1 Trends in Intergenerational Income Mobility and Its Relationship wit - PDF document

1 1 Trends in Intergenerational Income Mob
1 Trends in Intergenerational Income Mobility and Its Relationship with Income Inequality in South Korea Jaehyun Nam INTRODUCTION Over decades, there has been a vast body of empirical studies on intergenerational income mobility in the United States as well as other developed countries. However, there is little evidence on intergenerational income mobility in South Korea (hereafter, Korea), and furthermore, there is no study on trends in intergenerational income mobility thus far. Korea has been undergoing rapid and significant societal changes in recent decades, in large part precipitated by the aftermath of the 1997 Economic Crisis, which brought about mass unemployment, sharp increases in poverty and income inequality(Kim 2012; Lee and Lee 2001). As such, exploring trends in intergenerational income mobility provides information on how the equality of economic opportunities in Korea has been shaped in recent decades. Beyond these trends, the association between income inequality and intergenerational income mobility has given much attention to researchers and policymakers, as income inequality has sharply risen in recent decades. In light of the widely-accepted finding that countries with greater income inequality also experience less income mobility across generations, higher income inequality may undermine intergenerational income mobility (Corak 2013). Comparative studies using cross-national data provide evidence that income inequality decreases intergenerational income mobility. For example, countries with high income inequality, such as the United States and Great Britain, have significantly less economic mobility than countries where income is more evenly distributed, such as Finland, Sweden, Norway, and Germany (Björklund and Jäntti 1997; Corak 2013). However, there is little evidence from studies within single countries that intergenerational income mobility is associated with income inequality (Bloome 2015), even though intuitively, income inequality may shape i

2 ntergenerational income mobility. Drawin
ntergenerational income mobility. Drawing on the Korean Labor and Income Panel Study (KLIPS), I will explore trends in intergenerational income mobility in recent decades, and then examine its relationship with income inequality in Korea. The proposed study will contribute to an understanding of how intergenerational income mobility in Korea changes and the extent to which intergenerational income mobility is responsive to changes in inequality. RESULTS Descriptive Statistics 7UDQVLWLRQPDWUL[DFURVVLQFRPHTXDQWLOHVEHWZHHQSDUHQWV¶JHQHUDWLRQDQGFKLOGUHQ¶Vgeneration is a commonly used way to examine the patterns of changing mobility. The matrix shows the probabiliW\RIFKLOGUHQ¶VSRVLWLRQLQDJLYHQTXDQWLOHRIWKHLQFRPHGLVWULEXWLRQFRQGLWLRQDORQWKHLUSDUHQWV¶SRVLWLRQLQWKHLQFRPHGLVWULEXWLRQ7RLOOXVWUDWHWKLVDSSURDFK,used quintile groups of both parents and children over-time average log income. Table 2 shows the transition probability matrices with the rows of table representing parental position in income quintile and the columns of table representing FKLOGUHQ¶VSRVLWLRQLQLQFRPHTXLQWLOH3DQHO$shows the patterns of changing mobility overall: It presents quite disproportion income 2 mobility across generations. For example, 37% of adult children whose parents, on one hand, were in the bottom quintiles of the family income remain themselves in the same quintiles, while only 8% of those children moved up to the highest income quintile; on the other hand, 34% of children whose parents were in the highest income quintile are themselves in the highest income quintile, while only 8% of those children moved down to the bottom income quintile. These overall

