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Intergenerational earnings mobility in Taiwan: 1990–2010

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Abstract

In this paper, we study the intergenerational earnings mobility between fathers and sons in Taiwan. We apply the two-sample approach developed by Björklund and Jäntti (Am Econ Rev 87(5):1009–1018, 1997) and find that the intergenerational earnings elasticity in Taiwan was around 0.4–0.5 in both the early 1990s and the late 2000s. We also estimate the intergenerational rank association in earnings to have been around 0.3 in both periods. Intergenerational earnings mobility in Taiwan is similar to that in less mobile countries such as the USA, and it appears to remain stable during a period of rapid economic development.

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Notes

  1. This traditional view has recently been challenged by many researchers. For example, in his popular book Capital in the Twenty-First Century, Piketty (2014) provides substantial historical evidence of long-term inequality and argues that the Kuznets curve is in fact a post-World World II anomaly.

  2. We have coined the term “imputation bias.” However, the importance of comparability between actual fathers and potential fathers has been observed by Björklund and Jäntti (1997), and the implication in a fast-growing economy has been discussed by Kim (2017).

  3. There is growing concern that intergenerational mobility is declining due to the large rise in overall inequality. However, the literature has not suggested any broad trends in intergenerational mobility in many developed countries, and the evidence sometimes appears conflicting, perhaps due to the demanding data requirements that these studies face (Aaronson and Mazumder 2008; Bratberg et al. 2005; Chetty et al. 2014b; Clark and Cummins 2015; Fan et al. 2015; Ferrie 2005; Hertz 2007; Lee and Solon 2009; Lefranc et al. 2014; Lefranc and Trannoy 2005; Long and Ferrie 2013; Markussen and Røed 2017; Mayer and Lopoo 2005; Modalsli 2017; Nicoletti and Ermisch 2007; Olivetti and Paserman 2015; Pekkala and Lucas 2007; Pekkarinen et al. 2017; Xie and Killewald 2013).

  4. The two-sample method is an application of the two-sample instrumental variables estimators that are developed by Angrist and Krueger (1992) and Arellano and Meghir (1992).

  5. Kim (2017) uses a secondary sample that better approximates actual fathers’ birth cohorts and estimates the intergenerational earnings elasticity in Korea to be around 0.4, which is substantially larger than the previous findings in Choi and Hong (2011) and Ueda (2013). The problem of imputation bias is not unique to the Asian literature, though the magnitude of imputation bias is probably smaller in developed countries. For example, Leigh (2007) and Mendolia and Siminski (2016) impute fathers’ earnings by occupation. However, their data (the Household, Income and Labour Dynamics in Australia Survey, HILDA) is available only since 2001 and does not have retrospective information on father’s earnings predictors like industry and occupation.

  6. Year 2011 is the base year, in which the GDP deflator equals 100. The USD values are based on the official exchange rates at each year.

  7. From 2005–2010, the earnings variable in the TSCS is recorded in 19 brackets in TWD 10,000 (about USD 300): TWD 1–10,000, TWD 10,001–20,000… TWD 190,001–200,000, and two top brackets: TWD 200,001–300,000 and TWD 300,001 and above. For the top bracket, we take TWD 300,000 as the respondents’ earnings. For the lower brackets, we take mid-points to be the respondents’ earnings: 5000, 15,000 … 195,000, 250,000. The estimates from interval regressions are reported in Tables 6 and 9.

  8. We use two surveys in 2005 and one survey in 2007, 2009, and 2010. The 2010 data are from the sixth round of the TSCS. All other data are from the fifth round of the TSCS. Since the second round of the TSCS, each round lasts for 5 years, with ten different questionnaires in use. Two random samples of respondents are selected each year to complete two questionnaires. The sample size in each survey is about 2000 adults (18 and above).

  9. The average self-reported retirement age was 54.9 in 2005 and 56.6 in 2010, based on the Survey on Turnover and Movement of Employees. As some people continue to work after they retire from their primary jobs, the average retirement age based on labor force participation was around 61.

  10. The SFIE was first conducted biennially from 1964–1970 with a sample size of about 3000 households. The SFIE microdata are available only after 1975.

  11. The earnings measure is the sum of the following three sources of labor incomes: wages, net agricultural income, and mixed income that consists of net operation surplus and net professional income.

  12. We combine illiterate and no formal education into one category. We treat both 2-year and 4-year military and police academies as vocational colleges. Cadet school is coded as vocational high school.

  13. All the agriculture, fishing, and forestry workers are in the agriculture, fishing, and forestry industry. However, that industry also includes managerial workers such as farm owners.

  14. We use one survey in 1990 and two surveys in 1991, 1992, and 1994. All data are from the second round of the TSCS.

  15. The top income bracket is TWD 200,000 in the 1990–1994 TSCS. Earnings in the 1990 TSCS are recorded in TWD 1000 brackets. Earnings are recorded in TWD 20,000 brackets in the 1991 TSCS, while earnings in the 1992 and 1994 TSCS are recorded in TWD 10,000 brackets, as in the 2005–2010 TSCS. Only the 1992 and 1994 TSCS separate vocational high school from academic high school.

