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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.) World GDP World GDP $45,000,000,000 $40,000,000,000 $35,000,000,000 $30,000,000,000 $25,000,000,000 $20,000,000,000 $15,000,000,000 $10,000,000,000 $5,000,000,000 20 00 19 98 19 96 19 94 19 92 19 90 19 88 19 86 19 84 19 82 19 80 19 78 19 76 19 74 19 72 19 70 $0 World Population World Population 6,000,000 5,000,000 4,000,000 3,000,000 2,000,000 1,000,000 20 00 19 98 19 96 19 94 19 92 19 90 19 88 19 86 19 84 19 82 19 80 19 78 19 76 19 74 19 72 19 70 0 GDP Per Capita World GDP Per Capita $8,000 $7,000 $6,000 $5,000 $4,000 $3,000 $2,000 $1,000 20 00 19 98 19 96 19 94 19 92 19 90 19 88 19 86 19 84 19 82 19 80 19 78 19 76 19 74 19 72 19 70 $0 -1% -2% 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976 1975 1974 1973 1972 1971 1970 World Growth Rate World Growth Rate 5% 4% 3% 2% 1% 0% -Divergence Figure 2. Variance of Log- Per Capita Income: 125 Countries 1.40 1.30 1.20 1.10 1.00 0.90 0.80 19 70 19 71 19 72 19 73 19 74 19 75 19 76 19 77 19 78 19 79 19 80 19 81 19 82 19 83 19 84 19 85 19 86 19 87 19 88 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 0.70 Variance of Log Per Capita Income Across Countries 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 China Income share of Quintile 1 Quintile 1 Linear (Quintile 1) Quintile 2 China Income share of Quintile 3 1998 1996 1994 1992 1990 1988 1986 Linear (Quintile 2) China Income share of Quintile 4 y = -0.002x + 0.2025 R2 = 0.6571 0.25 1984 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 1970 0 1982 0.02 1980 0.04 1978 0.06 1976 0.08 y = -0.0022x + 0.1613 R2 = 0.6646 1974 0.1 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 1972 y = -0.0021x + 0.1126 R2 = 0.6565 1970 0.12 China Income share of Quintile 2 0.35 0.3 0.2 0.25 0.15 0.2 0.15 0.1 y = -2E-05x + 0.2506 R2 = 1E-05 0.1 0.05 0.05 0 Quintile 3 Linear (Quintile 3) Linear (Quintile 5) 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 1970 y = 0.0063x + 0.2753 R2 = 0.661 Quintile 5 Linear (Quintile 4) 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 Quintile 4 China Income share of Quintile 5 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 1970 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 1970 0 India Income share of Quintile 1 0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.00 India Income share of Quintile 2 0.14 0.12 y = 4E-05x + 0.0873 R2 = 0.0123 y = -0.0002x + 0.1294 R2 = 0.1602 0.10 0.08 0.06 0.04 0.02 Quintile 1 Linear (Quintile 1) 1998 1996 1994 1992 1990 1988 1986 1984 1982 Quintile 2 India Income share of Quintile 3 Linear (Quintile 2) India Income share of Quintile 4 y = -0.0003x + 0.1694 R2 = 0.2915 0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 1980 1978 1976 1974 1972 1970 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 1970 0.00 0.25 0.2 y = -0.0006x + 0.2234 R2 = 0.4097 0.15 0.1 0.05 Quintile 3 Linear (Quintile 3) Linear (Quintile 5) 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 1970 y = 0.001x + 0.3903 R2 = 0.2291 Quintile 5 Linear (Quintile 4) 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 Quintile 4 India Income share of Quintile 5 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 1970 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 1970 0 USA Income share of Quintile 1 USA Income share of Quintile 2 0.06 0.14 0.05 0.12 0.10 0.04 0.08 y = -0.0004x + 0.056 R2 = 0.7013 Quintile 1 Linear (Quintile 1) Quintile 2 USA Income share of Quintile 3 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1970 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 0.00 1970 0.02 0.00 1976 0.04 0.01 1974 0.02 y = -0.0007x + 0.1218 R2 = 0.9503 0.06 1972 0.03 Linear (Quintile 2) USA Income share of Quintile 4 0.3 0.25 0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 0.2 y = -0.0007x + 0.1795 R2 = 0.8822 0.1 0.05 1998 1996 1994 Linear (Quintile 3) 1992 1990 1988 1986 1984 Quintile 3 1982 1980 1978 1976 1974 1972 1970 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 Quintile 5 Linear (Quintile 5) 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 y = 0.002x + 0.4002 R2 = 0.9307 Quintile 4 Linear (Quintile 4) 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 1970 0 USA Income share of Quintile 5 1970 y = -0.0002x + 0.2426 R2 = 0.1933 0.15 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, HighIncome 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. Start with a Histogram (Non-Parametric) Figure. 