Extending the common baseline model in various dimensions does not fundamentally change the low contribution of labor quality to productivity growth in Germany. Labor quality growth is low owing to a small increase in the share of workers with higher education, a negative contribution from a higher share of females and declines in relative returns. The contribution of actual labor market experience is lower than suggested by an age proxy.Human capital, labour quality, productivity growth

Guido Schwerdt

Jarkko Turunen

English

Research Papers in Economics

 
 
 
 
 
 
 
 
 
 
 
Labor Quality Growth in Germany 
 
 
Guido Schwerdt 
Jarkko Turunen 
 
 
 
 
Ifo Working Paper No. 77 
 
 
 
 
August 2009 
 
 
 
 
 
 
An electronic version of the paper may be downloaded from the Ifo website www.ifo.de. Ifo Working Paper No. 77 
Labor Quality Growth in Germany* 
 
Abstract 
 
Extending the common baseline model in various dimensions does not fundamentally 
change the low contribution of labor quality to productivity growth in Germany. Labor 
quality growth is low owing to a small increase in the share of workers with higher edu-
cation, a negative contribution from a higher share of females and declines in relative 
returns. The contribution of actual labor market experience is lower than suggested by 
an age proxy. 
 
JEL Code: E24, J24, O47. 
Keywords: Human capital, labour quality, productivity growth. 
 
 
 
 
 
 
 
Guido Schwerdt 
Ifo Institute for Economic Research 
at the University of Munich 
Poschingerstr. 5 
80539 Munich, Germany 
Phone: +49(0)89/9224-1310 
schwerdt@ifo.de  
Jarkko Turunen 
European Central Bank 
ECB 
Kaiserstr. 29 
60311 Frankfurt am Main, Germany 
Phone: +49(0)69/1344-0 
 
 
 
 
 
 
 
 
 
 
* The authors would like to thank Diane Gahl for excellent research assistance. The views reflect those of 
the authors and not those of the ECB.   2
1.  Introduction 
Previous studies suggest that improvements in human capital, as measured by labor quality 
growth, have been lower in Germany than in other European countries and in the United States (see 
Card and Freeman, 2004, Jorgenson, 2005 and Schwerdt and Turunen, 2007). Using representative 
microdata from the German Socio-Economic Panel (GSOEP) we improve on previous estimates by 
using an actual labor market experience measure (instead of an age proxy) and by carefully 
considering the impact of German unification on determinants of labor quality. We also provide a 
methodological contribution: empirical approaches relating to what variables should be used and 
how weights should be determined are typically driven by data limitations. We evaluate the 
robustness of our estimates along several of these dimensions. 
 
2.  Alternative estimates 
We take the BLS (1993) approach as our baseline. First, we estimate cross-sectional wage 
equations using weighted OLS: 
i Z ε η β α + i i i + X + = logW       (1) 
The dependent variable is the individual hourly wage. In the baseline model, X includes 
dummies for two education (with secondary education as the omitted category) and five age 
categories (with those between 34 and 45 as the omitted category).PF
3
FP Equation 1 is estimated 
separately for males and females and gender is therefore included as a third characteristic. The right 
hand side includes part-time employment status and sector as control variables in Z.  
We consider three alternatives to this baseline. First, we include a full set of interactions 
among the human capital variables. Fitzenberger and Kohn (2006) find systematic differences across 
cohorts in the evolution of skill wage premia in Germany.PF
4
FP These effects are captured by the 
interaction between age and education in the flexible model. The interacted model is equivalent to 
using mean wages for each group (after controlling for characteristics included in Z), similar to the 
approach in Ho and Jorgenson (1999).  
Second, we replace age with a measure of actual labor market experience. Age is commonly 
used as a proxy for labor market experience when information on actual experience is not available. 
                                                 
