LABOR DEMAND ELASTICITIES IN
EUROPE: A META-ANALYSIS
ANDREAS LICHTER, ANDREAS PEICHL AND SEBASTIAN
SIEGLOCH
NEUJOBS WORKING PAPER NO. D10.7
SEPTEMBER 2013
NEUJOBS Working Documents are intended to give an indication of work being
conducted within the NEUJOBS research project and to stimulate reactions from
other experts in the field. Texts published in this series are ultimately destined for
academic publishing. The views expressed in this paper are those of the author and
do not necessarily represent any institution with which he is affiliated. See the back
page for more information about the NEUJOBS project.
Available for free downloading from the NEUJOBS website (http://www.neujobs.eu)
© Lichter, Peichl & Siegloch / IZA, 2013
1
Labor Demand Elasticities in Europe: A
Meta-Analysis∗
Andreas Lichter
Andreas Peichl
Sebastian Siegloch
September 30, 2013
Abstract
In this paper, we perform a meta-regression analysis on own-wage elasticities
of labor demand for European countries. As this elasticity explains the firm’s
responsiveness of labor demand with respect to a change in the wage rate,
knowledge about the sign and size of this figure is of particular importance in
both economic research and policy analysis. Overall, we obtain 784 own-wage
labor demand elasticity estimates from 82 different micro-level studies. Our
meta-regression analysis shows that own-wage elasticities depend on both the
theoretical set-up and the empirical specification applied by the researcher.
Moreover, we find own-wage elasticities of labor demand to vary among different groups of countries, which indicates that firms’ demand for labor is
more flexible in some countries than others. Relying on our meta-regression
analysis we further predict reliable benchmark elasticities of labor demand,
which will be used in later stages of the NEUJOBS project.
JEL Classification: J23
Keywords: labor demand, meta-analysis
∗
Andreas Lichter ([email protected]) and Sebastian Siegloch ([email protected]) are affiliated
to IZA and the University of Cologne. Andreas Peichl ([email protected]) is affiliated to IZA, the
University of Cologne, ISER and CESifo. Corresponding author: Sebastian Siegloch, IZA Bonn,
P.O. Box 7240, 53072 Bonn, Germany, Tel.: +49(0)228 3894–160, Fax: +49(0)228 3894–510.
1
Introduction
Own-wage elasticities of labor demand serve as a key figure in both empirical research and policy analysis. The effectiveness of policy reforms concerning, for example, wage subsidies or payroll taxes crucially depends on the employers’ labor
demand responsiveness with respect to changes in the workers’ wage rate (Hamermesh, 1993). Knowledge about the sign and size of the particular own-wage labor
demand elasticity thus helps policy makers to better assess the costs and benefits
of a particular reform. Moreover, rising unemployment among the poorly educated
and intensifying wage inequality within the workforce over the last decades has initiated public debates and continuous empirical research on the presence and effects
of skill-biased technological and organizational change. Here, own-wage elasticities of firms’ demand for differently-skilled labor serve as important indicators for
structural changes in production. Likewise, own-wage elasticities of labor demand
further provide important insights when assessing labor market effects of deepening
international integration. Increases in the own-wage elasticity due to exacerbated
competition or global production sharing indicate negative consequences for workers, as employment and wages become more volatile and bargaining power decreases
(Rodrik, 1997). Lastly, own-wage elasticities of labor demand often serve as input variables in general computational equilibrium models or long-run labor market
projections.
In this paper, we corroborate the importance of own-wage labor demand elasticities for empirical research and policy analysis by providing a comprehensive
meta-regression analysis of the literature. Overall, our sample comprises 784 estimates obtained from 82 micro-level studies. Contrary to a qualitative review, a
meta-analytic analysis of this literature allows us to explicitly analyze whether labor
demand theory is backed up by empirical evidence, whether the own-wage elasticity
of labor demand depends on the empirical specification applied or the data used.
Moreover, this method allows the explicit analysis of differential demands for skills
as well the test for cross-national differences in the own-wage elasticity of labor demand. With respect to the NEUJOBS project, this methodology further allows us
to predict benchmark own-wage elasticities of labor demand. These elasticities will
1
serve as input variables in later stages of the project and may be more reliable than
single-study estimates.
To be more precise, recall that when firms face exogenous changes in wage
rates, adjustment of the size or structure of the workforce is desired yet costly.
Costs due to hirings or separations are likely to limit firms’ labor demand reaction
in the short-run. Moreover, substitution of labor through other input factors is
limited, as adjusting the stock of materials and capital requires time in incurs costs.
Intuitively, firms’ responses to wage rate changes should hence be less pronounced
in the short- than in the intermediate- and long-run. A firm’s response also depend
on its choice to adjust the level of output due to the given increase in production
costs. Labor demand responses should be less pronounced, and hence the own-wage
elasticity of labor demand should be lower in absolute terms, in case the level of
output is fixed.