2a. Income Distribution: China 100000 80000 60000 40000 20000 0 5 6 6 7 7 Series1 8 9 9 China China 90,000 80,000 thousands of people 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 $100 $1,000 $10,000 1970 1970 1980 1970 1980 1970 1990 1980 1990 2000 $100,000 India India 90,000 80,000 thousands of people 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 $100 $1,000 1970 $10,000 1980 1990 2000 $100,000 USA USA 18,000 16,000 thousands of people 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 $100 $1,000 1970 $10,000 1980 1990 2000 $100,000 USA (corrected scale) USA 18,000 16,000 thousands of people 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 $1,000 $10,000 1970 $100,000 1980 1990 2000 $1,000,000 Indonesia Indonesia 20,000 18,000 16,000 thousands of people 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 $100 $1,000 1970 $10,000 1980 1990 2000 $100,000 Brazil Brasil 8,000 7,000 thousands of people 6,000 5,000 4,000 3,000 2,000 1,000 0 $100 $1,000 1970 $10,000 1980 1990 2000 $100,000 Japan Japan 14,000 12,000 thousands of people 10,000 8,000 6,000 4,000 2,000 0 $100 $1,000 1970 $10,000 1980 1990 2000 $100,000 Mexico Mexico 6,000 thousands of people 5,000 4,000 3,000 2,000 1,000 0 $100 $1,000 1970 $10,000 1980 1990 2000 $100,000 Nigeria Nigeria 7,000 6,000 thousands of people 5,000 4,000 3,000 2,000 1,000 0 $100 $1,000 1970 $10,000 1980 1990 2000 $100,000 Nigeria (corrected scale) Nigeria 7,000 6,000 thousands of people 5,000 4,000 3,000 2,000 1,000 0 $10 $100 1970 $1,000 1980 1990 2000 $10,000 The Collapse of the Soviet Union USSR-FSU 25,000 thousands of people 20,000 15,000 10,000 5,000 0 $100 $1,000 $10,000 1970 1970 1970 1980 1980 1990 1990 2000 1989 1989 1970 1980 1970 1980 1989 $100,000 USSR and FSU Figure 1g: Distribution of Income in USSR-FSU 25,000 thousands of people 20,000 15,000 10,000 5,000 0 $100 $1,000 1970 $10,000 1980 1989 1990 $100,000 2000 World Distribution 1970 Figure 2a: The WDI and Individual Country Distributions in 1970 200,000 $1/day World thousands of people 160,000 120,000 China 80,000 India 40,000 USSR Japan 0 $100 $1,000 $10,000 Individual Countries World USA $100,000 World Distribution 2000 Figure 2b: The WDI and Individual Country Distributions in 2000 280,000 $1/day World 240,000 thousands of people 200,000 160,000 120,000 India 80,000 China 40,000 0 $100 Nigeria FSU $1,000 Japan $10,000 Individual Countries World USA $100,000 World Distribution Over Time WDI-Various Years 300,000 thousands of people 250,000 200,000 150,000 100,000 50,000 0 $100 $1,000 $10,000 1970 1990 1970 1970 1980 1970 1980 1980 1990 2000 $100,000 If use a Parametric Approach (countries are Log Normal) Figure 3b: Parametric and Non-Parametric WDI 300,000 250,000 200,000 150,000 100,000 50,000 0 $100 $1,000 Non-Parametric $10,000 Parametric (Country LogNormality) $100,000 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): $826 Poverty Rates Poverty Rates 40% 35% 30% 25% 20% 15% 10% 5% 0% 1970 1975 1980 1985 570$ 826$ 1990 495$ 1995 2000 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 Figure 4: Cumulative Distribution Functions (Various Years) 1 $570/year $5000/year $2000/year 0.8 78% 75% 73% 0.6 67% 0.4 62% 54% 20% 50% 41% 16% 0.2 0 $100 10% 7% $1,000 1970 $10,000 1980 1980 2000 $100,000 Rates or Headcounts? • Veil of Ignorance: Would you Prefer your children to live in country A or B? • (A) 1.000.000 people and 500.000 poor (poverty rate = 50%) • (B) 2.000.000 people and 666.666 poor (poverty rate =33%) • If you prefer (A), try country (C) • (C) 500.000 