BS Thesis

in Economics

Trade Openness and Inequality

An Empirical Analysis

Ólafur Kjaran Árnason

Advisor: Professor Gylfi Zoëga

Faculty of Economics

October 2017

Trade Openness and Inequality An Empirical Analysis

Ólafur Kjaran Árnason

Thesis towards a BS degree in Economics

Advisor: Professor Gylfi Zoëga

Faculty of Economics

School of Social Sciences, University of Iceland

October 2017

Trade Openness and Inequality: an Empirical Analysis.

This thesis is equivalent to 12 ECTS credits towards a BS degree from the

Faculty of Economics at the University of Iceland.

© 2017 Ólafur Kjaran Árnason

This thesis cannot be reproduced without the author’s consent.

Printing: Samskipti

Reykjavík, 2017

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Foreword

This thesis is equivalent to 12 ECTS credits towards a BS degree from the Faculty of

Economics at the University of Iceland. I would like to thank my advisor, Professor Gylfi

Zoëga, for his patience and helpful suggestions, and my ever supportive parents, Árni

Sigurjónsson and Ásta Bjarnadóttir. They are the finest people. My fellow economist,

Maja Jóhannsdóttir my love, jeg glæder mig til at spise dansk frokost sammen i næste

uge. Finally, I want to acknowledge the blessing of having had such encouraging

conversations with Árni Þór Lárusson, Gunnar Jörgen Viggósson, and my grandmother,

Gerður G. Óskarsdóttir, at various stages on the long path of thorns towards writing this

thesis.

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Abstract

This paper investigates the empirical relationship between openness to trade and within-

country income inequality. The estimation is carried out with group and time fixed

effects, using an unbalanced panel of 112 countries over the period of 1988-2008. A key

result is that the impact of increased openness on inequality is negatively related to

education levels. For most countries the estimated effect is positive but in countries

where the share of population with secondary education is high the effect is reversed and

increased openness is expected to lower inequality. Thus, more widespread education

may provide protection against increased inequality due to globalization, in direct

conflict with the Stolper-Samuelson theorem. This supports the view that rising within-

country inequality in many rich countries in recent decades is driven by other forces than

globalization. The results are highly significant, robust to various changes in model

specification, and not sensitive to the omission of subsets of the sample. In general, the

estimated impact of openness is quite small in terms of changes in the Gini coefficient,

but in some cases substantial when examined specifically for individual quintiles of the

income distribution.

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Contents

Foreword .................................................................................................................... 4

Abstract ...................................................................................................................... 5

Contents ...................................................................................................................... 6

Figures ........................................................................................................................ 7

Tables ......................................................................................................................... 7

1 Introduction .......................................................................................................... 8

2 Literature review ................................................................................................ 14

3 Data and methodology ....................................................................................... 20

3.1 Dependent variable ..................................................................................... 21

3.2 Independent variables ................................................................................. 22

3.3 Empirical strategy ....................................................................................... 24

4 Results ................................................................................................................ 26

4.1 Regression analysis ..................................................................................... 26

4.2 Robustness .................................................................................................. 28

5 Discussion .......................................................................................................... 32

References ................................................................................................................ 38

Appendix 1 ............................................................................................................... 42

Appendix 2 ............................................................................................................... 43

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Figures

Figure 1. World trade, 1870 to 2010. ................................................................................ 10

Figure 2. Global inequality, 1988 to 2008. ....................................................................... 11

Figure 3. Trade and inequality in several countries, 1988 to 2008. .................................. 13

Figure 4. The estimated effect of increased openness conditional on education. ............. 34

Tables

Table 1. Global inequality, 1988 to 2008. ........................................................................ 11

Table 2. Comparison of cross-country studies of openness and income inequality. ........ 19

Table 3. Number of observations by year and region. ...................................................... 21

Table 4. Fixed effects regression results: Gini. ................................................................. 27

Table 5. Fixed effects regression results: Quintile shares. ................................................ 28

Table 6. Robustness tests. ................................................................................................. 31

Table 7. Interpretation: Gini. ............................................................................................ 33

Table 8. Interpretation: Income of bottom quintile. .......................................................... 34

Table A1. Summary statistics. ..........................................................................................42

Table A2. Number of observations by country and region. .............................................43

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1 Introduction

Most economists agree that increased international trade is an effective means of raising

the living standards of people across the globe.1 The fundamental insight of David

Ricardo’s trade theory, which he presented 200 years ago, is that because of comparative

advantage this should in fact apply equally to countries rich and poor, irrespective of

factor endowments (Ricardo, 1817/2001). Many analysts have addressed the issue

empirically and there is a vast literature focused on identifying the link between openness

and economic growth. Although the empirical evidence has generally been supportive of

a positive impact of openness on growth (Hallaert, 2006), there remain significant

methodological obstacles, and many economists are skeptical of the results (Rodriguez &

Rodrik, 2000).

However, while most economists believe in the potential benefits of trade, they also

tend to agree that there are winners and losers from trade. According to the Stolper-

Samuelson theorem, which follows directly from the Heckscher-Ohlin model of trade,

the winners will be those who command factors in which their country is abundant

relative to its trading partners and the losers will be those who command factors in which

their country is relatively scarce (Stolper & Samuelson, 1941).2 The implications for

income inequality will therefore depend on relative factor endowments of countries and

the distribution of factors within countries. A simple version of the model assumes two

factors of production, low-skilled and high-skilled labor, where poor countries are

relatively abundant in low-skilled labor and rich countries in high-skilled labor, and thus

increased trade is expected to lower inequality in poor countries while raising inequality

in rich countries. The relationship between openness and inequality has not been studied

as extensively as the effects of trade on growth and so far the empirical evidence is

inconclusive. The cross-country literature on openness to trade and inequality is reviewed

in Chapter 2.

This paper will leave aside the question of growth and focus on distributional

consequences. Regardless of the relation between globalization and growth, its

1 Openness (to trade), globalization, and (international) trade will be used interchangably in the paper. 2 Factors of production, or factors for short, are any inputs used in the production of goods and services (e.g. low-skilled and high-skilled labor, capital, natural resources, etc.).

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connection with inequality is important enough on its own. If, for example, it is true that

globalization can potentially increase the well-being of everyone and it is also true that

globalization increases inequality in a systematic way, then it is absolutely necessary to

understand how its fruits can be better shared in order to maintain public support for

increased globalization. This perspective motivated the International Monetary Fund

(IMF), the World Bank (WB), and the World Trade Organization (WTO), all ardent

supporters of globalization, to issue a joint report last April on ways to share the gains

from trade more widely, stating that “trade is leaving too many individuals and

communities behind, notably also in advanced economies” (IMF, WB, & WTO, 2017, p.

4).

The period between 1988 and 2008 was an era of unprecedented globalization and is

in many respects ideal for studying the relationship between openness and inequality.

Never in history has globalization accelerated so rapidly and so universally, which is why

it has been referred to as the era of hyperglobalization (Subramanian & Kessler, 2013).

Figure 1 shows this remarkable rise of world trade as a share of world GDP. It is

interesting to note that not until the 1970s did globalization reach the same heights as

immediately after World War I but thereafter the ratio of trade to world GDP remains

fairly stable until shortly before 1990.

The late 1980s was a time when many developing countries were integrating into the

world economy and the fall of Communist regimes in Eastern Europe and the Soviet

Union was about to transform the global political landscape. Together these

developments led to hundreds of millions of people entering world markets who

previously had been living in autarkic or semi-autarkic countries. The year of 2008 is an

appropriate endpoint for the analysis, as the global financial crisis put a halt to the

hyperglobalization of the two preceding decades.

Apart from the period being very suitable for studying globalization, comparable high-

quality data on income distribution become much more readily available around 1990,

which allows us to study the development of inequality with greater accuracy than before

that time (Milanovic, 2016a). For data on inequality, this paper relies on the database

constructed by Lakner and Milanovic (2016), which is described in Chapter 3.

But what happened to income inequality during the era of hyperglobalization? It is

well documented that for the last couple of decades income inequality has been on the

rise in many Western countries (see for example OECD, 2015). That is not the whole

10

Figure 1. World trade, 1870 to 2010.

