Inequality aversion and the natural rate of subjective...

Journal of Public Economics 87 (2003) 1061–1090www.elsevier.com/ locate/econbase

Inequality aversion and the natural rate of subjectiveinequality

a b , b*Peter J. Lambert , Daniel L. Millimet , Daniel SlottjeaUniversity of York, York, UK

bDepartment of Economics, Southern Methodist University, Box 750496,Dallas, TX 75275-0496,USA

Received 16 June 2000; received in revised form 21 October 2000; accepted 26 November 2000

Abstract

This paper analyzes inequality aversion across countries and identifies factors whichexplain the empirical heterogeneity observed across these countries. We do this byhypothesizing a ‘natural rate’ of subjective inequality across countries and solving for theexplicit country-specific value of the inequality aversion parameter that is consistent withthe hypothesized natural rate. We present evidence consistent with the existence of a naturalrate of subjective inequality by verifying that countries with low (high) tolerance forinequality have low (high) inequality as measured by the Gini coefficient as well. Finally,we explore the socio-economic factors that are consistent with observed differences ininequality aversion across these countries, finding important effects of female empower-ment, public education expenditures, per capita income, economic growth, and populationsize. 2003 Elsevier Science B.V. All rights reserved.

Keywords: Natural rate of inequality; Inequality; Inequality aversion; Redistribution; Income dis-tribution

JEL classification: D30; O15

1. Introduction

In this paper we make, and then investigate, a hypothesis about incomeinequality. There are multiple measures of inequality, but here we focus on two of

*Corresponding author. Fax:11-214-768-1821.E-mail address: [email protected] (D.L. Millimet).

0047-2727/03/$ – see front matter 2003 Elsevier Science B.V. All rights reserved.PI I : S0047-2727( 00 )00171-7

1062 P.J. Lambert et al. / Journal of Public Economics 87 (2003) 1061–1090

the most common: the Gini coefficient,G, and the Atkinson index,I(e). Forconvenience and to contrast the origins of these two indices, we will henceforthrefer to the Gini coefficient as measuringobjective inequality; the Atkinson index

1as capturingsubjective inequality. The Atkinson index depends on the inequalityaversion parameter,e, of the decision-maker or society, and measures the fractionof total income which could be given up with no loss of social welfare if theremainder were to be distributed equally. Policymakers rely on ‘objective’measures of inequality as well as preferences when evaluating redistributivepolicies.

Many studies have compared the level of objective inequality across countries.Others have compared the level of subjective inequality across countries by fixingthe aversion parameter at various values. In this paper, however, the hypothesis wewish to explore is the following. Suppose countries implicitly arrange their affairsto result in the same degree of subjective inequality. Thus all countries would giveup the same percentage of income to eliminate inequality in a welfare neutralmanner. (Of course, they do not actually do this as the disincentive effects areprohibitive.) According to this framework, there is a proffered ‘natural rate ofinequality’ which gives rise in each country to an associated level of inequalityaversion. It is, then, the extent of inequality aversion — rather than subjectiveinequality — which is country-specific. Under this hypothesis, cross-countrydifferentials in objective inequality — as measured by indices such as the Ginicoefficient — are actually accounted for by differences in the degree of inequalityaversion across countries.

The argument is hardly startling that economists should be prepared to evaluatesubjective inequality in different societies using different, society-specific parame-ters. Income convergence across countries has become an acceptable notion; whynot convergence in terms of subjective inequality? Thus, in this paper, we ask fourfundamental questions. First, what values of country-specific inequality aversionparameterse would account for the differences in objective inequality perceivedby an outsider, and yet still be compatible with the hypothesis that commonpolitical and economic forces are at work so that all societies actually perceive andtolerate the same degree of subjective inequality, call itw, among their citizens?Second, conditional on the country-specific parameter values which rendersubjective inequality equal tow, what empirical factors account for the variation ininequality aversion? Third, given thatw, the assumed ‘natural rate of inequality,’ isunobserved, how do the values of the aversion parameter as well as the predictiveability of the empirical factors examined depend on the choice ofw? Finally, whatcan we infer about the adjustment process across different countries whichmaintains the international equilibrium at a given natural ratew?

Our data come from 96 countries. The factors we examine under our ‘natural

1It is important to note up front that our emphasis on the Atkinson and Gini indices is a choice. Othermeasures could have been examined. See also Footnote (5).

P.J. Lambert et al. / Journal of Public Economics 87 (2003) 1061–1090 1063

rate’ hypothesis as potential determinants of inequality aversion within a particularcountry include political, economic and human development indicators such as thepopulation growth rate, literacy rates, real GDP per capita, percentage of femalesin government positions, female activity as a percentage of male activity, thepercentage of total public expenditure on education, and bureaucratic corruption,among others.

After examining the determinants of inequality aversion, we throw the processinto reverse. What if one of the determining factors of inequality aversion is alteredin a particular country; for example, through a change in political or socialclimate? The resultant change in inequality aversion, while leaving the actualincome distribution unaltered, will throw subjective inequality into disequilibriumin the country concerned, away from the valuew. What adjustment in the observedobjective income distribution would we expect to see, in response to the change inthe political or social environment, in order to return the level of subjectiveinequality to the natural ratew?

The answers to these questions will shed light on the redistributive forces atwork, and throw new light as well on some observations pertaining to differencesin objective inequality across countries with different political and social arrange-ments. We conclude our study with some forward-looking remarks, suggestingfuture work which may reveal some new insight across countries in terms of thedegree of redistribution undertaken through the income tax system. Just as uniqueinequality experiences across countries have motivated the present study, interna-tional differences in the degree of tax progressivity have long captured econom-ists’ attention, without producing convincing explanations.

The paper unfolds as follows. In Section 2 we discuss the relevant existingliterature on inequality aversion. Section 3 introduces Atkinson’s (1970) measureof inequality, describes the hypothesis we wish to test, and presents the data andestimation strategy. In Section 4, we analyze the factors that account fordifferences in inequality aversion across countries and link these differences todifferences in observed objective inequality. Section 5 concludes the study.

2. Previous literature

Atkinson (1998) comments on the divergence of national experiences in the G-7countries with respect to levels of objective inequality. He states that when theinfluences of production, supply, demand and international trade on inequalityhave been taken into account, significant residual discrepancies between countriesremain to be explained. Atkinson suggests that ‘differing social norms’ may be atwork, these factors indicating the degree of socially acceptable inequality in eachplace. For our hypothesis, the variation in social norms is the central andgoverning factor.

Subjective inequality comparisons between countries are typically made byassuming a range of alternative, fixed, illustrative values for the inequality


aversion parametere, and applying each value to all countries. Thus, Atkinson(1970) compares subjective inequality in 12 countries, usinge values of 1.0, 1.5and 2.0, and shows graphically how a change ine affects ordinal rankings. Moore(1996) selectse 50.5, 1.0 and 2.0 for his comparisons. Comparative analysisderiving and using country-specifice values has not, to our knowledge, beenundertaken.

There is a literature which tries to identifye as the elasticity of the marginalsocial utility of income. Leaky bucket experiments can be set up to reveal themeaning of ane value (Atkinson, 1980), and individuals can be questioned aboutsocial choices to reveal thee values implicit in their answers. Amiel et al. (1999)find ane value of 0.25 among large groups of student respondents, a value that iswell outside the range of fixed values usually adopted by distributional analysts.

Another approach has been to derive governmental values ofe from observedpolicies. Gouveia and Strauss (1994) fit the equal sacrifice model to effectivefederal US income taxes between 1979 and 1989, finding impliede values

2between 1.72 and 1.94. Young (1990) does a similar exercise, findinge 5 1.61 for1957,e 5 1.52 for 1967 ande 51.72 for 1977. He reports slightly higher values ofe for the nominal schedules in those years. In addition, Young reports findings forother countries:e 5 1.63 for the West German nominal schedule in 1984,e 5 1.40for Italy in 1987, ande 51.59 for Japan in 1987. He gets a poor fit of the equalsacrifice model for the UK. Stern (1977) founde 5 1.97 for the UK income taxcode in fiscal year 1973/4. We shall return to the equal sacrifice model in theconcluding section of the paper.

Stern’s (1977) paper also contains a comprehensive survey of many otherapproaches to evaluating the elasticities of both private and social marginalutilities of income, reporting values found by a range of authors for differentcountries using various methodologies as high ase 510 and as low ase 5 0.4.Frisch (1959) argued in the context of demand analysis that a world-wide ‘atlas’ ofe’s is desirable, and that we would expect highere’s in poorer countries. Atkinson(1970) remarked that as the general level of income rises, we may become moreconcerned about inequality. We might then expect to observe highere’s in richercountries.

