Decomposing economic mobility transition matrices

Abstract

The intergenerational mobility literature has consistently found that the distribution of adult economic outcomes differ markedly depending on parental economic status, yet much remains to be understood about the drivers or determinants of this relationship. Existing literature on potential drivers focuses primarily on mean effects. To help provide a more complete picture of the potential forces driving economic persistence, we propose a method to decompose transition matrices and related indices. Specifically, we decompose differences between an estimated transition matrix and a benchmark transition matrix into portions attributable to differences in characteristics between individuals from different households (a composition effect) and portions attributable to differing returns to these characteristics between individuals from different households (a structure effect). We also incorporate a detailed decomposition, based on copula theory, that decomposes the composition effect into portions attributable to specific covariates and their interactions. We illustrate our method using data on white men from the 1979 National Longitudinal Survey of Youth. Estimation is based on an extended Mincer equation that includes cognitive and non-cognitive measures. To address the potential endogeneity of education, we implement an IV strategy that allows us to estimate causal effects and investigate the role of unobserved ability.JEL Classification: C14, C20, J31, J62

Decomposing joint distributions via reweighting functions: an application to intergenerational economic mobility

“…The composition and structure effect each account for about 50% of the measured mobility gap, meaning differences in characteristics of offspring across households and differences in returns to characteristics both contribute evenly to the measured mobility gap. While the structure effect is commonly interpreted as a measure of discrimination in many traditional decomposition settings, Richey and Rosburg (2016b) argue that a more fitting interpretation in this context is some form of (loosely-termed) 'privilege' such as parental connections, parental knowledge/awareness of job market and education opportunities or perhaps greater financial flexibility to facilitate job search. All of these would result in higher returns to similar productive characteristics.…”

“…The literature that seeks to understand the drivers of this intergenerational link has focused primarily on simple mean effects, such as the intergenerational elasticity of income (IGE) (Björklund et al, 2006;Blanden et al, 2007;Bowles and Gintis, 2002;Cardaket al, 2013;Lefgren et al, 2012;Liu and Zeng, 2009;Mayer and Lopoo, 2008;Richey and Rosburg, 2016a;Shea, 2000). Two exceptions are Bhattacharya and Mazumder (2011), who estimate conditional directional mobility measures and transition matrices, and Richey and Rosburg (2016b), who decompose transition matrices and related indices. While both studies are revealing and provide important additions to the literature, they have limitations that restrict their generalizability.…”

Distributional Effects of a Continuous Treatment with an Application on Intergenerational Mobility

“…Of these, transition matrices are most closely related to our approach and have received considerable attention in the intergenerational income mobility literature (see Jantti et al ., ; Bhattacharya and Mazumder, ; Black and Devereux, ; Richey and Rosburg, , among others). In principle, one could use a transition matrix to calculate the probability that a child's income is below the poverty line for different values of parents’ income.…”

“…Ao, Calonico and Lee () consider decompositions with a multi‐valued discrete treatment. Bowles and Gintis (); Groves (); Blanden, Gregg and Macmillan (); Richey and Rosburg () have decomposed intergenerational mobility parameters into parts that are explained by various background characteristics.…”

Individual, Structural and Exchange Mobility: Decomposition and Axiom based measures

“…In the case of a continuous variable such as income, the copula framework can be used to neatly separate association from the marginal distributions of initial and final income, so that these two distributions take care of structural mobility and the copula represents exchange mobility (e.g., Aaberge et al, 2018;Chetty et al, 2017). For discretized variables however, the copula is generally not unique (Richey and Rosberg, 2018). For ordinal or nominal data (such as those describing social and occupational mobility), the copula approach is generally inapplicable.…”