P
3
P Education categories include those with lower secondary education (ISCED categories 0-2), upper secondary education 
(3 and 4) and tertiary education (5-6).  
P
4
P Laquesta et al. (2008) argue that a more flexible model is important for measuring labor quality growth in Spain.   3
However, using such a proxy may lead to important biases. The GSOEP includes detailed calendar 
information on main activity that is used to construct a measure of actual experience in full-time 
employment (in months).PF
5
FP Zoghi (2008) constructs actual experience measures for the United States 
and illustrates their impact on labor quality growth. 
Third, we construct measures of labor quality separately for the unified Germany and for 
former West German regions. Both the composition of hours and relative returns are likely to have 
been different in East and West Germany. Recent studies also suggest that these differences may 
persist (see Orlowski and Riphahn, 2008). 
Estimates of labor quality growth are computed by weighting total hours worked with 
weights constructed from the predicted wages from equation (1). Specifically, we construct predicted 
wages  t j W ,
~
 for each worker group j and year t. Weights are the average of the share of each group in 
total compensation in adjacent years: 
() 1 , , , 2
1
− + = t j t j t j s s s        (2) 
where  
∑
=
j
t j t j
t j t j
t j H W
H W
s
, ,
, ,
, ~
~
       (3) 
and H is total hours worked. Change in aggregate labor input is: 
() ( ) ∑ − − =
j t j t j t j t t H H s L L 1 , , , 1 / ln / ln     (4). 
Finally, growth in labor quality is equal to the difference between growth in aggregate labor 
input and growth in raw hours: 
H L Q ln ln ln Δ − Δ = Δ       (5). 
We use GSOEP data from 1984-2007. Our full sample includes 200,482 person-year 
observations with positive hours data. Information on individuals from East Germany are included 
since 1990. Cross-sectional population weights have been used to aggregate hours worked and to 
perform weighted OLS regressions. 
                                                 
P
5
P This measure has been previously used to estimate returns to experience in Germany (see for example, Orlowski and 
Riphahn, 2008). The sample with information on actual experience is somewhat smaller than the total sample. We have 
recomputed all estimates using the smaller sample to confirm that differences in the results are not driven by different 
samples.   4
 
3.  Results 
All four estimates indicate a continuous increase in labor quality in the last 20 years (see 
Figure 1). The average growth rate for the baseline model, 0.32 percent year-on-year, is very close to 
the estimate based on data from the European Labor Force Survey (LFS) (0.27 percent) in Schwerdt 
and Turunen (2007). Our GSOEP based estimate is well below the estimate in Jorgenson (2005) 
(0.52 percent), but above the estimate in Card and Freeman (2004) (0.21 percent).  
Differences in the average growth rates across the alternative measures are below 0.1 
percentage point. This supports the view that the simple baseline model is sufficient to describe the 
overall contribution of labor quality to labor productivity growth. This result also confirms those 
from a similar exercise for the United States: Zoghi (2008) finds that the “simplest specification … 
yields results on labor composition … that are quite similar to the more elaborate specifications”. 
The estimates also show a common pattern over time. The late 1980s were characterized by low 
growth, followed by a period of strong growth immediately after unification. Labor quality growth 
has moderated thereafter.PF
6
FP  
While the empirical choices reflected in the alternative estimates are not crucial for overall 
labor quality growth, they do matter for understanding its underlying determinants. Changes in the 
composition of total hours worked in Germany are characterized by three broad trends. First, while 
the shares of total hours worked by education categories were relatively stable until the early 1990s, 
thereafter the share with low (high) education has been declining (increasing). Second, population 
ageing has a strong impact on the composition. Following the increasing share of prime-aged 
workers, the share of workers with 10-20 year of actual labor market experience increased strongly 
in the mid 1990s. The share of those with less than 5 years of actual experience has been declining, 
coinciding with the declining share of young workers. Third, the share of women in total hours 
worked has increased substantially.  
Changes in the weights reflect changes in relative returns. Previous studies suggest that the 
wage distribution across skill groups has been relatively stable over the sample period (for a review, 
see Fitzenberger and Kohn, 2006). However, since the East German wage distribution was more 
compressed than in the West, trends in weights reflect the mixing of the two distributions, as well as 
a gradual adjustment process thereafter. Table 1 presents ratios of imputed wages from equation (1). 
                                                 
