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Empirical Essays on Poverty, Inequality, and Social Welfare

by

Brian Daniel McCaig

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Department of Economics University of Toronto

© Copyright by Brian McCaig 2009

ii

Empirical Essays on Poverty, Inequality, and Social Welfare

Brian McCaig

Doctor of Philosophy

Department of Economics University of Toronto

2009

Abstract

This thesis consists of three empirical chapters related to distributional outcomes, such as

poverty and inequality, in three different contexts.

Chapter 1 outlines a class of statistical procedures that permit testing of a broad range of

multidimensional stochastic dominance hypotheses. We apply the procedures to data on income

and leisure hours for individuals in Germany, the UK, and the USA. We find that no country

first-order stochastically dominates the others in both dimensions for all years of comparison.

Furthermore, while in general the USA stochastically dominates Germany and the UK with

respect to income, in most periods Germany stochastically dominates with respect to leisure

hours. Finally, we find evidence that bivariate poverty is lower in Germany than in either the UK

or the USA. On the other hand, poverty comparisons between the UK and the USA are sensitive

to the subpopulation of individuals considered.

Chapter 2 provides a detailed description of the evolution of income inequality in

Vietnam between 1993 and 2006. We construct consistent estimates of annual household income

using five nationally representative household surveys. Our main finding is that Vietnam’s rapid

growth was accompanied by a reduction in inequality between 1993 and 2002 and an unchanged

level of inequality between 2002 and 2006. We find that strong growth in employment income

and robust growth of cropping income played an important role in decreasing rural inequality,

while the growth of wage income and the stagnation of household business income similarly

contributed to the reduction in urban inequality.

Chapter 3 examines the impacts of the 2001 U.S.-Vietnam Bilateral Trade Agreement on

provincial poverty level in Vietnam. My main finding is that provinces that were more exposed

iii

to the U.S. tariff cuts experienced faster decreases in poverty between 2002 and 2004. I

subsequently explore three labour market channels from the trade agreement to poverty

alleviation. Provinces that were more exposed to the tariff cuts experienced (1) increases in

provincial wage premiums for low-skilled workers, (2) faster movement into wage and salaried

jobs for low-skilled workers, and (3) more rapid job growth in formal enterprises.

iv

Acknowledgments I would like to thank professors Loren Brandt, Dwayne Benjamin, Gustavo Bobonis, Dan

Trefler, and Adonis Yatchew for their guidance.

Most importantly, I would like to thank my wife, Sara, for her support and encouragement

throughout the Ph.D. program. I truly could not have done it without her.

I would also like to acknowledge financial support from the Social Sciences and Humanities

Research Council.

v

Table of Contents

ACKNOWLEDGMENTS IV

TABLE OF CONTENTS V

LIST OF TABLES VIII

LIST OF FIGURES XI

INTRODUCTION 1

1 CHAPTER 1 INTERNATIONAL WELFARE COMPARISONS AND NONPARAMETRIC

TESTING OF MULTIVARIATE STOCHASTIC DOMINANCE 4

1.1 INTRODUCTION 4 1.2 NOTATION AND STATISTICAL PROCEDURE 7 1.3 SIMULATION RESULTS 11 1.4 EMPIRICAL RESULTS 15 1.5 CONCLUDING REMARKS 27 1.6 APPENDIX 27

2 CHAPTER 2 THE EVOLUTION OF INCOME INEQUALITY IN VIETNAM, 1993-2006 29

2.1 INTRODUCTION 29 2.2 BACKGROUND 30 2.2.1 LAND AND AGRICULTURAL REFORM 31 2.2.2 PRIVATE BUSINESS REFORM 31 2.2.3 SOE AND GOVERNMENT REFORM 32 2.2.4 TRADE REFORM 32 2.2.5 INVESTMENT 32 2.2.6 LABOUR 33 2.3 LITERATURE REVIEW 33 2.3.1 GENERAL EVIDENCE ON POVERTY 34 2.3.2 EVIDENCE ON INEQUALITY, 1993-1998 35

vi

2.3.2.1 Urban-rural inequality from 1993 to 1998 35 2.3.2.2 Regional inequality 1993 to 1998 37 2.3.2.3 Inequality based on household characteristics 38 2.3.2.4 Some evidence relating to income and employment 40 2.3.3 INEQUALITY EVIDENCE SINCE 1998 42 2.4 OUR CONTRIBUTION 42 2.5 DATA 43 2.5.1 HOUSEHOLD SURVEY DESIGN 44 2.5.2 HOUSEHOLD MEMBERSHIP AND MISSING MEMBERS 46 2.6 ESTIMATION OF HOUSEHOLD INCOME 49 2.7 MAIN EVIDENCE 51 2.7.1 IMPACTS OF REFORMS ON EMPLOYMENT 52 2.7.2 EVOLUTION OF HOUSEHOLD AND INDIVIDUAL ENDOWMENTS 55 2.7.2.1 Household size 55 2.7.2.2 Distribution of land 56 2.7.2.3 Distribution of human capital (education) 57 2.7.3 INCOMES 59 2.7.3.1 Mean per capita income 60 2.7.3.2 Comparison of Consumption and Income Behaviour 62 2.7.3.3 Composition of mean per capita income 63 2.8 DISTRIBUTIVE DIMENSIONS OF VIETNAM’S GROWTH 71 2.8.1 POVERTY TRENDS 72 2.8.2 INEQUALITY 73 2.8.2.1 Initial levels in 1993 73 2.8.2.2 Inequality trends 76 2.8.2.3 Comparison of consumption and income inequality 80 2.8.2.4 Role of spatial differences 81 2.8.2.5 Decompositions by source of income 83 2.9 ROBUSTNESS CHECKS 92 2.10 DISCUSSION AND CONCLUSION 96 2.11 DATA APPENDIX 100 2.11.1 ESTIMATION OF HOUSEHOLD INCOME 100 2.11.1.1 Income from crops 100 2.11.1.2 Income from agricultural sidelines 104 2.11.1.3 Household business income 105

vii

2.11.1.4 Wage income 109 2.11.1.5 Remittances and gifts 112 2.11.1.6 “Other” residual sources of income 113

3 CHAPTER 3 EXPORTING OUT OF POVERTY: PROVINCIAL POVERTY IN VIETNAM

AND U.S. MARKET ACCESS 115

3.1 INTRODUCTION 115 3.2 BACKGROUND 118 3.3 OVERVIEW OF THE U.S.-VIETNAM BILATERAL TRADE AGREEMENT 121 3.4 DATA 122 3.4.1 TARIFF DATA 122 3.4.2 HOUSEHOLD SURVEYS 124 3.4.3 EMPLOYMENT DATA 126 3.5 EMPIRICAL METHODOLOGY 127 3.5.1 EXOGENEITY OF U.S. TARIFF CUTS 129 3.5.2 UNDERLYING TRENDS AND CONTEMPORARY SHOCKS 131 3.6 EMPIRICAL RESULTS 131 3.6.1 ROBUSTNESS OF RESULTS 135 3.7 LABOUR MARKET TRANSMISSION MECHANISMS 137 3.7.1 WAGES 138 3.7.2 JOB CREATION 140 3.8 DISCUSSION OF RESULTS 144 3.9 CONCLUDING REMARKS 145 3.10 APPENDIX A: MEASUREMENT ERROR 146 3.11 APPENDIX B: DATA 150

REFERENCES 153

viii

List of Tables Table 1.1 Parameter values of the simulated bivariate normal distributions................................ 13

Table 1.2 Level and power of testing procedure........................................................................... 14

Table 1.3 Summary statistics for all individuals 25 years of age or older.................................... 16

Table 1.4 Estimated p-values for tests of stochastic dominance for individuals 25 years of age

and older........................................................................................................................................ 19

Table 1.5 Estimated p-values for tests of restricted first-order stochastic dominance for

individuals 25 years of age and older ........................................................................................... 21

Table 1.6 Summary statistics for all working individuals ............................................................ 22

Table 1.7 Estimated p-values for tests of stochastic dominance for all working individuals....... 23

Table 1.8 Estimated p-values for tests of first-order stochastic dominance for all working

individuals..................................................................................................................................... 24

Table 1.9 Summary statistics for all singles 25 years of age or older .......................................... 25

Table 1.10 Estimated p-values for tests of stochastic dominance for all singles 25 years of age

and older........................................................................................................................................ 26

Table 1.11 Estimated p-values for tests of restricted first-order stochastic dominance for singles

25 years of age and older .............................................................................................................. 26

Table 2.1 Summary information on the sample design of each household survey....................... 44

Table 2.2 Regional deflators included in the household surveys.................................................. 50

Table 2.3 Percentage of workers by major industrial sector......................................................... 53

Table 2.4 Shares of workers that are self-employed versus working for others........................... 53

Table 2.5 Share of wage earners working for the state, collectives, the private sector, and the

foreign sector ................................................................................................................................ 54

ix

Table 2.6 Mean household size..................................................................................................... 56

Table 2.7 Landlessness and land inequality in rural Vietnam ...................................................... 57

Table 2.8 Mean levels of education of all workers....................................................................... 58

Table 2.9 Gini coefficient estimates of educational inequality for all workers ............................ 58

Table 2.10 Mean real per capita income and consumption (000 VND) ....................................... 61

Table 2.11a Mean per capita income and growth by income activity in rural Vietnam............... 64

Table 2.12a Mean per capita income and growth by income activity in urban Vietnam ............. 66

Table 2.13 Percentage of household with non-zero income by income activity .......................... 67

Table 2.14 Estimates of the poverty headcount ratio using real per capita income...................... 72

Table 2.15 Estimated Gini coefficients based on real per capita income and consumption......... 74

Table 2.16 Contribution of location to income inequality and number of households per location

....................................................................................................................................................... 82

Table 2.17 Shorrocks decomposition............................................................................................ 91

Table 2.18 Comparison of mean income in the 2002 VHLSS between households in communes

that also appear in the 1998 VLSS and households in communes that only appear in the 2002

VHLSS.......................................................................................................................................... 94

Table 2.19 Comparison of the households reporting non-zero income in the 2002 VHLSS

between households in communes that also appear in the 1998 VLSS and households in

communes that only appear in the 2002 VHLSS.......................................................................... 95

Table 2.20 Gini coefficients from the 2002 VHLSS for households from "overlapping" and "non-

overlapping" communes ............................................................................................................... 96

Table 3.1 Vietnamese exports to and imports from the U.S., 1997-2004................................... 116

Table 3.2 Main commodity exports from Vietnam to the U.S. .................................................. 117

x

Table 3.3 Summary of U.S. tariffs applied to imports from Vietnam ........................................ 122

Table 3.4 Summary statistics ...................................................................................................... 125

Table 3.5 Countries subject to Column 2 U.S. tariffs, 1996-2005.............................................. 130

Table 3.6 Correlations of U.S. tariffs, 1997, 2001 and 2005...................................................... 130

Table 3.7 Ordinary Least Squares regression results of the impact of provincial exposure

(TariffDrop) on poverty between 2002 and 2004 ....................................................................... 134

Table 3.8 Ordinary Least Squares regression results of the impact of provincial exposure

(TariffDrop) on poverty between 2002 and 2004 controlling for other trade influences ........... 137

Table 3.9 Provincial wage premiums and provincial exposure .................................................. 140

Table 3.10 Enterprise job growth................................................................................................ 142

Table 3.11 Impact of provincial exposure (TariffDrop) on the share of workers working for a

wage or salary ............................................................................................................................. 144

Table 3.12 Regressions addressing measurement error .............................................................. 149

Table 3.13 Regressions with alternative measures of poverty.................................................... 150

xi

List of Figures Figure 1.1 Empirical income distributions for individuals 25 years of age and older in Germany

and the U.S., 2000......................................................................................................................... 17

Figure 1.2 Empirical leisure time distributions for individuals 25 years of age and older in

Germany and the U.S., 2000......................................................................................................... 18

Figure 1.3 Contour plots of the empirical CDFs of income and leisure for individuals 25 years of

age and older in Germany and the U.K., 1990.............................................................................. 20

Figure 2.1 Difference between the projected 1993 population using the 1999 census and the 1993

VLSS............................................................................................................................................. 47

Figure 2.2 Difference between the projected 1993 population using the 1999 census and the 2006

VHLSS.......................................................................................................................................... 48

Figure 2.3 Lorenz curves for rural income per capita, 1999-2006 ............................................... 78

Figure 2.4 Lorenz curves for rural income per capita, 1993-2006, for the bottom of the income

distribution .................................................................................................................................... 78

Figure 2.5 Lorenz curves for urban income per capita, 1993-2006.............................................. 79

Figure 2.6 Lorenz curves for urban income per capita, 1993-2006, for the bottom of the income

distribution .................................................................................................................................... 80

Figure 2.7 Composition of income by source for rural households by income quartile in 1993 and

2006............................................................................................................................................... 84

Figure 2.8 Composition of income by source for urban households by income quartile in 1993

and 2006........................................................................................................................................ 85

Figure 3.1 U.S. tariff cuts by 2-digit ISIC industry .................................................................... 123

Figure 3.2 Histogram of the proportional drop in provincial poverty rates, between 2002 and

2004............................................................................................................................................. 126

xii

Figure 3.3 Relationship between the proportional drop in povery and provincial exposure to U.S.

tariff cuts (TariffDrop) ................................................................................................................ 132

Figure 3.4 Relationship between the proportional drop in poverty and provincial exposure to U.S.

tariff cuts (TariffDrop) controlling for region ............................................................................ 132

Figure 3.5 Relationship between provincial poverty and enterprise employment...................... 141

Figure 3.6 Relationship between growth in jobs between 2000 and 2004 and provincial exposure

to U.S.-Vietnam Bilateral Trade Agreement .............................................................................. 141

Figure 3.7 Relationship between provincial poverty and the share of workers working for a wage

or salary in 2002.......................................................................................................................... 143

Figure 3.8 Difference between an under-reported distribution of per capita expenditures and the

true distribution........................................................................................................................... 147

1

Introduction

In recent decades development economics has moved beyond its historical focus on

growth, broadening the concept of development to include distributional issues and non-

monetary outcomes. A strong proponent of this shift in focus has been Amartya Sen (see for

example Sen (1982)). This change in focus has been motivated by a number of issues. These

include, but are not limited to, examples of countries that experienced sustained growth in mean

income, but stagnation in other welfare measures such as poverty, malnutrition, infant mortality,

etc., and theoretical findings suggesting that inequality and poverty may hamper overall growth.

The broadening of the concept of economic development requires better tools for analyzing

distributional issues, more detailed high-quality data on changes in the distribution of important

welfare indicators in developing countries, and the analysis of the distributional impacts of

economic reforms.

This thesis contributes to the literature focusing on distributional issues in three ways: by

proposing a statistical testing procedure useful for comparisons of multidimensional social

welfare and poverty, by providing a detailed description of the evolution of income inequality in

Vietnam between 1993 and 2006, and by examining the impacts of a major policy shock on

poverty in Vietnam between 2002 and 2004.

In the first chapter, which is co-authored with Adonis Yatchew, we address the need for

better tools for analyzing changes in the distribution of welfare across populations. As the focus

of development economics has moved beyond simply using mean outcomes, to including

measures sensitive to the distribution of welfare, such as inequality and poverty, it has also

expanded to include the use of multiple measures of welfare simultaneously. The increased

dimensionality and attention to the distribution of welfare introduce numerous challenges for

comparing changes in welfare in a statistical sense. First, how important are gains in one welfare

indicator relative to another indicator? Second, how sensitive are the comparisons to changes in

the chosen index or, in the case of poverty, to the poverty line used? These shortcomings make

approaches based on stochastic dominance highly appealing. Not only have these approaches

proven useful for robust rankings of univariate distributions but, as we show in Chapter 1, tests

for stochastic dominance can be extended to multivariate distributions. We propose a statistical

2

testing procedure that can be applied to tests of multidimensional stochastic dominance in the

context of comparing social welfare and poverty between two populations.

The second and third chapters both focus on Vietnam during its period of transition from

a centralized to a decentralized economy. Ever since Vietnam’s major economic reforms began

in 1986 under Doi Moi, Vietnam has grown exceptionally fast, whether measured by GDP per

capita in the national accounts or income per capita in household surveys. Vietnam’s rapid

growth has been accompanied by a rapid reduction in absolute poverty and a slight rise in

relative inequality, as measured by per capita consumption. Chapter 2, which is co-authored with

Dwayne Benjamin and Loren Brandt, contributes to the literature analyzing the trends in

Vietnam’s distributional outcomes by shifting the focus to income per capita as a measure of

welfare. While many researchers argue that consumption is preferred to income as a measure of

welfare, it is not a sufficient statistic for identifying all dimensions of welfare (hence the appeal

of multivariate approaches such as the one proposed in Chapter 1). Moreover, in an economy

that is growing as rapidly as Vietnam’s, consumption and income may diverge in systematic

ways as savings rates change throughout the distribution over time. Finally, using income allows

for additional sets of inequality decompositions that cannot be performed using consumption

data. It is with these reasons in mind that we present a detailed set of facts related to the

evolution of income inequality within Vietnam between 1993 and 2006. We find that Vietnam

experienced a period of initially falling inequality, between 1993 and 2002, followed by a period

of stable levels of inequality, between 2002 and 2006.

During the 1990s and the first decade of the new century, a number of large policy

shocks have hit the Vietnamese economy. Many of these shocks are likely to have both

efficiency and distributional implications for the population. In this context, the third chapter

compliments the descriptive nature of Chapter 2 with an econometric analysis of the distributive

implications of a major trade agreement. Specifically, the goal of the third chapter, the core

chapter of the thesis, is to explore the poverty impacts of the 2001 U.S.-Vietnam Bilateral Trade

Agreement. In a cross-province framework, I find that provinces that were most exposed to the

U.S. tariff cuts experienced the most rapid reductions in poverty. One of the mechanisms at work

appears to be an increase in the relative demand for low-skilled workers in response to the large

increase in low-skilled-labour-intensive exports from Vietnam to the U.S. The increase in

relative demand for low-skilled workers may be one of the reasons for relatively equal growth

3

between 2002 and 2006 in Vietnam as it helped to promote income growth for those most likely

to be in the lower end of the income distribution.

This thesis aims to contribute to the growing literature concerning the distributional and

multidimensional aspects of economic development. It does so in three distinct, but connected

chapters. Chapter 3 explores the poverty implications of a major trade shock to the Vietnamese

economy. As such, it is a very nice exploration of one of the possible mechanisms underlying

Vietnam’s growth with equality, which is documented in a stylized manner in Chapter 2. By

comparison, Chapter 1 is slightly more abstract, but presents a statistical testing procedure useful

for exploring multidimensional welfare analysis. It is hoped that all three chapters can be useful

additions to the literature.

4

1 Chapter 1 International Welfare Comparisons and

Nonparametric Testing of Multivariate Stochastic Dominance

1.1 Introduction

Cross-country welfare comparisons commonly use statistics such as median or mean per-

capita incomes, average hours worked, the proportion of the population living below the poverty

line and so on. The statistical theory for testing hypotheses about one or more such statistics is

generally available. Individual statistics, however, capture only one characteristic of a

distribution. Often, there is interest in making point-wise comparisons of entire distributions to

each other, for example using measures of stochastic dominance in the context of comparing

social welfare, inequality, and poverty. In the simplest case the distribution of income in country

“a” first-order stochastically dominates country “b” if for any income level “x” the proportion of

the population with income at or below “x” is lower in country “a” than in country “b”.

More generally, one may be interested in simultaneous comparisons between multiple

indicators of welfare. For example, one might want to test whether one country stochastically

dominates another with respect to several variables. Or, one might want to test whether one

country dominates in some dimensions while another dominates in others. For example, while

U.S. per capita GDP is substantially higher than in France, the French work fewer hours per

week.1

The primary advantage of stochastic dominance criteria is their robustness to changes in

the functional form of the social welfare or poverty index. Unlike indices, stochastic dominance

conditions do not require specification of the functional form of the utility or poverty function.

This allows practitioners to draw strong conclusions when stochastic dominance conditions are

met. If stochastic dominance holds, one can make robust inferences over all indices that share a

common set of properties. On the other hand, stochastic dominance conditions do not always

1 Such comparisons have filtered into the media and inform the debate on societal as well as individual choices. See e.g., Krugman (2005).

5

allow for two populations to be ranked. It is in this sense that orderings provided by stochastic

dominance are partial as compared to the complete rankings provided by indices. However,

when stochastic dominance does not hold over the entire support, it may hold over subsets of the

support that are of particular interest for poverty comparisons.

This paper outlines a class of statistical procedures that permit testing of a broad range of

multi-dimensional stochastic dominance hypotheses and more generally, hypotheses that rely

upon multiple stochastic dominance conditions. We conduct a small Monte Carlo study to

examine the size and power properties of the test procedure. We then apply the procedures to

data on income and leisure hours from Germany, the U.K. and the U.S. For individuals 25 years

of age or older we find that no country first-order stochastically dominates the others in both

dimensions. Furthermore, while the U.S. stochastically dominates Germany and the U.K. with

respect to income, in most periods Germany is stochastically dominant with respect to leisure

hours. In addition, we find evidence of lower bivariate poverty in Germany as compared to both

the U.K. and the U.S. We check the robustness of our main results using alternative population

groups and find similar results. Before proceeding, we provide a brief and selective outline of

related work.

Atkinson and Bourguignon (1982), working in a bivariate context, summarize the

theoretical relationship between comparisons of social welfare and first-order and second-order

stochastic dominance conditions. Specifically, assume a bivariate social welfare function of the

form , where ( ) (1 2 1 2,W U z z dG z z= ∫ ∫ ), ( )1 2,G z z is a cumulative distribution function defined

over the rectangle ( ) [ ] [ ]1 2 1 2, 0, 0,z z z∈ × z .2 Atkinson and Bourguignon (1982) show that the

difference in social welfare between two populations can be written as:

( ) ( ) ( )

( ) ( ) ( )

( ) ( ) ( )

1 2

1

2

12 1 2 1 2 1 2 2 10 0

1 1 2 1 2 1 2 10

2 1 2 1 2 1 2 20

, , ,

, , ,

, , ,

z z

a b a b

z

a b

z

a b

W W U z z G z z G z z dz dz

U z z G z z G z z dz

U z z G z z G z z dz

− = −⎡ ⎤⎣ ⎦

− −⎡⎣

− −⎡ ⎤⎣ ⎦

∫ ∫

∫

∫ .

⎤⎦

(0.1)

2 It is not necessary that the lower bound equal 0, only that it be finite. However, for most indicators of social welfare 0 is a natural lower bound.

6

This leads to a set of sufficient, but not necessary, conditions for social welfare in population “a”

to be at least as great as that for population “b”. If ( ) ( )1 2 1 2,a bG z z G z z≤ , ) for all ( 1 2,z z within

the support of the cumulative distribution functions, that is, if first-order stochastically

dominates , then social welfare is at least as large in population “a” as in population “b” for

all

aG

bG

{ }1 2 12: , 0, 0U U U U U∈ ≥ ≤ where subscripts denote own and cross-partial derivatives.

An alternative set of sufficient conditions exist when the cross-partial derivative of U is

non-negative. Define ( ) ( ) ( ) ( )1 2 1 2 1 2 1 2, , ,K z z G z z G z z G z z≡ + − , . Then the difference in social

welfare can be expressed as:

( ) ( ) ( )

( ) ( ) ( )

( ) ( ) ( )

1 2

1

2

12 1 2 1 2 1 2 2 10 0

1 1 1 1 10

2 2 2 2 20

, , ,

,0 ,0 ,0

0, 0, 0, .

z z

a b a b

z

a b

z

a b

W W U z z K z z K z z dz dz

U z K z K z dz

U z K z K z dz

− = − −⎡ ⎤⎣ ⎦

− −⎡ ⎤⎣ ⎦

− −⎡ ⎤⎣ ⎦

∫ ∫

∫

∫

(0.2)

Now, if for all ( ) (1 2 1 2,a bK z z K z z≤ ), ( )1 2,z z within the support of the cumulative distribution

functions, that is, if first-order stochastically dominates , then social welfare is at least as

large in population “a” as in population “b” for all

aK bK

{ }1 2 12: , , 0U U U U U∈ ≥ .3 In the former case

of a non-positive cross-partial, one can think of the two indicators of well-being as substitutes.

Hence, a social planner could increase social welfare by decreasing the correlation of indicators

across individuals while holding the marginal distributions constant. In the latter case of a non-

negative cross-partial, indicators are complements and a social planner could increase social

welfare by increasing the correlation of indicators across individuals.

More recently, Bourguignon and Chakravarty (2002) and Duclos et al. (2004) adapt this

framework to the study of multidimensional poverty. Both papers use a similar motivation to

Atkinson and Bourguignon (1982), but arrive at somewhat different stochastic dominance

conditions. Bourguignon and Chakravarty (2002) integrate from 0 to the poverty cutoff in both

3 Atkinson and Bourguignon (1982) also provide the various derivative conditions required for social welfare comparisons based on second-order stochastic dominance, while Crawford (2005) extends these results to comparisons based on third-order stochastic dominance.

7

dimensions, whereas Duclos et al. (2004) integrate over the poverty space where the poverty

threshold in one dimension is a function of the level of well-being in the other dimension. For a

poverty space that exists strictly within the interior of the support, the Duclos et al. (2004)

conditions are weaker, as they only require stochastic dominance within this interior space,

whereas the Bourguignon and Chakravarty (2002) conditions also require stochastic dominance

along the marginal distributions, up to the respective poverty thresholds.

These bivariate conditions, along with the univariate counterparts, are part of the

motivation for employing statistical tests of stochastic dominance. Recently, several such tests

have been proposed in the literature. Tests of univariate stochastic dominance available to date

can be broadly divided into two groups: those that use a modified Kolmogorov-Smirnov (KS)

statistic and those that perform simultaneous tests at multiple points. In the first category are the

tests proposed by McFadden (1989), Klecan et al. (1991), Kaur et al. (1994), Maasoumi and

Heshmati (2000), Barrett and Donald (2003), and Linton et al. (2005). A central issue for KS-

type tests of stochastic dominance is the determination of critical values. The second category of

tests includes those suggested by Anderson (1996) and Davidson and Duclos (2000). These tests

suffer from possible inconsistency, but they do not require bootstrapping, simulation, or

subsampling procedures to determine critical values.

Multivariate tests of stochastic dominance may be found in Crawford (2005) and Duclos

et al. (2004). Both procedures involve multiple comparisons on a grid of points. Crawford

extends Anderson (1996) to multiple dimensions while Duclos et al. similarly extend Davidson

and Duclos (2000). For both procedures, at each point of comparison a t-statistic is calculated

and compared to the appropriate critical value obtained from the studentized maximum modulus

(SMM). As stated by Tse and Zhang (2004), the nominal size of the SMM test overstates the

actual size when the tests are not independent.

The present paper is organized as follows. Section 1.2 establishes notation and sets out

the statistical procedures. These are an extension of tests found in Hall and Yatchew (2005).

Section 1.3 describes the results of simulations and Section 1.4 discusses empirical results.

1.2 Notation and Statistical Procedure

The test procedures outlined in this paper are applicable in univariate and multivariate

settings. They require use of the bootstrap and involve integration over the support, thereby

8

avoiding the multiple test problem described above. In practice, this allows the researcher to test

for stochastic dominance over as dense a grid of points as desired, without affecting the size and

power properties of the test. Furthermore, in contrast to the univariate KS-type test procedures,

our procedure should have more power over deviations from the null hypothesis that are small in

magnitude, but persist over a large portion of the support. However, KS-type test procedures

would be expected to have greater power against alternatives that feature a large deviation from

the null that is not very persistent.

Let and denote two right-continuous k-dimensional cumulative distribution functions (CDFs). For convenience, assume that the support of the CDFs is , the unit cube in

.

aG bGΛ

kR 4 We are interested in testing hypotheses of the form

0 : a s bH G G

where s denotes weak stochastic dominance of order s. Let ( ) (1a aD G=z )z and define

( ) ( )1s sa aD D d−= ∫

z

0

z u u

for integers . (An analogous definition applies to 2s ≥ ( )sbD z .) Weak stochastic dominance of

order holds iff while strong stochastic dominance additionally

requires a strict inequality over some region of

s ( ) ( ) for all s sa bD D≤z z z∈Λ

Λ , denoted by s . For ∈Λλ , let

( ) ( ) ( ){ }max ,0s sa bD Dψ = −λ λ λ . Then the null hypothesis is true iff ( ) 0ψ =λ for all ∈Λλ .

Define

( )1/ 2

2T ψ

Λ

d⎧ ⎫

⎡ ⎤= ⎨ ⎬⎣ ⎦⎩ ⎭∫ λ λ . (0.3)

The objective is to estimate and to test whether it is statistically different from zero. Let T

( )1,..., aa anw w and ( )1,..., bb bnw w be independently and identically distributed observations from

the two respective populations with corresponding empirical distribution functions aG and .

Our test statistic, say bG

T , is obtained by substituting numerical analogues of saD and s

bD , say saD

and sbD , into T . When examining indicators of well-being one may want to form hypotheses based on

subsets of the indicators. For example, one might want to test that the income distribution of

k

4 Rescaling of the data, which may be implemented in a variety of ways, becomes important later when we introduce hypotheses involving more than one stochastic dominance condition. In large samples, the method should not affect the conclusions, however, in modestly sized samples one approach or another may possess superior finite sample properties.

9

country “a” dominates that of country “b”, but the leisure-time distribution of country “b”

dominates that of country “a”. In this spirit, partition the k-dimensional vectors into sub-

vectors of dimension and ( respectively, where 0 '

,aw wb

)'k 'k k− k k< < ; i.e., ( ),a a a=w x y and

. A more general hypothesis is given by ( ,b b b=w x y )

:x yx x ys so a b bH G G and G G

ya

1

where we write and the other distribution functions are defined similarly. We

allow for the order of dominance to vary between the two subsets of indicators as denoted by

( ) ( ),xa aG G=x x

xs

and . In the multivariate case, the above hypothesis asserts stochastic dominance with respect

to marginal distributions (which may themselves be multi-dimensional) without requiring

stochastic dominance everywhere. Let

ys

xΛ be the unit cube in and let be the unit cube in 'kR yΛ

( )'k k−R . For x x∈Λλ , let ( ) ( ) ( ){ }max ,x x

x x

s sx x a x b xD Dψ = −λ λ 0λ y and for y ∈Λλ , let

( ) ( ) ( ){ }max ,y y

y y

s sy y b y a yD Dψ = −λ λ 0λ

d

. Define

( ) ( )1/ 21/ 2

22

x y

x x x y y yT dψ ψΛ Λ

⎧ ⎫⎧ ⎫⎪ ⎪ ⎪ ⎡ ⎤⎡ ⎤= +⎨ ⎬ ⎨⎣ ⎦ ⎣ ⎦⎪ ⎪

⎪⎬

⎪ ⎪⎩ ⎭ ⎩ ⎭∫ ∫λ λ λ λ . (0.4)

Defining the support of and as unit cubes in and x y 'kR ( )'k k−R ensures that the additive terms

in (2.2) are not of radically different orders of magnitude. This becomes important for ,

as integrating over the CDFs would otherwise introduce the units of the variable into the

integrated values.

, 2x ys s ≥

As noted above, Atkinson and Bourguignon (1982) derive second-order stochastic

dominance conditions that are sufficient to identify a positive difference in social welfare in the

bivariate case. One example would be the compound hypothesis:

2 2:x x yo a b a b aH G G and G G and G G2 yb .

To properly test this null hypothesis requires the simultaneous testing of all three conditions.5 To

test the above null hypothesis we define xΛ and yΛ as unit intervals in and R Λ as the unit

5 This is not generally a concern for first-order stochastic dominance conditions as the conditions on the marginal distributions are a part of the condition on the bivariate distribution. However, the second-order stochastic

10

square in . For 2R x xλ ∈Λ , let ( ) ( ) ( ){ }2 2max ,0x xx x a x b xD Dψ λ λ= − λ y; for yλ ∈Λ , let

( ) ( ) ( ){ }2 2max ,0y yy y a y b yD Dψ λ λ= − λ ; and for ∈Λλ , let ( ) ( ) ( ){ }2 2max ,a bD Dψ = −λ λ 0λ

d

.

Define

( ) ( ) ( )1/ 21/ 2 1/ 2

22 2

x y

x x x y y yT d dψ λ λ ψ λ λ ψΛ Λ Λ

⎧ ⎫⎧ ⎫ ⎧ ⎫⎪ ⎪ ⎪ ⎪⎡ ⎤⎡ ⎤ ⎡ ⎤= + +⎨ ⎬ ⎨ ⎬ ⎨⎣ ⎦ ⎣ ⎦⎣ ⎦ ⎩ ⎭⎪ ⎪ ⎪ ⎪⎩ ⎭ ⎩ ⎭∫ ∫ ∫ λ λ⎬ . (2.3)

In practice we estimate the value of from (2.1), (2.2) or (2.3) over a finite number of points.

Throughout the paper we report results based on an evenly spaced, 25 by 25 grid of points. The

empirical counterparts of

T

saD and s

bD are calculated at each of these points, before being

substituted into the expression for ψ and then T .

The test statistics in (2.1), (2.2) and (2.3) do not have known asymptotic distributions.

However, following Hall and Yatchew (2005), we obtain consistent bootstrap critical values

using the following algorithm. Combine the two datasets into one bootstrap dataset. Draw two

samples of size and for bootstrap samples “a” and “b” respectively. The data generating

mechanisms for the two bootstrap samples will weakly satisfy the null hypothesis since they are

drawn from the same distribution. We use an elevated asterisk, *, to denote a bootstrap estimate.

From the bootstrap samples calculate , and insert these into (2.1), (2.2) or (2.3)

to obtain

T

an bn

( )*

saD z ( )

*sbD z

*T . Repeating this procedure many times (we use 200 bootstrap iterations throughout

the paper), allows one to bootstrap the distribution of under the respective null hypothesis.

From the bootstrap distribution of we calculate the critical values.

T̂

T̂

Our bootstrap procedure imposes what is known in the literature as the “least favourable

case.” This approach is common to many of the stochastic dominance tests that require

calibration of critical values such as McFadden (1989) and Barrett and Donald (2003). Consider

two univariate, empirical distribution functions and the null hypothesis 1:o aH G Gb

. Suppose

dominance conditions require dominance over the integrals of the marginal distribution functions and the double integral of the bivariate distribution function. It is no longer the case that the conditions on the marginal distributions are embedded within the condition on the bivariate distribution.

11

that bG crosses once from below. Then the null hypothesis would appear to be true to the

right of the “crossing” but false to the left of the crossing. Hence, to satisfy the null hypothesis

our bootstrap procedure only needs to alter the relative positions of the functions below their

crossing point. However, we impose a stronger condition of equality of distributions throughout

the support. It is in this sense that we impose the “least favourable case” and it can be expected

that the power of the test procedure would be lower. Among the procedures requiring calibration

of critical values, Linton et al. (2005) avoid this problem by subsampling while Maasoumi and

Heshmati (2000) similarly avoid imposing the “least favourable case” by resampling from the

ordinates.

aG

A second comment on our bootstrapping procedure is warranted for tests of second- or

higher order stochastic dominance. To satisfy the null hypothesis at a higher order, we impose

equality of the distribution functions as opposed to equality of the integrated distribution

functions. This is stronger than is strictly necessary, but also common to many of the stochastic

dominance tests found in the literature. 6,7

1.3 Simulation results

To examine the properties of our testing procedure we conduct simulations using various

data generating mechanisms (DGMs). In each case, the simulated data are generated using a

6 We would like to thank an anonymous referee for bringing this to our attention. 7 One potential heuristic method to deal with both of the problems identified above would be to divide the support into regions where the empirical functions satisfy and do not satisfy the null hypothesis. For :

so aH G G

b, define the

region of the support for which the null hypothesis is empirically false as ( ) ( ){ }: s s

a bD DΛ = ∈Λ >λ λ λ . Next,

calculate the bootstrapped version of ( )ψ λ according to ( ) ( ) ( ){ }* **

max , 0s s

a bD Dψ = −λ λ λ for ∈ Λλ and

( ) ( ) ( ){ }*

max , 0s s

a bD Dψ = −λ λ λ for ∉ Λλ . That is, where the null hypothesis is not satisfied use the bootstrapped

empirical functions derived from pooling the observations to estimate ψ and use the original empirical functions otherwise. Though the statistical theory of this approach is beyond the scope of the current paper, some univariate simulations suggest dramatic improvement in power against certain alternatives.