2The equal sacrifice model assumes that income taxes are set such that (for some particular utilityfunction) the loss in individual utility is equated across all income levels. Given a sufficientlydiminishing marginal utility of income, the equal sacrifice tax is progressive (see, e.g., Young, 1994).Ok (1995) and Mitra and Ok (1996, 1997) have studied intensively the question whether a given taxschedulet(y), where y is income, can be rationalized as an equal absolute sacrifice tax for someplausible (increasing and concave) utility functionU(y). They show that tax schedulest(y) satisfyingt9(y). 0 andt0(y).0 for all y are equal absolute sacrifice taxes (Ok, 1995, Theorem 2); that amongincreasing piecewise-linear tax schedules, essentially only the convex ones are equal absolute sacrificetaxes (Mitra and Ok, 1996a, Corollary 3.10); and that more generally some non-convex progressive taxschedules are, and some are not, equal absolute sacrifice taxes (Mitra and Ok, 1996b, Theorem 1 andexamples). Mitra and Ok were able to demonstrate, in particular, that the statutory personal income taxcodes in Turkey between 1981 and 1985, and in the USA between 1988 and 1990, though progressive,were not equal absolute sacrifice taxes.


Finally, researchers have also been interested in the determinants of within-country income redistribution. Persson and Tabellini (1994) posit a model ofinequality and economic growth relying on the median voter hypothesis. Theauthors claim that greater inequality increases the likelihood that the median voterwill support redistribution as he/she will be lower in the income distribution (andthey go on to argue that redistribution is harmful for growth). However, Perotti(1996) rejects this claim empirically, finding no increased propensity to redistri-bute income in countries with greater levels of objective inequality (as measuredby the Gini coefficient). Ravallion and Lokshin (2000) (building on the model of

´Benabou and Ok (2000) and Hirschman and Rothschild’s (1973) theory of a‘tunnel effect’) argue that preferences for redistribution depend not just on themedian voter’s current position in the income distribution, but also his /herexpectations of future income mobility. Milanovic (1999) examines redistributiveeffects in 17 democracies in terms of Lorenz shifts. Consistent with Perotti’sfindings, the author finds that the median voter hypothesis does not explain

3differences in redistribution across these countries. In conclusion, Milanovic callsfor ‘‘a totally different mechanism to explain redistribution.’’ Our final remarkspick up on this point.

3. Model

3.1. Preliminaries

We begin the present analysis by providing a brief sketch of the theoryunderpinning the Atkinson index, and explaining why this index may be called‘subjective’ (for further details, see Lambert (2001, Chapter 4) and Atkinson(1970)). Let the distribution function and frequency density function for income beF(y) and f(y), respectively, and mean income given bym . Welfare is additivelyF

separable and symmetric, taking the form

W 5EU(y) f(y) dy (1)F

3Alternatively, Bearse et al. (2000) argue that in poor countries the median voter prefers in-kindredistribution (through greater provision of public services) rather than cash income. Kula and Millimet(1999) put forth a model relying on the median voter hypothesis as well where thethreat ofredistribution in objectively unequal societies causes the wealthy to alter their consumption patterns(over time) such that the median voter no longer supports redistribution. In other words, inequality stillgives rise to ‘redistribution’, only it is self-imposed rather than undertaken by official means. Gradsteinand Milanovic (2000) present evidence suggesting that democracies (specifically countries with greatervoting participation by the general population) — except for the recent experiences in Eastern Europe— tend to favor redistribution and lower inequality.


where U(y) is strictly increasing and concave. One can think ofU(y) as theimposed utility-of-income function of a decision-maker. By Jensen’s inequalitythere is an income levelz [ (0, m ), known as the equally distributed equivalentF F

income, such thatW 5U(z ).F F

If each individual received an income ofz , the remainder, amounting toF

C 5N(m 2z ), whereN is the population size, could be discarded with no lossF F F

of social welfare. Atkinson’s index expressesC as a fraction of total incomeY ,F F

whereY 5Nm :F F

C zF F] ]I 5 5 12 (2)Y mF F

I is an index of relative inequality (invariant to equiproportionate incomechanges) only whenU(y) takes the isoelastic form. LetU (y) be the utilitye

function with constant elasticity of marginal utilitye $ 0:12eby]]U (y)5 a 1 if e ±1 (3)e 12 e

and

U (y)5 a 1 b ln(y) if e 51 (4)1

4wherea, b . 0 are constants. The parametere measures inequality aversion. TheAtkinson index for the utility functionU (y) and income distributionF is denotede

I (e).F

The Atkinson indexI (e) differs from the Gini coefficient and other summaryF

statistics-based inequality indices in its explicitly ethical foundation. It embodiesthe inequality aversion parameter of a decision-maker, and captures the amount thesocial decision-maker would pay to eliminate inequality; thus, it is referred to as‘subjective’ in contrast to the Gini coefficient. Although the ethical underpinningsof the Gini coefficient have been exposed (see, e.g., Slottje et al. (1989)), the index

5is regarded as authoritative by many; we call it ‘objective’ for the present study.Two entirely different income distributionsF and F could be attributed the1 2

same value of the Atkinson index by two different decision-makers:

I (e )5 I (e ) (5)F 1 F 21 2

where e ± e . This is the crux of the hypothesis we shall examine. Much is1 2

4As stated in Atkinson (1970), inequality aversion is assumed to beconstant relative to ensure themeasurement of inequality is invariant to equiproportionate changes in income. In the remainder of theanalysis we maintain this assumption.

5To emphasize, we use the Gini coefficient to approximate ‘objective’ inequality given its historicalas well as current importance in the study of inequality. Clearly other indices could have been used(e.g., Section 4 also utilizes the Lorenz coordinates and the Theil index). The use of the Gini coefficientparticularly, as opposed other measure(s) based on the extended Gini, entails some implicit preferencesregarding inequality.


known about how the Atkinson index responds to changes in the inequalityaversion parameter. For any given, unequal income distributionF(y), I (e) isF

increasing ine (i.e., ≠I (e) /≠e . 0 ;F ). As e → 0, we have the polar case ofF

inequality neutrality; utility becomes linear in individual incomes. No loss of totalincome would be accepted in exchange for complete equality:

I (e) → 0 as e → 0 ;F (6)F

At the other end of the spectrum, ase →`, the welfare ranking of incomedistributions approaches the Rawlsian leximin.

Finally, in the case of a discrete income distributionhy , y , . . . , y j, the1 2 N

formula for the Atkinson index is:

1 / (12e)y1 i 12e] ]I (e)5 12 O( ) (7)F GF N mFi

for e ± 1, whilst for e 51 the index takes the value:

m̃F]I (1)5 12 (8)F mF

˜where m is geometric mean income. For data partitioned intok equal-sizedF

groups (e.g., quintiles), whereq is the share of aggregate income belonging toj

group j, the Atkinson index fore ± 1 can be estimated as:

1 / (12e)1 12e]I (e)5 12 O(kq ) (9)F GF jk j

For e 51 we haveI (1)5 12 kq , whereq is the geometric mean income share.F F F

3.2. The approach

To analyze differences in inequality aversion across countries, begin by lettingthere ben countries indexedi 5 1, 2, . . . , n. F (y) is the income distributioni

function in countryi. Given the natural rate of subjective inequality,w, we firstidentify the inequality aversion parameter,e , in countryi such that the subjectivei

inequality in distributionF equalsw, ;i:i

I (e )5w ;i. (10)F ii

We then analyze the determinants of the inequality aversion parameterse ,e , . . . ,1 2

e , seeking to identify factors that explain the heterogeneity across countriesn

required by our hypothesis. Ifx is a vector of possible explanatory variables andxj

the jth component, we attempt to identify a functionc(x) such that

ie ¯c(x ) (11)i


iwhere x indicates the values ofx for country i; or, at least, to sign the partialderivatives≠c /≠x .j

With the explanatory variables inx and functionc(x) determined, if even onecountry were to change its political or social institutions, leading to a new value

ifor one of its explanatory variables, sayx , then the level of subjective inequalityj

in that country would diverge from the world-wide natural rate of inequalityw. Fori i i iexample, suppose≠c /≠x . 0 and thatx increases tox 1Dx . Thene increasesj j j j i

i ito e 1De , where De 5 ≠c /≠x Dx .0. This causes the level of subjectives di i i j j

inequality to increase since the decision-maker is now more inequality averse andwould be willing to sacrifice more ‘distributive income’ to eliminate the inequalityin the given income distribution,F :i

≠I (e )F ii]]I (e 1De )5w 1 De .w. (12)FS D GF i i ii ≠ei

Given the new degree of inequality aversione 1De in country i, how can thei i

natural rate of inequalityw be restored? Only by altering the income distribution(e.g., directly through the tax system or indirectly through increased expenditureson education, government sponsored training programs, improved child carebenefits, etc.) fromF to H , say, where:i i

I (e 1De )5 I (e )5w , I (e 1De ). (13)H i i F i F i ii i i

The condition in (13) requiresH to be objectively more equal thanF (so that ai i

decision-maker with inequality aversione 1De would pay less to eliminatei i

inequality in H than in F ). Taking the Gini as our measure of objectivei i

inequality, thenH has a lower Gini coefficient than F (i.e., G ,G ).i i H Fi i

If this is the mechanism whereby the natural ratew is restored, and dulymaintained world-wide, then the Gini coefficient should be related to the same setof variables which explain inter-country differences in inequality aversion. Theonly difference is that the relationship between the set of explanatory variables andthe Gini coefficient should have inverse properties to those ofc. In other words,we can express the Gini coefficient in countryi as a function of the same set ofexplanatory variables in (11):

iG ¯v(x ) (14)Fi

and if ≠c /≠x .0 (as we assumed in the exposition), then≠v /≠x , 0. Thisj j

constitutes the testable implication of our theory which is examined in theempirical section below. If there is in fact a ‘natural rate’ of subjective inequality,the country-specific attributes that have a positive (negative) effect on inequalityaversion should have a negative (positive) impact on the Gini coefficient.


