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Trends in Earnings Inequality

Trends in Earnings Inequality. Start with some facts. Comparisons of wage inequality across countries and changes over time Have tried to have as consistent a measure of earnings as possible but some caution warranted E.g. earnings measure for France is net, for others gross.

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Trends in Earnings Inequality

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  1. Trends in Earnings Inequality

  2. Start with some facts • Comparisons of wage inequality across countries and changes over time • Have tried to have as consistent a measure of earnings as possible but some caution warranted • E.g. earnings measure for France is net, for others gross. • But some conclusions probably safe e.g. less wage inequality in Sweden than US • For most it is weekly earnings for FT workers, both men and women

  3. Overall Wage Inequality: Panel A

  4. Overall Wage Inequality: Panel B

  5. Overall Wage Inequality: Panel C

  6. Top-End Wage Inequality: Panel A

  7. Top-End Wage Inequality: Panel B

  8. Top-End Wage Inequality: Panel C

  9. Bottom-End Wage Inequality: Panel A

  10. Bottom-End Wage Inequality: Panel B

  11. Bottom-End Wage Inequality: Panel C

  12. Some General Conclusions • Sizeable differences in wage inequality across countries: • High in US, lower in Europe, lowest in Nordic • Countries like US, UK show marked upward trend, other countries a smaller trend, some stability or slight fall • Rise in top-end wage inequality perhaps more pronounced • Rises in bottom-end wage inequality perhaps more episodic

  13. Understanding Changes in Wage Inequality • Think of an earnings equation for log wages • Take variance: Var(w)=β’Cov(X) β+Var(ε) • Think of one ‘X’ variable: Var(w)=β2Var(X)+Var(ε) Not quite P90/P10 but closely related so discussion still relevant

  14. Change in wage inequality might be due to: • Rise in Var(X) i.e. the composition of the population is changing in such a way as to lead to rising wage inequality • Rise in β2 i.e. change in returns to characteristics that raises inequality for given distribution of X e.g. rise in return to education. • Rise in Var(ε) i.e. residual wage inequality – ‘within-group’ wage inequality

  15. Most developed literature is for US • Change in Var(X) trivial • Change in β important: • Rise in returns to education • Rise in returns to experience • But falling gender pay gap (often missed as focus often just on men and women separately) • Rise in residual wage inequality

  16. Rise in Returns to Education

  17. Rising Returns to Experience

  18. Interpreting Rising Residual Wage Inequality • Sometimes interpreted as identical people now get more different wages – more noise/luck in the system • But X very crude so residual contains many things so not sure this line of reasoning is sensible • R2 in earnings function actually very stable • 1-R2=Var(ε)/Var(w)

  19. Stable R2 in Earnings Function- UK NES Data

  20. Lemieux Interpretation • Residual wage inequality varies with X – heteroscedasticity • Higher for more education and more experienced • Change in distribution of X can explain upward rise • Also different wage series show different trends • Controversial – Autor, Katz, Kearney dispute it

  21. Lemieux Picture

  22. Interpretation of Rising Returns to education/experience • Wages positively correlated with education/experience • Most common view is that this represents a rise in the return to skill • Why might this have occurred • Demand shifts • Globalisation • technology • Supply shifts • institutions

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