In this paper I investigate how the labor productivity growth is affected from various institutions of the labor market using the empirical evidence from a panel data of OECD countries. I find that benefit replacement rate, benefit duration index, and the tax wedge appear to be significant labor market institutions affecting the labor productivity growth. A higher benefit replacement rate, a longer duration of unemployment benefits, and a higher tax wedge are expected to generate a lower labor productivity growth.Labor Market Institutions, Labor Productivity Growth

Macit, Fatih

English

Research Papers in Economics

LABOR MARKET INSTITUTIONS AND LABOR
PRODUCTIVITY GROWTH
Fatih Macit1
S￿leyman ‚ Sah University
June 2011
Abstract
In this paper I investigate how the labor productivity growth is a⁄ected from
various institutions of the labor market using the empirical evidence from a panel
data of OECD countries. I ￿nd that bene￿t replacement rate, bene￿t duration
index, and the tax wedge appear to be signi￿cant labor market institutions
a⁄ecting the labor productivity growth. A higher bene￿t replacement rate, a
longer duration of unemployment bene￿ts, and a higher tax wedge are expected
to generate a lower labor productivity growth.
Keywords: Labor Market Institutions, Labor Productivity Growth
1Correspondence: Fatih Macit, Department of Economics, S￿leyman ‚ Sah University, • Is-
tanbul, TURKEY, 34865. Email: fmacit@ssu.edu.tr
11 Introduction
The structure of labor markets has been an important area of research for many
economists. Many of these studies have focused on explaining the unemploy-
ment di⁄erences across the countries by the di⁄erences in their labor market
institutions. Blanchard and Wolfers (2000) investigate the interaction between
shocks and labor market institutions in explaining the cross country di⁄erences
in the rise of European unemployment. Fialova and Schneider (2008) explore
the role of labor market institutions on di⁄erent labor market developments in
European Union member countries particularly focusing on new member coun-
tries.
The labor market institutions have also been incorporated in dynamic sto-
chastic general equilibrium models in order investigate their e⁄ect on business
cycle dynamics. Macit (2010) incorporates search and matching frictions in an
otherwise New Keynesian model and investigates whether the level of unem-
ployment bene￿ts and ￿ring costs a⁄ect the business cycle dynamics. He ￿nds
that a higher level of unemployment bene￿t and a stricter employment protec-
tion legislation generate less volatile and more persistent movements in in￿ ation
and real wages and the level of these labor market institutions a⁄ect how wages
and in￿ ation respond to exogenous shocks. Thomas (2006) investigates the re-
lationship between output and employment volatility and ￿ring costs and ￿nds
that countries with lower levels of ￿ring costs tend to have lower output and
employment volatility. Campolmi and Faia (2007) explore whether the di⁄er-
ences in labor market structures observed among European Union countries are
important in explaining the in￿ ation di⁄erentials.
In this paper I investigate the link between labor market institutions and
labor productivity growth. To the best of my knowledge, it is the ￿rst paper that
explores whether the labor productivity growth is a⁄ected from labor market
2institutions. For this purpose I take a panel data of 20 OECD countries covering
the period from 1970 to 2006. Bene￿t replacement rate, bene￿t duration index,
union density, employment protection legislation index, and the tax wedge are
the labor market variables that capture di⁄erent aspects of the labor market. I
￿nd that bene￿t replacement rate, bene￿t duration index, and the tax wedge
are signi￿cant in explaining the labor productivity growth.A more generous
unemployment bene￿t system and a longer duration of unemployment bene￿ts
are expected to generate a lower productivity growth. A higher tax burden is
also expected to lead to a lower labor productivity growth.
The paper proceeds as follows. The next section presents the empirical model
and gives a description of the data. Section III presents the estimation results
and Section IV concludes.
2 Empirical Model and Data
2.1 Empirical Model
This section presents the empirical model that I use to investigate the rela-
tionship between labor market institutions and labor productivity growth. The
reduced form equation that is going to be estimated can be summarized as
follows:
prodit = ￿ + ￿
0LMIit + ￿i + "it (1)