3 patterns hold when I examine the matrice
patterns hold when I examine the matrices separately by gender presented in Panels B and C of Table 2. But, intuitively sons whose parents were in the bottom and middle quintiles are more likely to remain in the bottom and middle quintiles than their counterparts, whereas daughters whose parents were in the highest quintile are more likely to remain in the same quintile but less likely to move down to the bottom quintile than their counterparts. For instance, 41% of daughters and 22% of sons with parents in the highest quintile were themselves in the highest quintile; only 6% of those daughters moved down to bottom quintile, compared to 11% of those sons who transitioned to the bottom quintile. Intergenerational Elasticity of Income Table 3 presents the estimates of the IGEsPanel A for IGEs not adjusted for family size and Panel B for IGEs adjusted for family size. The second column of Table 3 reports the estimates of the IGEs with pooled sample. The IGE not adjusted for family size 0.201, which implies that a GLIIHUHQFHLQFKLOGUHQ¶VLQFRPHLVDVVRFLDWHGZLWKGLIIHUHQFHLQSDUHQWDOLQFRPH7KHGHJUHHof the Korean IGE is quite small, compared to the US where the best estimate of the IGE is 0.4 or higher (Mazumder 2005; Solon 1992). Quite lower estimates of the Korean IGEs are understandable because, as mentioned, all incomes measured in KLIPS data are after-tax income, instead of before-tax income. Tax system might play important roles in promoting intergenerational mobility through institutions outside the family investing equally in all children, emphasizing that poor children are likely to gain more than affluent children, because SRRUFKLOGUHQ¶VSDUHQWVKDYHQRWLQYHVWHGDVPXFKThe third column and the fourth column of Table 3 present the estimates of the IGEs for sons and daughters, respectively. Overall the IGEs for daug

4 hters are greater than those of sons, wh
hters are greater than those of sons, which align with a prior study by Choi and Ahn (2015) reporting that the Korean IGEs for daughters and sons are 0.17 and 0.11, respectively. When estimating the IGEs adjusted for family size, I see that these overall patterns are nearly identical. Panel B reports the estimates of the IGEs adjusted for family size. Although adjusting for family size does not greatly alter the estimates, the sizes of the estimates adjusted for family size become slightly smaller. It is worth noting that the estimates of the IGEs should not be interpreted as the causal influence of parental income given the potential problems associated with unobserved heterogeneity and measurement error (Mayer and Lopoo 2005). Figure 1 plots trends in the IGEVRIFKLOGUHQ¶VLQFRPHZLWKUHVSHFWWRSDUHQWDOLQFRPHfrom 2007 to 2012 when FKLOGUHQ¶V income was observed (parental income was observed from 1997 to 2002). My emphasis is on the trend rather than the degree of the IGEs hereafter. The first Panel in Figure 1 shows trends in the IGEs not adjusted for family size. Trends in the IGEs have been quite up and down, but there is no clear-cut trend. Even though the intuition is that the IGEs increased slightly, I used Wald statistics to examine statistically significant changes in point-estimates. Wald statistic is not significant, indicating all 6 year-point estimates are identical. This finding aligns with prior studies that intergenerational mobility in the US has been unchanged, even slightly increased in recent decades (Chetty et al. 2014b; Hertz 2007; Lee and Solon 2009; 3 Mayer and Lopoo 2005); on the other hand, it is inconsistent with the other studies providing evidence that the trends in mobility have declined (Aaronson and Mazumder 2008; Bloome and Western 2011). Similarly trends in the IGEs adjusted for family size (the second Panel) are shown to be almost identical patternsthere are no statistica

5 lly significant changes in the trends.
lly significant changes in the trends. The dash-dot line with triangle maker shows the estimate of the IGEs for sons and the dot line with circle marker indicates the estimate of the IGEs for daughters. Although trends intuitively have increased, both have quite up-and-down trends ranging from 0.07 to 0.35 for sons and from 0.05 to 0.41 for daughters, respectively. These greater variations may be caused by small sample sizes. In a similar manner, trends in the IGEs adjusted for family size are altered very little. The adjusted estimates for sons have hovered between 0.06 and 0.38 while those for daughters have ranged between 0.06 and 0.31. The increase in the IGEs is relatively evident for sons, especially when family size is taken into accounted, while the IGEs for daughters stay in a higher level, indicating lower mobility than sons. When taking into account that the income measured by KLIPS data is after-tax measure, these estimates of the IGEs are underestimated compared to those of the US IGEs, which were mostly estimated with before-tax income. If the income collected by KLIPS is before-tax measure, the estimates would be greater to the same degree as US estimates. This indicates that Korean society could be somewhat immobile. The detail numbers of the trends in the IGEs are presented in Appendix. Intergenerational Mobility and Inequality After the 1997 financial crisis, Korean income distribution has sharply been polarized, showing the top decile (P90-100) income share accounted for over 40 percent of the total in 2010 (Kim 2012). The gradual rise in inequality since 1998 is corresponding to the underlying changes in the gain concentrating in the top decile. This indicates that the gain from growth has not been evenly distributed to all classes, while the market mechanism under capitalism has played roles in polarizing income inequality (Kenworthy and Smeeding 2013; Piketty and Saez 2003) . The gini coefficient, which is the most widely used measure of inequality, i

