Recovering and Coding Occupational Data in U.S. Tax Returns

Awarded Scholars:
Michael Hout, New York University
David Grusky, Stanford University
Project Date:
Mar 2016
Award Amount:
Project Programs:
Social, Political, and Economic Inequality

How has rising economic inequality over the last four decades compromised upward social mobility? The limited evidence available suggests that the answers may differ for two key indicators of social mobility. While analyses of income tax-return data report no clear trend in economic mobility for recent cohorts of young adults, analyses of survey data suggest an increasing intergenerational association by occupation. These studies differ in the source of data and in the type of social mobility considered, and it is difficult to reconcile the discrepancy between the two without analyzing income and occupational mobility simultaneously.

Michael Hout and David Grusky will devise an innovative method for developing a single data source that is large enough to allow the analysis of occupational and income mobility at the same time, with the reliability that tax records allow for income. They will use machine-learning techniques to develop an algorithm and protocol that will exploit the under-utilized occupation fields available in Internal Revenue Service (IRS) tax return data and to code the occupational information in a way that will allow researchers to simultaneously measure occupational mobility and income mobility.


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