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Poverty, Inequality, and the World Distribution of Income. By Xavier Sala-i-Martin. 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).
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Poverty, Inequality, and the World Distribution of Income By Xavier Sala-i-Martin
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)
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)
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)
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
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.)
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
Method • Use micro surveys to anchor the dispersion • Use GDP Per Capita to anchor de MEAN of the distribution. • This is subject to CONTROVERSY.
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?
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
(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…
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
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
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)
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).
Methodology: GROUP C • Use the level shares and the slopes of countries that belong to the same “region”
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.
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
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.
Once we have the distribution • Can Compute Poverty Rates • But Poverty Rates are Arbitrary… • Can Compute various measures of inequality
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
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…
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…