No Country For Old Men': Explaining the Age-Earnings Profiles in Russia
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‘No Country For Old Men’: Explaining the
Age-Earnings Profiles in Russia
Alexey Bessudnov
D.Phil. candidate
Department of Sociology, Oxford University
alexey.bessudnov@sant.ox.ac.uk
October 2, 2009
This is the first and unfinished draft of the paper. The re-
sults are preliminary. Please do not cite, quote or circulate
this paper.
Abstract
In this paper I construct cross-sectional and longitudinal age-earnings
profiles for men in Russia for 1991-2006. Two patterns have been iden-
tified. First, in Russia men’s earnings tend to peak and decline earlier
compared to Western countries. Second, there is some change in the
shape of age-earnings profiles within the period considered. In the
mid-1990s the profiles tend to be flatter than in the beginning of the
1990s and the 2000s, with little difference between predicted earnings
of men under 45. In the 2000s the age of maximum predicted earnings
is somewhat younger than in the beginning of the 1990s. In this paper I
focus attention on the first pattern. I argue that the difference between
the shapes of age-earnings profiles in Russia and Western countries can
be explained by higher age-based occupational and job segregation in
Russia that is a result of rapid market reforms and changes in economy
in the 1990s. While it is hard to test this hypothesis empirically due to
the data limitations, I present some evidence in its favour. The anal-
ysis presented in the paper will be extended, developed and revised
later.
1 Introduction. The puzzle
Figures 1(a) and 1(b) show the age-earnings profiles for men in Great Britain
and the USA, constructed with the data from the 2006 Labour Force Survey
and the March supplement to the 2006 Current Population Survey. The
profiles follow the pattern, previously well documented in the literature.
Men’s earnings rapidly increase until the age of 35-40 years, then continue
to rise slowly until about 50 years and then somewhat decline.
140000
350
Weekly net earnings, £
Annual earnings, $
30000
300
20000
250
10000
200
25 30 35 40 45 50 55 60 25 30 35 40 45 50 55 60
Age Age
(a) Great Britain, LFS 2006, n=5656 (b) The USA, CPS March 2006,
n=46546
10000
9000
Monthly earnings, rub
8000
7000
6000
5000
25 30 35 40 45 50 55 60
Age
(c) Russia, RLMS 2006, n=1722
Figure 1: Age-earnings profiles, Great Britain, the USA and Russia, men
22-60 y.o., nonparametric spline scatter plot smooths with 95% confidence
bands (dashed lines)
The observed age-earnings profile in Russia in 2006 is very different from
Britain and the USA (see Figure 1(c)). Men’s earnings increase until the age
of about 35 years and then sharply decrease. Average earnings of men over
50 years old in Russia in 2006 were smaller than average earnings of men in
22, in the very beginning of their careers. This is a pattern that is unusual
for developed industrial countries. It has been noticed before (Lukyanova,
2007), but never properly examined and explained.
In this paper I explore the age-earnings association in post-Soviet Russia
in more detail and suggest explanations for it.
22 Dynamics of earnings over the life cycle
There is a number of theories that explain the dynamics of men’s earnings
over the life cycle. The most well known among them is a human capital
model, developed in the 1960s by Ben-Porath (1967). He suggested that
earnings depend on the amount of human capital accumulated by individu-
als. People have more incentives to invest in human capital (i.e., general and
specific training) in the early stage of their lives to have more time to enjoy
the returns to accumulated capital. As time passes, people’s investments in
human capital diminish, until they finally stop investing. Therefore, earn-
ings rapidly increase in young age, then keep increasing with a slower pace,
reach a plateau and finally decrease due to depreciation of human capital.
The well-known Mincer earnings equation (Mincer, 1974; Willis, 1986;
Weiss, 1986) is partly based on this theoretical model. Mincer suggested re-
gressing earnings on education, age, age squared 1 and a number of controls.
The dependence of earnings on age is modelled to be quadratic, reflecting
nonlinearity of the age-earnings association.
