The Genetic Heritability of Lifetime Income *

Transcription

1 Draft -- please do not quote or redistribute The Genetic Heritability of Lifetime Income * Ari Hyytinen University of Jyväskylä and Yrjö Jahnsson Foundation Pekka Ilmakunnas Aalto University School of Economics Edvard Johansson Åland University of Applied Sciences Otto Toivanen Katholieke Universiteit Leuven This version: January 7, 01 Abstract: We decompose the variation of lifetime income into its genetic and environmental components, using longitudinal data on Finnish twins. Lifetime income is measured by the long-run average of income, calculated over a fifteen-year period that covers the prime working age of the individuals in our data. We find that 40% of the variance of women s lifetime income is due to genetic factors and that the corresponding estimate of heritability for men is about 50%. These estimates are robust to using alternative income measures and for controlling for variation in schooling. We also find that genetics account for a smaller but non-negligible fraction of the variance of (individual-specific) income uncertainty. Key words: permanent income, income uncertainty, heritability, twins, genetics JEL code: J31, J6 * Ari Hyytinen, University of Jyväskylä, P.O. Box 35, FI University of Jyväskylä, Finland, ari.hyytinen@jyu.fi; Pekka Ilmakunnas, Aalto University School of Economics, P.O. Box 140, FI Aalto, Helsinki, Finland; pekka.ilmakunnas@aalto.fi; Edvard Johansson, Åland University of Applied Sciences, Neptunigatan 17, PB 1010, AX-111 Mariehamn, Åland, Finland, edvard.johansson@ha.ax; Otto Toivanen, Department of Managerial Economics, Strategy and Innovation, Katholieke Universiteit Leuven, Naamsestraat 69 Leuven, B-3000 Belgium. otto.toivanen@econ.kuleuven.be. We would like to thank Jaakko Kaprio for access to the twin data and seminar participants at the Summer Meeting of the Finnish Economists (Jyväskylä, 011) for useful comments. This research has been financially supported by the Academy of Finland (project 17796). Hyytinen thanks the Bank of Finland for hospitality. The usual caveat applies. 1

2 1 Introduction Understanding the determinants of inequality and intergenerational income mobility are subject to an intensive research program in economics and social sciences more broadly. As Black and Devereux (011) conclude in their recent review, an important and robust finding of this literature is that the relative equitable Nordic countries have high intergenerational mobility, exceeding clearly that of the UK and US. Consistent with this, the correlation of incomes among siblings is also much lower in the Nordic countries than in the U.S. (Solon 1999, Björklund et al. 00, Black and Devereux 011). Because genetic heritability of income and other (shared) environmental determinants, such as fixed family characteristics, contribute to both intergenerational and sibling correlations of earnings, these findings appear to suggest only a limited role for them in determining the lifetime income of individuals in the Nordic countries. This paper asks whether the data support this conclusion. In particular, we study whether genetic heritability and other shared environmental determinants matter and which of them is more important. A particular challenge in the branch of the empirical literature that has studied the heritability of income is that the object of primary interest, lifetime income, can often be measured only using poor proxies (Haider and Solon 006, Böhlmark and Lindquist 006). 1 Table 1 summarizes the twins studies from which one can infer a proxy for the heritability of income. It shows that most of this prior work uses a single cross-section and short-term income measures, such as annual earnings or hourly salary. Notable exceptions are Isacsson (1999) and Björklund et al. (005), which both use three years of earnings data on Swedish twins over a spell of seven years. Unlike the prior analyses, our empirical analysis is based on a data set that allows using both long-term income measures and standard behavioural genetics designs to measure the importance of genetic heritability and shared environmental factors in generating individual variation in life-time incomes. Our sample consists of 1173 monozygotic (MZ) and 396 dizygotic (DZ) twin pairs, who were born between 1950 and 1957 and for which we observe accurate administrative data on prime working-age incomes from 1990 to 004. Our primary measure 1 This may lead, for example, to a gross underestimation of the strength of the intergenerational links (Haider and Solon 006).