12

bivariate lognormal pair ( where the underlying joint normal random variables ),X Y ( ),x y have

means ,x yμ μ , variances 2 , 2x yσ σ and covariance xyσ .

For each DGM we run 1000 simulations, with sample sizes and 500. We

conduct tests of the following five hypotheses:

50a bn n= =

10

10

10

1 10

1 10

:

:

:

:

: .

x x

y y

x x y y

x x y

Aa b

Ba b

Ca b

Da b b a

Eb a a b

H G G

H G G

H G G

H G G and G G

H G G and G Gy

Under the first DGM the two distributions are identical. The parameter values of the underlying

normal distribution are set to ( ) ( )2 2, , , , 0.85,0.85,0.36,0.36,0.2x y x y xyμ μ σ σ σ = for both

populations. We chose the means and variances to allow for easy comparability with the

univariate simulations of Barrett and Donald (2003). We expect the rate of rejection for all five

null hypotheses to be approximately at the level of the test. The second DGM maintains the same

parameter values for population “b” but those for population “a” change to

. These parameter values imply that all five null

hypotheses are false since the marginal distributions cross for both variables

( ) (2 2, , , , 0.6,0.6,0.64,0.64,0.2x y x y xy aμ μ σ σ σ = )

X and Y . The

third DGM uses the original parameter values for population “a” and

for population “b.” That is, both populations

have the same parameters except for the covariance parameter. Thus, the marginal distributions

are identical, in which case, hypotheses B, C, D and E are true; however, hypothesis A which

tests bivariate stochastic dominance of “a” over “b” is false.

( ) (2 2, , , , 0.85,0.85,0.36,0.36, 0.2x y x y xy bμ μ σ σ σ = )−

8 The fourth DGM uses the original

parameter values for population “b” and ( ) ( )0.22 2, , , , 0.65,2.1,0.36,0.36,x y x y xy aμ μ σ σ σ =

for

population “a.” These parameters imply that hypotheses A, B and D are false while C and E are

true. Table 1.1 summarizes the parameter values of the underlying normal distribution for each

DGM. Paramet valu of the simul biv norm distr

8 Though, bivariate stochastic dominance of “b” over “a” is true.

13

Table 1.1 Parameter values of the simulated bivariate normal distributions

DGM 1 DGM 2 DGM 3 DGM 4 A b a b a b a b

xμ 0.85 0.85 0.60 0.85 0.85 0.85 0.65 0.85 yμ 0.85 0.85 0.60 0.85 0.85 0.85 2.10 0.85 2xσ 0.36 0.36 0.64 0.36 0.36 0.36 0.36 0.36 2yσ 0.36 0.36 0.64 0.36 0.36 0.36 0.36 0.36 xyσ 0.20 0.20 0.20 0.20 0.20 -0.20 0.20 0.20

Table 1.2 shows the results of the simulations described above.9 We find that our test

procedure performs well for all four DGMs. The power of the test procedure improves

substantially as the sample size increases from 50 to 500 observations. Our results for hypotheses

B and C, which involve only the marginal distributions, are very similar to those of Hall and

Yatchew (2005). Where hypothesis B or C is weakly true, as under DGMs 1 and 3, the null

hypothesis is rejected at approximately the test level. Furthermore, when hypothesis B or C is

false, as under DGM 2, the test procedure has substantial power even with a sample size of 50.

Finally, the test procedure correctly fails to reject hypothesis C when it is strongly true, as is the

case with DGM 4.

Our results concerning the combined marginal hypotheses, D and E, show similar

patterns. When hypothesis D or E is weakly true, such as with DGMs 1 and 3, the rejection rate

is approximately equal to the test level. Furthermore, when hypothesis D or E is false, there is

substantial power even with a sample size of 50. Finally, when hypothesis E is strongly true, as

under DGM 4, the rejection rate is close to zero.

We now turn to a discussion of tests of hypothesis A, which asserts bivariate stochastic

dominance. When this hypothesis is weakly true, as is the case for DGM 1, the rejection rates

remain close to the nominal significance levels. Under DGM 2 where univariate stochastic

dominance fails at both margins, it is perhaps not surprising that the bivariate test has substantial

9 All simulations were run using R, which is freely available from http://www.r-project.org. The simulation code is available at http://www.chass.utoronto.ca/~bmccaig.

14

power. In this instance, one could have rejected bivariate stochastic dominance by performing

univariate tests on either or both margins.

Table 1.2 Level and power of testing procedure

50a bn n= = 500a bn n= = 10% 5% 1% 10% 5% 1% Hypothesis DGM 1 A: weakly true 0.105 0.055 0.014 0.121 0.068 0.021 B: weakly true 0.093 0.044 0.008 0.105 0.055 0.016 C: weakly true 0.074 0.044 0.010 0.101 0.055 0.018 D: weakly true 0.098 0.044 0.010 0.093 0.048 0.013 E: weakly true 0.088 0.040 0.009 0.104 0.058 0.016 DGM 2 A: false 0.605 0.458 0.214 1.000 1.000 1.000 B: false 0.605 0.452 0.233 1.000 1.000 0.998 C: false 0.496 0.350 0.144 1.000 1.000 0.997 D: false 0.480 0.324 0.159 1.000 1.000 0.997 E: false 0.381 0.252 0.102 1.000 1.000 0.996 DGM 3 A: false 0.489 0.344 0.149 1.000 0.997 0.956 B: weakly true 0.113 0.050 0.015 0.112 0.066 0.014 C: weakly true 0.090 0.041 0.013 0.095 0.051 0.015 D: weakly true 0.141 0.071 0.022 0.114 0.056 0.013 E: weakly true 0.081 0.041 0.010 0.104 0.049 0.017 DGM 4 A: false 0.120 0.058 0.012 0.973 0.914 0.739 B: false 0.598 0.443 0.236 1.000 1.000 0.998 C: true 0.000 0.000 0.000 0.000 0.000 0.000 D: false 1.000 1.000 1.000 1.000 1.000 1.000 E: true 0.001 0.001 0.000 0.000 0.000 0.000

DGM 3 is more interesting. In this case, univariate stochastic dominance holds (weakly)

at both margins but bivariate dominance does not. The bivariate test has substantial power even

for a sample size of 50.

For DGM 4, bivariate dominance does not hold and indeed univariate dominance fails on

one of the margins. In this case, bivariate dominance is rejected with greater power by

performing univariate tests. Under DGM 4, bivariate dominance actually holds for a substantial

portion of the support, which would appear to underlie the weaker power of the bivariate test.

15

1.4 Empirical results

The empirical analysis in this section is motivated by the literature on the differences in

time spent working between continental European countries, (in particular Germany and France),

and the U.S. This literature is largely focussed on trying to understand the determinants of the

differences in average hours worked (see Alesina, Glaeser and Sacerdote (2005), Prescott (2004),

and Schettkat (2003)). Thus far, it has not been concerned with trying to robustly compare the

social welfare and poverty consequences of the associated differences in income and non-labour

market time across the countries.

We compare the joint distributions of income and non-labour market time in Germany,

the United Kingdom, and the United States using data from the Cross-National Equivalent File

(CNEF). 10,11 The German data within the CNEF dataset originate from the German Socio-

Economic Panel Study (GSOEP), the U.K. data come from the British Household Panel Survey

(BHPS), and the U.S. data come from the Panel Study of Income Dynamics (PSID). The years of

comparison are 1983, 1990 and 2000 for Germany and the U.S. but only 1990 and 2000 for

comparisons involving the U.K. (Consistent earlier data for the U.K. are not available in the

CNEF.) We define leisure as the difference between total hours in a year and the reported

number of annual hours spent in the formal labour market.

We focus on the welfare of individuals aged 25 years or older, using post-government

income and leisure time as our indicators of well-being. Table 1.3 provides summary statistics

for individuals 25 years of age or older. Not surprisingly, average income is highest in the U.S.

for all three years of comparison and the variation in incomes is also greatest in the U.S.

Germany displays the highest average annual hours of leisure time. The average number of

leisure hours increases in Germany and the U.K. over time, but decreases by over 200 hours in

the U.S. between 1983 and 2000.

10 For data prior to 1991 Germany refers to the former territory of West Germany. From 1991 onward Germany refers to the unified territories of East and West Germany. 11 Please refer to http://www.human.cornell.edu/pam/gsoep/equivfil.cfm for further information.

16

Table 1.3 Summary statistics for all individuals 25 years of age or older

1983 1990 2000Germany

Mean of income 8533 12916 17133Mean of leisure time 7875 7904 7966St. dev. of income 4927 7402 9500St. dev. of leisure time 953 870 906Correlation -0.278 -0.285 -0.274Percentage working 52.2 54.9 48.4No. of observations 9549 7522 18692

United Kingdom Mean of income n.a. 13945 21165Mean of non-labour market time n.a. 7575 7647St. dev. of income n.a. 7524 13811St. dev. of non-labour market time n.a. 1073 1063Correlation n.a. -0.231 -0.159Percentage working n.a. 64.7 60.1No. of observations n.a. 6469 7224

United States Mean of income 11446 17046 26852Mean of non-labour market time 7507 7415 7279St. dev. of income 7985 13197 27521St. dev. of non-labour market time 1053 1079 1073Correlation -0.193 -0.230 -0.120Percentage working 70.1 73.4 75.1No. of observations 10394 11563 11444

Note: All income values are reported in current year U.S. dollars and reported in per adult equivalent units. “n.a.” denotes not available due to lack of consistent data.

Figure 1.1 and Figure 1.2 present the empirical marginal distributions of income and

leisure time, respectively, for individuals 25 years of age and older in Germany and the U.S. in

2000. The U.S. income distribution appears to be first-order stochastically dominant over most of

the income support, although the empirical income distributions appear to cross at the lower end

of the income support. The integral test allows us to check whether this crossing is statistically

significant. As for the leisure distribution, the German distribution appears to first-order

stochastically dominate that of the U.S. The distributions are discontinuous at the upper limit of

leisure time, reflected by the sharp jumps in the plots, due to non-working individuals.

17

Figure 1.1 Empirical income distributions for individuals 25 years of age and older in Germany and the U.S., 2000

0 20000 40000 60000 80000 100000

0.0

0.2

0.4

0.6

0.8

Germany 2000U.S. 2000

Annual post-government income per adult equivalent in 2000 USD

0 20000 40000 60000 80000 100000

0.0

0.2

0.4

0.6

0.8

Table 1.4 displays our results for tests of first- and second-order stochastic dominance of

the income, leisure, and joint distributions for each country-pair in the years 1983, 1990, and

2000. The table reports the estimated p-value of each indicated hypothesis. Some consistent

patterns emerge from the results. First, the German leisure distribution is first-order

stochastically dominant with respect to the U.K. and U.S. leisure distributions while the U.K.

leisure distribution is first-order stochastically dominant with respect to the U.S. Second, the

U.S. income distribution is stochastically dominant at first-order except for in comparison to the

U.K. in 2000, while the U.K. income distribution first-order stochastically dominates the German

distribution. Finally, the integral test definitively rejects first-order stochastic dominance of the

bivariate surfaces for each comparison except for Germany and the U.K. in 1990. In this

instance, see Figure 1.3, the German empirical bivariate distribution lies everywhere below that

of the U.K. except along the upper limit of leisure time. The p-value of 0.110 for the null

hypothesis of German stochastic dominance is indicative of the low power of the integral test

under the alternative hypothesis when the violation of the null hypothesis is not very persistent, a

18

point raised earlier in our discussion of our fourth simulation. This claim is supported by the

strong rejection of German stochastic dominance at first-order when the integral test is applied

strictly to the income distribution.

Figure 1.2 Empirical leisure time distributions for individuals 25 years of age and older in Germany and the U.S., 2000

5000 6000 7000 8000

0.0

0.2

0.4

0.6

0.8

1.0

Germany 2000U.S. 2000

Annual leisure hours

5000 6000 7000 8000

0.0

0.2

0.4

0.6

0.8

1.0

Overall, the results of tests for first-order stochastic dominance suggest that bivariate

social welfare cannot be robustly ranked for individuals 25 years of age and older at any order of

stochastic dominance. The sufficient conditions for ranking bivariate distributions require the

direction of stochastic dominance to be the same for both marginal distributions and the overall

bivariate distribution. Since first-order stochastic dominance implies higher-order stochastic

dominance, the direction of stochastic dominance over the income and leisure distributions will

conflict for all orders of stochastic dominance, given our results above. Thus, we will be unable

to establish sufficient conditions for ranking social welfare between the populations.

19

Table 1.4 Estimated p-values for tests of stochastic dominance for individuals 25 years of age and older

First-order Second-order Null hypothesis 1983 1990 2000 1983 1990 2000

Germany and the U.K. income s incomeGer UK n.a. 0.000 0.000 n.a. 0.000 0.000

income s incomeUK Ger n.a. 0.325 0.935 n.a. 0.545 0.690

leisure s leisureGer UK n.a. 1.000 1.000 n.a. 1.000 1.000

leisure s leisureUK Ger n.a. 0.000 0.000 n.a. 0.000 0.000

sGer UK n.a. 0.110 0.000 n.a. 0.955 0.995

sUK Ger n.a. 0.000 0.000 n.a. 0.000 0.000 Germany and the U.S.

income s incomeGer US 0.000 0.000 0.000 0.000 0.000 0.000

income s incomeUS Ger 0.325 0.155 0.470 0.380 0.375 0.560

leisure s leisureGer US 1.000 1.000 1.000 1.000 1.000 1.000

leisure s leisureUS Ger 0.000 0.000 0.000 0.000 0.000 0.000

sGer US 0.000 0.000 0.000 1.000 1.000 1.000

sUS Ger 0.000 0.000 0.000 0.000 0.000 0.000 The U.K. and the U.S.

income s incomeUK US n.a. 0.000 0.000 n.a. 0.000 0.000

income s incomeUS UK n.a. 0.350 0.010 n.a. 0.455 0.440

leisure s leisureUK US n.a. 1.000 1.000 n.a. 0.680 1.000

leisure s leisureUS UK n.a. 0.000 0.000 n.a. 0.000 0.000

sUK US n.a. 0.000 0.000 n.a. 0.355 1.000

sUS UK n.a. 0.000 0.000 n.a. 0.055 0.000

Bivariate poverty dominance (see Duclos et al., 2004) provides an alternative way to

interpret the stochastic dominance results presented in Table 1.4. In particular, consider all

bivariate poverty indices of the form ( ) ( ),P x y dG xπΩ

= ∫∫ , y where is the poverty region

defined by a poverty frontier and

Ω

( ),x yπ is an individual’s contribution to poverty, given well-

20

being indicators x and y (income and leisure in our case).12 The “poverty focus axiom” induces

if ( ) and ( ), 0x yπ ≥ ,x y ∈Ω ( ),x yπ 0= otherwise.

Figure 1.3 Contour plots of the empirical CDFs of income and leisure for individuals 25 years of age and older in Germany and the U.K., 1990

0 10000 20000 30000

Annual post-government income per adult equivalent in 1990 USD

6500

7000

7500

8000

8500

Annu

al le

isur

e ho

urs

0.25

0.5

0.75

0.25

0.5

0.75

Germany 1990U.K. 1990

Duclos et al. (2004) show the equivalence between first-order stochastic dominance and

poverty rankings for all poverty indices that are continuous along the poverty frontier, regardless

of how the poverty frontier is defined and with , 0x yπ π ≤ , 0xyπ ≥ . Moreover, if we strengthen

the conditions on ( , )x yπ to include ,xx yy 0π π ≥ (i.e., the poverty index obeys the transfer

12 It is common to define bivariate poverty by either a union or intersection approach. In the former, an individual is considered to be poor if either indicator of well-being falls below its respective poverty line, whereas in the intersection approach an individual is considered poor only if both indicators fall below the respective poverty lines. An example of the latter would be the working poor who have little leisure and low income. More generally, the poverty cutoff in one dimension can be a function of the level of well-being in the second dimension, as in Duclos et al. (2004).

21

principle in both dimensions), ,xxy yyx 0π π ≤ (i.e., the transfer principle is stronger in one

dimension the lower the level of the other indicator), and 0xx yyπ ≥ then second-order stochastic

dominance of the bivariate distributions over the poverty space implies robust rankings for all

poverty indices that feature these properties. As an example, this class of poverty indices

includes the two-dimensional poverty gap measure, an extension of the FGT index (Foster et al.,

1984) to two dimensions. Thus, based on the test results for bivariate second-order stochastic

dominance, we conclude that bivariate poverty is robustly lower in Germany than in either the

U.K. or the U.S. in the sense described above and is also lower in the U.K. as compared to the

U.S. for individuals 25 years of age and older.

The above poverty orderings are quite general, as they are for all poverty frontiers that

one could care to define within the bivariate support. Stronger poverty orderings, which place

fewer restrictions on ( , )x yπ , are possible if the poverty frontier is restricted to lie within a sub-

region of the support. The obvious drawback is that poverty frontiers that extend beyond this

sub-region are excluded from the analysis. Table 1.5 displays our results of restricted first-order

stochastic dominance tests.

Table 1.5 Estimated p-values for tests of restricted first-order stochastic dominance for individuals 25 years of age and older

1983 1990 2000 Income cut-off 25000 32000 40000 Leisure cut-off 7000 7000 7000 Null hypothesis P-value

1Ger UK n.a. 0.985 0.990

1UK Ger n.a. 0.000 0.000

1Ger US 1.000 1.000 1.000

1US Ger 0.000 0.000 0.000

1UK US n.a. 0.040 1.000

1US UK n.a. 0.000 0.000

We restrict the domain to a lower-left quadrant based on an income upper limit of

$25,000, $32,000, and $40,000 (these are U.S. dollars) for the years 1983, 1990, and 2000,

respectively, and a time-invariant leisure upper limit of 7,000 hours annually. These regions

capture a large portion of the populations, as 14 to 20 percent of the German observations, 32 to

22

44 percent of the U.K. observations, and 41 to 44 percent of the U.S. observations, respectively

fall within these regions. Our results indicate that within these restricted supports the German

bivariate distribution first-order stochastically dominates the U.K. and the U.S. bivariate

distributions, while the U.K. joint distribution first-order stochastically dominates the U.S. in

2000.

We check the robustness of our main results by examining whether the differences in

employment rates are heavily influencing our conclusions by restricting the data to only those

individuals that reported positive hours of work. Table 1.6 displays summary statistics for all

working individuals. In this group, average income and leisure tend to show the same pattern as

for individuals 25 years of age or older.

Table 1.6 Summary statistics for all working individuals

1983 1990 2000Germany

Mean of income 9471 14488 19135Mean of leisure time 7095 7219 7132St. dev. of income 4908 8109 10197St. dev. of leisure time 605 534 559Correlation -0.162 -0.161 -0.224No. of observations 6557 5090 10150

United Kingdom Mean of income n.a. 14615 21840Mean of non-labour market time n.a. 6970 6946St. dev. of income n.a. 7794 14584St. dev. of non-labour market time n.a. 793 742Correlation n.a. -0.213 -0.175No. of observations n.a. 5122 5536

United States Mean of income 11709 17921 27609Mean of non-labour market time 7083 6968 6898St. dev. of income 7752 13281 24451St. dev. of non-labour market time 826 820 808Correlation -0.202 -0.190 -0.110No. of observations 9382 10077 10292

Note: All income values are reported in current year U.S. dollars and reported in per adult equivalent units. “n.a.” denotes not available due to lack of consistent data.

Our results for tests of first- and second-order stochastic dominance among all working

individuals are presented in Table 1.7. We reject first-order stochastic dominance of the bivariate

distributions for all comparisons except the U.S. relative to the U.K. in 1990. This result is

23

puzzling, however, when one considers the results comparing the two marginal distributions. The

integral test rejects the null hypotheses of weak first-order stochastic dominance of the U.K.

income distribution relative to the U.S. and the U.S. leisure distribution relative to the U.K.

Hence, the null of bivariate stochastic dominance should be rejected in both directions. This

inconsistency is similar to that noted above when comparing the bivariate distributions of

Germany and the U.K. in 1990 for all individuals 25 years of age or older. Other results suggest

that the German leisure time distribution second-order stochastically dominates both the U.K.

and U.S. distributions, the U.S. income distribution first-order stochastically dominates those of

Germany and the U.K., and the U.K. is an intermediate case.

Table 1.7 Estimated p-values for tests of stochastic dominance for all working individuals






sGer UK n.a. 0.000 0.000 n.a. 0.985 0.170






sGer US 0.000 0.000 0.000 0.000 0.370 0.075






sUK US n.a. 0.000 0.000 n.a. 0.000 0.005

sUS UK n.a. 0.110 0.005 n.a. 0.825 0.235

24

The second-order stochastic dominance results concerning the bivariate distributions

reverse some of the poverty orderings arrived at previously for all individuals 25 years of age

and older. German poverty remains lowest by all poverty indices with the properties associated

with tests of second-order stochastic dominance outlined above, while U.S. bivariate poverty is

robustly lower than in the U.K., contrasting the results for the previous group of individuals.

These results hold for any poverty frontier. Stronger poverty orderings are presented in Table

1.8. The poverty rankings implied by these results mirror those suggested by tests of second-

order stochastic dominance over the entire support: bivariate poverty is lowest in Germany,

followed by the U.S.

Table 1.8 Estimated p-values for tests of first-order stochastic dominance for all working individuals

1983 1990 2000 Income cut-off 25000 32000 40000 Leisure cut-off 7000 7000 7000 Null hypothesis P-value

1Ger UK n.a. 1.000 0.985

1UK Ger n.a. 0.000 0.000

1Ger US 0.945 1.000 1.000

1US Ger 0.000 0.000 0.000

1UK US n.a. 0.000 0.045

1US UK n.a. 0.415 0.115

An additional check on our primary results is offered by looking only at single-person

households, where the individuals are 25 years of age or older (see Table 1.9 for summary

statistics). For this population subset we need not be concerned with choice of an equivalence

scale for income, nor with our implicit assumption that there are no economies associated with

leisure time for a multi-person household. We present results for tests of first- and second-order

stochastic dominance over the entire support in Table 1.10. We find that the U.S. income

distribution is strongly first-order stochastically dominant with respect to Germany and second-

order dominant with respect to the U.K. Similarly, Germany’s leisure distribution is first-order

stochastically dominant with respect to the U.S. and second-order dominant with respect to the

U.K. Results of tests for bivariate second-order stochastic dominance also follow a similar

pattern to those of our primary population group. Germany is stochastically dominant with

25

respect to both the U.K. and the U.S., although only weakly in 2000 with respect to the U.K., and

the U.K. is stochastically dominant relative to the U.S.

Table 1.9 Summary statistics for all singles 25 years of age or older

1983 1990 2000Germany

Mean of income 8998 13914 17460Mean of non-labour market time 8121 8072 8084St. dev. of income 5937 8777 11219St. dev. of non-labour market time 886 867 898Correlation -0.444 -0.462 -0.392Percentage working 37.1 41.8 39.6No. of observations 1166 918 2712

United Kingdom Mean of income n.a. 14500 23008Mean of non-labour market time n.a. 8074 8082St. dev. of income n.a. 7408 18713St. dev. of non-labour market time n.a. 1015 1015Correlation n.a. -0.433 -0.211Percentage working n.a. 35.5 34.3No. of observations n.a. 930 1082

United States Mean of income 11940 16543 26973Mean of non-labour market time 7674 7567 7389St. dev. of income 7918 13881 25931St. dev. of non-labour market time 1051 1124 1105Correlation -0.435 -0.415 -0.270Percentage working 61.3 60.2 69.1No. of observations 1313 1653 1432

Note: All income values are reported in current year U.S. dollars. “n.a.” denotes not available due to lack of consistent data.

Our results for tests of restricted first-order stochastic dominance, which are provided in

Table 1.11, display patterns that are similar to those obtained for all individuals 25 years of age

or older. For singles 25 years or age or older, Germany is first-order stochastically dominant over

the restricted bivariate surface, while the U.K. is first-order stochastically dominant relative to

the U.S.

26

Table 1.10 Estimated p-values for tests of stochastic dominance for all singles 25 years of age and older






sGer UK n.a. 0.010 0.000 n.a. 0.555 0.145






sGer US 0.045 0.205 0.055 1.000 1.000 1.000






sUK US n.a. 0.275 0.340 n.a. 1.000 1.000

sUS UK n.a. 0.000 0.000 n.a. 0.000 0.000 Table 1.11 Estimated p-values for tests of restricted first-order stochastic dominance for singles 25 years of age and older

1983 1990 2000 Income cut-off 25000 32000 40000 Leisure cut-off 7000 7000 7000

0H P-value

1Ger UK n.a. 0.965 0.955

1UK Ger n.a. 0.000 0.000

1Ger US 1.000 1.000 1.000

1US Ger 0.000 0.000 0.000

1UK US n.a. 0.930 1.000

1US UK n.a. 0.010 0.015

27

1.5 Concluding Remarks

In this paper, we introduce testing procedures for multi-dimensional stochastic

dominance. In particular, we present a framework for testing hypotheses of stochastic dominance

over the entire multidimensional distribution and compound hypotheses involving two or more

stochastic dominance conditions. We use these testing procedures to compare social welfare and

poverty for individuals 25 years of age or older in Germany, the U.K, and the U.S., using income

and leisure as measures of well-being. We find that sufficient conditions for evaluating

differences in bivariate social welfare using stochastic dominance relationships do not hold.

Furthermore, we find that the U.S. income distribution stochastically dominates those of

Germany and the U.K., usually at first-order, while the U.K. income distribution dominates that

of Germany. However, when comparing the leisure distributions the directions of dominance are

reversed. The German leisure distribution dominates that of the U.K. and the U.S., usually at

first-order.

As mentioned previously, when stochastic dominance does not hold over the entire

support, it may be that it holds over a subset of the support. Of particular interest are regions that

could encompass the multidimensional poor. Using stochastic dominance conditions we find

bivariate poverty to be lowest in Germany while orderings for the U.K. relative to the U.S.

depend on the specific subpopulations being compared (e.g., workers only, workers and non-

workers together). These poverty rankings are based on two sets of tests. The first set relies on

first-order stochastic dominance over a sub-region of the support that extends outward from the

origin. The second set of tests employs second-order stochastic dominance, but includes the

entire support. The latter approach requires a more detailed specification of the properties of the

poverty index, but the established rankings are robust to all possible poverty frontiers.

1.6 Appendix

We remove all individuals for whom the “income” variable or the “annual hours worked”

variable is missing or otherwise invalid.

28

INCOME:

We employ the CNEF series I11102XX for Germany and the U.K. and I11113XX for the

U.S. as our measure of post-government household income, where XX refers to the year of the

survey. We convert household income to per adult equivalent units by dividing total household

income by , where A and K represent the number of adults and children,

respectively, in the household, and a child is defined as being between 0 and 14 years of age

inclusive. (This particular equivalence scale was supplied with the cross-national data files.)

Next, we convert from local currency units to U.S. dollars using purchasing power parities for

actual individual consumption taken from the OECD’s National Accounts database. The

conversion factors are 2.1434, 1.8731, and 1.8162 DM per USD for Germany in 1983, 1990, and

2000 respectively, and 0.5688 and 0.6066 pounds per USD for the U.K. in 1990 and 2000.

1 0.7( 1) 0.5e A= + − + K

HOURS WORKED:

For annual hours worked, we employ E11101XX. The GSOEP data does not include

estimates of time off work due to holidays, vacations, sick leave, or other reasons for each year

of the survey. As such, the CNEF-GSOEP uses an estimate of actual weekly time spent working

extrapolated over the year. This procedure overestimates the amount of time actually spent

working. We adjust for this by subtracting the average number of days Germans spent away from

work, as reported by Bach and Koch (2003). We checked this procedure using the 1985 and 2000

GSOEP surveys, which contain estimates of individual time away from work during the previous

year. For both years, we found that the two distributions of annual hours worked, one using the

Bach and Koch (2003) estimates of time away from work and the other using GSOEP estimates,

were very similar. Given the large differences in leisure distributions across countries, the small

difference between these two methods of estimating the German leisure distribution is unlikely

to affect our conclusions.

WEIGHTING:

The GSOEP, BHPS, and PSID are stratified samples. To draw inferences on the

respective populations we use weighting factors available in the CNEF. We employ W11101XX

for Germany, the multiple of W11107XX and W11110XX for the U.K., and the multiple of

W11101XX and W11104XX for the U.S.

29

2 Chapter 2 The Evolution of Income Inequality in Vietnam,

1993-2006

2.1 Introduction

In the late 1980s, Vietnam embarked on an ambitious program of economic reform

popularly referred to as Doi Moi. The reform process began with the devolution of land from

communal to household control, soon extended to the liberalization of agricultural markets, and

has entailed the opening up of nearly every segment of the domestic economy to the international

economy, culminating with Vietnam’s entry into the World Trade Organization in 2007. The

Doi Moi process, with its elements of transition from plan to market and development from

agriculture to non-agriculture, has been accompanied by impressive economic growth. On a per

capita basis, GDP grew 6.0 percent between 1995 and 2006, while per capita incomes increased

by 8.37 percent per annum between 1993 and 2006.

The distributive dimensions of this process are equally important, especially in a

comparative perspective. Over a slightly longer period, China, which has gone through a process

not unlike that of Vietnam’s, has achieved similarly high growth rates, but is noteworthy for the

rapid run-up in inequality in the last two decades (see Ravallion and Chen (2006) and Benjamin

et al. (2008)), albeit in the context of falling poverty rates. Japan, Taiwan and Korea, in contrast,

are often cited as examples of “growth with equity” at similar points in the development process

(see Fei, Ranis and Kuo (1979), Rodrik (1995), and Adelman and Morris (1973)).

The objective of this chapter is to carefully document the behaviour of income inequality

in Vietnam over much of this period following the initiation of Doi Moi. To do so, we draw on

five nationally representative household-level surveys conducted by the General Statistics Office

(GSO) of Vietnam spanning the period between 1993 and 2006. Differences in survey design,

sampling, coverage, as well as mundane issues such as deflators make this less than automatic.

Important differences in the starting conditions between North and South reflecting historical

legacies must also be factored in to any assessment.

30

We construct our own income estimates, but draw on existing estimates of consumption

made by the GSO and the World Bank. In future work, we will revisit the consumption

estimates. The two measures of welfare offer slightly different pictures, but at a minimum

suggest that the rapid growth has not been accompanied by a significant rise in inequality.

Consumption-based inequality estimates show a slight rise in inequality whereas income-based

measures offer a more favourable assessment, with inequality falling significantly in the first

decade before levelling off.

The plan of the chapter is as follows. Section 2.2 provides a background to major policy

changes during Vietnam’s period of economic reform. Next, in Section 2.3, we discuss the

existing literature on inequality within Vietnam. This literature has focused almost exclusively

on per capita consumption, whereas we focus on per capita income for reasons outlined in

Section 2.4. We subsequently describe the five household surveys used for our analysis in

Section 2.5 and discuss the estimation of household income in Section 2.6. In Section 2.7 we

describe the impacts that the reforms have had on employment, endowments, mean income, and

the composition of income by source. Next, in Section 2.8 we explore the distributive

implications of Vietnam’s growth, both for absolute poverty and relative inequality. To increase

our understanding of how income inequality has evolved, we conduct a series of decompositions,

by both geography and source of income. As a final data exercise, we explore the robustness of

our main results in Section 2.9 using various subsamples of households from the 2002 household

survey. Finally, in Section 2.10 we highlight our main conclusions before describing in detail, in

the data appendix, our methodology for constructing consistent definitions of household income

across the five household surveys.

2.2 Background

In the late 1980’s, Vietnam’s government embarked on an ambitious program of

economic and social reform.13 The policy of Doi Moi (or renovation), which was officially

adopted in 1986 by the Sixth Communist Party Congress, aimed to replace a highly bureaucratic

and centralized economic system under state planning with a more decentralized and market-

13 Information for this section is garnered from World Bank (1999), World Bank (2005), and Glewwe (2004).

31

oriented economy, in which the private sector was the key driver of growth. Key elements of the

reform program included land and agricultural reform; overhaul of the SOE (State-owned

enterprise) sector; private business reform; increasing integration with the international economy,

including a more hospitable environment for FDI; and labour and social policy reform.

2.2.1 Land and agricultural reform

The 1987 Land Law and Decree 10 in 1988 were key pieces of reform legislation that

saw the dismantling of the system of agricultural communes, and the distribution of almost all

agricultural land to rural households belonging to each former commune. Under Decree 10, farm

households were defined as autonomous economic units, and effectively made residual claimants

to farm output after paying of taxes. Over time, use rights have become more secure with the

issuing of land use certificates (LUC), and the set of property rights enjoyed by holders of LUCs

has been extended to include the right to sell or mortgage land use rights, to lease or exchange,

and to pass on LUCs through inheritance.

In addition to land reform, agricultural production has been aided by a series of price and

marketing reforms that liberalized the domestic market and removed restrictions on exports of

key farm products. Internal trade was also relaxed for agricultural commodities, particularly in

1997 with Decree 140.

2.2.2 Private business reform

By 1989, most private economic activities were legal in Vietnam. In 1990, a legal

framework for private enterprise was established with the approval of the Private Enterprise Law

and the Company Law, and in 1992 the Vietnamese Constitution officially recognized the role of

the private sector in the economy. The Enterprise Law of 2000 merged the Private Enterprise

Law and Company Law but, in fact, went much further in terms of reform. Previously private

enterprises were subject to a wide range of government approvals and controls. The Enterprise

Law, on the other hand, protected the rights of individuals to establish and operate businesses

largely free of excessive government oversight. Registration procedures were simplified,

allowing businesses to “register first, then check” and over 100 types of business licences were

eliminated, resulting in much less red-tape for businesses.

32

2.2.3 SOE and government reform

Macroeconomic imbalances and fiscal deficits prompted government spending reductions

and SOE sector reform beginning in the late 1980s. Unprofitable SOEs were closed or sold off,

and the number of employees in the sector was cut. Between 1989 and 1992 alone, the number of

SOE’s was halved and one-third of employees were laid off. In 2002 a social safety net fund,

more generous than that fixed in the Labour Code, was instituted for redundant SOE workers to

assist in the equitization process of the SOEs. Further SOE reforms enacted in the 2000s include

the setting up of the Debt and Asset Trading Corporation (DATC) in 2003, under the Ministry of

Finance, to oversee restructuring of poor performers among remaining SOEs.

2.2.4 Trade reform

As evidenced by the application for WTO accession and the signing of the U.S.-Vietnam

Bilateral Trade Agreement in 2001, Vietnam has made significant efforts to conform to

international standards in domestic regulations and administration. Foreign trade and investment

changes were initially more gradual, but beginning in the late 1980s tariffs were reduced, a large

number of quantitative restrictions on trade were abolished, and the foreign exchange rate regime

was liberalized. Other reforms helped to form a legal basis for the equitable treatment of foreign,

private and state firms in an integrated economy.