With respect to the empirical specification, the choice of the empirical model
applied or the econometric treatment of the wage variable may significantly affect
the absolute value of the own-wage elasticity of labor demand. Assuming the wage
rate to be an exogenous regressor in an empirical model of labor demand is common
practice in empirical work, yet subject to criticism. Labor demand estimation further
shows considerable heterogeneity with respect to the data employed and we examine
whether the source (admin or survey data), the type (panel data or cross-sectional
and time-series data), or the observational level (industry or firm-level data) of the
data affects the elasticity estimate. We also examine whether firms respond more
heavily to wage rate changes for low- than high-skilled workers and whether there is
a trend in the absolute value of the own-wage elasticity of labor demand over time.
As national differences in labor market institutions are likely to affect firm behavior
in general, we also test for differences in own-wage elasticities of labor demand for
different groups of countries, clustered along institutional characteristics.
Our empirical results are manifold. Firstly, we find that the empirical results
back up the underlying theory. Firms respond less to wage rate changes in the shortthan in the intermediate- or long-run and in case being output constrained. We also
report differences in the own-wage elasticities of labor demand for certain groups
2
of countries and among different skills. Moreover, we find that both the study’s
empirical treatment of the wage rate as well as the data employed significantly
affect the absolute value of the own-wage elasticity.
The rest of the paper is structured as follows. In Section (2) we provide a short
overview on the theory of labor demand as well as on the empirics of labor demand
estimation. We follow up by presenting the sample of studies used in our analysis
in Section (3), providing a qualitative review as well as basic descriptive statistics.
In Section (4) we report our meta-regression results, check the robustness of our
results and derive benchmark elasticities for different groups of European countries.
These elasticities will be used as input variables in future stages of the NEUJOBS
project. Section (5) concludes.
2
The elasticity of labor demand - Theory and
estimation
As noted in the introduction of this paper, a meta-regression analysis of own-wage
elasticities of labor demand allows us to study whether labor demand theory is
backed up by empirical evidence. In the first part of this section, we thus shortly
discuss the basic relevant propositions of labor demand theory.1 It is followed by an
overview about the different estimation techniques prevailing in the literature.
In a nutshell, labor demand theory assumes firms to face short-, intermediateand long-run demands for inputs to facilitate production of output that maximizes
their profits. However, due to factor adjustment costs, firms do not fully adjust
their level of input factors to the optimal level after an change in input prices
immediately, i.e. in the short-run. Among others, institutional regulations affecting
individual and collective dismissals or restricting the placement of workers prevent
firms from employing the desired level of labor. Likewise, firms cannot adjust the
stock of capital employed or materials used immediately. In the intermediate-run
though, firms are assumed to adjust the level of labor and material to the optimal
level, whereas capital input remains fixed. Investment in physical capital takes time
1
For an in-depth see, for example, Hamermesh (1993) or Cahuc and Zylberberg (2001).
3
such that full flexibility in input demand is only allowed for in the long-run. Thus,
theory implies that firms’ labor demand responses are more limited in the shortthan in the intermediate- and long-run. For our empirical analysis we thus label
each elasticity estimate according to the above classification. Precisely, assignment
follows by means of the (dis)equilibrium status of labor and capital inputs and
whether a dynamic or static model of labor demand is estimated.
Firms’ responses to wage rate changes are further assumed to differ depending
on whether firms actual alter the level of output or keep production constant when
experiencing changes in the costs of production. Intuitively, the response to an
increase in the wage rate is lower in case output is fixed. Own-wage elasticities
of labor demand unconditional on output should thus exceed those conditional on
output in absolute terms.
When considering the empirical models used to estimate labor demand, we
detect heterogeneity in the approaches applied. In general, the elasticity of labor
demand is either derived from a reduced-form or a structural model, where the latter accounts for the underlying theoretical relationship more explicitly. In structural
models, production or cost functions as well as the corresponding demand functions
are jointly estimated in a first step. In a second step, the elasticities are calculated
ex post by using equation parameters. Whereas the use of cost functions has become standard in the literature, its specification differs among studies. In contrast,
reduced-form models follow theory less strict by employing log-linear specifications
of models for labor demand. Given the nature of the model, the estimated parameter for the wage rate variable may then be easily interpreted as the elasticity of
labor demand.
3
Literature review and descriptive analysis
We next provide a short qualitative summary of the literature estimating (impacts
on) the own-wage elasticity of labor demand. It is followed by a descriptive analysis
of our dataset, providing detailed information on all estimates in our sample.
A variety of papers assesses the absolute value of own-wage labor demand
4
elasticities in order to analyze costs and benefits of policy reforms, the effect of
institutional regulations on labor demand or the substitutability of different types
of workers. Among others, FitzRoy and Funke (1998) as well as Koebel et al. (2003)
and Jacobi and Schaffner (2008) investigate patterns of substitution among different
types of workers and other input factors such as materials and capital. Peichl and
Siegloch (2012), however, derive own-wage elasticities of labor demand to explicitly
assess the demand side effects of policy reforms. Much attention has also been put
on analyzing potential effects of technological and organizational change on labor
demand. Technology and innovations are found to have a positive effect on overall
employment (VanReenen, 1997, e.g.), whereas evidence is mixed when analyzing
potential effects for skilled and unskilled labor (Falk and Koebel, 2004; Addison
et al., 2008).