3 for life-time income is the average of individuals wage and salary earnings and self-employment income over this fifteen-year period. We also employ alternative measures, such as age-adjusted versions of our primary measure and long-term taxable income. Twin data are not a panacea, but its great advantage is that it allows measuring how genetic, shared environmental and individual-specific (non-shared environmental) factors contribute to the variance of life-time incomes. The relative contributions of these factors to the variance can be identified, because MZ and non-identical DZ twins have a shared (family) environment, but unlike the identical MZ twins, the non-identical DZ twins share, like non-twin siblings, only onehalf of their genes on average. Greater similarity in outcomes between the MZ twins is therefore indicative of the importance of genes. Table 1 also reports the sibling correlations of incomes for MZ and DZ twins as well as a standard additive variance decomposition implied by the siblings correlations. While the decomposition relies on a number of restrictive assumptions, two preliminary observations can be made: First, the U.S. estimates for the importance of the genetic component, h, are close to those reported for Sweden. Second, the genetic component accounts for as much as 40% of income variation. [Insert Table 1 here] In this paper, we find that 40% of the variance of women s lifetime income is due to genetic factors and that the corresponding estimate of genetic heritability for men is about 50%, with some estimates indicating even greater heritability for men. Unlike the earlier work, we can show that these estimates are robust to using alternative income measures and, strikingly, for controlling for the effects of schooling. Moreover, we show that if we use annual income data (instead of our measures for lifetime income), the yearly heritability estimates are of similar magnitude and relatively stable over time in most cases. 3 The estimates of heritability that we get are consistent with the earlier Swedish evidence (see Table 1) See Sacerdote (011) for a review. We discuss the assumptions that underlie this calculation in the next section and relax some of them in our empirical analysis. 3 However, they are clearly not constant and vary over time in certain standard behavioural genetic models more than in others. 3

4 and suggest that genes may have a surprisingly large contribution to the correlation in the lifetime incomes of siblings and to intergenerational income persistence even in the equitable Nordic countries. This finding is a bit puzzling, as it appears to suggest that genetic heritability is relatively more important in the Nordic countries than in the U.S. in explaining the similarity in the earnings between children and parents and across siblings. An alternative way to look at the importance of heritability for income outcomes is to ask how much of the variance in the within individual variation in (annual) incomes can be attributed to genetic, shared environmental and individual-specific (non-shared environmental) factors. That is, how much of the variance in intertemporal income uncertainty these factors account for? It turns out that 0% of the variance of women s income uncertainty, as measured by the standard deviation of their annual income, is due to genetic factors. The corresponding estimate of genetic heritability for men is about 30%. This finding suggests that genes matter for the income risks that people face in their prime working age. Simplifying a bit, this finding also suggests that genetic heritability is about twice as important for the first moment of the earnings distribution as they are for determining its second moment. Besides the studies that focus on the heritability of income, there are a number of papers that are related to our work. A common denominator of them is that they all apply various variance decompositions to twin data in order to determine the importance of genetic and environmental factors for the variation of economic outcomes (see also Sadercote 011 for a review). This branch of the literature include Behrman and Taubman (1989) and Miller et al. (001), who investigate the genetic heritability of education, Miller et al. (1996) and Schnittker (008), who focus on occupational status and socioeconomic position, and Nicolaou et al. (008), who examine the effect of genetics on the likelihood of becoming an entrepreneur. More recent work has extended the literature by studying the genetic heritability of the formation of preferences (Cesarini et. al 009, and Simonson and Sela 011) and financial decision-making (Barnea et al. 010, and Cesarini et al. 010). 4 4 There are two other closely related branches in the literature. The first of them uses (non-twins) siblings and/or adoption data. Examples of this work include Björklund et al. (006, 007), Jäntti et al. (001), Plug and Vijverberg (003), and Sacerdote (00, 007). The second related branch focuses on the intergenerational mobility and elasticity of incomes; see Solon (1999) for a review. 4

5 The remainder of this paper is organized as follows. In the next section, we present the Finnish twin and register data in more detail. The third section describes how we measure lifetime income and estimate the contribution of genes to its variance. The fourth section offers, to the best of our knowledge, the first ever look at the heritability of income uncertainty. Section 5 concludes. Data Our twin sample is based on the Finnish Twin Registry (of The Department of Public Health in University of Helsinki) that we have matched to the Finnish Longitudinal Employer-Employee Data (FLEED) of Statistics Finland. The Finnish Twin Registry was established in 1974 and was initially compiled from the Central Population Registry of Finland. Initial twin candidates were persons born before 1958 with the same birth date, commune of birth, sex, and surname at birth (Kaprio et al., 1979; Kaprio and Koskenvuo, 00). A questionnaire was mailed to these candidates in 1975 to determine zygosity and to collect baseline data for follow-up studies. The zygosity of the twin pairs was determined using a deterministic method. It classified twin pairs on the basis of their responses to two questions on similarity in appearance in childhood. A subsample was taken for which the classification was redone using eleven blood markers. The classification results agreed completely, with the probability of misclassification of a blood marker concordant pair being 1.7% (Kaprio et al., 1979). We focus on the youngest cohort, born in Our sample contains nearly all same-sex DZ and MZ twins of this cohort of the Finnish population. Most of the attrition is due to death (e.g., of fatal diseases) and migration. We had the twin data linked to FLEED using personal identifiers. FLEED is constructed from a number of different administrative registers on individuals, firms and establishments that are collected or maintained by Statistics Finland. Importantly for this study, FLEED includes information on salaries and other income, taken directly from tax and other registers. This implies that our income data do not suffer from underreporting or recall error. Nor is it top-coded. The income data available to us cover years from 1990 to

