Skill-Biased Technological Change and the Business Cycle
Almut Balleer and Thijs van Rens
Abstract
Over the past two decades, technological progress in the United States has been biased towards skilled labor. What does this imply for business cycles? We construct a quarterly skill premium from the CPS and use it to identify skill-biased technology shocks in a VAR with long-run restrictions. Hours fall in response to skill-biased technology shocks, indicating that at least part of the technology-induced fall in total hours is due to a compositional shift in labor demand. Investment-specific technology shocks reduce the skill premium, indicating that capital and skill are not complementary in aggregate production.
Published
in the Review of Economics and
Statistics, 95(4), pp.1222-1237.
May
2012 [download pdf] - Also available as Kiel
Working Paper 1775.
Previous versions:
May 2011 (also available as CEPR
Discussion Paper 8410) and June 2009
(titled “Cyclical Skill-Biased Technological Change”, also available as IZA
Discussion Paper 4258). First draft: February 15, 2008
Codes and data
All codes
and data in a single file (includes readme file) [download zip]
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)
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