An Empirical Investigation on the Great Gatsby's Curve

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1 Autonomous Technological Institute of Mexico From the SelectedWorks of Carlos Urrutia February, 2106 An Empirical Investigation on the Great Gatsby's Curve Carlos Urrutia Marina Mendes Tavares Available at:

2 An Empirical Investigation on the Great Gatsby s Curve Marina Mendes Tavares ITAM and IMF Carlos Urrutia ITAM February 2016 Abstract Cross country evidence suggests a negative relation between inequality and intergenerational mobility, sometimes dubbed as the Great Gatsby s Curve. From a time series perspective, we analyze the relation between the increase in inequality and the observed trends in intergenerational mobility in the U.S. over the last three decades. As an empirical contribution, we document a significant increase in the intergenerational elasticity of earnings, both at the family level and at the individual level, between 1977 and However, we do not observe a significant trend in the intergenerational elasticity of family income, suggesting an important role for transfers in mitigating the impact of earnings inequality on intergenerational mobility. Keywords: Intergenerational Mobility, Inequality, Great Gatsby Curve. JEL codes: D31, E24, H31, J32. We thank Dirk Krueger, Gianluca Violante and participants at ITAM Brown Bag Seminar, 2013 Midwest Macro Meetings, 2013 Society for Economic Dynamics Meetings, and the 2013 North American Summer Meetings of the Econometric Society for useful comments. We benefited from the superlative research assistance of Jorge Mondragon. marinamendestavares@gmail.com. currutia@itam.mx. 1

3 1 Introduction The last three decades have witnessed a dramatic increase in inequality in the U.S.. A large fraction of this increase can be explained by two facts: (i) an increase in the wage premium for college graduates; (ii) an increase in earnings disparity among individuals with the same age and level of education, reflecting higher volatility in both persistent and transitory earnings shocks. 1 There is a growing concern on whether a long period of increasing inequality could have a significant impact on intergenerational mobility and, ultimately, on the equality of opportunities in the U.S. society for the youngest cohorts. 2 Cross country evidence suggests a negative relation between inequality and intergenerational mobility, sometimes dubbed as the Great Gatsby s curve. As reported in Corak (2011), countries with relatively low income inequality (as the Nordic countries or Canada) also exhibit a lower intergenerational elasticity of income, implying less persistence across generations. A mechanic extrapolation of cross-sectional evidence to time series behavior would suggest a causal relation between the increase in inequality in the U.S. and a decline in intergenerational mobility. However, from a theoretical point of view, the relation between inequality and intergenerational mobility is not straightforward. Consider a simple model with borrowing constraints for parental investments in the human capital of their offsprings. 3 Changes in the predictable component of earnings, due for instance to an increase in the wage premium for college graduates, would provide richer parents more incentives to invest in their children s human capital, increasing both inequality and the intergenerational persistence of earnings. But changes in inequality driven by an increase in the volatility of earnings would have the opposite prediction in terms and children s outcomes. 1 See Heathcote et al. (2010). An additional fact is the reduction in the gender wage gap during this period, but in their calibrated model Heathcote et al. (2008) find a smaller role for it in accounting for the disparity in household earnings. 2 This concern has been raised to the policy debate by Alan Krueger, as the Chairman of the Council of Economic Advisers to President Obama. See Krueger (2012) and the 2012 Economic Report of the President. 3 See, for instance, Becker and Tomes (1979) and Loury (1981). Aiyagari et al. (2002) provide a fully dynamic general equilibrium model of parental investments with heterogeneity in abilities and borrowing constraints. Restuccia and Urrutia (2004) incorporate this mechanism in a quantitative model, distinguishing between early education and college education stages. 2