In 1995, Vietnam became a member of ASEAN and its associated Asian Free Trade Area

(AFTA). Vietnam agreed to reduce to less than 5% all tariffs on imported goods from other

AFTA members, excepting a small number of sensitive products. By 2003, Vietnam had

eliminated all import quotas except for those on sugar and petroleum products, and retained

quantitative restrictions on only a small number of items. The average import tariff in 2003 was

9.3 percent, a fall from 12.7 percent in 1996.

2.2.5 Investment

In 1988, an important law encouraging private investment was implemented, loosening

regulations on joint ventures, and allowing for 100 percent foreign-owned enterprises to operate

33

in the country. The Domestic Investment Promotion Law of 1994 (and later revised in 1998)

expanded access to investment incentives, previously available only to foreigners, to Vietnamese

investors. This focus on strengthening the investment climate was further supported by the Ninth

Party Congress in 2001 when it adopted a goal of increasing FDI by further attracting investment

from multinational companies, particularly those with a technological focus. Further, in 2002,

foreign ownership in nonstate enterprises was permitted in 35 industries, including agriculture,

forestry and fisheries, science and technology, education and medicine.

Until the Common Investment Law of 2005, there had been separate investment schemes

for domestic firms, foreign firms, and cooperatives. The new law harmonized these schemes and

established one comprehensive set of regulations for all types of businesses. Firms no longer had

to give preference to domestic suppliers of goods and services, or purchase from a specific

domestic supplier. Restrictions on the quantity, value or type of goods and services to be

exported were dropped, along with regulations requiring firms to export a fixed percentage of

their production.

2.2.6 Labour

In 1994, the Vietnamese Labour Code came into force, requiring participation in

compulsory social insurance for all workers engaged in a labour contract longer than 3 months.

Businesses engaged in agriculture, forestry, fisheries and salt production were made responsible

for participating in suitable types of social insurance programs. In 2005, coverage was expanded

to include all formal workers regardless of contract type, in all registered enterprises, including

private enterprises. Both workers and employers were to contribute payments that would offset

the costs of their future benefits. Labour reforms proceeded in other areas as well, and in 2002

revisions to the Labour Code set up procedures to deal with collective labour disputes.

2.3 Literature review

The issues surrounding inequality in Vietnam have been popular topics for academic study

in recent years, particularly with the fertile research ground provided by the rapid and wide-

ranging reforms outlined in the previous section. These papers rely almost exclusively on

measures of per capita consumption data, with a limited number of studies using income data.

34

There are a few broad trends in the literature. The first encompasses research that provides

estimates and statistics relating to poverty and inequality. Evidence about inequality in Vietnam

is often reported firstly at the country level, and subsequently broken down into numerous

subsamples, the most common being urban-rural, north-south and regional. Other authors link

data about poverty and consumption to other household characteristics, notably ethnic

background, education level of the head of household and occupation of the head of household,

to reveal patterns of inequality at the household level. There are also a number of authors who

examine the changing structure of employment and its impact on inequality throughout the

country. Almost without exception, these studies rely on the 1993 and 1998 VLSSs, although

there are a small number of studies who report on the period after 1998.

Our review of the literature begins with a brief discussion of results relating to poverty,

followed by an in-depth discussion of papers reporting on inequality in 1993 and 1998, including

some evidence on important household determinants of consumption, and concludes with an

overview of evidence on inequality after 1998. Unless otherwise mentioned, research outlined in

this section uses consumption data.

2.3.1 General evidence on poverty

Over the past two decades, the incidence of poverty in Vietnam has been dramatically

reduced. Van de Walle and Cratty (2004) find that poverty fell significantly over the 1990s.

Molini and Wan (2008) and Glewwe, Gragnolati and Zaman (2002) report that poverty fell from

just over 58 percent in 1993 to 37.4 percent in 1998. Similarly, Litchfield and Justino (2004) use

poverty dominance analysis to find that no matter where the poverty line is situated, the

cumulative distribution function of real annual household expenditure for 1997-98 is always

below that of 1992-93, indicating that consumption per capita poverty was reduced between

1993 and 1998. They also find that although all decile groups benefited from increased mean per

capita consumption levels, only the top two deciles increased their share in consumption

expenditure, revealing an accompanying rise in inequality between deciles.

This trend continued into the 2000s, according to VASS (2007), which reports that the

incidence of poverty fell from 58.1 percent in 1993 to only 19.5 percent in 2004. The poverty

gap also decreased, indicating that those who remain poor are becoming less so. However, some

areas remain very poor (notably the northern mountains, north-central coast and central

35

highlands with poverty headcount ratios over 30 percent) and the gap between the best and worst

performing regions has widened.

2.3.2 Evidence on inequality, 1993-1998

The general consensus in the literature relying on consumption or expenditure data is that

per capita consumption inequality rose slightly between 1993 and 1998. According to Gini

coefficient estimates, expenditure inequality rose from about 0.34 in 1993 to 0.35 in 1998 for all

of Vietnam. This pattern is reported by the Vietnamese Academy of Social Sciences (VASS)

(2007), Gallup (2004), Glewwe, Gragnolati, and Zaman (2002, 2000) and Molini and Wan

(2008), although their estimate of the Gini coefficient in 1993 is a bit lower than most other

estimates.

Several authors break the distribution into deciles to offer a more nuanced view of

inequality in Vietnam. VASS (2007) reports that although the rise in Gini has been modest, the

absolute gap between the top and bottom deciles (or quintiles) has grow more rapidly. Glewwe,

Gragnolati and Zaman (2002) also find that each decile shows an increase in per capita

expenditures, but the increases were consistently higher for better-off deciles. Further evidence

of rising inequality is provided by Litchfield and Justino (2004). They show that growth rates in

mean per capita expenditures by decile generally increase as the decile increases. Consequently,

the share of total expenditures decreased for the bottom eight deciles and increased for the top

two. Conversely, Van de Walle and Cratty (2004) find that households with worse initial

characteristics in 1993 tended to see higher expenditure growth rates, perhaps signalling a

convergence or catching-up process. We now examine evidence on inequality broken down

spatially.

2.3.2.1 Urban-rural inequality from 1993 to 1998

Within rural areas, per capita consumption inequality is estimated to have fallen between

1993 and 1998. VASS reports a Gini coefficient of 0.28 in 1993 and 0.27 in 1998. Similarly,

expenditure inequality within urban areas also reportedly fell from a Gini coefficient of 0.35 to

0.34 between 1993 and 1998. Thus, given the decrease in inequality within rural and urban areas,

and the increase in overall inequality, this suggests that the relative gap between rural and urban

36

areas must have widened between 1993 and 1998. This is reported by Glewwe, Gragnolati, and

Zaman (2000). Using the Theil index of inequality, they show that the urban-rural gap grew in

terms of its contribution to overall inequality between 1993 and 1998. In 1993, they estimate that

about 21 percent of overall inequality was due to the urban-rural gap. By 1998 however, the

urban-rural gap was responsible for about 31 percent of overall inequality. As reported by VASS

(2007), the ratio of urban to rural per capita expenditures increased from 1.91 in 1993 to 2.23 in

1998 and 2.24 in 2004. Fully 96 percent of the increase in expenditure inequality, as measured by

the Theil L index, between 1993 and 1998 could be attributed to the increase in the urban-rural

gap. Similarly, the Theil index calculated by Molini and Wan (2008) shows that in 1993 within

rural inequality was the largest contributor to total inequality, followed by urban inequality and

between group inequality, but by 1998, the urban-rural gap has increased in importance and

explains most of the increase in inequality.

Benjamin and Brandt (2004)’s analysis measures both consumption and income

inequality. They find that income inequality, based on the Gini coefficient, dropped from 0.50 to

0.43 in urban areas between 1993 and 1998, primarily from reductions within regions (north or

south), rather than between regions. Consumption-based Gini coefficients show no change. In

terms of rural income inequality, their data again show a stable coefficient of 0.41 in both years,

but when decomposed by north and south, reveal a slight rise (0.37 to 0.39) in the rural north and

a sharp drop (0.46 to 0.42) in the rural south. Consumption-based measures of inequality show

slight increases between north and south and within these regions

Nguyen et al. (2007) decompose the urban-rural log RPCE gap across the distribution

into two components, separating differences in household characteristics and differences in

returns to those characteristics. The authors find that in 1993, the greatest explanatory factor in

the urban-rural gap is differences in covariates such as education, ethnicity and age. By contrast,

in 1998 they find that this is true only in the lowest quintiles, and above the 30th percentile of the

distribution, the gap is explained more by the differences in returns to these characteristics

between urban and rural. They further find that between 1993 and 1998, changes in covariate

distributions across quantiles are small compared to urban–rural differences.

37

2.3.2.2 Regional inequality 1993 to 1998

Urban-rural inequality has been shown to be an important part of the overall story of

inequality in Vietnam. Another important area of study in the literature is spatial variation

between north-south and different regions in Vietnam. The difference between north and south

Vietnam stems in large part from historical separation and economic policy, while regional

variations are also pronounced.

VASS (2007) reports that the difference in per capita expenditures across regions grew

between 1993 and 2004. Real per capita expenditures grew by 133, 117, and 111 percent

between 1993 and 2004 in the South East, North East, and Red River Delta. By comparison,

growth in real per capita expenditures was a more modest 52, 63 and 67 percent in the North

West, the Mekong River Delta, and the South Central Coast. Since the South East and Red River

Delta, containing Ho Chi Minh City and Hanoi respectively, were already the two richest regions

in 1993, their faster growth has widened the relative gap between the richest and poorest regions.

Using the seven administrative regions listed in the 1992/93 VLSS, Glewwe, Gragnolati, and

Zaman (2000) report that inter-region differences in mean real per capita expenditures

contributed only 13.4 percent of overall inequality in 1992/93 but 21.8 percent by 1997/98.

Benjamin and Brandt (2004) find that mean per capita consumption data do not show a

widening gap between regions, in contrast to other studies. Consumption in urban areas in the

north increased from 90 percent to 93 percent of the levels of the south, while consumption in

rural areas in the north, remained at 83 percent that of the south in both survey years. Their

income results tell a slightly different story. The ratio of per capita income from north to south in

urban areas rose slightly from 0.95 in 1993 to 0.96 in 1998. For rural areas, the north to south

ratio fell dramatically from 0.99 in 1993 to 0.79 in 1998. This implies that inequality between

rural regions may be higher than between urban regions.

Varying rates of decreasing poverty across regions also supports this story. Glewwe,

Gragnolati and Zaman (2002) find that the relative probability of escaping poverty is 474 percent

greater in the South East than in the North Central region. They find that the Central Highlands,

Mekong Delta and Red River Delta households also have a much higher probability than other

regions. They report the largest decline in poverty in the Red River Delta, dropping by 34

percentage points, while the Central Coast and Mekong Delta lagged behind. Van de Walle and

Cratty (2004) and Litchfield and Justino (2004) find similar patterns. The former note that

38

poverty is still very high in the Northern Uplands and very low in the Southeast. The latter find

that households in the Southeast benefited from the largest decreases in poverty by region.

Among rural populations, those households most likely to remain in poverty in both the 1993 and

1998 time period include those in the Northern Uplands. We now move on to research into the

relationships between inequality and household characteristics.

2.3.2.3 Inequality based on household characteristics

Another important stream in the literature examines the correlations between inequality

and household characteristics. Although these covariates are often strongly linked to spatial

features, several authors have teased out the relative importance of a variety of household

characteristics. Results indicate that ethnic background is a very important indicator of poverty

and outcomes, while education, access to infrastructure (particularly markets), employment

status of the head of household, and several other covariates are important predictors.

3.2.3.1 Ethnic minorities and inequality

A dominant explanatory characteristic in the literature is the ethnic background of the

head of household. In the literature, the dominant ethnic Kinh group is found to be better off than

ethnic minorities, without exception. Ethnic Chinese households are even better off than Kinh.

Glewwe, Gragnolati and Zaman (2002), VASS (2007), Molini and Wan (2008), Litchfield and

Justino (2004), Nguyen et al (2007) and Van de Walle and Gunewardena (2001) all report higher

levels of poverty or lower probabilities of exiting poverty for ethnic minorities. In 1993, poverty

is 60 percent for Kinh and Chinese households together, and 80 percent for minorities, the

disparity in living standards showing first order dominance (Van de Walle and Gunewardena,

2001). By 1998, Glewwe, Gragnolati and Zaman (2002) report ethnic Chinese, who live

predominantly in urban areas and particularly in the South East, are slightly better off (poverty

rates falling from 12 to 8 percent) than the majority Kinh (poverty rates falling from 55 to 32

percent). In aggregating all other ethnic groups, poverty rates for minorities remained very high,

at over 75 percent in 1998 (down from 86.4 percent in 1993). They also find the relative

probability of a minority household escaping poverty is 63 percent lower than a Kinh household.

VASS (2007) also notes the relatively limited progress out of poverty of ethnic minorities, with a

39

much higher percentage (61 percent) remaining in poverty compared to the Kinh-Hoa group (14

percent) and the average ethnic minority poor person having a lower consumption level than

Kinh (67 percent of poverty line, as compared to 80 percent of poverty line).

Rationale for these lower outcomes is often discussed as well. Glewwe, Gragnolati and

Zaman (2002) find that concentration in remote areas, higher levels of gender inequity within

households, lower levels of education and linguistic differences are all factors associated with the

high poverty rates of ethnic minorities. Van de Walle and Gunewardena (2001) provide a

detailed analysis of the other household characteristics linked to these lower outcomes for

minorities. Education and literacy statistics reflect the same disparity found in poverty rates, and

contributing geographic factors include decreased access to infrastructure facilities and services,

and larger distances to these facilities on average for minorities. Access to non-farm employment

opportunities are also less common in communes where predominantly minority households

reside. Majority households are also much more likely to derive some income from non-farm

activities (56 percent versus 26 percent).

Further, they find that differences between the minority and majority also occur within

geographic areas, after controlling for household characteristics. Factors such as living in areas

with worse access to infrastructure, education and markets have a stronger negative effect on

minority households. The authors find that a larger component in the difference in minority

consumption is due to geographic location, but that returns to education and land also play a role.

They posit that an inherent difference in the quality of education and land would be consistent

with their results. When geographic effects are examined along with returns to education a

positive effect on minority living standards is revealed, but its magnitude is less than for majority

households in all but primary education. The geographic effect on majority households is much

weaker. Molini and Wan (2008) also find that further education raises expenditures, although

returns are fairly low.

2.3.2.3.1 The impact of other household characteristics

Other household characteristics are predictive of a household being in poverty or having

lower per capita expenditures. Numerous authors have explored these covariates (Gallup (2004),

Molini and Wan (2008), Litchfield and Justino (2004), Glewwe, Gragnolati and Zaman (2002),

Van de Walle and Cratty (2004) and Nguyen at al (2007)). Factors commonly associated with

40

lower measures of welfare include: access to infrastructure (particularly markets according to

Glewwe, Gragnolati and Zaman (2002)) , larger household size, greater number of children in the

household, younger head of household, head of household with lower levels of education, and

head of household engaged only in farm labour. This final characteristic is often seen as

relatively important, but will be discussed in the following sub-section.

Education is often discussed by these authors as well. Glewwe, Gragnolati and Zaman

(2002) reveal that an additional year of formal education by the household head increased the

consumption of households by 3 percent in 1993 and by 3.3 percent in 1998. However, the

returns to vocational education fell from slightly positive to slightly negative. More specifically,

Nguyen et al. (2007) find that education has greater pay-offs in urban areas and in the south, and

in 1993, most in the upper quantiles of urban southern households while in 1998 the greatest

returns are in the urban and rural south.

2.3.2.4 Some evidence relating to income and employment

A final strand of importance in the literature is the correlations between employment and

inequality. Studies can be grouped largely into research on the changing structure of employment

in Vietnam, the shift towards wage employment being of primary concern and the impact of

employment type on inequality. This is an area of concern for the present study, as we examine

the effect on inequality of various types of income over time.

Farming households are often cited as having significantly worse outcomes than

households engaged in other types of labour. Many authors (Van de Walle and Cratty (2004),

Gallup (2004), Glewwe, Gragnolati and Zaman (2002), and Nguyen et al. (2007)) have found

evidence of this, including; low average per capita expenditures, high incidences of poverty,

lower average income, a higher probability of remaining in poverty and negative relationships

between agricultural employment and log RPCE.

Alternate wage employment has been found to improve outcomes for households.

Glewwe, Gragnolati and Zaman (2002) find that white-collar workers, heads of households in

sales, and households with heads in production activities continued to have better outcomes than

farmers in both 1993 and 1998. Vijverberg and Haughton (2004) examine the prevalence of

nonfarm household enterprises (NFHEs) between 1993 and 1998. They find that in 1998 55

percent of ‘solidly affluent’ households operate a NFHE, and only 35 percent of chronically poor

41

households. Similarly, households that rose at least 2 expenditure quintiles from one survey to

the next were more likely to be operating a NFHE than those that fell.

Type of employment has also been shown to have interesting correlations with inequality.

Gallup (2004) reveals that households with primarily farming income have much more equal

expenditures than non-farm households, with a particularly high inequality among households

with predominantly wage income. Gallup (2004) also finds that inequality of household

expenditure increased between 1993 and 1998, while wage inequality was falling. Wage

employment was responsible for an increasing share of inequality, from 26 percent in 1993 to 33

percent in 1998. In addition, wage employment was highly correlated with having high incomes

from other sources, suggesting that an increase in wage earning households had increased overall

inequality. Gallup (2004) also finds that wages in two areas, Ho Chi Minh City and Hanoi, were

significantly higher than the rest of the country, but that wages outside these centres were

similar. The distribution of wages became more equal throughout the 1990s, with the exception

of HCMC and Hanoi, where the wage distribution became more unequal. Related to the urban-

rural inequality gap previously discussed, in Nguyen et al. (2007) sectoral dummies show that

the vast majority of household heads in the rural sector are self-employed. In the urban sector a

majority in the lower quintiles is self employed as well. Benjamin and Brandt (2004) find that

between 1993 and 1998 average incomes increased dramatically, particularly in the rural south,

with the lowest increases recorded in the rural north.

There is also evidence that changes in markets have a substantial impact on the shares of

different types of employment in Vietnam. Vijverberg and Haughton (2004) find that non-farm

household enterprises have increased. However, in the poorest areas of the country, these

enterprises are less important. The importance rises through middle-income cities, and then falls

again for the most affluent area (HCMC). Enterprise survival is clearly related only to market

access. Edmonds and Pavcnik (2006) find that larger rice price increases in a community are

associated with more time spent in off-farm wage labour activities. Between 1993 and 1998,

average weekly hours worked for wages increased by 23 percent across the sample. They posit

that rises in rice prices are a proxy for increasing growth in trade in an area, allowing households

to specialize. McCaig (2009) also finds an increase of the number of workers reporting wage

labour as their most time-consuming job from 29 percent in 2002 to 31 percent in 2004. He also

finds that provinces that were more exposed to the tariff cuts associated with the United States-

Vietnam Bilateral Trade Agreement (USBTA) experienced (1) increases in provincial wage

42

premiums for low-skilled workers, (2) faster movement into wage and salaried jobs for low-

skilled workers, and (3) more rapid job growth in formal enterprises.

2.3.3 Inequality evidence since 1998

Following 1998, there is much less evidence in the literature concerning the evolution of

inequality. VASS (2007) reports an increase in expenditure inequality between 1998 and 2002,

but a stable level of expenditure inequality between 2002 and 2004 for all of Vietnam. Further,

they report that within urban areas expenditure inequality increased between 1998 and 2002,

before falling again by 2004. By comparison, expenditure inequality within rural areas slowly

increased between 1998 and 2004, such that inequality within rural areas was approaching

inequality levels in urban areas. Hence, between 1998 and 2004, only 39 percent of the increase

in expenditure inequality was due to the increase in the urban-rural gap while 61 percent was due

to changes in inequality with urban and rural areas. Again, this is based on the Theil's index of

inequality.

McCaig (2009) also examines the trends in poverty reduction between 2002 and 2004 in

his analysis of the impact of the USBTA. He finds that the national poverty rate fell by 9.4

percent between 2002 and 2004. In addition, the average decrease in provincial poverty was 31.1

percent in the same time period. Between 2002 and 2004, provinces that were more exposed to

the USBTA experienced faster decreases in poverty. He finds that an increase of one standard

deviation in provincial exposure approximately equivalent to an increase in exposure from the

25th percentile, the province of Ha Tinh, to the 75th percentile, the province of Tien Giang, leads

to a reduction in the poverty headcount ratio of approximately 11 to 14 percent, but this effect

diminishes the further the province is from a major seaport.

2.4 Our Contribution

In contrast to most of the existing literature on inequality within Vietnam, we focus on

per capita income instead of per capita consumption. We do this for a number of reasons. First,

income itself is an important determinant of well-being and hence deserves analysis. Second, the

existing consumption/expenditure estimates generated by the GSO and the World Bank have

some deficiencies. For example, as explained in Glewwe (2005), the imputed value of housing

43

services is estimated as a constant share of other non-food expenditures. In urban areas housing

is valued at 21.4 percent of other non-food expenditures and in rural areas the corresponding

share is 11.8 percent. Hence, the comparable consumption data series may be severely

undervaluing housing if the value of housing has grown more rapidly than other non-food

expenditures. This seems a likely outcome. Third, using income provides us the basis for

additional decomposition exercises to help understand the impact of Vietnam’s changing

economic structure on inequality. Fourth, in an economy that has been growing as rapidly as

Vietnam’s, consumption may lag behind income as households take time to adjust to their higher

permanent income.

With these reasons in mind, we construct estimates of household income that are as

consistent as possible across the five household surveys and that are based on market prices. We

are not aware of similar exercises that have been done for Vietnam. As we allude to throughout

the paper, changes in the questionnaire and changes in the survey framework make this a

challenging task.

2.5 Data

The data used for our analysis comes from five nationally representative household

surveys conducted by the General Statistics Office (GSO) of Vietnam: The 1992/93 and 1997/98

Vietnam Living Standards Surveys (VLSS) and the 2002, 2004 and 2006 Vietnam Household

Living Standards Surveys (VHLSS).14 Each survey collected detailed information from

households on income generating activities, expenditures, employment, education, health, land

holdings, and remittances.

Table 2.1 provides summary information relating to these surveys. A total of 4,800 and

6,000 households were interviewed for the 1993 VLSS and the 1998 VLSS. Beginning with the

2002 VHLSS, the number of households interviewed was greatly increased however not all

households were asked the full set of modules in the questionnaires. In 2002, 2004 and 2006, a

total of 74 347, 45 945, and 45 945, households were surveyed, of which 29 530, 9 188 and 9

189 were asked the full questionnaire.

14 For simplicity, from hereon we refer to the 1992/93 VLSS survey as the 1993 VLSS and the 1997/98 VLSS as the 1998 VLSS since the majority of households were interviewed in 1993 and 1998 respectively.

44

Table 2.1 Summary information on the sample design of each household survey

Survey Year 1993 1998 2002 2004 2006

Number of households 4,800 5,999 29,530 9,188 9,189 Number of communes 150 194 2,901 3,063 3,063

Number of strata 2 10 122 128 128

We restrict our analysis to households asked the full questionnaire in the three VHLSS

for two reasons. First, McCaig (2008a, 2008b) documents large differences in mean income

between households that were asked the entire questionnaire and those that were not asked the

expenditures module in both the 2002 and 2004 VHLSS. In all other respects, e.g. household

size, demographic composition, landholding, economic activity, etc., these households are

identical.15 Second, some questions related to land rental income and payments were only part of

an extended land module in the 2004 VHLSS, which was asked of the smaller 9 188 household

sample. Thus, in an effort to be as consistent as possible in our definition of income across the

surveys, we exclude the households from the 2002, 2004 and 2006 VHLSS that were not asked

the expenditure module.

2.5.1 Household survey design

In 1993, 80 percent of the households interviewed were living in rural areas, with the

remaining 20 percent drawn from urban areas. By 2006, the share of urban households

interviewed had grown slightly to 25 percent. These numbers are based on the contemporaneous

definition of urban and rural within each survey. Note, however, that the shares of urban and

rural households in the surveys are not necessarily the same as the shares in the overall

population as the five surveys used stratified sampling procedures. The GSO estimates the rural

population to be 23.6 percent of the total population as of April 1, 1999 according to the 1999

population census.

15 There are several potential explanations for this. First, it is possible that households asked about expenditures recall income differently than households that are only asked about incomes. Second, enumerators may use information on consumption as a check on reported income information. And third, because of the extended length of interviews involving both income and expenditure, more able enumerators may have been assigned to these households.

45

The 1993 VLSS was self-weighted with each household in the survey having an equal

probability of being selected.16 A total of 150 communes throughout the country were selected

with probability proportional to their size, with 20 percent in urban areas and 80 percent in rural

areas. In the next stage, two clusters in urban areas or two villages in rural areas were randomly

drawn from each commune with probability proportional to their size. Finally, 20 households

were randomly selected from each of the 300 clusters or villages. The 1993 sample includes

households from rural areas in 51 provinces and from urban areas in 21 provinces out of 53

provinces at the time. There are two provinces from which no households were sampled.

The 1998 VLSS had a much more complicated survey framework made up of three

groups of households: households that were reinterviewed from the 1993 VLSS, households

replacing those from the 1993 VLSS that could not be located, and households that were added

to the sample to increase the sample size.17 Also, in contrast to the 1993 VLSS, the 1998 VLSS

was stratified not just by urban-rural, but also by three urban and seven rural areas, for a total of

10 strata. The three urban strata are (1) Ha Noi and Ho Chi Minh City, (2) other cities, and (3)

the remaining urban population in towns. The seven rural areas are the Northern Mountains, the

Red River Delta, the North Central Coast, the South Central Coast, the Central Highlands, the

Southeast, and the Mekong River Delta. The finer level of stratification meant that the survey

included households in 59 of Vietnam’s 61 provinces.18 Households were surveyed in rural areas

of 58 provinces and in urban areas of 29 provinces.

Starting with the 2002 VHLSS the sampling framework changed in two important

ways.19 First, the stratification was now done for urban and rural areas in each province,

bringing the total number of strata to 122. The GSO wanted to be able to produce reliable

16 The discussion of the sampling framework for the 1993 VLSS is taken from World Bank (2000), “Viet Nam Living Standards Survey (VNLSS), 1992-93: Basic Information.” 17 The discussion of the sampling framework for the 1998 VLSS is taken from World Bank (2001), “Vietnam Living Standards Survey (VLSS), 1997-98: Basic Information.” 18 The number of provinces within Vietnam increased from 1993 to 2006. In 1993 the number of provinces was 53. Between 1993 and 1998 eight provinces were split into two new provinces, bringing the total number of provinces to 61. Between the 2002 and 2004 surveys three more provinces split in two, bringing the total number of provinces to 64. To be consistent across the surveys we code households and provinces according to the 61-province definitions. 19 The discussion of the sampling framework for the 2002 and 2004 VHLSSs is taken from GSO (year unknown), “Vietnam Household Living Standards Surveys (VHLSS), 2002 and 2004: Basic Information.”

46

estimates of poverty at the provincial level, which necessitated the stratification by province.

Second, the sampling framework was based on the master list from the 1999 Population and

Housing Census. Communes within each stratum were selected with a probability proportional to

the square root of its population, followed by the selection of one ward in urban areas or one

village in rural areas per commune, again with the selection probability being proportional to the

square root of the population. Finally, households were randomly selected within each ward or

village. One further noteworthy difference is the large increase in the number of communes

covered by the 2002 VHLSS relative to the two earlier VLSSs. In 2002 a total of 2 901

communes are included in the sample as compared to only 150 in the 1993 VLSS and 194 in the

1998 VLSS.

The sampling framework for the 2004 and 2006 VHLSSs is essentially identical to that of

the 2002 VHLSS with a few minor exceptions. First, the number of households selected per ward

or commune was decreased from an average of ten to three. Second, the number of communes

selected increased slightly to 3 063. Third, since three provinces split in two between 2002 and

2004, raising the number of provinces to 64, the total number of strata increased to 128.

2.5.2 Household membership and missing members

For the 1993 VLSS household members were defined to include “all the people who

normally live and eat their meals together in this dwelling.” Those who were absent more than

six of the last twelve months were excluded, except for the head of the household and infants less

than six months old. If the individual was away for more than six months out of the past twelve,

they were only considered a household member if they were a student living away from home

but still supported by the household, or if a new member had recently joined the household and

they would be living there permanently in the future. There were a few minor changes in

definition of household member between the two VLSSs, and then between the VLSSs and the

VHLSSs, but this only affected a very small percentage of individuals and households.

Despite the high degree of comparability of the definition of household membership, a

comparison of gender-age cohorts across the household surveys with the projections based on the

1999 Population and Housing Census suggests some “missing” individuals within the surveys.

Essentially, the problem is that the household surveys seem to be under enumerating young

individuals and young couples with children relative to the census-based projections. Figures 2.1

47

and 2.2 demonstrate the issue. Figure 2.1 compares the age structure for males and females in the

1993 VLSS with an age distribution projection based on the 3 percent sample of the census,

while Figure 2.2 does the same for 2006. Each figure is adjusted by the appropriate sampling

weight. A positive value for the difference in population between the two sources indicates that

the census numbers suggest a larger cohort than the corresponding household survey. A negative

value suggests the opposite. Two key insights emerge. First, the household surveys appear to

under-enumerate very young children and adults in their 20s and 30s, which is offset by a

disproportionately larger share of teenagers and older adults. Second, the patterns in the 1993

and 2006 comparisons are very similar, but the magnitude of the differences appears to be

growing over time. In 1993, the sum of one-half20 of the absolute value of the difference was

equal to 3.5 percent of the population, but by 2006 this increased to 9 percent.21

Figure 2.1 Difference between the projected 1993 population using the 1999 census and the 1993 VLSS

-500000-400000-300000-200000-100000

0100000200000300000400000500000

1 3 5 7 9 11 13 15 17 19 21

Census Projections minus Weighted 1993

Gap_Males

Gap_Females

Note: The population has been grouped into five-year age groups. Thus, the label 1 on the horizontal axis corresponds to individuals aged 0 to 4, the label 2 corresponds

20 Because the two populations are the same size, an overestimate in the size of one group is exactly offset by an underestimate elsewhere. Thus, we divide by 2. 21 This is partially confirmed using information from the 2006 VHLSS to identify those individuals in the 2004-2006 household panel that drop out. Those individuals leaving because of work or household division represent 3.6 percent of the population in 2004 of households in the 2004-2006 panel.

48

to individuals aged 5 to 9, etc.

Figure 2.2 Difference between the projected 1993 population using the 1999 census and the 2006 VHLSS

Census Projections minus Weighted 2006 VLSS

-1000000

-500000

0

500000

1000000

1500000

1 3 5 7 9 11 13 15 17 19 21

Demographic Cohort

Gap_MalesGap_Females

Note: The population has been grouped into five-year age groups. Thus, the label 1 on the horizontal axis corresponds to individuals aged 0 to 4, the label 2 corresponds to individuals aged 5 to 9, etc.

The most likely reason for this is migration to urban areas, which is not being picked up

in the urban samples of the household surveys. The combination of “missing” young children

and males and females in their 20s and 30s suggests that some of this is due to entire families

migrating as opposed to single individuals. The biases that these missing individuals will

introduce into our estimates of mean income and inequality are hard to assess. On the one hand,

the missing adults are likely better educated than older cohorts and thus their per capita incomes

may be higher than the individuals observed in the household surveys. However, part of their

income may be picked up by the surveys in terms of gifts and remittances. Trying to decipher the

impact on inequality is even more problematic. We do not have a solution to this problem, but

raise the issue because it is important for evaluating the comparability of the surveys across time

and the representativeness of the surveys relative to the overall population. This problem would

impact all studies using the same five household surveys, using either income or consumption

estimates. To our knowledge this issue is overlooked in the relevant literature.

49

2.6 Estimation of household income

We aggregate annual household income into six major categories: income from crops,

income from agricultural sidelines, household business income, wage income, gifts and

remittances, and finally “other” residual sources of income. Each income component is gross of

taxes and fees. Income from crops is net income (gross revenue22 minus current expenditures)

from rice; other cereal, vegetable, and annual crops; industrial crops; fruit crops; and crop by-

products such as straw, leaves, etc. Agricultural sidelines include livestock, aquaculture, other

animal products, and agricultural services. Household business income is net income from non-

agriculture, non-forestry, and non-aquaculture businesses run by the household and includes the

processing of agricultural, forestry, and aquacultural products. Wage income includes salary or

wage payments plus additional payments such as holiday contributions, social insurance

payments, etc. for all jobs worked by the individual during the past 12 months. Gifts and

remittances include payments from both domestic and overseas sources. Finally, “other” residual

sources of income include items such as government transfers and earned interest.

We convert all nominal incomes into nationally representative January 2006 prices using

three sets of deflators. First, since the households within each survey are interviewed during

different months we use the monthly deflators included in the datasets to convert the reported

values to January prices of the respective survey year. Second, to reflect differences in the cost of

living across regions we employ regional deflators. And third, to link January prices of each

respective survey year to January 2006, we utilize GSO monthly CPI figures. The respective CPI

inflators are 2.028, 1.393, 1.279, and 1.193 for 1993, 1998, 2002, and 2004.

For 2002 and 2004, we selected not to use the regional deflators provided by the GSO

with the household datasets. Table 2.2 helps show why, especially for the urban deflators. In

1993, the deflator for the urban Southeast, which includes Ho Chi Minh City, is 1.23 implying

that prices were, on average, 23 percent higher than the national average. However, in 2002 and

2004, the regional deflator was only 1.04, before increasing to 1.23 again in 2006. This extreme

22 We calculate the value of harvested output at market prices following Benjamin, Brandt, and Giles (2005). See

the appendix for more details.

50

movement seems highly improbable as do prices in the urban Southeast being only 4 percent

higher than the national average. Thus, instead of using the deflators supplied with the 2002 and

2004 VHLSS datasets, we impute the regional deflators for these years based on the regional

deflators for 1998 and 2006. These are shown in the bottom panel of Table 2.2.

To assist the reader in interpreting the income levels reported below, note that in 2005 the

market exchange rate was 15 900 Vietnamese dong per US dollar. In purchasing power parity

terms, the International Comparison Program estimated the value of a US dollar in 2005 at 4 700

Vietnamese dong (World Bank, 2008).