6 3 Heritability of lifetime income 3.1 Measuring lifetime income and its genetic variation Lifetime income Because we use a sample of individuals born between 1950 and 1957, the individuals are from 33 to 40 years old at the beginning of our sample period in 1990 and from 48 to 55 years old at the end of the sample period in 004. We thus observe the incomes of individuals who are at their prime working age. Our first measure for the life-time income of an individual is the average of (the logarithm of) the individual s wage and salary earnings and self-employment income, calculated over the sample period. The findings of Haider and Solon (006) for the U.S. and those of Böhlmark and Lindquist (006) for Sweden suggest that this long-term sample average ought to be a reliable measure for life-time income. While our results are robust to not trimming our sample (see below), we truncate ( winzorize ) those observations that fall outside the 1 st and 99 th percentiles to account for outliers in our baseline analysis. Table reports the means and standard deviations of income and age, separately for MZ and DZ twins by gender. [Insert Table here] Our alternative measure for life-time income adjusts for the stage of lifecycle. We obtain it from a regression of the annual income on a constant, fourteen year dummies and a third order polynomial of age, using the panel data on individuals but run separately for men and women. The age-adjusted life-time income is then computed as the within-individual average of these residuals. Variance decompositions We measure the importance of genetic factors for life-time income using two approaches. First, we present the standard behavioral genetics decomposition where the genetic heritability of life-time income is twice the difference in the correlations of the life-time income between MZ and DZ twins, i.e., h rmz rdz ( ), 6

7 and where the fraction of variance explained by the shared environment is c r h. This model assumes i) that genes and environment have additive MZ effects, ii) that MZ twins experience environments that are similar to those of DZ twins, iii) that there is no correlation between genetic factors and the shared environment (i.e., within-pair genetic differences are not correlated with the withinpair environmental differences), and iv) that there is no assortative mating, which would be the case if the long-term income genotypes of the parents are correlated. Albeit simple, this approach is a useful starting point, as it provides us with estimates that are comparable to those presented in Table 1. As shown below, our results are robust to using approaches which do not impose all of these assumptions. Our second approach is to use the regression model proposed by DeFries and Fulker (1985), and further developed by Waller (1994), Kohler and Rodgers (001) and Rodgers and Kohler (005), among others. 5 The simplest version of DeFries and Fulker (DF) model is a regression model that relies on the assumptions of the additive genetic model, i.e. assumptions i)-iv). It is typically called the ACE-model and can be written as INC INC R INC ) (1) ( R where INC 1 is a measure of the lifetime income of twin 1 in a pair of twins, INC is the corresponding measure for twin from the same pair of twins, R is the coefficient of genetic relatedness (R=1.0 for MZ twins and R=0.5 for DZ twins), is an error term, and s are regression coefficients. Given the assumptions of the ACE-model, 1 and 3 are unbiased estimates of c and h, respectively (De- Fries and Fulker, 1985, Rodgers and McGue, 1994). If the estimate of 1 is statistically not significant, the shared environmental term is often dropped. The model is then called AE-model. Genetic effects need not be additive, but can be of a dominant form. Such effects can be accommodated in the DF-model by reformulating it to 5 This model and its closely related variants are not unfamiliar to economists (see, e.g., Miller et al. 001). 7

8 INC R INC R) ( INC ) () 1 0 3( 4 D where D is the coefficient of dominant genetic relatedness, with D = 1 for MZ twins and D = 0.5 for DZ twins (Waller, 1994, Rodgers et al. 001). This model is often called the ADE-model. In (), 3 estimates narrow-sense heritability, 4 the dominance effect, and estimates broad-sense heritability (Waller, 1994) Main results Table 3 presents the correlation coefficients for our two measures of lifetime income, separately for MZ and DZ twins. For a trait to be heritable, the correlation of life-time income within the MZ twin pairs, r MZ the DZ twin pairs, r DZ, should be bigger than that of. This is clearly the case for our measures of lifetime income. In fact, the estimate for r MZ is more than twice that of r DZ. [Insert Table 3 here] Applying the standard additive decomposition to these numbers shows that h ( ) % for males and h ( ) % for females. Especially the estimate for males is at the upper end, if we compare it to the estimates presented in Table 1. The estimates for the fraction of variance explained by the shared environment are negative. Table 4 presents the results of our DF-analyses. The baseline results are presented in the columns with title ACE, for the two measures of lifetime income. Recalling that 1 and 3 are estimates of the shared environment ( c ) and genet- ic heritability ( h ), respectively, we can see that the estimates of h are large and highly statistically significant for both men and women. However, the estimates 6 In (1) and (), the value for twin of a pair of twins is an explanatory variable for twin 1 s outcome. However, it is not possible a priori to decide which of the twins is twin 1 and which is twin. Our regression analysis is therefore performed in the double entry form, i.e. each twin pair is entered into the data twice: The first observation uses the outcome of twin 1 as the dependent variable and that of twin as the explanatory variable. The second observation reverses the roles. This procedure means that standard errors should be clustered for correct inference (see Kohler and Rodgers, 001). 8