4 In this paper, we analyze the relation between the increase in inequality and the observed trends in intergenerational mobility in the U.S. in the last three decades. Our contribution is empirical. Using all available waves of the PSID dataset, we document a significant increase in the intergenerational elasticity of earnings, both at the family level and at the individual level, between 1977 and 2012, suggesting a reduction in intergenerational mobility. However, we also report a decline in the intergenerational elasticity of total family income, although not statistically significant. The difference between these two results seem to be driven by the increase in the share of transfers in total income, in particular for the bottom quintile of the income distribution. Moreover, we show that the upward trend in the intergenerational elasticity of earnings is driven mostly by parents with college education, which have been able to better transmit their earnings potential to their children. 4 A recent empirical literature analyzes the evolution of intergenerational mobility in the U.S. over the last decades. totally opposite trends. The results are far from conclusive, as different studies estimate For example, Mayer and Lopoo (2005) use the PSID dataset to show a decrease in the intergenerational elasticity of family income between 1980 and 1995, while Mazumder and Levine (2002) show an increase in persistence in a similar period using NLS data. Both estimates are highly imprecise, though, due to short sample issues. Lee and Solon (2009) propose a methodology to use the PSID data more efficiently, obtaining more precise estimates and showing no clear trend over time in the intergenerational elasticity of family income. We implement the methodology in Lee and Solon (2009) to analyze the evolution over time of the intergenerational elasticity of different income categories and decompose the results across income and education groups. Our results confirm their original finding about the intergenerational elasticity of total family income, namely that there is no clear trend over time, but show a significant increase in the intergenerational elasticity of earnings over time. 4 Our empirical findings open an interesting research agenda. In a companion paper (Tavares and Urrutia (2016)), we further explore the relation between the evolution of inequality in the U.S. and the observed changes in intergenerational mobility through the lens of a model. The key transmission channel from inequality to intergenerational mobility in our model is the existence of borrowing constraints for parental investments in the human capital of their offsprings. In particular, we pose the question of whether changes in the earnings process and transfers policies, which we take as given, can simultaneously account for the trends in inequality and intergenerational mobility. 3

5 This paper is organized as follows. In Section 2 we present our data sample and describe the estimation strategy. Section 3 reports the main results of the paper, this is, the estimated trends in intergenerational mobility in the U.S. for both family income and family earnings We show the results for the whole sample, but also conditioning on parental income and college attainment. Section 4 presents some additional evidence with respect to the evolution of cross-sectional inequality in family income and family earnings and revisits the theme on the Great Gastby s curve. Finally, we conclude. 2 Methodology Our methodology follows closely the work of Lee and Solon (2009), both in the sample selection (within the same database) and in the estimation strategy. However, we look at a broader set of variables (family income, labor earnings, transfers), explicitly test for linear trends in the intergenerational elasticities and decompose the results across (parental) income and education groups. We briefly describe in this section the details of the empirical approach. 2.1 Data We base our empirical analysis on the Panel Study of Income Dynamics (PSID), one of the few databases which provide measures of the income of parents and their adult children for different cohorts and in different points of their life-cycle. 5 For comparison purposes, we use the same sample selection as Lee and Solon (2009). However, while their sample ended in 2000, we include the last available PSID years until We focus only on sons, to abstract from issues related with the female labor force participation of daughters. 6 Because we need their adult income observations to be at least somewhat informative about their long-run 5 The PSID is a longitudinal survey administered by the University of Michigan s Survey Research Center. It began in 1968 with a national probability sample of almost 5,000 U.S. families. The survey attempted to follow all members of this original sample annually, as well all individuals that joined PSID households until After this year, the survey started to follow individuals biannually instead of annually. 6 Goldin (2006), among others, documents a big increase in female labor force participation in this period. 4

6 income we only use income for children observed no earlier than age We also use only the Survey Research Center component of the PSID and exclude income observation imputed by major assignments as well as outliers observations for which income (in 1967 dollars as measured by CPI-U) is less than $100 and more than $150,000. As Lee and Solon (2009) and Mayer and Lopoo (2005), we focus on the intergenerational mobility of family income as this variable provides a more comprehensive measure of economic status. We measure son s family income by the total income in the household in which they have become head or head s spouse. We also consider an intergenerational mobility measure based on family earnings, obtained adding the labor earnings of head and spouse. We think this is a better measure of the transmission of earnings potential or human capital across generations. The main differences between family income and family earnings are capital income and transfers, both private and public. Notice that the PSID only provides pre-tax measures of income and earnings. 2.2 Econometric Specification Intergenerational mobility estimates are very sensitivity to the age in which family income is measured. Ideally, they should be obtained using long-run income of both parents and sons. However, no intergenerational data set contain lifetime income data on both generations. We measure parental family income and earnings when the child is 15, 16, and 17 years old, as the average log of each variable over these three years. For example, for children in the 1952 cohort, the parental family income is the average of parental family income in 1967, 1968 and In order to account for differences in age among parents when sons were 15 to 17 years old, we control for a quadratic polynomial in the average age of the household head over those years. 7 The children in our sample range then from 25 years old in the 1987 cohort to 60 years old in the 1952 cohort. This is an unbalanced panel with repeated observations of the same individuals. It contains 17,509 observations of 1,834 individuals, averaging almost 11 observations per individual and about 60 individuals for each of the 1952 to 1969 cohorts and 46 individuals for the 1970 to 1987 cohorts. 5

