Roles of income and equality in poverty reduction: recent cross-country evidence

Journal of International Development

J. Int. Dev. 19, 919–926 (2007)

Published online 26 April 2007 in Wiley InterScience

(www.interscience.wiley.com) DOI: 10.1002/jid.1348

ROLES OF INCOME AND EQUALITY INPOVERTY REDUCTION: RECENTCROSS-COUNTRY EVIDENCE

RATI RAM*

Economics Department, Illinois State University, Normal, Illinois, USA

Abstract: This paper uses a reasonable model and recent cross-country data to study

empirically the effects of income and equality on poverty. Three main points are noted.

First, the estimates show highly significant roles of income and equality in poverty reduction,

and the effects of increased income and lower inequality are both substantial. Second, the

elasticity of poverty with respect to inequality is substantially larger than that relative to

income. Third, the estimates suggest a ‘growth elasticity’ of poverty that is much smaller than

the values used in almost every study. Therefore, most of the well-known and influential recent

research seems to have overstated the impact of income growth on poverty alleviation by de-

emphasising the role of inequality, to which poverty is highly responsive, and by using an

income (growth) elasticity of poverty that is much larger than what seems reasonable.

Copyright # 2007 John Wiley & Sons, Ltd.

Keywords: poverty; gini; income; elasticity

1 INTRODUCTION

High incidence of poverty, particularly in the developing world, has been viewed with great

concern recently. For example, the main theme of World Development Report 2000/2001

was ‘attacking poverty’. Similarly, noting that 1.2 billion people lived on less than one

(PPP) dollar per day and 2.8 billion on less than two dollars per day, United Nation

Development Programme (2001, pp. 9, 21) recalled the U.N. Millennium Development

Goals (MDG) in which a top target was to halve the proportion of world’s population living

below one (PPP) dollar per day by the year 2015. In fact, besides many other recent studies

*Correspondence to: Rati Ram, Economics Department, Illinois State University, Normal, IL 61790-4200, USA.E-mail: [email protected]

Copyright # 2007 John Wiley & Sons, Ltd.

920 R. Ram

of poverty, almost every issue of World Development Report and Human Development

Report since 2001 has discussed the MDG poverty target.

While numerous correlates, causes and consequences of poverty have been studied,

income (or its growth rate) seems to have receivedmost attention, and the role of inequality

has typically been considered in an indirect or secondary manner. For instance,World Bank

(2001, pp. 46–48) stated, ‘Not surprising, the richer the country...the smaller on average the

fraction living on less than $1 a day’. It also emphasised the role of income growth by

noting, ‘...growth in the 1980s and 1990s was a powerful force for reducing income

poverty’ and (the evidence) ‘highlights the importance of economic growth...for moving

people out of poverty’. Dollar and Kraay (2002, p. 195) summarised their research by

stating ‘This evidence emphasizes the importance of economic growth for poverty

reduction’. Even United Nation Development Programme (2003, p. 67) focused almost

entirely on growth, and indicated rates of increase in per capita income required for

meeting the poverty target. Agenor (2004) embedded the analysis of poverty reduction in

the context of macroeconomic adjustment, and inequality was just one of the many

variables considered. Adams (2004) included both inequality and income growth in his

poverty regressions, but focused more on ‘growth elasticity of poverty’. Dollar (2005)

considered the impact of globalisation on poverty, and focused mainly on income growth.

Agenor (2005) reviewed the channels through which macroeconomic policies affect the

poor with an emphasis on the role of labour markets, and the possibility of income

redistribution was considered only briefly. Jalilian and Kirkpatrick (2005) stressed the

poverty-alleviating role of financial development through economic growth. World Bank

(2005) provided an extensive discussion of the role of equity in economic development.

However, the focus is on explaining how equity can affect development in many ways, and

the consideration of poverty is largely indirect. Bourguignon’s (2003) work is one of the

few recent studies that discuss the impact of income and inequality on poverty. Although,

like most of the literature, he focuses on the growth elasticity of poverty, a considerable

segment of the paper discusses how change in income inequality affects poverty.