Note: World trade as a share of world GDP. The shaded part shows the period that has been referred to as the era of hyperglobalization. Source: Klasing and Milionis (2014) for 1870-1949 and the Penn World Tables (Version 8.1) by Feenstra, Inklaar and Timmer (2015) for 1950-2010. story, however. Economists studying global inequality, measured as if the world was one

country, have consistently found that it has been fairly stable in recent decades and that at

some point between the late 1980s and the early 2000s global inequality in fact started

decreasing (Bourguignon, 2015; Lakner and Milanovic, 2016).3 As a result, the era of

hyperglobalization coincides with the first period since the dawn of the Industrial

Revolution that global inequality is on the decline (Bourguignon and Morrison, 2002;

Milanovic, 2016a).

The concept of global inequality can be thought of as including two components,

inequality between countries and inequality within countries, where in today’s world the

between-component accounts for a larger share of global inequality than the within-

component.4

3 Bourguignon (2015) uses household survey data normalized to GDP per capita, because of the noted discrepancy between household survey means and GDP per capita based on national accounts, and he finds that global inequality started decreasing in the 1990s. Lakner and Milanovic (2016) make no such correction and find that global inequality started to decline in the 2000s. 4 The Theil coefficient is an alternative to the Gini coefficient which has the advantage of being explicitly decomposable into between- and within-components. However, the Gini is preferred in this study due to its widespread use.

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Table 1. Global inequality, 1988 to 2008.

1988 1993 1998 2003 2008 Global inequality 72.0 71.9 71.8 71.9 69.4 Between inequality (unweighted) 46.3 56.1 55.1 58.2 55.5 Between inequality (population-weighted) 65.9 64.9 64.8 64.5 61.3 Within inequality (unweighted) 35.1 39.6 39.5 39.0 37.6 Within inequality (population-weighted) 32.8 36.6 37.7 38.4 39.2

OECD countries 31.3 34.2 35.0 35.7 36.2 Non-OECD countries 33.2 37.2 38.3 39.0 40.0

Number of countries 72 112 118 130 117 Share of world population 81.0 92.0 91.6 93.2 89.9

Note: Inequality of net personal (PPP-adjusted) incomes in terms of the Gini coefficient. Global inequality consists of inequality between and within countries. The values for between and within inequality are averages across countries. Source: Author’s calculations based on inequality data from Lakner and Milanovic (2016) and population data from the World Bank (2017).

Figure 2. Global inequality, 1988 to 2008.

Note: Inequality of net personal (PPP-adjusted) incomes expressed in terms of the Gini coefficient. Between and within inequality refer to population-weighted averages across countries. Source: Author’s calculations based on data from Lakner and Milanovic (2016).

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While inequality within countries has been rising in both rich and poor countries, and in

fact even more so in poor countries, inequality between countries has been on the

decline, with the net effect being a slight decrease in global inequality.5

Figure 2 shows the development of global inequality during the period of 1988 to

2008 and the corresponding numbers are provided in

Table 1. The fall in global inequality has certainly accelerated since 2008 due to the

much higher economic growth in emerging economies relative to advanced economies.

Figure 3 shows the trends in trade and inequality for several countries over the same

period.

Considering these general trends in trade and inequality, one could hypothesize that

the increase in within-country inequality and the decrease in between-country inequality

are both connected to the rise in globalization. It is possible, for example, that

globalization allows poor countries to catch up with rich countries at a faster rate, thereby

lowering between-country inequality, and at the same time it benefits rich people

disproportionately at the country level, thereby raising within-country inequality across

the board. Obviously, this hypothesis is difficult to evaluate, but motivated by the trends

discussed above, this paper sets out to study to what extent the rise in globalization on

the one hand and within-country income inequality on the other hand are related.

The rest of the paper is organized as follows. Chapter 2 provides a review of previous

cross-country studies on openness to trade and inequality and explains how this paper

contributes to the literature. Chapter 3 describes the data and methodology used in the

empirical analysis and the results are presented in Chapter 4 along with a set of

robustness tests. Chapter 5 discusses the findings their implications.

5 It should be noted, however, that this small change in the Gini coefficient of global inequality between 1988 and 2008 conceals what Branko Milanovic describes as “the greatest reshuffle of individual incomes since the Industrial Revolution” (Milanovic, 2016b). This reshuffle of incomes is perhaps best demonstrated by the so-called ‘elephant curve’ (Milanovic, 2016a; Milanovic, 2016b).

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Figure 3. Trade and inequality in several countries, 1988 to 2008.

Note: The path of trade and inequality across five benchmark years (1988, 1993, 1998, 2003, and 2008) in a few large advanced economies (Australia, Japan, Germany, South Korea, and the USA) and a few large emerging economies (Brazil, China, India, Indonesia, and South Africa). Data are missing for Australia in 2008 and South Africa in 1988. Source: Trade data from the World Bank (2017) and inequality data from Lakner and Milanovic (2016).

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2 Literature review

The empirical literature on trade openness and within-country income inequality consists

of two different approaches. Cross-country studies make use of cross-country variation in

openness and inequality, often over a period of time, while case-specific studies usually

involve a detailed analysis of trade liberalization episodes in individual countries over a

shorter time period. Each approach has its strengths and weaknesses.

In their survey of the literature on trade liberalization and income inequality in

developing countries, Goldberg and Pavcnik (2007) “abstain from relying on cross-

country regressions”, primarily because of data constraints, stating that “[i]nconsistencies

in the measurement of inequality across countries, changes in the household survey

response rates over time as incomes rise, and frequent changes in the design of household

surveys within the same country make inference based on cross-country evidence (…)

potentially less reliable compared to inference that relies on within-country evidence over

shorter periods of time” (p. 40).

However, if imperfect data and comparability are the main shortcomings of the cross-

country approach, the most obvious drawback of case-specific studies is that they are,

indeed, case-specific. As a result, the findings of individual case-specific studies on

openness and inequality cannot be easily generalized, and if the case in question is in fact

a very special or unique one, even an ‘outlier case’, then the study’s findings may simply

be misleading. Although taking a number of case-specific studies together provides a

better chance of reaching some valid, general conclusions, it is still a problematic

approach. Nothing ensures that it will be possible to identify which case-specific factors

actually explain different results across different case-specific studies. It may also be that

most case-specific studies are conducted for countries with particular characteristics,

causing a bias in any general conclusions drawn from a survey of such studies.6

To some extent, the weaknesses discussed above reflect a straightforward trade-off

between applicability and accuracy, with cross-country studies gaining greater

6 For example, case-specific studies on developing countries in the 1990s were essentially limited to Latin America, which is indeed reflected in the survey by Goldberg and Pavcnik (2007). What if Latin America is for some reason a special case? Similarily, most case-specific studies on developed countries focus on the USA, which could just as well be a special case among developed countries. If so, then the validity of general conclusions drawn from a survey of such studies does not improve, even as their number increases.

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applicability by giving up some accuracy, and vice versa for case-specific studies.

Neither approach is impeccable but both are important. This paper follows the cross-

country approach to studying the relationship between openness to trade and income

inequality, while also taking seriously its aforementioned limitations, which will be

addressed further below. First, however, a brief review of the cross-country literature is

in order.7

An early empirical study focused on the question of openness and inequality was

conducted by Bourguignon and Morrisson (1990), who utilized a cross-section of 36

countries in 1970. The study reported a negative effect of openness on inequality but was

severely constrained by data availability. A few papers on the topic emerged in the late

1990s, hailing from the vibrant debate on the relationship between openness and

economic growth which reached its peak at the time. Among those were Edwards (1997);

Savvides (1998); Higgins and Williamson (1999); and Spilimbergo, Londono, and

Szekely (1999), all of which made use of a new World Bank database on inequality,

constructed by Deininger and Squire (1996).8

More recent studies that have focused exclusively on the effects of openness on

income inequality are Reuveny and Li (2003); Milanovic (2005); Gourdon, Maystre, and

de Melo (2008); Dreher and Gaston (2008); Bergh and Nilsson (2010); and Jaumotte,

Lall, and Papageorgiou (2013). These studies have benefited greatly from better data on

inequality as well as other variables. Better data not only improve the quality of cross-

country studies by means of lower measurement errors and greater comparability;

increased data coverage also allows for the use of more sophisticated estimation

techniques, yielding more reliable estimates.