7 We also observe sons family income and earnings over a range of ages and years. As in Lee and Solon (2009), we retain all the available data but we control for life cycle variations in our econometric specification. We estimate the following regression using ordinary least squares: y ict = α D t + β t X ic + γ 1 A ic + γ 2 A 2 ic + γ 3 A 3 ic + γ 4 A 4 ic (1) + δ 1 (t c 40) + δ 2 (t c 40) 2 + δ 3 (t c 40) 3 + δ 4 (t c 40) 4 + θ 1 X ic (t c 40) + θ 2 X ic (t c 40) 2 + θ 3 X ic (t c 40) 3 + θ 4 X ic (t c 40) 4 + ε ict where y ict is log family income (earnings) in year t for child i in birth cohort c = 1952, 53,..., 87. The vector D t contains year dummy variables, switching to every other year near the end of our sample period. The key explanatory variable X ic is the parental log family income (or earnings). Because our main point is to study changes over time, the intergenerational mobility coefficient β t is allowed to vary each year. The regression includes controls for a quartic in parental age A ic at the time parental income (earnings) is observed, a quartic in son s age (t c 40) at the time child s income (earnings) is observed, and interactions of the child s income or earnings with the same variables for parents. The son s age variable is normalized to be equal to 0 at 40 years old. This normalization may affect the size of the estimates, but not their variation over time. 8 We label trends in terms of years instead of in terms of cohort, since the two effects cannot be disentangled. Our main specification, shown as equation (1), assumes that the age-income profiles of different cohorts have the same shape. The inclusion of year dummy variables allows the height of the trajectories to vary across cohorts (or, equivalently, across years), but otherwise the coefficients of all variables involving sons age are assumed to be common across cohorts. 8 The positive autocorrelation of the error term ε ict over different years for the same individual complicates the proper estimation of our standard errors. Hence, we correct standard error by clustering at the household level following Cameron and Trivedi (2005). 6

8 Finally, our analysis requires to infer the existence of a time trend in the intergenerational elasticity (β t ). For this, we re-estimate the regression model (1) assuming that this elasticity is a linear function of time, using the following augmented regression: y ict = α D t + β 0 X ic + β 1 X ic (t 1977) + everything else (2) In this specification, the intergenerational mobility coefficient is given by β 0 + β 1 (t 1977), consequently a positive and significant β 1 is suggestive of the existence of a positive trend. 3 Trends in Intergenerational Mobility in the U.S. In this section we present the main results of the paper. We document a positive and significant trend over time for the intergenerational mobility of family earnings. However, and consistently to the findings of Lee and Solon (2009), this trend is no longer significant for the intergenerational mobility of family income. To reconcile these two findings, we show that the transfer component of income (and not capital income) is responsible for mitigating the increase in the intergenerational persistence in earnings, especially at the bottom of the income distribution. Furthermore, the upward trend in the intergenerational elasticity of earnings is driven mostly by parents with college education. 3.1 Intergenerational Mobility in Family Income and Earnings In Figure 1 we report our estimates of the intergenerational elasticity β t for sons in each year from 1977 through 2012 obtained using regression model (1) and show separately the results for family income and family earnings. Panel (a) shows the evolution of the intergenerational family income elasticity. Consistent with the results by Lee and Solon (2009), there is no clear trend over time. However, as shown in panel (b) of Figure 1, we do observe an upward trend in our estimate of the intergenerational family earnings elasticity. Performing a Wald test to 7

