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Decomposition of Changes in Korean Wage Inequality, 1998–2007

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Abstract

South Korea experienced a steep increase in wage inequality between the Asian financial crisis in 1997 and the global financial crisis in 2008. This paper investigates the causes of the sharp change during that time period by looking at the contributions of changes in the distributions of schooling and unionization. The effects are estimated applying two robust distributional function based decomposition methods on Korean Labor and Panel Survey data for the time period 1998–2007. The results suggest changes in the distribution of schooling can explain about 10 % of the changes in the 90/50 percentile wage gap. The declining unionization rate does not have much impact on the upward trend of Korean wage inequality. In addition, aggregate decomposition results suggest that changes in labor force composition can explain a significant proportion of the total wage changes in the upper-tail of the distribution. Based on our findings we provide a list of policy recommendations to address the wage inequality issue in Korea.

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Notes

  1. By following the convention, from hereafter we denote Korea for South Korea.

  2. a measure of inequality ranging from 0 where everyone has identical incomes to 1 where all income goes to only one person.

  3. Kuznets (1955) predicted that in the process of economic development, income inequality first rises, reaches the peak and falls after a certain critical threshold development stage and income level.

  4. Income inequality in Korea reaches it’s peak in the mid-1970s when the gini coefficient was more than 0.37.

  5. The data source of 10 % income share is ‘The World Top Incomes Database’.

  6. The report is available at http://www.oecd.org/publications/factbook/

  7. We mainly focus on wage inequality in this paper because according to the 2011 OECD report about two thirds of the overall income inequality at household level can be explained by the changes in wage inequality.

  8. See Yoo (1998), Kang and Yun (2008), Sung (2009), Kim and Kim (2012) among others.

  9. The details of these two decomposition methods are discussed in the next section.

  10. The seminal work by Oaxaca (1973) and Blinder (1973) is known as Oaxaca-Blinder decomposition method.

  11. For example, if Λ(⋅) is a logistic function, β(y) can be estimated by creating a dummy variable 1{Y i <y} indicating whether the value of Y i is below y and running a logit regression of 1{Y i <y} on X i to estimate β(y). Similarly the link function is the identity function the probability model is a linear probability model and the probability model is probit when the link function is normal CDF.

  12. Note that alternatively we can also define a counterfactual distribution function as F α (w t=0;x t=1). In that case the composition effect is F α (w t=0;x t=1)−F α (w t=0) and the wage structure effect is F α (w t=1)−F α (w t=0;x t=1). The (Machado and Mata 2005) decomposition method gives qualitatively similar results by using either counterfactual distribution function.

  13. RIF regression consists of running a regression of a transformation, namely the recentered influence function. See (Firpo et al. 2009) for details.

  14. The basic control variables are age, square of age, household size, dummy variables for sex, married or not, types of workers and industry dummies. These standard control variables are used by Ghosh (2014), Chernozhukov et al. (2013), Firpo et al. (2009), Firpo et al. (2007), Autor et al. (2008), Machado and Mata (2005), Card (1998) and DiNardo et al. (1996) among others.

  15. The average age does not go up exactly 10 years because of the inclusion of additional observations in 2007 and also due to the small attrition rate.

  16. Scale measures the percentage change in wage at 75th qunatile compared to the 25th quantile workers. Skewness is a measure of the asymmetry of the probability distribution. For symmetric distribution tje mean is equal to the median and hence the distribution will have zero skewness. The peakedness of the probability distribution function is measured by kurtosis. Gini coefficient is a measure of inequality of income distribution where 0 means no income inequality and 1 corresponds to perfect inequality.

  17. The Korean Labor and Income Panel Survey consists of only urban population implying the gini coefficient for the entire population may differ from our sample.

  18. Kwon (2001) focuses on a subsidy for unemployed workers and redesigned national pension and health care system.

  19. Since the area under a probability density function represents probability, we interpret the increase in area under a wage density function as more job opportunities in that specific wage range.

  20. Nahm (2008) defines the middle class as those workers whose wages are 75 to 150 % of the median wage of that particular year.

  21. A small part of the left tail of wage density function remains unchanged while the rest of the part of the distribution shifts to the left in 2007.

  22. The third row of each block in the middle and bottom panel shows the percentage change of wage inequality explained by that block variable on the wage gap specified in the column variable.

  23. See Fortin et al. (2011) for detailed discussion on decomposition methods and causality.

  24. From the second panel of Table 6 we note that some college (14.5) and college workers (7.3) contribute 87 % to the total change of 24.8 % workers who moved from skilled to unskilled sector.

  25. From the third column of the middle panel of Table 6 we see that the employment share of post college graduate workers has increased about 0.06/0.31=19 % during the period 1998 to 2007.

  26. Highly skilled workers refer to only post college graduate workers.

  27. The trends of both 90/10 wage ratio is shown in left panel of Fig. 3.

  28. Kang and Yun (2008) used a modified Oaxaca-Blinder type decomposition method developed by Yun (2006).

  29. The wages of nonunion workers fall due to the increased supply of squeezed out workers from union jobs.

  30. For U.S. labor market data Freeman (1980), Kahn (2000), Card et al. (2004) and Koeniger et al. (2007) among others also find that unions reduce wage inequality.

  31. These results imply that unions have a stronger between effect (inequality increasing effect) on the 90/50 wage gap and larger within effect (inequality decreasing effect) on the 50/10 wage gap.

  32. The results are inconsistent because both the decomposition methods give exactly opposite sign.

  33. See Table 1 for details.

  34. This argument offers a possible explanation why the results in both the methods are not qualitatively same for unionization effect.

  35. The report is available at http://www.weforum.org/reports

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Authors and Affiliations

  1. Department of Economics, University of Oklahoma, 308 Cate Center Drive, Room 158 CCD1, Norman, OK, 73072, USA

    Pallab Kumar Ghosh

  2. Korea Institute for Industrial Economics & Trade, 370 Sicheong-daero, Sejong, 30147, Korea

    Jae Yoon Lee

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  1. Pallab Kumar Ghosh
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Correspondence to Pallab Kumar Ghosh.

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Ghosh, P.K., Lee, J.Y. Decomposition of Changes in Korean Wage Inequality, 1998–2007. J Labor Res 37, 1–28 (2016). https://doi.org/10.1007/s12122-015-9217-9

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