The human capital theory suggests a simple and elegant explanation
of observed age-earnings profiles. However, some studies have found that
the actual age-earnings profiles in many cases diverge from the pattern
predicted by the human capital theory. First, it was established that the
quadratic function is not a perfect fit for the actual age-earnings relation-
ship, as it understates early career earnings growth and overstates midcareer
growth (Murphy and Welch, 1990; Robinson, 2003). Second, the decline of
earnings in later age that is observed in cross-sections often dissappears in
longitudinal age-earnings profiles, most likely due to period effects: infla-
tion and general productivity growth (Thornton et al., 1997; Johnson and
Neumark, 1996; Heckman et al., 2003; Myck, 2007).
Apart from the human capital model, there are other theories that ex-
plain the association between earnings and age. It was suggested that em-
ployers employ earnings as a mechanism for solving the principal-agent prob-
lem in their relations with workers. Workers’ productivity is often difficult
to monitor, and in order to solve the problem of shirking and malfeasance
employers use delayed payment contracts (Lazear, 1981; Hutchens, 1989).
Young employees are paid less than their older colleagues even if their pro-
ductivities do not differ. As workers grow older, their earnings increase. This
creates an incentive for younger workers to work harder and stay longer with
the firm to receive the age premium that disappears if they move to another
firm.2
1
Age here is a proxy for work experience.
2
Goldthorpe (2000) uses a similar argument as a theoretical foundation for his well-
known class schema. According to Goldthorpe, managers and professionals have a ‘service
relationship’ with employers, characterized by higher work autonomy and specificity. It
is hard to monitor their performance and in order to create incentives for them to work
3Both human capital and incentive pay theories predict that men’s earn-
ings increase as people get older and more experienced. As far as age-
earnings profiles in younger age are concerned, two theories do not contra-
dict each other. However, the incentive pay theory leaves unexplained the
decline in earnings of older workers.
Another theory that should be considered when explaining age-earnings
profiles deals with demographic factors (Welch, 1979; Freeman, 1979). Some
birth cohorts are bigger than others and, therefore, supply of workers in
different cohorts varies. If we assume that workers of different ages are im-
perfect substitutes in the labour market, then equilibrium wages of workers
in smaller cohorts should be higher than wages of workers in bigger co-
horts. Empirical studies show that cohort size does have a predicted effect
on earnings not only in the USA, but also in European countries (Wright,
1991; Brunello, 2007).
Most of the literature on age-earnings profiles deals with men’s earn-
ings only. Women frequently have intermittent careers and, therefore, their
age and work experience are weaker correlated than in the case of men.
This makes building models of the age-earnings association more difficult.
Some studies show that cohort size affects earnings only for men, but not
for women, probably because younger and older women are better labour
market substitutes due to breaks in women’s careers (Freeman, 1979).
3 Data
In this paper I use the data from the Russia Longitudinal Monitoring Survey
(RLMS). The RLMS is a household panel survey conducted by the Carolina
Population Centre jointly with several Russian institutions.3 The first phase
of the project started in 1992. From July 1992 to January 1994 four rounds
of the survey have been conducted, with a sample of about 6300 households
and more than 17000 individuals. Although the sample was quite large, the
sampling frame was not entirely satisfacory due to the large average cluster
size. In 1994 the survey was resampled with smaller primary sampling units.
The sample size became smaller (about 4000 households), but because of the
improved sampling frame the efficiency of the estimates did not deteriorate.
Since 1994 surveys have been conducted annually (except 1997 and 1999)
more efficiently, part of the compensation is delayed until later stages of their careers.
On the other hand, in the case of manual classes, when monitoring is easier to imple-
ment and time-based or piece-rate payment is applied, delayed payment contracts are not
used (Goldthorpe and McKnight, 2006).