9 Figure 1: Estimated Intergenerational Family Income and Earnings Elasticities, (a) Income (b) Earnings Note: The dotted lines show the 95% confidence bands for the estimated elasticities based on the full samples. the period from 1977 through 2012 we can not reject the hypothesis that all the estimates β t are the same in the case of family income, but we can reject it for family earnings. 9 How important is this upward trend in the intergenerational earnings elasticity? A simple average of the estimates in the 80 s is 0.13, 0.22 in the 90 s, 0.25 in the 00 s and 0.33 in This implies that the persistency in earnings more than doubled in the last 30 years. For instance, adult children of parents with earnings 10 percent above the mean were on average 13 percent above the mean in the 80 s, while similar children born 30 years later had earnings 33 percent above the mean. As a final check for the upward trend in earnings we estimate a linear time trend for the intergenerational elasticities using regression model (2). The estimated slope coefficient in the case of family earnings turned out positive and highly significant, while in the case of family income it is negative and not significantly different from zero (see Table 2 further down). 9 The first column of Tables 6 and 7 in the appendix contain the estimated intergenerational elasticities for each year and the Wald statistics. Notice that, as expected, the standard errors are higher in the first years of the sample, due to the reduced number of observations in the beginning of the sample. 8

10 3.2 The Role of Transfers It is surprising that the observed increase in the persistence of earnings does not translate into an increase in the income persistence, since by far labor earnings is the largest component of family income. However, as shown in Table 1 constructed using our sample, the share of transfers in household income has increased over time, in particular for the bottom quintile of the income distribution. In contrast, the omitted component, capital income, has remained roughly constant. 10 To investigate the impact of the increasing share of transfers on intergenerational mobility, we present in Figure 2 two alternative estimates of the intergenerational family income elasticity using regression model (1). The first estimate considers family income without any transfers received by any member of the household, both for parents and sons, while the second excludes capital income. We focus only on the period between 1984 and 2012, because during this period all of our estimates are significant. Figure 2 shows that when we exclude transfers from family income, the intergenerational elasticity of income without transfer is surprising similar to the intergenerational earnings elasticity. In particular, we do observe a positive trend over time. This suggests that transfers (but not capital income) played an important role in mitigating the increase in the persistence of income between generations. 11 Furthermore, Table 2 shows that the slope of the linear trends obtained using regression model (2) for income excluding transfers is again positive and highly significant, as it was the case for earnings. In contrast, the slopes for family income and income without capital are negative and not significant. 10 The decrease in the share of earnings was already documented by Díaz-Giménez et al. (2011) using the Survey of Consumer Finances (SCF) from 1998 to One limitation of the PSID is that it cannot provide a disaggregation of transfers which remains consistent throughout the period of analysis. This precludes us to distinguish between private and public transfers, or to identify which specific government program (Social Security, Welfare, etc.) are more important to account for the role of public transfers. 9

11 Table 1: Decomposition of Income (percentage of household income) Average Bottom Quintile Top Quintile Earnings Tranfers Earnings Transfers Earnings Transfers 80 s s s s Figure 2: The Role of Transfers on the Estimated Intergenerational Elasticities Income Earnings Income Wihout Transfers Income Without Capital Table 2: Estimated Intergenerational Family Elasticities Earnings Income Without Transfers Income Without Capital Income β β *significant at 10 % level; **significant at 5 % level; ***significant at 1% level. 10

12 3.3 Intergenerational Mobility Across the Income Distribution Table 1 also suggests that the impact of transfers might be different across the income distribution. Conditioning our sample on parental characteristics makes the estimates less precise, as the number of observations in each parental category might drop substantially. Still, to understand the mechanisms that relate inequality and mobility it is key to analyse if the trends observed for the whole sample are similar for rich and poor households. In Figure 3, we estimate the same intergenerational elasticities of income and earnings over time as in the previous sub-section, but excluding in one case parents in the top quintile of the income distribution (according to our sample) and parents in the bottom quintile, in the second. 12 The results suggest that the upward trend in the intergenerational elasticity of earnings is robust to the exclusion of the top and bottom quintiles. In the case of the elasticity of family income, though, the exclusion of the bottom quintile implies now an upward trend after the 80 s, similar to trends found in earnings, while when excluding the top quintile, we obtain a downward trend. considering the whole sample, as shown in Figure These two opposite trends cancel out when Table 3 reports the estimated linear trends obtained using regression model (2). Consistently with Figure 3, we find that when excluding the top of the income distribution we observe a negative and significant trend in the intergenerational family elasticity, while removing the bottom of the income distribution delivers a positive linear trend, albeit not significant. With respect to earnings, we observe again that excluding parents that belong to the top or to the bottom of the distribution do not affect importantly the slope of the time trend. 12 Again, the standard errors are higher in the first years of the sample, due to the reduced number of observations in the beginning of the sample. Still, starting in 1984 all intergenerational elasticities become statistically significant at 5% level, so we focus on the intergenerational elasticities for the period 1984 to Notice also that excluding the bottom quintile increases the level of the elasticity of family earnings from 0.19 to 0.32, averaging over time, without changing much the level of the elasticity of family income, 0.45 to The typical estimates found in the literature using the PSID lie around 0.5 for family income and 0.4 for family earnings. 11