Moreover, in recent years, many studies have analyzed the effects of various
features of globalization on the elasticity of labor demand. Studies by Hijzen et al.
(2005) and Hijzen and Swaim (2010) show that conditional own-wage elasticities
for unskilled (skilled) workers increase (decrease) as firms decide to outsource internal production processes. Another set of studies analyzes whether labor demand
elasticities might be affected by firm structure. Görg et al. (2009) and Hakkala
et al. (2010) report higher absolute own-wage elasticities for multinational than for
domestic firms whereas Buch and Lipponer (2010) find no significant differences.
Barba Navaretti et al. (2003) in turn find labor demand of multinational firms to
be less elastic than the demand for labor of domestic firms. Krishna et al. (2001)
exploit exogenous variation stemming form a trade liberalization process in Turkey
to measure the causal effect of trade openness on the unconditional own-wage elasticity of labor demand, yet find no strong empirical support for rising own-wage
elasticities in response to trade liberalization and increased product competition.
Descriptive Statistics We continue this section by providing comprehensive descriptive statistics of our dataset. Table (1) summarizes the most important aspects.
Recall that we collected 782 estimates of the own-wage labor demand elasticity from
82 empirical studies. The number of estimates obtained per study differs significantly
as the mean number of observations is 9.56, with the minimum (maximum) number
5
of estimates per study being 1 (106). Overall, three out of four papers (73.21%) considered in our analysis have been published in a refereed journal. We further note
that the vast majority of estimates in our sample are based on panel data (94.25%),
originating from administrative sources (76.21%).
The overall mean own-wage elasticity is −0.559, the minimum and maximum
elasticity being −5.793 and 5.164, respectively. In line with our expectations, we
note that the mean short-run own-wage elasticity (-0.355) is lower than the mean
elasticity in the intermediate- (-0.613) and long-run (-0.640). Moreover, the mean
own-wage elasticity is lower in absolute terms in case firms keep output constant
(-0.504) than when changing the level of production (-0.790). Lastly, the mean wage
elasticity is higher in case being derived from a reduced-form (-0.598) rather than
from a structural model (-0.500).
Table 1: Descriptive statistics on estimated own-wage elasticities
Overall
Sample
782
58.06
94.12
82
73.21
Continental
Europe
354
62.99
93.05
41
67.13
Nordic
Countries
106
22.64
90.57
8
73.58
UK/
Ireland
92
70.65
98.91
18
92.39
Southern
Europe
42
76.19
100.00
5
38.09
Eastern
Europe
153
50.98
93.46
5
100.000
Aggregate
Europe
35
91.43
94.29
5
8.57
50.13
94.25
76.21
61.58
94.07
76.84
59.43
96.23
80.19
52.17
80.43
43.48
71.43
95.24
71.43
10.46
100.00
100.00
48.57
100.00
45.71
24.81
38.11
37.08
19.44
39.64
19.77
38.98
41.24
2.26
58.19
29.55
13.21
57.55
2.83
55.66
57.61
17.39
25.00
30.43
21.74
26.19
0.00
73.81
16.67
0.00
16.99
69.28
13.73
69.28
0.00
8.57
68.57
22.86
0.00
71.43
Mean elasticity
- overall
-0.559
-0.481
-0.481
-0.567
-0.667
-0.781
-0.474
- short-run
- intermediate-run
- long-run
-0.355
-0.613
-0.640
-0.318
-0.464
-0.575
-0.337
-0.690
-0.506
-0.428
-0.635
-0.838
-0.447
–
-0.745
-0.252
-0.833
-1.173
-0.68
-0.442
-0.489
- conditional on output
- unconditional on output
-0.504
-0.790
-0.479
-0.561
-0.480
-0.502
-0.504
-0.708
-0.633
-0.837
-0.664
-0.833
-0.474
–
- structural model
- reduced-form model
-0.500
-0.598
-0.489
-0.469
-0.553
-0.390
-0.547
-0.572
–
-0.667
–
-0.781
-0.428
-0.587
No. elasticity estimates
- significant (%)
- smaller than zero (%)
No. of studies
- published (%)
Estimates
- at industry-level (%)
- using panel data (%)
- using admin data (%)
Elasticities
- short-run (%)
- intermediate-run (%)
- long-run (%)
- unconditional on output (%)
- structural model (%)
6
When focussing on country-level differences, we facilitate the general discussion and interpretation by following Card et al. (2010) and cluster countries along
institutional features. Precisely, we group estimates from Germany, France as well
as Belgium, the Netherlands and Luxembourg (BeNeLux) to Continental Europe
whereas Denmark, Norway, Finland and Sweden constitute the Nordic European
countries. We further combine the estimates from the UK and Ireland and merge
Italy, Spain, Portugal to Southern Europe. Moreover, we group Turkey, Macedonia
and the former CIS states to Eastern Europe. In terms of elasticities, we find considerable variation across the different country groups. The mean own-wage elasticities
of labor demand for the UK and Ireland (-0.567), the Southern European (-0.667)
and Eastern European countries (-0.781) exceed the overall mean elasticity in absolute terms. Contrary to this, the mean own-wage elasticity of labor demand is rather
low for the Continental European and the Nordic countries (-0.481). This pattern
remains roughly the same when distinguishing the elasticity among theoretical or
empirical characteristics.