  16. The levels of education are from the Taiwan Demographic Fact Book, Republic of China because the Statistical Abstract of Interior of the Republic of China does not separate a university degree from a junior college degree. The data in both reports are from the same source. The distribution of education in column (3) is based on all males aged 15 and above who are not students, regardless of their employment status.

  17. The 1970 data are from the 1974 Report on the Survey of Personal Income Distribution in Taiwan Area, which includes information back through 1970. The Report on the Survey of Family Income and Expenditure series does not include the SFIE data from Taipei city (the capital) after 1968 due to the separation of responsible statistics departments. The 1981 data are from the 1981 Report on the Survey of Personal Income Distribution in Taiwan Area.

  18. The life-cycle bias arises because the slope coefficient in the linear projection of current (observed) earnings on permanent earnings differs from unity at the early or late stage of the life cycle. (ypermanent = λyshort-run + ε, where λ≠ 1.) In fact, life-cycle bias could result in amplification bias rather than attenuation bias.

  19. Inoue and Solon (2010) provide a consistent estimator for the standard error. We use the Stata codes provided by Pacini and Windmeijer (2016) that also account for heteroskedasticity and find that the standard errors are quantitatively similar to bootstrap standard errors reported in the paper. These results are available upon request.

  20. The estimated standard errors could be underestimated in the proxy method because we do not have a secondary sample and therefore ignore randomness in the average earnings. However, in Table 8, the estimated (bootstrap) standard errors using averages [columns (5) and (6)] are similar to those using microdata [columns (7) and (8)]. Therefore, the magnitude of bias should be small.

  21. We calculate these ratios using the 1981 SFIE microdata and find that they are fairly similar across occupations. These ratios range from 0.7–0.8, except for the category of agricultural workers, in which the ratio is 0.6. If these ratios are similar in the 1968 and 1970 SFIE data, the bias in our estimates due to measurement error in \( \overline{y}_{i}^{\text{o}} \) is probably not large.

  22. We find that the average share of household wages earned by household heads is about 80% in both 1966 and 1981. Unfortunately, this information is only available for wage income, and we are not able to compare it within occupations because the occupational categories in the 1966 report are not comparable to those in later reports.

  23. We estimate the following conditional mean by the Poisson quasi-maximum likelihood: E(\( y_{i}^{\text{s}} | \cdot ) = \exp (\beta \hat{y}_{i}^{\text{f}} ({\text{or}}\; \overline{y}_{i}^{\text{o}} ) + {\text{age}}_{i}^{\text{s}} + {\text{age}}_{i}^{{{\text{s}}2}} + {\text{TSCS}}\; {\text{year}} \;{\text{dummies}}) \), where \( y_{i}^{\text{s}} \) is son i’s earnings in levels and \( \hat{y}_{i}^{\text{f}} \) (\( \overline{y}_{i}^{\text{o}} \)) is his father’s imputed log earnings (or average earnings by occupation in logarithm).

  24. We first partial out age (and its square) and year dummies from sons’ earnings and rank the residuals (the permanent component of earnings). We then estimate: \( y_{i}^{\text{s}} = \beta \hat{y}_{i}^{\text{f}} ({\text{or}}\; \overline{y}_{i}^{\text{o}} ) + {\text{age}}_{i}^{\text{s}} + {\text{age}}_{i}^{{{\text{s}}2}} + {\text{TSCS}} \;{\text{year}}\; {\text{dummies}} + u_{i} \), where \( y_{i}^{\text{s}} \) is the percentile rank of son i’s residual earnings and \( \hat{y}_{i}^{\text{f}} \) (\( \overline{y}_{i}^{\text{o}} \)), is the percentile rank of his father’s imputed log earnings (or the percentile rank of average earnings by occupation).

  25. We thank an anonymous referee for this suggestion. Notice that although the variance of imputed earnings is roughly half of the size of the variance of actual earnings, the percentile ranks of imputed earnings and actual earnings have similar variances even without standardization. The estimates for rank-rank slopes are nearly identical without standardization.

  26. Grawe (2004) and Corak (2006) suggest a more deliberate approach of pairwise comparisons and comparing a particular pair of countries based on methodologies that are as much the same as possible. If we compare estimates for which fathers’ earnings are predicted by education and occupation, our estimate of 0.48 in column (4) of Table 5 is similar to the US estimates of 0.52 reported by Björklund and Jäntti (1997). Similarly, if we can compare estimates for which the fathers’ earnings are predicted by education, our estimate of 0.65 in column (7) of Table 5 is comparable to the UK estimates of 0.58 reported by Dearden et al. (1997) and Grawe (2004).

  27. To further address the linearity assumption, we impute the fathers’ earnings in levels instead of in logarithms and then regress the sons’ log earnings on the fathers’ log imputed earnings. The results remain quantitatively similar to those in Table 5 and are available upon request.