Table 2.2 Regional deflators included in the household surveys

Regional deflators included in the dataset Region 1993 VLSS 1998 VLSS 2002 VHLSS2004 VHLSS 2006 VHLSS

URBAN Red River Delta 1.066 1.056 0.990 1.017 1.084

Northern Uplands 1.082 0.993 North East 0.984 1.008 0.963 North West 1.059 1.061 1.020 North Central Coast 1.011 1.025 0.988 1.004 0.996 South Central Coast 1.055 1.053 1.011 1.013 1.073 Central Highlands 1.071 1.059 1.036

South East 1.232 1.134 1.044 1.043 1.234 Mekong River Delta 1.093 1.013 1.027 1.018 1.096 RURAL

Red River Delta 0.895 0.916 0.949 0.948 1.007 Northern Uplands 0.969 1.018

North East 0.977 0.974 0.908 North West 1.041 1.030 0.989 North Central Coast 0.981 0.938 0.967 0.974 0.862 South Central Coast 0.976 0.974 0.996 0.996 0.976 Central Highlands 1.064 1.060 1.068 1.043 0.931

South East 1.115 0.965 1.037 1.033 1.061 Mekong River Delta 1.020 1.027 1.023 1.015 0.958

51

Table 2.2 Continued Regional deflators used in the analysis

Region 1993 VLSS 1998 VLSS 2002 VHLSS2004 VHLSS 2006 VHLSS URBAN


North East 0.978 0.970 0.963 North West 1.007 1.013 1.020 North Central Coast 1.011 1.025 1.011 1.003 0.996 South Central Coast 1.055 1.053 1.063 1.068 1.073 Central Highlands 1.036 1.036 1.036

South East 1.232 1.134 1.183 1.208 1.234 Mekong River Delta 1.093 1.013 1.054 1.075 1.096 RURAL


North East 0.981 0.944 0.908 North West 1.024 1.006 0.989 North Central Coast 0.981 0.938 0.899 0.880 0.862 South Central Coast 0.976 0.974 0.975 0.976 0.976 Central Highlands 1.064 1.060 0.994 0.962 0.931

South East 1.115 0.965 1.012 1.036 1.061 Mekong River Delta 1.020 1.027 0.992 0.975 0.958

2.7 Main evidence As background to our analysis of the evolution of incomes using the five household

surveys, we begin by examining two related dimensions of household behaviour: the structure of

employment in Section 2.7.1; and household endowments in Section 2.7.2, including household

size, distribution of land and distribution of human capital (education). We outline the initial

statistics and trends across the relevant time period. Following this, in Section 2.7.3 we examine

changes in incomes. This includes a discussion of mean per capita income, a comparison

between consumption and income behaviour, and a decomposition of total income into its

various sources. This information will be used to make some preliminary correlations to our

results on the evolution of inequality.

52

2.7.1 Impacts of reforms on employment

The economic reforms from the Doi Moi period highlighted in Section 2.2 can be seen in

all facets of Vietnam’s economy. In this section, we provide a brief description of how the

composition of the labour market has changed between 1993 and 2006. We do not explicitly link

the labour market changes to any particular policy changes. Instead, our goal is to illustrate the

profound impacts that the reforms have had on the structure of the economy and also to provide a

picture of the initial conditions of our study.

In 1993, Vietnam’s labour force was overwhelmingly engaged in the primary sector,

which includes agriculture, forestry and aquaculture.23 Table 2.3 provides a breakdown on the

basis of the primary, secondary and tertiary sectors for an individual’s main job. In 1993, 73.7

percent of all labour was classified as working in the primary sector, followed by 15.6 percent in

the tertiary sector, and 10.6 percent in the secondary sector. Note the significantly higher

percentage in the primary sector in the north than in the south (81.2 versus 65.4). The share of

primary sector employment falls steadily over time, and by 2006 the percentage employed fell to

53.0, or a reduction of 20.7 percentage points over a fifteen year period. Labour leaving

agriculture was absorbed by rapidly growing employment in the tertiary sector, and to a slightly

lesser extent, the secondary sector. Also, by 2006, the gap in the role of primary sector

employment between the north and south had narrowed considerably.

Two other dimensions of the structure of employment are noteworthy. First, a significant

percentage of those working in Vietnam are self-employed as shown in Table 2.4. In 1993, the

percentage was 84.1 percent. A good portion of this reflects the fact that a high percentage of

those working in the dominant primary sector are working on their own farms, but in 1993 more

than half of those working in either the secondary or tertiary sector were also self-employed,

typically in small family businesses. By 2006, the percentage of the labour force that was self-

employed fell to 66.9 percent. Self-employment in the primary and tertiary sectors remained

especially important, but in the secondary sector, the percentage working for other households or

firms rose to three out of every four workers.

23 We define the labor force to include all individuals that report working during the past 12 months, regardless of age or duration of work.

53

Table 2.3 Percentage of workers by major industrial sector 1993 1998 2002 2004 2006 All of Vietnam Primary 73.7 69.6 60.2 56.0 53.0 Secondary 10.6 11.0 16.0 17.7 19.3 Tertiary 15.6 19.0 23.8 26.3 27.8 North Vietnam Primary 81.2 76.9 64.6 60.9 61.3 Secondary 8.6 9.1 15.4 17.6 16.8 Tertiary 10.2 14.0 20.0 21.5 21.8 South Vietnam Primary 65.4 60.9 56.7 50.9 51.7 Secondary 13.0 14.3 16.5 17.9 18.2 Tertiary 21.7 24.8 27.8 31.3 30.1 Rural Vietnam Primary 84.5 82.4 71.3 68.0 65.3 Secondary 6.8 7.5 13.1 15.0 16.6 Tertiary 8.8 10.1 15.6 17.0 18.1 Urban Vietnam Primary 24.3 17.9 19.4 17.0 15.7 Secondary 28.5 27.4 26.4 26.5 27.4 Tertiary 47.2 54.8 54.2 56.5 57.0 Note: This is based on the individual’s main job during the previous 12 months. All figures have been weighted to account for non-equal sampling probabilities. All workers are included regardless of age or duration of work.

Table 2.4 Shares of workers that are self-employed versus working for others 1993 2006 Self-employed 84.1 66.9 In primary 69.2 47.7 In secondary 4.9 4.9 In tertiary 10.0 14.4 Working for others 15.9 33.1 In primary 4.5 5.3 In secondary 5.8 14.4 In tertiary 5.6 13.4

Second, a significant percentage of those working for wages were in the state sector,

which was second only to the private sector (defined here to include working for other

households) as a source of wage employment in 1993 (see Table 2.5). As a result, the

distribution of earnings, and thus inequality, were affected by wage behaviour in the state sector.

In 1993, 38.6 percent of workers were in the state sector, with an even higher percentage, 54.6

percent, in the north, compared to 30.7 percent in the south. The role of the state sector is even

54

more pronounced if we focus solely on the urban sector.24 After 1993, the role of state sector

employment falls as the percentage of the labour force working for wages rises from 15.9 in

1993 to 33.1 percent in 2006. But even as late as 2006, nearly one out of every three individuals

that were not working for themselves was employed by the state. In the urban sector, it was 43.6

percent, with the percentage nearly sixty percent in the north, and a third in the south. By

comparison, the percentage of those working for wages that were employed by foreign firms was

only 6 percent, which amounts to only 2 percent of the entire labour force.

Table 2.5 Share of wage earners working for the state, collectives, the private sector, and the foreign sector

1993 1998 2002 2004 2006 All of Vietnam State 38.6 39.8 33.9 32.9 31.3 Collective 4.8 2.4 2.0 2.0 2.0 Private 56.0 56.4 61.1 60.5 61.0 Private enterprise 11.2 7.1 14.0 15.2 Other households 55.9 28.6 54.0 46.5 45.8 Other 0.1 16.6 Foreign 0.6 1.5 3.1 4.6 5.8 North Vietnam State 54.6 53.0 44.2 42.1 38.1 Collective 10.7 4.8 3.4 3.1 2.8 Private 34.7 41.9 50.3 52.1 55.0 Private enterprise 10.2 5.2 11.3 13.7 34.7 Other households 26.4 45.1 40.8 41.3 Other 0.0 5.3 Foreign 0.0 0.3 2.0 2.7 4.1 South Vietnam State 30.7 32.4 26.8 26.2 26.3 Collective 2.0 1.0 1.0 1.3 1.4 Private 66.5 64.4 68.4 66.6 65.2 Private enterprise 11.8 8.3 16.0 16.2 Other households 66.3 29.8 60.1 50.6 49.0 Other 0.1 22.8 Foreign 0.9 2.1 3.8 5.9 7.0

24 The estimates for 1993 already reflect significant layoffs in the state sector in the late 1980s, implying an even larger role for the state sector a few years earlier.

55

As we can see, there have been significant changes in the structure of employment in

Vietnam between 1993 and 2006. This information proves interesting when we decompose

inequality by source of income in the Section 2.8.

2.7.2 Evolution of household and individual endowments

Both the level and distribution of per capita incomes in Vietnam are influenced by

endowments, as well as the returns to these endowments. These relationships are often

formalized through decomposition exercises of inequality measures. Our immediate objective

here is more modest, and largely descriptive. We focus on two key endowments: land and human

capital, supplemented with a brief discussion of household size. In 1993, more than 70 percent of

the labour force was in the primary sector (see Table 2.3) and income from cropping accounted

for slightly less than half of total rural household income (see Table 2.11 below). Hence, the

distribution of land is important for income and inequality, especially early in the reform process.

In the transition from a planned to a more market-based economy, we also expect human capital

to figure more prominently as remuneration in the labour market increasingly reflects individual

productivity. This has been the case in China (Zhang et al. (2005)).

2.7.2.1 Household size

In Table 2.6, we report information on household size for each of the five household

surveys. In 1993, average household size was 4.97. Average household size was significantly

larger in the south than in north, 5.41 versus 4.59, however in both regions there were no

differences between urban and rural households. Over the next 14 years, average household size

falls to 4.26, a decline of nearly fifteen percent. We observe an even larger reduction in the

South, and by 2006 differences in average household size between the north and south have

largely disappeared.

Reductions in household size reflect a multitude of factors including changes in fertility

behaviour, a shift in household types, e.g. from extended to nuclear, and changing living

arrangements. These factors impact dependency ratios, typically measured as the ratio of

dependents in the household to those working, and thus household income and inequality. These

are potentially informative, but will not be examined here.

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Table 2.6 Mean household size 1993 1998 2002 2004 2006 All Vietnam 4.97 4.70 4.43 4.34 4.20 Rural 4.97 4.80 4.49 4.39 4.25 North 4.67 4.58 4.38 4.28 4.16 South 5.38 5.10 4.63 4.51 4.35 Urban 4.94 4.38 4.25 4.23 4.08 North 4.15 3.98 4.01 3.97 3.83 South 5.46 4.66 4.41 4.38 4.24

2.7.2.2 Distribution of land

As previously mentioned, access to land will be important in a country with such a high

share of income derived from cropping. In this section, we focus on access to land of rural

households. There are marked differences across regions in terms of land access and inequality,

which can be attributed to alternative historical and institutional factors at work (Brandt et al.,

2006). These differences are often glossed over in the literature, which tends to paint a single

picture of the evolution of land access, singling out as the watershed events the

decollectivizatioun of agriculture in the late 1980s and the 1993 Land Law. For major parts of

Vietnam, collectivized agriculture (and thus, decollectivization) was never part of the story.

Agricultural land in Vietnam includes several major categories of land including: annual,

perennial, water surface area, and forestry land. Also usually included are gardens and ponds

adjacent to households’ residential land that is not included in the four primary categories, but

which is used for agriculture-related purposes. In Table 2.7, we report summary data on the

inequality of household agricultural land holdings for select years.25 We focus on landholdings

for which households report having long-term use rights.26 In the regions in the north, a highly

egalitarian redistribution of land in the late 1980s contributed to low levels of landlessness, and

low overall inequality of landholdings. In the south, especially in the densely populated

Southeast and Mekong River Delta, we observe significantly higher degrees of landlessness, and

25 Differences in 2002 in how land is reported make the data less than perfectly comparable. 26 We exclude land that households may have rented in. Land rental remains relatively minor in rural Vietnam, and inequality of landholdings based on cultivated holdings, i.e. land with long-term use rights, land rented out + land rented in, is very similar to that based on land with long-term use rights.

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inequality. In 1993, nearly one out of every six households in these two regions is landless. The

contrast with the Red River Delta is especially large.

Table 2.7 Landlessness and land inequality in rural Vietnam

% Landlessness Gini 1993 1998 2004 1993 1998 2004 North Vietnam Red River Delta 2.81 1.13 6.04 0.28 0.30 0.38 North East 1.74 5.45 4.52 0.42 0.54 0.59 North West 0.00 4.98 1.47 0.38 0.30 0.52 North Central Coast 4.17 8.37 8.54 0.41 0.59 0.49 South South Central Coast 10.62 6.35 13.86 0.34 0.43 0.69 Central Highlands 6.25 13.46 4.15 0.52 0.56 0.43 South East 17.50 21.97 38.83 0.54 0.62 0.75 Mekong River Delta 15.90 21.32 26.22 0.51 0.56 0.62 All rural Vietnam 7.20 10.30 14.38 0.49 0.57 0.64

Over time, we observe an increase both in the degree of landlessness and inequality of

landholding. The increase is especially sharp in the Southeast and the Mekong River Delta,

where by 2004 landlessness rises to 38.8 and 26.2 percent of all rural households, respectively. In

the north, the percentage of households that are landless rises as well, but still remains

considerably below that in the south, with the exception of the Central Highlands. More

noticeable in the north are the increases in land inequality, which rises fairly monotonically over

time.

All else being equal, we expect rising inequality in land access to be associated with

rising income inequality. Of course, this will be offset by the declining role of land and

agriculture in the economy, and the growth in off-farm opportunities, both of which are

important parts of the story.

2.7.2.3 Distribution of human capital (education)

Productivity of individuals is linked to their human capital, which includes both health

and education. In a socialist economy, the link between earnings and productivity is often

dampened as a result of more egalitarian institutions. These institutions tend to break down with

the emergence of a market-based economy and we expect factors like education to play a larger

role in income-determination. Two factors are potentially at work. First, on the margin,

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individuals will be able to capture a larger portion of their marginal product. Second, the reform

process may also put a premium on education in the labour market, and the returns to education

will likely rise. A discussion of the evolution of educational attainment in Vietnam will provide

some tentative insights into the accompanying evolution in income inequality.

In Tables 2.8 and 2.9, we report for each survey, and for all individuals in the labour

force, mean educational attainment and the Gini coefficient for educational attainment. In 1993,

mean educational attainment was 6.15 years, which rises fairly smoothly to 7.62 years by 2006.

Especially noteworthy are the initial differences between the north and south, and urban and

rural. On average, individuals in the north had nearly 1.5 more years of education. In both

regions, education attainment of individuals living in rural areas lags behind that of urban

residents.

Table 2.8 Mean levels of education of all workers

1993 1998 2002 2004 2006 All of Vietnam 6.15 6.59 7.22 7.59 7.62 Urban Vietnam 7.97 8.53 9.18 9.44 9.25 Rural Vietnam 5.75 6.10 6.69 7.01 7.08 North Vietnam 6.75 7.21 7.96 8.29 8.32 South Vietnam 5.33 5.85 6.46 6.85 6.91 Note: Workers include all individuals that reported working during the last 12 months regardless of age or duration of work.

Table 2.9 Gini coefficient estimates of educational inequality for all workers

1993 1998 2002 2004 2006 All of Vietnam 0.344 0.309 0.289 0.279 0.267 Urban Vietnam 0.275 0.246 0.239 0.235 0.210 Rural Vietnam 0.353 0.316 0.291 0.282 0.277 North Vietnam 0.297 0.262 0.240 0.228 0.216 South Vietnam 0.389 0.356 0.328 0.323 0.309 Note: Workers include all individuals that reported working during the last 12 months regardless of age or duration of work.

Higher levels of educational attainment in the north in 1993 were accompanied by much

lower levels of inequality. For example, the Gini coefficient for the educational inequality for all

workers was 0.297 in the north compared to 0.389 in the south. Over time, however, and in the

context of a secular rise in educational attainment, we observe a significant reduction in

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inequality. This is reflected in a reduction in urban-rural and north-south differences, as well as

in the Gini coefficients reported in Table 2.9 for all of Vietnam and separately for urban and

rural and north and south. Between 1993 and 2006, the aggregate Gini falls from 0.344 to 0.267,

a reduction of more than twenty percent. At every level, we observe a reduction in inequality in

educational attainment.27 More equal access to education over time is likely contributing to this

fall through lower within-cohort educational inequality for younger cohorts.

National income accounts suggest that between fifty and sixty percent of GDP represents

the returns to labour. Hence, all else equal, we might expect falling inequality in educational

achievement in the labour force to be associated with reductions in income inequality.

In the remainder of the paper we do not explicitly link the changes in educational

attainment inequality to changes in income inequality. Instead, the purpose of the above

discussion was to highlight how the distribution of an important determinant of individual

remuneration, education, has evolved over time as changes in individual endowments will

influence income.

2.7.3 Incomes

We being our discussion of income by examining the trends in mean per capita income

for all of Vietnam, rural and urban Vietnam, and the rural and urban areas disaggregated by

North and South. Next, we compare our estimates of mean per capita income with estimates of

mean per capita consumption using the GSO/World Bank consumption estimates. Subsequently,

we focus on the individual components of income (crops, wages, etc.) to elucidate which sources

of income have been important contributors to the rapid income growth experienced in Vietnam

between 1993 and 2006. Furthermore, since participation in and earnings from income activities

are often correlated with a household’s position in the income distribution (e.g., poorer

households are more likely to derive income from crops), examining which income sources grew

the most will start to provide us with a sense of the distributional consequences.

27 Rising enrolment rates for primary and secondary education suggest that this trend may continue. See World Bank (2004), p. 15.

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2.7.3.1 Mean per capita income

We begin our analysis of income by examining the behaviour of our estimates of mean

real per capita income. For reference, we also include the GSO/World Bank’s estimate of

consumption.28 In Table 2.10, we report means for all five years and growth rates for select

periods for all of Vietnam, rural Vietnam, urban Vietnam, and then rural and urban separately for

both north and south. Per capita incomes in 1993 (in 2006 VND) were 2.69 million VND. Urban

incomes were more than two times larger than rural, while incomes in the south were also

slightly higher than in the north. The largest difference between the two regions was with respect

to urban incomes, which were 20 percent higher in the south.

Over the entire period, per capita incomes grew at an impressive rate of 8.4 percent,

which implies a more than tripling of per capita incomes over the 13 year period. In 2006,

average per capita incomes were 7.99 million VND. Income growth was especially rapid

between 1993 and 1998, slowed sharply between 1998 and 2002 (presumably this was partially

due to the Asian Financial Crisis), and then rose to 8.9 percent between 2002 and 2006, only

modestly below the rate of growth achieved between 1993 and 1998.29

Overall, growth was relatively balanced between the urban and rural sectors. In sharp

contrast to China’s experience during a similar period in its development, rural per capita income

growth outpaced that in urban Vietnam, at 8.4 percent versus 7.2 percent, which contributed to a

slight decline in the urban-rural income ratio. However, the aggregate figures for Vietnam

conceal differences between the two regions. In the north, urban growth was slightly faster than

rural growth (8.7 versus 7.8 percent), but in the south, income growth in the urban sector lagged

significantly behind its rural counterpart (6.5 versus 9.1 percent). As a result, the urban-rural

income ratio in the north drifted upwards slowly, while that in the south fell sharply from 2.11 to

1.51 percent. Note also the more rapid growth in the rural south than in the north (9.1 versus 7.8

percent), and the exact opposite with respect to urban incomes (6.5 versus 8.7 percent).

28 In future work, we intend to construct our own estimates of consumption, with an eye to reconciling the behaviour of income and consumption. 29 Estimates of nominal GDP growth from the World Development Indicators suggest a similar pattern of high growth, followed by moderate growth, and then high growth again. Between 1993 and 1998 nominal GDP grew at a per annum rate of 8.3 percent, followed by 6.4 percent between 1998 and 2002, and 7.9 percent between 2002 and 2006.

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Table 2.10 Mean real per capita income and consumption (000 VND)

Growth (Annual) 1993 1998 2002 2004 2006 1993-06 1993-98 1998-2002 2002-06All Vietnam Income 2,692 4,312 5,603 7,019 7,993 8.37% 9.42% 6.55% 8.88%Consumption 2,755 3,824 4,458 5,340 5,856 5.80% 6.56% 3.84% 6.82%Rural Income 2,232 3,561 4,635 5,798 6,676 8.43% 9.34% 6.59% 9.12%Consumption 2,312 3,006 3,391 4,044 4,620 5.33% 5.26% 3.01% 7.73%Urban Income 4,565 6,922 8,798 10,523 11,602 7.18% 8.33% 6.00% 6.92%Consumption 4,559 6,664 7,979 9,056 9,242 5.44% 7.59% 4.50% 3.67%Urban/Rural Ratio Income 2.04 1.94 1.90 1.82 1.74 Consumption 1.97 2.22 2.35 2.24 2.00 North Rural Income 2,173 3,189 4,079 5,331 5,991 7.80% 7.67% 6.16% 9.61%Consumption 2,079 2,790 3,159 3,750 4,162 5.34% 5.88% 3.11% 6.89%South Rural Income 2,305 4,027 5,280 6,344 7,480 9.06% 11.16% 6.77% 8.71%Consumption 2,598 3,278 3,661 4,390 5,158 5.27% 4.65% 2.77% 8.57%North Urban Income 3,946 5,953 8,254 10,023 12,160 8.66% 8.23% 8.17% 9.69%Consumption 3,987 6,039 7,800 8,685 9,458 6.64% 8.30% 6.40% 4.82%South Urban Income 4,871 7,484 9,114 10,789 11,275 6.46% 8.59% 4.92% 5.32%Consumption 4,842 7,028 8,084 9,254 9,116 4.87% 7.45% 3.50% 3.00%

Note: All values have been spatially and temporally deflated to January 2006 prices.

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2.7.3.2 Comparison of Consumption and Income Behaviour

In comparison to income, consumption growth has been slower at 5.80 percent per annum

as compared to 8.37 percent per annum. This could be due to a number of factors. First, if the

value of housing services has been rising more rapidly than the value of other non-food

consumption, then we may be under-estimating the value of true consumption, especially in the

latter surveys. If this is true, then the measurement error affects essentially all papers that use the

GSO/World Bank consumption series contained within the datasets as we are not aware of any

authors that have attempted to deal with this problem. Second, the estimates of mean per capita

income and consumption in 1993 suggest that household savings was negative, subject to

measurement error in both income and consumption of course. Thus, it seems likely that either

(1) per capita income in 1993 is under-estimated, (2) per capita consumption in 1993 is over-

estimated, (3) or both sources of error exist. Third, our estimates of household income do not

include the service value of household durables and housing, whereas the consumption estimates

do.

We can get a rough estimate of the impact of excluding the service value of durables and

housing by looking at the estimates presented by Benjamin and Brandt (2004). They construct

their own estimates of household and per capita consumption, which suggest that the per capita

savings rate in 1993 was 15.3 percent in urban areas and 7.8 percent in rural areas when income

and consumption estimates both include the imputed service value of durables. Excluding the

difficult to measure value of durables, the savings rates are 19.1 percent and 8.7 percent in urban

and rural areas, respectively. Unlike Benjamin and Brandt (2004) our income estimates do not

include the value of services whereas the consumption estimates do. Adding their value of

services for urban and rural areas onto our estimates of income would lead to a positive savings

rate nationally and in particular in urban areas.30

Despite these measurement issues it is clear that per capita consumption grew very

rapidly between 1993 and 2006, both in urban and rural areas. The estimates also show the

marked slowdown in growth between 1998 and 2002, followed by recovery. Over the entire time

30 The estimated values of services from household durables presented by Benjamin and Brandt (2004) for 1993 are for a slightly different sample of households as they focus on only the panel households between the 1993 and 1998 VLSS.

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period, per capita consumption in urban and rural areas grew at nearly identical rates, 5.44 versus

5.33 percent per annum. However, consumption growth was more rapid in rural areas between

later surveys and was more rapid in urban areas between earlier surveys. Within rural areas, per

capita consumption initially grew more quickly in the north, but between 2002 and 2006 this was

reversed and consumption grew more quickly in the south.

2.7.3.3 Composition of mean per capita income

As mentioned earlier, one of the advantages of studying income, relative to consumption,

as an indicator of welfare is its ability to be disaggregated into various subcomponents, such as

cropping income, agricultural sidelines income, etc. This allows us to discover which income

generating activities have been important sources of growth, which income activities households

participate in, and the distributive implications of each source of income. In this subsection we

address the first two exercises. We return to discuss the distributive implications of each income

activity later in the paper.

The key income categories are: cropping income, agriculture sidelines, family business

income, wages, remittances and gifts, and “other” income. We organize our discussion into rural

and urban, and also discuss north and south differences within these categories. These

breakdowns are important not only because the regions are so different from each other, but also

because of the insights that can be derived when examining trends in inequality decomposed in

the same way. Tables 2.11 and 2.12 do this separately for rural and urban Vietnam, with

additional breakdowns then provided for the north and south.

These tables are complemented by Table 2.13, which provides information on the

percentage of households reporting non-zero income by source. It is useful to remember here that

mean per capita income by activity is influenced by two margins: the share of households that

participate in the activity and the amount of income generated by each participating household.

This detailed picture will also be useful when we examine trends in inequality later on.

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Table 2.11a Mean per capita income and growth by income activity in rural Vietnam Growth 1993 1998 2002 2004 2006 1993-06 1993-98 1998-2002 2002-06 North and South Crop 989 1,503 1,397 1,673 1,801 4.61% 8.38% -1.83% 6.35%Sidelines 245 421 616 788 885 9.88% 10.84% 9.52% 9.04%Family Business 486 676 883 1,020 1,189 6.89% 6.62% 6.68% 7.43%Wages 265 521 1,117 1,370 1,706 14.32% 13.50% 19.07% 10.59%Remittances 117 252 395 610 703 13.79% 15.38% 11.20% 14.40%Other Income 131 188 227 337 393 8.47% 7.24% 4.70% 13.79%Total 2,232 3,561 4,635 5,798 6,676 8.43% 9.34% 6.59% 9.12% North only Crop 1,014 1,254 1,228 1,503 1,516 3.09% 4.25% -0.52% 5.26%Sidelines 305 500 550 713 816 7.56% 9.85% 2.42% 9.85%Family Business 380 602 775 903 1,063 7.91% 9.20% 6.32% 7.89%Wages 164 342 903 1,212 1,438 16.69% 14.66% 24.28% 11.62%Remittances 117 237 352 592 651 13.18% 14.08% 9.85% 15.39%Other Income 192 254 270 408 507 7.46% 5.57% 1.54% 15.76%Total 2,173 3,189 4,079 5,331 5,991 7.80% 7.67% 6.16% 9.61% South only Crop 958 1,815 1,593 1,872 2,135 6.17% 12.78% -3.26% 7.33%Sidelines 170 323 693 876 965 13.34% 12.77% 19.09% 8.30%Family Business 615 769 1,008 1,157 1,337 5.97% 4.44% 6.79% 7.05%Wages 389 744 1,364 1,555 2,019 12.66% 12.96% 15.15% 9.80%Remittances 116 271 445 630 763 14.46% 16.91% 12.39% 13.47%Other Income 55 105 176 254 260 11.90% 12.86% 12.88% 9.73%Total 2,305 4,027 5,280 6,344 7,480 9.06% 11.16% 6.77% 8.71%

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Table 2.11b Share of Total Income North and South Crop 44.3% 42.2% 30.1% 28.9% 27.0% Sidelines 11.0% 11.8% 13.3% 13.6% 13.3% Family Business 21.8% 19.0% 19.1% 17.6% 17.8% Wages 11.9% 14.6% 24.1% 23.6% 25.5% Remittances 5.2% 7.1% 8.5% 10.5% 10.5% Other Income 5.9% 5.3% 4.9% 5.8% 5.9% North only Crop 46.7% 39.3% 30.1% 28.2% 25.3% Sidelines 14.0% 15.7% 13.5% 13.4% 13.6% Family Business 17.5% 18.9% 19.0% 16.9% 17.7% Wages 7.6% 10.7% 22.1% 22.7% 24.0% Remittances 5.4% 7.4% 8.6% 11.1% 10.9% Other Income 8.8% 8.0% 6.6% 7.6% 8.5% South only Crop 41.6% 45.1% 30.2% 29.5% 28.5% Sidelines 7.4% 8.0% 13.1% 13.8% 12.9% Family Business 26.7% 19.1% 19.1% 18.2% 17.9% Wages 16.9% 18.5% 25.8% 24.5% 27.0% Remittances 5.1% 6.7% 8.4% 9.9% 10.2% Other Income 2.4% 2.6% 3.3% 4.0% 3.5%

Table 2.12b Share of total income North and South Crop 3.2% 2.9% 3.5% 3.5% 3.2% Sidelines 1.0% 1.1% 3.2% 3.1% 3.0% Family Business 50.3% 39.2% 32.6% 32.8% 32.5% Wages 21.9% 30.9% 40.4% 37.0% 39.0% Remittances 13.6% 12.5% 11.4% 12.5% 10.7% Other Income 9.9% 13.5% 8.9% 11.1% 11.6% North only Crop 6.4% 4.1% 3.1% 2.8% 2.1% Sidelines 3.0% 1.7% 2.4% 2.8% 3.4% Family Business 45.9% 32.1% 28.1% 26.4% 26.8% Wages 19.9% 29.5% 43.5% 43.1% 41.7% Remittances 10.9% 11.4% 11.1% 10.6% 9.3% Other Income 13.9% 21.3% 11.9% 14.2% 16.7% South only Crop 2.0% 2.3% 3.7% 3.9% 3.9% Sidelines 0.2% 0.8% 3.7% 3.3% 2.7% Family Business 52.0% 42.5% 35.0% 36.0% 36.1% Wages 22.8% 31.5% 38.7% 34.0% 37.2% Remittances 14.6% 13.0% 11.6% 13.4% 11.6% Other Income 8.4% 9.9% 7.3% 9.5% 8.4%

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Table 2.12a Mean per capita income and growth by income activity in urban Vietnam Growth 1993 1998 2002 2004 2006 1993-06 1993-98 1998-2002 2002-06 North and South Crop 148 198 308 370 369 7.04% 5.82% 11.08% 4.52%Sidelines 47 74 284 328 349 15.35% 8.96% 33.52% 5.16%Family Business 2,295 2,714 2,867 3,452 3,769 3.82% 3.36% 1.37% 6.84%Wages 1,002 2,138 3,553 3,894 4,522 11.59% 15.16% 12.69% 6.03%Remittances 619 863 1,004 1,315 1,244 5.37% 6.63% 3.80% 5.36%Other Income 453 934 781 1,164 1,348 8.38% 14.46% -4.49% 13.65%Total 4,565 6,922 8,798 10,523 11,602 7.18% 8.33% 6.00% 6.92% North only Crop 252 246 254 283 250 -0.06% -0.50% 0.85% -0.40%Sidelines 120 99 197 284 419 9.61% -3.85% 17.20% 18.85%Family Business 1,812 1,908 2,317 2,648 3,255 4.50% 1.03% 4.85% 8.49%Wages 784 1,755 3,591 4,323 5,073 14.37% 16.13% 17.90% 8.64%Remittances 430 679 915 1,066 1,137 7.48% 9.14% 7.47% 5.41%Other Income 548 1,266 979 1,419 2,026 10.06% 16.76% -6.43% 18.18%Total 3,946 5,953 8,254 10,023 12,160 8.66% 8.23% 8.17% 9.69% South only Crop 96 170 339 417 439 11.67% 11.36% 17.30% 6.43%Sidelines 12 60 335 351 308 25.24% 32.87% 43.00% -2.06%Family Business 2,534 3,183 3,187 3,880 4,072 3.65% 4.56% 0.04% 6.12%Wages 1,110 2,361 3,531 3,665 4,199 10.23% 15.10% 10.06% 4.33%Remittances 713 969 1,056 1,448 1,308 4.67% 6.15% 2.15% 5.34%Other Income 407 741 665 1,027 949 6.52% 12.00% -2.70% 8.88%Total 4,871 7,484 9,114 10,789 11,275 6.46% 8.59% 4.92% 5.32%

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Table 2.13 Percentage of household with non-zero income by income activity 1993 1998 2002 2004 2006All rural Crop 93.7% 93.4% 84.3% 84.5% 84.2%Sidelines 89.2% 87.0% 81.3% 79.9% 77.2%Family Business 44.8% 43.5% 35.7% 37.2% 35.8%Wages 47.3% 48.1% 56.2% 61.5% 63.4%Remittances 49.0% 79.2% 80.5% 87.4% 89.6%Other Income 46.5% 38.8% 43.7% 41.4% 42.5%Rural north Crop 97.7% 98.5% 92.5% 93.4% 92.7%Sidelines 96.7% 97.0% 90.9% 89.2% 85.7%Family Business 46.1% 48.0% 36.9% 38.4% 36.7%Wages 40.6% 41.7% 51.0% 59.8% 61.5%Remittances 45.7% 76.3% 80.1% 85.5% 86.9%Other Income 60.9% 41.1% 44.9% 42.4% 45.9%Rural south Crop 88.7% 87.1% 74.9% 74.2% 74.2%Sidelines 79.9% 74.5% 70.1% 68.9% 67.1%Family Business 43.2% 37.8% 34.2% 35.9% 34.8%Wages 55.5% 56.0% 62.3% 63.4% 65.6%Remittances 53.1% 82.7% 80.9% 89.5% 92.8%Other Income 28.8% 35.9% 42.4% 40.2% 38.6%Urban Crop 22.3% 20.4% 21.9% 22.5% 22.1%Sidelines 33.1% 22.3% 23.8% 21.2% 19.3%Family Business 69.2% 66.3% 55.3% 56.7% 56.8%Wages 69.9% 68.5% 71.5% 72.6% 74.4%Remittances 59.6% 73.8% 78.8% 88.6% 90.3%Other Income 44.2% 45.2% 45.3% 52.1% 49.7%Urban north Crop 33.9% 29.2% 25.3% 27.2% 25.2%Sidelines 53.2% 32.5% 31.3% 28.2% 22.7%Family Business 68.9% 63.2% 53.2% 52.3% 52.9%Wages 66.2% 64.2% 71.9% 75.9% 76.0%Remittances 59.9% 64.7% 81.4% 87.0% 89.8%Other Income 66.4% 59.8% 56.2% 58.9% 60.4%Urban south Crop 16.6% 15.2% 20.0% 20.0% 20.3%Sidelines 23.1% 16.4% 19.4% 17.5% 17.2%Family Business 69.4% 68.1% 56.5% 59.1% 59.0%Wages 71.7% 71.1% 71.2% 70.9% 73.5%Remittances 59.5% 79.1% 77.3% 89.4% 90.7%Other Income 33.3% 36.7% 38.9% 48.5% 43.4%

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2.7.3.3.1 Rural

Early in the reforms, the rural sector was primarily agricultural, with more than half of all

income coming directly from cropping and agricultural sidelines.31 All but a relatively small

percentage of households earned income from agriculture, with the percentage of households

involved in farming higher in the north than in the south. Hence, strong growth in agriculture is

likely to benefit a very large portion of the rural population. Family-run businesses were next in

importance; they involved slightly less than half of all households, and were the source of 22

percent of all income. Wages contributed 12 percent, but this may underestimate slightly the role

of income from off-farm wage opportunities, some of which is indirectly captured in remittances

from household members that are not included in the GSO’s definition of household

membership. Overall, differences between the north and south in the structure of incomes were

relatively modest, with income from family businesses and wages more important in the south

than in the north. These differences in the composition of incomes between the two regions

largely disappear by 2006.

Over time, off-farm wage opportunities, followed by remittances and gifts, are the two

largest contributors to growth of income for rural households, with a growing percentage of

households reporting income from both sources. Combined, income from wages and remittances

and gifts grew nearly 15 percent per annum, and by 2006 was the source of 36 percent of all rural

income. Growth in incomes in Vietnam’s cropping sector is the slowest of all components of

household income and averaged 4.6 percent over the entire period, but for many farming

households this was partially offset by more robust growth in agriculture sidelines such as animal

husbandry and aquaculture. Overall, however, the primary sector declined in importance and by

2006 was the source of forty percent of rural household income, compared to 55 percent in 1993.

Lastly, income from family-run businesses grew steadily over the entire period and averaged just

below 7 percent. Nonetheless, its share of total rural income declined slightly as did the

percentage of households involved in family businesses. These broad shifts in the composition

of rural incomes are in line with the changes we discussed earlier in the structure of rural

employment.