6 s presented at the national level in Fig
s presented at the national level in Figure 2, from 1997 to 2012. With a range of 0 to 1, a low value of the gini coefficient indicates less inequality, whereas a high value indicates more inequality. Overall, the gini coefficients have increased from 0.37 in 1997 to 0.43 in 2012. Much of this increase occurred in 1998, and the gini coefficients have increased only modestly thereafter. Intuitively, trends in the intergenerational elasticity of income do not seem to covary with increasing income inequality, when Figure 1 is overlaid with Figure 2. However, gini coefficients at cities/provinces levels vary greatly by cities/provinces and time (Figure 3). These variations play a powerful role in providing heterogeneous effects of income equality on mobility. To examine the relationship I next relate parental income to regional gini coefficients and I fully exploit the heterogeneities. Table 4 presents the association between the IGEs and regional gini coefficients. Panel A shows the estimates not adjusted for family size and Panel B shows those adjusted for family size. The estimate of the IGE in Model 1 in Panel A, which is controlled for individual covariates, is 0.19 and is statistically significant. The estimate of the interaction between the IGE and gini coefficients is 0.03, indicating that the IGEs are higher with rising income inequality, but it is not statistically significant. To control for regional factors which can affect income inequality, I include regional covariates such as unemployment rate and population into the model and interact 4 these effects with the parental income in Model 2. The inclusion of these factors does not alter the estimates in Model 1. The results in Model 1 are robust to the inclusion of these factors in Model 2. Furthermore, I include the year and region dummies to purge unobservable regional factors as described in method section. The estimate in Model 3 shows that the interaction is still indistinguishable from zero, but the IGE is lowe

7 r with rising income inequality. The co
r with rising income inequality. The columns 5 to 7 and the columns 8 to 10 of Table 4 present the estimates for sons and daughters, respectively. In a simil manner, Model 1 controls for individual covariates, Model 2 includes the regional covariates, and Model 3 includes region and year fixed effects. Overall the IGEs for daughters are still greater than those for sons. The estimates of the interaction between the IGEs and gini coefficients are indistinguishable from zero for both. When estimating the IGEs adjusted for family size, these overall patterns hold. Panel B of Table 4 reports the estimates of the IGEs adjusted for family size. Adjusting for family size does not greatly alter overall patterns and significance from the estimates not adjusted for family size, but the sizes of the estimates adjusted for family size become a little smaller and bigger across the samples. From these exercises through Models 1 to 3, I found that there is no systematic relation between rising income inequality and the IGEs in Korea. CONCLUSION The primary purpose of this study is to examine trends in the intergenerational income mobility and the association between rising income inequality and intergenerational income mobility in Korea. This study is the first empirical analysis of how intergenerational income mobility has changed over time and how income inequality is linked to intergenerational income mobility across contexts within Korea. My analysis has four main findings. First, I find that Korea is a less mobile society. The IGE measured by family income is approximately 0.20, which seems to be small at a glance, compared to the US IGE. However, this figure underestimates the true Korean IGE because all incomes measured in KLIPS are after-tax income instead of before-tax income. The IGE for after-tax income are fairly smaller than those for before-tax income (Mitnik et al. 2015). The Korean IGEs would be close to the US estimates if the income collected by KLIPS is before-tax measur

