1. Inequality of What? Building a Distribution and Summarizing Inequality Peter Lanjouw, DECRG Poverty and Inequality Analysis Module 5: Inequality and Pro-Poor Growth

2. Outline Constructing a measure of welfare – Income/consumption? – Components – Adjustments Scalar Inequality Measures – Statistical measures – An axiomatic approach The distribution – Lorenz curves – Stochastic dominance

3. Alternative measures in practice Consumption per capita – Value of total consumption (whether or not transacted) Income per capita – Poor indicator when incomes vary; hard to measure Consumption per equivalent single adult – Problems with forming scales; composition versus size economies; intra-household inequality. Food-share ("Engel's Law") – Sources of noise: other parameters; problem of income elasticity near unity. Identification problems. Nutritional indicators – "Welfarist" critique (welfare and nutrition are different things); nutritional requirements/ anthropometric standards.

4. Recommendations Use a comprehensive consumption measure, spanning consumption space Choice between income and consumption is largely driven by the greater likelihood of accuracy of information on consumption. Recognize the limitations of consumption based measures; look for supplementary measures, especially access to public services, subjective welfare as a clue to measuring objective welfare.

5. Constructing a Consumption Measure The consumption measure serves as the foundation upon which much of the subsequent analysis rests. Principles – Should be comprehensive – Retain transparency and credibility – Goal is to be able to rank individuals credibly in terms of welfare

6. Common Steps Construct a food consumption measure Add basic non-food items (from consumption module) Add other non-food items (other modules) Add housing expenditures Add use-value of consumer durables

7. Aggregating Food and Basic Non-Food Expenditures Food expenditures should include not only purchased items but also consumption out of home production. Key issue is to distinguish between investments and consumption (avoid double counting). Health expenditures are usually excluded Two Additional omissions: – Leisure – Public goods

8. Imputing Housing and Durables Expenditures Many surveys ask specifically what an owner- occupier household would pay in rent if it were renting. Where credible this number can be used for home owners. Elsewhere one can try to predict rent paid based on regression models estimated on subset of renting households. Purchases of irregular, lumpy items such as consumer durables (tv, car, etc.) cannot be directly added to the consumption definition. – If supplementary information is available, it is sometimes possible to impute a stream of consumption services from durables ownership

9. AggregateGini Coefficient Food Spending 0.359 Food Spending plus Basic Non-Food Spending 0.420 Food plus Basic Non-Food Spending Including Energy and Education Spending 0.421 Above With Actual or Imputed Water Expenditures 0.415 Above With Actual or Imputed 0.420 Value of Housing Services Above With Imputed Value 0.430 of Owned Consumer Durables Source: Encuesta de Condiciones de Vida, 1994 (Instituto Nacional de Estatisticos y Censo, Ecuador). Impact on Inequality of Using Alternative Consumption Aggregations - Ecuador

10. Adjustments to Consumption Aggregate Spatial price variation – Cost of living adjustments Demographic Composition – Equivalence Scales – Economies of Scale Which unit of analysis? – Household or individual?

11. Summarizing Inequality in the Consumption Distribution Some options from basic statistics:

12. Inequality measurement and normative judgements All summary measures of inequality embody some normative judgements regarding inequality and the relative importance of different parts of the income distribution. – Most measures make these judgements implicitly rather than explicitly. Some measures are clearly unattractive: – Range: reflects well the distance between the poorest and richest, but is totally unaffected by changes in the distribution of income within these two extremes.

13. Inequality measurement: Some attractive axioms The principle of transfers: a transfer from a poorer person to a richer person, all things equal, must result in an increase of inequality (aka Pigou-Dalton principle). – This property is not satisfied by the range. Scale-neutrality: If we double all incomes, measured inequality should not change – This property is not satisfied by the variance Anonymity: If we simply switch location of two individuals in the income distribution, measured inequality should not change.