31 This estimate of primary sector income excludes wage income from working in the primary sector, or land rental payments, which would increase slightly primary sector activity.

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Trends in cropping income merit more detailed examination. First, the growth rate

calculated over the entire period conceals a sharp drop in income from the cropping sector that

occurred between 1998 and 2002, which is largely a product of forces exogenous to Vietnam.

Over this four year period, growth in cropping incomes fell to negative 1.8 percent per annum

compared to positive growth of 8.4 percent between 1993 and 1998, a swing of more than 10

percent. Indeed, a significant portion of the reduction in rural income growth over 1998-2002

compared to 1993-1998 (see Table 10) is a product of this behaviour.32 Much of the reduction in

cropping income can be attributed to the adverse effect on revenues of the decline in agricultural

prices in the wake of the Asian Financial Crisis. Perennial prices were especially hard hit. The

reduction in real agricultural output growth between the two periods was actually marginal.33

Prices and cropping sector income soon recovered and between 2002 and 2006 the latter

increased at a very reasonable 6.4 percent.

Second, rates of growth in cropping income were higher in the south than in the north.

The differences are especially pronounced in the early years of the reforms, as the south was able

to take better advantage of the domestic and international market liberalization for rice and other

agricultural commodities (Benjamin and Brandt, 2004). The south, and in particular, regions

such as the Central Highlands and the Southeast that moved heavily into perennials, were

especially hard hit by the collapse of world agriculture markets in the late 1990s, but cropping

income growth soon recovered, and averaged 7.3 percent between 2002 and 2006. Over the

entire period, income from the cropping sector grew twice as fast in the rural south as in the

north. This gap may help to explain why economic growth did not have a more favourable

impact on inequality levels in the north.

32 Between 1998 and 2002, there is also a reduction in the percentage of households reporting cropping income from 93.4 to 84.3 percent, with the reduction larger in the south. We cannot rule out that some of the behaviour of cropping income is tied to this decline. This further highlights concern over comparability between the two VLSS surveys and the three VHLSS surveys, which we discuss in more detail in the appendix. 33 Between 1993-98 and 1998-2002, real annual growth in crop output in Vietnam fell from 7.4 percent to 6.2 percent. By comparison, the aggregate crop price index for Vietnam fell at an annual rate of 1.8% between 1998 and 2002 after rising 11.9 percent between 1993 and 1998. See Brandt et al. (2008).

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2.7.3.3.2 Urban

In the early 1990s, the structure of urban incomes in the north and south looked very

similar. In both, there was an important informal component. Self-employment and self-

employed income from small, family-run businesses was the largest source of urban household

income, involving nearly 70 percent of all households, and generating more than half of total

household income. These family-run businesses, which operated on average 8 months out of the

year, and drew primarily on the labour of household members, were most prominent in tertiary

sector activity followed by the secondary sector. Wage income, on the other hand, was reported

by nearly the same percentage of households in north and south, but was the source of a much

smaller share of income, 21.9 percent.34 In the north, wage income was heavily tied to

employment in the state sector (68.5 percent of those working for wages), but even in the south

41.7 percent of all of those employed and working for wages in their main job reported working

for the state. Income from remittances, 13.6 percent of total income, and “other” income, 9.9

percent, made up most of the rest of total income. In both regions, upwards of a third of all urban

households were marginally involved in agriculture, with income from cropping and sidelines

representing only 4.2 percent of urban incomes in 1993.

Over time, wage income is the most rapidly growing component of urban incomes,

averaging 11.6 percent growth per annum over the entire 13 year period. Partially contributing to

the rapid growth is wage reform in the state sector, which increased wages by 38 percent in 2003

(World Bank, 2004). As a result of this rapid growth, wage earnings represented 40.4 percent of

all urban income by 2002, or nearly double the level in 1993. This rapid growth comes largely at

the expense of the share of income from family-run businesses, which grows less than 4 percent

per annum, and falls to 32.6 percent of household income.

Cumulatively, the urban north fares better than the south. In 1993, urban incomes in the

south were twenty percent higher. This gap was maintained between 1993 and 1998 as growth in

the two regions was nearly identical (8.2 versus 8.6 percent growth). Between 1998 and 2002

however, growth in household incomes in the south slipped to 4.9 percent per annum compared

34 The much higher percentage of income coming from family-run businesses reflects the fact that upwards of 2 members per household were working in these businesses, and that income from this source represents the returns to both labour and capital.

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to 8.2 percent in the north. Several factors appear to be underlying this difference. First, growth

in incomes from family-run businesses and remittances both dropped off significantly in the

south, possibly a consequence of the fallout from the Asian Financial Crisis. Second, the north

enjoyed much more rapid wage growth, which may be partially linked to the wage reform in the

state sector, and the much larger role of state employment in the north. Between 2002 and 2006,

growth in incomes from family-run businesses and remittances in the south recovered, but over

this four-year period, growth in every source of income in the south lagged behind those in the

north. There is an especially sharp contrast in the behaviour of “other” income, which grew twice

as fast in the north between 2002 and 2006.35 Overall, per capita incomes in the south grew 5.3

percent compared with 9.7 percent growth in the north. As a result, by 2006, per capita incomes

in the south trailed those in the north by 7 percent.

With this detailed information on the changes in employment, household endowments,

mean income and composition of income by source the background to the picture of income and

inequality in Vietnam becomes clearer. We next move on to our analysis of the behaviour of

inequality between 1993 and 2006.

2.8 Distributive dimensions of Vietnam’s growth

In this section, we examine two important indicators of how the benefits of Vietnam’s

impressive growth have been distributed amongst the population. As previously outlined,

although other authors have reported on these changes, we add to the discussion by extending the

time period beyond the scope of most previous studies, and also by using income data. This new

approach to the issues at hand helps to broaden the knowledge base and the available information

on this crucial time in Vietnam’s development. We first look at poverty trends, then discuss the

behaviour of inequality.36 Our primary purpose in reporting headcount ratios is to capture how

35 In future work, we plan to disaggregate “other” income into two components: government transfers, and non-government transfers such as interest from savings. 36 These estimates are subject to the criticism that some of the poor, and possibly a rising number in absolute as well as percentage terms, are missing from the household surveys. Prominent among this group would be migrants, who we discussed earlier. Depending on their incomes relative to those households surveyed, their exclusion could bias upwards or downwards our estimates of rural poverty, urban poverty, as well as overall poverty, and likewise, similar measures of inequality. Nonetheless, this critique holds for all other papers that have used the same household surveys to examine distributional concerns, such as poverty and inequality, in Vietnam.

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rapidly economic growth was pulling households up from the bottom. It is also important to

remember that there is an element of arbitrariness in any poverty line.

2.8.1 Poverty trends

In Table 2.14 we report estimates of poverty headcount ratios using real per capita

incomes, along with standard errors of our estimates. The poverty line we use is 2.478 million

VND in January 2006 prices, and has been adjusted to reflect differences in the cost of living

between regions, as well as between the rural and urban sectors.37 In 1993, nearly two-thirds of

all households were living below this poverty line. Consistent with the higher average incomes

among urban households reported in Table 2.10, we also observe a significantly smaller

percentage of households identified as poor in the cities compared to the countryside (40.8

versus 70.1 percent).

Table 2.14 Estimates of the poverty headcount ratio using real per capita income

1993 1998 2002 2004 2006 All of Vietnam Poverty headcount ratio 0.643 0.380 0.194 0.108 0.078 Standard error 0.017 0.017 0.004 0.004 0.003 Number of observations 9,089 9,086 Rural Vietnam Poverty headcount ratio 0.701 0.435 0.237 0.137 0.101 Standard error 0.016 0.021 0.005 0.005 0.005 Number of observations 6,863 6,806 Urban Vietnam Poverty headcount ratio 0.408 0.187 0.050 0.026 0.016 Standard error 0.049 0.021 0.005 0.004 0.003 Number of observations 2,226 2,280

Note: The poverty line used is 2 478.11 thousand dong in January 2006 prices.

Between 1993 and 1998, the rapid growth in per capita incomes in the country was

accompanied by a sharp fall in the head count ratio. Recall that in absolute terms, per capita

incomes in Vietnam more than doubled over this five year period. The headcount ratio fell by

37 This poverty line is based on inflating the GSO/World Bank recommended poverty line used in 2004 by the CPI inflation factor we use to convert from 2004 January prices to 2006 January prices.

73

26.3 percentage points between 1993 and 1998, and by an additional 18.6 percentage points over

the next four years despite declining economic growth between 1998 and 2002. By 2002, less

than a quarter of the rural population was identified as “poor,” with only 5 percent of all urban

individuals so classified. After 2002, poverty levels continued to fall, albeit at declining rates.

By 2006, only 1.6 percent of the urban population was still classified as poor, with 10 percent of

the rural population reported to have per capita income levels below the poverty line. Overall,

the poverty headcount ratio was 7.8 percent.

In short, the rapid reduction in the headcount ratios speaks to the success of the growth in

Vietnam in raising the incomes of those households in the lower tail of the income distribution.

We now examine the changes in income inequality that accompanied changes in poverty.

2.8.2 Inequality

We begin our discussion of inequality first by reporting initial 1993 levels using Gini

coefficients, and then moving on to discuss trends between 1993 and 2006. We supplement our

discussion based on Gini coefficient estimates with Lorenz curves. We also use information

gained in Section 2.7 to give further insight into conditions within the relevant regions which

may explain our statistics. Also included in the discussion of inequality is a comparison of

income and consumption data, the role of spatial differences and decompositions by source of

income.

2.8.2.1 Initial levels in 1993

Table 2.15 reports Gini coefficients for the same breakdown of regions and sectors as our

estimates of mean incomes and growth rates. Directly below our estimates we also report 95

percent confidence intervals, which are a good reminder of the lack of precision with which we

estimate Ginis, and the care that must be taken in making inferences about changes in inequality

over time. Early in the reforms, Vietnam had a relatively high level of inequality, with a Gini

coefficient of 0.45. Urban inequality was markedly higher, with a Gini of 0.48 compared to 0.40

for the rural sector. In both sectors, but especially in the rural sector, inequality was also

appreciably higher in the south.

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Table 2.15 Estimated Gini coefficients based on real per capita income and consumption

1993 1998 2002 2004 2006 All Vietnam Income 0.449 0.434 0.387 0.380 0.384 (0.437,0.464) (0.424,0.442) (0.381,0.395) (0.372,0.390) (0.377,0.394)Consumption 0.330 0.348 0.368 0.367 0.353 (0.323,0.338) (0.341,0.356) (0.359,0.375) (0.360,0.375) (0.347,0.362)Rural Income 0.399 0.391 0.349 0.344 0.356 (0.389,0.415) (0.383,0.400) (0.343,0.355) (0.336,0.352) (0.348,0.364)Consumption 0.275 0.270 0.278 0.291 0.297 (0.268,0.281) (0.264,0.278) (0.274,0.282) (0.286,0.299) (0.291,0.303)Urban Income 0.481 0.444 0.370 0.365 0.362 (0.455,0.510) (0.424,0.471) (0.360,0.388) (0.347,0.386) (0.347,0.378)Consumption 0.334 0.338 0.350 0.329 0.323 (0.319,0.353) (0.327,0.350) (0.340,0.361) (0.318,0.340) (0.312,0.335) North Rural Income 0.358 0.369 0.330 0.328 0.342 (0.344,0.373) (0.354,0.388) (0.324,0.339) (0.320,0.339) (0.328,0.353)Consumption 0.250 0.251 0.266 0.277 0.282 (0.241,0.259) (0.242,0.262) (0.261,0.271) (0.270,0.284) (0.274,0.291)South Rural Income 0.444 0.403 0.353 0.356 0.361 (0.429,0.464) (0.393,0.416) (0.346,0.365) (0.346,0.368) (0.347,0.372)Consumption 0.285 0.283 0.285 0.301 0.302 (0.273,0.295) (0.271,0.292) (0.278,0.289) (0.292,0.314) (0.292,0.310)North Urban Income 0.437 0.433 0.344 0.345 0.336 (0.397,0.479) (0.398,0.468) (0.332,0.366) (0.319,0.365) (0.308,0.357)Consumption 0.320 0.321 0.340 0.333 0.309 (0.295,0.345) (0.301,0.341) (0.325,0.354) (0.317,0.348) (0.292,0.329)South Urban Income 0.495 0.444 0.382 0.374 0.376 (0.463,0.523) (0.425,0.470) (0.365,0.402) (0.351,0.407) (0.357,0.399)Consumption 0.334 0.342 0.355 0.326 0.331 (0.313,0.355) (0.326,0.374) (0.347,0.373) (0.312,0.343) (0.317,0.346)

Note: The 95% confidence interval is provided below the estimated Gini. These are bootstrapped, bias- corrected estimated confidence intervals.

This high initial inequality seems unusual for a “socialist” economy early in its transition.

China provides an obvious basis for comparison. In 1985, or six to seven years into China’s

reforms, the Gini coefficient for per capita household incomes was 0.29, and only 0.17 in the

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urban sector. In the rural sector, it was 0.27 (Ravallion and Chen, 2007).38 There are a number

of potential explanations for Vietnam’s high initial inequality related to institutional features of

the urban and rural economies. One factor we can probably rule out is urban-rural income

differences, which were actually lower in Vietnam.

We begin by examining the urban sector, with its relatively high level of inequality as

reflected by a Gini coefficient of 0.48. First, a very high percentage of the urban labour force

(60.4 percent) was self-employed, largely in family-run businesses, in which the distribution of

human capital, access to credit and other key inputs, and so on could be highly skewed. Recall

that income from family-run businesses was the source of half of urban incomes in 1993.39

Second, the role of the state sector as a source of employment was never as large as in other

socialist economies. In general, we expect the wage distribution to be more compressed in the

state sector than in private firms. In 1993, only 39.6 percent of the urban workforce was engaged

in wage employment, of which 50.5 percent were employed by the state for their primary job.

This implies that only 20 percent of the entire urban workforce was employed by the state.40 By

comparison, the share of the urban labour force in China working for the state in 1978 was over

75 percent, with the rest of the labour force primarily working in collectively-run enterprises

owned by local municipalities. Even as late as 1988, the percentage working for the state in the

urban sector had only fallen to 65 percent, with less than 5 percent either self-employed or

working in the private sector. Third, the level of state subsidies for food, housing, medical, and

so on, which might have been equalizing, was also relatively low.41

In the case of the rural sector, with a Gini of 0.40, inequality is greater in the south than

in the north. Higher inequality with respect to both land and human capital likely underpins the

difference between the rural south and the rural north. Both are a product of historical legacy. As

38 The household data that are the basis for these estimates likely underestimate differences in household income in China. However, these biases are not sufficient to overturn the basic comparison. Potentially offsetting this is the larger size, and possibly greater regional heterogeneity in China. 39 The disequalizing role of family business income is confirmed by the Shorrock decompositions that are reported below. 40 The larger role of state sector employment in the north compared to the south may help explain the slightly lower level of urban inequality in the north. 41 In urban China, these subsidies amounted to nearly a quarter of urban incomes as late as the early 1990s. See Benjamin et al., 2008.

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we noted in the context of discussion of Table 2.7, lower land inequality in the north in the early

1990s reflected the relatively egalitarian redistribution of land that occurred as part of the process

of decollectivization in the late 1980s. Landlessness was minimal. Most of agriculture in the

south avoided collectivisation after reunification in 1975, and landholding patterns reflect a

complex mixture of demographic, economic, and social factors at work for decades or more.

With half of all income in the rural sector in 1993 originating in farming, and off-farm labour

markets incomplete at this time, access to land played a particularly important role in the

income-determination process.

Differences in land endowments were likely reinforced by a much more unequal access

to education in the south compared to the north that is reflected in our inequality measures of

educational attainment of the labour force (see Table 2.9). In the south, household landholdings

and educational attainment are highly positively correlated. In agriculture, as well as in family-

run businesses, human capital played an important role in a household’s ability to deal with an

increasingly complex, and rapidly changing local economy.

2.8.2.2 Inequality trends

We observe a significant reduction in inequality between 1993 and 2006 from a Gini

coefficient of 0.45 to 0.38 for all of Vietnam. All of this occurs between 1993 and 2002 with a

significant reduction in inequality in the north as well as the south, in urban as well as rural, with

one exception: the rural north, in which the level of inequality in 2006 is within measurement

error of its level in 1993. After 2002, the levels of inequality, as expressed by the Gini

coefficient, remain more or less the same in all regions.

The overall experience for Vietnam is in sharp contrast to China, where inequality fell

slightly very early in the reform, a product of the more rapid growth in the rural sector compared

to urban, but then rose sharply in the aggregate, as well as in urban and rural areas separately

(Benjamin et al., 2008; Ravallion and Chen, 2007).42 By 2006, the Gini for per capita household

42 Several caveats are in order in making this comparison. First, inequality in China in the late 1970s starts from a much lower base than it does in Vietnam. Second, China’s population is nearly twenty times that of Vietnams, with enormous heterogeneity. Most of China’s provinces are the size of Vietnam. Third, official Chinese estimates invariably underestimate the true level of inequality, and the increase.

77

incomes in China was probably between 0.45 and 0.50, which is higher than contemporaneous

estimates for Vietnam.

The Lorenz curve provides a non-parametric representation of the income distribution,

and has several advantages relative to the Gini coefficient for analysing changes in inequality.

On the one hand, it allows us to examine changes in inequality throughout the entire distribution

rather than on the basis of a single summary statistic. On the other hand, inequality orderings

based on Lorenz curves are robust to the inequality index. This means that for a large set of

inequality indices, e.g., the Gini coefficient, Atkinson index, variance of the log, and so on, each

will produce the same inequality ranking so long as the Lorenz curves can be ranked. The

primary drawback to using Lorenz curves is that they do not always provide a complete ranking.

When Lorenz curves cross, we cannot unambiguously say whether inequality has increased or

decreased, whereas inequality indices would still rank the two distributions, albeit perhaps

differently depending on how they are constructed.

Figures 2.3 and 2.4 show the Lorenz curves for rural Vietnam for each survey year.

Figure 2.3 shows the Lorenz curve for the entire income distribution, while Figure 2.4 is a

scaled-up version of the Lorenz curve for the bottom 40 percent of the income distribution. The

patterns demonstrated by the Lorenz curves confirm the pattern observed over time for the Gini

coefficients: income inequality has been decreasing over time, as represented by an inward shift

of the Lorenz curve over time (the more equal the distribution of income the closer the Lorenz

curve is to the 45 degree line), but after 2002 shows no further change. Furthermore, there are no

evident crossings of the Lorenz curves. Hence, based on visual inspection, we are able to confirm

that the previous results reported using Gini coefficients are likely robust to a large number of

possible inequality indices. In other words, the reported drop in inequality is not an artefact of

how the Gini coefficient summarizes the income distribution.

78

Figure 2.3 Lorenz curves for rural income per capita, 1999-2006

0

.2

.4

.6

.8

1

Lorenz curve

0 .2 .4 .6 .8 1Cumulative population share

1993 19982002 20042006

Figure 2.4 Lorenz curves for rural income per capita, 1993-2006, for the bottom of the income distribution

0

.05

.1

.15

.2

Lorenz curve

0 .1 .2 .3 .4Cumulative population share

1993 19982002 20042006

79

Figures 2.5 and 2.6 do the same thing for urban Vietnam. Again, the pattern is similar to

that demonstrated by the Gini coefficients: urban income inequality fell between 1993 and 2002

and then remained relatively constant between 2002 and 2006. Furthermore, similar to the

shifting of the Lorenz curves for rural Vietnam, there are no obvious crossings of the Lorenz

curves, confirming the robustness of the pattern implied using the Gini coefficients.

Figure 2.5 Lorenz curves for urban income per capita, 1993-2006

0

.2

.4

.6

.8

1

Lorenz curve

0 .2 .4 .6 .8 1Cumulative population share

1993 19982002 20042006

80

Figure 2.6 Lorenz curves for urban income per capita, 1993-2006, for the bottom of the income distribution

0

.05

.1

.15

.2

Lorenz curve

0 .1 .2 .3 .4Cumulative population share

1993 19982002 20042006

2.8.2.3 Comparison of consumption and income inequality

As most of the existing literature uses consumption data to report inequality in Vietnam,

we also create consumption estimates and present a comparison of Gini coefficients based on our

consumption estimates with our income estimates in Table 2.15. Our estimates of inequality

using per capita consumption show trends similar to much of the existing literature. Per capita

consumption inequality is found to have increased in Vietnam between 1993 and 2004, but then

decreased slightly in 2006. This hides important differences between rural and urban areas. In

rural Vietnam, consumption based inequality was relatively stable between 1993 and 2002,

before increasing moderately between 2002 and 2006. In urban Vietnam consumption inequality

increased between 1993 and 2002 and then fell below the 1993 level by 2006. Note, however,

that given the smaller sample size in urban areas, none of the changes in consumption inequality

are statistically significant.

As the focus of our paper is on income, not consumption, this is the last time we will

discuss consumption. As noted earlier, we have concerns about the construction of the

81

consumption estimates, in particular the estimation of the imputed value of services derived from

housing. However, we leave this for future work.

2.8.2.4 Role of spatial differences

Changes in disparities between locales are often viewed as an important contributor to

changes in overall inequality. This usually includes differences between regions, as well as

urban-rural differences. We have already noted the existence of differences between the north

and south of Vietnam as reflected in a comparison of mean per capita incomes. In this section we

take this analysis further by documenting the contribution to overall inequality made by

disparities between regions, as well as by differences in incomes between the urban and rural

populations.

The Gini coefficient is not easily decomposed in this manner and thus we need an

alternative measure of inequality. We follow the approach taken by Benjamin, Brandt and Giles

(2005), decomposing the variance of log income inequality index. This entails estimating the

following equation:

ln i L iy D uγ′= +

where DL is a vector of dummy variables indicating the location of individual i (e.g. north-south,

region (8 in total), or province). Controlling for the role of location differences in mean incomes,

we also look at the contribution of urban–rural income differences to overall inequality: how

much do mean differences of income between urban and rural households contribute to overall

inequality? We do this by including in the spatial decomposition: regression location dummies,

an urban indicator, and interactions between the location indicator and urban dummies. The

interactions allow the urban–rural gap to vary across locations. To help in the interpretation,

recall from Table 2.10 that in 1993 the urban-rural income ratio was approximately 2. The R-

squared from these regressions indicates the proportion of inequality that is explained by the

location dummy variables, plus urban-rural differences. The remaining variation is the within-

location proportion of inequality.

Table 2.16 reports the results of the spatial decompositions for all of Vietnam and for

rural and urban separately. Care must be taken in our interpretation of these numbers because of

the changes that occurred in the sampling structure of the survey in 1998 and again in 2002 (see

the discussion in Section 2.5). At a minimum, the 2002, 2004 and 2006 estimates are highly

82

comparable, subject possibly to the caveat that the number of observations per locale are smaller

in 2004 and 2006. This can lead to a slightly higher R-squared and convey the impression that

the role of differences between locales in overall inequality is rising, when in fact they are

falling.

Table 2.16 Contribution of location to income inequality and number of households per location

1993 1998 2002 2004 2006 Contribution to variance All Vietnam Contribution of North/South 0.000 0.024 0.038 0.022 0.019 Contribution of region 0.041 0.073 0.128 0.093 0.078 Contribution of province 0.167 0.198 0.230 0.183 0.161 Rural Vietnam Contribution of North/South 0.004 0.014 0.035 0.013 0.024 Contribution of region 0.043 0.039 0.113 0.060 0.073 Contribution of province 0.123 0.143 0.170 0.112 0.131 Urban Vietnam Contribution of North/South 0.003 0.001 0.001 0.000 0.008 Contribution of region 0.084 0.050 0.092 0.078 0.057 Contribution of province 0.214 0.229 0.211 0.176 0.147 All Vietnam Urban/Rural 0.061 0.088 0.149 0.141 0.127 North/South 0.000 0.024 0.038 0.022 0.019 North/South + Urban/Rural 0.065 0.108 0.172 0.149 0.145 Region 0.041 0.073 0.128 0.093 0.078 Region + Urban/Rural 0.111 0.127 0.241 0.197 0.187 Province 0.167 0.198 0.230 0.183 0.161 Province + Urban/Rural 0.198 0.241 0.303 0.255 0.246

With these limitations in mind, several important findings emerge. First, location plays a

relatively small role in overall inequality. North-south differences contribute no more than a few

percent. Regional and provincial effects are more pronounced, however provincial differences

still account for at most twenty percent. Far more important are differences among households

within provinces. Second, urban-rural differences, which were around a factor of two in the early

1990s, also explain a relatively small percentage of overall inequality. Combined, locale plus

urban-rural explains about 10 percent more of the inequality than locale does alone.

83

Overall, the results from the decomposition point to the significant contribution of

differences within urban areas and the countryside to inequality in Vietnam over this period.

They also suggest that it is here, namely, within the rural and urban sectors that we need to look

for clues as for the reasons for the behaviour of inequality. To this we now turn with our

discussion of inequality decomposed by source of income.

2.8.2.5 Decompositions by source of income

In this section we present information on the composition of income by income quartiles

to provide some insight into which households might be likely to gain from changes in a

particular source of income. We then use Shorrocks decompositions to examine what share of

inequality can be attributed to different income sources. This will help to shed light on the

changes in inequality within urban and rural regions.

Why did income inequality fall in both urban and rural Vietnam and then level off?

Endowments, as well as the institutions that map household endowments into family income are

important here. Earlier, we discussed the behaviour of two key endowments: the distribution of

land, which figures prominently in the rural economy, and human capital, which matters to both

the urban and rural sectors. Recall that land inequality was rising over this period, while

differences amongst the labour force in educational attainment, our measure of human capital,

were falling in nearly every dimension, while average educational attainment was rising.

We do not have income data disaggregated on the basis of the returns to individual

factors, e.g. land, human capital, physical capital, nor do we know much about the rapidly

evolving market and non-market institutions mapping endowments into incomes. However, we

do have detailed data by sources of income, which we can put to use. Our limited objective here

is to sketch some of the correlates of inequality, particularly those related to the composition of

household income. The key tools in our analysis are descriptive statistics of the structure of

income across the income distribution, and Shorrocks (1982, 1983) decompositions.

84

2.8.2.5.1 Composition of income by income quartiles

We begin by examining the composition of income by income quartiles for 1993 and

2006. We carry out this exercise separately for urban and rural areas in Figures 2.7 and 2.8.43

The aim of this exercise is to a provide a sense of which households, those at the bottom, in the

middle or at the top of the distribution, are likely to gain the most from growth in a particular

source of income on the basis of the initial structure of incomes. For example, if income from

family-run businesses is highly concentrated amongst high income households, we might expect

rapid growth in income from this source to benefit most this group, and thus, all else equal, for

inequality to rise. The reverse would also be true. Of course, the structure of income may

converge across income groups over time, in which case growth in any particular source of

income may have more modest effects on inequality.

Figure 2.7 Composition of income by source for rural households by income quartile in 1993 and 2006

020

4060

8010

0pe

rcen

t

1993 20061 2 3 4 1 2 3 4

By Income Quartile and YearRural composition of real income by source

Crop Sidelines BusinessWages Remittances Other

43 Tables 11 and 12 provide the complementary information on growth rates by source of income for urban and rural

households, respectively.

85

The upper panel of Figure 2.7 reveals marked differences in the composition of income

across income quartiles in rural Vietnam early in the reform process. Incomes are rather

narrowly concentrated in a few activities at the low end of the income distribution, but the

structure of incomes becomes much more diversified as incomes rise. For households in the

bottom quartile in 1993, cropping income accounts for over 70 percent of total household

income, with wages making up 20 percent. Across the quartiles, the share of income earned from

crops falls steadily, and for households in the highest income quartile, crop income accounts for

only about 35 percent of household income. Some of this is offset by an increase in the role of

farm sidelines, especially by households in the middle quartiles. Far more important at the upper

end of the distribution is income from family-run businesses, which was the source of more than

a third of income for the richest quartile. The role of wage income, measured as a share of total

income, is very similar through the first three income quartiles, but then drops slightly in the

highest income group.

Figure 2.8 Composition of income by source for urban households by income quartile in 1993 and 2006

020

4060

8010

0pe

rcen

t

1993 20061 2 3 4 1 2 3 4

By Income Quartile and YearUrban composition of real income by source

Crop Sidelines BusinessWages Remittances Other

This basic pattern is still present in 2006, but there are several changes to note. First,

cropping income is a much lower share of household income across all quartiles. Offsetting this

86

is a pronounced growth in the share of wages throughout the income distribution, including the

richest quartile. We observe a similar, albeit much smaller increase, in the role of remittances

across all quartiles, which is also correlated with the rapidly growing role of off-farm

opportunities in the rural economy. Second, the role of family-run businesses declines in the

context of a narrowing in the differences across the quartiles in share of the source of income.

Third, for lower income households farm sidelines have also become more important as a share

of income.

Figure 2.8 displays the same information for urban Vietnam. Especially prominent is the

unequal role that household business income plays in urban areas. In 1993, close to 60 percent of

total income in the upper quartile was derived from household business income compared to

about 20 percent in the bottom quartile. However, by 2006 the role of income from family

businesses becomes much less important at the same time that the share of income from this

source becomes much more evenly distributed across the quartiles. A second important feature of

Figure 2.8 is the importance of wage income in 1993 for urban households in the bottom quartile.

Wage income represented over 40 percent of total income for these households, in contrast to 10

percent of total income for upper quartile households. By 2006, the difference across quartiles in

terms of the share of income derived from wages has dramatically reduced, but upper quartile

households still earn a lower share of income from wages than the other quartiles. Some of these

differences in the role of wage income are offset in 1993 by the more important role of

remittance income for households in the upper quartile household. These differences decline

over time, but remittance income remains disequalizing in the sense that upper quartile

households benefit more from this source of income than households in other quartiles.

Perhaps the most noteworthy feature of both Figures 2.7 and 2.8 is how the structure of

income across the quartiles has become much more similar over time. In rural areas, poorer

households have been able to move into the same income generating activities as richer

households. Poorer households earn a larger share of their income from wages and agricultural

sidelines in 2006 than in 1993, making them look much more like rich households from 1993. In

contrast, the convergence in urban areas is due more to changes in the top quartile between 1993

and 2006, converging more closely with the poorer quartiles. The upper quartile of urban

households earns a much smaller fraction of total income from households businesses and a

larger fraction from wages. This helps to explain the patterns in inequality observed over time.

87

2.8.2.5.2 Shorrocks Decompositions

A big advantage of using income over consumption data is the ability to disaggregate

income by activity and examine the relationship of overall inequality to inequality by income

source. At a basic level, this is what Figures 2.7 and 2.8 show. We now formalize this

decomposition using Shorrocks decompositions. The Shorrocks decompositions tell us the

proportion of total inequality that can be attributed to a particular income source. The

decompositions are primarily a descriptive tool and are not meant not to illustrate the impact of

an increase in the inequality of particular source of income without further specifying the nature

of the change in inequality of that income source.

With this limitation in mind, we begin the discussion with a simply outline of the

procedure. Consider a decomposition of household i’s income according to K income generating

activities:

1

K

i ik

y y=

= k∑ .

Mean income can be written as the sum of mean income from each income source. Then a 1

percent increase in income from source k will lead to a Wk percent increase in total income,

where Wk is the share of income from source k. The decomposition of inequality is designed in a

similar manner. We wish to estimate Sk, the proportion of inequality attributable to the inequality

of income source k:

( ) ( )1

K

k kk

I Y S I Y=

= ∑

where I(Y) is the inequality index for total income Y, and I(Yk) is the inequality index for income

source k. Shorrocks demonstrated that for any additively decomposable inequality index, Sk can

be estimated by:

( )( )

cov ,ˆvar

ik ik

i

y yS

y= .

Thus, Sk captures the degree to which income source k is correlated with total income. If an

income source is earned primarily by the rich, then Sk will be relatively large. If it is earned

primarily by the poor then Sk will be relatively small.

88

We next turn to how one should interpret estimates of Sk. Unlike the share of income

earned from a particular source, Wk, Sk can be negative.44 Thus, one interesting benchmark is 0.

If Sk is negative then the income source is disproportionately earned by the poor and a marginal

increase in that income source, maintaining the same correlation with total income, would

decrease overall inequality. In practice, very few sources of income will have a negative value of

Sk. A second helpful benchmark is the share of income earned from that activity, Wk. If Sk>Wk

then income source k contributes more to inequality than it does to mean income, which we

define as a disproportionate effect on inequality.

As a matter of computation Sk can be computed using the following regression:

ik k k i iky y uα β= + + .

This regression formation makes clearer the interpretation of Sk. We are only estimating the

correlation of a particular source of income with total income. Moreover, the regression

formation illustrates the important role that measurement error will have on our estimates.

Overestimates of income from a particular source of income will cause us to overestimate the

“true” value of kβ . This will lead to a corresponding underestimate of the true value of kβ for

other sources of income. We want to highlight the implication then for our analysis. Our

estimates of Sk are only correlations of measured total income and measured income by source.

They are not consistent estimates of the correlation between true total income and true income

by source. This limitation, which plagues all such decompositions, should be kept in mind when

interpreting the estimates.

In Table 2.17 we present the results separately for rural and urban areas, disaggregated by

north and south. We begin our discussion of the Shorrocks decompositions for rural areas. For

1993, a key observation is that crop income is relatively equalizing, that is, it contributes less to

inequality than its share of total income (20.0 percent versus 44.3 percent). This lines up with the

intuition provided by Figure 2.5, which showed the role of income from cropping highly

negatively correlated with household incomes. This same pattern applies to agricultural sidelines.

Hence, primary sector income as a whole is relatively equalizing, contributing 55.3 percent to

total income, but only 28.2 percent of inequality. Wage incomes are also relatively equalizing,

44 Of course if one source of income is, on average, negative, then Wk can also be negative but this is very unlikely in practice.

89

but in 1993 are a relatively modest portion of total incomes. By comparison, household business

income is extremely disequalizing.45 Again, this is consistent with Figure 2.5, and the much

larger role it portrays for family-business income amongst the top quartile of households. It alone

accounts for almost half of overall rural inequality. Remittances are also disequalizing,

accounting for 10.3 percent of overall inequality despite representing only 5.2 percent of overall

income.

As we have seen in other dimensions, there are differences between the rural north and

south in terms of those activities that are most responsible for the inequality. In particular, crop

income, while still relatively equalizing, accounts for a much larger share of rural inequality in

the south than in the north, 25.1 versus 13.9 percent. This is likely due to higher landlessness and

greater land inequality in the south, which prevented a greater share of households from

participating in the rapid expansion in cropping income. In contrast, the share of inequality

accounted for by remittances and “other” income in the north is noticeably larger than in the

south.

The results in Table 2.17 for 1993, combined with information reported in Tables 2.11 on

growth rates by source of income, are helpful in explaining the behaviour of rural inequality. In

particular, throughout the 1990s, the acceleration in growth in agricultural incomes that

accompanied the reform of the farm sector, and rapidly expanding off-farm employment

opportunities played important roles in reducing rural inequality. As we described earlier, the

decline in inequality in rural Vietnam is coming almost exclusively from the reduction amongst

rural households in the south. In the north, inequality remained the same, albeit at a much lower

level than in the south. In the north, growth in cropping income was too slow to offset the

disequalizing influence of the growth in family businesses, and to a much less extent,

remittances. Wage growth in the north worked to the benefit of lower income households, but

their relatively low share of incomes in 1993 (7.6 percent) limited any impact they could exert on

inequality. In contrast, in the south, rapid growth in cropping income and wage growth

45 The disequalizing impact of household business income may be slightly overstated. Household businesses generate income

both for the household and for hired employees. This second stream of income is included under wage income, which tends to be

more evenly distributed than household business income. Hence, a larger view of the impact of household businesses on overall

inequality may lead to a lower impact than that suggested by looking at the profits (which also includes the returns to the

households own labour) accruing to the household.