However, the empirical evidence from cross-country studies on openness and

inequality is still far from conclusive. Table 2 provides a comparison of the methods and

results of a few important papers belonging to this literature. The results of other papers

mentioned above but not presented in the table are summarized very briefly in a

footnote.9 Although the results seem to comprise almost the whole range of possible

7 For case-specific studies, see for example Goldberg and Pavcnik (2007) for developing countries, and Autor, Dorn, and Hanson (2013) for the US. 8 Additionally, a few widely cited papers around 2000 addressed the issue as a side note: Li, Squire, and Zou (1998); Barro (2000); Ravallion (2001); Dollar and Kraay (2002); and Lundberg and Squire (2003). 9 The results of other papers mentioned here are as follows: Edwards (1997), Li et al. (1998), and Dollar and Kraay (2002) find an insignificant effect of openness on inequality; Savvides (1998), Barro (2000), and Ravallion (2001) find a positive effect in poor countries; Lundberg and Squire (2003) find a small positive

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results – increasing or decreasing inequality, conforming to or contradicting the Stolper-

Samuelson theorem, mostly affecting rich countries or mostly affecting poor countries,

and so on – there are in fact signs of some consistency. First, most studies find evidence

of a positive impact of openness on inequality; out of the fifteen studies discussed in

Table 2 and footnote 9, only two report a negative relationship. Second, by and large

there is limited empirical support for the Stolper-Samuelson theorem.10 This matter is not

addressed specifically in all of the studies, but it is noticable that only Gourdon et al.

(2008) report estimates that are generally in line with the predictions of the Stolper-

Samuelson theorem.11

The absence of empirical evidence in favor of the Stolper-Samuelson theorem, and in

fact the Heckscher-Ohlin model of trade more generally, has long been a puzzle.12 Most

problematic is the prediction that increased openness lowers inequality in poor countries

while, according to Goldberg and Pavcnik (2007), “there is overwhelming evidence” (p.

39) supporting the opposite. As a response, some theoretical papers have proposed

variations of the Heckscher-Ohlin model that seek to reconcile the theory with the

empirical evidence. For example, Wood (1994) provides a Heckscher-Ohlin model with

three factors (unskilled, medium-skilled, and high-skilled labor) in which the effect of

increased openness on inequality is ambiguous in countries relatively abundant in

medium-skilled labor, such as Latin American countries in the 1980s and 1990s.

Therefore, he argues, trade liberalization episodes in these countries did not have the

Stolper-Samuelson effects expected for countries abundant in unskilled labor. Davis

(1996) comes to a similar conclusion assuming two factors and three types of goods that

differ in their capital intensiveness.

The estimated positive effect of openness on inequality has often been relatively

small. This gave rise to a consensus in the late 1990s among labor and trade economists

that increased inequality had more to do with factors such as skill-biased technological

effect; and Reuveny and Li (2003) find a negative effect of trade openness but a positive effect of financial openness, similar to Jaumotte et al. (2013). 10 According to Goldberg and Pavcnik (2007), there has been “no [empirical] support for the predictions of the model, at least not in its strict version” (p. 58). 11 The results of Spilimbergo et al. (1999) are in line with Stolper-Samuelson for skilled-labor but not for capital. Dreher and Gaston (2008), and Bergh and Nilsson (2010) find a positive effect of openness on inequality in general, but especially for rich countries, which conforms to Stolper-Samuelson, but they do not find any evidence of a negative impact in poor countries, which should also follow from the theorem. 12 Feenstra (2004) provides a good discussion of the empirical problems of the Heckscher-Ohlin model and the Stolper-Samuelson theorem. Adrian Wood has long argued that these difficulties are in fact much exaggerated, see for example Wood (1994), Wood (1995), and Wood (2017).

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change and less with globalization (Goldberg, 2015).13 However, the subject has received

renewed interest in recent years as several studies have reported a larger impact of

globalization on inequality,14 leading IMF et al. (2017) to emphasize trade-related

adjustment costs that “can bring a human and economic downside that is frequently

concentrated, sometimes harsh, and has too often become prolonged” (p. 4).

This paper contributes to the existing literature by paying special attention to the

concerns of Goldberg and Pavcnik (2007) highlighted above. First, and most importantly,

this is done by using data of higher quality than most previous studies, prioritizing data

comparability. Although this emphasis limits the feasible time-frame of the analysis, and

thus reduces the number of observations available, the shorter time-frame itself may also

increase the accuracy of the results by reducing measurement inconsistencies within

countries over time. Moreover, the analysis includes the entire era of hyperglobalization

as it has been defined and also covers most of the world’s population during that period

(see Chapter 3). The inequality database used in this paper, which is described in Chapter

3, relies completely on data taken directly from household surveys. Unfortunately, other

studies have usually had to rely on data of lower quality. In most cases, previous studies

have used the Deininger and Squire database (Deininger and Squire, 1996), in which

observations are much less comparable across countries and (more importantly for fixed

effects estimation) over time within countries.15 Alternatively, some studies have relied

on databases that include datapoints not taken from actual household surveys. For

example, the SWIID database (Solt, 2009) is based on a missing data algorithm and the

EHII database (Galbraith & Kum, 2005) estimates total income inequality based on the

systematic relationship between the Deininger and Squire database and data on industrial

pay-inequality. It should be noted that the studies by Milanovic (2005) and Jaumotte et

13 Borjas, Freeman, and Katz (1997), studying the effects of trade and immigration on wage inequality, come to a common conclusion in this respect: “[A]cceleration of skill-biased technological change, a slowdown in the growth of the relative supply of college graduates, and institutional changes in the labor market are probably more important than immigration and trade in explaining the widening of the U.S. wage structure since the late 1970s” (p. 62-63). A recent review of the literature on the relationship between globalization and wage inequality by Helpman (2016) concludes that “trade played an appreciable role in increasing wage inequality, but that its cumulative effect has been modest” (p. 1). 14 Autor et al. (2013) has perhaps received most widespread attention. The paper reports a sizable impact of rising Chinese import competition on cumulative earnings, employment, and benefits payments across local US labor markets over the period of 1992 to 2007. 15 As noted by the authors of the database themselves, “variation in the definition of the variables used to measure inequality – gross income or net income, income or expenditure, data per capita or data per household – can seriously affect the magnitude of the indicators of inequality and undermine the international and intertemporal comparability of the data” (Deininger and Squire, 1996, p. 566).

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al. (2013) are exceptions, but the former is limited to a much lower number of

observations than the present analysis and the latter includes data on much fewer

countries.

Second, by using fixed effects estimation, persistent data inconsistencies across

countries are absorbed by the country fixed effects, and thus do not affect the estimates.

This comes at the cost of leaving significantly less variation to be explained in the

estimation, since fixed effects estimation only makes use of variation within countries

while ignoring variation between countries, but it also produces much more reliable

ceteris paribus estimates. It seems to be generally agreed upon in the literature that, if

possible, applying fixed effects estimation is the optimal approach.16 The empirical

strategy is discussed further in Chapter 3.

16 Most of the papers in Table 2 use fixed effects estimation, if not as their main approach then at least as a robustness check. Spilimbergo et al. (1999), who are chiefly interested in the impact of relative factor endowments, do not use fixed effects “because many countries have few observations and change in relative endowments is relatively slow” (p. 85) and Milanovic (2005) does not do it because his “panel is very short” (p. 27), or only three observations.

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Table 2. Comparison of cross-country studies of openness and income inequality.