4
Meta-Analysis
We now turn to our meta-regression analysis of own-wage labor demand elasticities.
The underlying meta-regression model regresses the own-wage elasticity of labor
demand on a constant term and various control variables:
η i = β0 +
K
X
αK ZiK + i .
(1)
k=1
Here, the term ηi denotes the reported own-wage elasticity of labor demand, β0 the
constant term, ZiK a vector containing all independent variables of interest and i
the error term. To account for the considerable variation in the number of estimates
coming from one single study, we estimate the model by Ordinary Least Squares
(OLS), weighting each estimate by the inverse of the number of estimates per study.
Standard errors are clustered at the study-level.
We start our analysis by regressing the own-wage elasticity of labor demand
on those variables characterizing both the theoretical and empirical specification
7
employed to derive the estimate of own-wage labor demand elasticity.
Table 2: Meta-regression analysis for own-wage labor demand elasticities
Model
Timing (omitted: Short-run)
Intermediate-run
Long-run
Structural model
Unconditional on output
(1)
(2)
(3)
(4)
(5)
-0.151∗
(0.083)
-0.182∗∗
(0.082)
-0.154∗
(0.091)
-0.201∗∗
(0.082)
-0.143
(0.091)
-0.231∗∗∗
(0.083)
-0.142
(0.091)
-0.256∗∗∗
(0.081)
-0.212∗∗
(0.092)
-0.301∗∗∗
(0.084)
0.052
(0.094)
-0.194∗∗
(0.085)
-0.004
(0.094)
-0.177∗
(0.090)
-0.022
(0.090)
-0.245∗∗
(0.097)
-0.029
(0.090)
-0.230∗∗
(0.102)
-0.027
(0.074)
-0.220∗∗
(0.098)
0.100
(0.117)
-0.076
(0.102)
-0.071
(0.184)
-0.332∗
(0.184)
-0.043
(0.137)
0.073
(0.121)
-0.111
(0.100)
-0.083
(0.176)
-0.326∗
(0.182)
-0.010
(0.140)
-0.007∗
(0.004)
0.087
(0.120)
-0.162∗
(0.096)
-0.107
(0.181)
-0.260
(0.168)
-0.064
(0.141)
-0.005
(0.004)
-0.179∗
(0.096)
-0.033
(0.115)
0.195∗∗
(0.090)
-0.351∗∗∗
(0.050)
-0.290∗∗∗
(0.080)
-0.040
(0.160)
-0.038
(0.197)
0.118
(0.104)
-0.179∗∗
(0.087)
-0.082
(0.160)
-0.287
(0.193)
-0.084
(0.135)
-0.004
(0.004)
-0.163∗
(0.092)
-0.042
(0.093)
0.199∗∗
(0.089)
-0.189∗
(0.109)
0.026
(0.075)
-0.260∗∗∗
(0.084)
0.058
(0.215)
782
0.034
0.029
782
0.059
0.048
782
0.070
0.058
782
0.087
0.071
782
0.142
0.124
Country dummies (omitted: Continental Europe)
Nordic European Countries
UK/Ireland
Southern European Countries
Eastern European Countries
Aggregate Europe
Mean year of observation (normalized)
Administrative data
Panel data
Industry level data
Unskilled labor
Published in refereed journal
Wage treated as endogenous
Constant
No. of observations
R-Squared
Adjusted R-Squared
Note: Standard errors (in parentheses) are clustered at the study level. Significance levels are 0.1
(*), 0.05 (**), and 0.01 (***).
Column (1) of Table (2) presents the corresponding regression results and we
note that our results are in line with our expectations, as short-run own-wage elasticities are significantly lower – in absolute terms – than intermediate- and long-run
elasticities of labor demand. We also note that firm’s labor demand response to
wage rate changes is significantly higher in case firms adjust their level of produc8
tion than when keeping output constant. The researcher’s choice of the empirical
model (structural versus reduced-form) has, however, no statistically significant effect on the own-wage elasticity of labor demand when controlling for the theoretical
specification.
In the second column of this table, we additionally control for differences in the
own-wage elasticity among different groups of countries. Compared to the Continental European countries, the own-wage elasticity of labor demand is considerably
higher in the UK/Ireland, the Southern European and Eastern European countries,
yet only statistically different for the last set of countries. In order to analyze
whether the demand for labor has become more elastic over time, we additionally
control for the normalized mean year of observation (see column (3)). From the
parameter of this variable we infer that the own-wage elasticity of labor demand has
become larger in absolute terms over time. This effect remains quantitatively persistent yet becomes insignificant when controlling for data characteristics. In column
(4) we add these variables to our model. Interestingly, we find that estimates of
the own-wage elasticity of labor demand are significantly higher when being based
on data that originates from administrative rather than survey sources. Moreover,
estimates based on industry-level data are significantly lower in absolute terms than
those based on firm-level data. In column (5), we also investigate whether the empirical treatment of the wage variable affects the estimate. We find that treating
the wage variable as endogenous, and hence instrumenting the wage rate, has a positive and significant effect on the absolute value of the own-wage elasticity of labor
demand. Moreover, we infer that the demand for unskilled labor responds more
heavily to wage rate changes and that the elasticities reported in studies published
in refereed journals are not statistically different.
