Measuring Inequality

1. Measuring Inequality A practical workshop On theory and technique San Jose, Costa Rica August 4 -5, 2004

2. Panel Session on: Econometric Analysis Using Inequality Measures

3. by James K. Galbraith and Enrique Garcilazo The University of Texas Inequality Project http://utip.gov.utexas.edu Session 5

4. A Global Coup? Looking Beyond Technology and Trade at the Causes of Rising Inequality in the Age of Globalization

5. With the UTIP data, we can review changes in global inequality both across countries and through time. Nothing comparable can be done with the Deininger and Squire data set, for the measurements are too sparse and too inconsistent.

6. The Scale Brown: Very large decreases in inequality; more than 8 percent per year. Red Moderate decreases in inequality. Pink: Slight Decreases. Light Blue: No Change or Slight increases Medium Blue: Large Increases -- Greater than 3 percent per year. Dark Blue: Very Large Increases -- Greater than 20 percent per year. h

7. 1963 to 1969

8. 1970 to 1976 The oil boom: inequality declines in the producing states, but rises in the industrial oil-consuming countries, led by the United States.

9. 1977 to 1983

10. 1981 to 1987 … the Age of Debt Note the exceptions to rising inequality are mainly India and China, neither affected by the debt crisis…

11. 1984 to 1990

12. 1988 to 1994 The age of globalization… Now the largest increases in inequality in are the post-communist states; an exception is in booming Southeast Asia, before 1997…

13. Simon Kuznets in 1955 argued that while inequality could rise in the early stages of industrialization, in the later stages it should be expected to decline. This is the famous “inverted U” hypothesis. Recent studies based on Deininger & Squire find almost no support for any relationship between inequality and income levels. We believe, however, that in the modern developing world the downward sloping relationship should predominate, particularly in data drawn from the industrial sector.

14. A regression of pay inequality on GDP per capita and time, 1963-1998. The downward sloping income-inequality relation holds, but with an upward shift over time…

15. Milanovic Unweighted Inequality Between Countries The time effect from a two-way fixed effects panel data analysis of inequality on GDP per capita, with time and country effects.

16. This pattern resembles the general pattern we associate, within countries, with the coup d’etat:

17. Unemployment, Inequality and the Policy of Europe1984-2000 A Presentation to the European Commission Lectures Program Brussels June 29, 2004

18. The Standard View • Employment is determined in a labor market. • Labor markets are national. • Flexibility reduces unemployment. • The United States has more jobs than Europe, but only at the expense of more inequality. • Is this good or bad? A political question

19. The U.S. Case • In the American case, we have measured inequalities of pay (weekly earnings) in the manufacturing sector on a monthly basis going back to January, 1947, for sectors that are continuously measured since that time. The result gives us a time series of pay inequalities in a key part of the American industrial economy.

20. Wage Inequality and Some Historical Events Recession Vietnam War Recession Recession Korean War Recession Recession TRUMAN EISENHOWER JFK LBJ NIXON FORD REAGAN BUSH CARTER CLINTON

21. Wage Inequality and Unemployment Open UnemploymentRate A strong positive correlation between the unemployment rate and wage inequality in the US is exhibited here.

22. The U.S and Europe • First, let’s compare U.S. inequality to that in each European country. • Then, let’s compare U.S. inequality to that in Europe-as-a-whole • Finally, we ask, what is the relationship between unemployment and inequality in Europe?

23. EHII -- Estimated Household Income Inequality for OECD Countries Low High

24. Now, is pay inequality in Europe really lower than in the U.S.? It depends on how you count… The value for the U.S. on this scale is about 0.29, or roughly the height of the blue bar. Overall European manufacturing pay inequality –including differences between countries – is higher than in the US.

25. “Data! Data! Data! I can’t make bricks without clay.” Sherlock Holmes The Adventure of the Copper Beeches

26. European Regional Panel Data Set • Pay across Sectors by European Region • From Eurostat’s REGIO • Annual 1984-2000, up to 159 Regions • Enables us to compute measures of inequality within and between regions. • Permits construction of a panel with which we can isolate regional, national and continental effects

28. Contribution of European Provinces in Inequality Across the European continent, late 1990s.

30. A Simple Theory of European Unemployment • Demand Factors: • GDP Growth and Investment • Wealth and Demand for Services • Supply Factors: • Inequalities of Pay • Transition to Work for Youth

31. Hypotheses • Growth reduces unemployment. (-) • Higher incomes mean fewer unemployed. (-) • Inequality increases unemployment (+) • More younger workers means more unemployed. (+)

32. Regression analysis of European unemployment

33. Country Fixed Effects Show the Differences Between Countries Not Explained by the Explanatory Variables. Centralized wage bargains? Emigration?

35. Time Fixed Effects Show the Movements of Unemployment Across All Regions, After Taking Account of the Regressors

36. Conclusions • Labor markets are not national. • Macroeconomic conditions matter. • Youth is a problem. • Equality of pay helps. • Flexibility does not. • Small countries have an advantage. • EU policies started off very poorly. • But there is hope for the future.

37. Beating the Bank at its Own Game:Estimating Income Inequalityfrom measures ofpay inequalityand other economic information

38. Estimating the DS Gini Coefficients from Pay Inequality and other variables. Dependent variable is log(DSGini)

39. EHII -- Estimated Household Income Inequality for OECD Countries Low High

40. Mean Value and Confidence Interval of Differences • eap: East Asia and Pacific • eca: Eastern Europe and Central Asia • lac: Latin and Central America • mena: Middle East and North Africa • na: North America • sas: South Asia • ssa: Sub Saharan Africa • we: Western Europe

41. Major Differences Between D&S Gini and EHII Gini

42. Trends of Inequality in the D&S Data

43. Trends of Inequality in subset of EHII 2.2 Data matched to D&S

44. Trends of Inequality in Full EHII 2.2 Dataset (N=3,179)

45. Income Inequality in North America

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