3.3. Data

To test our hypothesis, we use data from the1999 World DevelopmentIndicators published by the World Bank. Table A.1 (see Appendix A) contains the

6income shares by quintile for 96 countries. There is significant variation acrosscountries. We then use the data on income shares, along with the Atkinson indexfor partitioned data in (9) and the formula forI (1), to find the value ofe whichF

achieves a given level of subjective inequality,w. Because Eq. (9) cannot besolved explicitly fore, we conduct a grid search over potential values ofe until wefind the value which yields the desired level of subjective inequality. To do this,we compute the value of (9) fore between 0 and 200,e ± 1, (and the value of

25I (1) for e 5 1) with a step size of 1.0310 . Since we do not presume to knowF

the natural rate of subjective inequality,w, we perform this exercise for severalpossible values. Table A.2 (see Appendix A) reports the values ofe which yield an

7Atkinson index value of 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, and 0.40. Recall, thevalue of the Atkinson index reports the percentage of income which could bediscarded with no loss in social welfare if the remainder is equally divided; thus,these values span a wide range.

Table 1 contains the summary statistics for the potential explanatory variableswe examine. The variables emphasized reflect a country’s economic growth, levelof human development, corruption, and attitudes toward education and empower-ing women. From these variables, seven were selected as the factors thatpotentially best explain variation in thee values across countries based on a priorigrounds as well as data availability. Variable definitions and sources are relegatedto Appendix A.

6Depending on data availability, the data reflect per capita income for some countries and per capitaexpenditure in others. In addition, we do not attempt to limit our analysis to countries deemeddemocratic. While one may question the ability of individuals to translate preferences concerninginequality and redistribution into changes in the objective income distribution in extreme nondemoc-racies, such avenues do exist. For example, ‘fair wage’ models and models based on social customargue that societal standards and perceptions of fairness may affect the income distribution throughemployment decisions, work effort conditional on employment, propensity for collective action on thepart of workers, wage-setting practices of employers, as well as other channels (see, e.g., Ackerlof,1980, 1982; Naylor, 1989; Ackerlof and Yellen, 1990; Blinder and Choi, 1990; Agell and Lundborg,1992). Moreover, it has been shown to be difficult to distinguish between democracies andnondemocracies and, despite attempts to do so, empirical evidence linking income inequality and lowergrowth have found as much (if not more) support when analyzing all countries, rather than a subset of‘democracies’ (Weede, 1997). Finally, since (as we shall see) the data based on the full sample supportour natural rate hypothesis, if indeed the relevant avenues are missing in nondemocracies, then theresults would be even stronger upon restricting our attention to the sub-sample of democratic regimes.

7The corresponding values ofI (e) are not presented in the table, but in all cases except one we areF

able to find a value ofe which bringsI (e) to exactly the desired value. For the lone exception,I (e)F F24only deviates from the desired value by2 6.03 10 .


Table 1Summary statistics

Variable Mean S.D. Minimum Maximum

Annual populationgrowth, 1995–2015 1.32 1.09 20.70 3.48

Adult literacy rate, 1995 0.79 0.24 0.14 0.99Dependency ratio, 1995 0.69 0.18 0.44 1.04Life expectancy at birth, 1995 65.47 10.57 34.70 79.10Real GDP per capita, 1995 7004 7674 455 34004Annual GNP per capita

growth, 1980–1995 0.71 2.16 23.88 8.64Annual inflation rate, 1980–1995 49.71 148.78 1.30 961.60% School enrollment (combined

1st, 2nd, and 3rd level), 1995 0.63 0.20 0.15 1.00% Females in Government,

ministerial level, 1995 0.09 0.09 0.00 0.48% Females in Government,

sub-ministerial level, 1995 0.09 0.08 0.00 0.46Female economic activity

rate (% of male), 1995 0.69 0.20 0.27 1.03Gender empowerment measure (GEM) 0.46 0.16 0.12 0.79Human development index (HDI) 0.67 0.23 0.19 0.96Public expenditure on education

(% of Government expenditure), 1993–1995 0.15 0.06 0.00 0.33Corruption index (CPI), 1998 4.74 2.41 1.50 10.00

4. Results

4.1. Preliminaries

To ensure our results are robust to the choice ofw, we use several values forw,ranging from 0.10 to 0.40. Tables 2 and 3 present the correlation matrix betweenthe country-specific inequality aversion parameters for each value ofw with and

Table 2Inequality aversion parameters: correlation matrix

e e e e e e e Gini0.10 0.15 0.20 0.25 0.30 0.35 0.40

e 1.00000.10

e 0.9996 1.00000.15

e 0.9981 0.9994 1.00000.20

e 0.9940 0.9965 0.9988 1.00000.25

e 0.9832 0.9873 0.9920 0.9969 1.00000.30

e 0.9420 0.9484 0.9573 0.9692 0.9851 1.00000.35

e 0.5761 0.5856 0.6025 0.6306 0.6817 0.7938 1.00000.40

Gini 20.9011 20.8957 20.8859 20.8686 20.8354 20.7502 20.3062 1.0000


Table 3Inequality aversion parameters: correlation matrix, excluding the Slovak Republic

e e e e e e e Gini0.10 0.15 0.20 0.25 0.30 0.35 0.40

e 1.00000.10

e 0.9996 1.00000.15

e 0.9981 0.9994 1.00000.20

e 0.9948 0.9973 0.9992 1.00000.25

e 0.9886 0.9924 0.9959 0.9987 1.00000.30

e 0.9773 0.9826 0.9881 0.9934 0.9979 1.00000.35

e 0.9534 0.9607 0.9687 0.9773 0.9863 0.9948 1.00000.40

Gini 20.9329 20.9298 20.9246 20.9159 20.9019 20.8787 20.8355 1.0000

without the inclusion of a single outlier (Slovak Republic). For notation,e0.10

refers to the country-specific value ofe which makesI (e)50.10, etc. For allF

values of the Atkinson index below 0.40, the correlations are extremely close tounity, implying that over a fairly wide range ofw, the arbitrary choice ofw does

8not appear to be problematic. Tables 2 and 3 also provide the correlations betweenthe country-specific aversion parameters and the Gini coefficient. In all cases, thecorrelations between thee values and the Gini coefficient are negative and close tounity in absolute value, particularly in Table 3 when the outlier is excluded.Consequently, countries which are less averse to inequality have higher levels ofobjective inequality, as our theory of a ‘natural rate of subjective inequality’predicts.

´Our finding is consistent with Benabou (2000) who posits a theoretical modelbased on inequality, social contract, and credit constraints containing multiplesteady states. The inferences from the model are used to explain why somecountries (e.g., the US) are characterized by high objective inequality and lowredistribution and others (e.g., Western Europe) are characterized by the reverse.Our theory predicts the same outcome and relies on a much simpler argument. Ourresult is also consistent with the empirical finding in Perotti (1996); specifically,countries with high objective inequality are no more likely to undertake redistribu-tive taxation. Under the ‘natural rate’ hypothesis, the only countries which shouldalter their level of redistribution are countries which have experienced some shock(e.g., change in political regime, recession, greater female labor force participation,etc.) such that subjective inequality is no longer equal to the equilibrium naturalrate.

Since the potential validity of our natural rate of subjective inequality hypoth-esis rests on the finding that countries with greater aversion to inequality havelower objective inequality, we would like to ensure that the conclusion suggestedby Tables 2 and 3 is not driven by the representation of objective inequality

8When the single outlier is excluded, the correlations among the aversion parameters are well above0.9 for all values up to and includingw 50.4.


