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.)

World GDP

World Population

GDP Per Capita

World Growth Rate

 -Divergence

β-divergence

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 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.

Start with a Histogram (Non-Parametric)

China

India

USA

USA (corrected scale)

Indonesia

Brazil

Japan

Mexico

Nigeria

Nigeria (corrected scale)

The Collapse of the Soviet Union

USSR and FSU

World Distribution 1970

World Distribution 2000

World Distribution Over Time

If use a Parametric Approach (countries are Log Normal)

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

Poverty Rates

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…

Cumulative Distribution Function

Rates or Headcounts? Veil of Ignorance: Would you Prefer your children to live in country A or B? (A) people and poor (poverty rate = 50%) (B) people and poor (poverty rate =33%) If you prefer (A), try country (C) (C) people and poor.

Poverty Headcounts

World Poverty: Summary All Rates fall dramatically over the last thirty years Drop is largest for higher poverty rates (so if you want to argue that the poverty rates are large, you must agree that there has been a lot of improvement and if you want to argue that there has been little improvement, you must agree that poverty rates are small)

But Evolution of Poverty is not Uniform Across Regions of the World

Regional Poverty

Poverty in USSR and FSU

Poverty and Growth The regions of the world that have experienced high growth (Asia), have also experienced huge reductions in poverty The regions of the world that have experienced negative growth (Africa), have also experienced huge increases in poverty The regions of the world that have experienced little growth (Latin America, Arab World) have experienced little improvements in poverty

Income Inequality Popular View: –FACT 1: Inequality within the USA, within China, within Latin America, etc. has been increasing –FACT 2: Per Capita Income Across countries has been diverging (so cross-country inequality has been increasing) –Conclusion: HENCE, global income inequality has been increasing Right?

Wrong!!! FACT 1: refers to citizens FACT 2: refers to countries The correct definition of “Across-Country Inequality” should be: “inequality that we would have in the world if all citizens within each country had the same level of income but there were differences in income per capita across countries”. Notice that this would correspond to a “population-weighted concept of dispersion”.

Decomposition Global Inequality = Inequality Across Countries + Inequality Within Countries

Within Country Inequality Inequality that would exist if all countries had the same per capita income, but had the existing differences across its citizens

It could be the case that a few very poor and very populated countries had converged (so the incomes of many CITIZENS had converged) and that many poor countries with few inhabitants had diverged.

Far Fetched? The few but very populated countries are China and India The many but little populated countries are in the African continent

Convergence Across Countries

Convergence Across Citizens who live in Different Countries

Income Inequality Need to estimate measures of PERSONAL income inequality. Question is: what measures to use? Various Measures –Ad Hoc Indexes (gini, variance of incomes, variance of logs). Some have nice properties, some do not. –Social Welfare Function Indexes (Atkinson) –Axiomatic Indexes (Some nice properties are pre- specified)

Income Inequality Axiomatic Indexes –Pigou-Dalton Transfer principle (a good measure should rise with mean preserving redistribution from poor to rich). Varlog violates this principle. –Scale Independence (variance violates) –Decomposability: I(total)=I(within)+I(across). Only Generalized Entropy Indexes (Mean Logarithmic Deviation, Theil and Squared of CV).

Income Inequality What measure to use? Problem is that different measures might give different answers so if you can pick and choose your measure of inequality, you can pick and choose your conclusion We will use estimate and report ALL measures so you can decide which one you like

Gini

Variance of Log Income

Atkinson (0.5)

Atkinson (1)

Mean Log Deviation

Theil Index

Ratio Top 20% to Bottom 20%

Ratio Top 10% to Bottom 10%

Decomposition Not all measures can be “decomposed” in the sense that the within and the across- country component add up to the global index of inequality Only the “Generalized Entropy” indexes can be decomposed: MLD and Theil

MLD Decomposition

Theil Index Decomposition

Lessons Across-Country inequalities decline Within-Country inequalities increase, but not enough to offset the decline in across-country inequalities so that overall inequality actually falls Across-Country inequalities are much larger: if you want to reduce inequalities across citizens, promote AGGREGATE growth in poor countries!

Inequalities have fallen… Because Asia has been catching up with OECD. If Africa does not start growing soon, inequalities will start increasing again...

Projected Inequalities if Africa does not Grow…

Not All is Income UNDP suggests that other things matter also. –Life Expectancy –Child Mortality –Caloric Intake –Literacy Rates and School Enrollment –Access to Water and Sanitation UNDP creates and index with various of these measures. But how did these measures evolve over time?

Life Expectancy

Child Mortality

Caloric Intake

Starving Population

Literacy Rates

Primary Schooling

Secondary Schooling

Access to Water

Sanitation

Third World Wages

Conclusion: The World is Improving… Poverty Rates are falling because some large nations are GROWING Poverty Headcounts are falling even though population is growing Inequalities are falling because some poor and large economies are GROWING Other measures of welfare are also improving (they probably correlate with income well). But, unless AFRICA does not start growing: –Inequalities will rise again –Poverty will rise again (because Asia will stop reducing poverty when they are close to zero)

FINAL CONCLUSION: GROWTH MATTERS! Key Questions for Economists Today: –Why doesn’t Africa grow? –How do we make Africa grow? –Fewer questions in economics (or in any other science) are more relevant for human welfare.