where prodit refers to labor productivity growth for country i at time t.
LMIit is a vector of labor market institutions and ￿i measures the country
￿xed e⁄ects. The model is estimated using the ￿xed e⁄ecs estimation method.
32.2 Data
The sample that I use includes data from 20 OECD countries namely Australia,
Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy,
Japan, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzer-
land, United Kingdom, and the United States. The labor market institutions
data is taken from Nickell (2006) and is an annual data covering the period from
1970 to 2006. The data for labor productivity growth is obtained from OECD
Economic Outlook database. The labor market institutions that are used in
the model are employment protection legislation index, union density, bene￿t
replacement rate, bene￿t duration index, and tax wedge.
Employment protection legislation index, EPLit, takes a value between 0
and 2 and a higher number implies that there are stricter employment protection
legislations in that country. The index captures the features of the labor market
such as notice of dismissal, di¢ culty of dismissal, severance pay etc.
Union density, UDit, is the ratio of total union members to total employ-
ment. The series is calculated using the administrative and survey data from
OECD labor market statistics database.
The bene￿t replacement rate, BRRit, measures the level of unemployment
bene￿ts as a percentage of average earnings before tax. It is calculated as the
average across the ￿rst ￿ve years of unemployment.
Bene￿t duration index, BDit, is taken as an indicator of how long the un-
employment bene￿ts last for. Nickell (2006) calculate the index as follows:
BD = 0:6 ￿
BRR2
BRR1
+ 0:4 ￿
BRR4
BRR1
(2)
where BRR1 is the bene￿t replacement rate that prevails during the ￿rst year
of unemployment, BRR2 is the bene￿t replacement rate that prevails during the
second and third year of unemployment, and BRR4 is the bene￿t replacement
4rate received during the fourth and ￿fth year of unemployment. For instance, if
the worker cannot get any unemployment bene￿ts after one year then BRR2 =
BRR4 = 0 and the index will take a value of zero.
The total tax wedge, TWit, measures the total tax burden and is calculated
as the sum of employment tax rate, the direct tax rate, and the indirect tax
rate.
Table 1 gives a summary of the labour market institutions for the 20 OECD
countries. It gives the average values of labour market variables for the period
1970 to 2006. The table shows that there is a huge cross country variation in
terms of labour market institutions. For instance, in the bene￿t replacement
rate one can observe countries like Denmark and Netherlands who pay unem-
ployment bene￿ts more than 50 percent of average earnings before tax. However,
one can also see countries like Japan and Italy who pay only 10 percent of av-
erage earnings before tax in the form of unemployment bene￿ts. For the other
labour market variables the same type of large variation can be observed.
3 Estimation Results
There are two very commonly used estimation techniques used in panel data
estimation namely the ￿xed e⁄ects estimation and random e⁄ects estimation.
The ￿xed e⁄ects model treats the ￿i in equation (1) as ￿xed unknown parame-
ters. The random e⁄ects model on the other hand treats the individual country
e⁄ects as random. The important assumption behind the random e⁄ects model
is that the ￿
0
is are independent of the explanatory variables in LMIit. In or-
der to decide which model to use I use the Hausman test which tests the null
hypothesis that the explanatory variables and ￿i are uncorrelated. The ￿xed
e⁄ects estimator is consistent both under the null and alternative hypothesis
whereas the random e⁄ects estimator is consistent only under the null hypothe-
5sis. For Hausman test rejecting the null hypothesis implies that the ￿xed e⁄ects
estimator should be preferred to random e⁄ects estimator as the latter one is
inconsistent. The Hausman test statistic can be computed as:
￿H = (b ￿FE ￿ b ￿RE)0[V (b ￿FE) ￿ V (b ￿RE)]￿1(b ￿FE ￿ b ￿RE) (3)
Under the null hypothesis the Hausman test statistics has an asymptotic ￿2
distribution with degrees of freedom equal to the number of explanatory vari-
ables in LMIit vector. The value of the test statistic is obtained as 29.71 which
is signi￿cantly higher than ￿2(5) even at 1% signi￿cance level. Therefore, one
can reject the null hypothesis which implies that the model should be estimated
with ￿xed e⁄ects model.
Table 2 shows the results under ￿xed e⁄ects estimation. As the data for tax