3
The Russian State Statistical Bureau, Institute of Sociology of the Russian Academy
of Sciences and the Russian Centre for Preventive Medicine participated in the first phase
of the project. In the second phase only the Institute of Sociology took part. The second
phase was funded by the USAID and NIH, Higher School of Economics and Pension Fund
of Russia.
4with the same sampling frame. Since the sampling frames in Phase 1 and
Phase 2 were different, the data sets from two phases represent two different
surveys and cannot be merged into a single panel file.
In Phase 1 the response rate at the household level was 88.8% in round
1, 84.3% in round 2, 81.8% in round 3 and 76% in round 4. In Phase 2 the
response rate at the household level was 87.6% in round 5, 82.1% in round
6, 79.4% in round 7, 77.7% in round 8 and 75.3% in round 9. Due to the
higher sample attrition in Moscow and St.Petersburg, in round 10 (2001)
the sample for these two cities was replaced by a new sample. This is the
reason why the response rate in round 10 dropped to 57.9%. It was 57.3%
in round 11, 54.8% in round 12, 54.3% in round 13 and 50.8% in round 14
(for the areas outside Moscow and St.Petersburg the response rate for these
rounds was over 70%, but in the two biggest Russian cities it was much
lower). In 2005 some new households were added to the sample to restore
the regional balance in the original 1994 sample. The overall response rate
further dropped to 44.9%, but for the part of the sample, comparable with
round 14, the response rate was higher (50.6% for the whole sample and
69.9% for regions outside Moscow and St.Petersburg). The response rate
for individuals within households in both phases was over 89% and in most
rounds over 97% (for details see RLMS, 2009).
Each round in the RLMS is represented by two samples. The first sam-
ple is a nationally representative cross-sectional sample based on the origi-
nal 1994 sampling frame (with adjustments made in 2001 and 2005). When
people moved to another place, attempts were made to follow them and
interview them at their new addresses. In this case there were not included
in the cross-sectional sample, but included in the second, longitudinal sam-
ple. In this paper I use cross-sectional RLMS samples for models fitted for
separate years and longitudinal samples for longitudinal models.
The RLMS began in autumn 1992, after the collapse of the USSR and the
start of radical market reforms in Russia. To compare age-earnings profiles
in Russia before and after the beginning of market reforms I use another
data set, the General Social Survey of the USSR. This is a survey conducted
in April-May 1991 by Michael Swafford and a number of Russian sociologists
from the Institute of Sociology of the USSR Academy of Sciences (ICPSR
6500). The survey used a probability sample representing 18+ population of
the European part of the USSR (Russia to the west of the Urals, Ukraine,
Belorussia, Moldavia, and Lithuania). The sample size was about 3000
respondents and the response rate exceeded 84% (Swafford et al., 1995).
I used 2006 March Current Population Survey and 2006 Labour Force
Survey to constuct the age-earnings profiles for men in the USA and Britain,
presented in the Introduction.
The crucial variables for this paper are age and earnings. Coding age in
all data sets is straightforward. As for earnings, in the RLMS I used the
variable for after-tax earnings received in the last 30 days at the primary
5job. The GSS-USSR asked about after-tax monthly earnings at the primary
job, without mentioning the last 30 days.
In all further analysis I limit the sample to men 22-60 years old. Inclusion
of younger men would strongly bias the sample to less educated people who
enter the labour market earlier. In Russia students usually start university
education when they are 16 years old and an average university course lasts
for four or five years. By the age of 22 most people finish full-time education
and enter the labour market. Sixty years is the official age of retirement in
Russia.
For the purposes of this paper, I focus on men’s earnings only.
4 Method
The usual way to model the age-earnings association is to model it quadrat-
ically. This approach implies a certain functional form for the age-earnings
association, and the rise of earnings in early age is assumed to be symmetric
to their decline in older age.
The use of quadratic function to model the association between age and
earnings has been criticised in the literature before (Murphy and Welch,
1990; Robinson, 2003). In our case it may be particularly misleading, as the
age-earnings association in Russia may be far from the usual parabolic shape.