Observations /

Countries Estimation approach

Inequality measure

(source)

Paper Period Definition of openness Effects of openness on inequality

Jaumotte, Lall, and Papageorgiou (2013) 1981-2003 290 / 51 Fixed effects

Trade as a share of GDP and 100 minus average

tariff rates Gini (Povcal and LIS) Negative, effects of financial openness

and technology positive

Bergh and Nilsson (2010) 1970-2005 493 / 78 Fixed effects and system GMM EFI and KOF indices Gini (SWIID) Positive, especially in rich countries

Dreher and Gaston (2008) 1970-2000 349 / 100 Fixed effects and system GMM KOF index

Estimated Gini, not based on household surveys

(EHII) Positive, especially in rich countries

Gourdon, Maystre, and de Melo (2008) 1975-2000 210 / 64 Fixed effects Ratio of tariff revenues to

imports Gini (DS and WIDER)

and deciles (WYD)

Positive in rich countries, negative in poor countries; positive in skill- and

capital-abundant countries

Milanovic (2005) 1988-1998 201 / unknown System GMM Trade as a share of GDP Income share of each decile (WYD)

Positive in poor countries, negative in rich countries

Higgins and Williamson (1999)

1960s-1990s (decade

averages) 449 / 44 Pooled OLS and

fixed effects Sachs-Warner index Gini and the ratio of top

and bottom quintile incomes (DS)

Insignificant

Spilimbergo, Londono, and Szekely (1999) 1965-1992 320 / 34 Pooled OLS

Own index of openness, adjusted for factor

endowments Gini (DS) Positive in skill-abundant countries,

negative in capital-abundant countries

Note: When possible, the number of observations and countries applies to specifications estimated with fixed effects. The openness indices mentioned in the table are the Economic Freedom Index (EFI) by Gwartney, Lawson, and Norton (2008), the KOF index of globalization by Dreher (2008), and the Sachs-Warner (0-1) index by Sachs and Warner (1995). The following databases are mentioned: DS by Deininger and Squire (1996), SWIID is the Standardized World Income Inequality Database by Solt (2009), EHII is the Estimated Household Income Inequality dataset by Galbraith and Kum (2005), WIDER is from the World Institute for Development Economics Research at the United Nations University, Povcal and the World Income Distribution (WYD) are from the World Bank, and LIS refers to data from the Luxembourg Income Study. Source: Author’s compilation.

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3 Data and methodology

The database on income inequality used in this paper is the Lakner-Milanovic World

Panel Income Distribution database (LM-WPID) by Lakner and Milanovic (2016) which

derives mostly from PovcalNet and the World Income Distribution (WYD) database.17

Here, income refers to total personal income, net of taxes and transfers, as recorded in

household surveys. The data are given for five benchmark years (1988, 1993, 1998,

2003, and 2008) and come from 550 household surveys in 159 countries,18 on average

representing 95 percent of world GDP and 90 percent of the world’s population.

When corresponding data have been collected for control variables used in the main

model specification, the result is an unbalanced panel of 112 countries and 440

observations, covering on average 85 percent of the world’s population.19 An extended

model is also presented for which these numbers are somewhat lower. Countries with

only one observation are not included in the panel as they are of no use in a fixed effects

estimation, which is the preferred estimation approach.

Table 3 gives information about the number of observations by year and region.

Member countries of the Organization for Economic Co-operation and Development

(OECD) in 2008 are considered as one separate region and other countries are grouped

into four geographical regions (all excluding OECD countries): Africa and the Middle

East, Asia, Eastern Europe and Central Asia, and Latin America and the Caribbean. As

can be seen from Table 3, roughly 30 percent of the data come from OECD countries.

Appendix 1 provides summary statistics for all variables used in the main and extended

specifications.

17 The PovcalNet was developed for poverty measurement by the Development Research Group at the World Bank and the WYD was constructed by Milanovic. Together, PovcalNet and WYD account for almost 98 percent of the data in the LM-WPID database. Other sources are the Luxembourg Income Study (LIS), the European Union Survey of Income and Living Conditions (SILC), the British Household Panel Survey (BHPS), and the statistics offices of Finland and Portugal. 18 All surveys were conducted within two years of a benchmark year, and at least three and no more than seven years from the previous and next survey. Approximately 75 percent of the surveys were conducted within one year from the benchmark year. 19 Hong Kong and Singapore are excluded from the panel because the relation between trade and inequality is presumably different for cities and city-states than for countries that do not fall into that category. It should be noted that the significance of the results does not depend on this.

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Table 3. Number of observations by year and region.

1988 1993 1998 2003 2008 Total (%) Total number of observations 57 90 99 99 95 440 100.0

Africa and the Middle East 11 23 23 24 23 104 23.6 Asia 9 12 14 16 15 66 15.0 Eastern Europe and Central Asia 1 6 13 12 13 45 10.2 Latin America and the Caribbean 15 20 20 18 17 90 20.5 OECD 21 29 29 29 27 135 30.7

Population (millions) 3898 4827 5193 5544 5776 Share of world population 76.3 87.1 87.2 87.2 85.4

Note: The number of observations used to estimate the main specification below. Appendix 2 provides a list of the countries included in the estimation and the number of observations for each country. Source: Population data from the World Bank (2017).

3.1 Dependent variable Apart from relying solely on high-quality data drawn from household surveys, the LM-

WPID database has many desirable features for the present analysis.20 First, it covers a

large number of countries from all regions of the world during a period of rapid

globalization. Second, the database provides information on the average personal income

of each decile of the income distribution, which allows for a more detailed study of

inequality than synthetic measures such as the Gini coefficient. Third, for a given country

the data report either total income (net of taxes and transfers) or total consumption of

individuals, which gives a more complete picture of the development of inequality than

for example data on wages.21 As there are important differences between measuring

inequality of income and inequality of consumption, it is required that for a given country

the data come from either income surveys or consumption surveys, so it is never the case

that a country is represented by income data in one year and consumption data in

another.22

20 For a more detailed description of the database, see Lakner and Milanovic (2016). 21 The data are adjusted for purchasing power parity (PPP). This is crucial for comparison of income and/or consumption across countries, and hence for the calculation of global inequality, but it is not particularly important in the current context. 22 Inequality of consumption is generally lower than inequality of income, and therefore the data on inequality are not perfectly comparable across countries. But the requirement mentioned in the text makes the data comparable across time for individual contries, which is critical for the present analysis, since the preferred estimation approach relies completely on the variation within countries (see Section 3.3).

22

It should be noted that a well-known feature of household surveys is their tendency to

underestimate top incomes (Korinek, Mistiaen, & Ravallion, 2006) and the data used

here will therefore understate the true level of inequality. Also, since the data express

only the average income of each decile of the income distribution, all calculations of

inequality will fail to take into account inequality within deciles, which may be

considerable in the top decile. This has to be kept in mind when interpreting the results.

3.2 Independent variables When possible, independent variables are calculated as five-year averages, so data

corresponding to the benchmark year 1988 are the average for 1984-1988. This is

standard in the cross-country literature on openness and inequality, and is done for a

couple of reasons. Most importantly, this is done to reflect that the independent variables

are not assumed to affect the income distribution instantaneously. Another reason is that

taking five-year averages may reduce the risk that the results are excessively affected by

temporary fluctuations and measurement errors, and moreover, it may help mitigating the

endogeneity problem which will be addressed in greater detail in Chapter 4.

The main variable of interest, openness to trade, is proxied by the sum of exports and

imports as a share of GDP, based on data from the World Bank (2017). The decision to

use a de facto measure of actual flows, instead of a de jure measure of trade policy such

as barriers to trade, comes from the fact that the aim of this paper is to study how

inequality is affected by globalization per se, regardless of whether it is driven by

changes in policy or something else. If the focus was specifically on studying the effects

of trade policy on inequality, then it would be problematic to use a measure such as the

ratio of trade to GDP, since there are obviously many other factors than policy that affect

such a measure.

The variable is not expressed as a natural logarithm because an increase in the ratio of

trade to GDP from 10 to 20 percent is assumed to have the same effect on inequality as

an increase from 100 to 110 percent, not the same as an increase from 100 to 200 percent.

This applies to all other variables which are denoted in percentage terms.