wedge is missing or incomplete for some countries I run two di⁄erent models
with the ￿rst one not including the tax wedge and the second one having the
tax wedge as an explanatory variable. Before getting into interpretation of the
results I ￿rst carry out a test for the joint signi￿cance of the country ￿xed
e⁄ects. That is I test the null hypothesis that all ￿
0
is are equal to zero against
the alternative that at least some of them are di⁄erent from zero using an F
test. The resulting F values for the ￿rst and second model are 7.16 and 5.86
respectively. Both of these values are higher than the critical F values which
allows one to reject the null hypothesis.
Table 2 shows that under both models the bene￿t replacement rate and the
bene￿t duration index are statistically signi￿cant and they have a negative im-
pact on labor productivity growth. That is in countries where workers receive
higher levels of unemployment bene￿ts and they are entitled for unemployment
bene￿ts for longer durations that is expected to generate a lower labor produc-
tivity growth. Intuitively this makes sense as a more generous unemployment
6bene￿t and a longer duration for those bene￿ts imply a better outside option for
the worker and that reduces the incentive of the worker to increase his produc-
tivity. The second model shows that tax wedge is also a signi￿cant labor market
institution in explaining the labor productivity growth. If there is a higher tax
burden on the worker that is expected to reduce the labor productivity growth.
4 Conclusion
In this paper I investigate whether the labor market institutions play a role in
explaining the labor productivity growth. I ￿nd that if there are high unemploy-
ment bene￿ts and workers are entitled for these been￿ts for a longer duration
that is expected to generate a lower labor productivity growth. The tax wedge
also appears to have a signi￿cant impact on labor productivity growth. The re-
sults show that a higher tax wedge is expected to reduce the labor productivity
growth.
7References
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in the Rise of European Unemployment: The Aggregate Evidence", The
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tion Volatility in the Euro Area". Available on the web: http://www.aei-
ecsa.de/dokumente/tagung_eurozone_faia.pdf.
[3] Christo⁄er, K. and T. Linzert (2005). "The Role of Real Wage Rigidity
and Labor Market Frictions for Unemployment and In￿ ation Dynamics" ,
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[4] Fialova, K. and O. Schneider (2008). "Labor Market Institutions and their
E⁄ect on Labor Market Performance in the New EU Member Countries" ,
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[5] Hausman, J. A. (1978), "Speci￿cation Tests in Econometrics", Economet-
rica, 46, 1251-1271.
[6] Krause, Michael and Thomas Lubik (2007). "The (Ir)relevance of Real
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[9] OECD, 2008. OECD Economic Outlook. Paris.
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9Table 1: Average values of the labor market institutions over the period
1970-2006
Country EPL UDEN BRR BD TW
Australia 0.324 42.888 23.238 1.017 34.975
Austria 0.872 50.194 28.141 0.636 55.032
Belgium 0.959 52.400 42.497 0.802 54.320
Canada 0.270 34.276 18.012 0.000 42.561
Denmark 0.669 74.191 50.212 0.694 59.373
Finland 0.744 70.406 30.147 0.539 56.738
France 0.998 14.881 32.838 0.379 60.788
Germany 0.974 31.852 28.318 0.602 51.821
Ireland 0.269 51.216 27.509 0.583 34.744
Italy 1.124 41.670 9.576 0.075 50.668
Japan 0.690 28.110 10.374 0.000 30.251
Netherlands 0.871 29.776 51.091 0.603 52.250
Norway 0.948 56.128 29.432 0.452 60.806
New Zealand 0.324 44.900 29.279 1.025 NA
Portugal 1.507 39.452 22.012 0.236 39.155
Spain 1.835 12.500 29.135 0.215 42.412
Sweden 0.356 79.700 23.741 0.042 70.179
Switzerland 0.337 26.625 19.447 0.080 32.662
United Kingdom 0.196 41.870 20.482 0.680 41.906
United States 0.070 18.682 12.697 0.187 32.850
10Table 2: Estimation Results for the Labor Productivity Growth
EPL -0.0653 -0.0517
BRR -0.0395￿￿￿ -0.0304￿
UDEN 0.0029 0.0186
BD -1.9750￿￿ -2.5862￿￿￿
TW - -0.0422￿￿
#observations 573 501
R2 0.05 0.09
Notes: In terms of the statistical signi￿cance of the coe¢ cient estimates ￿ denotes the
signi￿cance at 10% level, ￿￿ denotes signi￿cance at the 5% level, and ￿￿￿ denotes signi￿cance
at the 1% level. The regression also includes dummy variables for each country to represent
the ￿xed country e⁄ects but they are not reported here.
11

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