Table 4Inequality aversion and quintile shares: correlation matrix

e q q q q q0.10 1 2 3 4 5

e 1.00000.10

q 0.9162 1.00001

q 0.8912 0.9630 1.00002

q 0.8490 0.8984 0.9694 1.00003

q 0.5594 0.5513 0.6621 0.8154 1.00004

q 20.8838 20.9429 20.9840 20.9919 20.7764 1.00005

implicit in the Gini coefficient which has been criticized for giving more weight tothe middle portion of the income distribution (Cowell, 1977; Slottje, 1989). Toassess the robustness of our findings, Tables 4 and 5 present the correlationsbetweene (the value of the aversion parameter whenw 5 0.10) and the quintile0.10

shares (Table 4) as well as the cumulative quintile shares, or Lorenz ordinates9(Table 5). We find that increases in inequality aversion are associated with

significantly higher income shares for the four lowest quintiles, examined either10individually or cumulatively. Thus, the inference that relatively inequality averse

countries have lower levels of objective inequality appears robust to the choice of11objective inequality measure.

As a preliminary exploration of the empirical factors responsible for thevariation in country-specific inequality aversion as well as objective inequality,Table 6 presents the Spearman rank correlations betweene and selected0.10

explanatory variables, as well as similar correlations for the Gini coefficient. Morestriking than the signs and magnitudes of the correlations is the fact the correlationbetween a given explanatory variable ande is almost the negative of the0.10

correlation between the same explanatory variable and the Gini coefficient. Thus,

Table 5Inequality aversion and lorenz ordinates: correlation matrix

e q q 1 q q 1 q 1 q q 1 q 1 q 1 q0.10 1 1 2 1 2 3 1 2 3 4

e 1.00000.10

q 0.9162 1.00001

q 1 q 0.9114 0.9895 1.00001 2

q 1 q 1 q 0.9024 0.9722 0.9944 1.00001 2 3

q 1 q 1 q 1 q 0.8835 0.9426 0.9735 0.9914 1.00001 2 3 4

9Here after, we present results only fore . Recall, assumingw 50.10 implies that 10% of national0.10

income is ‘redundant’ and could be given up without any loss in social welfare if the remainder isequally distributed. We confirmed that the inferences are invariant to the choices ofw we tried.

10As in Table 3, the correlations become even closer to unity if we exclude the Slovak Republic.11In addition, we also examined the correlations between inequality aversion and the Theil index.

Correlations betweene and the Theil index were consistently well below20.80.0.10


Table 6Spearman rank correlations

Variable e Gini coefficient0.10

r Probability r ProbabilityH : r 5 0 H : r 5 0o o

Annual population 20.56 0.00 0.57 0.00growth, 1995–2015

Adult literacy rate, 1995 0.51 0.00 20.52 0.00Dependency ratio, 1995 20.55 0.00 0.55 0.00Life expectancy at birth, 1995 0.43 0.00 20.43 0.00Real GDP per capita, 1995 0.42 0.00 20.42 0.00Annual GNP per capita 0.42 0.00 20.42 0.00

growth, 1980–1995Annual inflation rate, 1980–1995 20.22 0.05 0.23 0.04% School enrollment combined 0.37 0.00 20.38 0.00

1st, 2nd, and 3rd level, 1995% Females in Government, 0.15 0.15 20.15 0.16

ministerial level, 1995% Females in Government, 20.07 0.50 0.06 0.58

sub-ministerial level, 1995Female economic activity 0.37 0.00 20.37 0.00

rate % of male, 1995Gender empowerment measure (GEM) 0.44 0.00 20.45 0.00Human development index (HDI) 0.43 0.00 20.44 0.00Public expenditure on education 20.33 0.00 0.32 0.00

% of Government expenditure, 199321995Corruption index 0.43 0.00 20.43 0.00

the values of the inequality aversion parameters appear to be approximatelymonotonically related to the values of the Gini coefficient. This is confirmed inFig. 1 which depicts an approximate log-linear relationship between the aversionparameters,e, and the Gini coefficient regardless of the choice ofw.

4.2. Factors impacting inequality aversion

We now turn our focus to exploring the determinants of the country-specific12inequality aversion parameters. To do so, we estimate several different simple

regression specifications using bothe and the Gini coefficient as the dependent0.10

variable. The linear version (Table 7) usese and the Gini coefficient as the0.10

dependent variables; the semi-log version (Table 8) uses the natural log ofe0.10

and the Gini coefficient as dependent variables. The general results are robust to

12We use the word ‘determinants’ loosely as we do no attempt to deal with issues of endogeneity.


Fig. 1. Inequality aversion and the Gini coefficient.

13choice of specification, explanatory variables, andw. The most important resultto examine — that which is the basis for this paper — is whether changes in thepolitical or social climate of a country which change the inequality aversion of thedecision-maker result in alterations of the observed income distribution such thatthe natural rate of inequality is restored. Thus, attributes (in a cross-section) whichare associated with greater inequality aversion must also be associated with lowerlevels of objective inequality, if subjective inequality in each country is to remain

14equal tow. Examining Tables 7 and 8 shows that this is precisely the case. Invirtually every case, the sign (and significance) of the coefficient is identical acrossthe model usinge as the dependent variable and the model using the Gini0.10

coefficient but of the opposite sign. In fact, in Table 8 where we first take the logtransformation of the dependent variables, the coefficients in the Gini regressions(b ) are approximately negative one-half of the coefficients in the inequalityG

13Even using the values of the aversion parameters whenw 5 0.40 (where the correlations were thelowest in Table 4) does not change the basic results.

14Alternatively, we could test the natural rate hypothesis using time-series (or panel) data. Forexample, Li et al. (1998) find that income inequality is relatively stable over time within countries, butvaries considerably across countries. Thus, inequality aversion should also be fairly stable withincountries, but exhibit significant cross-country variation.

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Table 7aDeterminants of inequality aversione and objective inequality (Gini): linear specification0.10

Variable Model I Model II Model III Model IV

e Gini e Gini e Gini e Gini0.10 0.10 0.10 0.10

Population growth 20.150 4.467 20.151 4.574 20.205 7.645 20.225 8.306

(23.596) (3.297) (23.896) (3.280) (24.429) (4.654) (24.822) (5.000)

Adult literacy 20.002 0.080 20.003 0.094 20.004 0.156 20.005 0.178

(21.268) (1.347) (21.620) (1.564) (22.063) (2.358) (22.373) (2.625)

GDP per capita 8.146 2410.827 6.737 2355.073

(1.554) (22.411) (1.362) (21.998)

GNP per capita growth 0.032 21.280 0.079 22.758

(2.008) (22.287) (2.531) (22.486)

GNP per capita growth, 20.009 0.283

squared (22.868) (2.669)

GNP per capita growth* 20.109 3.474

[I51/Growth.0] (22.594) (2.326)

Females in Government, 0.008 20.094 0.007 0.001 0.008 20.069 0.008 20.084

ministerial level (1.897) (20.676) (1.727) (0.008) (2.321) (20.595) (2.431) (20.717)


sub-ministerial level (21.594) (1.885) (21.338) (0.965) (20.842) (0.244) (20.728) (0.134)

Female economic 0.003 20.121 0.002 20.109 0.002 20.101 0.002 20.082

activity (1.807) (22.493) (1.291) (22.191) (1.445) (21.744) (1.111) (21.421)

Public education 20.009 0.327 20.010 0.391 20.011 0.436

spending (21.897) (2.007) (21.977) (2.218) (22.212) (2.409)

]2R 0.40 0.41 0.49 0.45 0.60 0.57 0.59 0.56

Observations 93 76 60 60

a Notes: t-statistics in parentheses. Refer to the text and/or Appendix A for variable definitions.

1076P.J.

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Table 8aDeterminants of inequality aversione and objective inequality (Gini): semi-log specification0.10

Variable Model I Model II Model III Model IV

e Gini e GINI e Gini e Gini0.10 0.10 0.10 0.10

Population growth 20.252 0.121 20.260 0.124 20.401 0.193 20.438 0.211

(23.571) (3.607) (23.617) (3.576) (24.801) (4.751) (25.173) (5.119)

Adult literacy 20.004 0.002 20.005 0.002 20.008 0.004 20.009 0.004

(21.371) (1.431) (21.618) (1.600) (22.377) (2.316) (22.668) (2.604)

GDP per capita 19.195 29.986 15.869 28.303

(2.166) (22.344) (1.733) (21.882)

GNP per capita growth 0.066 20.032 0.149 20.071

(2.328) (22.293) (2.638) (22.600)

GNP per capita growth, 20.016 0.008

squared (22.911) (2.864)

GNP per capita growth* 20.194 0.093

[I51/Growth.0] (22.550) (2.511)


ministerial level (1.130) (21.080) (0.583) (20.535) (1.154) (1.168) (1.277) (21.290)


sub-ministerial level (21.941) (1.844) (21.192) (1.080) (20.515) (0.426) (20.394) (0.308)

Female economic 0.006 20.003 0.005 20.002 0.005 20.002 0.004 20.002

activity (2.289) (22.365) (1.871) (21.957) (1.634) (21.706) (1.281) (21.360)

Public education 20.017 0.008 20.020 0.010 20.022 0.011

spending (22.028) (1.991) (22.217) (2.195) (22.428) (2.405)

]2R 0.42 0.43 0.47 0.48 0.60 0.60 0.59 0.59

Observations 93 76 60 60

a Notes: t-statistics in parentheses. Refer to the text and/or Appendix A for variable definitions.


aversion regressions (b ); i.e., b ¯ 2 0.5b . This is a natural consequence of thee G e15log-linear relationship depicted in Fig. 1.