In order to get more flexible estimates of age-earnings profiles I employ a
nonparametric approach. I model the association between earnings and age
as:
log earni = f (agei ) + εi (1)
f (agei ) is a function that is estimated locally at some focal point of age.
There are two main types of functions that can be used as f (agei ): local
polynomial regression and spline functions (Fox, 2000b; Keele, 2008). While
mathematically different, in practice they produce very similar smooths.
In this paper I mostly use thin plate regression spline function with auto-
mated choice of smoothing parameters, as implemented in the R package
mgcv (Wood, 2001, 2003, 2006). In addition, in some cases I fit local poly-
nomial smooth, as implemented in the R command loess.4
The main advantage of nonparametric models is flexibility. The analyst
does not have to make many assumptions about the functional form of the
association between two variables (although it is assumed to be smooth).
4
Some nonparametric regression models can be fitted in Stata with lowess, lpoly,
running, or for multivariate analysis, with mlowess and mrunning. However, R provides a
larger number of more versatile tools for fitting and interpreting nonparametric regressions.
In particular, semiparametric models that I use in this paper are more easily estimated
with mgcv in R. At the moment, Stata’s ability to fit semiparametric models with mrunning
is quite limited.
6Another advantage is that standard errors for predictions are produced lo-
cally5 and, therefore, nonparametric regression is sensitive to the distribu-
tion of independent variables. This is a data driven approach that does
not allow to make predictions outside tha range of data. However, the dis-
advantage of nonparametric regression is that, contrary to ordinary OLS
regression, it does not produce two parameters (coefficients for an intercept
and a slope) that describe the relationship. Therefore, results of nonpara-
metric regressions should be analyzed visually. This is the method I employ
in the paper.
Nonparametric regression can be extended to include several predic-
tors (Fox, 2000a; Keele, 2008):
log earni = f (agei , xi ) + εi , (2)
where xi is a control variable. However, model 2 becomes difficult to
estimate when it includes more than three predictors, as it requires a very
large sample size. (Even in the case of two predictors think of a three-
dimensional space that is divided into small “cubes”, and each of these
cubes should contain enough observations to allow for estimation of local
regression). Besides, the results of estimation of model 2 in case of several
predictors are hard to visualize.
Model 2 can be modified into a more restrictive additive model:
log earni = b0 + f (agei ) + f (xi ) + εi , (3)
This model does not allow for interactions between age and x, but it
is easier to estimate and interpret. Furthermore, we can assume that x
is associated with the dependent variable (in our case, logged earnings)
parametrically. This would yield a semiparametric model:
log earni = b0 + f (agei ) + b1 xi + εi , (4)
The association between age and earnings may change conditional on
x. For instance, age-earnings profiles for people belonging to different social
classes may look differently. Therefore, we may want to allow for interactions
between age and parametric terms.
log earni = b0 + f1 (agei ) + b1 xi + f2 (agei )xi + εi , (5)
5
In mgcv standard errors for predictions are based on the posterior distribution of model
coefficients.
75 Age-earnings profiles in cross-sectional perspec-
tive
Figures 4 and 5 show cross-sectional age-earnings profiles for Russia for each
year from 1991 to 2006, except for 1997 and 1999 when the data are not
available. Solid lines represent spline-based smoothing estimates and dashed
lines represent 95% confidence bands. Dotdash lines are estimates from local
polynomial regression smoothing.
As the figures demonstrate, spline and local polynomial regression es-
timates are close to each other. The spline function produced somewhat
smoother curves. There are only two, out of sixteen, graphs, for which dif-
ferences between spline and local polynomial regression smooths can lead to
different interpretations of the age-earnings association. These are curves
for 1996 and 2002, when two methods produce varying predictions for work-
ers under 35 years old. In these two cases the spline function is possibly
oversmoothed.