90

disproportionately benefitted the lowest income groups. Growth in wages were, in all likelihood,

especially important for the rural landless.

In Table 2.17 we also report the results for the Shorrocks’ decomposition for 2006. These

patterns reflect the changes in rates of growth across income activity, as well as changes in role

of these activities across households in the income distribution. Broadly speaking, the general

patterns are similar to those in 1993. Cropping income and wages still remain relatively

equalizing, and family businesses the exact opposite. However, in the case of cropping income

and family income, their impacts have been dampened slightly as their shares of income have

fallen, and the distribution of income from these activities has become less concentrated across

the income distribution. The disequalizing role of remittances, on the other hand, has increased,

while sideline farm activities have also become disequalizing. There is also one noteworthy

difference between the north and south: crop income has a much lower correlation with overall

income in the north, 0.036, compared to 0.28 in the south. Hence, crop income has become more

evenly distributed in the north and less evenly distributed in the south.

Urban inequality in 1993 is largely shaped by the distribution of family-business income,

which was the source of 69.2 percent of overall inequality as compared to representing only 50.3

percent of total income. The other relatively disequalizing sources of income in 1993 are

remittances and gifts, 17.7 percent of overall inequality in comparison to 13.6 percent of overall

income, and also “other” income, 12.2 percent of overall inequality versus 9.9 percent of overall

income. In contrast, wage income contributes very little to overall inequality in comparison to its

share of income, 1.6 percent versus 21.9 percent, and in this regard is highly equalizing. Finally,

farming income, both crops and sidelines, make negligible contributions to overall inequality, as

we expect given their very small contribution to incomes.

These relationships provide clues to the factors underlying the falling inequality we

observe in urban Vietnam through 2002. Rapid growth in wage income, which was especially

important for lower income households, helped to narrow income differentials in the north as

well as the south. This was complemented by relatively slow growth in family business incomes

between 1993 and 2002 (see Table 2.14), which initially were highly concentrated among the

richest households.

91

Table 2.17 Shorrocks decomposition

1993 2006 1993 2006 Share OLS Share OLS Share OLS Share OLSRural Urban Crop 0.443 0.20016 0.26973 0.18312 Crop 0.03238 -0.0086 0.03181 -0.0062Sidelines 0.10968 0.08247 0.13253 0.15056 Sidelines 0.0104 0.00218 0.0301 0.05607Family Business 0.21755 0.49264 0.1781 0.28588 Family Business 0.50275 0.6918 0.32488 0.4742Wages 0.11881 0.05395 0.25548 0.12566 Wages 0.2195 0.01582 0.38979 0.1755Remittances 0.0524 0.10344 0.10525 0.18404 Remittances 0.13563 0.17683 0.10725 0.19613Other Income 0.05855 0.06733 0.0589 0.07073 Other Income 0.09935 0.12197 0.11616 0.10429Rural north Urban north Crop 0.46662 0.13911 0.25301 0.03597 Crop 0.06387 -0.009 0.02057 -0.0108Sidelines 0.1405 0.10031 0.13623 0.13568 Sidelines 0.03044 0.00606 0.03448 0.07925Family Business 0.17491 0.45135 0.17743 0.32498 Family Business 0.45933 0.82584 0.2677 0.24718Wages 0.07563 0.05074 0.24005 0.14201 Wages 0.19857 0.00587 0.41717 0.21804Remittances 0.05402 0.15582 0.10873 0.23549 Remittances 0.10896 0.09244 0.09348 0.36627Other Income 0.08832 0.10266 0.08455 0.12586 Other Income 0.13883 0.0788 0.16661 0.10006Rural south Urban south Crop 0.41561 0.2509 0.28547 0.27991 Crop 0.01976 -0.0069 0.03893 -0.0033Sidelines 0.07393 0.0702 0.12906 0.16281 Sidelines 0.00238 0.00229 0.02733 0.04433Family Business 0.26701 0.52356 0.17873 0.26151 Family Business 0.52014 0.65534 0.36113 0.59135Wages 0.16891 0.05329 0.26999 0.10517 Wages 0.22788 0.01523 0.37244 0.15192Remittances 0.05052 0.06118 0.10197 0.15114 Remittances 0.14631 0.19802 0.11598 0.11259Other Income 0.02402 0.04088 0.03478 0.03946 Other Income 0.08353 0.13607 0.08419 0.10309

92

After 2002, inequality fails to decline any further in either the north or the south. Several

factors appear to be potentially important. First, wage growth, especially in the south, slowed

appreciably after 2002, while wages became much less equalizing in both regions (see Table

2.12). Slower growth in wage earning among households in the lower income quartiles is

consistent with both of these observations. Second, in the north, remittance incomes also became

highly disequalizing, and by 2006 were the source of a third of the inequality. This may also be

tied to important differences amongst households throughout the quartiles in terms of their

ability to access off-farm labour markets. And third, after 2002 growth in family-business

income, the most disequalizing source of urban incomes, recovers. Most of this influence is

coming through its effect on inequality in the south. In the north, family business incomes are

much less important, and much more equally distributed, than they were in 1993.

In summary, the main contributors to measured inequality are household businesses, in

both rural and urban areas, followed by remittances. Both sources of income contribute more to

overall inequality than to overall income. In the rural sector, rapid growth in the more equally

distributed income from farming and wages helped to offset the influence of family business

income on overall inequality. As a result, income growth in the lower end of the income

distribution outstripped that at the top, and inequality fell. Wages performed a similar role in the

urban areas, and played a prominent role in reducing urban inequality up through 2002. After

2002, the equalizing role of wages became much less pronounced, while other sources of income

such as remittances began to exert a more pronounced influence on the distribution of incomes.

Growth no longer disproportionately benefited lower income households, and thus, the level of

inequality in 2006 remained very similar to that in 2002.

2.9 Robustness checks

There are reasons to be concerned about the comparability of the surveys between 1998

and 2002. Changes to the sampling framework between 1998 and 2002 are associated with

unusual movements in the share of household participating in various income generating

activities. There are a number of possible explanations for these unusual trends. First, the

summary statistics generated from the household surveys could be indicative of true rapid

changes in the structure of income between 1998 and 2002. Second, the expansion of the number

93

of communes in which households were surveyed could have dramatically altered the

composition of communes included in the surveys, despite the random nature in which

communes were chosen. Third, changes in the actual questionnaire could have influenced how

households reported various activities. Unfortunately, given the data available we are only able

to test the plausibility of the second reason listed above.

There are 70 communes that appear in both the 1998 VLSS and 2002 VHLSS. We can

compare various summary statistics for these “overlapping” communes with those for “non-

overlapping communes” (i.e., communes that appear in the 2002 VHLSS but not in the 1998

VLSS) to test if the two sets of communes represent different subpopulations. We begin by

comparing the means of household size, total per capita income, and per capita income by

activity between households in the two sets of communes in the 2002 VHLSS. Table 2.18 reports

the mean for each variable within the non-overlapping commune sample, the difference in means

between the two sets of communes, the percentage difference in means, and the t-statistics for

the null hypothesis of equal means. We see mixed evidence as to whether or not the two samples

represent the same population. For rural households we observe no statistically significant

differences in means except for per capita income from remittances and gifts and from other

sources. Similarly, for urban households only two of the variables examined display statistically

significant differences in means between the two samples. Nonetheless, there are some

particularly large differences in the percentage differences in means.

In Table 2.19 we repeat the same analysis, except based on the share of households that

report receiving income from each activity. For both rural and urban households we find no

statistically significant differences in the share of households reporting positive income by

activity between the two sets of communes. Hence, most of the differences observed in Table

2.18 are coming from differences in earnings per activity per active household.

94

Table 2.18 Comparison of mean income in the 2002 VHLSS between households in communes that also appear in the 1998 VLSS and households in communes that only appear in the 2002 VHLSS

Mean fornon-

overlappingcommune

sample

Difference in means

between theoverlappin

g

and non-overlapping

Percenta

communes

gedifferencein means

between theoverlapping

and non-overlapping

communes

t-statistic of null

hypothesis of equal

means Rural Vietnam Household size 5.17 0.20 3.95 1.41Total per capita income 4764.2 58.9 1.24 0.14Per capita income from agriculture 1411.5 288.9 20.47 1.35Per capita income from ag. sidelines 632.6 -88.2 -13.95 -1.13Per capita income from household business 917.5 118.1 12.88 0.31Per capita income from wages 1126.7 -34.0 -3.02 -0.19Per capita income from remittances and gifts 444.9 -153.8 -34.57 -2.59Per capita income from other sources 231.0 -72.1 -31.20 -2.54 Urban Vietnam Household size 4.94 -0.10 -2.12 -0.43Total per capita income 9209.8 -935.3 -10.16 -0.98Per capita income from agriculture 317.5 -179.4 -56.50 -2.50Per capita income from ag. sidelines 284.9 238.3 83.64 0.81Per capita income from household business 3217.9 -1099.4 -34.16 -3.42Per capita income from wages 3589.9 -50.7 -1.41 -0.07Per capita income from remittances and gifts 1010.5 178.7 17.69 0.44Per capita income from other sources 789.2 -22.8 -2.89 -0.12

Note: “Non-overlapping commune sample” refers to households from communes that appear in the 2002 VHLSS but not in the 1998 VLSS. The difference in means should be interpreted relative to the mean for the non-overlapping commune sample. Thus, 0.20 means that the mean household size is 0.20 persons greater in the overlapping sample of households than in the non-overlapping sample.

95

Table 2.19 Comparison of the households reporting non-zero income in the 2002 VHLSS between households in communes that also appear in the 1998 VLSS and households in communes that only appear in the 2002 VHLSS

Share fornon-

overlappingcommune

sample

Difference in shares

between theoverlappin

g

and non-overlapping

Percenta

communes

gedifferencein shares

between theoverlapping

and non-overlapping

communes

t-statistic of null

hypothesis of equal

means Rural Vietnam Per capita income from agriculture 0.844 0.030 3.59 0.83Per capita income from ag. sidelines 0.813 -0.017 -2.03 -0.37Per capita income from household business 0.354 -0.015 -4.24 -0.30Per capita income from wages 0.559 -0.027 -4.92 -0.58Per capita income from remittances and gifts 0.803 0.035 4.37 1.00Per capita income from other sources 0.437 -0.019 -4.39 -0.35 Urban Vietnam Per capita income from agriculture 0.226 -0.089 -39.22 -1.64Per capita income from ag. sidelines 0.243 -0.072 -29.51 -1.31Per capita income from household business 0.549 0.031 5.72 0.60Per capita income from wages 0.712 -0.001 -0.20 -0.03Per capita income from remittances and gifts 0.787 0.028 3.53 0.51Per capita income from other sources 0.449 0.070 15.66 1.06

Note: “Non-overlapping commune sample” refers to households from communes that appear in the 2002 VHLSS but not in the 1998 VLSS. The difference in means should be interpreted relative to the mean for the non-overlapping commune sample. Thus, 0.20 means that the mean household size is 0.20 persons greater in the overlapping sample of households than in the non-overlapping sample.

As a final check we compare inequality estimates between households in the two sets of

communes in Table 2.20. Similar to the results in Table 2.18, we find mixed evidence on

whether or not the two sets of communes are equally representative. The Gini coefficients for

overall Vietnam are very similar, differing by only 0.011, and they are not statistically different.

However, the similarity of the overall Gini coefficients hides important differences within rural

and urban areas. In rural areas, the Gini coefficient is 0.025 points higher in the overlap set of

communes. Again though, this is not a statistically significant difference. However, the

difference in the Gini coefficient is even larger in urban areas. It is 0.071 points higher in the

non-overlapping set of communes and it is a statistically significant difference. It is perhaps not

surprising that we observe a larger difference in urban areas than in rural areas, given the change

in the sampling framework described previously. Recall that the 1998 VLSS was stratified into

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ten strata, three of which were urban areas. By comparison the 2002 VHLSS was stratified into

122 strata, 61 of which were urban areas. The difference in sampling strategies meant that urban

households were only interviewed in 29 out of 61 provinces in the 1998 VLSS as compared to all

61 provinces in the 2002 VHLSS. Hence, despite the random selection of communes within

strata, it is likely that the 2002 sampling framework produced a more nationally representative

selection of households, particularly in urban areas.

Table 2.20 Gini coefficients from the 2002 VHLSS for households from "overlapping" and "non-overlapping" communes

Confidence interval

Sample Gini

coefficientLower bound

Upper bound

Urban & Rural Communes in 1998 and 2002 0.392 0.358 0.435 Communes in 2002 only 0.403 0.395 0.416 Rural only Communes in 1998 and 2002 0.388 0.339 0.486 Communes in 2002 only 0.363 0.355 0.373 Urban only Communes in 1998 and 2002 0.322 0.291 0.368 Communes in 2002 only 0.393 0.376 0.415

Note, however, that the difference in urban inequality between the two samples of

communes reported in Table 2.20 does not explain away the fall in urban inequality shown in

Table 2.15. Recall that our estimates point to a fall in urban inequality between 1998 and 2002,

from 0.444 to 0.370 as measured by the Gini coefficient. Hence, the difference between

communes in 2002 goes in the opposite direction of the fall between 1998 and 2002. Thus, if we

restrict our 2002 sample to only those communes that also appeared in the 1998 VLSS, then the

fall in urban inequality would have been even greater.

2.10 Discussion and conclusion

In this paper we have tried to present a consistent set of facts about the evolution of

Vietnam’s income distribution between 1993 and 2006. Although we have not directly linked

these changes to specific reform policies associated with Doi Moi we have nonetheless presented

a large amount of information. We believe the most important results from this paper are the

following points.

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1. Income growth has been very rapid between 1993 and 2006. Over the 13 year period

between 1993 and 2006, per capita incomes in Vietnam experienced growth averaging 8.4

percent. Growth was especially rapid between 1993 and 1998, but then fell between 1998 and

2002, before recovering between 2002 and 2006. This pattern was slightly more pronounced in

the south than in the north, reflecting the region’s greater exposure to international market forces,

including the impact of the Asian Financial Crisis and ensuing downturn in global economic

activity.

2. Rural income growth was especially rapid. Overall, rural growth outstripped urban per

capita income growth, but the opposite was true in the North. Especially important to the growth

in rural incomes was the increase in wage opportunities as reflected in growth in wages and

remittances.

3. Poverty fell very quickly. Poverty levels in Vietnam were very high in 1993, as nearly

two-thirds of the population had incomes below the poverty line. Poverty was considerably

higher in the countryside compared to the cities, with 70.1 percent classified as poor in rural

areas compared to 40.8 percent in the cities. The rapid growth in incomes was accompanied by a

rapid reduction in the poverty headcount ratio that slows over time. By 2006, only 7.8 percent of

the entire population, 1.6 percent of the urban population, and 10.1 percent of the rural

population is classified as “poor”. These estimates are subject to the potential criticism that some

of the poor, and possibly a rising number in absolute terms, are missing from the household

surveys.

4. Income inequality is high initially and falls through 2002. Inequality in Vietnam early

in the reform process is very high by international standards. It is higher in the south than in the

north, and in urban areas compared to rural areas. Much of this can be attributed to institutional

factors and other differences between the two regions. Inequality declines appreciably, with this

largely occurring between 1993 and 2002. There is no further reduction after 2002. We observe a

marked reduction in urban inequality, rural inequality, as well as a narrowing in the urban-rural

gap, the latter driven by behaviour in the south.

5. Rapid wage growth and robust growth in cropping income drove down rural

inequality. In rural areas, growth of wage income, which is especially important to poorer

households, is central to the reduction in income inequality. Robust growth of incomes in

agriculture from both cropping and sidelines are also important at the beginning and end of the

period we examine. Liberalization of farm markets domestically and internationally, and the

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ability of households to take advantage of these opportunities, is playing an important role in the

rapid growth in agriculture, especially in the south. Although land was much more equally

distributed in the north than in the south, significantly slower growth in the farm sector prevented

this from having an even larger impact on inequality. This helps explain the narrowing in rural

inequality between the north and south.

6. Wage income growth also helped to reduce inequality in urban areas. Rapid growth in

wages, which were the most equalizing source of income in urban areas, is largely responsible

for the reduction in urban inequality. Slower growth in incomes from family run businesses,

which were more unequal, also contributed. This shift partially reflects the changing nature of

employment relationships in urban Vietnam, and the fall in self-employment, and rise in wage

employment.

We also briefly examined two key household and individual endowments – land and

human capital (education). We find that educational attainment is becoming more evenly

distributed amongst workers. We do not formally investigate the impact that this has had on the

distribution of income, but we conjecture, and leave for future research, that growing equality in

educational attainment is contributing to the relatively equalizing role of wage earnings in the

distribution of overall income.

A final outcome from this paper is a greater understanding of some of the data issues

involved with creating consistent estimates of income and distributional measures such as

inequality or poverty. We have identified three main issues that influence the comparability of

various summary statistics across the five household surveys that are not generally addressed in

the literature. The first concern, well mundane, is nonetheless important. The regional price

deflators included with the 2002 and 2004 VHLSS household datasets are improbable and likely

deeply flawed. Since these deflators are used by all authors creating national and comparable

regional estimates of poverty and inequality, all such work will be sensitive to these regional

deflators. We create our own regional deflators for 2002 and 2004. The second concern is the

substantial change to the sampling framework between the 1998 VLSS and the 2002 VHLSS.

Many authors focus only on the 1993 to 1998 period or from 2002 onwards, but there are a

handful of papers that cover the entire time period. Unlike these papers we have examined the

sensitivity of our results to the change in the sampling framework and find mixed evidence as to

whether or not the change in the sampling framework introduced a change in how representative

the sample is of the true population. Finally, the household surveys are missing people. Relative

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to population projections based on the 1999 census, the household surveys are not adequately

capturing young adults and young children. This is most likely due to household migration and

indeed we find supporting evidence using the household panels between 2002, 2004 and 2006.

The “missing” people problem is becoming more severe overtime and calls into question how

indicative summary statistics are of the true underlying population. Unfortunately, this is not a

problem that is easily addressed.

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2.11 Data Appendix

2.11.1 Estimation of Household Income

This section describes our methodology for estimating household income in a consistent

manner across the five household surveys. We organize the section according to the six major

income activities discussed in the body of the paper: crops, agricultural sidelines, household

businesses, wages, remittances and gifts, and “other” sources. For each income activity we

describe which sections of the household questionnaires contain the relevant questions, for both

income and, where relevant, expenditures.

2.11.1.1 Income from crops We estimate revenue from crops according to the following steps. First, for each crop we

calculate the unit value of sold output by household. There are many households that produce a

particular crop yet do not report selling any of the output. For these households we cannot

construct a unit value of sold output. Second, we remove any unit values that are extreme outliers

as a means to prevent recording errors from skewing the results. Specifically, we remove any

unit values that are more than three standard deviations away from the median unit value, where

both the median and standard deviation are calculated across all unit values within the sample for

the particular crop. Third, for any households that reported harvesting a particular crop but do

not have an associated unit value (either because they did not report selling any of the output or

because the unit value of sold output was an extreme outlier) we calculate a unit value as the

median unit value within the commune, province, region, or the entire sample. We use the most

local median unit value that is available. Fourth, we estimate the market value of crop revenue as

the reported quantity harvested times the unit value. For some crops, information was not asked

about the quantity and value of sold output. This makes it impossible to calculate unit values and

we thus use the self-reported value of the harvest as an estimate of revenue from that particular

crop.

For crop by-products there is no information on the quantity or value of sold output.

Thus, we use the self-reported value of the by-product item as an estimate of revenue.

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The following subsections list the specific sections of each household questionnaire that

we use for calculating revenue and expenditures for crops, as well as any deviations from the

above procedure.

2.11.1.1.1 1993 VLSS

Crop revenue is the sum of revenue from all crops listed in Parts B1 through B6 of

Section 9 of the household questionnaire as well as crop by-products listed in Part C of Section

9. This includes paddy, other food crops, annual industrial crops, perennial industrial crops, fruit

crops, agro-forestry crops, and crop by-products.

In Section 9, Part B1, respondents could report the quantity and value of sold output in

terms of rice or paddy. We convert from rice to paddy by dividing the rice quantity by 0.66 for

observations that report the quantity of sold or bartered output in terms of rice, not paddy, since

the quantity harvested is reported in terms of kilograms of paddy.

For agro-forestry crops in Section 9 Part B6 there is no information collected on the

quantity harvested or sold. Hence, the calculation of unit values is impossible. We therefore use

the reported value of the harvest as an estimate of the revenue from each agro-forestry item.

Crop expense is the sum of annual expenditures on seed, chemical fertilizers, organic

fertilizers, insecticide or herbicide, transportation, and other expenses (storage costs, payments in

cash and in-kind for services received from the cooperative or government (land preparation for

planting irrigation, plant protection, protection of land) renting animals; renting equipment or

machinery; maintenance and repairs of agricultural implements and machinery; gasoline,

electricity, and other fuels; other services; labour costs for ploughing, planting and tending, and

harvesting). For each expenditure item, we use the self-reported bought and bartered value

during the past 12 months (see Section 9 Part D of the household questionnaire).

2.11.1.1.2 1998 VLSS

Crop revenue is the sum of revenue from all crops listed in Parts B1 through B6 and Part

C of Section 9 of the household questionnaire. This includes the following crop items: rice, other

food crops, annual industrial crops, perennial industrial crops, fruit crops, agro-forestry crops,

and crop by-products.

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In Section 9, Part B1, respondents could report the quantity and value of sold output in

terms of rice or paddy. We convert from rice to paddy by dividing the rice quantity by 0.66 for

observations that report the quantity of sold or bartered output in terms of rice, not paddy, since

the quantity harvested is reported in terms of kilograms of paddy.

In Section 9 Part B3 there are two crop categories (“Other annual industrial crops” and

“Other annual crops”) for which information on the quantity sold was not gathered. This

prevents the calculation of unit values for sold output. Furthermore, for these two crop categories

information on the quantity harvested was also not collected. We thus have no way of valuing

the total harvest and hence simply use the reported value of sold output.

For crop item 51, mangosteen, in Section 9 Part B5 no households reported selling any of

the output. Thus, we use the reported value of the harvest instead of estimating the value of the

harvest using the unit value procedure for all households that reported harvesting mangosteen.

Similarly, there is only one observation for crop item 44, grapes, in Section 9 Part B5, which

reports selling any output. We thus use the reported value of the harvest instead of estimating the

value of the harvest for all households that harvested grapes.

For agro-forestry crops in Section 9 Part B6 there is no information collected on the

quantity harvested or sold. Hence, the calculation of unit values is impossible. We therefore use

the reported value of the harvest as an estimate of the revenue from each agro-forestry item.

Crop expense is the sum of annual expenditures on seed, chemical fertilizers, organic

fertilizers, insecticide or herbicide, transportation, and other expenses (storage costs, payments in

cash and in-kind for services received from the cooperative or government (land preparation for

planting, irrigation, plant protection, protection of land) renting animals; renting equipment or

machinery; maintenance and repairs of agricultural implements and machinery; gasoline,

electricity, and other fuels; other services; labour costs for ploughing, planting and tending, and

harvesting). For each expenditure item, we use the self-reported bought and bartered value

during the past 12 months (see Section 9 Part D of the household questionnaire).

2.11.1.1.3 2002 VHLSS

Crop revenue is the sum of revenue from all crops listed in Parts B2.1 through B2.4 and

Part B5.1 of Section 5 of the household questionnaire. This includes the following crop items:

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rice and other food crops, annual and perennial industrial crops, fruit crops, crop by-products,

and silviculture.

For silviculture crops, there is no information on the quantity harvested or sold or

bartered. Thus, we cannot calculate unit values and instead rely on the reported value of the

harvest as an estimate of revenue.

Crop expenditures is the sum over all crops of the following expenditure items from

Section 5 Part B2.5: seeds; organic fertilizer; chemical fertilizer; pesticides; non-durable items;

energy and fuel; repairs and maintenance; rental cost of land; renting machinery, equipment, and

others; plough rental; outside labour; irrigation; and other costs.

2.11.1.1.4 2004 VHLSS



rice; other starchy, vegetable and annual crops; annual and perennial industrial crops; fruit crops;

crop by-products; and silviculture.


Section 4 Part B1.6: seeds; saplings; organic fertilizer (bought); chemical fertilizer; pesticides;

herbicides; non-durable items; energy and fuel; repairs and maintenance; rental cost of land;

renting machinery, equipment, and others; plough rental; outside labour; irrigation; and other

costs.

2.11.1.1.5 2006 VHLSS



rice; other starchy, vegetable and annual crops; annual and perennial industrial crops; fruit crops;

crop by-products; and silviculture.


Section 4 Part B1.6: seeds; saplings; organic fertilizer (bought); chemical fertilizer; pesticides;

herbicides; non-durable items; energy and fuel; repairs and maintenance; rental cost of land;

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renting machinery, equipment, and others; plough rental; outside labour; irrigation; and other

costs.

2.11.1.2 Income from agricultural sidelines

Agricultural sideline activities include livestock, other animal products, and water

products. Unlike cropping income, we often do not have the required information for

constructing unit values of sold output and thus we estimate revenue as the self-reported value of

total output.

2.11.1.2.1 1993 VLSS

Revenue from livestock is the sum of the self-reported value of consumed and sold

quantities for each livestock item listed in Section 9 Part F of the household survey. Revenue

from other animal products is the sum of the self-reported value of sales from all items listed in

Section 9 Part G of the household questionnaire. Finally, revenue from aquaculture products is

the sum of the self-reported value of sold output. Since the module does not ask about the value

of aquaculture products consumed by the household, we inflate the sold values by a region-

product inflator.

We estimate expenses for livestock, other animal products, and aquaculture using

information in Section 9 Part J of the household questionnaire. We include all expenditure items

listed in the module and sum over the self-reported values. Unlike in the three VHLSSs, it is not

possible to separate expenses for livestock from those for aquaculture products.

2.11.1.2.2 1998 VLSS

For both revenues and expenses, the 1998 VLSS collected the same information as the

1993 VLSS for livestock, other animal products, and aquaculture. Thus, we follow the exact

same approach as for the 1993 VLSS. One small difference is that information on the cost of

labour was asked in a different section of the income module. Whereas labour costs for livestock,

other animal products and aquaculture was asked along with other associated expenses in Section

9 Part J of the 1993 VLSS, in the 1998 VLSS it is asked along with labour costs for crop

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production in Section 9 Part D. We separate labour costs for “livestock raising” and “raising

water products” from other farm labour uses and include these as the cost of labour for

agricultural sidelines.

2.11.1.2.3 2002 VHLSS

The list of livestock and other animal products included in the 2002 VHLSS was shorter

than in the two VLSSs (see Section 5 Part B3). For example, respondents were no longer asked

directly about horses, bees, silk worms, or snakes. Nor were they asked about hides and furs or

manure. All of these items were individually included in the 1993 and 1998 VLSSs. In the 2002

VHLSS some households may be reporting some of these items in the residual categories “Other

cattle, poultry, etc.” and “Livestock by-products” but we cannot be sure. In contrast, the 2002

VHLSS included questions on aquaculture products that were caught as opposed to simply

asking about raised aquaculture products, as was done in the two VLSSs. With these changes in

the questionnaire in mind, we estimate the annual revenue from livestock, other animal products,

and aquaculture as the sum of the self-reported value of sold and self-consumed output.

We estimate total expenses as the sum of all expense items listed in Section 9 Parts B3.2

and B6.2 except for depreciation of fixed assets, payment of loan interest, and turnover tax.

2.11.1.2.4 2004 and 2006 VHLSS

For the 2004 and 2006 VHLSS we estimate revenue as the sum of the self-reported value

of sold and consumed output for all items listed in Section 4 Parts B2.1 and B5.1. We estimate

expenses as the sum of the self-reported value of all expense items listed in Section 4 Parts B2.2

and B5.2 except for depreciation, payment of loan interest, and business taxes.

2.11.1.3 Household business income

Household business income covers profits from all non-farm, non-forestry, and non-

aquaculture household production. In the 1993 and 1998 VLSSs this information is collected in

two sections: processing of self-grown crops for sale and the non-farm self-employment module.

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Starting with the 2002 VHLSS we draw on the non-farm, non-forestry, non-aquaculture

household business module.

2.11.1.3.1 1993 VLSS

For the 1993 VLSS, household business income is defined as the sum of income from

processing crops at home (Section 9 Part E) and income from non-farm self-employment

(Section 10).

Revenue from the processing of crops at home to make products for sale is estimated as

the number of months the item was made for sale times the number of times the item was sold

per month times the usual amount received each time. The first and the last time items in the

expression are directly available from the data. The number of times the item is sold per month is

estimated from question 5 of Section 9 Part E. We take the number of times reported and

multiply this by the number of times the reported period occurs within a month. For example, if

the respondent reported selling two times and the time period as a week, then the number of

times sold per month is 2×4.33, where 4.33 is the average number of weeks per month.

Expenses from the processing of crops at home to make products for sale are simply the

self-reported cost over the past 12 months.

Revenue from non-farm self-employment is estimated based on information in Section 10

Part C. Revenue is the sum of the value of cash and in-kind payments received by the business as

well as the self-reported value of household consumption of business products including raw

materials during the past 12 months. Payments received by the business are adjusted by the

household’s ownership share of the business if the business is owned with others.

Expenditures from non-farm self-employment are estimated based on information in

Section 10 Parts A and B. We include the following expenditure items in our estimates: wages or

other remuneration; raw materials; articles for resale; rental of equipment, buildings, land,

vehicles, and machinery; maintenance and repairs; transport; fuel; electricity; water; licence fee;

and other expenditures. For each included expenditure item we estimate the annual value of

expenditures according to the following algorithm. We multiply the usual value paid for the item

with the number of times the payment is made within 12 months. We estimate the number of

times that the payment is made within the past 12 months by using the number of months the

business was operating, the time period for the payment, and the number of times the reported

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time period would occur in a month. For expenditure items reported on a daily basis, we also use

information on the number of days per month the business usually operated. Some household

businesses do not report any expenses in the dataset. For these businesses we predict the value of

annual expenses using the value of annual cash payments received by the business and the

median ratio of expenses to cash payments for businesses that report both data. Finally, we adjust

businesses expenses by the ownership share of the household if the household jointly owns the

business.

2.11.1.3.2 1998 VLSS

As for the 1993 VLSS, we include income from the processing of crops at home for sale

and income from non-farm self-employment in household business income. The estimation of

income from the processing of crops for sale is handled in the same manner as for the 1993

VLSS. For household business income, the only difference is that information on remuneration

for employees was collected in a different manner as compared to the 1993 VLSS. We estimate

the value of remuneration for employees using question 12 from Section 10 Part A. As the

question collects information in a manner similar to that for other expenditure items, we follow a

similar approach. We multiply the cost per worker per period by an estimate of the number of

periods within the past 12 months that the payment was made and subsequently multiply by the

number of employees.

2.11.1.3.3 2002 VHLSS

Starting with the 2002 VHLSS (see Section 5 Part C of the household questionnaire),

information on the processing of crops for sale is no longer collected in a separate section from

other self-employment activities. Both activities are asked about simultaneously in the module

on non-farm, non-forestry, and non-aquaculture businesses. Unfortunately, we do not know what

impact this substantial change in the questionnaire has on the comparability of the data. There

are, however, a few things that we can say. First, in the 2002 VHLSS food and beverage

production is the second most frequent industry for the primary household business. A total of

1,435 households list this industry for the primary business out of a total of 11,405 households

reporting a business (12.6 percent). By comparison, in the 1998 VLSS 153 out of 5,999

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households reported processing home-produced crops for sale. Moreover, 299 households in the

1998 VLSS reported operating a business in food and beverage processing within the self-

employment module (this includes all business activities of the household, not simply the

primary activity). Only 16 households reported both the processing of home-grown crops for sale

and operating a household business within the food and beverage processing industry. Hence, it

does not seem as though there is much evidence to suggest double counting of income from crop

processing (i.e., the activity being reported in the section of crop processing and also in the

section on non-farm self-employment).

For each business activity, we estimate income as the sum of (1) monthly turnover

multiplied by the number of months in operation and (2) the self-reported value of self-

consumption. We include the following expenditure items in our estimate of business expenses:

materials; non-durable items; electricity, petrol, oil, fuel, and water; small repair and

maintenance; rent of factory, workshop, machinery, or other tools; labour costs; and other

expenses. Unlike the 1993 and 1998 VLSSs, the respondent was asked to provide the annual

value of each expense as opposed to the usual expense per period. This format remained in the

2004 and 2006 VHLSS. Finally, unlike the other four surveys, the 2002 VHLSS did not ask

about the household share of ownership in the business. Hence, we cannot deflate business

income (revenue minus expenses) by the household ownership share and are thus likely

overestimating household business income from some businesses. The impact of this

measurement error is likely to be fairly small however, as most household businesses are owned

only by one household. For example, in the 1998 VLSS only 134 businesses out of 3,440 (3.9

percent) were owned by more than one household.

2.11.1.3.4 2004 and 2006 VHLSS

The household business modules of the 2004 and 2006 household questionnaires are

extremely similar in terms of the information we use to estimate revenue and expenses. The

relevant section of the questionnaires is Section 4 Part C. We estimate business revenue as the

sum of household self-consumption and the product of monthly revenue times the number of

months in operation. We estimate annual business expenses as the sum of self-reported annual

expenses on materials and sub-materials; small and non-durable tools; electricity; water;

gasoline, oil, petrol, lubricants, and burning fuels; small repair and maintenance; rent of land,

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workshops, shops, machines, and other production means; transportation; labour costs;

expenditure on water and solid waste disposal; and other expenditures. We adjust revenue

(excluding the value of household self-consumption) and expenses by the household’s ownership

share if it differs from 100 percent.

2.11.1.4 Wage income

Wage income is the sum of all wage/salary payments, plus other forms of remuneration,

such as holiday payments, for all jobs during the past 12 months. While the general definition of

wage income is straightforward, creating a consistent estimate is challenging due to changes in

the employment modules across the surveys. In particular, the employment module was

substantially shortened in the 2002 VHLSS, as compared to the 1993 and 1998 VLSSs, and then

lengthened somewhat starting with the 2004 VHLSS. We describe the estimation of wage

income for each survey below.

For each survey, we check for wage outliers according to the following procedure. First,

we estimate the annual value of wage & salary payments and the annual number of hours

worked. Second, we estimate the corresponding hourly wage rate. Third, we identify hourly

wage outliers based on values that are greater than or less than five standard deviations from the

median hourly wage by occupation. Fourth, the hourly wage outliers are replaced by the median

hourly wage by occupation. Finally, for all observations with an hourly wage outlier we re-

estimate the annual value of wage and salary payments with the respective median hourly wage

and the annual number of hours worked by the individual.

2.11.1.4.1 1993 VLSS

The 1993 VLSS collected detailed information on employment for all household

members aged six years old and over. Section 4 of the household questionnaire begins by asking

about the individual’s primary and secondary job during the past seven days in Parts B and C. In

addition, the employment module also collected information on the individual’s primary and

secondary jobs during the past 12 months if these jobs differed from those already reported for

the past seven days. For our purposes, we are interested in the employment of the individual over

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the past 12 months. As such, we focus on the primary and secondary jobs during the past 12

months.

The primary job during the past 12 months can fall into one of three categories: (1) the

same job as the primary job during the past seven days, (2) the same job as the secondary job

during the past seven days, (3) neither the primary or secondary job during the past seven days.

