Estimates Of Intergenerational Mobility Research Articles

Intergenerational Mobility in Lifetime Income

Estimates of intergenerational mobility in lifetime income derived from incomplete income histories routinely incorporate in the estimation process, necessary life‐cycle adjustments to annual income data. The two‐stage method presented here first estimates proxies for fathers’ and sons’ lifetime family incomes from annual income observations, schooling and race; and then uses these income proxies to derive mobility measures. Applying this to United States PSID data for sons born between 1952 and 1981, we find a decline in intergenerational mobility in lifetime family income, as measured by the intergenerational elasticity of income, the rank‐rank correlation, absolute upward mobility, and other indicators.

Intergenerational mobility across Australia and the stability of regional estimates

We produce the first estimates of intergenerational mobility in Australia using administrative data, covering a million individuals born between 1978 and 1982. Australia emerges as one of the more mobile advanced economies, with an intergenerational elasticity of income of 0.185 and a rank-rank slope of 0.215. This picture of mobility remains under a range of exercises designed to test traditional methodological concerns. While mobility is rapid through most of Australia, there is meaningful dispersion: the mining boom in particular appears to have lifted incomes for those raised in affected regions over the period in question. More generally, regions with higher incomes and lower unemployment rates tend to have higher expected ranks for those raised there; while regions with less segregation and higher school attendance rates have weaker intergenerational persistence in income ranks. We end by extending a generalised error-in-variables model to provide a framework for thinking about the stability of these regional mobility measures over time. In line with this model, regional rank-rank slopes steadily increase over the period we observe, while the expected ranks of children in the national income distribution fluctuate in ways that partly mirror the changing economic fortunes of Australian regions.

Occupational Dualism and Intergenerational Educational Mobility in the Rural Economy: Evidence from China and India

This paper extends the Becker-Tomes model of intergenerational educational mobility to a rural economy characterized by farm-nonfarm occupational dualism and provides a comparative analysis of rural China and rural India. The model builds a micro-foundation for the widely used linear-in-levels estimating equation. Returns to education for parents and productivity of financial investment in children’s education determine relative mobility, as measured by the slope, while the intercept depends, among other factors, on the degree of persistence in nonfarm occupations. Unlike many existing studies based on coresident samples, our estimates of intergenerational mobility do not suffer from truncation bias. The sons in rural India faced lower educational mobility compared with the sons in rural China in the 1970s to 1990s. To understand the role of genetic inheritance, Altonji et al. (2005) biprobit sensitivity analysis is combined with the evidence on intergenerational correlation in cognitive ability in economics and behavioral genetics literature. The observed persistence can be due solely to genetic correlations in China, but not in India. Father’s nonfarm occupation was complementary to his education in determining a sons’ schooling in India, but separable in China. There is evidence of emerging complementarity for the younger cohorts in rural China. Structural change in favor of the nonfarm sector contributed to educational inequality in rural India. Evidence from supplementary data on economic mechanisms suggests that the model provides plausible explanations for the contrasting roles of occupational dualism in intergenerational educational mobility in rural India and rural China. (Stone Center on Socio-Economic Inequality Working Paper Series)

Are estimates of intergenerational mobility biased by non-response? Evidence from the Netherlands

Intergenerational mobility is often studied using survey data. In such settings, selective unit or item non-response may bias estimates. Linking Dutch survey data to administrative income data allows us to examine whether selective responses bias the estimated relationship between parental income and children’s mathematics and language test scores in grades 6 and 9. We find that the estimates of these relationships are biased downward due to parental unit non-response, while they are biased upwards due to item non-response. In the analyses of both unit and item non-response, the point estimates for language and mathematics test scores point in the same direction but only one of the two relationships is significant. These findings suggest that estimates of intergenerational mobility based on survey data need to be interpreted with caution because they may be biased by selective non-response. The direction of such bias is difficult to predict a priori. Bias due to unit and item non-response may work in opposing directions and may differ across outcomes.

Racial and Ethnic Differences in the Geography of Intergenerational Mobility

We use the NLSY79 to produce the first estimates of intergenerational mobility in the U.S. by both region and race/ethnicity. We show that gaps in intergenerational mobility by race are significantly larger than those by region. In particular, there is no region in the United States where it is better to be poor and black compared to being equally poor and white. We also show that the expected rank of Hispanics falls between that of whites and blacks. We find that the low mobility in the Southeast of the US documented by Chetty et al (2014) is actually driven by low mobility by whites and that blacks who grew up in the Southeast actually experience higher mobility than blacks growing up in the Northeast and Midwest. We also directly examine the role of migration and find that it plays little role in explaining the regional heterogeneity in intergenerational mobility. Finally, we use a rich set of individual covariates available in the NLSY — including test scores — and show that these can explain much of the gaps by race/ethnicity.