8 e. Second, overall the IGEs for daughter
e. Second, overall the IGEs for daughters are larger than those for sons, indicating that sons are more likely than daughters to have greater mobility within income distribution. Third, there is no clear-cut trend in the IGEs. Finally, there is no systematic relation between rising income inequality and the IGEs in contemporary Korea. My analysis provides little evidence that intergenerational income mobility is responsive to the income inequality they experienced growing up. The finding appears to be inconsistent with the GGC that supports the framework of the negative association between intergenerational income mobility and income inequality across countries. This difference could be explained by larger heterogeneity in family roles and institutions across countries than within a country (Bloome 2015). 5 REFERENCES Aaronson, D.and B. Mazumder. 2008. "Intergenerational economic mobility in the United States, 1940 to 2000." Journal of Human Resources 43(1):139-172. Becker, G.S.and N. Tomes. 1979. "An equilibrium theory of the distribution of income and intergenerational mobility." The Journal of Political Economy:1153-1189. Becker, G.S.and N. Tomes. 1986. "Human capital and the rise and fall of families." Journal of labor economics 4(2):S1-39. %HKUPDQ-5DQG37DXEPDQ7KHLQWHUJHQHUDWLRQDOFRUUHODWLRQEHWZHHQFKLOGUHQ¶VDGXOWHDUQLQJVDQGWKHLUSDUHQWV¶LQFRPH5HVXOWVIURPWKH0LFKLJDQSDQHOVXUYH\RILQFRPHdynamics." Review of Income and Wealth 36(2):115-127. Björklund, A.and M. Jäntti. 1997. "Intergenerational income mobility in Sweden compared to the United States." The American Economic Review 87(5):1009-1018. . 2009. "Intergenerational income mobility and the role of family background." Oxford Handbook of Economic Inequality, Oxford University Press, OxfordBlack, S.E.and P.J. D

9 evereux. 2011. "Recent developments in i
evereux. 2011. "Recent developments in intergenerational mobility." Handbook of labor economics 4:1487-1541. Bloome, D. 2015. "Income inequality and intergenerational income mobility in the United States." Social Forces 93(3):1047-1080. Bloome, D.and B. Western. 2011. "Cohort change and racial differences in educational and income mobility." Social Forces 90(2):375-395. Chetty, R., N. Hendren, P. Kline, and E. Saez. 2014a. "Where is the land of opportunity? The geography of intergenerational mobility in the United States." Quarterly Journal of Economics 129(4):1553-1623. Chetty, R., N. Hendren, P. Kline, E. Saez, and N. Turner. 2014b. "Is the United States still a land of opportunity? Recent trends in intergenerational mobility." The American Economic Review 104(5):141-147. Choi, J.and K. Hong. 2011. "An analysis of intergenerational earnings mobility in Korea: Father-son correlations in labor earnings." Korean Social Security Studies 27(3):143-163. Choi, K.and T. Ahn. 2015. "Intergenerational income mobility among daughters in Korea: The role of marital sorting." Journal of Women and Economics 12(2):45-66. Corak, M. 2013. "Income inequality, equality of opportunity, and intergenerational mobility." The Journal of Economic Perspectives 27(3):79-102. Couch, K.A.and D.R. Lillard. 1998. "Sample selection rules and the intergenerational correlation of earnings." Labour Economics 5(3):313-329. Eide, E.R.and M.H. Showalter. 1999. "Factors affecting the transmission of earnings across generations: A quantile regression approach." Journal of Human Resources:253-267. Fox, L.E. 2015. "Parental Wealth and the BlackWhite Mobility Gap in the US." Review of Income and Wealth. Francesconi, M.and C. Nicoletti. 2006. "Intergenerational mobility and sample selection in short panels." Journal of Applied Econometrics 21(8):1265-1293. Grawe, N.D. 2004a. "Intergenerational mobility for whom? The experience of high-and low-earning sons in international perspective." Generational income mobility in N

