Xavier Sala-i-Martin Columbia University June 2009

Goal Estimate WDI consistent with the empirical growth evidence (which uses GDP per capita as the mean of each country/year distribution). Estimate Poverty Rates and Counts resulting from this distribution Estimate Income Inequality across the world’s citizens Estimate welfare across the world’s citizens Analyze the relation between poverty and growth, poverty and inequality

Data GDP Per capita (PPP-Adjusted). We usually use these data as the “mean” of each country/year distribution of income (for example, when we estimate growth regressions)

Note: I decompose China and India into Rural and Urban Use local surveys to get relative incomes of rural and urban Apply the ratio to PWT GDP and estimate per capita income in Rural and Urban and treat them as separate data points (as if they were different “countries”) Using GDP Per Capita we know…

GDP Per Capita Since 1970

Annual Growth Rate of World Per Capita GDP

β-Non-Convergence

σ -Divergence (191 countries)

Histogram Income Per Capita (countries)

Adding Population Weights

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β-Non-Convergence

Population-Weighted β-convergence ( )

We can use Survey Data Problem Not available for every year Not available for every country Survey means do not coincide with NA means But NA Numbers do not show Personal Situation: Need Individual Income Distribution

Surveys not available every year Can Interpolate Income Shares (they are slow moving animals) Regression Near-Observation Cubic Interpolation Others

Strategy 1: (Sala-i-Martin 2006)

Missing Countries Can approximate using neighboring countries

Strategy 2: Pinkovskiy and Sala-i-Martin (2009)

Method: Interpolate Income Shares Break up our sample of countries into regions(World Bank region definitions). Interpolate the quintile shares for country-years with no data, according to the following scheme, and in the following order: Group I – countries with several years of distribution data We calculate quintile shares of years with no income distribution data that are WITHIN the range of the set of years with data by cubic spline interpolation of the quintile share time series for the country. We calculate quintile shares of years with no data that are OUTSIDE this range by assuming that the share of each quintile rises each year after the data time series ends by beta/2^i, where i is the number of years after the series ends, and beta is the coefficient of the slope of the OLS regression of the data time series on a constant and on the year variable. This extrapolation adjustment ensures that 1) the trend in the evolution of each quintile share is maintained for the first few years after data ends, and 2) the shares eventually attain their all-time average values, which is the best extrapolation that we could make of them for years far outside the range of our sample. Group II – countries with only one year of distribution data. We keep the single year of data, and impute the quintile shares for other years to have the same deviations from this year as does the average quintile share time series taken over all Group I countries in the given region, relative to the year for which we have data for the given country. Thus, we assume that the country’s inequality dynamics are the same as those of its region, but we use the single data point to determine the level of the country’s income distribution. Group III – countries with no distribution data. We impute the average quintile share time series taken over all Group I countries in the given region.

Method 2: Step 1: Find the σ of the lognormal distribution using least squares for the country/years with survey data

Step 2: Compute the resulting normal distributions for each country-year

Step 3: Estimate implied Gini coefficients for country/years with available surveys

Step 4: Three Types of countries Countries with multiple surveys Intrapolate ginis Estimate location parameter as a function of sigma(Gini) for intrapolated years and then estimate the mean with sigma and GDP per capita Countries with ONE survey We keep the single year of data, and impute the Ginis for other years to have the same deviations from this year as does the average Gini time series taken over all Group I countries in the given region, relative to the year for which we have data for the given country (ie, we assume that the country’s inequality dynamics are the same as those of its region, but we use the single data point to determine the level of the country’s income distribution.) Countries with NO distribution data We impute the average Gini time series taken over all Group I countries in the given region.

Step 5: Integrate across countries and get the WDI

Summary of Baseline Assumptions We use GDP data from PWT 6.2 Sensitivity: WB, Madison We break up China and India into urban and rural components, and use POVCAL surveys for within country inequality. Sensitivity: China and India are treated as unitary countries We use piecewise cubic splines to interpolate between available survey data, and extrapolate by horizontal projection. Sensitivity Interpolation: 1) nearest-neighbor interpolation, 2) linear interpolation. Sensitivity Extrapolation: 1) assuming that the trends closest to the extrapolation period in the survey data continue unabated and extrapolating linearly using the slope of the Gini coefficient between the last two data points, and 2) a mixture of the two methods in which we assume the Gini coefficient to remain constant into the extrapolation period, except if the last two years before the extrapolation period both have true survey data. Lognormal distributions Sensitivity: 1) Gamma, 2) Weibull, 3) Optimal (Minimum Squares of residuals), 4) Kernels

Results

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Poverty Rates: $1/day

Poverty Rates

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 Counts

Regional Analysis

Poverty Rates

Counts $1/day

Poverty Rates $2/day

Counts $2/day

Inequality

MLD and Theil

Decomposable Measures (Generalized Enthropy, GE)

Welfare

Sen Index (=Income*(1-gini))

Atkinson Welfare Index certainty equivalent for a person with a CRRA utility with risk aversion parameter gamma of a lottery over payoffs, in which the density is equal to the distribution of income. Hence, the Atkinson welfare index is the sure income a CRRA individual would find equivalent to the prospect of being randomly assigned to be a person within the community with the given distribution of income

Sensitivity of Functional form: Poverty Rates ($1/day) with Kernel, Normal, Gamma, Adjusted Normal, Weibull distributions

Sensitivity of Functional form: Gini with Kernel, Normal, Gamma, Weibull distributions

Sensitivity of GDP Source: Poverty Rates ($1/day) with PWT, WB, and Maddison

Sensitivity of Source of GDP: Gini with PWT, WB, and Maddison

Sensitivity of Interpolation Method: Poverty Rates 1$/day with Nearest, Linear, Cubic and Baseline

Sensitivity of Interpolation Method: Gini with Nearest, Linear, Cubic and Baseline

Missreporting Rich don’t answer Poor don’t have houses Eliminate quintiles 1 and 3 and repeat the procedure

The WB revised China and India GDP (PPP) Following the conclusion of the International Comparisons Project (ICP) in November 2007, the World Bank has changed its methodology with respect to calculating country GDPs at PPP. This change lowered Chinese and Indian GDPs by 40% and 35% respectively Several criticisms have been made of this finding; It considers prices in urban China only (so prices are too high and real income too low). Chinese GDP in 1980 is implied to be $465, and by applying the old WB growth rates, it is $308 in 1970, which may be below the lower limit of survival In comparing the original and revised World Bank series, we see that the effect of the revision was largely to multiply each country’s GDP series by a time- invariant constant, which is the expected effect of applying the PPP adjustments derived from the ICP to all years from 1980 to We nevertheless compare the poverty and inequality estimates arising from the new WB series to our baseline estimates.

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All is not money! Easterlin Paradox: 1) Within a society, rich people tend to be much happier than poor people. 2) But, rich societies tend not to be happier than poor societies (or not by much). 3) As countries get richer, they do not get happier (after a given threshold) Implications Economic Sociologists: Relative Income UN: Human Development Index (as opposed to GDP) Environmental Movement: No growth

Problem with Easterlin Paradox: Old data (1974) No poor countries in the data set Gallup conducted a poll in Analyzed by Stevenson and Wolfers (2008)

Source: Stevenson and Wolfers (2008). Economic Growth and Subjective Well-Being: Reassessing the Easterlin Paradox *Economic Growth and Subjective Well-Being: Reassessing the Easterlin Paradox