Robustness checks In order to infer the robustness of our results, we next restrict
our sample along four dimensions. Firstly, in column (1) we show the results for
our full meta-regression model without applying weights. The results remain mostly
unchanged, with the limiting remark that some effects turn statistically insignificant
in this case. In column (2), we exclude those estimates based on aggregate European
data. Here, the results mirror those of column (5) from table (1) in sign and signifi9
cance. In columns (3) and (4), we further restrict the sample to those estimates that
are in line with economic theory, i.e. smaller than zero, and have been published in
refereed journals, respectively. Again, the general conclusions remain unchanged.
Table 3: Meta-regression analysis – restricted samples
Model
Timing (omitted: Short-run)
Intermediate-run
Long-run
Structural model
Unconditional on output
Country dummies (omitted: Continental Europe)
Nordic European Countries
UK/Ireland
Southern European Countries
Eastern European Countries
Aggregate Europe
Mean year of observation (normalized)
Administrative data
Panel data
Industry level data
Unskilled labor
Published in refereed journal
Wage treated as endogenous
Constant
No. of observations
R-Squared
Adjusted R-Squared
no weights no aggregate in line with theory
published in
in regression
EU data
η<0
refereed journal
-0.276∗∗∗
(0.099)
-0.508∗∗∗
(0.094)
-0.225∗∗
(0.093)
-0.314∗∗∗
(0.087)
-0.269∗∗∗
(0.094)
-0.338∗∗∗
(0.087)
-0.182
(0.114)
-0.282∗∗∗
(0.098)
-0.052
(0.088)
-0.254∗∗
(0.102)
-0.040
(0.076)
-0.227∗∗
(0.099)
-0.005
(0.073)
-0.182∗
(0.099)
-0.055
(0.085)
-0.220∗∗
(0.102)
-0.032
(0.085)
-0.196∗∗
(0.095)
-0.202
(0.128)
-0.183
(0.145)
0.034
(0.189)
0.118
(0.104)
-0.184∗∗
(0.089)
-0.083
(0.163)
-0.289
(0.191)
0.004
(0.082)
-0.191∗∗
(0.089)
-0.058
(0.157)
-0.404∗∗∗
(0.151)
-0.077
(0.138)
0.148
(0.118)
-0.179∗
(0.094)
-0.089
(0.232)
-0.298
(0.180)
0.012
(0.078)
-0.012∗∗
(0.005)
-0.020
(0.128)
0.063
(0.151)
0.146
(0.101)
-0.030
(0.109)
-0.020
(0.088)
-0.141
(0.101)
0.223
(0.243)
-0.005
(0.004)
-0.166∗
(0.094)
-0.046
(0.093)
0.190∗∗
(0.090)
-0.150
(0.115)
0.013
(0.080)
-0.256∗∗∗
(0.087)
0.108
(0.224)
-0.004
(0.004)
-0.196∗∗
(0.090)
-0.042
(0.083)
0.266∗∗∗
(0.080)
-0.262∗∗∗
(0.092)
0.050
(0.074)
-0.270∗∗∗
(0.079)
0.031
(0.216)
-0.006
(0.005)
-0.121
(0.097)
0.005
(0.091)
0.226∗∗
(0.095)
-0.066
(0.124)
782
0.103
0.084
747
0.142
0.124
736
0.211
0.194
574
0.136
0.113
-0.163
(0.098)
0.013
(0.211)
Note: Standard errors (in parentheses) are clustered at the study level. Significance levels are 0.1
(*), 0.05 (**), and 0.01 (***).
10
Benchmark own-wage elasticities Based on our meta-regression analysis, we
next predict benchmark own-wage elasticities of labor demand for various groups of
countries, which will serve as input variables in later stages of the NEUJOBS project.
Based on our preferred specification, given by column (5) of Table 2, we obtain the
following own-wage elasticities of labor demand: We note that the predicted ownTable 4: The own-wage elasticity of labor demand - benchmark estimates
Group of countries
Continental European Countries
Nordic European Countries
UK/Ireland
Southern European Countries
Eastern European Countries
Overall Europe
Own-wage elasticity
-0.423
-0.333
-0.529
-0.479
-0.929
-0.529
wage elasticities show some considerable degree of heterogeneity. In absolute terms,
our results show highest and lowest absolute own-wage elasticities of labor demand
for Eastern European and Nordic European countries, respectively. All predicted
own-wage elasticities are negative and below one in absolute terms.
5
Conclusion
The own-wage elasticity of labor demand serves as a key figure in economic research
and policy analysis. Indicating the firms’ responsiveness to changes in the wage rate,
the sign and size of this elasticity provides, among others, valuable insights for the
assessment of costs and benefits of policy reforms or the effects of globalization on
labor demand.