The main objective of this study is to estimate, from sizable recent cross-country data, a

reasonable model of poverty so as to judge empirically the role of equality as well as

income in poverty alleviation, and to make a direct comparison of their impacts. As noted

in the preceding paragraph, such a comparison is lacking in the literature, which has

generally tended to emphasise the role of income growth. In the few studies that considered

both income and equality, the empirical context is one country or a few countries or a series

of ‘growth spells’.1 There is hardly any empirical research based on a sizable cross-country

sample that makes a direct comparison of roles of income and equality in reducing poverty.

2 MODEL, DATA AND THE MAIN RESULTS

A parsimonious model for poverty may be written as

LPOVi ¼ a0 þ a1ðLPCYÞi þ a2ðLINEQÞi þ a3ðSOCÞi þ ui (1)

1Bourguignon (2003, p. 6) mentions several studies that carefully distinguish (decompose) the effects on povertyreduction of growth and distributional changes. However, he notes that these studies are generally restricted to aspecific country or a limited number of countries or regions.

Copyright # 2007 John Wiley & Sons, Ltd. J. Int. Dev. 19, 919–926 (2007)

DOI: 10.1002/jid

Poverty-Reducing Roles of Income and Equality 921

where LPOVi is logarithm of a measure of poverty in country i, LPCY is logarithm of real

GNP per capita for the country, LINEQ is the logarithm of an index of inequality, SOC is a

simple dichotomous variable that takes the value one for present or former socialist

countries and zero for others and u is a well-behaved stochastic error term. Bourguignon

(2003) pointed out that mean and distribution of income are the only two theoretical

determinants of ‘headcount’ poverty, which is defined as the proportion of population that

lives below a predefined income level. While the role of (mean) income is perhaps obvious,

income distribution (inequality) can, as Bourguignon (2003, pp. 9–10, 21) explained, affect

poverty in three different ways. First, a redistribution of income that lowers inequality (for

a constant mean) reduces poverty directly through what he called the ‘distribution effect’

because of a reduction in the proportion of population that lies below the poverty threshold.

Second, as recent research suggests, lower inequality may increase the rate of growth of

income which also contributes to poverty reduction. Third, due to variability in the effect

of inequality on poverty, the effect of an increase in income is likely to be larger when

inequality is lower. Although Bourguignon (2003) has argued that elasticities of

(headcount) poverty with respect to income and inequality are not constant, the log-log

specification of Equation (1) seems to be a reasonable approximation and has the merit of

showing the elasticities directly. A similar specification was used by Adams (2004) and

some other scholars.

In addition to income and inequality, which are the primary variables, an intercept

dummy has been included to let the constant term for present or former socialist countries

differ from that for the rest of the sample. This reflects the possibility that these countries

tend to be more egalitarian, and may be marked with lower poverty at given income and

inequality levels. The expected sign on this term is negative.

The primary proxy for poverty (POVERTY1) is one well-known measure of headcount

poverty defined as the percentage of population that lives below the poverty line of one

international (PPP) dollar per day in 1985 prices. TheMDG poverty target is stated in terms

of this measure, which has been widely used. However, for a sensitivity check, the model is

also estimated from the more ‘stringent’ headcount poverty measure (POVERTY2) which

is defined as the proportion of population that lives below two international (PPP) dollars

per day. Information of both poverty indexes is taken from World Bank (2002, pp.

234–235).

Income is proxied by GNP per capita in international dollars. The variable values are for

the year 1994 which harmonises most with the data on poverty and inequality. The

information is taken from World Bank (1995, pp. 18–19).

Income inequality is represented by Gini index which is based on the entire distribution

of income and is used widely as an inequality measure. Data on Gini are also taken from

World Bank (2002, pp. 234–235). However, since the numbers for some countries are

based on income and for other on consumption, following Deininger and Squire (1996,

p. 582), consumption-based Gini has been adjusted upward by 6.6 (on 0–100 scale) to

enhance cross-country comparability of the data.

Every country for which information on the variables could be found in the sources has

been included. The resulting sample is 61. As might be expected, none of the countries

belongs to the ‘developed’ group as usually defined. Appendix 1 lists the countries, and

identifies those in the SOC group. Table 1 provides some descriptive statistics for the

sample.