13 Figure 3: Estimated Intergenerational Elasticities Conditioning on Parent s Family Income Excluding Quin7le I Excluding Quin7le V Excluding Quin7le I Excluding Quin7le V (a) Income (b) Earnings Table 3: Estimated Intergenerational Family Elasticities Conditional on Family Income Estimates Income Income Earnings Earnings Excluded Quintile Top Bottom Top Bottom β β *significant at 10 % level; **significant at 5 % level; ***significant at 1% level. 12

14 3.4 Intergenerational Mobility by Parental Education We also estimate linear trends for the intergenerational elasticities of family income and family earnings separately for parents with college education and parents without college education. In Table 4, we present the results only from 1984 to 2012, when the elasticities themselves were significant. Consistent with our previous results, we find an upward trend in the earnings elasticity, but is only significant for parents with college education. This suggests that the observed increase in the intergenerational elasticity of family earnings for is mostly driven by the increase in the degree by which college educated parents have been able to transmit their earnings potential to their children. In terms of the intergenerational income elasticity, we do observe again a significant decline over time for parents without college education. This is again consistent with the findings in Table 3, as these parents are likely to belong to the bottom half of the income distribution. 4 The Great Gatsby s Curve Revisited Finally, we summarize our previous results by analyzing the relation between inequality and intergenerational mobility in the US from 1980 to This relation is akin to the Great Gatsby s curve that we discussed in the introduction, but obtained from time series for these variables in the US instead of a cross-country comparison. We document that the observed increase in inequality is indeed associated to an increase in the intergenerational persistence of family earnings, consistent with an upward-sloping Great Gatsby s curve. However, this relation disappears when we consider the intergenerational persistence of family income. 13

15 4.1 Growing Inequality Inequality in the U.S. is growing over time, a fact which is well documented in the literature. 14 We find a similar trend in our sample of sons using the standard measures of inequality (the Gini coefficient, the P90/P50 ratio, and P50/P10 ratio). The main results are summarized in Table 5, where we report the average Gini, average P90/P10 ratio, and P50/P10 ratio for family income and earnings in the last four decades. All measures show an increase in inequality, consistent with the literature. 15 In addition, we find that there is more inequality in family earnings than in family income and that inequality in earnings is growing faster. This fact, reported also by Díaz-Giménez et al. (2011) using the SCF data, suggest an important role of transfers in mitigating the increase in inequality. As we have seen in the previous section, transfers have also being important in attenuating the decline in intergenerational mobility. 4.2 The Great Gatsby s Curve for the US The observed trends in inequality have a counterpart in the evolution of intergenerational mobility that we have analyzed in the previous section. Figure 4 shows the implied Great Gatsby s curves for the U.S. in the period from 1980 to Each point in the figure corresponds to a year, for which we plot our estimate of the intergenerational elasticity against the Gini coefficient computed in our sample. In Panel (a) we plot the Great Gatsby s curve using family income, both for the Gini and for the intergenerational elasticity, and in Panel (b) using family earnings. 14 Some examples are Heathcote et al. (2010) and Díaz-Giménez et al. (2011). 15 Regarding the P90/P50 ratio our findings are on the same order and similar to Heathcote et al. (2010) which use hourly wages from the Current Population Survey (CPS). Notice, though, that our P50/P10 ratios are higher than the ones reported by these authors. The main reason for this discrepancy is that in our sample we included many households that have zero earnings, work very little hours, but have positive income from transfers. Heathcote et al. (2010) restrict his sample to head and spouse that make more half of the federal minimum wage and excludes households in which the head or spouse has positive labor income but zero weeks worked in the CPS. 14

