Poverty, Inequality, and the World Distribution of Income

1. Poverty, Inequality, and the World Distribution of Income By Xavier Sala-i-Martin

2. Interesting Quotes • “The number of people living on less than $1 a day grew from 1.18 billion in 1987 to 1.20 billion in 1998—an increase of 20 million” The World Bank (World Development Report 2000/2001)

3. Interesting Quotes • “The number of people living on less than $1 a day DID NOT CHANGE between 1987 to and 1998” The World Bank (Globalization, Growth and Poverty, 2001)

4. Interesting Quotes • “Over the past 20 years, the number of people living on less than $1 a day has fallen by 200 million, even as the world's population grew by 1.6 billion." The World Bank (The Role and Effectiveness of Development Assistance, March 2002)

5. Interesting Quotes • “One of the U.N. Millennium Development Goals is to ‘halve, between 1990 and 2015, the proportion of people whose income is less than one dollar a day.’ A lot depends on whether the scorecard is being credibly tallied, and the apparent discrepancies in the World Bank's numbers deserve serious scrutiny” Angus Deaton, 2002

6. Goal Today • Provide a simple, transparent method to estimate the World Distribution of Individual Income • Once the distribution is estimated, analyze various of its characteristics (fraction of people below specific thresholds –poverty rates-, dispersion –inequality-, etc.)

7. World GDP

8. World Population

9. GDP Per Capita

10. World Growth Rate

11. -Divergence

12. β-divergence

13. Aggregate Numbers do not show Personal Situation: Need Individual Income Distribution • Problem: we do not have each person’s income • We have • (A) Per Capita GDP (PPP adjusted) • (B) Income Shares for some years • We can combine these two data sources to estimate the WORLD DISTRIBUTION OF INCOME

14. Method • Use micro surveys to anchor the dispersion • Use GDP Per Capita to anchor de MEAN of the distribution. • This is subject to CONTROVERSY.

15. Controversy: Scaling by National Accounts or Survey Means? • The surveys that we use to compute income shares have “means” • World Bank uses those means to estimate income inequality (Milanovic (2001)) and Poverty (Chen and Ravallion (2001)) • But this mean is much smaller than Per Capita income (or Consumption) from the National Accounts • Moreover, the ratio of Survey Mean to National Account mean tends to go down over time • Ravallion criticizes that if we do not trust the mean, why do we trust the variance?

16. Anchoring the Distribution with National Accounts Data • I anchor the distribution with National Accounts data because: • (a) the mean of our distribution corresponds to the per capita variables that people are used to using (ie, we cannot cross-check the variance… but we can cross-check the mean) • (b) the NA are available every year (so we do not have to forecast the data for years in which there are no surveys) • (c) Surveys have problems of underreporting and systematic non-compliance

17. (d) Survey means are very “strange” • Survey says Hong Kong income is 5% richer than USA (NA says USA GDP is 25% larger) • Survey says Korea is 2% richer than Sweden (NA says Sweden is 49% richer) • Survey says Nicaragua is 77% richer than Thailand (NA says Thailand is 83% richer) • Survey says Ghana is 112% richer than India (NA says they are about the same) • Survey says that Kenya is 81% richer than Senegal (NA says Senegal is 20% richer) • Survey says Tanzania is 16% richer than Indonesia (NA says Indonesia is 168% richer) • And the list goes on and on…

18. Methodology: The Dispersion • Based on data availability, we have 4 types of countries • (A) Countries for which we have GDP data and MANY SURVEYS (70 countries –85 countries after collapse of Soviet Union- with 5.1 billion people or 84% of world population) • (B) Countries for which we have only ONE SURVEYS and GDP data (29 countries with 329 million people or 5.4% of population) • (C) Countries with NO SURVEYS but we have GDP data (28 countries with 242 million citizens or 4.0% of world’s population) • (D) Countries for which we do not have Surveys or GDP data

19. From Surveys… • Let s(ikt) is the income share for quintile k, for country i during year t. • For countries where we have many annual surveys, realize that the income shares are fairly constant over time

23. Methodology: GROUP A • Regress s(ikt) on a time trend for k=1,2,4,5 (and use k=3 as a default to add up to 1) and use the projections as a measure of yearly income shares. • We will not be able to say anything about sudden changes in inequality trends (except for FSU) • Experimented with two different slopes for India and China • Experimented with using actual vs projected slopes for years in which we have hard shares • Note: The WB uses the shares of the closest available year (horizontal projection)

24. Methodology: GROUP B • Use the level shares for the only year in which we have a survey and use the “average slopes” of countries that belong to the same “region” • Regions are defined by the World Bank (East Asia and Pacific, Europe and Central Asia, Latin American and Caribbean, Middle East and North Africa, South Asia, Sub-Saharan Africa, High-Income Non-OECD and High-Income OECD).

25. Methodology: GROUP C • Use the level shares and the slopes of countries that belong to the same “region”

26. Methodology: USSR and FSU • We use USSR survey and GDP data until 1989 • Then we have data for individual republics for 1990-2000 • All the republics have more than one survey so they all belong to group A • Thus, the evolution of inequality (shares) is common for all republics before 1989, but independent for each republic after 1990.

27. Methodology: Anchoring Quintiles with National Account Data • Once we have the income shares for each country/year, we multiply by National Accounts GDP Per capita to get the level of income that each quintile gets every year

28. Two Methods… • Parametric: Fix the shape of the distribution (say, log normal), and with mean and variance we can construct the entire distribution. • Non-Parametric: Do not force the distribution to have a particular shape.

29. Start with a Histogram (Non-Parametric)

30. China

31. India

32. USA

33. USA (corrected scale)

34. Indonesia

35. Brazil

36. Japan

37. Mexico

38. Nigeria

39. Nigeria (corrected scale)

40. The Collapse of the Soviet Union

41. USSR and FSU

42. World Distribution 1970

43. World Distribution 2000

44. World Distribution Over Time

45. If use a Parametric Approach (countries are Log Normal)

46. Once we have the distribution • Can Compute Poverty Rates • But Poverty Rates are Arbitrary… • Can Compute various measures of inequality

47. Poverty Lines are Arbitrary • Consumption or Income? UN Millenium Goals talk about Income Poverty. WB talks about Consumption poverty… • Original Line: 1 dollar a day in 1985 prices • Mysterious Change in Definition by the World Bank: 1.08 dollars a day in 1993 prices (which does not correspond to 1 dollar in 85 prices) • We use Original Line, adjust it for US inflation to convert to 1996 prices: $495/year • Allow for 15% adjustment for underreporting of the rich: $570/year • To get a sense for Consumption (C/Y=0.69): $826

48. Poverty Rates

49. Inequality does not move fast enough… • To change the evolution of poverty. • We have seen that inequality is not related to growth, but when it goes up, it does not go up enough to increase poverty in the country… • To eradicate poverty, we need to promote growth NOT equality…

50. If you don’t like these definitions of poverty… • We can look at CDFs: pick your own poverty line and the CDF tells you the poverty rate for that particular year…