23

Following Bergh and Nilsson (2010), three control variables are included in all model

specifications. These are the log of mean income, the dependency ratio and the share of

population over 15 years old with secondary education.23

Mean income is included to correct for changes in the income distribution related to

income levels.24 The data on mean income are taken from the LM-WPID database, like

the data on inequality, and represent average net income as recorded in household

surveys. The variable is expressed in natural logarithm since proportional changes in

mean income, rather than absolute changes, are assumed to have the same effects on

inequality.

The dependency ratio, defined as the share of population under 15 years old and over

64 years old, enters the model to control for changes in demography. Higgins and

Williamson (1999) find evidence for strong effects of demography on inequality where

countries with large mature working age cohorts have lower inequality than countries

with large young working age cohorts. The theoretical reasoning is simple. Large cohorts

tend to receive lower earnings than small cohorts due to supply-effects in the labor

market, and life-cycle earnings are highest for mature working age individuals.

Therefore, when large cohorts are on top of the age-earnings curve, this contributes to

lower inequality. Although the dependency ratio does not account for this directly it is

assumed to be associated with higher inequality following similar logic. Data on

demography are from the World Bank (2017).

The share of population over 15 years old with secondary education is included to

account for the skill-level of the workforce. The source of the data is the Barro-Lee

Educational Attainment Dataset by Barro and Lee (2013) and the variable is defined as

the share of people who have completed secondary education, therefore also counting

people with tertiary education.25 The effect of a higher skill-level on inequality is

theoretically ambiguous. As more people receive a skill premium, inequality may go up,

23 To be exact, Bergh and Nilsson (2010) include the share of population over 25 years old with tertiary education, but here the share of population over 15 years old with secondary education is preferred instead. 24 Many empirical studies have included mean income along with the square of mean income, based on the well-known Kuznets hypothesis of inequality (Kuznets, 1955). However, since the Kuznets hypothesis is not the subject of this paper, and adding the square of mean income did not much improve the fit of the models estimated below, it is not included in the present analysis for the sake of simplicity. 25 The dataset provides data for five-year intervals, starting in 1950. Therefore, the data on education used here correspond to the years 1985, 1990, 1995, 2000, and 2005.

24

but a greater supply of skilled workers may also lead to a decrease in the skill premium

itself and thereby lower inequality.

An extended model specification includes three additional control variables. These are

foreign direct investment (FDI), democracy, and the share of industry in GDP. FDI enters

the model as the stock of (inward) FDI as a share of GDP and is included to allow for

different effects of financial openness in addition to openness to trade. Democracy is

expected to lower inequality by leading to greater redistribution via the median voter

hypothesis and the share of industry in GDP is added to control for the structure of the

economy. The sources for these variables are the United Nations Conference on Trade

and Development (UNCTAD, 2016) for FDI, the Center for Systemic Peace (CSP) for

democracy, and the World Bank (2017) for the share of industry in GDP. The democracy

variable takes values from -10 (strongly autocratic) to 10 (strongly democratic) and since

absolute changes, not proportional changes, are assumed to have the same effect on

inequality, the variable is not expressed in natural logarithm.

3.3 Empirical strategy All the empirical models estimated in this paper are fixed effects regression models of

the form

where i denotes country and t denotes time, Xit' is a matrix of control variables, Ci and Tt

represent country and time fixed effects, respectively, uit is an idiosyncratic error term,

and the coefficients of interest are btrade and binteraction. To begin with, the model is

estimated without an interaction term but in other specifications trade is allowed to

interact with the log of mean income or the share of population with secondary

education. This is done to investigate whether the impact of trade on inequality depends

on these variables in a systematic way. All model specifications are estimated with the

Gini coefficient as the dependent variable and the main specification is also studied in

greater detail by estimating the model separately for the income share of each quintile of

the income distribution.

The country fixed effects absorb all factors that are country-specific and do not vary

over time. In this context, the inclusion of country fixed effects is arguably very

important, since there are many factors that affect the level of inequality and do not vary

over time. For example, geography and history can be thought of as purely time-invariant

25

while factors such as institutions, culture, and ethnic or linguistic fragmentation can be

thought of as being at least close to time-invariant over the period 1988 to 2008.

Similarly, the time fixed effects absorb all factors that are time-specific and do not vary

among countries, for example factors such as global business cycles. Therefore, allowing

for fixed effects can be expected to greatly reduce bias due to omitted variables although

it is true, of course, that omitted variables which do vary over time may still cause bias in

the estimates. This can only be dealt with by including more relevant independent

variables in the model, which is done in the extended specification and also in the

robustness tests in Chapter 4.

Including fixed effects is equivalent to assuming that the marginal effects of the

independent variables are the same across countries but that there is a different intercept

for each country. By doing so, the fixed effects estimator uses only variation within

countries while ignoring variation between countries, which is why it is also known as

the ‘within estimator’. As a result, the method does not work well with data for which the

variation within countries is little or for variables that change very slowly over time. This

may be an issue here since the average number of observations is quite low, for instance

it is 3.9 for the main specification.

The critical assumption needed for the fixed effects estimator to be consistent is strict

exogeneity, meaning that the idiosyncratic error terms uit have zero mean conditional on

all explanatory variables and fixed effects at any point in time. The validity of this

assumption is evaluated in the robustness tests in Chapter 4. For the estimator to be

efficient, the idiosyncratic errors have to be homoscedastic and not serially correlated.

But since this is not likely to be the case, all regressions are run with heteroscedasticity

and autocorrelation consistent standard errors, assuming that the errors are clustered at

the country level. The method used is from Arellano (1987) and is optimal when the

time-dimension is relatively short and the cross-section is relatively large.26 (Stock and

Watson, 2015; Wooldridge, 2001)

26 The estimations are carried out in R using the plm-package. The robust standard errors are calculated by the Arellano method and are of type ‘sss’, which corresponds to the small-sample correction used by Stata.

26

4 Results

4.1 Regression analysis Table 4 displays the estimation results of four model specifications in which the

dependent variable is the Gini coefficient. The first column does not include an

interaction term, so trade is assumed to have the same marginal effects on inequality

across all countries. In the second and third columns trade is allowed to interact with

income levels and the share of population with secondary education, providing a test of

the simple version of the Stolper-Samuelson theorem described in Chapter 1.

In columns 1 and 2, the variables of interest are not statistically significant at any

reasonable level of significance. In column 3, however, both trade and the interaction

term become significant at the 1 percent significance level suggesting that the effect of

trade on inequality is highly dependent on education levels. Interestingly, the signs of the

coefficients are in stark contrast with those predicted by the Stolper-Samuelson theorem.

Instead of a negative trade coefficient and a positive interaction coefficient, with the net

effect being positive in countries abundant in skilled-labor and negative in countries

abundant in unskilled-labor, the results presented here indicate the opposite. That is, trade

has less of a positive effect on inequality as the share of its population with secondary

education increases until at some point the net effect is fully reversed so that more trade

is expected to lower inequality. This turning point occurs when approximately 55 percent

of the population have secondary education, such as in France and the UK in 2008.

The results carry over to the extended specification in column 4 where additional

control variables are included in the model. These are the stock of inward FDI as a share

of GDP, democracy, and the share of industry in GDP. The coefficients for trade and the

interaction term are similar in size and become even more significant than in column 3,

now at the 0.1 percent significance level. The additional control variables increase the fit

of the model as measured by R2 but none of them is statistically significant. The main

specification in column 3 will now be studied in greater detail by estimating the model

separately for each quintile of the income distribution.27

27 Estimating the extended specification for each quintile of the income distribution yields very similar results, so focusing on the main specification should be good enough.

27

Table 4. Fixed effects regression results: Gini.