In this NEUJOBS working paper, we provide a meta-regression analysis of
empirical research concerned with the estimation of own-wage elasticities of labor
demand. Our data covers 82 empirical studies from which we extract 782 estimates
of the own-wage labor demand elasticity. The meta-regression analysis focuses on
the effects of both the theoretical and empirical specification applied as well as on
differences between various groups of countries.
11
The results of our meta-analysis are manifold. We show that a firm’s response
to wage rate changes is smaller in the short- than in the intermediate- or long-run,
i.e. demonstrate that the absolute value of the intermediate and long-run elasticity of
labor demand exceeds the short-run elasticity. Costs arising from labor adjustment
as well as a rigid capital stock serve as plausible explanations for this pattern. In
line with our expectations, we further find that firms respond more heavily to wage
rate changes in case the level of production is flexible rather than fixed.
We further report differences in own-wage elasticities of labor demand for
different groups of European countries, which were defined along institutional characteristics likely to affect firm behavior in general and firms’ labor demand behavior
specifically. Our estimates show that own-wage elasticities of labor demand are
significantly higher in the UK/Ireland as well as in the Eastern European countries
than in Continental Europe in absolute terms. Recall that these results indicate that
firms located in Eastern Europe or the UK/Ireland might adjust their level of labor
to a greater extent when being confronted with changes in the wage rate than firms
located in Continental Europe. Among others, differences in institutional regulations such as the strictness of dismissal protection laws may help explaining these
differences. Lastly, we provide benchmark elasticities for each group of countries
considered. These elasticities – obtained from our meta-regression analysis – will
be used in later stages of the NEUJOBS project and are preferable to single-study
estimates.
12
Table 5: Empirical studies on labor demand in Europe (1993-2013)
Study
Model specifics
Data
Estimation
Characteristics
Source
Period
Magnus (1979)
static, K cost share
structural-form, conditional
industry-level, time-series
admin
1950-1976
Nickell (1984)
dynamic, no K
reduced-form, conditional
industry-level, time-series
admin
1958-1974
Nissim (1984)
dynamic, K quasi-fixed
structural-form, conditional
industry-level, time-series
admin
1963-1978
Symmons and Layard (1984)
dynamic, no K
reduced-form, unconditional
industry-level, time-series
admin
1956-1980
Carruth and Oswald (1985)
static/dynamic, K regressor
reduced-form, unconditional
industry-level, time-series
admin
1950-1980
Faini and Schiantarelli (1985)
dynamic, K regressor
reduced-form, (un)conditional
industry-level, panel
admin
1970-1979
Mairesse and Dormont (1985)
dynamic, K regressor
reduced-form, unconditional
firm-level, panel
survey
1970-1979
Wadhwani (1987)
dynamic, K regressor
reduced-form, unconditional
industry-level, time-series
admin
1962-1981
Pencavel and Holmlund (1988)
static/dynamic, K regressor
reduced-form, unconditional
industry-level, time-series
admin
1951-1983
Flaig and Steiner (1989)
dynamic, K regressor
reduced-form, conditional
industry-level, time-series
admin
1963-1986
13
Time horizon
Table 5: continued
Study
Model specifics
Data
Estimation
Characteristics
Source
Period
Wadhwani and Wall (1990)
dynamic, K regressor
reduced-form, unconditional
industry-level, panel
survey
1974-1982
Arellano and Bond (1991)
dynamic, K regressor
reduced-form, unconditional
firm-level, panel
survey
1979-1984
Blanchflower et al. (1991)
dynamic, no K
reduced-form, unconditional
firm-level, cross-section
survey
1984
Bergström and Panas (1992)
static, K cost share
structural-form, conditional
industry-level, panel
admin
1963-1980
Bresson et al. (1992)
dynamic, no K
reduced-form, conditional
firm-level, panel
survey
1980-1983
Fitzroy and Funke (1994)
dynamic, K regressor
reduced-form, conditional
industry-level, panel
admin
1979-1990
Konings and Roodhooft (1998)
static/dynamic, K regressor
reduced-form, conditional
firm-level, panel
admin
1986-1994
Konings and Vandenbussche (1995)
static, no K
reduced-form, conditional
firm-level, panel
survey
1982-1989
Lindquist (1995)
dynamic, K quasi-fixed
structural-form, conditional
firm-level, panel
admin
1972-1990
Draper and Manders (1996)
static, K cost share
structural-form, conditional
industry-level, time-series
admin
1972-1993
14
Time horizon
Table 5: continued
Study
Model specifics
Data
Estimation
Characteristics
Source
Period
Cahuc and Dormont (1997)
static/dynamic, K regressor
reduced-form, conditional
firm-level, panel
survey
1986-1989
Falk and Koebel (1997)
static, K cost share
structural-form, conditional
industry-level, panel
admin
1977-1994
VanReenen (1997)