9 for c are either negative or not significant. This suggests that alternative models ought to be considered and that dominance effects may be present (Waller, 1994, Rodgers et al., 001): Columns titled AE presents more parsimonious models from which the shared environment term is dropped, and columns titled ADE show the results for the ADE-models. The AE models suggest that the estimate of h is 38% for females and 47% for males. In the ADE-models, broad heritability refers to the sum and is 40% (53%) for women (men). The sum is highly significant in both cases. [Insert Table 4 here] Robustness We check the robustness of the results displayed in Table 4 as follows: First, we run the DF-regressions using a larger sample that includes twin pairs born between 1945 and 1949 and those born between 1950 and There is some evidence for dominance effects both for men and for women in these estimations, but the magnitude of genetic heritability remains intact (see Appendix 1, Table A1). Second, we run regressions using a broader income concept including capital income and transfers, such as unemployment benefits and parental leave benefits (see Appendix 1, Table A). This time there is some evidence for dominance effects for women, but not for men. The magnitude of the genetic effects is nevertheless comparable to those reported earlier in Table 4. Third, we rerun the regressions using an untrimmed, larger sample (i.e., with outliers included). These results are available on request and they are similar to our main findings. Our final robustness test is based on an approach developed by Bowles and Gintis (00). It relaxes the assumption that the environments experienced by MZ twins are similar to those of DZ twins, allows for a non-zero gene-environment correlation, and does not call for random mating. In particular, the model allows the environment of a sibling to depend on both the shared environmental factors and on genes. Appendix describes our implementation of this approach in great- 9

10 er detail; it suffices to note here that the results support our main qualitative findings. 3.3 Extensions: Macroeconomic conditions and schooling Given that most of the prior work uses a single cross-section and/or short-term income measures, a question of particular interest is whether the heritability estimates are stable in annual data. Figure 1 addresses this question and reports the results from behavioral genetic decompositions (DF-regressions) in which we use annual incomes, instead of our measures for lifetime income, as the dependent variable. We present the results by gender, separately for the ACE, AE and ADEmodels (left-axis) over time and plot also the real growth of GDP (right axis). [Insert Figure 1 here] The results show that the yearly heritability estimates are relatively stable (though not constant), except for the ACE model, in which the estimate varies from year to year more than in the other models. However, the yearly heritability estimates are, on average, of similar magnitude than the estimates we obtained using the lifetime income as the outcome measure. It seems that the estimates may correlate with the GDP growth, especially for males. We can also try to link the gender-specific yearly heritability estimates more systematically to the real growth of GDP and time-varying income inequality (as measured by the Gini-coefficient using a regression. The results are displayed in Appendix 3: In general, we do not find significant (partial) correlations, though there is some evidence that the estimates correlate with the GDP growth when we use the heritability estimates from AE and ADE models. We remind the reader not to take these regressions too literally due to our small sample size. While exploratory, this analysis highlights the possibility that the heritability estimates which use short-term data may depend both on the model specification as well as vary with the overall macroeconomic conditions. A natural source of heritability in lifetime income is education. To explore its importance for our main results, we run our DF-regressions in two new ways. First, we add each individual s education (i.e., schooling in years) to the ACE, AE 10