10 orth America and Europe:58-89. . 2004b.
orth America and Europe:58-89. . 2004b. "Reconsidering the use of nonlinearities in intergenerational earnings mobility as a test for credit constraints." Journal of Human Resources 39(3):813-827. 6 Haider, S.and G. Solon. 2006. "Life-cycle variation in the association between current and lifetime earnings." The American Economic Review 96(4):1308-1320. Haveman, R.and B. Wolfe. 1995. "The determinants of children's attainments: A review of methods and findings." Journal of economic literature 33(4):1829-1878. Hertz, T. 2007. "Trends in the intergenerational elasticity of family income in the United States." Industrial Relations: A Journal of Economy and Society 46(1):22-50. Korea Labor Institute [KLI]. 2015. "Korean Labor and Income Panel Study (KLIPS) Waves 1-17 User's guide.". Sejong-si: Korea Labor Institute. Kenworthy, L.and T. Smeeding. 2013. "GINI Country Report: Growing Inequalities and their Impacts in the United States." AIAS, Amsterdam Institute for Advanced Labour Studies. Kim, B., J. Seok, and E. Hyun. 2012. "Intergenerational income elasticities in Korea and their trend." Korean Journal of Labour Economics 35(2):25-41. Kim, M., B. Kim, and T. Ha. 2009. "Intergenerational income elasticity in Korea." Kukje Kyungje Yongu 15(2):87-102. Kim, N. 2012. "Income concentration in Korea, 1976-2010: Evidence from income tax statistics." Economic Analysis 18(2):75-114. Lee, C.-I.and G. Solon. 2009. "Trends in intergenerational income mobility." The Review of Economics and Statistics 91(4):766-772. Lee, J.and S. Lee. 2001. "Economic Crisis and income disparity: Income distribution and poverty before and after the 1997 Crisis." Kukje Kyungje Yongu 7(2):79-109. Mayer, S.E.and L.M. Lopoo. 2005. "Has the intergenerational transmission of economic status changed?" Journal of Human Resources 40(1):169-185. Mazumder, B. 2005. "Fortunate sons: New estimates of intergenerational mobility in the United States using social security earnings data." Review of Economics and Statistics 87(2

11 ):235-. 2012. "Is intergenerational econ
):235-. 2012. "Is intergenerational economic mobility lower now than in the past?" Chicago Fed Letter(Apr). Mazumder, B.and D.I. Levine. 2003. "The growing importance of family and community: An analysis of changes in the sibling correlation in earnings." Meyer, B.D., W.K. Mok, and J.X. Sullivan. 2009. "The under-reporting of transfers in household surveys: its nature and consequences." National Bureau of Economic Research. Mitnik, P., V. Bryant, M. Weber, and D. Grusky. 2015. "New estimates of intergenerational mobility using administrative data." SOI Working Paper, Statistics of Income Division, Internal Revenue Service. Piketty, T. 2014. Capital in the twenty-first century. Cambridge, MA, London. Piketty, T.and E. Saez. 2003. "Income Inequality in the United States, 1913-1998." Quarterly Journal of Economics 118(1):1-39 (Longer updated version published in A.B. Atkinson and T. Piketty eds., Oxford University Press, 2007) (Tables and Figures Updated to 2014 in Excel format, June 2015). Solon, G. 1992. "Intergenerational income mobility in the United States." The American Economic Review:393-408. . 1999. "Intergenerational mobility in the labor market." Handbook of labor economics3:1761-1800. Zimmerman, D.J. 1992. "Regression toward mediocrity in economic stature." The American Economic Review:409-429. 7 TABLES Table 1: Descriptive Statistics of the Children and Parents Demographics and Economic Characteristics Parents Children Children's Birth Cohorts Number of Observations Year Age at Measuring Income (SD) Log Family Income (SD) Year Age at Measuring Income (SD) Log Family Income (SD) Overall Sons Daughters 1997 49 10.077 2007 28 10.272 10.283 10.266 1978-1983 186 (5.7) (0.722) (1.3) (0.527) (0.449) (0.564) 1998 50 10.125 2008 29 10.353 10.291 10.383 1979-1984 157 (4.7) (0.725) (1.3) (0.561) (0.439) (0.611) 1999 49 10.299 2009 29 10.326 10.318 10.330 1980-1985 160 (4.6) (0.696) (1.3) (0.698)