The first profile is based on the data collected in spring 1991 that come
from the General Social Survey of the European USSR. That was the time
before the collapse of the USSR and, although some economic reforms had
already started in the USSR, the economy largely remained under the state
control. The age-earnings profile is quite close to the parabolic shape, with
maximum predicted earnings reached in the age of about 40 years.
In December 1991 the USSR formally dissolved, and in January 1992
Russian government started rapid market reforms that included liberaliza-
tion of prices, previously controlled by the state, and privatization. Eco-
nomic reforms induced hyperinflation that lasted for 1992 and most of 1993.
Russian GDP sharply contracted, and the GDP trend remained negative for
most of the 1990s (except for 1997 and 1999, when the economic recovery
began) (Shleifer and Treisman, 2000).
The age-earnings profiles for 1992 and 1993 are generally similar to the
profile for 1991. In 1991 the age-earnings parabola is more symmetric, and
predicted earnings of 22-year-old and 60-year old workers equal each other.
In 1992-93 the curve is skewed to the right, and the oldest workers earn less
than the youngest.
A change in the shape of the age-earnings profile happens in 1994. In this
year there is no association between age and earnings until the age of about
45 years, and after this age earnings decrease. In other words, compared
with 1993 the left side of the curve was “lifted up”. The same shape of the
profile is observed in 1995.
For 1996 spline and local polynomial regression estimations of the age-
earnings association give different results. According to spline estimation,
earnings somewhat increase until the age of 35 and then decrease. Local
polynomial regression estimates show a sharper increase of earnings between
84200
260
3800
250
Monthly earnings, rub
Monthly earnings, rub
240
3400
230
220
3000
210
2600
200
25 30 35 40 45 50 55 60 25 30 35 40 45 50 55 60
Age Age
(a) April - May 1991, n=857 (b) July - October 1992, n=3121
11000
34000
10000
30000
Monthly earnings, rub
Monthly earnings, rub
9000
26000
8000
22000
7000
6000
18000
25 30 35 40 45 50 55 60 25 30 35 40 45 50 55 60
Age Age
(c) January - April 1993, n=2639 (d) June - August 1993, n=2618
160000 180000 200000 220000 240000 260000
80000
70000
Monthly earnings, rub
Monthly earnings, rub
60000
50000
40000
25 30 35 40 45 50 55 60 25 30 35 40 45 50 55 60
Age Age
(e) October 1993 - January 1994, (f) Autumn 1994, n=1582
n=2028
300000 350000 400000 450000 500000 550000
450000 500000 550000 600000 650000 700000
Monthly earnings, rub
Monthly earnings, rub
25 30 35 40 45 50 55 60 25 30 35 40 45 50 55 60
Age Age
(g) Autumn 1995, n=1372 (h) Autumn 1996, n=1065
9
Figure 2: Age-earnings profiles, Russia (1991 - the European USSR), 1991-
96, men 22-60 y.o., the RLMS (except (a)). Solid lines represent thin plate
regression spline function estimation with 95% confidence bands (dashed
lines). Dotdash lines represent local polynomial regression estimates.900
2000
850
1800
800
Monthly earnings, rub
Monthly earnings, rub
750
1600
700
1400
650
600
1200
550
1000
500
25 30 35 40 45 50 55 60 25 30 35 40 45 50 55 60
Age Age
(a) 1998, n=1053 (b) 2000, n=1187
4000
3200
2800
3500
Monthly earnings, rub
Monthly earnings, rub
2400
3000
2000
2500
1600
25 30 35 40 45 50 55 60 25 30 35 40 45 50 55 60
Age Age
(c) 2001, n=1253 (d) 2002, n=1271
6500
5000
6000
4500
Monthly earnings, rub
Monthly earnings, rub
5500
4000
5000
4500
3500
4000
3000
25 30 35 40 45 50 55 60 25 30 35 40 45 50 55 60
Age Age
(e) 2003, n=1269 (f) 2004, n=1327
9500
7500
8500
7000
Monthly earnings, rub
Monthly earnings, rub
6500
7500
6000
5500
6500
5000
5500
4500
25 30 35 40 45 50 55 60 25 30 35 40 45 50 55 60
Age Age
(g) 2005, n=1275 (h) 2006, n=1722
10
Figure 3: Age-earnings profiles, Russia, 1998-2006, men 22-60 y.o., the
RLMS (all surveys conducted in autumn). Solid lines represent thin plate
regression spline function estimation with 95% confidence bands (dashed
lines). Dotdash lines represent local polynomial regression estimates.22 and 35 years.