  28. E[ln(y)|x] = α + βln(x) implies ln[exp[E(ln(y)|x)]] ≡ ln[GM(y|x)] = α + βln(x), where GM denotes the geometric mean operator. In contrast, in a Poisson model, \( E\left( {y|x} \right) = \exp \left( {\alpha + \beta x} \right) \), the slope measures the elasticity of conditional mean: β =  \( \frac{\partial \ln [E(y|x)]}{\partial \ln \left( x \right)} \).

  29. One advantage of the rank-rank regression is to include zero earnings. The estimate for rank-rank slope that includes zero earnings is 0.26 (nor reported).

  30. The MUS reports regular monthly earnings from the primary job, while SFIE and TSCS report annual/average monthly earnings from all jobs. 98% of working males aged 40–55 report having only one job. However, it is very common in Taiwan for employees to receive a substantial bonus at the end of the year. The reported earnings in the MUS sample are 25% lower than in the SFIE sample.

  31. In the 1968 SFIE report, transport workers and mining workers are separate from production workers and laborers. In the 1970 SFIE report, transport workers are reported as an individual category. In the 1981 SFIE report, transport workers, production workers, and laborers are reported as three individual categories.

  32. The TSCS also has information on where the respondents were living before age 15, but the information is only available for 2005, 2007, and 2010. Although a person’s birthplace is not necessarily where they grew up and where their father was working, only 7% of the sons report a different metropolitan status for their birthplaces than for their residences before age 15.

  33. We use the pre-1982 administrative divisions that include two special municipalities, 3 provincial cities, and 16 counties in Taiwan. The geographical boundaries of these administrative divisions were mostly the same from 1949 to 2010. We define the two special municipalities (Taipei city and Kaohsiung city), three provincial cities (Keelung city, Taichung city, and Tainan city), and one county (Taipei county) to be metropolitan areas. Taipei county surrounds both Taipei city (the capital) and Keelung city, and it is part of the Taipei-Keelung metropolitan area. In 2010, Taichung, Tainan, and Kaohsiung cities were combined with their counties and upgraded to special municipalities. Taipei county itself also became a special municipality in 2010.

  34. As many native residents in metropolitan areas in 2005–2010 are second-generation migrants from nonmetropolitan areas, migration and urbanization likely contribute to the improvement in intergenerational mobility. Indeed, while income inequality increased in Taiwan from 1990 to 2010, it generally increased at slower rates in metropolitan areas than in nonmetropolitan areas (Wu 2011).

  35. The intergenerational elasticity of years of education is estimated by the authors from the TSCS data and available upon request.

  36. Lefgren et al. (2012) and Cardak et al. (2013) apply decomposition methods and suggest that the intergenerational ability transmission accounts for the majority of intergenerational earnings transmission in Sweden and the USA. Since the estimate based on earnings imputed by education attainment is large (column (7) of Table 5), the intergenerational ability transmission is likely strong in Taiwan and probably plays a major role in determining intergenerational mobility.

  37. Since all Asian studies rely on imputed earnings, there is no comparable estimate in the literature that can be used to indicate the magnitude of bias in two-sample estimates. If we follow Corak’s (2006) approach and use the US estimates as a baseline to infer the size of bias, the magnitudes of the upward bias in two-sample estimates is approximately 10–15% when fathers’ earnings are imputed by occupation and/or education. (Corak’s preferred estimate is 0.47 and the lower and upper bounds are 0.40 and 0.52 for the USA. In the literature, the estimates based on fathers’ actual earnings tend to be closer to the lower bound while the estimates based on fathers’ imputed earnings tend to be closer to the upper bound.) To further address the issue, we use sons’ birth locations as additional earnings predictors and estimate conditional intergenerational earnings elasticity controlling for fathers’ education, industry, and occupation. The estimated intergenerational earnings elasticity goes down from 0.49 to 0.38 when fathers’ education, industry, and occupation are controlled in the regression. This conditional intergenerational elasticity could be interpreted as a lower bound of the intergenerational earnings elasticity.

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  1. School of Economics and Finance, Victoria University of Wellington, Wellington, New Zealand

    Yu-Wei Luke Chu

  2. Department of Economics, National Taiwan University, Taipei, Taiwan

    Ming-Jen Lin

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Appendix

Appendix

See Tables 11, 12 and 13.

Table 11 Summary of Kan et al. (2015) and Sun and Ueda (2015)
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Table 12 Estimates of Intergenerational Earnings Elasticity and Rank–Rank Slope in 2005–2010 Using MUS sample
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Table 13 Estimates of Rank–Rank Slope by Metropolitan Status in 1990–1994 and 2005–2010
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Chu, YW.L., Lin, MJ. Intergenerational earnings mobility in Taiwan: 1990–2010. Empir Econ 59, 11–45 (2020). https://doi.org/10.1007/s00181-019-01637-0

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