12 (0.666) (0.718) 2000 49 10.289
(0.666) (0.718) 2000 49 10.289 2010 29 10.226 10.216 10.232 1981-1986 161 (4.8) (0.739) (1.4) (0.663) (0.667) (0.663) 2001 49 10.360 2011 28 10.186 10.076 10.244 1982-1987 147 (4.8) (0.62) (1.4) (0.512) (0.449) (0.535) 2002 48 10.339 2012 28 10.155 9.885 10.292 1983-1988 143 (4.9) (0.615) (1.4) (0.654) (0.63) (0.626) 1997-2002 49 10.250 2007-2012 28 10.253 10.181 10.292 1978-1989 954 (4.9) (0.695) (1.4) (0.611) (0.583) (0.622) Source: Author's calculations from KLIPS data. Notes: Income is adjusted to 2010 dollars using the Korean CPI, which is retrieved from http://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1J0A001&conn_path=l2 Mean log income is based on the sample with income greater than zero. 3DUHQW¶VDJHUHIHUVWRWKHDJHRIWKHKRXVHKROG¶VUHIHUHQFHperson, such as the household head. The standard deviation is in parentheses. Weights are applied. 8 7DEOH3HUFHQW&KDQJHLQ7UDQVLWLRQ3UREDELOLWLHVIURP3DUHQWV¶,QFRPH4XLQWLOHVWR&KLOGUHQ¶VIncome Quintiles Panel A: Overall Children's Quintile Group Bottom 2nd 3rd 4th Top 3DUHQWV¶Quintile Group Bottom 37.0 20.3 18.8 15.6 8.3 2nd 24.5 18.8 21.9 19.3 15.6 3rd 17.9 25.3 19.5 19.0 18.4 4th 13.1 21.5 21.5 20.4 23.6 Top 7.9 14.1 18.3 25.7 34.0 Panel B: Son-Parents Pair Children's Quintile Group Bottom 2nd 3rd 4th Top 3DUHQWV¶Quintile Group Bottom 42.5 26.3 13.8 8.8 8.8 2nd 19.6 26.1 31.5 7.6 15.2 3rd 17.1 30.0 24.3 15.7 12.9 4th 18.3 28.2 19.7 16.9 16.9 Top 10.8 21.5 23.1 23.1 21.5 Panel C: Daughter-Parents Pair Children's Quintile Group Bottom 2nd 3rd 4th Top 3DUHQWV¶Quintile Group Bottom 33.0 16.1 22.3 20.5 8.

