Wage Inequality and Structural Change

1. Wage Inequality and Structural Change
Joanna Tyrowicz (GRAPE, IAAEU, UW and IZA )
Magdalena Smyk (GRAPE and WSE)
Statistics Poland, Annual Congress, Warsaw, 2018
1

2. Motivation
Income inequality is a major policy concern
• Theory: inequality should ↑ due to globalization and skill biased technological change
Feenstra and Hanson 1996; Acemoglu and Autor 2011
2

3. Motivation
Income inequality is a major policy concern
• Theory: inequality should ↑ due to globalization and skill biased technological change
Feenstra and Hanson 1996; Acemoglu and Autor 2011
• Transition countries are an interesting case, because of great inequality ↑
Milanovic 1999; Brainerd 2000
2

4. Motivation
Income inequality is a major policy concern
• Theory: inequality should ↑ due to globalization and skill biased technological change
Feenstra and Hanson 1996; Acemoglu and Autor 2011
• Transition countries are an interesting case, because of great inequality ↑
Milanovic 1999; Brainerd 2000
2

5. Motivation
Income inequality is a major policy concern
• Theory: inequality should ↑ due to globalization and skill biased technological change
Feenstra and Hanson 1996; Acemoglu and Autor 2011
• Transition countries are an interesting case, because of great inequality ↑
Milanovic 1999; Brainerd 2000
But there are substantial methodlogical challenges
• Majority of studies look at post-redistribution household (equivalized) income
Milanovic and Ersado 2012; Aristei and Perugini 2012, 2014
2

6. Motivation
Income inequality is a major policy concern
• Theory: inequality should ↑ due to globalization and skill biased technological change
Feenstra and Hanson 1996; Acemoglu and Autor 2011
• Transition countries are an interesting case, because of great inequality ↑
Milanovic 1999; Brainerd 2000
But there are substantial methodlogical challenges
• Majority of studies look at post-redistribution household (equivalized) income
Milanovic and Ersado 2012; Aristei and Perugini 2012, 2014
• Cannot account for individual characteristics
2

7. Motivation
Income inequality is a major policy concern
• Theory: inequality should ↑ due to globalization and skill biased technological change
Feenstra and Hanson 1996; Acemoglu and Autor 2011
• Transition countries are an interesting case, because of great inequality ↑
Milanovic 1999; Brainerd 2000
But there are substantial methodlogical challenges
• Majority of studies look at post-redistribution household (equivalized) income
Milanovic and Ersado 2012; Aristei and Perugini 2012, 2014
• Cannot account for individual characteristics
2

8. Motivation
Income inequality is a major policy concern
• Theory: inequality should ↑ due to globalization and skill biased technological change
Feenstra and Hanson 1996; Acemoglu and Autor 2011
• Transition countries are an interesting case, because of great inequality ↑
Milanovic 1999; Brainerd 2000
But there are substantial methodlogical challenges
• Majority of studies look at post-redistribution household (equivalized) income
Milanovic and Ersado 2012; Aristei and Perugini 2012, 2014
• Cannot account for individual characteristics
−→ Develop measures of earned income inequality, adjusting for individual characteristics
2

9. Motivation
Income inequality is a major policy concern
• Theory: inequality should ↑ due to globalization and skill biased technological change
Feenstra and Hanson 1996; Acemoglu and Autor 2011
• Transition countries are an interesting case, because of great inequality ↑
Milanovic 1999; Brainerd 2000
But there are substantial methodlogical challenges
• Majority of studies look at post-redistribution household (equivalized) income
Milanovic and Ersado 2012; Aristei and Perugini 2012, 2014
• Cannot account for individual characteristics
−→ Develop measures of earned income inequality, adjusting for individual characteristics
Question: is structural change conducive to growth in wage inequality?
2

10. Data

11. Collecting source data
• standardized data: (EU)SES, ECHP
3

12. Collecting source data
• standardized data: (EU)SES, ECHP
• standard methodology data: LSMS, census, ISSP
3

13. Collecting source data
• standardized data: (EU)SES, ECHP
• standard methodology data: LSMS, census, ISSP
• LFS and HBS
3

14. Collecting source data
• standardized data: (EU)SES, ECHP
• standard methodology data: LSMS, census, ISSP
• LFS and HBS
• ULMS, RLMS, GSOEP
3