11 and ADE models as a new R.H.S. variable. Second, we obtain a standard within twin estimate (using MZ data only) for the effect of the schooling on lifetime income and then adjust the lifetime income, for each individual in our data, by deducting the estimated effect times the individual s years of schooling from his or her lifetime income. The results from these adjusted DF-regressions are displayed in Table 5. They show, perhaps surprisingly, that the heritability estimates are only marginally lower when we condition on schooling. This result is robust across the various decompositions and by gender. [Insert Table 5 here] 4 Heritability of Income Uncertainty 4.1 Measuring Income Uncertainty How much of the variance in the within individual variation in annual incomes can be attributed to genetic, shared environmental and individual-specific factors? That is, how much of the variance in intertemporal income uncertainty these factors account for? To the best of our knowledge, the prior literature does not provide an answer to these questions, despite the fact that income uncertainty and risks have been subject to a considerable research program. As we understand it, estimates for the heritability of the second moment of the income distribution are not available in the prior literature for two reasons: First, earlier analyses have not relied on income data over several years, which is required to measure income uncertainty at the level of each individual. Second, measurement errors make the heritability analysis of income uncertainty difficult, as it is likely to bias the heritability estimates downwards. Albeit all data sets suffer from measurement errors to some extent, such errors are not likely to dominate the intertemporal variation of individuals income in administrative, computerbased registers. Such panel data have not been available for twins before. As earlier, we use a sample of individuals who are from 33 to 40 years old at the beginning of our sample period in 1990 and from 48 to 55 years old at the end of the sample period in 004. To obtain a measure for income uncertainty at the level of individuals, we use the annual income data and calculate the standard 11

12 deviation of the logarithm of income separately for each individual over the sample period. Table 6 displays the descriptive statistics for this measure in our sample. It shows that income uncertainty is, on average, greater for females than for males. The means of the uncertainty measure are very similar in the MZ and DZ subsamples. [Insert Table 6 here] 4. Main Results Table 7 displays the correlation coefficients for income uncertainty, separately for MZ and DZ twins. Applying the standard additive decomposition to these numbers shows that h ( ) % h ( ) % for males and for females. These numbers suggest a nonnegligible role for genes in explaining variation in income risk. [Insert Table 7 here] Table 8 presents the results of the DF-analyses. The estimates of h are highly statistically significant and they show that 0% of the variance of women s income uncertainty is can be attributed to genetic factors. The corresponding estimate of genetic heritability for men is at least 30%. These findings confirm the conclusion that genes matter for the income risks that people face in their prime working age. [Insert Table 8 here] As shown in Appendix 4, these results are robust to using the average of the absolute value of the difference between annual income and its individual-specific mean as the alternative measure for the income uncertainty. 5 Conclusions We match the Finnish twin registry with longitudinal administrative data and decompose the variation of lifetime income into its genetic and environmental com- 1

13 ponents. Unlike the prior literature, we can measure lifetime income by the longrun average of income, calculated over a fifteen-year period that covers the prime working age of the individuals in our data. Our main finding is that around 40% of the variance of women s lifetime in-come is due to genetic factors and that the corresponding estimate of genetic heritability for men is about 50%. These estimates are robust to using alternative income measures and for controlling for variation in schooling. We also find that genetics account for a smaller but nonnegligible fraction of the variance of (individual-specific) income uncertainty. How these income effects come about is unclear. Do they mostly reflect the direct effects of genetic composition on personality traits, cognitive abilities, intelligence, physical appearance or activity levels, to which the labor market attaches a price? Or are they more due to the more indirect gene-environment correlation effects? For example, to what extent can the heritability of long-term income be explained by the propensity of people with a certain genetic composition to pursue a certain (well-paid) occupation status? Our analysis provides only a first, partial answer to questions of this sort, as we find that the relatively high genetic heritability of long-term income cannot be explained by the propensity of people with a certain genetic composition to invest in their human capital (i.e., in better education). While considerable progress has been made in understanding some of these mechanisms (Black and Devereux 011, Sacerdote 011), there are many questions that still wait for answers. 13

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15 DeFries, J. and Fulker, D Multiple Regression Analysis of Twin Data. Behavior Genetics 15(5): Falconer, D Introduction to Quantitative Genetics. New York. Longman. Haider, S. and Solon, G Life-Cycle Variation in the Association between Current and Lifetime Earnings. American Economic Review 96(4): Isacsson, G Estimates of the return to schooling in Sweden from a large sample of twins. Labour Economics 6: Kaprio, Jaakko, and Koskenvuo, Markku, 00. Genetic and environmental factors in complex diseases: The older Finnish twin cohort. Twin Research 5(5): Kaprio, J. Koskenvuo, M. Artimo, M. Sarna, S. Rantasalo, I The Finnish Twin Registry: Baseline Characteristics. Section I. Materials, methods, representativeness and results for variables special to twin studies. Department of Public Health, University of Helsinki, Series M 47. Kohler, H. and Rodgers, G DF-Analyses of Heritability with Double-Entry Twin Data: Asymptotic Standard Errors and Efficient Estimation. Behavior Genetics 31(): Miller, P., Mulvey, C., and Martin, N What Do Twins Studies Reveal About the Economic Returns to Education? A Comparison of Australian and U.S. Findings. American Economic Review 85(3): Miller, P., Mulvey, C., and Martin, N Multiple regression analysis of the occupational status of twins: A comparison of economic and behavioral genetic models. Oxford bulletin of Economics and Statistics 58(): Miller, P., Mulvey, C., and Martin, N Genetic and environmental contributions to educational attainment in Australia. Economics of Education Review 0(3): Nicolaou, N., Shane, S., Cherkas, L., Hunkin, J. and Spector, T Is the Tendency to Engage in Entrepreneurship Genetic? Management Science 54(1): Plug, E. and Vijverberg, V Schooling, Family Background, and Adoption: is it Nature or is it Nurture? Journal of Political Economy 111(3): Rodgers, J. and MacGue, H A Simple Algebraic Demonstration of the Validity of the DeFries-Fulker Analysis in Unselected Samples with Multiple Kinship Levels. Behavior Genetics 4(): Rodgers, J., Kohler, H., Kyvik, K. and Christiansen, K Modelling of Human Fertility: Findings from a Contemporary Danish Twin Study. Demography 38(1):