13 0 2nd 29.0 12.0 13.0 30.0 16.0 3r
0 2nd 29.0 12.0 13.0 30.0 16.0 3rd 18.3 22.5 16.7 20.8 21.7 4th 10.0 17.5 22.5 22.5 27.5 Top 6.4 10.3 15.9 27.0 40.5 Source: Author's calculations from KLIPS data. 1RWHV(DFKFHOOUHSRUWVWKHSUREDELOLW\RIFKLOGUHQ¶VEHLQJLQWKHLQFRPHTXLQWLOHJLYHQE\WKHFROXPQFRQGLWLRQDORQSDUHQWV¶SRVLWLRQLQWKHLQFRPHTXLQWLOHJLYHQE\WKHURZ 9 Table 3: Estimates of the Intergenerational Elasticity of Income Pooled Estimates Estimates for Sons Estimates for Daughters Panel A: Not adjusted for Family Size 0.201*** 0.158*** 0.216*** (0.031) (0.048) (0.038) Panel B: Adjusted for Family Size 0.184*** 0.119** 0.214*** (0.029) (0.049) (0.034) Observations 954 376 578 Notes: Robust standard errors in parentheses *** p0.01, ** p0.05, * p0.1 Table 4: The Association between Intergenerational Elasticity of Income and Income Inequality. Pooled Estimates Estimates for Sons Estimates for Daughters Model1 Model2 Model3 Model1 Model2 Model3 Model1 Model2 Model3 Panel A: Not adjusted for Family Size Parental income 0.190*** 0.190*** 0.250*** 0.156*** 0.153*** 0.128 0.193*** 0.195*** 0.313*** (0.035) (0.036) (0.060) (0.053) (0.053) (0.080) (0.043) (0.045) (0.079) Parental income*gini 0.029 0.029 -0.055 0.003 0.000 -0.089 0.069 0.069 -0.041 (0.039) (0.039) (0.066) (0.056) (0.057) (0.106) (0.050) (0.050) (0.081) Individual Covariates × × × × × × × × × Regional Covariates × × × × × × Region-Year Dummies × × × Observations 954 954 954 376 376 376 578 578 578 R-squared 0.159 0.160 0.208 0.145 0.159 0.243 0.183 0.183 0.252 Panel B: Adjusted for Family Size Parental

14 income 0.170*** 0.176*** 0.226*** 0.
income 0.170*** 0.176*** 0.226*** 0.116** 0.117** 0.092 0.190*** 0.198*** 0.315*** (0.032) (0.033) (0.056) (0.053) (0.053) (0.082) (0.038) (0.040) (0.072) Parental income*gini 0.036 0.033 -0.042 0.007 0.003 -0.082 0.071 0.068 -0.028 (0.038) (0.038) (0.063) (0.056) (0.057) (0.107) (0.049) (0.049) (0.074) Individual Covariates × × × × × × × × × Regional Covariates × × × × × × Region-Year Dummies × × × Observations 954 954 954 376 376 376 578 578 578 R-squared 0.258 0.260 0.292 0.170 0.183 0.254 0.320 0.321 0.366 Notes: Robust standard errors in parentheses *** p0.01, ** p0.05, * p0.1 FIGURES Figure 1: Trends in Intergenerational Elasticity of Income from 2007 to 2012 Figure 2: Korean Income Inequality Measured by Gini Coefficients from 1997 to 2012. Figure 3: Korean Income Inequality Measured by Gini Coefficients by Cities/Provinces from 1997 to 2012. APPENDICES Appendix Figure 1: Trends in the Intergenerational Elasticity of Income from 2007 to 2012 Pooled Estimates Estimates for Sons Estimates for Daughters Panel A: Not adjusted for Family Size 2007 0.218 0.069 0.244 (0.045) (0.117) (0.053) 2008 0.287 0.352 0.259 (0.071) (0.139) (0.080) 2009 0.185 0.156 0.213 (0.076) (0.139) (0.086) 2010 0.215 0.178 0.218 (0.078) (0.131) (0.097) 2011 0.109 0.150 0.045 (0.133) (0.118) (0.245) 2012 0.328 0.200 0.405 (0.111) (0.160) (0.196) Panel B: Adjusted for Family Size 2007 0.213 0.062 0.237 (0.044) (0.117) (0.057) 2008 0.307 0.378 0.285 (0.064) (0.141) (0.067) 2009 0.185 0.126 0.230 (0.065) (0.143) (0.067) 2010 0.158 0.066 0.201 (0.072) (0.141) (0.086) 2011 0.032 0.008 0.057 (0.127) (0.155) (0.195) 2012 0.288 0.309 0.313 (0.097) (0.175) (0.125) Observations 954 376 578 Notes: Robust standard errors i