15. Collecting source data
• standardized data: (EU)SES, ECHP
• standard methodology data: LSMS, census, ISSP
• LFS and HBS
• ULMS, RLMS, GSOEP
3

16. Collecting source data
• standardized data: (EU)SES, ECHP
• standard methodology data: LSMS, census, ISSP
• LFS and HBS
• ULMS, RLMS, GSOEP
−→ Overall 1650+ datasets, 800+ with wage data
3

17. Match between the OECD and our data
4

18. The measures
• Entropy measures: Gini coefficient and mean log deviation
5

19. The measures
• Entropy measures: Gini coefficient and mean log deviation
• Positional measures: 9th to 1st decile ratio + top (9th:5th) and bottom (1st:5th)
−→ raw inequality measures from individual wage data
5

20. The measures
• Entropy measures: Gini coefficient and mean log deviation
• Positional measures: 9th to 1st decile ratio + top (9th:5th) and bottom (1st:5th)
−→ raw inequality measures from individual wage data
• Inequality may change because
5

21. The measures
• Entropy measures: Gini coefficient and mean log deviation
• Positional measures: 9th to 1st decile ratio + top (9th:5th) and bottom (1st:5th)
−→ raw inequality measures from individual wage data
• Inequality may change because
• individuals with the same characteristics get paid differently
5

22. The measures
• Entropy measures: Gini coefficient and mean log deviation
• Positional measures: 9th to 1st decile ratio + top (9th:5th) and bottom (1st:5th)
−→ raw inequality measures from individual wage data
• Inequality may change because
• individuals with the same characteristics get paid differently
• individual characteristics change (services, educational boom, etc.)
5

23. The measures
• Entropy measures: Gini coefficient and mean log deviation
• Positional measures: 9th to 1st decile ratio + top (9th:5th) and bottom (1st:5th)
−→ raw inequality measures from individual wage data
• Inequality may change because
• individuals with the same characteristics get paid differently
• individual characteristics change (services, educational boom, etc.)
• Counterfactual distributions
5

24. The measures
• Entropy measures: Gini coefficient and mean log deviation
• Positional measures: 9th to 1st decile ratio + top (9th:5th) and bottom (1st:5th)
−→ raw inequality measures from individual wage data
• Inequality may change because
• individuals with the same characteristics get paid differently
• individual characteristics change (services, educational boom, etc.)
• Counterfactual distributions
• Parametric (linear regression): works well at the mean
5

25. The measures
• Entropy measures: Gini coefficient and mean log deviation
• Positional measures: 9th to 1st decile ratio + top (9th:5th) and bottom (1st:5th)
−→ raw inequality measures from individual wage data
• Inequality may change because
• individuals with the same characteristics get paid differently
• individual characteristics change (services, educational boom, etc.)
• Counterfactual distributions
• Parametric (linear regression): works well at the mean
• Semi-parametric (DiNardo, Fortin and Lemieux, 1996): works well at tails
5

26. The measures
• Entropy measures: Gini coefficient and mean log deviation
• Positional measures: 9th to 1st decile ratio + top (9th:5th) and bottom (1st:5th)
−→ raw inequality measures from individual wage data
• Inequality may change because
• individuals with the same characteristics get paid differently
• individual characteristics change (services, educational boom, etc.)
• Counterfactual distributions
• Parametric (linear regression): works well at the mean
• Semi-parametric (DiNardo, Fortin and Lemieux, 1996): works well at tails
• Benchmark: the US economy
5