New Estimates of Intergenerational Mobility in Australia

We present new estimates of intergenerational earnings elasticity for Australia. We closely follow the methodology used by Leigh [BE Journal of Economic Analysis & Policy, 7 (2007) 1], but use considerably more data (12 waves of HILDA and four waves of PSID). Our adjusted estimates are intended to be comparable to those for other countries in Corak [Journal of Economic Perspectives, 27 (2013) 79]. Our preferred estimate (0.35) is considerably higher than implied by Leigh's study, and is less subject to sampling variation. In an international context, intergenerational mobility in Australia is not particularly high, and is consistent with its relatively high level of cross‐sectional inequality.

Estimating occupational mobility with covariates

The Altham statistic is often used to calculate intergenerational associations in occupations in studies of historical social mobility. This paper presents a method to incorporate individual covariates into such estimates of social mobility, and to construct corresponding confidence intervals. The method is applied to an intergenerational sample of Norwegian data, showing that estimates of intergenerational mobility are robust to the inclusion of controls for father’s and son’s age.

New Estimates of Intergenerational Mobility in Australia

We present new estimates of intergenerational earnings elasticity for Australia. We closely follow the methodology used by Leigh (2007), but use considerably more data (twelve waves of HILDA and four waves of PSID). Our adjusted estimates are intended to be comparable to those for other countries in Corak (2013). Our preferred estimate (0.35) is considerably higher than implied by Leigh’s study, and is less subject to sampling variation. In an international context, intergenerational mobility in Australia is not particularly high, and is consistent with its relatively high level of cross-sectional inequality.

Using Occupation to Measure Intergenerational Mobility

Scholarly investigations of intergenerational mobility typically focus on either the occupations of fathers and sons or their incomes. Using an identical sample of fathers and sons, we examine how estimates of intergenerational mobility in income and occupational prestige are affected by (1) measurement that uses long time averages and (2) varying the point in the life cycle when outcomes are measured. We find that intergenerational occupational mobility is overstated when using a single year of fathers’ occupation compared to a 10-year average centered on mid-career. We also find that for both income and occupation, mobility estimates are largest when sons are in their mid-career, suggesting that this may be the ideal period in which to measure their status. Finally, we see differences in the pattern of estimates across the two types of measures: for income, estimates of intergenerational persistence are highest when fathers are in their mid-career; for occupation, estimates are much larger when fathers’ occupations are accounted for late in their careers.

Intergenerational Earnings Mobility Revisited: Estimates Based on Lifetime Earnings*

AbstractUsing Norwegian intergenerational data, which include a substantial part of the life‐cycle earnings for children and almost the entire life‐cycle earnings for their fathers, we present new estimates of intergenerational mobility. Extending the length of fathers’ earnings window from 5 to 25 years increases estimated elasticities. Increasing the age at which fathers’ earnings are observed has the opposite effect. Biases in the estimated elasticities are related to both transitory earnings variation and life‐cycle measurement error; the former appear to be more important than the latter. Estimation bias stemming from persistence in transitory innovations plays only a minor role. Our findings indicate that intergenerational earnings mobility in Norway might have been strongly overstated in many earlier studies with shorter earnings histories. Some of our new estimates are twice as large as earlier estimates.

Estimates of Intergenerational Elasticities Based on Lifetime Earnings

Using Norwegian intergenerational data with a substantial part of the life-cycle earnings of children and almost the entire life-cycle earnings for their fathers, we present new estimates of intergenerational mobility. Extending the length of the fathers' earnings windows from 5 to 30 years increases the estimated elasticities. Increasing the age of father at observation has the opposite effect. Our findings indicate that intergenerational earnings mobility may have been strongly overstated in many earlier studies with shorter earnings histories. Biases in the estimated elasticities appear to be related to age and/or life-cycle measurement errors more than persistency in the transitory innovations.

The Intergenerational Transmission of Lifetime Earnings: Evidence from Brazil

Abstract This paper uses unique household survey data from Brazil and recently developed econometric techniques to estimate the transmission of lifetime earnings in Brazil and to examine effects of earnings measurement on estimates of intergenerational mobility. The level of intergenerational earnings transmission in Brazil is estimated to be among the highest observed for any country. Observations of earnings across the life-cycles of fathers and sons are used to form estimates of the transmission of lifetime earnings and to study life-cycle measurement effects on earnings transmission estimates. The use of earnings of relatively young sons, common in previous studies, is found to underestimate the true level of transmission of lifetime earnings. This paper provides two methods to obtain improved measures of lifetime earnings transmission. This paper also finds that education may be the most significant pathway by which earnings are transmitted intergenerationally.

Fortunate Sons: New Estimates of Intergenerational Mobility in the United States Using Social Security Earnings Data

Previous studies, relying on short-term averages of fathers' earnings, have estimated the intergenerational elasticity (IGE) in earnings to be approximately 0.4. Due to persistent transitory fluctuations, these estimates have been biased down by approximately 30% or more. Using administrative data containing the earnings histories of parents and children, the IGE is estimated to be around 0.6. This suggests that the United States is substantially less mobile than previous research indicated. Estimates of intergenerational mobility are significantly lower for families with little or no wealth, offering empirical support for theoretical models that predict differences due to borrowing constraints.