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