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The changing nature of wage inequality

Abstract

The paper reviews recent developments in the literature on wage inequality with a particular focus on why inequality growth has been particularly concentrated in the top end of the wage distribution over the last 15 years. Several possible institutional and demand-side explanations are discussed for the secular growth in wage inequality in the United States and other advanced industrialized countries.

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Notes

  1. Berman et al. (1994) dismiss the trade explanation on the basis that most of the increase in the use of skilled labor happens within industries, while a traditional Heckscher-Ohlin would predict that changes should happen between industries. More recently, however, authors such and Feenstra and Hanson (2003) have pointed out that trade in intermediate inputs, which can lead to within-industry changes, was a leading alternative explanation to SBTC for the increase in the relative demand for skill. Their focus on intermediate, as opposed to final goods and services, is consistent with popular perceptions that offshoring is now the key way in which globalization affects domestic employment and inequality.

  2. Katz and Autor (1999) re-examined the May-ORG data and concluded that within-group inequality had increased in the 1970s. Based on this evidence and the earlier findings from the March CPS data by Juhn et al. (1993), Acemoglu (2002) concluded that “...there is considerable evidence that residual and overall inequality started to increase during the 1970s”, while correctly noting that “there is less uniformity among data sources regarding the behavior of residual inequality than the returns to schooling”. (Acemoglu, 2002, page 65). This finding was based, however, on a comparison between May 1973 data where observations with missing wages were dropped from the sample and May 1979 data where missing wages were replaced by their allocated values. Both Lemieux (2006b) and Autor et al. (2005) have now confirmed that within-group inequality does not increase in the 1970s when allocated wages are dropped in 1979 too (as in Card and Lemieux, 1996, or DiNardo et al. 1996).

  3. This is a more general way of adjusting for experience than in a standard Mincer-type regression where a low-level polynomial in experience is included in the regression. The approach used here is equivalent to running separate regressions for each year with a full set of experience and education (five groups) dummies and then averaging out (using a fixed weighted average across experience groups) the returns to education within each experience cell.

  4. See, for example, Piketty (2003) for France, Piketty and Saez (2003) for the United States, Saez and Veall (2005) for Canada, and the summary piece by Piketty and Saez (2006).

  5. Gabaix and Lanthier (2007) report a more standard elasticity of compensation with respect to market capitalization of 0.3 in a standard panel data regression, which is more or less comparable to Bebchuk and Grinstein (2005). They include an aggregate measure of market capitalization instead of standard year dummies, however, and add the effect of that measure (0.7) to get a “full” elasticity of about 1. The problem, however, is that this purely time-series variable could also capture other proposed explanations (social norms, etc.) for the change in executive pay. Without this questionable approach for estimating the elasticity, their results would be very much in line with those of Bebchuk and Grinstein (2005).

  6. International Herald Tribune, Dec. 13, 2006.

  7. The updated numbers of the Piketty and Saez (2003) study indicate that the 99.9th percentile of the salary distribution was $837,892 in 2004 (data accessed on Emmanuel Saez’s website at: http://elsa.berkeley.edu/~saez/TabFig2004.xls). See also Kaplan and Rauh (2007) for some estimates of the contribution of high earners other than CEOs to the top end of the distribution.

  8. This is the top-code on annual earnings on the main job in the March CPS that prevailed until 2002. The top-code has been revised up to $200,000 for 2002 earnings, which is still, however, below the 99th percentile of the distribution of earnings in the tax data of Piketty and Saez ($251,000 in 2004). The top-code on earnings in the ORG supplement of the CPS is also $150,000 a year for individuals who have worked for all 52 weeks of the year (top-code of $2884.61 a week), while the top-code in the 2000 census is only slightly larger ($175,000 a year) than in the pre-2002 March CPS.

  9. The top-code on annual earnings in the PSID was $99,999 until 1982, which was well within the top 1% of the earnings distribution back then. It was subsequently increased to very large values that only bind for a handful of individuals.

  10. The overall effect of unions on wage inequality is usually understood as the interplay between the effect of the inequality enhancing “between” effect (union raise average wages of similar workers) and the inequality reducing “within” effect (union reduce wage dispersion among similar workers). At the bottom of the distribution, both the within and between effects go in the same direction (raise wages of low wage workers), which explains why the overall effect is large of positive. At high enough percentiles, however, (negative) within effects can possibly dominate (positive) between effects, explaining why the overall effect eventually turns negative in Fig. 6.

  11. Performance pay is measured by looking at whether a worker received piece rates, commissions, or bonuses on his current job (looking at all observations of a given worker–employer match). Bonuses are by far the most important component (in terms of workers affected) of this performance–pay measure.

  12. One major criticism of the earlier SBTC literature is that it often tended to simply attribute changes unexplained by other factors to technological change, instead of coming up with precise measures of technological change that can then be tested on equal footing against other proposed explanations such as de-unionization (see Card and DiNardo 2002, for more discussion of this). So even though the more sophisticated explanation of the effect of technological change based on the distinction between skilled and routine tasks is intuitively appealing, there is not enough direct evidence yet to conclude that it is the main source of change in wage inequality over the last 15 years.

  13. This is consistent with Card and DiNardo (2002) who also argue that the relative poor wage performance of technology-related occupations is hard to reconcile with the SBTC story.

  14. Redoing the graph for other wage thresholds such as the 90th percentile yield very similar results.

  15. By contrast, Goos and Manning (2007) find that the polarization of the labor market grew smoothly over both the 1980s and the 1990s in Britain.

  16. Mincer (1974) argues that returns to potential experience are higher for individuals who invest more in on-the-job training than for workers who invest less. Similarly, Becker (1967) develops a human capital investment model where workers have heterogenous returns to education and discount rates. The fact that different people face different returns to education is also front and central in the literature on the estimation of the causal effect of education on earnings (see, for example, Card, 2001).

  17. Dew-Becker and Gordon (2005) mention the superstar phenomena as a possible source of growth in top-end inequality.

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Acknowledgements

I would like to thank Christian Dustmann and three anonymous referees for their comments, and the Social Sciences and Humanities Research Council of Canada (through TARGET) for the research support.

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Correspondence to Thomas Lemieux.

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Responsible editor: Christian Dustmann

A preliminary version of this paper was presented at the 20th Annual Conference of the European Society for Population Economics held in Verona in June 2006.

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Lemieux, T. The changing nature of wage inequality. J Popul Econ 21, 21–48 (2008). https://doi.org/10.1007/s00148-007-0169-0

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Keywords

  • Inequality
  • Institutions
  • Technological change

JEL Classification

  • J24
  • J31
  • J51