(1) (2) (3) (4)

Gini Gini Gini Gini

Trade 0.0191 0.0982 0.0758** 0.0816***

(1.22) (1.34) (3.25) (3.40)

Log of mean income 3.87* 4.57* 3.67* 4.58**

(2.58) (2.65) (2.48) (2.82)

Dependency ratio 0.223 0.254 0.284' 0.159

(1.33) (1.45) (1.69) (0.88)

Share secondary 0.0508 0.0591 0.187** 0.0816***

(1.29) (1.48) (2.91) (2.65)

Trade * log of mean income -0.0100

(1.16) Trade * share secondary -0.00139** -0.00164***

(3.18) (3.42)

Foreign direct investment -0.00145

(0.09)

Democracy -0.0528

(0.39)

Share industry -0.0481

(0.55)

Number of observations 440 440 440 374

Number of countries 112 112 112 101

R2 (within) 0.0667 0.0698 0.0934 0.115 Significance levels: *** 0.1 percent, ** 1 percent, * 5 percent, ' 10 percent Note: The dependent variable is the Gini coefficient. Numbers in parentheses are t-statistics, computed from robust standard errors (see footnote 26). The independent variables are trade as a share of GDP, the log of mean income, the dependency ratio, the share of population over 15 years old with secondary education, the stock of (inward) foreign direct invest as a share of GDP, a measure of democracy from -10 to 10, and the share of industry in GDP.

The results are presented in Table 5 where the dependent variable is the income of

each quintile as a share of mean income. A clear pattern emerges from the table. For

quintiles one to three (the bottom 60 percent of the income distribution) the coefficient

for trade is negative and the interaction coefficient is positive, while for the top quintile

the coefficients have the opposite signs. There is no evidence that trade affects the

income share of the fourth quintile in a systematic way. Note that the results are

particularly stark for the bottom 20 percent and the top 20 percent of the income

distribution. Again, the turning point occurs when the share of population with secondary

education is approximately 55 percent.

28

Table 5. Fixed effects regression results: Quintile shares.

(1) (2) (3) (4) (5)

Q1 Q2 Q3 Q4 Q5

Trade -0.123*** -0.0931** -0.0611' 0.00269 0.278**

(4.20) (3.05) (1.95) (0.10) (2.70)

Log of mean income -3.19' -5.42** -5.10** -3.10* 16.7**

(1.80) (2.80) (2.74) (2.02) (2.59)

Dependency ratio -0.339' -0.383' -0.301 -0.253 1.20

(1.65) (1.74) (1.32) (1.42) (1.61)

Share secondary -0.303*** -0.207* -0.142' -0.0182 0.697*

(3.41) (2.52) (1.82) (0.27) (2.57)

Trade * share secondary 0.00216*** 0.00171** 0.00111* 0.0000271 -0.00520**

(3.34) (3.08) (2.17) (0.01) (2.88)

Number of observations 440 441 441 441 441

Number of countries 112 112 112 112 112

R2 (within) 0.0976 0.0991 0.0683 0.0286 0.0827 Significance levels: *** 0.1 percent, ** 1 percent, * 5 percent, ' 10 percent Note: The dependent variables are the incomes of each quintile of the income distribution, expressed as a share of mean income. Numbers in parentheses are t-statistics, computed from robust standard errors (see footnote 26). The independent variables are trade as a share of GDP, the log of mean income, the dependency ratio, and the share of population over 15 years old with secondary education.

The country fixed effects are jointly significant for all specifications, which indicates

that estimating pooled regression models would not be appropriate, and this is also true

for the period effects. In addition, the Hausman test is always rejected, supporting the

choice of fixed effects over random effects.

The R2 reported in the tables is the within-R2 which tells how much of the variation of

the independent variables is explained by the model after the fixed effects have been

accounted for, or equivalently, how much of the variation within countries is explained

by the model. As a result, the values of the within-R2 may seem low compared to what is

typical without fixed effects, but this is only because when they have been accounted for

there is much less variation left to explain. In general, the values of the within-R2 are

very acceptable but in the case of the fourth quintile it is quite low.

4.2 Robustness The robustness of the findings is studied by performing a series of robustness tests.

Potential problems that could threaten the validity of the results fall into roughly three

categories. First of all, there may be problems with endogeneity at a given point in time;

29

if any of the independent variables at time t is correlated with the idiosyncratic error term

at the same t, the model will suffer from omitted variable bias. Second, intertemporal

endogeneity may blemish the results; if any of the independent variables at time t is

correlated with the idiosyncratic error term at any point in time, say at time s, the

assumption of strict exogeneity of the independent variables fails and the fixed effects

estimator will be inconsistent. The third category of potential problems has to do with

sample; if the results are highly dependent on the inclusion of a subset of the sample, for

example one particular region or a few countries with extreme within variation in one or

more variables, they will not be of much value. Table 6 summarizes some of the

robustness tests performed, reporting the coefficients and t-values of the variables of

interest.

As discussed earlier, the only way to deal with the omitted variable bias is to include

more relevant independent variables in the model.28 This was already done in the

extended specification in Table 4 but as a further check, the first row of Table 6 presents

an extension of the extended specification that adds three more control variables. These

are domestic credit to the private sector as a share of GDP to account for financial depth,

inflation as a proxy for macroeconomic stability, and government spending as a share of

GDP to control for government redistribution. The data for all three variables are from

the World Bank (2017). It turns out that the coefficients for trade and the interaction term

are somewhat lower for this extended extended specification than the main specification,

but they are still statistically significant, now at the 10 percent level. (However, the

turning point at which increased openness is expected to lower inequality is still the

same, when share of population with secondary education is around 55 percent.)

Problems that fall into the second category, violation of strict exogeneity, could be

just as serious as the omitted variable bias. It is absolutely possible that trade not only

influences inequality but that the reverse is also true, i.e. inequality influences trade. For

example if we assume trade disproportionately benefits the rich and that greater

inequality increases the political power of the rich, then one could hypothesize that

increased inequality may lead to increased openness to trade, resulting in biased

estimation. A complete treatment of such problems would demand the use of a system

generalized method of moments estimator (system GMM), which is consistent in the

28 The fixed effects take care of relevant variables that do not change over time, so the only concern here is with omitted variables that do change over time.

30

presence of endogeneity, but this is beyond the scope of the paper. Instead a partial test

for strict exogeneity is implemented, as suggested by Wooldridge (2001), where future

values of possibly endogenous independent variables (at time t + 1) are included.29 This

is done in row two of Table 6 and in row three where lagged values (at time t - 1) are

included instead of future values.30 Fortunately, in both cases the future or lagged

independent variables are far from being statistically significant and do not affect the

significance of the contemporaneous variables, which is reassuring.

Next, the main specification is estimated excluding each of the five regions defined

above, one at a time. The coefficients for trade and the interaction term are significant

throughout, but it is interesting to note that the results are stronger when OECD countries

are not included in the sample, indicating that the model is perhaps better at explaining

the relationship between trade and inequality in low- and middle-income countries. The

findings are neither dependent on the inclusion of small countries nor the inclusion of

city-states, as can be seen from rows nine and ten in Table 6.

To check if the results are dominated by outliers, countries with extreme variation in

one or more of the variables of interest are dropped from the sample,31 without affecting

the results much. Finally, the model is estimated without time fixed effects to see if the

coefficients are still significant when global cycles that affect inequality in all countries

are not absorbed by the time effects. Robustness tests performed but not reported in

Table 6 include using a different measure of openness,32 excluding observations for each

benchmark year and excluding observations from each individual country in the sample.

29 A similar test is performed by Gourdon et al. (2008), but they use the future variable (at time t + 1) instead of the variable at time t, rather than including both variables in the regression. 30 A similar test is performed by Bergh and Nilsson (2010), but they use the future variable (at time t – 1) instead of the variable at time t, rather than including both variables in the regression. 31 The method used to determine if the variation of a variable is ‘extreme’ is as follows: The variable is demeaned and the standard deviation of the (demeaned) variable is calculated. Countries with (demeaned) observations more than three standard deviations away from the mean of the demeaned variable are defined as having ‘extreme’ variation in that variable. 32 The different measures of openness used were the ‘economic’ and ‘actual economic flows’ subindices of the KOF index of globalization by Dreher (2008).

31

Table 6. Robustness tests.