16 Table 4: Linear Trends of Intergenerational Elasticities Conditioning on Parent s Education Income Earnings All Non-College College All Non-College College β β * significant at 10 % level; ** significant at 5 % level; *** significant at 1% level. Table 5: Income and Earnings Inequality Income Earnings Gini P90/P50 P50/P10 Gini P90/P50 P50/P10 80 s s s s Figure 4: The Great Gatsby Curve for Income and Earnings 's 90's 00's 10's 80's 90's 00's 10's (a) Income (b) Earnings 15

17 Consistent with our previous results, we find an upward sloping Great Gatsby s curve for family earnings, indicating a positive correlation between the growth in inequality, measured by the Gini index, and the growth in intergenerational persistence of family earnings during this period. Contrarily, in the case of family income we find a weak negative correlation between inequality and intergenerational persistence. 5 Conclusions In this paper we perform an exploratory analysis on the evolution of intergenerational mobility in the U.S. Our results show a significant increase in the intergenerational elasticity of family earnings, both at the family level and at the individual level, between 1977 and This seems to be mainly driven by an increase in the ability of college educated parents to transmit their earnings potential to their children, perhaps amplified by a higher degree of assortative matching and selectivity in the marriage market. However, we also report a decline in the intergenerational elasticity of family income, although not statistically significant, suggesting a compensatory role of transfers. Our results suggest that the concern about higher inequality leading to less intergenerational mobility in the US is well founded, at least in terms of the earnings potentials of different generations. However, it also shows that transfers, in particular government programs as Social Security and Welfare, might have been already effective in attenuating its impact on the intergenerational mobility of economic status. 16

18 References Aiyagari, S Rao, Jeremy Greenwood, and Ananth Seshadri, Efficient investment in children, Journal of Economic Theory, 2002, 102 (2), Becker, Gary S and Nigel Tomes, An equilibrium theory of the distribution of income and intergenerational mobility, The Journal of Political Economy, 1979, pp Cameron, A.C. and P.K. Trivedi, Microeconometrics: Methods and Applications, Cambridge University Press, Corak, Miles, Inequality from Generation to Generation: The United States in Comparison, The Economics of Inequality, Poverty, and Discrimination in the 21st Century, Díaz-Giménez, Javier, Andy Glover, and José-Víctor Ríos-Rull, Facts on the distributions of earnings, income, and wealth in the united states: 2007 update, Federal Reserve Bank of Minneapolis Quarterly Review, 2011, 34 (1), Goldin, Claudia, The Quiet Revolution That Transformed Women s Employment, Education, and Family, AEA Papers and Proceedings, 2006, May 2006, Ely Lecture, American Economic Asssociation Meetings, Boston MA (Jan. 2006). Heathcote, Jonathan, Fabrizio Perri, and Giovanni L Violante, Unequal we stand: An empirical analysis of economic inequality in the United States, , Review of Economic dynamics, 2010, 13 (1), , Kjetil Storesletten, and Giovanni L Violante, The macroeconomic implications of rising wage inequality in the United States, Technical Report, National Bureau of Economic Research Krueger, Alan B, The Rise and Consequences of Inequality in the United States, Remarks delivered to the Center for American Progress. Washington. January, 2012, 12. Lee, C.I. and G. Solon, Trends in Intergenerational Income Mobility, The Review of Economics and Statistics, 2009, 91 (4),

19 Loury, Glenn C, Intergenerational transfers and the distribution of earnings, Econometrica: Journal of the Econometric Society, 1981, pp Mayer, S.E. and L.M. Lopoo, Has the Intergenerational Transmission of Economic Status Changed?, Journal of Human Resources, 2005, 40 (1), Mazumder, Bhashkar and David Levine, Choosing the Right Parents: Changes in the Intergenerational Transmission of Inequality Between 1980 and the Early 1990s, Restuccia, Diego and Carlos Urrutia, Intergenerational persistence of earnings: The role of early and college education, The American Economic Review, 2004, 94 (5), Tavares, M. M. and C. Urrutia, Accounting for the Trends in Inequality and Intergenerational Mobility in the U.S.,