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17 Source Table 1: Earlier studies on the genetic heritability of income Type of Income measure data Gender Country r MZ r DZ h c e Taubman (1976, Table ) Log of annual earnings Twins Male U.S Ashenfelter and Kruger (1994, Table ) Log of hourly wage rate Twins Both U.S Schnittker (008, Table 1) Family earnings Twins Both U.S Miller, Mulvey, Martin (1995, Table ) Ave. occupat. earnings Twins Both Australia Isacsson (1999, Table ) Log of 3-year ave. earnings Twins Both SWE Björklund, Jäntti and Solon (005, Table 1) Log of annual earnings Twins Male SWE Björklund, Jäntti and Solon (005, Table 1) Log of annual earnings Twins Female SWE Avg. (U.S.) Avg. (non-u.s.) Notes: h = *(r MZ -r DZ ), c = r MZ -h, and e =1-h -c refer to the standard additive behavioral genetics variance decomposition. Earnings (income) data refer to a cross-section, except that of Isacsson (1999) and Björklund et al. (005), who use annual labour earnings from years 1987, 1990, and In Miller et al. (1995), the earnings measure is the average full time income from the occupation of employment, measured at the level of -digit, gender-specific occupational groups (i.e., is not measured at the level of individuals). Ashenfelter and Kruger (1994, p. 1161) report that r MZ =0.55 and r DZ =0.30 in Behrman et al. (1980). We do not add these estimates to the table, as they apparently refer to the twins data on which Taubman (1976) builds. Sacerdote (in press, Table IV) reports that in a study by Behrman, Taubman and Wales, h =

18 Table : Descriptive statistics Females Males MZ DZ MZ DZ Income ( ) Average Standard deviation Log(income) Average Standard deviation Age 1990 (years) Average Standard deviation Number of twin pairs Number of persons Notes: The income numbers are within-person averages for , the averages are across persons, and age refers to the average age in Table 3: Correlation coefficients Panel A: Long-term average income Females Males MZ DZ MZ DZ Correlation coefficients, r MZ and r DZ Number of twin-pairs Chow-test of hypothesis r MZ = r DZ (h = 0) p-value Chow-test of hypothesis r MZ = r DZ (c = 0) p-value 7.00 < < Panel B: Age-adjusted income Females Males MZ DZ MZ DZ Correlation coefficients, r MZ and r DZ Number of twin-pairs Chow-test of hypothesis r MZ = r DZ (h = 0) p-value Chow-test of hypothesis r MZ = r DZ (c = 0) p-value 6.41 < <

19 Panel A: Females Long-term average income Age-adjusted income ACE AE ADE ACE AE ADE (0.08) (0.08) (1.0) (0.38) (1.0) (0.14) (0.14) (0.14) (0.1) (0.04) (0.14) (0.1) (0.04) (0.14) (0.17) (0.17) (0.71) (0.10) (0.71) (0.11) (0.10) (0.11) AIC: N(pairs): F-statistic: p-value < 0.01 < 0.01 Panel B: Males Table 4: ACE, AE, and ADE -regressions Long-term average income Age-adjusted income ACE AE ADE ACE AE ADE (0.09) (0.09) (1.10) (0.41) (1.10) (0.14) (0.14) (0.14) (0.1) (0.04) (0.15) (0.1) (0.04) (0.15) (0.17) (0.17) (0.77) (0.11) (0.77) (0.11) (0.11) (0.11) AIC: N(pairs): F-statistic: p-value < 0.01 < 0.01 Notes: Standard errors in parentheses, clustered at twin pair level. AIC is the Akaike information criterion. The F-statistic and p-value refer to the test that the sum of narrow-sense heritability and dominance effect is zero, i.e., = 0. 19