The secondary job over the past 12 months can be similarly categorized. This categorization is

important because it influences what type of remuneration information was collected about the

job. For the primary job during the past seven days we have separate information on (1) wage

and salary payments and (2) various non-wage/salary payments such as payments for Tet and

other holidays, bonuses, payments for lunch, etc. By comparison, for the secondary job during

the past seven days all forms of remuneration were asked in one single question. For the primary

job during the past 12 months, we have the same information as above if the job was already

reported on as being the primary or secondary job during the past seven days. If it is a different

job, then we have separate information on (1) wage and salary payments and (2) various non-

wage/salary payments. Hence, for some individuals we have more detailed information about the

various components of their compensation package. A similar situation arises for the secondary

job during the past 12 months. If it was the same as the primary or secondary job during the past

seven days then we have the compensation information as listed above. If, however, it was

neither the primary or secondary job during the past seven days then we only have information

on total compensation (i.e., all forms of compensation are asked about in one question).

The impact of the differences in how remuneration data was collected could influence our

estimates of wage income. For example, if the individual is responding about his or her primary

job during the past seven days, he or she may better recall the value of all payments when being

asked separately about each component as compared to respond about his or her secondary job

during the past seven days where he or she is asked about the total value of remuneration in just

one question. Intuitively, one suspects the measurement error to be less when the individual is

asked more detailed questions.

A second noteworthy issue, especially in comparison to the 2002 through 2006 VHLSSs,

is that the individuals were asked about both the value of the payment and the time unit of the

payment (i.e., daily, weekly, monthly, quarterly, per half year, or annually). Unfortunately, the

1993 employment module did not always collect the necessary information to know how many

days the individual worked in the year. For example, in Part B, which collects information on the

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individual’s primary job during the past 12 months, the respondent is asked about the number of

weeks worked in the past 12 months as well as the average number of hours worked per week.

This allows us to estimate the number of hours worked per year, but does not allow us to

estimate the number of days worked per year without further assumptions. Without an estimate

of the number of days worked per year, we cannot estimate the annual value of any payments

reported by day. To solve this problem, we estimate the mean number of days worked during the

past seven days by industry and use this as an estimate of the number of days worked per week

by the individual.

More broadly, the reporting of various payments by different payment periods introduces

concerns over how to interpret each payment period. For example, how does one estimate the

annual value of a yearly payment if the individual only worked for four months during the past

year? We assume that an annual payment is a one-time payment that is made to the individual

regardless of the employment duration during the past months. For all other payment periods, we

pro-rate the payment by the number of payment periods worked during the year. For example,

the value of a monthly payment is multiplied by the number of months worked during the year.

2.11.1.4.2 1998 VLSS

The structure of the employment module is extremely similar to the 1993 VLSS. For our

purposes, there are only two important differences relative to the 1993 VLSS. The first key

difference is that the module asked about the number of weeks worked per year and the number

of days worked per week for the primary job during the past seven days. Thus, we no longer

have to estimate the number of days worked per week, as we had to do for the 1993 employment

data. The second notable difference is a change in the categories of non-wage/salary

compensation. As with the 1993 VLSS, all payments were reported according to various

payment periods. We follow the same procedures for estimating the annual value of these

payments as described above for the 1993 VLSS.

2.11.1.4.3 2002 VHLSS

The changes in the employment module for the 2002 VHLSS represent the biggest

changes across the five surveys. First, employment information was only collected for

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individuals aged 10 years of age and older, as compared to 6 years of age and older as in the

other four surveys. Second, the module no longer collected information on the value of the

payment per period. Instead, the respondent was asked to provide the total value received for

each form of compensation over the past 12 months. This is the same as in the subsequent 2004

and 2006 VHLSSs. Third, the categories of non-wage/salary payments changed once again.

Fourth, the module does not ask for detailed information on a second wage/salary job. Instead,

all wage/salary jobs, other than the first, are grouped into “other paid works.” By comparison,

the 1993 and 1998 VLSSs as well as the 2004 and 2006 VHLSSs asked detailed questions about

the individual’s secondary job during the past year. For these surveys, “other paid work” consists

of all jobs other than the individual’s first and second job. No doubt this introduces greater

measurement error in the recollection of employment income in the 2002 VHLSS. However, a

relatively small number of households report employment income from “other paid works.” Only

1,306 households out of 29,532 (about 4.4 percent) report employment income for jobs other

than the main wage/salary job. Thus, the differences in recollection are likely to have a minimal

impact on our overall estimate of household income.

2.11.1.4.4 2004 and 2006 VHLSS

The employment modules for the 2004 and 2006 VHLSSs were almost identical for our

purposes. The only marginally relevant difference between the two surveys was the separation of

maternity allowance from other social allowance payments in the 2006 VHLSS.

The employment modules for the 2004 and 2006 VHLSSs are in many respects a

compromise between the 1993 and 1998 VLSSs and the 2002 VHLSS. They collected detailed

information on the primary and secondary job during the past year, but retained the format of

asking for the annual value of various payments.

2.11.1.5 Remittances and gifts

For both the 1993 and 1998 VLSS, income from remittances is recorded in Section 13

Part A. We use the self-reported value of money received during the past 12 months by the

household, including in-kind payments, from all sources (including remittances from both

domestic and overseas sources).

113

Starting with the 2002 VHLSS, information on remittances was collected in a different

manner. Instead of collecting detailed information on remittances by sender, the questionnaires

simply asked for the total value of remittances received by the household over the past 12

months, separated by domestic and overseas sources. The relevant questionnaire sections are

Section 5 Part D1 in the 2002 VHLSS and Section 4 Part D1 in the 2004 and 2006 VHLSSs.

Note however, that the VHLSSs also asked about gifts in the same questions as remittances. This

is why we group remittances and gifts together: it is impossible to separate them in the VHLSSs.

For the 1993 and 1998 VLSSs, information on income received as gifts was asked in

Section 13 Part B and we add this to the value of remittances to arrive at an estimate of income

from remittances and gifts.

2.11.1.6 “Other” residual sources of income

This final category of income covers items such as various transfer payments from the

government and interest earned from various household assets. We describe the included income

items for each survey below.

2.11.1.6.1 1993 VLSS

For the 1993 VLSS the relevant module of the household questionnaire is Section 13 Part

B. We sum the self-reported value of income over the following items: social fund of the

government; social subsidies; interest on savings, stocks, and loans; insurance; dowry or

brideprice and inheritance; lottery winnings; income from the lease of buildings, equipment,

house, land, and draft animals; income from the lease of durable goods and everyday utensils;

and other income.

2.11.1.6.2 1998 VLSS

For the 1998 VLSS the relevant module of the household questionnaire is Section 13 Part

B, which is very similar to that for the 1993 VLSS. We sum the self-reported value of income

over the following items: social insurance fund of the government; social subsidies; funds or

programs related to the government poverty alleviation program; assistance from private and

114

international non-governmental organizations; interest on savings, stock, and loans; insurance;

dowry or brideprice and inheritance; lottery winnings; income from the lease of buildings,

equipment, house, land, and draft animals; income from the lease of durable goods and everyday

utensils; and other income.

2.11.1.6.3 2002 VHLSS

For the 2002 VHLSS the relevant module of the household questionnaire is Section 5

Part D1. We sum the self-reported value of income over the following items: pension, sickness

and one-time job loss allowance; social insurance allowance; other income from social

insurance; interest of savings, shares, coupons, and loans; income from workshop, machinery,

asset and tool leasing; and other income, including lottery winnings, charity and support from

other organizations.

2.11.1.6.4 2004 and 2006 VHLSS

For the 2004 and 2006 VHLSS, the relevant module of the household questionnaire is

Section 4 Part D1. We sum the self-reported value of income over the following items: pension,

one-time sickness and job loss allowance; social welfare allowance; lump sum retirement

allowance (only asked in the 2006 VHLSS); other social welfare allowance (only asked in the

2006 VHLSS); allowance for recovery from disaster and fire (only asked in the 2006 VHLSS);

income from various types of insurance; interest on savings, shares, bonds and loans; income

from leasing workshops, machines, assets, and equipment; income and support form charity

organizations, associations or firms; and other sources.

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3 Chapter 3 Exporting Out of Poverty: Provincial Poverty in

Vietnam and U.S. Market Access

3.1 Introduction

Can a small, poor country reduce poverty by gaining increased market access to a large,

rich country? A contingent of international policy makers seem to think the answer is yes. For

example, the Doha Ministerial Declaration, part of the World Trade Organization’s (WTO) most

recent round of negotiations, stated “International trade can play a major role in the promotion of

economic development and the alleviation of poverty.” The WTO’s Doha agenda called for

developed countries to reduce barriers to trade in agricultural goods and labour-intensive

manufacturers. The reductions are predicted to stimulate exports from developing countries as

they are thought to have a comparative advantage in the production of these goods. But what do

economists really know about the impact of increased market access on developing countries?

Unfortunately, little ex post empirical evidence exists to answer this question. This paper seeks to

contribute to filling in this knowledge gap.

The paper uses the United States-Vietnam Bilateral Trade Agreement (BTA) to examine

the impact of increased market access on poverty in Vietnam. A key attraction of the BTA is the

simplicity and extent of the changes in tariffs faced by Vietnamese exports to the U.S. As

discussed in greater detail below, the U.S. committed to granting Vietnam the status of Normal

Trade Relations (or Most Favored Nation status) upon entry into force of the agreement. This

straightforward reclassification of Vietnamese exports implies that the tariff cuts offered by the

U.S. are less susceptible to endogeneity concerns from political lobbying by Vietnamese or

American industry groups. Moreover, unlike many other trade agreements, such as the North

American Free Trade Agreement, the U.S. tariff cuts were immediate as opposed to being phased

in over a number of years.

Since the BTA came into force in December 2001, Vietnamese exports to the U.S. have

grown very rapidly. From 2001 to 2002, Vietnamese exports to the U.S. grew by 128 percent

followed by an additional 90 percent from 2002 to 2003 (see Table 3.1).

116

Table 3.1 Vietnamese exports to and imports from the U.S., 1997-2004

1997 1998 1999 2000 2001 2002 2003 2004 2005 Value (million USD) Exports 388 553 609 822 1053 2395 4555 5276 6630 Imports 278 274 291 368 461 580 1324 1163 1192 Growth over previous year (%) Exports 22 43 10 35 28 128 90 16 26 Imports -55 -1 6 27 25 26 128 -12 2

Source: U.S. International Trade Commission. Note: Imports are general imports, and exports are FAS exports.

By 2004, the General Statistics Office (GSO) of Vietnam estimates exports to the U.S. accounted

for 20.2 percent of Vietnam’s total exports or about 13 percent of GDP.46 By comparison, in

2000, exports to the U.S. represented only 5.1 percent of total exports or 2.8 percent of GDP.

Hence, the growth in exports to the U.S. represents a quick and substantial shock to Vietnam’s

economy. At a more disaggregated level, exports soared in the 2-digit SITC categories of

articles of apparel and clothing accessories. This commodity category showed an annual growth

of 276.5 percent from 2001 to 2004. Table 3.2 presents information on the value, growth, and

share of exports for Vietnam’s top seven commodity exports to the U.S. according to 2004 value.

With the exception of petroleum products, Vietnam’s top seven exports to the U.S. are all

commodities that are conventionally classified as being low-skilled labour intensive. As low-

skilled workers are more likely to be poor, this suggests the potential for the increase in exports

to have positive impacts on alleviating poverty in Vietnam through increased demand for low-

skilled labour.

Following the entry into force of the BTA, the incidence of poverty in Vietnam declined

dramatically. Between 2002 and 2004 the national poverty rate fell from to 28.9 to 19.5

percent.47 While there is clearly a coincident trend in poverty alleviation and U.S. market access,

it remains an empirical question whether there is a causal connection running from the cut in

U.S. tariffs to the fall in poverty.

46 According to the GSO, exports of goods and services in 2004 were 65.74 percent of GDP. 47 There is some concern over the magnitude of the decline, in particular that the national poverty rate in 2002 may be overestimated (see Glewwe (2005)). I address this issue rigorously in Appendix A.

117

Table 3.2 Main commodity exports from Vietnam to the U.S.

SITC SITC Description 2004 Value Annual Growth Share of exportsCode (million USD) 2001 to 2004 to U.S. in 2004 (%) (%)

84 Articles of apparel and clothing accessories 2571 276.5 48.7

3 Fish 568 5.9 10.8 85 Footwear 475 53.2 9.0 82 Furniture 386 206.4 7.3 33 Petroleum 349 24.0 6.6 5 Vegetables and fruit 184 54.2 3.5 7 Coffee and tea 144 17.3 2.7

Source: U.S. International Trade Commission

The paper measures the immediate short-run impacts of U.S. tariff cuts on provincial

poverty in Vietnam. Following Topalova (2007), I construct provincial measures of exposure to

the U.S. tariff cuts by weighting the tariff cuts by the pre-existing share of employment by

industry within each province. I find that provinces that were more heavily exposed to the tariff

cuts (i.e., had a greater share of workers in industries with large tariff cuts) experienced more

rapid decreases in poverty. The impact on provincial poverty rates between 2002 and 2004 is

large. An increase of one standard deviation in provincial exposure leads to a reduction in the

incidence of poverty by approximately 14 percent, although the effect diminishes the further the

province is from a major seaport. The results are robust to alternative measures of poverty,

alternative poverty lines, plausible measurement error in provincial poverty rates, and differential

provincial poverty trends induced by variation in observable initial conditions. Regarding

transmission mechanisms, I provide evidence that provincial wage premiums increased, low-

skilled workers moved into wage and salaried jobs quicker, and employment in formal

enterprises grew more rapidly in more exposed provinces.

The paper proceeds by providing an overview of the literature on trade and poverty and a

theoretical discussion of the impact of changes in foreign market access when sub-national units

vary in their initial industrial structure. Next, the BTA is discussed in detail, followed by an

overview of the data and empirical methodology used in the paper. Subsequently, regression

results are reported and discussed, before concluding remarks are presented.

118

3.2 Background

The trade and poverty literature provides little direct empirical evidence about the ex post

economic impact of changes in trade policy on the poor (see reviews by Winters et al. (2004) and

Goldberg and Pavcnik (2004)). Nonetheless, the associated literature is very large and generally

falls into one of two literature strands. The first strand relies on the relationship between growth

and openness to trade combined with the relationship between growth and poverty alleviation.48

The second strand relies on indirect evidence of the impact of changes in trade policy on poverty.

This often takes the form of evidence linking labour market correlates of poverty, such as

unemployment, employment in the informal sector, and unfavorable changes in wages for

unskilled workers, with trade liberalization, often focusing only on urban and or manufacturing

workers.49

Very recently, however, empirical evidence on trade liberalization and poverty has

emerged. These studies fall into two categories of methodologies: the first examines what did

happen, and the second predicts what could happen. Topalova (2007) studies India’s unilateral

trade liberalization over the late 1980s and early 1990s and the subsequent variation in regional

impacts. She finds that rural Indian districts that were more exposed to the import tariff

reductions experienced slower declines in poverty than districts that were less exposed. Porto

(2003), Porto (2006), and Nicita (2004) predict the impact of changes in trade policy on

households. These papers use ex post estimates of the impact of tariff changes on prices and

predict the subsequent impact on household income or expenditures as suggested by initial

household production and consumption patterns. This study follows a methodology similar to

Topalova (2007) to examine what occurred after the implementation of the BTA in Vietnam.

Most of the studies on trade and poverty use national trade reforms, such as own country

tariff reductions or quota removals, as their source of variation in trade policy.

48 See Hallack and Levinsohn (2004) for a recent review of the trade and growth literature. Kraay (2006) provides evidence across a panel of developing countries that suggests that most of the long-run variation in changes in poverty can be explained by growth of average incomes. Besley and Burgess (2003) provide evidence of the elasticity of poverty with respect to income per capita. 49 For recent empirical evidence of the impact of trade on labour markets in developing countries see Attanasio, Goldberg and Pavcnik (2004), Goldberg and Pavcnik (2003), Pavcnik, Blom, Goldberg, and Schady (2004), Galiani and Sanguinetti (2003), and Goldberg and Pavcnik (2005), among others.

119

Few papers look at the converse question – can countries use new trade opportunities as a

mechanism for poverty reduction? Porto (2003) estimates the impact of possible domestic and

international trade reform for Argentina. He predicts that the elimination of agricultural subsidies

and trade barriers on agricultural manufactures and industrial manufactures in industrialized

countries would cause poverty to decline in Argentina.

Hence, this paper makes two main contributions to the literature. The first contribution is

the ex post analysis of trade impacts on poverty across all geographic and economic sectors. This

is in contrast to many other papers that focus solely on urban or rural areas or only on

manufacturing or agricultural activities. Second, the paper makes use of a large trade shock

induced by a trading partner as opposed to domestic trade liberalization. This provides two

benefits relative to the existing literature. First, it provides evidence on an important question:

can developing countries benefit from improved market access to large foreign market. Second,

for establishing a causal relationship the exogeneity of the foreign tariff cuts are more plausible

than the exogeneity of domestic tariff cuts.

The empirical section of this paper directly focuses on the impact on poverty of new

export opportunities induced by increased market access. The framework addresses whether all

provinces in Vietnam derived similar benefits from the decreases in U.S. tariffs. Should one

expect variation in impacts at the sub-national level? Traditional theories of international trade

do not address this question. As such, I provide a brief adaptation of the Ricardo-Viner model,

also known as the Specific Factors model, to illustrate why one might expect differences in the

impact across provinces.50 The Specific Factors model seems most appropriate as it focuses on

short-run impacts and the empirical section concentrates on the first two years immediately

following the implementation of the BTA.

In this model, labour is assumed to be completely mobile across industries, whereas

capital is immobile in the short run. As a simple example, consider a two-province country that

moves from international autarky to international free trade. For the current discussion, I abstract

away from internal trade between the two provinces and I further assume that the country takes

world prices as given. Let ( ),p pi i i i

pX f L K= denote the production of good 1, 2i = in

province , where it is assumed that each province uses the same technology to produce ,p A B=

50 See Feenstra (2004) for a discussion of the Ricardo-Viner model of international trade.

120

good i. Assume that prior to international trade, inter-province labour mobility has equalized the

wage rate A Bw w w= = . From the first-order condition with respect to labour demand, this

implies that the labour-capital ratio within an industry must be equal across provinces.51

Consider what happens in the short-run when the country opens up to trade. Suppose that this

increases the relative price, p, of good 1, where the price of good 2 has been normalized to one.

The percentage wage change can be expressed as:

( )( ) ( )

2 2 2

2 2 2 1 1 1

22

2 2

2 12 1

2 2 1 1

22

2

2 2 12 1

2 1 1

,, ,

1 ,1

1 1,1 ,1

,1

,1 ,1

LL

LL LL

LL

LL LL

LL

LL LL

f L Kdw dpw f L K pf L K p

LfK K dp

pL Lf p fK K K K

LfK dp

pL K Lf pfK K K

=+

⎛ ⎞⎜ ⎟⎝ ⎠=

⎛ ⎞ ⎛ ⎞+⎜ ⎟ ⎜ ⎟

⎝ ⎠ ⎝ ⎠⎛ ⎞⎜ ⎟⎝ ⎠=

⎛ ⎞ ⎛ ⎞ ⎛ ⎞+⎜ ⎟ ⎜ ⎟ ⎜ ⎟

⎝ ⎠ ⎝ ⎠ ⎝ ⎠

where I have suppressed the province superscripts. The second line comes from the assumption

of constant returns to scale in the production functions (i.e., they are homogeneous of degree

one). This implies the second partial derivatives are homogeneous of degree negative one

(Varian, 1992). Since the ratio of labour to capital is constant across provinces within an

industry, the percentage change in wages will differ across provinces according to the difference

in capital stocks ratios assuming that labour is imperfectly mobile across provinces. Thus, the

province with the higher share of its capital invested in good 1, the rising price industry, would

expect a greater percentage change in the nominal wage rate. This simple model helps to explain

why some provinces might be expected to benefit more than others in the immediate short-run

following entry into force of the BTA.

51 This is a result of fiL being homogenous of degree 0 from assuming constant returns to scale in fi.

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3.3 Overview of the U.S.-Vietnam Bilateral Trade Agreement

The BTA was signed on 13 July 2000 and came into force on 10 December 2001.52 The

commitments made by the United States and Vietnam are similar to those required by the World

Trade Organization (WTO). As such, the principal change for the U.S. was to grant Vietnam

Normal Trade Relations (NTR) or Most Favored Nation (MFN) access to the U.S. market

immediately upon entry into force of the BTA. The tariff cuts were largest in manufacturing

where the average ad valorem equivalent tariff dropped from 31.5 to 3.3 percent. The average ad

valorem tariff also fell substantially within agriculture, hunting and forestry as it was cut from

10.6 to 3.2 percent. In contrast, the tariff cuts within both fishing and mining were much smaller.

More detail on the U.S. tariff cuts is provided in Section 3.4.

In contrast, the scope of the commitments made by Vietnam is much larger. The bulk of

Vietnam’s commitments are scheduled for implementation within three to four years after entry

into force, but some commitments are not required until up to ten years after. The majority of

Vietnam’s commitments lie in the realm of legal and regulatory change as Vietnam had already

applied MFN tariffs to U.S. products before the BTA. These commitments include accordance of

national treatment to U.S. companies and nationals, customs system and procedures reform,

liberalizing and streamlining trading rights, liberalizing trade in services, and liberalizing and

safeguarding foreign investment, among others. As for trade policy commitments, the BTA

requires Vietnam to cut tariffs on approximately 250 tariff lines out of more than 6,000, typically

by 25 to 50 percent, mostly in agriculture. The overall impact of these cuts on industry level

tariffs has been very small. Industry level Vietnamese tariffs have been very stable over the

period of 1999 to 2004. Furthermore, the BTA has an extensive list of quantitative import

restrictions that must be eliminated, typically four to six years after entry into force. Almost all

of these were eliminated well ahead of schedule as part of an IMF/World Bank Agreement. By

the beginning of 2003, all import quotas except for those on sugar and petroleum products had

been lifted. Quotas on sugar and petroleum products are required to be removed after ten and

seven years from entry into force of the BTA.

52 This section draws heavily on the STAR-Vietnam report “An Assessment of the Economic Impact of the United States – Vietnam Bilateral Trade Agreement.”

122

3.4 Data

This section describes the three principal sources of data used in the subsequent analysis:

tariff data from the U.S. International Trade Commission, poverty estimates derived from the

2002 and 2004 Vietnam Household Living Standards Surveys (VHLSS), and employment data

from the 1999 Population and Housing Census in Vietnam. I describe each of them in turn.

3.4.1 Tariff Data

I use 2001 U.S. tariffs from the U.S. International Trade Commission’s online Tariff

Information Center. Prior to the BTA, Vietnam was subject to tariffs according to Column 2 of

the U.S. tariff schedule. Upon entry into force of the BTA, Vietnam became subject to MFN

tariff rates. For both tariff schedules I compute the ad valorem equivalent of any specific tariffs.

Details of the procedure can be found in the data appendix. I then match the tariff lines to

industries by the concordance provided by the World Bank via the World Integrated Trade

Solution database to construct industry-level tariffs according to 3-digit ISIC nomenclature.

There are 76 3-digit ISIC industries that experienced tariff cuts spread across agriculture

and forestry, fishing, mining, manufacturing, and other industries. Table 3.3 provides some

summary statistics on the tariff cuts by major sectors.

Table 3.3 Summary of U.S. tariffs applied to imports from Vietnam

Mean Mean pre-BTA post-BTA Standard Number of Tariff tariff Mean deviation Industry industries (Column 2) (MFN) tariff cut of tariff cutAgriculture, hunting & forestry 3 0.085 0.016 0.069 0.010 Fishing 1 0.013 0.002 0.011 Mining 9 0.027 0.001 0.026 0.045 Manufacturing 57 0.330 0.034 0.296 0.148 Other 6 0.080 0.002 0.077 0.111

Source: U.S. International Trade Commission Note: The tariffs reported are weighted average tariffs. For each commodity-line tariff, its weight is the share of imports within the sector based on 2001 U.S. imports.

As mentioned above, the average tariff cut was highest in manufacturing. There is large variation

in the tariff cuts, both across and within major sectors. The variation within sectors is highest

123

within manufacturing where the standard deviation of the cut in tariffs is 0.148 percentage

points. The variation is shown in more detail in Figure 3.1, which shows the tariff cut in

percentage points by industry. The empirical analysis below is done using 3-digit industry tariffs,

but to make the figure easier to read, the tariffs have been aggregated to the 2-digit industry

level. Industries 1 and 2 fall within agriculture, hunting & forestry; industry 5 is fishing;

industries 10 through 14 are mining; industries 15 through 36 are manufacturing; and industries

40 through 93 are other industries.

Figure 3.1 U.S. tariff cuts by 2-digit ISIC industry

0

10

20

30

40

50

60

1 2 5 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 40 74 92 93

Industry code

Tarif

f cut

(per

cent

age

poin

ts)

Tobacco Products Apparel

Textiles Furniture

Agriculture

Clearly the largest tariff cuts were in industry 18 (manufacture of wearing apparel;

dressing and dyeing of fur), industry 16 (manufacture of tobacco products), and industry 17

(manufacture of textiles), all within manufacturing. One of the smallest tariff cuts was also

within manufacturing, industry 23 (manufacture of coke, refined petroleum products, and nuclear

fuel). One thing that is clear from the figure is the variation in tariff cuts across industries, which

is important for the identification strategy outlined below.

124

3.4.2 Household surveys

The principal poverty measure used in the empirical analysis is the poverty headcount

ratio. It measures the share of the population that falls below the poverty line. As with most

studies of poverty in developing countries, this paper focuses on absolute deprivation. Thus, the

poverty line used does not change over time as living standards improve or decline, instead it is

meant to represent the same absolute level of welfare adjusted for price changes.

The 2002 and 2004 Vietnam Household Living Standards Surveys (VHLSS) are

representative at the provincial level and provide information on household expenditures,

occupation, employment, and various other household and individual characteristics.

Expenditure information is available for approximately 30,000 households in the 2002 VHLSS

and 9,000 households in the 2004 VHLSS. The 2002 VHLSS was conducted between January

2002 and December 2002. In contrast, the 2004 VHLSS interviewed households only from May

2004 through November 2004, with the majority of households being interviewed in June and

September. For both surveys the recall period for expenditures and employment is the past

twelve months. To construct estimates of provincial poverty, I use the official “general poverty

line”, which includes an estimate of the cost of a basket of food items required to consume 2100

calories per day and essential non-food items such as clothing and housing.53 The general

poverty line is 1,917 thousand VND in 2002 and 2,077 thousand VND in 2004. Glewwe (2005)

has reviewed the consistency of the expenditure data and concludes that they are broadly

consistent across the 2002 and 2004 VHLSS. Details of the expenditure variables and sample

weights used can be found in the data appendix.

There is a substantial variation in provincial poverty rates as well as the proportional drop

in poverty between 2002 and 2004. The latter is the primary dependent variable of the current

study. Table 3.4 provides summary statistics on the levels of poverty, the rate of poverty

reduction, patterns of employment, measures of education, and other provincial data used in the

analysis. The 2002 levels of poverty range from a high of 77 percent in Lai Chau to a low of 2

percent in Ho Chi Minh City. For the current study, it is not the level of poverty, but rather its

53 See World Bank (1999).

125

rate of decline that is most interesting. Here too there is considerable variation, as shown in

Figure 3.2.

Table 3.4 Summary statistics

Variable MeanStd. Dev. Min Max

Poverty Headcount Ratio 2002 0.322 0.182 0.020 0.766Poverty Headcount Ratio 2004 0.229 0.157 0.000 0.689Proportional Drop in Poverty, 2002 to 2004 0.311 0.221 -0.210 1.000Share of workers in: Agriculture, 1999 0.704 0.175 0.070 0.896 Aquaculture, 1999 0.024 0.035 0.000 0.187 Mining, 1999 0.006 0.016 0.000 0.122 Manufacturing, 1999 0.072 0.065 0.010 0.362Number of formal enterprise jobs, 2000 52468 114683 2860 788922Share of workers working for a wage or salary, 2002 0.287 0.124 0.087 0.602Share of population with at most: Primary education, 1999 0.750 0.089 0.444 0.910 Lower secondary education, 1999 0.139 0.047 0.043 0.270 Upper secondary education, 1999 0.083 0.038 0.030 0.216Urban share of the population, 1999 0.193 0.150 0.046 0.838Distance to nearest major seaport (km) 214.295 142.814 0.000 615.000Regional dummies: Red River Delta region 0.180 0.388 0 1 North East region 0.180 0.388 0 1 North Wests region 0.049 0.218 0 1 North Central Coast region 0.098 0.300 0 1 South Central Coast region 0.098 0.300 0 1 Central Highlands region 0.066 0.250 0 1 South East region 0.131 0.340 0 1 Mekong River Delta region 0.197 0.401 0 1

Two provinces experienced measured increases in the incidence of poverty, Khanh Hoa

and Bac Lieu, while Ho Chi Minh City eliminated all remaining poverty between 2002 and 2004.

The proportional drop in poverty between 2002 and 2004 is negatively correlated with the

incidence of poverty in 2002. This suggests that existing trends in economic performance may be

an important factor for explaining the decrease in poverty. In the empirical section I attempt to

address this concern by controlling for differences in initial provincial characteristics.

126

Figure 3.2 Histogram of the proportional drop in provincial poverty rates, between 2002 and 2004

05

1015

Num

ber o

f pro

vinc

es

-.5 0 .5 1Proportional drop in poverty, 2002 to 2004

3.4.3 Employment data

For constructing the measure of provincial exposure to U.S. tariff cuts, I use employment

data from the 3 percent sample of the 1999 Population and Housing Census. In general, it reports

industry of employment at the 3-digit ISIC level, but for some individuals it is only reported at

the 2-digit level.54 I restrict the sample to individuals 13 years of age and older, as individuals

below age 13 were not asked about their employment status.

Finally, between 2002 and 2004 three Vietnamese provinces were split. To be consistent,

I recode household observations from the 2004 VHLSS into the original 61 provinces, as in the

1999 census and the 2002 VHLSS.

54 To be exact, the industry codes used in the census do not match exactly with the ISIC nomenclature. There are a small number of industries for which the 3-digit industry assigned to the described industry does not match the ISIC code. I recode these observations according to ISIC nomenclature. This is the same for the 2002 and 2004 VHLSS. See the data appendix for further details.

127

3.5 Empirical Methodology

Following Topalova (2007), I exploit provincial variation in exposure to the trade

agreement based on the structure of employment prior to the trade agreement. I construct

provincial measures of the drop in U.S. tariffs as follows:

p ipi

TariffDrop iω τ= Δ∑ (5)

where p indexes provinces, ipω is the share of workers in province p in industry i (i.e.,

), and 1ipiω =∑ iτΔ is the tariff drop in industry i. The employment and tariff data cover over

seventy industries across agriculture, aquaculture, mining, and manufacturing. To establish the

robustness of the relationship between poverty reduction and exposure, I employ the following

regression model:

p py TariffDrop X p pα β= + + +δ ε (6)

where is the proportional drop in the poverty headcount ratio in province and Xpy p p is a vector

of control variables intended to help control for underlying trends in poverty reduction that could

be correlated with provincial exposure to the U.S. tariff cuts. In most specifications Xp includes

the natural logarithm of the poverty headcount ratio in 2002 to control for convergence in

poverty rates and regional dummy variables to control for unobserved trends in poverty that vary

by region. In other specifications, controls for other trade influences are added as are initial

provincial characteristics such as employment patterns.

I use the proportional drop in poverty (which is approximately equal to the difference in

the natural logarithm of poverty) as the dependent variable. I have chosen this form for the

dependent variable to be consistent with other key papers in the literature, such as Besley and

Burgess (2003) and McMillan, Zwane, and Ashraf (2007), which both use the natural logarithm

of poverty as their dependent variable.

It is important to understand the source of variation being used to identify β in equation

(2). The regression measures the partial correlation between the proportional drop in poverty and

exposure to U.S. tariff cuts. This implies that the framework cannot identify the average impact

of increased U.S. market access on poverty across provinces. This will be part of the estimated

constant term. Hence, the total impact of the trade agreement, which is comprised of the relative

impact, as measured by TariffDrop, and the average impact, cannot be determined. In the

128

discussion section I add additional assumptions that allow for an admittedly rough estimate of

the overall impact.

A second point to address is the weighting of national tariffs at the provincial level to

create a measure of provincial exposure to the tariff cuts. I use the industry of employment to

aggregate exposure at the industry level into a provincial measure of exposure. This implicitly

assumes that two workers in the same industry, one in the export-oriented manufacturing centre

of Ho Chi Minh City and the other working in predominantly rural Son La, for instance, will

experience the impact of tariffs cuts on clothing and apparel goods the same way. Ideally, one

would like to know whether the individual is involved in the production of goods destined for the

domestic or international market, but this information is not available in the census data. One

way to address this point is by considering how far away a province is from one of Vietnam’s

three major seaports, which are located in the provinces of Hai Phong, Da Nang, and Ho Chi

Minh City. Thus, I include the distance to the nearest major seaport as well as its interaction with

TariffDrop. This allows the net impact on a worker within an industry to vary geographically

within Vietnam.

Third, weighting national tariffs by industry of employment is not the only plausible

aggregation method. One could measure a province’s exposure by weighting tariffs with the

value of production within an industry by province or the value of exports and imports within an

industry by province. Unfortunately, national account estimates at the provincial level in

Vietnam are unreliable and thus I cannot check the robustness of my results to these alternative

aggregation procedures.

The timing of the tariff cuts and the choice of study period used for identifying the impact

of the tariff cuts are important. I use the 2002 VHLSS as my baseline from which to measure

changes in poverty. This raises two concerns. First, some of the households were surveyed close

to the end of the 2002. Hence, their expenditure and employment data are reported for a period

that is almost entirely after the entry into force of the BTA. Second, to the extent that firms and

individuals changed behavior in anticipation of the BTA, this implies that some of the impacts

were being felt prior to the date of implementation. Both observations suggest that by focusing

on the period of 2002 to 2004 I may be underestimating the impact that that BTA has had as of

2004 on provincial poverty. Unfortunately, due to lack of data, this problem is hard to avoid as

the 1998 Vietnam Living Standards Survey (VLSS), unlike the 2002 and 2004 VHLSS, was not

designed to be representative at the provincial level. Hence, the results should be interpreted as

129

the impact that the BTA had on the two-year period from 2002 to 2004 and not as the cumulative

impact up to 2004.

3.5.1 Exogeneity of U.S. Tariff Cuts

Since the trade agreement is bilateral, this raises concerns about endogenous protection

and endogenous market access through political lobbying by U.S. and Vietnamese industries. In

general, one would expect that U.S. industries would lobby for smaller cuts in the U.S. tariffs

protecting their industry and that Vietnamese industries would lobby for greater cuts in U.S.

tariffs. This concern, however, is unlikely to influence the U.S. tariff cuts in this particular

agreement. The U.S. tariff cuts were presented as an all-or-nothing package whereby exports

from Vietnam into the U.S. would immediately be covered by MFN tariff rates instead of

Column 2 tariff rates. The movement from one pre-existing tariff schedule to a second pre-

existing tariff schedule implies that both U.S. and Vietnamese industries did not have an

opportunity to influence the tariff cuts faced by their industry. This argument relies on the

assumption that both the Column 2 and MFN tariff schedules are exogenous to Vietnam, which I

turn to now.

The Column 2 tariff rates are arguably exogenous to Vietnam for a number of reasons.