dynamic, K regressor
reduced form, unconditional
firm-level, panel
admin
1976-1982
Blechinger et al. (1998)
static, no K
structural-form, conditional
firm-level, panel
survey
1993-1995
FitzRoy and Funke (1998)
dynamic, K regressor
reduced form, conditional
industry-level, panel
admin
1991-1993
Hatzius (1998)
static, no K
reduced-form, conditional
firm-level, panel
survey
1974-1994
Hine and Wright (1998)
dynamic, no K
reduced-form, conditional
industry-level, panel
admin
1979-1992
Koebel (1998)
static, K cost share
reduced-form, conditional
industry-level, panel
admin
1960-1992
Milner and Wright (1998)
dynamic, no K
reduced-form, conditional
industry-level, panel
admin
1972-1991
Rottmann and Ruschinski (1998)
dynamic, K regressor
reduced-form, conditional
firm-level, panel
survey
1980-1992
15
Time horizon
Table 5: continued
Study
Model specifics
Data
Estimation
Characteristics
Source
Period
Allen and Urga (1999)
dynamic, K cost share
structural-form, conditional
industry-level, time-series
admin
1965-1992
Abraham and Konings (1999)
static, K regressor
reduced-form, conditional
firm-level, panel
survey
1990-1995
Funke et al. (1999)
dynamic, no K
reduced-form, conditional
firm-level, panel
admin
1987-1994
Greenaway et al. (1999)
dynamic, no K
reduced-form, conditional
industry-level, panel
admin
1979-1991
Mellander (1999)
static, K quasi-fixed/cost share
structural-form, conditional
industry-level, panel
admin
1985-1995
Bellmann and Schank (2000)
static, K quasi-fixed
structural-form, conditional
firm-level, cross-section
leed
1995-1995
Braconier and Ekholm (2000)
static, no K
reduced-form, conditional
firm-level, panel
survey
1970-1994
Addison and Texeira (2001)
dynamic,no K
reduced-form, conditional
industry-level, panel
admin
1977-1997
Faini et al. (2001)
dynamic, K regressor
reduced-form, conditional
industry-level, panel
admin
1985-1995
Falk (2001)
static, K regressor
reduced-form, conditional
firm-level, panel
survey
1995-1997
16
Time horizon
Table 5: continued
Study
Model specifics
Data
Estimation
Characteristics
Source
Period
Falk and Koebel (2001)
dynamic, K quasi-fixed
reduced-form, conditional
industry-level, panel
admin
1976-1995
Flaig and Rottmann (2001)
static, no K / K quasi-fixed
structural-form, conditional
industry-level, panel
admin
1968-1995
Krishna et al. (2001)
static, K regressor
reduced-form, unconditional
firm-level, panel
admin
1983-1986
Bellmann et al. (2002)
static, K quasi-fixed
reduced-form, conditional
firm-level, panel
leed
1993-1998
Cuyvers et al. (2002)
static, K quasi-fixed
structural-form, conditional
firm(industry)-level, panel
survey
1994-1998
Falk and Koebel (2002)
static, K quasi-fixed
structural-form, conditional
industry-level, panel
admin
1978-1990
Kölling and Schank (2002)
static, K quasi-fixed
structural-form, conditional
firm-level, panel
leed
1994-1997
Barba Navaretti et al. (2003)
dynamic, K regressor
reduced-form, conditional
firm-level, panel
survey
1993-2000
Bruno et al. (2003)
dynamic, no K
reduced-form, conditional
industry-level, panel
admin
1970-1996
Koebel et al. (2003)
static, K cost share
structural-form, conditional
industry-level, panel
admin
1978-1990
17
Time horizon
Table 5: continued
Study
Model specifics
Data
Estimation
Characteristics
Source
Period
Falk and Koebel (2004)
static, K quasi-fixed
structural-form, conditional
industry-level, panel
admin
1978-1994
Konings and Murphy (2004)
static/dynamic, no K
reduced-form, conditional
firm-level, panel
survey
1993-1998
Amiti and Wei (2005)
static, no K
reduced-form, (un)conditional
industry-level, panel
admin
1995-2001
Arnone et al. (2005)
dynamic, (no) K
reduced-form, (un)conditional
firm-level, panel
survey
1998-2002
Basu et al. (2005)
dynamic, no K
reduced-form, conditional
firm-level, panel
admin
1989-1993
Becker et al. (2005)
static, K quasi-fixed
structural-form, conditional
firm-level, cross-section
admin/survey
[1998;2000]
Bruno and Falzoni (2005)
dynamic, no K
reduced-form , conditional
industry-level, panel
admin
1970-1997
Falk and Wolfmayr (2005)
static, no K
reduced-form, conditional
industry-level, panel
admin
1995-2000
Görg and Hanley (2005)
dynamic, no K
reduced-form, conditional
firm-level, panel
survey
1990-1995
Hijzen et al. (2005)
static, K quasi-fixed
structural-form, conditional
industry-level, panel
survey
1982-1996
18
Time horizon
Table 5: continued
Study
Model specifics
Data
Estimation
Characteristics
Source
Period
Bellmann and Pahnke (2006)
dynamic, K regressor
reduced-form, conditional
firm-level, panel
admin
1996-2004
Koebel (2006)
static, K cost share
structural-form, conditional
industry-level, panel
admin
1976-1995
Ekholm and Hakkala (2006)
static, K quasi-fixed
structural-form, conditional
industry-level, panel
admin
1995-2000
Benito and Hernando (2007)
dynamic, K regressor
reduced-form, conditional
firm-level, panel
survey
1985-2000
Crino (2007)
static, K quasi-fixed
structural-form, conditional