Examining the specific point estimates, several interesting results emerge. First,countries that have faster growing populations have significantly lower inequalityaversion and higher levels of objective inequality. One rationale for this could bethat policymakers view excessive population growth as self-induced and thereforefeel little need to correct for inequality in such populations. Another possiblerationale is that inequality in high population growth economies may reflectdifferences in individual preferences and, therefore, does not warrant concern bydecision-makers. For example, comparing two countries, if one country has higherpopulation growth and, as a result, fewer two-earner households, while in the othercountry every household has two income earners, then policymakers may not be

16averse to the higher inequality in the former. It is also possible, of course, thatthose countries with rampant population growth simply lack the resources,economic sophistication, or bureaucratic structure to deal effectively with a highlevel of income inequality. For example, Bearse et al. (2000) argue that a crucialdifference between developed and developing countries is that the governments indeveloped countries have access to a more productive tax system.

Second, relatively high levels of adult literacy rates and public spending oneducation are both associated with lower inequality aversion as well. Again, theexplanation possibly lies in the psyche of decision-makers. If adults are welleducated and the state helps provide a ‘level playing field’ by investing in thepublic education system, then inequality which results is more likely to be viewedas ‘justified.’ In other words, governments that spend relatively more on educatingtheir workforce may be less tolerant of individuals that do not map thateducational advantage into stronger labor market outcomes. Or, perhaps, govern-ments with well educated citizens and a strong public education system view

15One could gain a superficial understanding of these twin findings by supposing that the Atkinsonindex is a function of the Gini coefficient and the aversion parameter. It would then follow that the tworegressions are really one, as one dependent variable is an inverse function of the other. Thus, whateverfactors explain the Gini coefficient will also explain the aversion parameter with the opposite sign.However, this is not really the case. Since the Atkinson index is a function of the underlying incomedistribution and inequality aversion and the Gini coefficient is a different function of the underlyingincome distribution, fixing the Atkinson index at a specified value,w, generates a relationship betweeninequality aversion and the underlying income distribution and, hence, inequality aversion and the Ginicoefficient. However, given that the Gini coefficient and the Atkinson index (for a given level ofinequality aversion) are distinct in their representation of the underlying income distribution, therelationship between the Gini coefficient and inequality aversion is not clear a priori (as seen in thatFig. 1 is not a straight line). Thus, estimating two separate regressions in Table 7 and 8 is necessary toensure the validity of our natural rate hypothesis.

16In other words, the higher income inequality in the country where many choose not to be in thelabor force reflects stronger preferences by some for children relative to income. However, in thecountry with lower population growth and a higher percentage of two-earner households, inequalitymay be a concern as it is not a simple reflection of differential preferences for income versusnon-market goods.


inequality as more reflective of differences in individual preferences (e.g.,preferences for lower income and greater leisure), rather than symptomatic ofsome social ill. Regardless, the finding that well educated societies,ceterisparibus, are less inequality averse is also consistent with Ravallion and Lokshin(2000) who find that the less educated are more likely to favor redistribution.

Third, higher levels of per capita GDP are associated with higher levels ofinequality aversion and lower objective inequality. As discussed earlier, this isconsistent with the conjecture of Atkinson (1970) and contrary to Frisch (1959).The explanation may lie in the fact that modern, mature economies have a strongersense of ethical principles regarding the relatively poor. Another possibility is thatthe lack of inequality is a normal good and thus the demand for objectiveequalityrises with income. This is consistent with the observed rise in charitable giving asincome rises as well as findings that social transfers as a percentage of GDPincrease with GDP. It is also consistent with findings in the empirical developmentliterature on intra-household allocation which finds that aversion to inequalityamong resources devoted to sons and daughters increases with income (see, e.g.,Garg and Morduch, 1998; Behrman, 1988; Rosenzweig and Schultz, 1982).

Fourth, countries with greater levels of female ‘empowerment’ (as measured bytheir economic activity and political presence at the ministerial level of govern-ment) are more inequality averse. This is also consistent with the developmentliterature on intra-household allocation which finds that women on average aremore altruistic in terms of devoting resources to their children, in particulardaughters (see, e.g., Thomas, 1994). In addition, programs which empower womenwithin the household (e.g., the Grameen Bank and other micro-credit institutions)increase not only the absolute level of resources devoted to children, but also theresources devoted to daughters relative to boys (see, e.g., Pitt and Khandker, 1998;Pitt et al., 1998). In addition, Ravallion and Lokshin (2000) find that women aremore likely to favor redistribution. However, since the direction of causation in thepresent analysis is unclear, it may be that countries which are more averse toinequality are less discriminatory against women.

Next, we estimate two different specifications (Models III and IV) to examinethe effect of economic growth on inequality aversion: (i) a linear spline modelwith the kink point at a growth rate of zero, and (ii) a second-order polynomialmodel. We find a significant non-linear effect of the growth rate of per capitaincome on inequality aversion and objective inequality in both models (Fig. 2).Specifically, the relationship between economic growth and inequality aversion ischaracterized by an inverted U-shaped pattern. Inequality aversion increases withthe growth rate of per capita income until the growth rate reaches roughly 2%(Model III), but further growth reduces inequality aversion. For objective

17inequality, the relationship is U-shaped. The fact that economies with significant,

17The estimated peak value of inequality aversion occurs when the growth rate is 2.06%. Theestimated trough value of objective inequality occurs when the growth rate is 2.00%. Also, even if thetwo outliers with very high growth rates are excluded, the results from the polynomial model areunaffected.


Fig. 2. Effect of economic growth on inequality aversion and objective inequality.


positive growth rates have higher objective inequality and lower inequalityaversion is consistent with Quadrini (1999) who argues that growing economiesare characterized by higher income inequality and less redistribution. It is alsopartially consistent with Ravallion and Lokshin (2000) which argues that greaterupward economic mobility reduces the incentive to redistribute. However, negativeeconomic growth or prospects for downward mobility increases preferences forredistribution, whereas we find the opposite: significant negative economic growthis consistent with low inequality aversion and high objective inequality.

One possible explanation for this discrepancy may be reverse causation.According to Persson and Tabellini (1994) and Perotti (1996), objective incomeinequality lowers economic growth. Thus, the fact that objective inequality(inequality aversion) is high (low) when growth rates are significantly negativemay reflect this fact. However, since objective inequality (inequality aversion) ishigh (low) when growth rates are significantly positive as well indicates that otherforces are at work (e.g., Quadrini, 1999). Another possible explanation is the‘tunnel effect’ of Hirschman and Rothschild (1973). The ‘tunnel effect’ argues thatwhat matters for redistribution areexpectations of future mobility — either up ordown. If individuals expect to be better off in the future, then demand forredistribution may fall, even among the poor. Thus, if individuals expect to beupwardly mobile in the near future, then even countries experiencing negative

18growth may be inequality averse (and, hence, relatively objectively unequal).As a final exercise, we perform a simple simulation exercise. Takingw 5 0.10,

suppose that in the US and the UK, conditional on total government expenditure,the percentage of government expenditure allocated to education would double.This amounts to the fraction of government expenditure spent on public educationincreasing from 14.1% (11.4%) to 28.2% (22.8%) in the US (UK). Using theresults in Table 6 (Model IV), the initial impact would be to lower the value of theaversion parameter in each country by 2.2% (from 0.4241 to 0.4148 in the US andfrom 0.6682 to 0.6535 in the UK). Given that the income distribution has notchanged, the level of subjective inequality would be reduced from the natural rateof inequality (0.10) in each country to 0.0978 in each country. However, as aresult of the reallocation of government appropriations, the level of objectiveinequality would rise by 1.1% (with the Gini increasing from 0.401 (0.326) to0.405 (0.330) in the US (UK)). After some adjustment period, then, in this

18Alternatively, a possible explanation relates to the interaction between democracy and growth. Ifthe countries experiencing negative growth rates are more likely to be ruled by nondemocratic regimesand preferences for inequality are dictated by unsympathetic governments in such countries, then onewould expect countries experiencing negative growth rates to also be less averse to inequality.However, the link between democracy and growth is far from well established (see, e.g., Burnetti,1997; Weede, 1997; Burnetti and Weder, 1995).


scenario, subjective inequality would return to the natural rate with the newincome distribution objectively less equal in each country.

