Cyclical Skill-Biased Technological Change, Almut Ballleer and Thijs van Rens

Cyclical Skill-Biased Technological Change

Abstract

Over the past two decades, technological progress has been biased towards making skilled labor more productive. The evidence for this finding is based on the persistent parallel increase in the skill premium and the supply of skilled workers. What are the implications of skill-biased technological change for the business cycle? To answer this question, we use the CPS outgoing rotation groups to construct quarterly series for the price and quantity of skill. The unconditional correlation of the skill premium with the cycle is zero. However, using a structural VAR with long run restrictions, we find that technology shocks substantially increase the premium. Investment-specific technology shocks are not skill-biased and our findings suggest that capital and skill are (mildly) substitutable in aggregate production.

June 2009 [download pdf] - Also available as IZA Discussion Paper 4258.
First draft: February 15, 2008

Data

The data we used in this paper are available for download here. For the construction of the underlying micro-data from the CPS outgoing rotation groups, please see Haefke, Sonntag and van Rens.

· Dataset (comma-separated format, readable in Excel or in Stata using the insheet command)

· Stata do-files that were used to construct these data

The dataset contains quarterly time series (US, 1979-2006) for the skill premium, the relative employment of skill and the wage of low skilled workers. The variable naming convention is VAR_WW_SMPL_METHOD.

VAR is the name of the variable

· sp = skill premium (log ratio of the wage of high and low skilled workers)

· rele = relative employment of skill (as a fraction, ln(rele/(1-rele)) are the series used in the paper)

· wL = log wage low skilled workers

· Series named f_* and w_* contain fractions and average log wages for various subgroups of workers. The naming convention for these variables is different, please see the do-files for details.

WW denotes the sample weights and averaging procedure

· ew = simple average, weighting only by CPS earnings weights (ORG weights)

· hw = simple average, weighting by CPS earnings weights and hours worked

· hwp = predicted values from a Mincer regression, weighted by CPS earnings weights and hours worked (baseline)

SMPL denotes the subsample

· popu = the entire population (only available for some rele measures, which should be interpreted as the relative supply rather than the relative employment of skill)

· labf = the entire labor force (only available for some rele measures, see above)

· empl = all employed workers in the CPS

· priv = employed workers in the private, non-farm business sectors in the CPS (baseline)

METHOD refers to the method used to calculate the skill premium and relative employment, see the paper and the do-files for more details

· minc = coefficient on years of schooling in a Mincer regression of log wages on schooling, a quartic in potential labor market experience and some demographic controls, for rele these series contain the average years of schooling

· hlcm = high school degree or less versus a college degree of more education

· edu5 = using 5 education categories (less than high school, high school degree, some college, college degree, more than college)

· Gxedu5 = using 2 gender and 5 education categories

· Gxedu5xexp4 = using 2 gender, 5 education and 4 experience categories (baseline)

Thijs van Rens | CREI | Department of Economics and Business | Universitat Pompeu Fabra