industry-level, panel
admin
1990-2004
Lachenmaier and Rottmann (2007)
static, no K
reduced-form, conditional
firm-level, panel
survey
1982-2003
Addison et al. (2008)
static, K quasi-fixed
structural-form, conditional
firm-level, panel
admin
1993-2002
Benito and Hernando (2008)
dynamic, K regressor
reduced-form, conditional
firm-level, panel
survey
1985-2001
Jacobi and Schaffner (2008)
static/dynamic, K quasi-fixed
structural-form, conditional
industry-level, panel
admin
1999-2005
Micevska (2008)
dynamic, (no) K
reduced-form, conditional
firm-level, panel
admin
1994-1999
19
Time horizon
Table 5: continued
Study
Model specifics
Data
Estimation
Characteristics
Source
Period
Onaran (2008)
static/dynamic, no K
reduced-form, conditional
industry-level, panel
admin
1999-2004
Godart et al. (2009)
dynamic, no K
reduced-form, conditional
firm-level, panel
survey
1996-2005
Görg et al. (2009)
dynamic, no K
reduced-form, conditional
firm-level, panel
survey
1983-1998
Brixy and Fuchs (2010)
static/dynamic, no K
reduced-form, conditional
firm-level, panel
survey
2001-2006
Buch and Lipponer (2010)
dynamic, no K
reduced-form, conditional
firm-level, panel
survey
1997-2004
Freier and Steiner (2010)
static, K quasi-fixed
structural-form, conditional
industry-level, panel
admin
1999-2003
Hakkala et al. (2010)
dynamic, no K
reduced-form, conditional
industry-level, panel
admin
1990-2002
Hijzen and Swaim (2010)
static/dynamic, K regressor
reduced-form, (un)conditional
industry-level, panel
admin
1980-2002
Muendler and Becker (2010)
static, K quasi-fixed
structural, conditional
firm-level, panel
survey
1996-2001
Bohachova et al. (2011)
dynamic, no K
reduced-form, conditional
firm-level, panel
survey
2000-2008
20
Time horizon
Table 5: continued
Study
Model specifics
Data
Time horizon
Estimation
Characteristics
Source
Period
Crino (2012)
static, K quasi-fixed
structural-form, conditional
industry-level, panel
admin
1990-2004
Kölling (2012)
static, K quasi-fixed
structural-form, conditional
firm-level, panel
survey
2000-2007
Peichl and Siegloch (2012)
static, no K
structural-form, conditional
firm-level, panel
leed
1996-2007
Sala and Trivin (2012)
dynamic, (no) K
reduced-form, (un)conditional
industry-level, panel
admin
1964-2007
21
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ABOUT NEUJOBS
“Creating and adapting jobs in Europe in the context of a socio­ecological transition” NEUJOBS is a research project financed by the European Commission under the 7th
Framework Programme. Its objective is to analyse likely future developments in the
European labour market(s), in view of four major transitions that will impact
employment - particularly certain sectors of the labour force and the economy - and
European societies in general. What are these transitions? The first is the socioecological transition: a comprehensive change in the patterns of social organisation
and culture, production and consumption that will drive humanity beyond the current
industrial model towards a more sustainable future. The second is the societal
transition, produced by a combination of population ageing, low fertility rates,
changing family structures, urbanisation and growing female employment. The third
transition concerns new territorial dynamics and the balance between agglomeration
and dispersion forces. The fourth is a skills (upgrading) transition and and its likely
consequences for employment and (in)equality.
Research Areas NEUJOBS consists of 23 work packages organised in six groups:
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Group 1 provides a conceptualisation of the socio-ecological transition that
constitutes the basis for the other work-packages.
Group 2 considers in detail the main drivers for change and the resulting
relevant policies. Regarding the drivers we analyse the discourse on job
quality, educational needs, changes in the organisation of production and in
the employment structure. Regarding relevant policies, research in this group
assesses the impact of changes in family composition, the effect of labour
relations and the issue of financing transition in an era of budget constraints.
The regional dimension is taken into account, also in relation to migration
flows.
Group 3 models economic and employment development on the basis of the
inputs provided in the previous work packages.
Group 4 examines possible employment trends in key sectors of the economy in
the light of the transition processes: energy, health care and goods/services for
the ageing population, care services, housing and transport.
Group 5 focuses on impact groups, namely those vital for employment growth
in the EU: women, the elderly, immigrants and Roma.
Group 6 is composed of transversal work packages: implications NEUJOBS
findings for EU policy-making, dissemination, management and coordination.
For more information, visit: www.neujobs.eu
Project coordinator: Miroslav Beblavý ([email protected])