In 1998 the survey was conducted soon after a major economic crisis
that occurred in Russia in August. The 1998 profile is similar to 1994 and
1995, although with a more gradual decline of earnings in later age. The
analytic sample size in 1996 and 1998 was quite small and standard errors
of the estimates are large. 6
Since 1999 Russia experienced economic recovery, and in 1999-2006 both
GDP and real earnings were increasing. In 2000 the age-earnings profile
again takes the “bell” shape, however, skewed to the left side. Compared to
the early 1990s, predicted earnings peak earlier, in the age of about 35. The
shapes of the profiles in 2001 and 2003-06 are very similar to the one observed
in 2000. In 2002 the profile, estimated with the spline function, is identical
to 1998. However, the local polynomial regression fit gives estimates that
are closer to 2000-01 and 2003-06.
6 Age-earnings profiles in longitudinal perspective
In the previous section I constructed cross-sectional age-earnings profiles for
1991-2006. Another way to look at the age-earnings association is to analyze
dynamics of earnings for birth cohorts in longitudinal perspective. Several
studies showed that longitudinal age-earnings profiles may differ from cross-
sectional profiles. In longitudinal perspective the decline of earnings in older
age is usually not observed that can be explained by inflation and general
productivity growth.
The construction of longitudinal age-earnings profiles in Russia involves
solving some problems. First, because of high inflation, especially in the
1990s, and rapid economic growth in the 2000s that included growth of
real earnings, profiles based on earnings measured in rubles in a given year
would not be very informative. They would show increase of earnings for
all birth cohorts in all periods, in the 1990s because of inflation and in the
2000s because of both inflation and real earnings growth. Adjusting earnings
for inflation would not solve the problem as the dynamics of real earnings
(period effect) would still obscure the age-earnings relationship (age effect),
within birth cohorts.
To solve this problem I standardize earnings for each year with mean
equal zero and standard deviation equal one, and then construct longitu-
dinal profiles with standardized, rather than real earnings. For each year
standaridized earnings show how far the earnings of a given birth cohort
6
One could argue that the age-earnings profiles could have been affected by wage
arrears, widespread in Russia in 1996-99 (Gerber, 2006). To test this I constructed an
age-earnings profile for 1998 (the year when wage arrears were most severe) with the data
on contracted, rather than actual earnings. The shape of the resulting profile was identical
to the profile for the same year, based on actual earnings.
110.2
Relative logged earnings, st.dev.
0.1
0.0
−0.1
−0.2
born in 1971−75
−0.3
born in 1966−70
born in 1961−65
born in 1976−80
−0.4
1994 1996 2000 2002 2004 2006
RLMS round
Figure 4: Relative earnings in five-year cohorts, Russia, 1994-2006, men
born in 1961-80 (relative earnings measured in standard deviations, zero is
average earnings of men 22-60 y.o. in a given year)
0.1
0.0
Relative logged earnings, st.dev.
−0.1
−0.2
−0.3
born in 1956−60
born in 1951−55
born in 1946−50
born in 1941−45
−0.4
1994 1996 2000 2002 2004 2006
RLMS round
Figure 5: Relative earnings in five-year cohorts, Russia, 1994-2006, men
born in 1941-60 (relative earnings measured in standard deviations, zero is
average earnings of men 22-60 y.o. in a given year)
120.2
0.2
0.1
0.1
Relative logged earnings, st.dev.
