Labor Market Inequalities, Taxes and Transfers in the Welfare State: The Swedish Experience

1. Labor Market Inequalities, Taxes and Transfers in the Welfare State:The Swedish Experience Bertil Holmlund Uppsala University (UCLS), CESifo and IZA IZA Workshop on the Effects of the Economic Crisis on the Labor Market, Unemployment and Income Distribution Bonn, February 21– 22, 2013

2. The Economist Feb 2nd 2013:“The Nordic countries – The next supermodel.Politicians from both right and left could learn from the Nordic countries”

3. The Swedish welfare state • The traditional Swedish model: • Coordinated wage bargaining • High employment • Small wage differentials • Progressive taxes • Small income differentials • Generous social insurance • The model in crisis? • High unemployment • Increasing inequalities • Market forces • Policy changes (tax and benefit reforms, deregulations)

4. Purpose • Document inequality trends in the Swedish labor market • (un)employment • hours • wages and earnings • Focus on “skill groups” (age, education and gender) • Examine how taxes and transfers modify the linkages between labor market inequalities and income inequalities

5. Outline • The big picture: the labor market in booms and slumps • Labor market trends by skill: (un)employment and hours (LFS) • Earnings and wages (LINDA + LFS) • Taxes, transfers and the distribution of income (LINDA)

6. The bigpicture • GDP • Unemployment • Employment • Labor force participation • Hours worked • Trends in the income distribution

7. GDP gap (deviation from trend GDP), percent

8. Unemployment 16-64

9. LFPR and employment/population 16-64

10. Hours/population 16-64 (week)

11. Hours/worker and hours/population 16-64 (week)

12. Ginicoefficients

13. Topincomeshares

14. Labor market trends by skill • Skill measured by age/education/gender • Age groups: 20-24, 25-34, 35-44, 45-54, 55-64 • Prime age: 25-54 (or more narrow definition: 35-44) • Education groups: basic, high school, university • At most 30 skill groups: age/education/gender

15. Education shares of population 25-54, both sexesbasic educ in blue, university in green

16. Employment/population by educ, men 25–54basic educ in blue, university in green

17. Employment/population by educ, women 25–54basic educ in blue, university in green

18. Unemployment rates by educ, men 25–54basic educ in blue, university in green

19. Unemployment rates by educ, women 25–54basic educ in blue, university in green

20. Hours/population by educ (week), men 25–54basic educ in blue, university in green

21. Hours/population by educ (week), women 25–54basic educ in blue, university in green

22. Hours/worker by educ (week), men 25–54basic educ in blue, university in green

23. Hours/worker by educ (week), women 25–54basic educ in blue, university in green

24. The young and the old • So far prime ages 25-54 • What about the young and the old?

25. Employment/population by educ, men 20-24

26. Hours/population by educ, men 20-24

27. Unemployment rates by educ, men 20-24

28. Employment/population by educ, men 55-64

29. Employment/population by educ, women55-64

30. Hours/population by educ, men 55-64

31. Hours/population by educ, women55-64

32. Unemployment rates by educ, men 55-64

33. Unemployment rates by educ, women55-64

34. Summing up • Low educated people, especially women, are falling behind since the early 1990s • Falling employment rates Prime-aged women: - 30 perc. points since 1990 • Increasing unemployment rates Prime-aged women: + 10 perc. points since early 2000 • A marked trend increase in employment/pop and hours/pop among oldermen, all education groups, since the mid 1990s + 10 perc. points • A trend increase in hours/worker in all female education groups + 15 percent since the late 1980s among the low educated Convergence of hours/worker across education levels

35. Inequalities in (un)employment and hours • At most 30 groups (age/educ/gender) • Standard deviations of • ln (e/pop) • ln (hours/pop) • ln (e/lf) = ln (1-u) ≈ -uu is the unemployment rate • lnu

36. Dispersion of employment rates (30 groups)

37. Dispersion of employment and hours (30 groups)

38. Dispersion of unemployment and log unemployment

39. Dispersion of employment and hours (18 groups, 25-54)

40. Earnings and wages • At most 30 groups • Annual earnings: age/educ/gender (register data, LINDA) • Annual hours: age/educ/gender (labor force surveys) • Hourly wage = earnings/hours • Educational differentials (employment, earnings, wages) • Micro data on earnings and wages (LINDA) • So far only 1992 and 2004 (wage rates)

41. Dispersion ofearnings, hours and wages

42. Earnings, wage and employmentratios, menuniveduc/basiceduc, 35-44

43. Earnings, wageand employmentratios, womenuniveduc/basiceduc, 35-44

44. Educational wage ratios 35-44, men

45. Educational wage ratios 35-44, women

46. Real earnings, men 35-44

47. Real earnings, women 35-44

48. Real wages, men 35-44

49. Real wages, women 35-44

50. Wage dispersion in micro data (LINDA)