Equation (1) is estimated by the ordinary least-squares (OLS) procedure since no

significant feedback from poverty to income or inequality seems likely. However, in view


DOI: 10.1002/jid

Table 1. Descriptive sample statistics

Mean(unweighted)

SD(unweighted)

Minimumvalue

Maximumvalue

N

PCY (PPP $) 3388 2328 410 10540 61

GINI (%) 47.19 10.12 19.50 69.50 61

POVERTY1 (%) 20.86 21.43 1.50 72.80 61

POVERTY2 (%) 44.60 29.07 2.00 90.80 61

LPCY 7.86 0.79 6.02 9.26 61

LGINI 3.83 0.23 2.97 4.24 61

LPOVERTY1 2.34 1.31 0.41 4.29 61

LPOVERTY2 3.41 1.09 0.69 4.51 61

Note: PCY is real GNP per capita for 1994 in international dollars; GINI denotes Gini index on a 0–100 scale;POVERTY1 is the percentage of population below the poverty line by the criterion of one PPP dollar income (at1985 prices) per day; and POVERTY2 is the percentage of population below the poverty line by $2 per-daycriterion. LPCY, LGINI, LPOVERTY1 and LPOVERTY2 are logarithms of GDP per capita, GINI, POVERTY1and POVERTY2, respectively. Appendix 2 contains additional information about the variables and the datasources.

922 R. Ram

of the diversity of the cross-country sample, White’s (1980) heteroscedasticity-consistent

standard errors are used in every case.

Table 2 reports the main estimates and indicates two major points. First, high statistical

significance of the income and the inequality terms are evident. For each poverty measure,

both income and Gini show statistical significance at least at 5 per cent level. The effect of

each variable is in the expected direction. Increase in income reduces poverty as the

negative sign on the parameters shows, and increase in Gini (inequality) increases poverty.

Therefore, both income and inequality have important roles in poverty reduction.

Second, perhaps more important, poverty is substantially more responsive to changes in

inequality than to changes in income. The elasticity of poverty with respect to Gini is

almost twice as large as that with respect to income.2 In other words, not merely does

inequality have an important direct role in affecting poverty, but the role seems

considerably more important than that of income. This is the main outcome of the exercise

undertaken in this paper.

Table 2. Estimated effects of income and inequality on poverty: Based on Equation (1) of the text

POVERTY1 model ($1 criterion) POVERTY2 model ($2 criterion)

Constant 2.774 (1.11) 3.904y (1.94)

LPCY �0.975* (�8.39) �0.675* (�5.72)

LGINI 1.918* (3.47) 1.295* (2.74)

SOC �0.525 (�1.61) �0.670y (�1.68)

Adj. R2 0.67 0.54

N 61 61

Note: Table 1 defines the main variables. SOC is a 0–1 dummy that takes the value 1 for present or former socialistcountries, and 0 for others. Appendix 2 provides additional information about the variables and the data sources.The numbers in parentheses are t-statistics based on White-consistent standard errors. An asterisk indicatessignificance at least at the 5% level, and y denotes significance at the 10% level.

2This scenario is consistent with the observations by Squire (1993, p. 378) and Bruno et al. (1998, p. 128).


DOI: 10.1002/jid


Three secondary points may also be noted from Table 2. First, elasticities with respect to

both income and Gini are larger for $1 poverty ratio than for $2 poverty measure. However,

since the incidence of $2 poverty is much higher, as Table 1 shows, a given percentage

change in income or Gini has a slightly larger (absolute) effect on $2 poverty than on $1

index. For example, at the sample mean, 1 per cent reduction in Gini lowers the $2 index by

0.58 (1.295% of 44.6) and $1 index by 0.40 (1.918% of 20.86). Second, the socialist

dummy carries the expected negative sign, but its statistical significance is somewhat

mixed. Third, given the parsimonious specification and the diverse cross-country sample,

the fit of the model seems good.