P
6
P Alternative measures also show differences. In particular, after the immediate unification period, the index for West 
Germany shows the highest growth rates, whereas the experience based measure shows the lowest growth rates.   5
Focusing on the first and last year, relative returns to education indeed appear relatively stable. 
However, immediately following unification, returns to higher education declined. Relative returns 
increase again in the 2000s, but do not fully reverse the loss. Returns to age have increased and more 
so for the oldest workers. In some contrast, with the exception of those with more than 30 years of 
experience, returns to experience have declined. Finally, the unification appears also to have 
contributed to a permanent decline in the gender gap. Overall the quantitative impact of trends in 
relative returns on labor quality growth is small. Keeping wages fixed at their early 1980s level 
suggests labor quality growth at 0.35 percent for the model based on experience (as opposed to 0.28 
percent with actual wages). 
Table 2 presents a full set of results for the experience based model. The rise in educational 
attainment is the main driver of the increase in labor quality, while increase in experience also 
contributes positively. However, the first order contribution of actual labor market experience is 
approximately half the contribution of age in the baseline index. The contribution of gender is 
negative, dampening labor quality growth on average by almost 0.1 percentage point over the sample 
period. While it is standard to include gender as a determinant, these results suggest -- assuming that 
only a part of the wage differential between men and women reflects productivity -- that the index 
including women may understate actual improvements.  
 
4.  Conclusion 
Our results show that going beyond a common baseline model does not fundamentally 
change the measured contribution of labor quality to productivity growth. However, including an 
actual experience measure and exploring differences across West and East Germany helps in 
understanding determinants of labor quality growth. We find that low labor quality growth is mainly 
driven by a relatively low increase in the share of hours worked by workers with higher education 
and a significant negative contribution from a higher share of hours worked by females. Results also 
suggest a lower direct contribution from actual experience than suggested by an age proxy. 
Increasing the share of educated workers and ensuring proper incentives for accumulating labor 
market experience are needed to boost productivity growth in Germany.   6
References: 
 
BLS (1993) “Labor Composition and US Productivity Growth, 1948–1990”, Bureau of Labor 
Statistics Bulletin 2426, Washington DC. 
Card, D. and Freeman, R. (2004): “What Have Two Decades of British Economic Reform 
Delivered?”, in Card, D., Blundell, R. and Freeman, R. (editors): Seeking a Premier Economy: The 
Economic Effects of British Economic Reforms, 1980-2000, NBER. 
Fitzenberger, B. and Kohn, K. (2006): “Skill Wage Premia, Employment, and Cohort Effects: 
Are Workers in Germany All of the Same Type?”, IZA Discussion Papers 2185 
Ho, M. and Jorgenson, D. (1999): “The Quality of the U.S. Work Force, 1948-95” , 
unpublished manuscript, Harvard. 
Jorgenson, D. (2005): “Accounting for Growth in the Information Age”, in Aghion, P. and 
Durlauf, S. (editors), Handbook of Economic Growth, Amsterdam, North-Holland. 
Lacuesta, A., Puente, S and Cuadrado, P. (2008): “Omitted Variables in the Measurement of 
a Labor Quality Index: The Case of Spain”, Bank of Spain working paper. 
Orlowski, R. and Riphahn. R. (2008): "The East German Wage Structure after Transition", 
IZA Discussion Papers 3861. 
Schwerdt, Guido and Jarkko Turunen (2007): "Growth In Euro Area Labor Quality," Review 
of Income and Wealth, vol. 53(4), pages 716-734. 
Zoghi, C. (2008): “Measuring Labor Composition: A Comparison of Alternate 
Methodologies”, forthcoming in Abraham, K., Spletzer, J. and Harper, M. (editors): HLabor in the 
New EconomyH, NBER, University of Chigago Press.   7
Figure 1: Alternative estimates 
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
1985‐89 1990‐94 1995‐99 2000‐07 1985‐2007
Baseline West Germany Fully interacted Actual experience
   8
Table 1: Relative imputed wages 
  Whole sample  West Germany 
  1985 1990 1995 2000 2007 1985 1990 1995 2000 2007
Male  1.40 1.43 1.29 1.29 1.29 1.40 1.39 1.33 1.35 1.32
Age  25-34  1.88 1.65 1.73 1.94 1.93 1.88 1.81 1.72 1.94 1.84
Age  35-44  2.18 1.86 1.99 2.22 2.40 2.18 2.08 2.05 2.28 2.30
Age  45-54  2.09 1.89 2.03 2.25 2.53 2.09 2.16 2.07 2.32 2.46
Age  55-64  2.05 1.66 2.01 2.32 2.52 2.05 1.96 2.01 2.40 2.40
Age  65+  1.06 1.36 1.82 2.09 2.06 1.06 1.67 1.86 2.10 1.92
Experience 1-5P
1
P  1.72 1.83 1.78 1.81 1.73 1.72 1.87 1.72 1.75 1.60
Experience 5-10P
1
P  1.99 1.97 2.03 2.03 2.00 1.99 2.09 1.98 1.96 1.83
Experience 10-20P
1
P  2.35 2.17 2.21 2.23 2.23 2.35 2.42 2.24 2.23 2.09
Experience 20-30P
1
P  2.47 2.25 2.43 2.31 2.44 2.47 2.55 2.45 2.32 2.32
Experience 30+P
1
P  2.20 2.06 2.26 2.30 2.44 2.20 2.36 2.29 2.32 2.31
Education  Middle  1.40 1.15 1.25 1.32 1.30 1.40 1.36 1.29 1.33 1.24
Education  High  1.90 1.46 1.64 1.80 1.81 1.90 1.90 1.76 1.86 1.74
        