First, the countries subject to Column 2 rates are all former or current communist countries,

suggesting that political concerns larger than industry lobbying dominate this category of the

U.S. tariff schedule. Table 3.5 shows the list of countries subject to Column 2 tariff rates from

1996 to 2005. At the time of the U.S.-Vietnam Bilateral Trade Agreement, the only remaining

countries were Afghanistan, Cuba, Laos, and North Korea. Second, imports into the U.S. under

Column 2 constitute a very small fraction of overall U.S. imports. Between 1996 and 2006, the

share of total U.S. imports originating in countries subject to Column 2 rates ranged between

0.00 and 0.09 percent. This implies that the returns to U.S. industries lobbying for protection are

very low within the Column 2 section of the U.S. tariff schedule. Third, as suggested by the

previous point, both prior and subsequent to the BTA, there has been little change in the

prevailing Column 2 rates.

130

Table 3.5 Countries subject to Column 2 U.S. tariffs, 1996-2005

1996 1997 1998 1999 2000 Afghanistan Afghanistan Afghanistan Afghanistan Afghanistan Cambodia

Cuba Cuba Cuba Cuba Cuba Laos Laos Laos Laos Laos

North Korea North Korea North Korea North Korea North KoreaVietnam Vietnam Vietnam Vietnam Vietnam

2001 2002 2003 2004 2005 Afghanistan Afghanistan

Cuba Cuba Cuba Cuba Cuba Laos Laos Laos Laos Laos

North Korea North Korea North Korea North Korea North KoreaVietnam

Table 3.6 presents simple correlations at the 4-digit Harmonized System (HS) commodity

level of the Column 2 and MFN ad valorem equivalent tariff rates between 1997, 2001, and

2005. Between 1997 and 2005, the correlation of Column 2 rates was 0.978 as compared to only

0.849 for MFN rates. Clearly the Column 2 rates have been very stable and much more so than

the MFN rates. These three arguments support the proposition that the Column 2 rates prevailing

in 2001 were exogenous to Vietnam.

Table 3.6 Correlations of U.S. tariffs, 1997, 2001 and 2005

MFN 1997 2001 2005

1997 1.000 2001 0.940 1.000 2005 0.849 0.912 1.000

Column 2

1997 2001 2005 1997 1.000 2001 0.991 1.000 2005 0.978 0.984 1.000

Note: The correlations are done at the 4-digit HS level.

The major argument for the exogeneity of the ex-post level of U.S. protection is that

overall imports from Vietnam into the U.S. represent a very small fraction of total U.S. imports.

By 2006, U.S. imports from Vietnam constituted only 0.46 percent of total U.S. imports. Hence,

131

it is hard to believe that the U.S. would set its trade protection structure based on conditions in

Vietnam.

3.5.2 Underlying trends and contemporary shocks

In the above econometric framework, identification fails if TariffDrop is correlated with

the error term. In this particular context the primary concern is likely to be omitted variable bias.

Since the dependent variable is expressed in terms of rate of poverty reduction, any time constant

provincial characteristics that influence the level of poverty are controlled for. Hence, I only

need to be concerned with time-varying omitted variables that may be correlated with the

measures of exposure. I address this problem by including in the regression various provincial

characteristics that might induce differential poverty reduction trends across provinces.

Furthermore, I include dummy variables for each of Vietnam’s eight geographic regions. This

absorbs any differential trends in poverty reduction that are common within a region and hence

helps to control for unobserved heterogeneity in provincial trends.

3.6 Empirical results

Figures 3.3 and 3.4 show plots of the proportional drop in poverty versus provincial

exposure to the U.S. tariff cuts, as measured by TariffDrop. Figure 3.3 shows the raw correlation

between the two variables. There is a strong positive correlation. All of the regression results

shown below include regional dummy variables and thus identification relies on within region

variation. Figure 3.4 shows the correlation with the difference between Vietnam's eight

administrative regions removed. It too shows a strong positive correlation.

132

Figure 3.3 Relationship between the proportional drop in poverty and provincial exposure to U.S. tariff cuts (TariffDrop)

-.50

.51

Pro

porti

onal

dro

p in

pov

erty

, 200

2 to

200

4

.06 .08 .1 .12 .14TariffDrop

Figure 3.4 Relationship between the proportional drop in poverty and provincial exposure to U.S. tariff cuts (TariffDrop) controlling for region

-.50

.5E

(Pro

porti

onal

dro

p in

pov

erty

|X)

-.02 0 .02 .04 .06E(TariffDrop|X)

coef = 7.3470229, se = 2.0959195, t = 3.51

133

The primary regression results are reported in Table 3.7. Column (1) includes the

smallest set of control variables – the natural logarithm of poverty in 2002 and regional

dummies. In this specification, the impact of TariffDrop is positive and statistically significant.

The inclusion of the natural logarithm of poverty in 2002 helps to control for any bias in the

estimate of the coefficient of TariffDrop introduced by convergence in poverty rates across

provinces, as TariffDrop is negatively correlated with poverty in 2002. In column (2) I add the

distance to the nearest major seaport. As mentioned above, Vietnam has three major seaports in

the provinces of Hai Phong in the north, Da Nang in the centre, and Ho Chi Minh City in the

south. Each of these provinces is also a major manufacturing area. Thus, the distance to the

nearest major seaport variable will also partially capture spillover effects based on proximity to

large manufacturing centres. This additional control variable does not alter the fundamental

result of more exposed provinces experiencing more rapid poverty reduction. The coefficient

estimate on TariffDrop in column (2) is slightly lower than in column (1), but remains positive

and statistically significant. Column (3) subsequently adds an interaction term between

provincial exposure to the U.S. tariff cuts, TariffDrop, and the distance to the nearest major

seaport. It yields some interesting results. First, the impact of TariffDrop on the proportional

drop in poverty remains positive and statistically significant. Second, this positive impact

diminishes the further the province is from a major seaport. In this regard, Vietnam is relatively

fortunate as the median province is only 180 km from a major seaport. Finally, in column (4) I

add the share of employment within agriculture, aquaculture, mining, and manufacturing in

2002.

Since the U.S. tariff cuts were, on average, largest within manufacturing, by construction

TariffDrop is positively correlated with the share of manufacturing employment within each

province. Thus, failing to control for differences in initial employment structure means that

TariffDrop will also be picking up any latent trends in poverty reduction associated with

differences in the employment structure. For example, if manufacturing oriented provinces are

growing more quickly than other provinces and subsequently experience more rapid reduction in

poverty, then this would introduce an upward bias into the estimate of TariffDrop. The results in

column (4) suggest that the positive and statistically significant effect of TariffDrop is not simply

due to underlying poverty reduction trends based on differences in initial employment patterns.

The point estimate on TariffDrop falls slightly compared to column (3), but remains positive and

134

statistically significant. As expected, provinces with a larger share of workers in manufacturing

did experience faster poverty reduction, but conditional on the included regressors this impact is

not statistically significant.

Table 3.7 Ordinary Least Squares regression results of the impact of provincial exposure (TariffDrop) on poverty between 2002 and 2004

Dependent variable: Proportional drop in poverty, 2002 to 2004 (1) (2) (3) (4) TariffDrop 9.133 8.037 10.140 8.002 (4.77)** (3.78)** (6.44)** (2.71)**Distance to nearest major seaport -0.000 -0.001 -0.001 (1.86) (3.52)** (3.57)**TariffDrop x Distance to nearest -0.042 -0.049 major seaport (2.26)* (2.89)**ln(Poverty 2002) 0.074 0.098 0.111 0.184 (1.67) (2.20)* (2.76)** (2.22)* Agriculture employment 2002 0.022 (0.05) Aquaculture employment 2002 0.151 (0.23) Mining employment 2002 0.917 (0.82) Manufacturing employment 2002 1.653 (1.31) Observations 61 61 61 61 R2 0.43 0.46 0.50 0.53 Direct impact of a 1 SD increase 0.125 0.110 0.139 0.110 in TariffDrop Indirect impact evaluated at the -0.123 -0.144 average distance from port Robust t statistics in parentheses. * significant at 5%; ** significant at 1% Includes regional dummy variables as additional regressors.

In all four specifications the impact of TariffDrop is positive and statistically significant.

More importantly, the result is meaningful in an economic sense. The bottom of Table 3.7

reports the proportional drop in poverty associated with a one standard deviation increase in

TariffDrop. This is approximately equivalent to an increase in exposure from the 25th percentile,

the province of Ha Tinh, to the 75th percentile, the province of Tien Giang. The estimated impact

ranges between a decrease in poverty of 11.0 to 13.9 percent. To help provide some perspective

135

on this result the average decrease in provincial poverty between 2002 and 2004 was 31.1

percent. Thus, the impact of an increase in exposure is predicted to be very large in comparison

to the average percentage decrease in poverty between 2002 and 2004.

Alternatively, one can benchmark this prediction against the Millennium Development

Goal of reducing poverty by 50 percent in 25 years. This goal requires poverty to drop at an

annual rate of 2.7 percent per year or 5.5 percent over a two-year period. Thus, the predicted

impact of a one standard deviation increase of provincial exposure to the U.S. tariff cuts implies

a rate of poverty reduction about twice as fast as required to meet the aforementioned

Millennium Development Goal.

3.6.1 Robustness of results

In principal, the variable TariffDrop may be correlated with other poverty reduction

factors for two reasons: correlation with the employment structure or correlation with the tariff

cuts. In the above section I addressed the first of these concerns, which is likely to be the more

important of the two concerns. In this section I turn to other possible trade influences that may be

correlated with TariffDrop.55

An additional concern with the measure of exposure is that it may be picking up trade

related influences other than the BTA. For example, if U.S. import demand is shifting to the

same industries that received the largest tariff cuts then I will be estimating this effect along with

the impact of the tariff cuts. I examine this possibility by constructing a measure of provincial

exposure to changes in U.S. imports over the period of 1999 to 2004. Specifically, the variable is

calculated according to:

,2004 ,1999ln lnp ip i ip ii i

ImpChanges Imports Importsω ω⎛ ⎞ ⎛= −⎜ ⎟ ⎜⎝ ⎠ ⎝∑ ∑ ⎞

⎟⎠

where ωip is the share of workers in province p in industry i, and Importsi,t is the value of U.S.

imports from all countries in industry i in year t=1999, 2004. Hence, provinces with a greater

share of workers in industries that experienced larger increases in U.S. import demand will be

55 I have also run regressions controlling for initial education levels, government spending, government transfers, FDI stocks, and measures of the provincial business environment. None of these qualitatively influence the presented results.

136

more exposed to this structural change. Table 3.8 displays regression results when ImpChangesp

is included as a control variable. I do not include the 2002 employment share variables as they

are not jointly significant in the last regression reported in Table 3.7. The coefficient estimate on

TariffDrop is still statistically significant at the 1 percent level.

Changes in Vietnam’s trade policies, aside from the BTA, may also be a source of

omitted variable bias. I explore this possibility by constructing a measure of provincial exposure

to changes in Vietnam’s import tariffs between 1999 and 2004. This is done in an analogous

method as for changes in U.S. tariffs. Results are shown in column (2) of Table 3.8. Similar to

Topalova’s (2007) results for Indian districts, I find that Vietnamese provinces that were more

exposed to Vietnam’s tariff cuts experienced slower reductions in poverty, although the estimate

is not statistically significant.

One final trade policy change that warrants attention is Vietnam’s tariff commitments

under the BTA. These are almost exclusively concentrated in crops and food processing. As of

2004, Vietnam had not cut these tariff lines. In addition, the tariff cuts are small in magnitude

compared to those made by the U.S. However, firms and farmers may be changing their

production patterns in anticipation of the impending tariff cuts. Column (3) shows regression

results when provincial exposure to future Vietnamese tariff cuts, as proscribed by the BTA, are

included. This exposure does not have a statistically significant impact, nor does it substantially

change the coefficient estimate of exposure to U.S. tariffs. Finally, column (4) of Table 3.8

presents regression results when all three trade influences are included. The results are similar to

those presented in the previous columns.

In Appendix A, I discuss the possible impacts of measurement error in the initial level of

poverty in 2002. Results indicate that the above results are not driven by plausible measurement

error. Furthermore, I check the robustness of my results to the poverty line used and alternative

measures of poverty. These results are also reported in Appendix A in Table 3.13. I consider a 25

percent increase in the poverty line, as well as the normalized poverty gap and the normalized

poverty severity at the original poverty line.56 The results are consistent with the primary results

presented above.

56 The normalized poverty gap is the average difference between actual expenditures and the poverty line for all poor individuals, expressed as a fraction of the poverty line, while the normalized poverty severity gap is the average squared differenced expressed as a fraction of the poverty line.

137

Table 3.8 Ordinary Least Squares regression results of the impact of provincial exposure (TariffDrop) on poverty between 2002 and 2004 controlling for other trade influences

Dependent variable: Proportional drop in poverty, 2002 to 2004 (1) (2) (3) (4) TariffDrop (US) 9.832 10.366 10.294 10.232 (6.00)** (7.05)** (7.16)** (5.93)**Distance to nearest major seaport -0.001 -0.001 -0.001 -0.001 (3.45)** (3.40)** (3.55)** (3.08)**TariffDrop x Distance to nearest -0.041 -0.043 -0.043 -0.042 major seaport (2.20)* (2.26)* (2.34)* (2.59)* ImpChanges 1.117 2.155 (0.64) (1.10) TariffDrop (VN 99-04) -3.830 -22.065 (0.27) (0.58) TariffDrop (VN BTA) 2.804 -11.267 (0.18) (0.26) ln(Poverty 2002) 0.132 0.093 0.099 0.097 (2.19)* (1.09) (1.18) (1.10) Observations 61 61 61 61 R2 0.50 0.50 0.50 0.51 Direct impact of a 1 SD increase 0.135 0.142 0.141 0.140 in TariffDrop Indirect impact evaluated at the -0.126 -0.123 average distance from port

Robust t statistic in parentheses. *significant at 5%; **significant at 1% Includes Regional dummy variables as additional regressors.

3.7 Labour market transmission mechanisms

This section aims to confirm and to explain the above results. Given the extent of the

poverty reductions, intuitively, one would expect to find changes in the labour market that are

consistent with this pattern. If contradictory results were found, then this would lead one to be

suspicious of the previous results. Furthermore, these same labour market channels help to

explain how the tariff cuts led to reductions in poverty.

138

3.7.1 Wages

One channel from tariff cuts to household welfare is the wage labour market. In the 2004

VHLSS, among individuals aged 15 to 64, 82 percent of individuals reported working in the past

12 months. Of these workers, 31 percent reported working for a wage in the past twelve months

for their most time-consuming job. In the 2002 VHLSS, 83 percent of individuals between the

ages of 15 and 64 reporting working in the past 12 months, while 29 percent of these workers

reported working for wages for their most time-consuming job.57

I examine how the drop in U.S. tariffs influenced provincial wage premiums.58 The

provincial wage premium is the variation in individual wages that cannot be explained by

individual characteristics, such as age, gender, or industry affiliation, but can be explained by the

province of the worker. In essence, it is a conditional average wage by province. If labour is

imperfectly mobile across provinces, one would expect to find a relationship between changes in

provincial wage premiums and exposure to the tariff cuts. According to the 1999 census, only

approximately 3 percent of individuals moved across provinces between 1994 and 1999,

suggesting that labour is imperfectly mobile across provinces, at least prior to the BTA.

The empirical analysis follows a two-stage procedure. In the first stage, the log of real hourly

wages for worker i at time t ( )( )ln ijptw is regressed on a vector of individual characteristics

, a vector of industry dummies ( itH ) ( )itI , and a vector of provincial dummies : ( )itP

( )ln it it t it jt it pt ijptw α ε′ ′ ′= + + + +H β I wp P wp .

The vector of individual characteristics includes a dummy for the individual’s gender, a

quadratic in age, dummies for the highest level of completed education, dummies for sector of

ownership, and the number of months, days per month, and hours per day spent working. The

coefficient of the provincial dummy represents the variation in wages that cannot be explained

by individual characteristics or industry affiliation, but can be explained by province of

residence. Following Krueger and Summers (1988), I normalize the sum of the employment-

weighted provincial wage premiums to zero and I express the provincial wage premiums as

57 For both surveys, these are simple averages, unadjusted for sampling weights. 58 See for example Attanasio, Goldberg, and Pavcnik (2004) who report results on industry wage premiums.

139

deviations from zero. In the second stage, the change in the provincial wage premium is

regressed on the drop in tariffs by province and the provincial wage premium in 2002:

,2002p pwp TariffDrop wp up pα β γΔ = + + + .

Since the dependent variable is an estimate, I use weighted least squares. The weights are the

inverse of the variance from the first stage regression, corrected according to Haisken-DeNew

and Schmidt (1997). The results are reported in Table 3.9 for all wage earners and then

subsamples of workers based on education and by the level of skill according to occupation. For

all wage earners the drop in tariffs is positively associated with provincial wage premiums, but

this result is not statistically significant. However, dividing the sample according to education

reveals a more nuanced pattern. For workers with at most a primary education the impact of

TariffDrop on the change in the provincial wage premium is both positive and statistically

significant. A one-standard deviation increase in exposure to the U.S. tariff cuts is associated

with a 1.9 percent increase in the provincial wage premium for primary educated workers. The

results are positive, but statistically insignificant for workers with both a lower secondary and an

upper secondary education. Note also that the estimate of the impact of TariffDrop drops as the

level of education increases. This is consistent with the large increase in exports in low-skilled

labour-intensive goods creating a positive labour demand shock for unskilled workers. A similar

picture emerges when the sample of workers is divided according to whether or not their job is

considered a skilled or unskilled occupation. For unskilled workers, the estimate of TariffDrop

on the provincial wage premium is positive and statistically significant. A one standard deviation

increase in TariffDrop is associated with a 1.8 percent increase in the provincial wage premium

for unskilled workers. By comparison, the impact is estimated to be negative for skilled workers,

although it is not statistically significant.

140

Table 3.9 Provincial wage premiums and provincial exposure

Dependent variable: Change in provincial wage premium, 2002 to 2004 Education Occupation

All Primary Lower

SecondaryUpper

Secondary Unskilled Skilled TariffDrop 0.065 1.396 0.708 0.072 1.312 -0.566 (0.16) (2.77)** (1.25) (0.11) (2.55)* (1.06) Provincial Wage -0.293 -0.561 -0.352 -0.321 -0.451 -0.255 Premium 2002 (5.87)** (10.12)** (5.48)** (4.35)** (7.63)** (4.20)** Observations 61 61 61 61 61 61 R2 0.51 0.65 0.38 0.48 0.51 0.52 Impact of a 1 SD 0.09 1.91 0.97 0.10 1.80 -0.78 increase in TariffDrop

Robust t statistics in parentheses. *significant at 5%; **significant at 1%

3.7.2 Job Creation

To further explore the labour demand impacts, I investigate the growth of jobs in

enterprises and the movement of workers into jobs that pay either a wage or salary. I use data

collected annually by the GSO from nationally representative firm surveys to examine the impact

on formal enterprise jobs. The firm surveys exclude cooperatives involved in agriculture and

forestry as well as household businesses and farms. Hence, the employment estimates essentially

cover off-farm employment. Figure 3.5 displays a scatter plot of the poverty headcount ratio in

2002 versus the natural logarithm of enterprise employment in 2000, while Figure 3.6 displays a

scatter plot of the percentage growth in jobs between 2000 and 2004 versus provincial exposure

to the BTA.

141

Figure 3.5 Relationship between provincial poverty and enterprise employment

0.2

.4.6

.8In

cide

nce

of p

over

ty, 2

002

8 10 12 14ln(Enterprise employment, 2000)

Figure 3.6 Relationship between growth in jobs between 2000 and 2004 and provincial exposure to U.S.-Vietnam Bilateral Trade Agreement

0.5

1G

row

th ra

te o

f job

s, 2

000-

2004

-.02 0 .02 .04 .06TariffDrop

142

The figures display a negative correlation between the incidence of poverty and

employment in enterprises and a positive correlation between job growth and provincial

exposure. The former cross-sectional relationship suggests that enterprise job creation may be an

important source of poverty alleviation. To explore the robustness of the positive correlation I

employ the following regression model:

( ) ( ) ( )04 00 00ln ln ln 'p p p p pjobs jobs TariffDrop jobs pα β λ− = + + + X γ ε+

where tpjobs is the number of employees in enterprises in province p at time and

is a vector of regional dummies. The results are shown in Table 3.10. I find strong evidence

of convergence in enterprise employment as provinces with lower levels of enterprise

employment experienced more rapid job growth between 2000 and 2004, all else equal. Related

to previous results, provincial exposure to the trade agreement is positively and significantly

correlated with job growth, even after controlling for regional trends and convergence in

employment levels.

2000, 2004t =

pX

Table 3.10 Enterprise job growth

Dependent variable: Proportional drop

in poverty, 2002 to 2004 (1) TariffDrop 7.486 (2.70)** ln(Jobs 00) -0.094 (-2.61)* Regional dummies yes Observations 61 R-squared 0.28

Robust t statistics in parentheses. *significant at 5%; **significant at 1%

I find a similar relationship between the level of poverty and the share of workers

working for a wage or salary in 2002, as demonstrated in Figure 3.7.

143

Figure 3.7 Relationship between provincial poverty and the share of workers working for a wage or salary in 2002

0.2

.4.6

.8In

cide

nce

of p

over

ty, 2

002

.1 .2 .3 .4 .5 .6Share of workers working for a wage or salary, 2002

Moreover, I find provincial exposure to the U.S. tariff cuts is positively associated with

the change in the share of workers working for a wage or salary. This is particularly true for

workers with only a primary education. These regression results are shown in Table 3.11. As

exhibited in the provincial wage premium regressions, the impact of TariffDrop is strongest for

workers with a primary level of education and the impact diminishes for higher levels of

education. A one standard deviation increase in TariffDrop induces a 1.74 percentage point

increase in the share of primary-educated workers working for either a wage or salary.

144

Table 3.11 Impact of provincial exposure (TariffDrop) on the share of workers working for a wage or salary

Dependent Variable: Change in share of workers working for wage or salary Education

Sample All Primary Lower

Secondary Upper

Secondary (1) (2) (3) (4) TariffDrop 0.800 1.269 0.873 -0.654 (1.36) (2.67)* (1.97) (0.91) Share worked for -0.230 -0.630 -0.303 0.056 wage 2002 (1.88) (5.55)** (2.90)** (0.45) Regional dummies yes yes yes Yes Observations 61 61 61 61 R-squared 0.44 0.64 0.45 0.16 Impact of a 1 SD 1.10 1.74 1.20 -0.90 increase in TariffDrop Robust t statistics in parentheses. *significant at 5%; **significant at 1%

3.8 Discussion of results

This study is unusual among most of the trade and development literature as it focuses on

a very short time period. This obviously raises questions about the plausibility of the results. Can

a trade agreement really influence poverty in only two years? Previous sections of this paper

presented additional labour market evidence that confirms the poverty results, while the current

section provides a series of simple calculations to demonstrate the magnitude of the increase in

export flows relative to the drop in poverty. The calculations are based on estimating the amount

of money required to lift individuals out of poverty and comparing this value to a prediction of

the increase in value of exports under the BTA relative to a scenario without the BTA. It is

meant as an illustrative example only.

Consider the province of Lao Cai, located in northwest Vietnam. Lao Cai is a relatively

isolated province with a low level of integration with both the domestic and international

economy. As a benchmark, I will assume that the overall impact of the BTA was zero in Lao Cai

145

(recall that the overall impact is the sum of the relative and average impacts across provinces).

Conditional on the coefficient estimate on TariffDrop presented in column (3) of Table 3.7, this

implies that the average impact of the BTA across provinces was an 8 percent drop in the

incidence of poverty. Combining the average and relative effects suggests that approximately 1.6

million Vietnamese (about 2 percent of the population) were lifted out of poverty by the BTA

between 2002 and 2004. Furthermore, if I assume that each individual lifted out of poverty was

the average distance from the poverty line, then approximately 63.6 billion VND (approximately

4 million USD) must reach these individuals on an annual basis to keep them out of poverty.

With an admittedly crude estimate of the amount of money required to lift the individuals out of

poverty, this can now be compared to the amount of money flowing into Vietnam due to the rise

in exports to the U.S. In 2003, annual exports from Vietnam to the U.S. totaled about 4.55 billion

USD. Based on the three-year trend of growth in exports from 1998 to 2001, in the absence of

the BTA exports from Vietnam to the U.S. would have been closer to 2.39 billion USD. This

suggests that only 0.6 percent of the estimated growth in export value is required to reach these

individuals implying that the above regression results are far from implausible given the growth

in exports from Vietnam to the U.S.

3.9 Concluding Remarks

In this paper, I estimate the poverty impacts of a large, developed country lowering

import barriers to goods from a small, developing country. Specifically, I examine the effect of

the U.S.-Vietnam Bilateral Trade Agreement (BTA), which came into force in December 2001,

on the incidence of poverty at the provincial level in Vietnam between 2002 and 2004. The

econometric framework establishes that provinces that were more exposed to the BTA (i.e.,

provinces that had a higher share of workers employed in industries that benefited from larger

tariff cuts) experienced greater proportional drops in poverty. I find a large and statistically

significant impact. An increase in exposure to the BTA of one standard deviation is estimated to

lead to approximately an 11 to 14 percent decrease in the incidence of poverty within a province.

By comparison, between 2002 and 2004, the average proportional drop in provincial poverty is

31.1 percent. Moreover, I show that this result is robust to a number of concerns. In particular, I

control for possible trends in provincial poverty based on provincial characteristics. I also

address concerns of potential measurement error and consider alternative measures of poverty.

146

I demonstrate labour market effects that are consistent with the estimated general

equilibrium impacts. I show that provincial wage premiums increased in provinces more exposed

to the trade agreement. This effect is strongest for unskilled workers. In addition, more exposed

provinces experienced greater rates of job creation within formal enterprises. Finally, provinces

that were more exposed experienced an increase in the share of workers working for a wage or

salary. Again, this effect is strongest for unskilled workers.

The estimated impacts are consistent with predictions from the Specific Factors, or

Ricardo-Viner, model of international trade. In the most frequent interpretation of this model,

labour is assumed to be mobile across industries, but capital is immobile in the short-run. With

the additional assumption of imperfect mobility of labour between provinces, the model predicts

that provinces more exposed to an exogenous increase in prices will experience a greater

percentage increase in nominal wages. I find exactly this effect when estimating changes in

provincial wage premiums, specifically for unskilled workers. Although the Ricardo-Viner

model does not make predictions specifically about poverty, the relative increase in wages is

consistent with my empirical finding of more rapid poverty alleviation in provinces more

exposed to the tariff cuts.

The paper focuses exclusively on immediate, short-run impacts. While these impacts are

important to understand and suggestive of positive impacts of international integration for the

poor, the paper does not address the medium- to long-run potential for poverty alleviation via

increased exporting opportunities.

3.10 Appendix A: Measurement Error

One concern that is always present when using household surveys is the consistency of

the data. Based on a comparison of the mean per capita consumption in the VHLSS and the

national accounts, Glewwe (2005) suggests that the 2002 VHLSS may have underestimated

household per capita expenditures relative to the 2004 VHLSS. One possible explanation is

problems with the commencement of the 2002 VHLSS, due to its large size and it being the

GSO’s first time implementing the survey on its own. However, Glewwe finds no evidence of an

experience effect. A second plausible explanation is pressure to make the expenditure and

income variables match in 2002. However, in both the 2002 and 2004 VHLSS nominal per

capita expenditures are about 77 percent of nominal per capita income. This implies that there is

147

no evidence of interviewers systematically doing something to lower consumption in the 2002

VHLSS. Overall, Glewwe concludes that the 2002 and 2004 VHLSS are broadly consistent,

although it may be possible that the 2002 survey underestimated household expenditures relative

to the 2004 survey. If this is true, then the poverty rates for 2002 may be overestimated.

To explore this issue, consider an example where all households report the same fraction,

1θ < , of true expenditures in 2002. As an example, Figure 3.8 shows an observed distribution of

per capita expenditures where 0.8θ = and the true, unobserved distribution. It also shows two

poverty lines at 1917 and 8000.

Figure 3.8 Difference between an under-reported distribution of per capita expenditures and the true distribution

0.0

001

.000

2.0

003

.000

4

0 2000 4000 6000 8000 10000Per capita expenditures

Observed dist. True dist.

From the figure, it is clear that the measurement error in the poverty headcount ratio will

be most severe when the poverty line is close to the mode of the observed distribution. The

difference between the observed and the true incidence of poverty will be greatest at the point of

crossing between the observed and true distributions. In addition, as the poverty line moves past

the mode of the distribution the difference between the observed and true poverty headcount

ratio will diminish. Finally, if the observed poverty headcount ratio is 0 than the true poverty

148

headcount ratio will also be 0 under the assumption that all households under reported their

expenditures.

Let denote the true level of poverty in province p at time t and let denote the

observed level. Given the shape of the distribution, a natural approximation would be to model

the measurement error as a quadratic function of the observed incidence of poverty:

ptP ptP

( )( )2

measurement error

pt pt pt ptP P aP b P≅ − +

with the restrictions a>0, b<0 and a+b>0. Then the true proportional drop in poverty can be

approximated as:

( ) ( )

( ) ( )( ) ( ) ( )

2002 20042002 2004

2002

2002 2002 2002 2004

2002 2002 2004

measurement error

ln ln

ln ln

ln ln 1 ln .

p pp p

p

p p p p

p p

P PP P

P

P P a bP P

P a bP P

−≅ −

⎡ ⎤= − + −⎣ ⎦

= + − − − p

p

This suggests including a non-liner function of the initial level of poverty on the right-hand side

of the regression:

( )2002p p py TariffDrop f P uα β= + + + .

If this measurement error is correlated with the drop in tariffs, then the previous estimates are

biased.

I address possible measurement concerns in three ways. First, optimal first-differencing

weights are used to remove the nonparametric component of the regression (Yatchew, 2003).

Second, the measurement error is explicitly modeled as a quadratic function of the initial

incidence of poverty. Third, the incidence of poverty in 2002 in each province is recalculated

based on the assumption that each household under reports their expenditures by the same

percentage. Specifically, I follow Glewwe (2005) and rescale household expenditures by the

ratio 0.838/0.805, the respective ratios of mean expenditures in the 2004 and 2002 VHLSS to the

national accounts estimates. The results are shown in columns (1) through (3), respectively, of

Table 3.12. The coefficient estimates are a similar magnitude as previous results and are

statistically significant. This suggests that possible measurement error in the initial incidence of

poverty is not driving the results.

149

Table 3.12 Regressions addressing measurement error

Dependent variable: Proportional drop in poverty, 2002 to 2004 (1) (2) (3)

Estimation method First-Diff.

NLS OLS

TariffDrop 11.152 11.050 9.139

(3.12)** (4.80)** (3.93)**

North East -0.136 -0.187 -0.095 (-1.28) (-2.07)* (-1.13)

North West -0.243 -0.390 -0.146

(-2.14)* (-3.16)** (-1.92)

North Central Coast -0.117 -0.165 -0.083 (-1.65) (-2.20)* (-1.15)

South Central Coast -0.070 -0.243 -0.253

(-0.54) (-1.99) (-2.01)*

Central Highlands -0.077 -0.157 -0.026 (-0.68) (-1.13) (-0.22)

South East 0.094 -0.058 -0.071

(1.03) (-0.50) (-0.57)

Mekong River Delta -0.039 -0.093 -0.114 (-0.41) (-0.93) (-1.08)

Constant -0.419

(-2.01)*

Observations 61 61 61 R-squared 0.42 0.35

Standard deviation of TariffDrop

0.0137 0.0137 0.0137

Economic impact 0.153 0.151 0.125 Robust t statistics in parentheses. *significant at 5%; **significant at 1%

150

Table 3.13 Regressions with alternative measures of poverty

(1) (2) (3)

Dependent variable

Proportional drop in

headcount ratio, 2002 to 2004

Proportional drop in poverty gap ratio, 2002

to 2004

Proportional drop in poverty severity ratio, 2002 to 2004

Poverty line (percentage of overall poverty line) 125 100 100 TariffDrop 6.915 10.502 14.050 (3.00)** (3.00)** (2.38)* Regional dummies yes yes yes Observations 61 61 61 R-squared 0.40 0.30 0.25 Robust t statistics in parentheses. *significant at 1%; **significant at 5%

3.11 Appendix B: Data

Poverty Measures: I use the 2002 and the 2004 Vietnam Households Living Standards Surveys

to estimate provincial poverty. From the 2002 VHLSS household expenditure file,

hhexpe02.dta, I use the real per capita expenditure series pcexp1rl, which has been regionally

and temporally deflated to national average January 2002 prices. I weight each household

observation by household size and the household’s associated sample weight. From the 2004

VHLSS household expenditure file, hhexpe04.dta, I use the real per capita expenditure series

pcexp1rl, which has been regionally and temporally deflated to national average January 2004

prices. Again, I weight each household observation by household size and the associated sample

weight. These expenditure series and weights reproduce the national and regional poverty

estimates for 2002 reported by the World Bank (2003). I obtained these datasets from the GSO.

Employment Shares: I use the 3 percent sample of the 1999 Vietnam Census, made available

by IPUMS International59, to construct estimates of employment by industry within each

province. Individuals are considered employed if the variable empstat takes the value 1000. The

59 See http://www.ipums.org/international/index.html.

http://www.ipums.org/international/index.html

151

variable ind records the industry affiliation for employed individuals. For the majority of

industries, the code and description match with the 3-digit ISIC, revision 3 codes. However,

there are a few industries for which the Vietnamese census code differs from the corresponding

3-digit ISIC code. I make the changes documented below.

Old industry code

New industry code

Old industry code

New industry code

701 731 702 732 711 701 712 702 721 711 722 712 723 713 731 721 732 722 733 723 734 724 735 725 739 729 901 921 902 922 903 923 904 924 911 910 913 911 920 900

Finally, I assign individuals based on the province of official residence on the night of the census

using provvn and weight individuals using wtper.

U.S. Tariffs: The 2001 U.S. tariff data from the U.S. International Trade Commission’s

(USITC) website. I convert specific tariffs to ad valorem equivalents by estimating the unit value

of imports within each 8-digit HTS tariff line using total annual imports from all countries. I

calculate the unit value of imports by dividing customs value of total imports by the total

quantity by first unit for each 8-digit HTS tariff line that features a specific tariff component.

Concordance from HS to ISIC: The U.S. tariff data is reported according to the 8-digit

Harmonized Tariff Schedule (HTS) of the United States. I match the 8-digit HTS codes to 6-digit

Harmonized System (HS) codes by dropping the last two digits of the code. I convert the 6-digit

HS codes to 3-digit ISIC codes with the concordance supplied by the World Bank. These

concordances are also available as part of the WITS software program. I calculate a weighted

average of the ad valorem equivalent of all tariff lines within an industry using U.S. imports in

each tariff line as the weights.

Hourly wages: For the 2004 VHLSS, nominal hourly wages are estimated by dividing the wage

and salary received during the past 12 months for the most time consuming job (variable

152

m4ac10a from file m4a.dta) by an estimate of annual hours. Annual hours are estimated by

multiplying the number of months (m4ac6) by the number of days per month (m4ac7) and by

the number of hours per day (m4ac8). I convert the nominal hourly wage series to national

average January 2004 prices by regionally and temporally deflating using the series rcpi and

mcpi available in hhexpe04.dta.

For the 2002 VHLSS, the wage and hours data comes from the file muc3.dta. I take

annual wages from m3c1a and construct annual hours from months (m3c9), days per month

(m3c10) and hours per day (m3c11). As for the 2004 wages, I convert the nominal hourly wage

series to national average January 2002 prices by regionally (rcpi) and temporally (mcpi)

deflating using deflators in the file hhexpe02.dta.

153

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