Besides a comparison of the elasticities, it appears useful to get a more detailed picture

by simulating predicted values of POVERTY1 and POVERTY2 for several different levels

of income and inequality on the basis of the regression estimates in Table 2. Table 3

provides a flavour of such simulations. It reports predicted (simulated) values of

POVERTY1 and POVERTY2 when (a) income varies from 1500 to 5000 while Gini and

SOC are held at the sample means, and (b) Gini varies from 60 to 25 while income and SOC

are held at the sample means. These ranges for income and Gini are approximately 50%

below and above the sample means reported in Table 1, and are well within the sample

Table 3. Simulated effects of changes in GNP per capita and Gini on poverty: Based on theestimates in Table 2

Changing GNP per capita holding GINI andSOC at sample means (47.19, 0.213)

Changing GINI holding GNP percapita and SOC at sample means

(3388, 0.213)

A. Model for POVERTY1 (based on $1 per day criterion)

GNP per capita POVERTY1 GINI POVERTY1

1500 46.1 60 33.0

2000 34.9 55 28.0

2500 28.0 50 23.3

3000 23.5 45 19.0

3500 20.2 40 15.2

4000 17.7 35 11.8

4500 15.8 30 8.7

5000 14.3 25 6.2

B. Model for POVERTY2 (based on $2 per day criterion)

GNP per capita POVERTY2 GINI POVERTY2

1500 77.2 60 60.8

2000 63.6 55 54.3

2500 54.7 50 48.0

3000 48.4 45 44.5

3500 43.6 40 41.9

4000 39.8 35 36.0

4500 36.8 30 30.2

5000 34.2 25 19.6

Note: Since the model is in log-log form, the original predicted values are in logarithms of poverty. Thecorresponding ‘raw’ values have been rescaled so that predicted poverty values for the means of GNP per capitaand Gini equal sample means for the two poverty measures. The rescaling is necessitated by the consideration thatlogarithm of the mean does not equal mean of the logarithms.


DOI: 10.1002/jid

924 R. Ram

maxima and minima. Note that the simulations are intended to compare the partial effects

of income and Gini even though changes in income might be associated with variations in

inequality.

Table 3 shows that both inequality and income have quantitatively large effects on

poverty. Relative to POVERTY1 (based on $1 criterion), increasing GNP per capita from

1500 to 5000 (ceteris paribus) reduces the poverty ratio from nearly 46% to 14%, and

reducing Gini from 60 to 25 lowers the poverty ratio from about 33% to 6%. Changes in

income and inequality are indeed large, but so are the consequent changes in poverty.

Similarly, relative to POVERTY2 (based on $2 criterion), increasing GNP per capita from

1500 to 5000 lowers poverty ratio from about 77% to 34%, and reducing Gini from 60 to 25

reduces poverty ratio from about 61% to 20%.

The main point suggested by Table 3 is that while increased income is indeed important

for alleviating poverty, as almost all studies have emphasised, reduction of inequality might

be at least as important. Therefore, a focus on income growth, without an appropriate

attention to inequality, is not likely to be a good approach to poverty alleviation.3 A few

additional points in regard to the simulations in Table 3 may be noted. First, despite the

end-point comparisons mentioned above, one need not compare such large changes. The

table permits comparisons across much smaller variations in income and Gini, and one can

still see that both have large effects. Second, it is not suggested that changes in inequality

are as easy to achieve as increases in income.4 The point is that changes in inequality are

also important and should not be overlooked. Even if there is difficulty in reducing

inequality, one may make an effort to prevent it from increasing, and, to the extent feasible,

try to spread the benefits of growth more widely. Last, the simulations reinforce Table 2 in

providing a vivid illustration of the high sensitivity of poverty to changes in inequality.

It is perhaps useful to note that the importance of a change in inequality is distinct from

the role of ‘initial’ inequality in the effect of income-growth on poverty. The role of initial

inequality in this sense has been indicated in many studies, notably by Ravallion (1997,

2001).

In the context of a discussion of elasticities, Table 2 reveals another important aspect.