Note: Ratios of imputed wages over baseline categories. Baseline categories are Female, Age 15-24, 
Experience 0-1, and Education low. The results from experience categories have been calculated 
from the experience model, the rest are from the baseline model.   9
Table 2: Contributions to labor quality 
     First order  Second order  Third order 
   Total  Sex  Experience Education SexExp ExpEdu SexEdu  SexExpEdu 
1983 100.00  100.00  100.00 100.00  100.00  100.00  100.00 100.00 
1985 101.19  99.77  100.47 100.85  100.28  99.86  100.00 99.96 
1986 101.28  99.56  100.42 101.05  100.56  99.77  100.03 99.90 
1987 100.70  99.53  100.16 100.59  100.54  99.94  100.05 99.89 
1988 100.66  99.38 99.86  100.89  100.67  99.95  100.06 99.85 
1989 100.38  99.19 99.81  100.79  100.76  99.93  100.06 99.84 
1990 101.44  98.68  100.95 102.01  100.39  99.57  99.92  99.93 
1991 102.26  98.92  101.52 101.98  100.39  99.63  99.90  99.94 
1992 103.10  98.83  102.14 102.62  100.37  99.34  99.88  99.94 
1993 103.14  98.73  102.17 102.76  100.44  99.26  99.85  99.94 
1994 103.99  98.81  102.54 103.46  100.38  99.01  99.80 100.00 
1995 104.34  98.82  102.56 103.97  100.36  98.87  99.78 100.01 
1996 104.40  98.78  102.66 104.20  100.29  98.70  99.76 100.04 
1997 104.47  98.80  102.36 104.33  100.36  98.85  99.79 100.00 
1998 104.81  98.71  102.81 104.45  100.33  98.73  99.73 100.06 
1999 104.61  98.62  102.64 104.43  100.37  98.77  99.78 100.04 
2000 105.30  98.58  102.14 105.86  100.41  98.56  99.75 100.05 
2001 105.20  98.40  102.33 105.68  100.49  98.57  99.75 100.03 
2002 105.69  98.40  102.50 106.01  100.51  98.56  99.75 100.01 
2003 106.19  98.26  103.00 106.27  100.47  98.46  99.77 100.01 
2004 106.20  98.08  103.23 106.11  100.54  98.51  99.80  99.99 
2005 105.99  97.92  103.03 106.14  100.69  98.49  99.81  99.98 
2006 106.92  97.95  103.57 106.87  100.60  98.19  99.80 100.01 
2007 106.62  97.94  103.16 106.91  100.64  98.24  99.78 100.02 
                
Average 0.28  -0.09  0.14  0.29  0.03  -0.08  -0.01  0.00 
    
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Accounting for Growth in the Information Age”,

Growth In Euro Area Labor Quality,&quot;

Labor Composition and US Productivity Growth, 1948–1990”, Bureau of Labor Statistics Bulletin 2426,

Measuring Labor Composition: A Comparison of Alternate Methodologies”, forthcoming

Omitted Variables in the Measurement of a Labor Quality Index: The Case of Spain”, Bank of Spain working paper.

Skill Wage Premia, Employment, and Cohort Effects: Are Workers in Germany All of the Same Type?”,

The East German Wage Structure after Transition&quot;,

What Have Two Decades of British Economic Reform Delivered?”,

Labor Quality Growth in Germany

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Labor Quality Growth in Germany

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