Observations / Countries

Trade * share secondary Variation Trade

(1) Extended extended specification 357 / 98 0.0479' -0.000872' (1.73) (1.73)

(2) Including future trade and trade * share secondary (at time t + 1) 440 / 112 0.0783* -0.00138' (2.27) (1.90)

(3) Including lagged trade and trade * share secondary (at time t - 1) 422 / 111 0.0716' -0.00128* (1.89) (2.04)

(4) Excluding countries in Africa 336 / 81 0.0671' -0.00132* (1.88) (2.29)

(5) Excluding countries in Asia 374 / 96 0.0725*** -0.00124** (3.42) (2.65)

(6) Excluding countries in Eastern Europe and Central Asia 390 / 97 0.0685** -0.00110* (2.88) (2.58)

(7) Excluding countries in Latin America and the Caribbean 345 / 90 0.0809** -0.00134** (3.02) (3.00)

(8) Excluding OECD countries 305 / 82 0.0897*** -0.00218*** (3.64) (3.85)

(9) Excluding countries with population under 1 million 417 / 103 0.0537** -0.00121** (2.75) (2.66)

(10) Including city-states (Hong Kong and Singapore) 446 / 114 0.0480* -0.000695' (2.16) (1.83)

(11) Excluding countries with extreme variation in inequality 421 / 107 0.0527** -0.00107** (3.03) (2.81)

(12) Excluding countries with extreme variation in trade 427 / 109 0.0809** -0.00141** (3.22) (2.78)

(13) Excluding countries with extreme variation in share secondary 422 / 108 0.0850*** -0.00174*** (3.565) (3.84)

(14) Excl. countries with extreme var. in inequality, trade or share secondary 390 / 100 0.0632** -0.00145** (3.32) (2.96)

(15) No time fixed effects 440 / 112 0.0723** -0.00151***

(3.11) (3.426) Significance levels: *** 0.1 percent, ** 1 percent, * 5 percent, ' 10 percent Note: All regressions estimate the main specification with the Gini coefficient as the dependent variable. Rows 1 to 3 address endogeneity concerns, rows 4 to 14 address sensitivity to sample changes, and row 15 estimates the main specification without time fixed effects. Except for row 15, all regressions include country and time fixed effects. Numbers in parentheses are t-statistics, computed from robust standard errors (see footnote 26). The extended extended specification in row 1 adds three more variables to the extended specification presented above (see column 4 of Table 4). These are five-year averages of domestic credit to the private sector as a share of GDP, inflation, and government spending as a share of GDP. The source for the three additional variables is the World Bank (2017).

32

5 Discussion

The evidence presented in this paper indicates that the impact of openness on inequality

is negatively dependent on education levels. In other words, openness seems to increase

inequality except in countries where a high share of the population has secondary

education. This finding is statistically significant and appears to be fairly robust, but so

far nothing has been said about its economic significance. How large is the estimated

impact of openness on inequality? Is it large enough to matter?

When interpreting the coefficients in Table 4 and Table 5 it is important to keep in

mind that the estimated impact of openness on inequality can only be stated conditional

on the share of population with secondary education. The trade coefficient conveys little

information alone because it only indicates how trade is expected to affect inequality

conditional on the share of population with secondary being equal to zero, which is not

an interesting case. To get a sense of magnitudes it is necessary to plug in different

values of the share of population with secondary education and calculate the ceteris

paribus effects of a given change in trade openness.

Let us consider an increase in the trade to GDP ratio of 30 percentage points, which is

typical for the period between 1988 and 2008, in three countries with very different

education levels: Uganda, Thailand, and Canada. The share of population with secondary

education was 7 percent in Uganda in 2008, 24 percent in Thailand, and 72 percent in

Canada. Using the estimates from the main specification in column 3 of Table 4, a 30

percent increase in trade to GDP translates into an increase of 2.0 and 1.3 Gini points in

Uganda and Thailand, respectively, and a decrease of 0.7 Gini points in Canada. These

are not dramatic changes in the Gini coefficient but they are still far from negligible.

Table 7 shows the same calculation based by region based on their average levels of the

share of population with secondary education in 1993 and 2008.

However, it is perhaps more informative to calculate the estimated relative effect on

the income of a particular income group, for example the bottom quintile of the income

distribution. Based on the estimates from column 1 of Table 5, the effect of an increase in

the trade to GDP ratio of 30 percentage points will be a decrease of 10.8 percent and 7.1

33

Table 7. Interpretation: Gini.

Region 1993 2008 Africa and the Middle East 1.7 1.6 Asia 1.6 1.0 Eastern Europe and Central Asia 0.3 -0.3 Latin America and the Caribbean 1.4 0.9 OECD 0.5 -0.1 World 1.1 0.6

Note: The estimated ceteris paribus effect on the Gini coefficient following a 30 percentage points increase in trade to GDP, which is typical for the period between 1988 and 2008. The estimates are calculated for the average shares of population with secondary education in each region in 1993 and 2008. The year 1993 is shown instead of 1988 due to better data coverage. Source: Author’s calculations based on the fixed effects regression results reported in column 3 of Table 4. Education data are from Barro and Lee (2013) and population data from the World Bank (2017).

percent in the income of the bottom quintiles in Uganda and Thailand, respectively, and

an increase of 3.3 percent in Canada.33 It is important to emphasize that these are ceteris

paribus effects so it is assumed that increased openness only has distributional

consequences. But this calculation also tells us approximately how much distribution-

neutral growth would be needed in order to offset the distributional effects of increased

openness, i.e. keep the income of the bottom quintile unchanged. For example, the

poorest 20 percent in Uganda would need 10.8 percent growth to compensate for the

negative distributional impact. This interpretation presents a more intuitive

comprehension of the results and better reveals the economic significance of the

estimates. Finally, Figure 4 plots the estimated ceteris paribus effect of a 10 percentage

points increase in trade to GDP, conditional on the share of population with secondary

education. The estimates are shown both for the Gini coefficient and the income of the

bottom quintile as a share of mean income. The estimated effect on the Gini coefficient is

a decreasing function of the share of population with secondary education, ranging from

an increase of 0.75 Gini points to a decrease of -0.63 Gini points, while in terms of the

income share of the bottom quintile, the estimated effect is an increasing function of the

share of population with secondary education that ranges from a decrease of -1.2 percent

Table 8 implements this calculation for each region based on the average levels of the

share of population with secondary education and the income share of the bottom

quintiles in 1993 and 2008.

33 The income of the bottom quintile in each country is around 30 percent of mean income, which is close to the sample average.

34

Finally, Figure 4 plots the estimated ceteris paribus effect of a 10 percentage points

increase in trade to GDP, conditional on the share of population with secondary

education. The estimates are shown both for the Gini coefficient and the income of the

bottom quintile as a share of mean income. The estimated effect on the Gini coefficient is

a decreasing function of the share of population with secondary education, ranging from

an increase of 0.75 Gini points to a decrease of -0.63 Gini points, while in terms of the

income share of the bottom quintile, the estimated effect is an increasing function of the

share of population with secondary education that ranges from a decrease of -1.2 percent

Table 8. Interpretation: Income of bottom quintile.

Region 1993 2008 Africa and the Middle East 9.5 8.7 Asia 6.9 4.9 Eastern Europe and Central Asia 2.0 -1.0 Latin America and the Caribbean 12.1 8.5 OECD 2.6 -0.2 World 6.2 3.3

Note: The distribution-neutral economic growth needed to keep the income of the bottom quintile of the income distribution unchanged following a 30 percentage points increase in trade to GDP. For example, people in the bottom quintile of the average African country in 2008 would have needed a distribution-neutral growth of approximately 8.7 percent following a 30 percentage points increase in trade to GDP to be fully compensated for its estimated negative impact on their income. The estimates are calculated for the average shares of population with secondary education in each region in 1993 and 2008. Source: Author’s calculations based on the fixed effects regression results reported in column 1 of Table 5. Education data are from Barro and Lee (2013), income of bottom quintile from Lakner and Milanovic (2016), and population data from the World Bank (2017).

35

Figure 4. The estimated effect of increased openness conditional on education.