The ‘growth elasticity’ of POVERTY1 ($1 criterion) is�0.97, and that for POVERTY2 ($2

criterion) is �0.67. These are much smaller than the (absolute) values suggested in almost

all studies, particularly relative to the poverty ratio based on $1 (PPP) criterion. Some of the

most well-known and influential studies that use the value of �2.0 include those by World

Bank (2001, p. 47), Collier and Dollar (2001, p. 1789) and United Nation Development

Programme (2003, p. 67). Although a sharp contrast from the high numbers used in almost

every study, the elasticities in Table 2 are remarkably similar to the values implied by

recent developing-country aggregates for changes in income and poverty.5 Therefore, most

of the well-known and influential studies are likely to have exaggerated the role of income

growth in poverty reduction both by deemphasising inequality and by suggesting an

unreasonably high responsiveness of poverty to income.

3Despite the lack of a direct comparison of the roles of income and inequality on the basis of sizable recentcross-country data, the importance of lower inequality has been suggested by several researchers. More notableamong these are White (2001) and Dagdeviren et al. (2002).4Limitations of most types of redistributive efforts are well known, and have been noted by several scholars,including Agenor (2005, p. 423), Bourguignon (2003, p. 21) and Fields (2001, pp. 101–102).5For example, World Bank (2006, pp. 8–9) data on actual or projected growth of GDP per capita and poverty rates(for all developing countries) from 1990 to 2015 imply $1-poverty elasticity to be�0.90 to�0.98 and $2-povertyelasticity to be �0.60 to �0.72. These are almost identical with the estimates in Table 2.


DOI: 10.1002/jid


3 CONCLUDING OBSERVATIONS

This paper uses a reasonable model and sizable cross-country data to make a direct

comparison of the effects of lower inequality and increased income on poverty reduction.

Subject to the caveats that are appropriate for such cross-country studies, the estimates and

the simulations indicate three major points. First, both increased income and reduced

inequality have statistically significant and quantitatively large roles in poverty

amelioration. Second, perhaps more important, the elasticity of poverty with respect to

inequality (Gini) is much larger than that with respect to income. Third, estimates of the

poverty elasticity with respect to income (‘growth’) are much smaller than those that have

been used and suggested in almost every study. Therefore, most of the recent research,

which has been well known and highly influential, seems to have exaggerated the impact of

income growth on poverty alleviation by deemphasising inequality, to which poverty is

highly responsive, and through the use of unreasonably high elasticities of poverty-ratio

relative to growth of income.

ACKNOWLEDGEMENTS

Many insightful comments from an anonymous referee are thankfully acknowledged.

Of course, the usual disclaimer applies.

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APPENDIX 1

List of sample countries

Algeria, Azerbaijan�, Bangladesh, Belarus�, Bolivia, Brazil, Burkina Faso, Central African Republic, Chile,

China�, Colombia, Costa Rica, Cote d’Ivoire, Dominican Republic, Ecuador, Egypt, El Salvador, Ethiopia,

Georgia�, Ghana, Guatemala, Honduras, Hungary�, India, Indonesia, Jamaica, Jordan, Kazakhstan�, Kenya,

Korea (South), Lesotho, Madagascar, Mali, Mauritania, Mexico, Mongolia�, Morocco, Mozambique, Nepal,

Niger, Nigeria, Pakistan, Panama, Paraguay, Peru, Poland�, Romania�, Russia�, Senegal, Sierra Leone,

Slovak Republic�, Sri Lanka, Thailand, Tunisia, Turkey, Ukraine�, Uruguay, Uzbekistan�, Venezuela,

Zambia, Zimbabwe

Note: An asterisk (�) indicates a present or former socialist economy.

APPENDIX 2

Variable notation and data sources

POVERTY1 Percentage of population living below the poverty

line of $1 PPP (1985 prices) per day

World Bank (2002, pp. 234–235)

POVERTY2 Percentage of population living below the poverty

line of $2 PPP (1985 prices) per day

World Bank (2002, pp. 234–235)

PCY GNP per capita in international (PPP) dollars for

the year 1994

World Bank (1995, pp. 18–19)

GINI Gini coefficient on 0–100 scale, with an upward

adjustment of 6.6 for consumption-based Gini

World Bank (2002, pp. 234–235)

SOC Dummy variable that takes the value 1 for present

or former socialist (communist) countries and

0 for others


DOI: 10.1002/jid

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Roles of income and equality in poverty reduction: recent cross-country evidence