14. Inequality measurement and normative judgements Gini Coefficient: – Attaches more weight to transfers affecting middle income classes

15. Inequality measurement and normative judgements Coefficient of variation: – Attaches equal weights to all income levels – No less arbitrary than other judgements. Standard deviation of logarithems: – Is more sensitive to transfers in the lower income brackets. Bottom line: The degree of inequality cannot in general be measured without introducing social judgements.

16. The Atkinson class of inequality measures Atkinson (1970) introduces the notion of ‘ equally distributed equivalent ’ income, Y EDE. This represents the level of income per head which, if equally shared, would generate the same level of social welfare as the observed distribution. This then defines a measure of inequality of the following form: I A = 1- (Y EDE /μ)

17. The Atkinson class of inequality measures A low value of Y EDE relative to μ implies that if incomes were equally distributed the same level of social welfare could be achieved with much lower average income. So I A would be large. Everything hinges on the degree of inequality aversion in the social welfare function. With no aversion, there is no welfare gain from redistribution so Y EDE is equal to μ and I A = 0.

18. The Atkinson class of inequality measures Atkinson proposes the following form for his inequality measure:

19. The Atkinson class of inequality measures A key role here is played by the distributional parameter ε. In calculating I A you need to explicitly specify a value for ε. When ε=0 there is no social concern about inequality and so I A = 0 (even if the distribution is “ objectively ” unequal). When ε=∞ there is infinite weight to the poorer members of the population ( “ Rawls ” ) How to choose ε?

20. Selecting ε (Okun ’ s leaky bucket) Okun (1975) asks the hypothetical question: “ Consider a transfer of $4000 per head from the top 5 percent giving, in principle, $1000 to each of the bottom 20 percent. But some of the money “ leaks ” away in the process. How much leakage would you be willing to accept before abandoning the proposed redistribution? ”

21. Selecting ε (Okun ’ s leaky bucket) Atkinson shows that the answer to this question can be used to back out a value of ε. g represents the ratio of the average income of the “ transferee ” to the “ recipient ”. x represents proportion of transfer received by the recipient.

22. Selecting ε (Okun ’ s leaky bucket) Suppose a hypothetical transfer from someone with twice the income level of the proposed recipient (g=2) If you will not tolerate less than half of the transfer going to the poor (x=0.5) then ε=1. If you will accept up to 75% leakage, then x=0.25 and ε=2.

23. Recommendations No inequality measure is purely ‘ statistical ’ : each embodies judgements about inequality at different points on the income scale. To explore the robustness of conclusions: Option 1: measure inequality using a variety of inequality measures (not just Gini). Option 2: employ the Atkinson measure with multiple values of ε. Option 3: look directly at Lorenz Curves, apply Stochastic Dominance results.

24. The Lorenz Curve To compare inequality in two distributions: – Plot the % share of total income received by the poorest nth percentile population in the population, in turn for each n and each consumption distribution. – The greater the area between the Lorenz curve and the hypotenuse the greater is inequality. Second Order Stochastic Dominance (Atkinson 1970): – If Lorenz curves for two distributions do not intersect, then they can be ranked irrespective of which measure of inequality is the focus of attention. – If the Lorenz curves intersect, different summary measures of inequality can be found that will rank the distributions differently.

26. Useful Readings Atkinson, A. B. (1983) The Economics of Inequality 2 nd Edition (Oxford: Clarendon Press). Atkinson, A. B. (1983) Social Justice and Public Policy (Cambridge, Mass: MIT Press). Atkinson, A.B. and Bourguignon, F. (eds) (2000) Handbook of Income Distribution Vo1. 1 (North Holland: Elsevier Science Publishers). Deaton, A. and Zaidi, S. (1999) ‘ Guidelines for Constructing Consumption Aggregates for Use as a Money-Metric Welfare Measure ’, LSMS working paper. Foster, J. and Sen, A. (1997) On Economic Inequality: Expanded Edition with Substantial Annexe (Oxford: Oxford University Press).