1. Inequality and Globalization: Judging the Data A Presentation at The World Bank June 18, 2002 A comparison of the UTIP data set on world pay inequalities with the Deininger-Squire data set on world income inequalities

2. by James K. Galbraith and Hyunsub Kum The University of Texas Inequality Project http://utip.gov.utexas.edu

3. Two Data Sets Deininger & Squire Income inequality Household surveys Comprehensive Official & Unofficial Bibliographic Gini coefficient UTIP-UNIDO Pay inequality Establishment surveys Narrow Official Data Only Calculated “in house” Theil’s T statistic

4. Key Questions Is the coverage sufficient? Are the numbers accurate? Are the data right for the research question? Can we do better?

5. The Deininger-Squire data set suffers from major deficiencies of coverage... Version of D&S used by Dollar and Kraay, “Growth is good for the poor.”

6. The UTIP-UNIDO Data Set has fewer gaps ….

7. Comparing Coverage UTIP coverage count for this table is based on UNIDO ISIC 2001 edition only, where matching data for GDP per capita also available; total UTIP coverage is about 3200 observations.

8. Judging Accuracy Some Useful Indicators: Consistency over time Consistency across space Correspondence to known events Consistency with “common knowledge”

9. Consistency across time… UTIP-UNIDOD&S Bars indicate standard deviation around average value for each year

16. Note the reported heterogeneity of North America and Europe, and the homogeneous measurements for Asia, with low inequality comparable to northern Europe and Canada.

17. Elementary economics suggests these differences in inequality are implausible in an integrated region. If inequality were really so much greater in France than in Germany, wouldn’t low- skilled French workers migrate to Germany to sweep the streets?

18. The UTIP data and the D&S data cannot both be right. If Indonesia or India has highly unequal pay, how does it arrive at highly equal incomes – more equal than Australia? Through a strongly redistributive welfare state? Ha! Alternatively, if low Ginis in those countries reflect egalitarian but impoverished agriculture, then why are Ginis so high in agrarian Africa?

19. Correspondence to known events…

20. Revolution Military Coup GATT Entry Falklands War Banking Crisis War Tiananmen Data for China drawn partly from State Statistical Yearbook

22. Samples from the World Bank High Quality Data Set

24. European Countries Ranked in Order of Inequality by the World Bank, Low to High 1970 UK Sweden Belgium Netherlands Finland Germany Denmark Greece Spain Norway Portugal Italy France 1992 Spain Finland Belgium Netherlands Italy Germany UK Sweden Denmark Norway France Greece Portugal Source: Deininger and Squire Data are for nearest available year Consistency with “common knowledge”…

25. Using the UTIP inequality rankings, one finds that countries in Europe that have less inequality also have less unemployment. European Countries Ranked in Order of Industrial Earnings Inequality Using the Theil Statistic, Low to High 1970 Norway Finland Denmark Germany Netherlands UK Belgium Sweden Greece France Austria Italy 1992 Norway Denmark Finland Netherlands Sweden UK Germany Belgium Austria Greece Portugal France Spain Italy Source: OECD STAN and authors’ calculations Data are for year reported.

26. Consistency over time and space together… 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.

27. 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

28. 1963 to 1969

29. 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.

30. 1977 to 1983

31. 1981 to 1987 … the Age of Debt

32. 1984 to 1990

33. 1988 to 1994 The age of globalization…

34. A regression of pay inequality on GDP per capita and time, 1963-1998.

35. The time effect from a two-way fixed effects panel data analysis of inequality on GDP per capita, with time and country effects.

36. Globalization and Inequality Overall, pay data reveal a strong upward trend in inequality across countries, over time, with an inflection point in the early 1980s. It is a reasonable inference that global macroeconomic forces, notably rising real interest rates and the debt crisis, followed by the implementation under pressure of neoliberal policies, were responsible for this worldwide pattern of rising inequality in pay structures.

37. Are Pay Data Appropriate? There are some instances of selection bias: where industrial job losses affect mainly low-income workers, increasing inequality will be understated – especially in the UK. In very rich countries, trends in capital income can lead to large differences between the trend of pay inequality and of income inequality – especially in the U.S.

38. But in General… Trade and technology do affect income mainly through pay. Manufacturing pay is a fair indicator of the movement of all pay. Pay is a large subset of all income. Most income dynamics are derived from pay dynamics.

39. But in General… Trade and technology do affect income mainly through pay. (Galor and Tsiddon 1997, Aghion and Howitt 1997.) Manufacturing pay is a fair indicator of the movement of all pay. (Cf. Galbraith & Wang on China, 2001.) Pay is a large subset of all income. (Williamson 1982) Most income dynamics are derived from pay dynamics. (Acemoglu 1997).

40. Conclusion: Used with care, good pay data are better than bad income data.

41. Can we do better? UNIDO industrial data has 28 sectors. OECD’s STAN has 39 sectors. Chinese State Statistics ~ 500 cells Russian State Statistics ~ 900 cells Brazil, Mexico: monthly observations Will the World Bank take up the challenge of collecting national data in detail?