5. Conclusion

We begin with a wide sample of countries, including those with a variety ofpolitical structures, to examine which politico-socioeconomic factors couldaccount for differences in objective inequality and inequality aversion in across-section. We find evidence to support our hypothesized ‘natural rate’ ofsubjective inequality; societies with high objective inequality (as measured by theGini coefficient) are less averse to inequality. In addition, the socio-economicvariables that we expected a priori to reflect a given society’s inequality aversiondo in fact affect a country’s tolerance for inequality and help merge the findingsfrom many different bodies of literature. In particular, countries that are moreenlightened towards the treatment of women have higher inequality aversion. Thesame is true for countries with higher per capita incomes (inequality aversion is anormal good) and fairly stable per capita incomes (i.e., growth rates close to zero).On the other hand, countries which provide a more level playing field for theirpopulations (i.e., allocate more resources to public education and have higher adultliteracy rates) are more tolerant of resulting inequality. The same is true forcountries with high population growth rates.

As discussed in an earlier section, several authors, beginning with Stern (1977),have sought to infere values for different countries by fitting the equal sacrificetax model. This amounts to (i) characterizing asT (y) the actual or effective taxi

liability in country i, and (ii) identifying ase the e value for whichU (y)2i e

U [ y 2 T (y)] best approximates a constantu for all income levelsy. Sterne i oi

(1977, pp. 236–237) somewhat wryly asks, ‘‘What is the use of deriving valuesfrom policies if the only point of having explicit values is to derive policies?’’.Under our ‘natural rate’ hypothesis, we can approach this question from an entirelynew angle. Withe inferred from the natural rate of subjective inequality, we cani

ask how much redistribution would be caused by an equal sacrifice tax for aparticular utility function U (y) and some constantu ? Could we make ane oi

association between the explanatory variablesx for inequality aversion and theredistributive effect of the tax system as we are able to do for the Gini coefficient?

Buchholtz et al. (1988) have examined the properties of a tax scheduleT(y)satisfying the equal sacrifice ruleU (y)2U [ y 2 T(y)] 5 u for some constantu .e e o o

They showinter alia that if inequality aversion increases and there is no change inthe pre-tax income distribution, the new equal sacrifice tax which raises the sametotal revenue induces an upward shift in the Lorenz curve for post-tax income.However, adapting this finding to our present situation is not straightforward aschanges in politico-socioeconomic factors trigger not only changes in inequality


aversion, but also in the pre-tax income distribution if subjective inequality is to berestored to the natural rate. Clearly, more research is warranted along these lines.

Acknowledgements

The authors wish to thank Valentino Dardanoni, Rajat Deb, EsfandiarMaasoumi, Efe Ok, two anonymous referees, and the co-editor for many helpfulcomments and suggestions.

Appendix A

The explanatory variables used in Tables 4–6 come from the United NationsDevelopment Program Human Development Report (except corruption) and areavailable at http: / /www.undp.org/hdro. Data definitions are available from thesame source unless otherwise indicated and are as follows:

• Gender empowerment measure (GEM): Reveals whether women can takeactive part in economic and political life. It focuses on participation, measuringgender inequality in key areas of economic and political participation anddecision-making. It tracks the percentages of women in parliament, amongadministrators and managers and among professional and technical workers andwomen’s earned income share as a percentage of men’s. Differing from theGender-related development index (GDI), it exposes inequality in opportunitiesin selected areas.

• Human development index (HDI): Human Development Reports, since the firstin 1990, have published the human development index as a measure of humandevelopment. Recognize, however, that the concept of human development ismuch broader than the HDI. It is impossible to come up with a comprehensivemeasure or even a comprehensive set of indicators because many vitaldimensions of human development are non-quantifiable. But a simple compo-site measure of human development can draw attention to the issues quiteeffectively. The HDI is not a substitute for the fuller treatment of the richnessof the concerns of the human development perspective. The HDI measures theoverall achievements in a country in three basic dimensions of humandevelopment: longevity, knowledge and a decent standard of living. It ismeasured by life expectancy, educational attainment (adult literacy andcombined primary, secondary and tertiary enrollment) and adjusted income.

• Dependency ratio: The ratio of nonworking-age population to working-agepopulation. Typically, working-age population is defined as 15–64 years of age(Gillis et al., 1996).