people and 499.999 poor. Poverty Headcounts Poverty Counts 1,400,000 1,200,000 1,000,000 800,000 600,000 400,000 200,000 0 1970 1975 1980 1985 570$ 826$ 1990 495$ 1995 2000 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 Rates ($570) 60% 50% 40% 30% 20% 10% 0% 1970 1975 Africa Latin America 1980 East Asia 1985 1990 South Asia Middel East and NA 1995 2000 Eastern Europe and CA Regional Poverty Poverty Counts ($570) 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000 0 1970 Africa 1975 Latin America 1980 East Asia 1985 South Asia 1990 Middel East and NA 1995 Eastern Europe and CA 2000 Poverty in USSR and FSU Poverty Rates ($570) 1.40% 1.20% 1.00% 0.80% 0.60% 0.40% 0.20% 0.00% 1970 1975 1980 1985 Eastern Europe and CA 1990 1995 2000 Poverty in USSR and FSU Poverty Counts ($570) 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 1970 1975 1980 1985 Eastern Europe and CA 1990 1995 2000 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 prespecified) 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 Figure 7. Bourguignon-Morrisson and Sala-i-Martin: Global and Across-Country Gini 0.7 0.65 0.6 0.55 0.5 0.45 0.4 1820 1850 1880 1910 Bourguignon-Morrisson 1940 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 Sala-i-Martin Global Sala-i-Martin Across Gini Gini 0.665 0.66 0.655 0.65 0.645 0.64 0.635 0.63 1970 1975 1980 1985 1990 1995 2000 Variance of Log Income Variance of Log Income 1.68 1.66 1.64 1.62 1.6 1.58 1.56 1.54 1.52 1.5 1970 1975 1980 1985 1990 1995 2000 Atkinson (0.5) Atkinson with coefficient 0.5 0.365 0.36 0.355 0.35 0.345 0.34 0.335 0.33 1970 1975 1980 1985 1990 1995 2000 Atkinson (1) Atkinson with Coefficient 1 0.595 0.59 0.585 0.58 0.575 0.57 0.565 0.56 0.555 0.55 1970 1975 1980 1985 1990 1995 2000 Mean Log Deviation Mean Logarithmic Deviation 0.91 0.9 0.89 0.88 0.87 0.86 0.85 0.84 0.83 0.82 0.81 0.8 1970 1975 1980 1985 1990 1995 2000 Theil Index Theil 0.85 0.84 0.83 0.82 0.81 0.8 0.79 0.78 0.77 1970 1975 1980 1985 1990 1995 2000 Ratio Top 20% to Bottom 20% Figure 7e: World Income Inequality: Ratio Top 20% / Bottom 20% 12 11 10 9 8 7 1970 1975 1980 1985 1990 1995 2000 Ratio Top 10% to Bottom 10% Figure 7f: World Income Inequality: Ratio Top 10% / Bottom 10% 32 30 28 26 24 22 20 1970 1975 1980 1985 1990 1995 2000 Decomposition • Not all measures can be “decomposed” in the sense that the within and the acrosscountry component add up to the global index of inequality • Only the “Generalized Entropy” indexes can be decomposed: MLD and Theil MLD Decomposition Mean Logarithmic Deviation 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1970 1975 1980 Global 1985 Across-Country 1990 Within-Country 1995 2000 Theil Index Decomposition Theil 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1970 1975 1980 Global 1985 Across-Country 1990 Within-Country 1995 2000 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… Global Projections if Same Growth as 1980-2000 1.20 1.00 0.80 0.60 0.40 0.20 0.00 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014 2018 2022 2026 2030 2034 2038 2042 2046 2050 Theil MLD 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 68 66 64 62 60 58 56 1970 2000 Life Expectancy Child Mortality 12% 10% 8% 6% 4% 2% 0% 1970 2000 Child Mortality Caloric Intake 3,000 2,500 2,000 1,500 1,000 500 0 1970 2000 Calory Intake per capita (Third World) Starving Population 40% 35% 30% 25% 20% 15% 10% 5% 0% 1970 2000 Fraction of Starving Population % Literacy Rates 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1970 2000 Literacy Rates Primary Schooling 100% 80% 60% 40% 20% 0% 1970 2000 Primary Enrollment Ratio Secondary Schooling 100% 80% 60% 40% 20% 0% 1970 2000 Secondary Enrollment Ratio Access to Water 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1970 2000 Access to Water Sanitation 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1970 2000 Access to Water Third World Wages $3,000 $2,500 $2,000 $1,500 $1,000 $500 $0 1970 2000 Wages (Third World 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.