Data and Code for: Location, Location, Location

Principal Investigator(s):  View help for Principal Investigator(s) David Card, University of California-Berkeley; Jesse Rothstein, University of California-Berkeley; Moises Yi, United States Department of Commerce. Bureau of the Census

Version:  View help for Version V1

Name File Type Size Last Modified
  portion1_acs 05/07/2024 03:54:PM
  portion2_rdc 05/07/2024 03:55:PM
  portion3_postdisclosure 05/07/2024 03:54:PM
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README.docx application/vnd.openxmlformats-officedocument.wordprocessingml.document 37.2 KB 05/07/2024 11:54:AM
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tables.xlsx application/vnd.openxmlformats-officedocument.spreadsheetml.sheet 265.8 KB 05/07/2024 11:54:AM

Project Citation: 

Card, David, Rothstein, Jesse, and Yi, Moises. Data and Code for: Location, Location, Location. Nashville, TN: American Economic Association [publisher], 2025. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-01-03. https://doi.org/10.3886/E193945V1

Project Description

Summary:  View help for Summary Abstract:
We use data from the Longitudinal Employer-Household Dynamics program to study the causal effects of location on earnings. Starting from a model with employer and employee fixed effects, we estimate the average earnings premiums associated with jobs in different commuting zones (CZs) and different CZ-industry pairs. About half of the variation in mean wages across CZs is attributable to differences in worker ability (as measured by their fixed effects); the other half is attributable to place effects. We show that the place effects from a richly specified cross sectional wage model overstate the causal effects of place (due to unobserved worker ability), while those from a model that simply adds person fixed effects understate the causal effects (due to unobserved heterogeneity in the premiums paid by different firms in the same CZ). Local industry agglomerations are associated with higher wages, but overall differences in industry composition and in CZ-specific returns to industries explain only a small fraction of average place effects. Estimating separate place effects for college and non-college workers, we find that the college wage gap is bigger in larger and higher-wage places, but that two-thirds of this variation is attributable to differences in the relative skills of the two groups in different places. Most of the remaining variation reflects the enhanced sorting of more educated workers to higher-paying industries in larger and higher-wage CZs. Finally, we find that local housing costs at least fully offset local pay premiums, implying that workers who move to larger CZs have no higher net-of-housing consumption.

This replication archive contains all code for the project, and the public-use portion of the data. These data come primarily from the public use microdata files of the American Community Survey, 2010-2018.

Scope of Project

Subject Terms:  View help for Subject Terms replication; wage differentials ; urban economics
JEL Classification:  View help for JEL Classification
      J31 Wage Level and Structure; Wage Differentials
      J61 Geographic Labor Mobility; Immigrant Workers
      R23 Urban, Rural, Regional, Real Estate, and Transportation Economics: Regional Migration; Regional Labor Markets; Population; Neighborhood Characteristics
Geographic Coverage:  View help for Geographic Coverage United States
Time Period(s):  View help for Time Period(s) 2010 – 2018
Universe:  View help for Universe Full-time wage and salary workers covered by unemployment insurance
Data Type(s):  View help for Data Type(s) administrative records data; program source code; survey data

Methodology

Data Source:  View help for Data Source
American Community Survey
Longitudinal Employer-Household Dynamics
Unit(s) of Observation:  View help for Unit(s) of Observation Individuals

Related Publications

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Published Versions

V1 [2025-01-03]

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DDI 2.5

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