Note: The estimated effect of a 10 percentage points increase in trade to GDP, conditional on the share of population with secondary education. The turning point, where increased openness is expected to lower the Gini coefficient and raise the income share of the bottom quintile, occurs when the share of population with secondary education is approximately 55 percent, such as in France and the UK in 2008. Source: Author’s calculations based on the fixed effects regression results reported in column 3 of Table 4 and column 1 of Table 5.

of mean income to an increase of 0.9 percent of mean income. Note, however, that since

only a few countries have a share of population with secondary education high enough to

be on the right side of the turning point, the estimates indicate that in most countries

increased openness to trade can be expected to raise income inequality. The income of

the bottom quintile is on average around 30 percent of mean income, and in many

countries less than 20 percent, so even a modest increase in trade to GDP of 10

percentage points can have a substantial negative effect on the income of this group in

countries where the share of population with secondary education is low. However, for

countries where the share is around 50 to 60 percent, as is the case in many Western

countries, the estimated effect of increased openness is close to zero. This suggests that

the observed increase in inequality in rich countries over the last decades is driven by

other forces than trade.

Now, let us have a quick look at the other explanatory variables. The log of mean

income is significantly pro-inequality in all specifications, suggesting that periods of

36

rapid economic growth disproportionately benefit the rich.34 Since the variable is

expressed in logs, the coefficient divided by 100 indicates the effect of a 1 percent

increase in mean income. This converts into an increase of around 0.4 Gini points

corresponding to 10 percent economic growth. The dependency ratio is also pro-

inequality but only statistically significant for quintiles 1 and 2. Therefore, there is some

evidence that a larger share of people under 15 or over 65 goes hand in hand with

increased inequality.

The share of population with secondary education is statistically significant on its own

in most specifications, but because of the interaction term it has to be interpreted

conditionally on the ratio of trade to GDP. A higher share of population with secondary

education is associated with higher inequality, but less so in economies that are more

open. For example, an increase in the share of 15 percentage points, typical for the period

1988 to 2008, results in a 1.5 points rise in the Gini coefficient conditional on the trade to

GDP ratio being around 70 percent, which is the average across all time periods. This,

coupled with the effects of openness on inequality, suggests that higher education levels

may cause a one-off increase in inequality as more people receive a skill premium.

However, it also suggests that more widespread education may provide protection in the

future against inequality due to openness.

As mentioned above, the results of this paper are in direct conflict with the Stolper-

Samuelson theorem. How can this be explained? It is possible that the Stolper-Samuelson

effects are simply dominated by other countervailing forces which do not influence

inequality through demand for different factors based on their relative abundance. One

potential explanation is that more widespread education increases the flexibility of the

workforce, allowing a larger share of the population to take advantage of the

opportunities created by international trade, but also at the same time lessening the

adjustment costs for those adversely affected by increased openness.

Whatever the cause, the estimates reported here suggest that it is unlikely that the

observed rise in within-country inequality in the Western world is driven by increased

international trade. This is not to imply, however, that there will not be winners and

losers from trade in well-educated countries, but the evidence presented here does not

34 This result disappears when the square of mean income is included in the model, based on the Kuznets hypothesis (Kuznets, 1955). However, since mean income is only included in the analysis as a control variable, this is not of concern. When the square of mean income is included, both coefficients are statistically insignificant, indicating that there is no evidence of a Kuznets relationship in the data.

37

suggest that trade increases inequality in a systematic way in countries where education

is widely distributed. On the other hand, in countries where a low share of the population

has secondary education, globalization indeed appears to be one of the drivers behind

increased within-country inequality. Even though the estimates are quite small in terms

of the Gini coefficient, the findings show that in countries where the share of the

population with secondary education is low to medium, the bottom 60 percent of the

income distribution is negatively affected by trade, and in particular the effects can be

substantial at the very bottom of the income distribution.

38

39

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Appendix 1 Table A1. Summary statistics.

Observations / Countries

Variable Explanation Mean St. dev. Min Max Source Gini Gini of net incomes, calculated from mean of each income decile 549 / 159 38.4 9.59 19.1 70.7 LM (2016) SQ1 Income share of quintile 1 (% of mean income) 549 / 159 32.1 11.4 5.7 59.8 LM (2016) SQ2 Income share of quintile 2 (% of mean income) 550 / 159 53.8 12.6 14.2 79.2 LM (2016) SQ3 Income share of quintile 3 (% of mean income) 550 / 159 75.2 11.6 27.3 103.2 LM (2016) SQ4 Income share of quintile 4 (% of mean income) 550 / 159 106.7 8.4 59.9 130.5 LM (2016) SQ5 Income share of quintile 5 (% of mean income) 550 / 159 232.1 41.4 156.5 391.4 LM (2016) Trade Exports plus imports (% of GDP) 717 / 153 78.0 48.3 12.8 419.5 WB (2017) Mean income Mean net income of total population 550 / 159 4638.6 5154.8 179 24273 LM (2016) Log of mean income Log of mean net income of total population 550 / 159 7.82 1.16 5.19 10.1 LM (2016) Dependency ratio Share of population under 15 and over 65 783 / 157 40.5 6.91 25.8 54.1 WB (2017) Share secondary Share of population with secondary education 645 / 129 29.8 21.2 0.319 95.1 BL (2013) Foreign direct investment Stock of inward foreign direct investment (% of GDP) 715 / 153 28.3 69.6 0.00964 1195.6 UNCTAD (2016) Democracy From -10 (strongly autocratic) to +10 (strongly democratic) 718 / 149 2.62 6.58 -10 10 CSP (2016) Share industry Size of industrial sector (% of GDP) 642 / 151 29.5 10.3 4.6 72.8 WB (2017)

Note: The following variables are calculated as five-year averages: Trade, dependency ratio, foreign direct investment, democracy, and share industry. LM is Lakner and Milanovic (2016), WB (2017) refers to the World Development Indicators by the World Bank, BL is Barro and Lee (2013), UNCTAD (2016) refers to a dataset on foreign direct investment by the United Nations Conference on Trade and Development, and CSP (2016) refers to the Polity IV database by the Center for Systemic Peace.

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Appendix 2

Table A2. Number of observations by country and region.

Note: The numbers refer to data used in estimating the main specification. In total, 440 observations are used from 112 countries, which gives an average of 3.9 observations per country.

Africa Asia Eastern Europe and

Central Asia Latin America and

the Caribbean OECD Algeria 2 Bangladesh 5 Albania 3 Argentina 5 Australia 4 Burundi 3 Cambodia 4 Armenia 3 Belize 2 Austria 5 Cameroon 3 China 5 Bulgaria 5 Bolivia 5 Belgium 5 Central Afr. Rep. 3 Fiji 2 Croatia 3 Brazil 5 Canada 5 Cote d'Ivoire 5 India 5 Estonia 2 Chile 5 Czech Republic 4 Cyprus 3 Indonesia 5 Kazakhstan 3 Colombia 5 Denmark 5 DR Congo 2 Laos 4 Kyrgyzstan 4 Costa Rica 5 Finland 5 Egypt 5 Malaysia 5 Latvia 3 Dominican Republic 5 France 5 The Gambia 3 Maldives 2 Lithuania 2 Ecuador 5 Germany 5 Ghana 4 Mongolia 2 Romania 3 El Salvador 5 Greece 5 Iran 4 Nepal 3 Russia 4 Guatemala 4 Hungary 4 Israel 5 Pakistan 5 Slovenia 3 Guyana 2 Iceland 2 Jordan 5 Philippines 5 Tajikistan 3 Honduras 5 Ireland 5 Kenya 3 Sri Lanka 5 Ukraine 4 Jamaica 4 Italy 5 Malawi 3 Thailand 5 Total: 45 Nicaragua 4 Japan 5 Mali 3 Vietnam 4 Panama 4 Luxembourg 4 Mauritania 5 Total: 66 Paraguay 4 Mexico 5 Morocco 4 Peru 4 Netherlands 5 Mozambique 3 Trinidad & Tobago 2 New Zealand 3 Namibia 2 Uruguay 5 Norway 4 Niger 2 Venezuela 5 Poland 4 Rwanda 3 Total: 90 Portugal 5 Senegal 2 Slovakia 4 South Africa 4 Spain 5 Swaziland 3 South Korea 5 Tanzania 3 Sweden 4 Tunisia 4 Switzerland 3 Uganda 5 Turkey 5 Yemen 3 United Kingdom 5 Zambia 3 United States 5 Zimbabwe 2 Total: 135

Total: 104

45