20 Table 6: Estimated Intergenerational Family Income Elasticities by Year for Sons Year Estimates Estimates Estimates Excluding Top Quintile Excluding Bottom Quintile (0.10) 0.63 (0.12) 0.52 (0.14) (0.09) 0.58 (0.15) 0.50 (0.13) (0.08) 0.67 (0.17) 0.47 (0.16) (0.07) 0.58 (0.14) 0.52 (0.13) (0.07) 0.76 (0.18) 0.50 (0.11) (0.06) 0.73 (0.14) 0.46 (0.10) (0.06) 0.66 (0.13) 0.45 (0.10) (0.06) 0.68 (0.15) 0.40 (0.09) (0.06) 0.58 (0.14) 0.43 (0.09) (0.06) 0.58 (0.11) 0.49 (0.09) (0.05) 0.53 (0.12) 0.34 (0.08) (0.06) 0.50 (0.10) 0.31 (0.08) (0.05) 0.54 (0.09) 0.40 (0.08) (0.04) 0.36 (0.08) 0.40 (0.07) (0.05) 0.52 (0.09) 0.45 (0.08) (0.04) 0.41 (0.09) 0.42 (0.07) (0.04) 0.48 (0.09) 0.49 (0.07) (0.04) 0.37 (0.08) 0.43 (0.06) (0.04) 0.39 (0.08) 0.43 (0.06) (0.04) 0.46 (0.08) 0.48 (0.06) (0.03) 0.49 (0.07) 0.49 (0.06) (0.04) 0.48 (0.07) 0.46 (0.06) (0.03) 0.34 (0.06) 0.44 (0.05) (0.03) 0.37 (0.06) 0.49 (0.05) (0.03) 0.37 (0.06) 0.49 (0.05) (0.03) 0.37 (0.06) 0.47 (0.05) (0.03) 0.35 (0.06) 0.51 (0.05) (0.03) 0.32 (0.07) 0.55 (0.06) Observations 20,336 15,525 19,434 Wald Statistic: Equality of elasticities across years (p = ) (p = ) (p = ) 19

21 Table 7: Estimated Intergenerational Family Earnings Elasticities by Year for Sons Year Estimates Estimates Estimates Excluding Top Quintile Excluding Bottom Quintile (0.10) 0.23 (0.11) 0.55 (0.12) (0.10) 0.09 (0.11) 0.29 (0.15) (0.08) 0.10 (0.09) 0.33 (0.15) (0.08) 0.08 (0.08) 0.36 (0.12) (0.10) 0.16 (0.12) 0.43 (0.17) (0.09) 0.14 (0.10) 0.30 (0.12) (0.07) 0.08 (0.08) 0.23 (0.10) (0.07) 0.08 (0.07) 0.23 (0.10) (0.06) 0.06 (0.07) 0.25 (0.09) (0.07) 0.12 (0.07) 0.31 (0.08) (0.06) 0.10 (0.07) 0.19 (0.08) (0.06) 0.11 (0.06) 0.17 (0.08) (0.06) 0.14 (0.06) 0.22 (0.08) (0.05) 0.09 (0.05) 0.28 (0.07) (0.05) 0.16 (0.06) 0.32 (0.08) (0.05) 0.12 (0.05) 0.29 (0.07) (0.05) 0.19 (0.06) 0.34 (0.07) (0.04) 0.14 (0.05) 0.31 (0.06) (0.05) 0.19 (0.06) 0.30 (0.06) (0.04) 0.12 (0.04) 0.35 (0.06) (0.04) 0.22 (0.04) 0.34 (0.06) (0.04) 0.21 (0.04) 0.36 (0.05) (0.03) 0.14 (0.03) 0.31 (0.05) (0.04) 0.15 (0.04) 0.36 (0.05) (0.03) 0.18 (0.04) 0.34 (0.05) (0.04) 0.21 (0.05) 0.37 (0.05) (0.04) 0.26 (0.05) 0.41 (0.05) (0.04) 0.26 (0.05) 0.47 (0.05) Observations 19,513 14,934 18,379 Wald Statistic: Equality of elasticities across years (p = ) (p = ) (p = ) 20

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