27. Results

28. Overall time trends: Gini coefficient
6

29. Overall time trends: Gini coefficient
7

30. Overall time trends: 9th to 1st decile ratio
8

31. Overall time trends: Gini coefficient
Monthly wage - Hourly wage - Hourly wage -
original Parametric ACS2000 Parametric ACS2010
OLS RE FE RE FE RE FE
9th-to-1st
Transition 0.243 0.006 -0.183*** -0.188***
(0.167) (0.111) (0.048) (0.055)
Time 0.005 0.004 0.005 -0.006 -0.012 -0.004 -0.008
(0.030) (0.023) (0.023) (0.014) (0.015) (0.016) (0.017)
Transition#Time -0.065 -0.052* -0.052* 0.075*** 0.078*** 0.071*** 0.071***
(0.042) (0.031) (0.031) (0.020) (0.020) (0.022) (0.023)
Gini Index
Transition 0.130*** 0.065*** -0.033*** -0.033***
(0.040) (0.017) (0.008) (0.008)
Time 0.015*** 0.014*** 0.012** -0.0002 -0.0008 -0.001 -0.001
(0.005) (0.005) (0.005) (0.002) (0.003) (0.002) (0.003)
Transition#Time -0.0242*** -0.0254*** -0.0242*** 0.012*** 0.012*** 0.012*** 0.012***
(0.007) (0.006) (0.006) (0.004) (0.004) (0.003) (0.004)
Mean Log Devation
Transition 0.078*** 0.077*** -0.029*** -0-.024***
(0.0201) (0.022) (0.008) (0.007)
Time 0.027*** 0.018*** 0.014** 0.0003 0.001 0.0003 0.001
(0.008) (0.006) (0.006) (0.002) (0.003) (0.002) (0.002)
Transition#Time -0.04*** -0.037*** -0.034*** 0.004 0.003 0.004 0.002
(0.011) (0.009) (0.009) (0.003) (0.003) (0.003) (0.003)
Obs. 548 548 548 418 418 418 418
Countries 31 31 31 30 30 30 30
9

32. Time trends: summary
• Essentially, immediate adjustment in wage inequality in transition countries
−→ decompression of wages srtong despite institutional arrangements
10

33. Time trends: summary
• Essentially, immediate adjustment in wage inequality in transition countries
• Growth in inequality mostly due to decompressing lower half of the earnings distribution
−→ decompression of wages srtong despite institutional arrangements
10

34. Time trends: summary
• Essentially, immediate adjustment in wage inequality in transition countries
• Growth in inequality mostly due to decompressing lower half of the earnings distribution
• Very slow negative trend towards lower earnings dispersion
−→ decompression of wages srtong despite institutional arrangements
10

35. Time trends: summary
• Essentially, immediate adjustment in wage inequality in transition countries
• Growth in inequality mostly due to decompressing lower half of the earnings distribution
• Very slow negative trend towards lower earnings dispersion
• Counterfactual distributions more compressed in transition countries
−→ decompression of wages srtong despite institutional arrangements
10

36. Time trends: summary
• Essentially, immediate adjustment in wage inequality in transition countries
• Growth in inequality mostly due to decompressing lower half of the earnings distribution
• Very slow negative trend towards lower earnings dispersion
• Counterfactual distributions more compressed in transition countries
• Convergence towards similar characteristics
−→ decompression of wages srtong despite institutional arrangements
10

37. Time trends: summary
• Essentially, immediate adjustment in wage inequality in transition countries
• Growth in inequality mostly due to decompressing lower half of the earnings distribution
• Very slow negative trend towards lower earnings dispersion
• Counterfactual distributions more compressed in transition countries
• Convergence towards similar characteristics
• No convergence towards similar wages
−→ decompression of wages srtong despite institutional arrangements
10

38. Structural change
Table 1: Wage compression and the indicators of structural change
5th-to-1st 9th-to-5th
Raw Parametric DFL Raw Parametric DFL
High-skilled Neg 0 Neg Neg Neg Neg
#Transition 0 0 Neg Neg Neg 0
Obs. 330 289 289 289 330 289
Countries 26 25 25 25 26 25
11

39. Structural change
Table 1: Wage compression and the indicators of structural change
5th-to-1st 9th-to-5th
Raw Parametric DFL Raw Parametric DFL
High-skilled Neg 0 Neg Neg Neg Neg
#Transition 0 0 Neg Neg Neg 0
Obs. 330 289 289 289 330 289
Countries 26 25 25 25 26 25
R&D Neg 0 Neg Neg Neg 0
#Transition Neg 0 Neg Neg 0 0
Obs. 387 344 344 344 387 344
Countries 31 30 30 30 31 30
11

40. Structural change
Table 1: Wage compression and the indicators of structural change
5th-to-1st 9th-to-5th
Raw Parametric DFL Raw Parametric DFL
High-skilled Neg 0 Neg Neg Neg Neg
#Transition 0 0 Neg Neg Neg 0
Obs. 330 289 289 289 330 289
Countries 26 25 25 25 26 25
R&D Neg 0 Neg Neg Neg 0
#Transition Neg 0 Neg Neg 0 0
Obs. 387 344 344 344 387 344
Countries 31 30 30 30 31 30
Trade Neg - - Neg 0 0
#Transition 0 0 Neg 0 Pos 0
Obs. 488 416 416 416 488 416
Countries 31 30 30 30 31 30
11