20 Table 5: ACE, AE, and ADE -regressions with education Panel A: Education included as a regressor Females Males ACE AE ADE ACE AE ADE (0.08) (0.08) (1.00) (0.38) (1.00) (1.08) (0.41) (1.10) (0.1) (0.04) (0.14) (0.1) (0.04) (0.14) (0.16) (0.17) (0.73) (0.4) (0.73) (0.78) (0.) (0.78) Education (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) AIC: N(pairs): F-statistic: p-value < 0.01 < 0.01 Panel B: Education effect deducted from income Females Males ACE AE ADE ACE AE ADE (0.08) (0.09) (0.64) (0.6) (0.64) (0.87) (0.34) (0.87) (0.1) (0.04) (0.14) (0.13) (0.04) (0.15) (0.16) (0.17) (0.45) (0.10) (0.45) (0.60) (0.11) (0.60) AIC: N(pairs): F-statistic: p-value < 0.01 < 0.01 Notes: Standard errors in parentheses, clustered at twin pair level. AIC is the Akaike information criterion. The F-statistic and p-value refer to the test that the sum of narrow-sense heritability and dominance effect is zero, i.e., = 0. 0

21 Table 6: Descriptive statistics of income risk Females Males MZ DZ MZ DZ Standard deviation of log(income) over time ( ) Average Standard deviation Number of twin pairs Number of persons Notes: The standard deviations of log(income) are within-person standard deviations for and the averages are averages of these numbers across persons. Table 7: Correlation coefficients of income risk Standard deviation of log(income) Females Males MZ DZ MZ DZ Correlation coefficients, r MZ and r DZ Number of twin-pairs Chow-test of hypothesis r MZ = r DZ (h = 0) p-value Chow-test of hypothesis r MZ = r DZ (c = 0) 8.00 < < p-value

22 Table 8: ACE, AE, and ADE -regressions for income risk Females Males ACE AE ADE ACE AE ADE (0.07) (0.08) (0.17) (0.11) (0.17) (0.16) (0.11) (0.16) (0.10) (0.03) (0.1) (0.11) (0.04) (0.13) (0.14) (0.15) (0.1) (0.07) (0.1) (0.1) (0.07) (0.1) AIC: N(pairs): F-statistic: p-value < 0.01 < 0.01 Notes: Standard errors in parentheses, clustered at twin pair level. AIC is the Akaike information criterion. The F-statistic and p-value refer to the test that the sum of narrow-sense heritability and dominance effect is zero, i.e., = 0.

23 Figure 1: Annual variation in heritability estimates 3

24 Appendix 1. Robustness analysis: Lifetime income Table A1: ACE, AE, and ADE -regressions for those born 1945 or later Panel A: Females Long-term average income Age-adjusted income ACE AE ADE ACE AE ADE (0.07) (0.07) (0.81) (0.31) (0.81) (0.1) (0.1) (0.1) (0.09) (0.03) (0.11) (0.09) (0.03) (0.11) (0.13) (0.13) (0.56) (0.09) (0.56) (0.09) (0.09) (0.09) AIC: N(pairs): F-statistic: p-value < 0.01 < 0.01 Panel B: Males Long-term average income Age-adjusted income ACE AE ADE ACE AE ADE (0.07) (0.07) (0.85) (0.3) (0.85) (0.1) (0.1) (0.1) (0.10) (0.03) (0.11) (0.10) (0.03) (0.11) (0.13) (0.13) (0.58) (0.09) (0.58) (0.09) (0.09) (0.09) AIC: N(pairs): F-statistic: p-value < 0.01 < 0.01 Notes: Standard errors in parentheses, clustered at twin pair level. AIC is the Akaike information criterion. The F-statistic and p-value refer to the test that the sum of narrow-sense heritability and dominance effect is zero, i.e., = 0. 4

25 Panel A: Females Average taxable income Age-adjusted taxable income ACE AE ADE ACE AE ADE (0.09) (0.09) (1.38) (0.54) (1.38) (0.07) (0.08) (0.07) (0.15) (0.06) (0.13) (0.15) (0.06) (0.13) (0.18) (0.18) (0.8) (0.06) (0.8) (0.06) (0.06) (0.06) AIC: N(pairs): F-statistic: p-value < 0.01 < 0.01 Panel B: Males Table A: ACE, AE, and ADE -regressions for taxable income Average taxable income Age-adjusted taxable income ACE AE ADE ACE AE ADE (0.1) (0.1) (1.53) (0.53) (1.53) (0.07) (0.07) (0.07) (0.16) (0.06) (0.) (0.16) (0.06) (0.) (0.4) (0.4) (1.18) (0.05) (1.18) (0.05) (0.05) (0.05) AIC: N(pairs): F-statistic: p-value < 0.01 < 0.01 Notes: Standard errors in parentheses, clustered at twin pair level. AIC is the Akaike information criterion. The F-statistic and p-value refer to the test that the sum of narrow-sense heritability and dominance effect is zero, i.e., = 0. 5