Table A.1Income distribution by country

Country Source Gini Percentage share of income or consumption

IndexLowest Lowest Second Third Fourth Highest Highest

10% 20% 20% 20% 20% 20% 10%

Algeria a,b 0.353 2.8 7.0 11.6 16.1 22.7 42.6 26.8

Austrailia c,d 0.337 2.5 7.0 12.2 16.6 23.3 40.9 24.8

Austria c,d 0.231 4.4 10.4 14.8 18.5 22.9 33.3 19.3

Bangladesh a,b 0.283 4.1 9.4 13.5 17.2 22.0 37.9 23.7

Belarus c,d 0.288 3.4 8.5 13.5 17.7 23.1 37.2 22.6

Belgium c,d 0.250 3.7 9.5 14.6 18.4 23.0 34.5 20.2

Bolivia c,d 0.420 2.3 5.6 9.7 14.5 22.0 48.2 31.7

Brazil c,d 0.601 0.8 2.5 5.7 9.9 17.7 64.2 47.9

Bulgaria c,d 0.308 3.3 8.3 13.0 17.0 22.3 39.3 24.7

Burkina Faso a,b 0.482 2.2 5.5 8.7 12.0 18.7 55.0 39.5

Canada c,d 0.315 2.8 7.5 12.9 17.2 23.0 39.3 23.8

Chile c,d 0.565 1.4 3.5 6.6 10.9 18.1 61.0 46.1

China c,d 0.415 2.2 5.5 9.8 14.9 22.3 47.5 30.9

Colombia c,d 0.572 1.0 3.1 6.8 10.9 17.6 61.5 46.9

Costa Rica c,d 0.470 1.3 4.0 8.8 13.7 21.7 51.8 34.7

Cote d’lvoire a,b 0.369 2.8 6.8 11.2 15.8 22.2 44.1 28.5

Czech Repulic c,d 0.266 4.6 10.5 13.9 16.9 21.3 37.4 23.5

Denmark c,d 0.247 3.6 9.6 14.9 18.3 22.7 34.5 20.5

Dominican Republic c,d 0.505 1.6 4.2 7.9 12.5 19.7 55.7 39.6

Ecuador a,b 0.466 2.3 5.4 8.9 13.2 19.9 52.6 37.6

Egypt, Arab Rep. a,b 0.320 3.9 8.7 12.5 16.3 21.4 41.1 26.7

El Salvador c,d 0.499 1.2 3.7 8.3 13.1 20.5 54.4 38.3

Estonia c,d 0.354 2.2 6.2 12.0 17.0 23.1 41.8 26.2

Ethiopia a,b 0.400 3.0 7.1 10.9 14.5 19.8 47.7 33.7

Finland c,d 0.256 4.2 10.0 14.2 17.6 22.3 35.8 21.6

France c,d 0.327 2.5 7.2 12.7 17.1 22.8 40.1 24.9

Gambia, The a,b 0.478 1.5 4.4 9.0 13.5 20.4 52.8 37.6

Germany c,d 0.281 3.7 9.0 13.5 17.5 22.9 37.1 22.6

Ghana a,b 0.327 3.6 8.4 12.2 15.8 21.9 41.7 26.1

Guatemala c,d 0.596 0.6 2.1 5.8 10.5 18.6 63.0 46.6

Guinea a,b 0.403 2.6 6.4 10.4 14.8 21.2 47.2 32.0

Guinea-Bissau a,b 0.562 0.5 2.1 6.5 12.0 20.6 58.9 42.4

Guyana a,b 0.402 2.4 6.3 10.7 15.0 21.2 46.9 32.0

Honduras c,d 0.537 1.2 3.4 7.1 11.7 19.7 58.0 42.1

Hungary c,d 0.279 4.1 9.7 13.9 16.9 21.4 38.1 24.0

India a,b 0.297 4.1 9.2 13.0 16.8 21.7 39.3 25.0

Indonesia c,d 0.365 3.6 8.0 11.3 15.1 20.8 44.9 30.3

Ireland c,d 0.359 2.5 6.7 11.6 16.4 22.4 42.9 27.4

Israel c,d 0.355 2.8 6.9 11.4 16.3 22.9 42.5 26.9

Italy c,d 0.312 2.9 7.6 12.9 17.3 23.2 38.9 23.7

Jamaica a,b 0.411 2.4 5.8 10.2 14.9 21.6 47.5 31.9

Jordan a,b 0.434 2.4 5.9 9.8 13.9 20.3 50.1 34.7

Kazakhstan c,d 0.327 3.1 7.5 12.3 16.9 22.9 40.4 24.9

Kenya a,b 0.445 1.8 5.0 9.7 14.2 20.9 50.2 34.9

Kyrgyz Republic c,d 0.353 2.7 6.7 11.5 16.4 23.1 42.3 26.2

Lao PDR a,b 0.304 4.2 9.6 12.9 16.3 21.0 40.2 26.4

Latvia c,d 0.285 3.3 8.3 13.8 18.0 22.9 37.0 22.4

Lesotho a,b 0.560 0.9 2.8 6.5 11.2 19.4 60.1 43.4

Lithuania c,d 0.336 3.4 8.1 12.3 16.2 21.3 42.1 28.0

Luxembourg c,d 0.269 4.2 9.5 13.6 17.7 22.4 36.7 22.3

Madagascar a,b 0.460 1.9 5.1 9.4 13.3 20.1 52.1 36.7


Table A.1. Continued

Country Source Gini Percentage share of income or consumption

IndexLowest Lowest Second Third Fourth Highest Highest

10% 20% 20% 20% 20% 20% 10%

Malaysia c,d 0.484 1.9 4.6 8.3 13.0 20.4 53.7 37.9

Mali a,b 0.505 1.8 4.6 8.0 11.9 19.3 56.2 40.4

Mauritania a,b 0.389 2.3 6.2 10.8 15.4 22.0 45.6 29.9

Mexico c,d 0.537 1.4 3.6 7.2 11.8 19.2 58.2 42.8

Moldova c,d 0.344 2.7 6.9 11.9 16.7 23.1 41.5 25.8

Mongolia a,b 0.332 2.9 7.3 12.2 16.6 23.0 40.9 24.5

Morocco a,b 0.392 2.8 6.6 10.5 15.0 21.7 46.3 30.5

Nepal a,b 0.367 3.2 7.6 11.5 15.1 21.0 44.8 29.8

Netherlands c,d 0.315 2.9 8.0 13.0 16.7 22.5 39.9 24.7

Nicaragua a,b 0.503 1.6 4.2 8.0 12.6 20.0 55.2 39.8

Niger a,b 0.505 0.8 2.6 7.1 13.9 23.1 53.3 35.4

Nigeria a,b 0.450 1.3 4.0 8.9 14.4 23.4 49.4 31.4

Norway c,d 0.252 4.1 10.0 14.3 17.9 22.4 35.3 21.2

Pakistan a,b 0.312 4.1 9.4 13.0 16.0 20.3 41.2 27.7

Panama c,d 0.571 0.7 2.3 6.2 11.3 19.8 60.4 43.8

Papua New Guinea a,b 0.509 1.7 4.5 7.9 11.9 19.2 56.5 40.5

Paraguay c,d 0.591 0.7 2.3 5.9 10.7 18.7 62.4 46.6

Peru c,d 0.462 1.6 4.4 9.1 14.1 21.3 51.2 35.4

Phillippines a,b 0.429 2.4 5.9 9.6 13.9 21.1 49.6 33.5

Poland a,b 0.272 4.0 9.3 13.8 17.7 22.6 36.6 22.1

Romania c,d 0.282 3.7 8.9 13.6 17.6 22.6 37.3 22.7

Russian Federation a,b 0.480 1.4 4.2 8.8 13.6 20.7 52.8 37.4

Rwanda a,b 0.289 4.2 9.7 13.2 16.5 21.6 39.1 24.2

Senegal a,b 0.538 1.0 3.1 7.4 12.1 19.5 57.9 42.3

Sierra Leone a,b 0.629 0.5 1.1 2.0 9.8 23.7 63.4 43.6

Slovak Republic c,d 0.195 5.1 11.9 15.8 18.8 22.2 31.4 18.2

Slovenia c,d 0.292 4.0 9.3 13.3 16.9 21.9 38.6 24.5

South Africa a,b 0.593 1.1 2.9 5.5 9.2 17.7 64.8 45.9

Spain c,d 0.325 2.8 7.5 12.6 17.0 22.6 40.3 25.2

Sri Lanka a,b 0.301 3.8 8.9 13.1 16.9 21.7 39.3 25.2

Sweden c,d 0.250 3.7 9.6 14.5 18.1 23.2 34.5 20.1

Switzerland c,d 0.361 2.9 7.4 11.6 15.6 21.9 43.5 28.6

Tanzania a,b 0.382 2.8 6.8 11.0 15.1 21.6 45.5 30.1

Thailand a,b 0.462 2.5 5.6 8.7 13.0 20.0 52.7 37.1

Tunisia a,b 0.402 2.3 5.9 10.4 15.3 22.1 46.3 30.7

Turkmenistan c,d 0.358 2.7 6.7 11.4 16.3 22.8 42.8 26.9

Uganda a,b 0.392 2.6 6.6 10.9 15.2 21.3 46.1 31.2

Ukraine c,d 0.473 1.4 4.3 9.0 13.8 20.8 52.2 36.8

UK c,d 0.326 2.4 7.1 12.8 17.2 23.1 39.8 24.7

US c,d 0.401 1.5 4.8 10.5 16.0 23.5 45.2 28.5

Venezuela c,d 0.468 1.5 4.3 8.8 13.8 21.3 51.8 35.6

Vietnam a,b 0.357 3.5 7.8 11.4 15.4 21.4 44.0 29.0

Yemen, Rep. a,b 0.395 2.3 6.1 10.9 15.3 21.6 46.1 30.8

Zambia a,b 0.498 1.6 4.2 8.2 12.8 20.1 54.8 39.2

Zimbabwe a,b 0.568 1.8 4.0 6.3 10.0 17.4 62.3 46.9

a Refers to expenditure shares by percentiles of population.b Ranked by per capita expenditure.c Refers to income shares by percentiles of population.d Ranked by per capita income.

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Table A.2Inequality aversion by country

Country Gini index e e e e e e e0.10 0.15 0.20 0.25 0.30 0.35 0.40

Sierra Leone 0.629 0.15770 0.23514 0.31216 0.38924 0.46690 0.54579 0.62672Brazil 0.601 0.18994 0.28800 0.38928 0.49492 0.60637 0.72554 0.85501South Africa 0.593 0.19004 0.28773 0.38911 0.49548 0.60853 0.73053 0.86466Guatemala 0.596 0.19298 0.29163 0.39272 0.49718 0.60617 0.72117 0.84414Paraguay 0.591 0.19848 0.30020 0.40467 0.51291 0.62623 0.74631 0.87538Panama 0.571 0.21020 0.31735 0.42696 0.54006 0.65795 0.78226 0.91518Colombia 0.572 0.21379 0.32631 0.44350 0.56695 0.69876 0.84174 0.99780Zimbabwe 0.568 0.21503 0.32843 0.44778 0.57521 0.71359 0.86707 1.04179Lesotho 0.560 0.21892 0.33166 0.44799 0.56924 0.69714 0.83396 0.98277Guinea 0.403 0.21992 0.32990 0.44161 0.55597 0.67407 0.79734 0.92763Chile 0.565 0.22389 0.33991 0.46120 0.58962 0.72764 0.87866 1.04754Honduras 0.537 0.24000 0.36577 0.49642 0.63379 0.78027 0.93908 1.11473Senegal 0.538 0.24225 0.36735 0.49674 0.63203 0.77526 0.92913 1.09734Mexico 0.537 0.24482 0.37253 0.50578 0.64659 0.79762 0.96252 1.14646Niger 0.505 0.26180 0.39228 0.52382 0.65765 0.79526 0.93856 1.09010Papua New Guinea 0.509 0.27281 0.41732 0.57021 0.73458 0.91476 1.11692 1.35032Dominican Republic 0.505 0.27646 0.42165 0.57416 0.73671 0.91298 1.10812 1.32962Mali 0.505 0.27709 0.42407 0.57978 0.74750 0.93177 1.13915 1.37951Nicaragua 0.503 0.28112 0.42854 0.58321 0.74786 0.92618 1.12329 1.34664El Salvador 0.499 0.28277 0.42912 0.58095 0.74042 0.91030 1.09434 1.29777Zambia 0.498 0.28924 0.43935 0.59677 0.76426 0.94550 1.14561 1.37202Burkina Faso 0.482 0.30225 0.46719 0.64491 0.84051 1.06142 1.31901 1.63166Malaysia 0.484 0.30287 0.46232 0.63030 0.81013 1.00631 1.22524 1.47647Russian Federation 0.480 0.31274 0.47401 0.64232 0.82040 1.01188 1.22174 1.45710Costa Rica 0.470 0.31354 0.47507 0.64227 0.81760 1.00426 1.20659 1.43073Gambia, The 0.478 0.31809 0.48311 0.65619 0.84039 1.03980 1.26009 1.50943Venezuela 0.468 0.32027 0.48655 0.65977 0.84281 1.03945 1.25489 1.49667Ukraine 0.473 0.32223 0.48843 0.66195 0.84566 1.04338 1.26033 1.50405Thailand 0.462 0.33270 0.51144 0.70326 0.91344 1.14965 1.42349 1.75357Ecuador 0.466 0.33314 0.51160 0.70254 0.91094 1.14384 1.41174 1.73106Peru 0.462 0.33483 0.50703 0.68652 0.87628 1.08028 1.30399 1.55532

