Replication data for: New Evidence on Earnings Volatility in Survey and Administrative Data

Principal Investigator(s): View help for Principal Investigator(s) Michael D. Carr; Emily E. Wiemers

Name	File Type	Size	Last Modified
LICENSE.txt	text/plain	14.6 KB	10/12/2019 09:00:PM
PandP_sipp.dta	application/octet-stream	17.6 KB	10/12/2019 09:00:PM
ReadMe.pdf	application/pdf	39 KB	10/12/2019 09:00:PM
pandpgraphs_submit.do	text/plain	2.1 KB	10/12/2019 09:00:PM
psidss.dta	application/octet-stream	16.4 KB	10/12/2019 09:00:PM
unemployment.dta	application/octet-stream	2 KB	10/12/2019 09:00:PM
volatility_analysis.do	text/plain	477 bytes	10/12/2019 09:00:PM
volatility_data.do	text/plain	3 KB	10/12/2019 09:00:PM
volatility_master.do	text/plain	2.2 KB	10/12/2019 09:00:PM

Project Citation:

Carr, Michael D., and Wiemers, Emily E. Replication data for: New Evidence on Earnings Volatility in Survey and Administrative Data. Nashville, TN: American Economic Association [publisher], 2018. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-13. https://doi.org/10.3886/E114453V1

Project Description

Summary: Despite the rise in cross-sectional inequality since the late 1990s, there is little consensus on trends in earnings volatility during this period. Using consistent samples and methods in administrative earnings data matched to the Survey of Income and Program Participation (SIPP GSF) and survey data from the Panel Study of Income Dynamics (PSID), we examine earnings volatility for men from 1978 through 2011. In contrast to the apparent inconsistency in trends across administrative and survey data in the existing literature, we find recent increases in volatility in the SIPP GSF and the PSID, though increases are larger in the PSID.

Scope of Project

JEL Classification:
      C81 Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
      C83 Survey Methods; Sampling Methods
      J31 Wage Level and Structure; Wage Differentials

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Published Versions

V1 [2019-10-13]

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