26 Appendix. Robustness analysis: Bowles and Gintis -model The approach we use to check the robustness of our main heritability results is based on Bowles and Gintis (00). It relaxes the assumption that the environments experienced by MZ twins are similar to those of DZ twins, allows for a non-zero gene-environment correlation, and does not call for random mating. In particular, the model allows the environment of a sibling to depend on both the shared environmental factors and on genes (via a correlation term ge ). It also allows the correlation between the relevant maternal and paternal genes to be non-zero ( m ), and unequal shared environment for MZ and DZ twins ( MZ DZ ). The two key correlation moments of this model are (see Bowles and Gintis 00, p. 3-7) where r e h eh (3) MZ y y MZ ge 1 DZ (1 m y ) y1y DZ (1 y) ge r e h m eh (4). It is easy to see that if m y = ge = 0 (and if MZ is normalized to 1 DZ MZ ge(1 my ) one), the model reduces to the standard additive model. Moreover, MZ DZ is positive if 0 (and m y < 1) and the difference is increasing in ge and larger for smaller values of m y. It is not clear which of the assumptions of the standard additive model should be relaxed for our data. The analysis of Björklund et. al. (005) suggests that in the Swedish earnings data, crosscorrelations between the genetic and shared environmental factors are negative (but insignificant), that for male (female) DZ twins the genetic correlation may be about 0.43 (0.39), as opposed to 0.5 of the standard model, and that for male (female) DZ twins, the correlation of environments may be as low as (0.8), when the corresponding correlation for MZ twins is standardized to one. Given the similarity of the Nordic countries, it seems prudent that we consider the possibility of being negative and allow ge vary from (small) negative to (small) positive values. The results are displayed in Table A3. Before going into the results, one property of the model is worth noting: the implied estimate for the shared environmental effect can in this model be posi- rdz tive only if my 0.5 r 1. When r MZ is more than twice times r DZ (as is the case in our data), this MZ means that we have to allow for a negative correlation between the relevant maternal and paternal genes. Interestingly, the estimates from the Swedish earnings data for twins and siblings, presented in Björklund et al. (005), also suggest that m y might be negative. y ge m y 6

27 Panel A: Females Table A3: Bowles-Gintis model Panel B: Males ge e h DZ ge e h DZ Notes: The numbers in the table refer to h and e estimates from Bowles and Gintis (00) model, solved by allowing the gene-environment to vary (the 1st column of each panel) and using the following estimates: For females, r MZ = 0.400, r DZ = 0.170, and for males, r MZ = 0.57, r DZ = In both panels, MZ = 1.0 and m y = The last column on the right displays the implied environment correlation of DZ twins (with that of MZ twins standardized to one). The table displays estimates for h and e for ge 0.5,0.5. The estimated degree of genetic heritability varies from 18% to 53% for females and from 43% to 60% for males. It is worth mentioning that we could also solve the Bowles-Gintis model by imposing and allowing h and e = 0 m y to adjust to match the two correlation moments of the model. If we do so, we find that the correlation between the maternal and paternal genes would be estimated at -3.3% for males and at -1.% for females, with corresponding heritability estimates at 53% and 40%. 7

28 Appendix 3. Annual regressions of heritability Table A4: Annual regressions of the share of genetic heritability h, ACE h, AE h, ADE Female (0.04) (0.01) (0.01) GDP growth (0.50) (0.5) (0.3) Gini index (0.01) (0.004) (0.004) Constant (0.8) (0.14) (0.11) R : N: Notes: Standard errors in parentheses, clustered at year level. 8

29 Appendix 4. Robustness analysis: Income uncertainty Table A5: ACE, AE, and ADE -regressions for an alternative measure of income risk Females Males ACE AE ADE ACE AE ADE (0.07) (0.08) (0.13) (0.09) (0.13) (0.13) (0.09) (0.13) (0.10) (0.03) (0.13) (0.1) (0.04) (0.13) (0.15) (0.16) (0.09) (0.06) (0.09) (0.09) (0.06) (0.09) AIC: N(pairs): F-statistic: p-value < 0.01 < 0.01 Notes: Standard errors in parentheses, clustered at twin pair level. AIC is the Akaike information criterion. The F-statistic and p-value refer to the test that the sum of narrow-sense heritability and dominance effect is zero, i.e., = 0. 9