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Table A.2. Continued

Country Gini index e e e e e e e0.10 0.15 0.20 0.25 0.30 0.35 0.40

Madagascar 0.460 0.33761 0.51735 0.70856 0.91569 1.14485 1.40491 1.70921Nigeria 0.450 0.33815 0.50803 0.68212 0.86287 1.05345 1.25809 1.48283Kenya 0.445 0.36117 0.55127 0.75171 0.96659 1.20147 1.46425 1.76665Jordan 0.434 0.38285 0.58934 0.81183 1.05690 1.33401 1.65743 2.04987Phillippines 0.429 0.38954 0.59655 0.81879 1.06265 1.33734 1.65682 2.04343Bolivia 0.420 0.39808 0.60824 0.83095 1.07163 1.33788 1.64091 1.99814China 0.415 0.40657 0.61967 0.84422 1.08535 1.35015 1.64897 1.99778Jamaica 0.411 0.42151 0.64500 0.88268 1.14059 1.42723 1.75515 2.14400US 0.401 0.42409 0.63804 0.85696 1.08444 1.32515 1.58560 1.87534Tunisia 0.402 0.44457 0.67906 0.92762 1.19654 1.49462 1.83494 2.23812Guinea-Bissau 0.562 0.44571 0.68620 0.94599 1.23343 1.56072 1.94662 2.42178Guyana 0.402 0.45721 0.70076 0.96301 1.25192 1.57892 1.96132 2.42701Yemen, Rep. 0.395 0.46213 0.70783 0.96986 1.25527 1.57395 1.94065 2.37877Ethiopia 0.400 0.46500 0.72359 1.01040 1.33828 1.72700 2.20850 2.83728Mauritania 0.389 0.47125 0.72136 0.98799 1.27849 1.60331 1.97815 2.42821Morocco 0.392 0.47175 0.72381 0.99635 1.29854 1.64389 2.05346 2.56236Uganda 0.392 0.48339 0.74216 1.02223 1.33289 1.68769 2.10754 2.62686Tanzania 0.382 0.49556 0.76407 1.05559 1.38047 1.75398 2.20007 2.75882Cote d’lvoire 0.369 0.53180 0.81391 1.11759 1.45287 1.83443 2.28521 2.84380Nepal 0.367 0.54756 0.85239 1.19173 1.58213 2.04938 2.63619 3.41852Turkmenistan 0.358 0.55099 0.84152 1.15067 1.48775 1.86624 2.30699 2.84494Ireland 0.359 0.55503 0.84829 1.16063 1.50128 1.88361 2.32825 2.86969Kyrgyz Republic 0.353 0.56165 0.85631 1.16885 1.50852 1.88874 2.33025 2.86779Indonesia 0.365 0.56261 0.87702 1.23163 1.64694 2.15641 2.81831 3.74236Estonia 0.354 0.56508 0.85155 1.14929 1.46526 1.80920 2.19563 2.64793Israel 0.355 0.56517 0.86443 1.18428 1.53508 1.93208 2.39920 2.97728Switzerland 0.361 0.56986 0.88118 1.22241 1.60775 2.05883 2.61078 3.32552Algeria 0.353 0.57127 0.87565 1.20250 1.56284 1.97293 2.45839 3.06317Vietnam 0.357 0.57164 0.88968 1.24409 1.65288 2.14469 2.76802 3.61227Moldova 0.344 0.60276 0.91581 1.24805 1.60969 2.01559 2.48903 3.06965Austrailia 0.337 0.61939 0.94374 1.28782 1.66241 2.08319 2.57489 3.17990Mongolia 0.332 0.63747 0.97544 1.33772 1.73704 2.19235 2.73414 3.41626Lithuania 0.336 0.65802 1.02509 1.43532 1.90998 2.48257 3.20982 4.19851France 0.327 0.65977 1.00830 1.37785 1.78008 2.23222 2.76174 3.41691Kazakhstan 0.327 0.66576 1.01950 1.39977 1.82068 2.30354 2.88321 3.62261

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UK 0.326 0.66815 1.01455 1.37948 1.77401 2.21427 2.72589 3.35340Ghana 0.327 0.67557 1.05503 1.48288 1.98473 2.60248 3.41008 4.55272Spain 0.325 0.67784 1.03793 1.42464 1.85190 2.34066 2.92513 3.66708Canada 0.315 0.70278 1.07498 1.47125 1.90538 2.39804 2.98302 3.72146Italy 0.312 0.71937 1.09985 1.50505 1.94962 2.45564 3.05948 3.82770Egypt, Arab Rep. 0.320 0.72540 1.13740 1.60665 2.16388 2.86002 3.78661 5.13092Netherlands 0.315 0.73546 1.13007 1.56118 2.04750 2.61801 3.32179 4.25212Bulgaria 0.308 0.76344 1.18387 1.64657 2.17384 2.80108 3.58999 4.66282Pakistan 0.312 0.77140 1.23378 1.78321 2.47201 3.39149 4.71232 6.81443Sri Lanka 0.301 0.81282 1.27525 1.79919 2.41794 3.18677 4.20729 5.69834Lao PDR 0.304 0.82786 1.31771 1.89932 2.62978 3.61208 5.05046 7.43680India 0.297 0.83901 1.31897 1.87101 2.53630 3.38551 4.55346 6.34575Belarus 0.288 0.87132 1.33564 1.83983 2.40828 3.08027 3.92632 5.09188Latvia 0.285 0.87377 1.33246 1.82414 2.37003 3.00372 3.78440 4.83112Slovenia 0.292 0.88423 1.38901 1.96913 2.66788 3.55993 4.78943 6.69214Rwanda 0.289 0.89837 1.42187 2.04091 2.81506 3.85199 5.36948 7.91429Romania 0.282 0.91217 1.41024 1.96193 2.59851 3.37213 4.38054 5.83893Germany 0.281 0.92150 1.42570 1.98620 2.63672 3.43413 4.48624 6.03413Bangladesh 0.283 0.93200 1.46192 2.06947 2.79989 3.73178 5.02011 7.03778Hungary 0.279 0.96767 1.53308 2.19617 3.01389 4.08583 5.61271 8.11492Poland 0.272 0.98944 1.53600 2.14916 2.86936 3.76665 4.97929 6.83754Luxembourg 0.269 0.99023 1.55009 2.18610 2.94524 3.91071 5.25137 7.38816Czech Repulic 0.266 1.08514 1.74763 2.56251 3.63626 5.17539 7.66025 12.75214Finland 0.256 1.11870 1.76398 2.51183 3.42817 4.63535 6.40237 9.50857Sweden 0.250 1.14016 1.76184 2.45204 3.25892 4.26847 5.65947 7.89578Belgium 0.250 1.14803 1.76230 2.43876 3.22189 4.19039 5.50585 7.57852Norway 0.252 1.15352 1.81149 2.56794 3.48751 4.69094 6.44590 9.53235Denmark 0.247 1.17966 1.81480 2.51673 3.33214 4.34474 5.72983 7.94647Austria 0.231 1.36112 2.12322 2.99601 4.06421 5.49622 7.70285 12.09634Slovak Republic 0.195 2.02655 3.23485 4.74693 6.85910 10.39346 19.13509 193.32671


• Female economic activity rate: Expresses women’s earned income share as apercentage of men’s.

• Corruption: Data are obtained from Transparency International’s CorruptionPerceptions Index (CPI) available at http: / /www.gwdg.de/|uwvw. The CPI isa means of enhancing understanding of levels of corruption from one country toanother. It does not attempt to assess the degree of corruption practiced bynationals outside their own countries. In an area as complex and controversialas corruption, no single source, or polling method, has yet been developed thatcombines a perfect sampling frame, large enough country coverage, and a fullyconvincing methodology to produce comparative assessments. This is why theCPI has adopted the approach of a composite index. It is a ‘poll of polls’. Itconsists of credible surveys using different sampling frames and varyingmethodologies and is the most statistically robust means of measuringperceptions of corruption. The 1998 CPI includes data from the EconomistIntelligence Unit (Country Risk Service and Country Forecasts), GallupInternational (50th Anniversary Survey), the Institute for Management De-velopment (World Competitiveness Yearbook), the Political & Economic RiskConsultancy (Asian Intelligence Issue), the Political Risk Services (Internation-al Country Risk Guide), World Development Report (Private Sector Survey)and the World Economic Forum (Global Competitiveness Report). The indexranges from 0 (least corrupt) to 10 (most corrupt).

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