41. Structural change
Table 1: Wage compression and the indicators of structural change
5th-to-1st 9th-to-5th
Raw Parametric DFL Raw Parametric DFL
High-skilled Neg 0 Neg Neg Neg Neg
#Transition 0 0 Neg Neg Neg 0
Obs. 330 289 289 289 330 289
Countries 26 25 25 25 26 25
R&D Neg 0 Neg Neg Neg 0
#Transition Neg 0 Neg Neg 0 0
Obs. 387 344 344 344 387 344
Countries 31 30 30 30 31 30
Trade Neg - - Neg 0 0
#Transition 0 0 Neg 0 Pos 0
Obs. 488 416 416 416 488 416
Countries 31 30 30 30 31 30
Employment Neg Pos Neg Neg 0 Neg
#Transition 0 Pos Neg Neg 0 Pos
Obs. 470 403 403 403 470 403
Countries 31 30 30 30 31 30
11

42. Structural change
Table 1: Wage compression and the indicators of structural change
5th-to-1st 9th-to-5th
Raw Parametric DFL Raw Parametric DFL
High-skilled Neg 0 Neg Neg Neg Neg
#Transition 0 0 Neg Neg Neg 0
Obs. 330 289 289 289 330 289
Countries 26 25 25 25 26 25
R&D Neg 0 Neg Neg Neg 0
#Transition Neg 0 Neg Neg 0 0
Obs. 387 344 344 344 387 344
Countries 31 30 30 30 31 30
Trade Neg - - Neg 0 0
#Transition 0 0 Neg 0 Pos 0
Obs. 488 416 416 416 488 416
Countries 31 30 30 30 31 30
Employment Neg Pos Neg Neg 0 Neg
#Transition 0 Pos Neg Neg 0 Pos
Obs. 470 403 403 403 470 403
Countries 31 30 30 30 31 30
High-tech export 0 0 Pos 0 Neg 0
#Transition Neg 0 Neg 0 Pos 0
Obs. 458 399 399 399 458 399
Countries 31 30 30 30 31 30
11

43. Conclusions

44. Conclusions
We provide consistent estimates of unconditional and conditional measures of earnings
inequality
• Wage dispersion increased rapidly in early transition, mainly through prices
12

45. Conclusions
We provide consistent estimates of unconditional and conditional measures of earnings
inequality
• Wage dispersion increased rapidly in early transition, mainly through prices
• Initially level was lower, now is higher than Western Europe
12

46. Conclusions
We provide consistent estimates of unconditional and conditional measures of earnings
inequality
• Wage dispersion increased rapidly in early transition, mainly through prices
• Initially level was lower, now is higher than Western Europe
• Decline, but very slow
12

47. Conclusions
We provide consistent estimates of unconditional and conditional measures of earnings
inequality
• Wage dispersion increased rapidly in early transition, mainly through prices
• Initially level was lower, now is higher than Western Europe
• Decline, but very slow
• Conditional estimates reveal that workers still more similar in transition countries than
in Western Europe
12

48. Conclusions
We provide consistent estimates of unconditional and conditional measures of earnings
inequality
• Wage dispersion increased rapidly in early transition, mainly through prices
• Initially level was lower, now is higher than Western Europe
• Decline, but very slow
• Conditional estimates reveal that workers still more similar in transition countries than
in Western Europe
12

49. Conclusions
We provide consistent estimates of unconditional and conditional measures of earnings
inequality
• Wage dispersion increased rapidly in early transition, mainly through prices
• Initially level was lower, now is higher than Western Europe
• Decline, but very slow
• Conditional estimates reveal that workers still more similar in transition countries than
in Western Europe
Structural change per se is not a big force
• Skill biased technological change is correlated with lower earnings inequality
• No stable results for globalization
Inequality form below more relevant than that of above.
12

50. Questions or suggestions?
Thank you!
w: grape.org.pl
t: grape org
f: grape.org
e: j.tyrowicz@grape.org.pl
13