Relative logged earnings, st.dev.
0.0
0.0
−0.1
−0.1
−0.2
−0.2
−0.3
−0.3
1998 2000 2001 2002 2003 2004 2005 2006 1994 1996 2000 2002 2004 2006
RLMS round RLMS round
(a) born in 1976-80, n=2117 (b) born in 1971-75, n=2856
0.2
0.2
0.1
0.1
Relative logged earnings, st.dev.
Relative logged earnings, st.dev.
0.0
0.0
−0.1
−0.1
−0.2
−0.2
−0.3
−0.3
1994 1996 2000 2002 2004 2006 1994 1996 2000 2002 2004 2006
RLMS round RLMS round
(c) born in 1966-70, n=2383 (d) born in 1961-65, n=2837
0.2
0.2
0.1
0.1
Relative logged earnings, st.dev.
Relative logged earnings, st.dev.
0.0
0.0
−0.1
−0.1
−0.2
−0.2
−0.3
−0.3
1994 1996 2000 2002 2004 2006 1994 1996 2000 2002 2004 2006
RLMS round RLMS round
(e) born in 1956-60, n=2728 (f) born in 1951-55, n=2443
0.2
0.2
0.1
0.1
Relative logged earnings, st.dev.
Relative logged earnings, st.dev.
0.0
0.0
−0.1
−0.1
−0.2
−0.2
−0.3
−0.3
1994 1996 2000 2002 2004 2006 1994 1996 2000 2002 2004
RLMS round RLMS round
(g) born in 1946-50, n=1841 (h) born in 1941-45, n=626
Figure 6: Time-earnings profiles of 13
five-year birth cohorts, Russia, 1994-
2006, the RLMS, men 22-60 y.o., with 95% confidence bandswere from the average earnings of men 22-60 years old. Of course, standard-
ization of earnings does not solve the problem of simultaneous identification
of age, period and cohort effects, which is hardly possible to solve without
making some assumptions (Glenn, 2005). Standardized earnings of a given
cohort depend on earnings of other cohorts who are active in the labour
market in a given period. In this sense standardized earnings are relative.
However, in a way this is what we are interested in. Our main interest is
not to know how many rubles members of a cohort earn on average or what
they can buy for these rubles, but what are their relative earnings compared
to other cohorts.
Another problem is that the sample size in the RLMS does not allow
us to construct longitudinal profiles for one-year birth cohorts. Standard
errors of the estimates would be too large. To avoid this, I use five-year
birth cohorts.
Figures 4 and 5 show longitudinal time-earnings profiles, constructed
for eight five-year birth cohorts.7 The figures should be interpreted in the
following way. Each line represents one five-year cohort. For each cohort
standardized relative earnings have been nonparametrically regressed on the
RLMS round (i.e., year, but with 1997 and 1999 missing). The lines demon-
strate the dynamics of relative earnings for cohorts in 1994-2006 (apart from
the youngest cohort that reached the age of 22 in 1998, and the oldest co-
hort that became older than 60 in 2005). The same profiles with confidence
bands are shown in Figure 8.
Figure 4 shows that relative earnings of three youngest cohorts (men
born in 1966-80) were increasing in 1994-2006. The youngest cohort (born
in 1976-80) experienced the steepest increase of earnings. In 1998 22-year
old workers’ earnings were on average 0.3 standard deviations smaller than
the mean earnings. In 2006 members of this cohort were from 26 to 30
years old, and their predicted earnings were 0.2 standard deviations larger
than mean for 2006. Relative earnings of cohorts born in 1971-75 and 1966-
70 were increasing as well, although the increase was not so steep. The
earnings dynamics for these two cohorts is very similar, despite a five-year
age difference. Men born in 1971-75 were 19 to 23 years old in 1994 (my
estimates are based on the data for people who were over 22 years) and 31 to
35 years old in 2006. The cohort born in 1966-70 were, correspondingly, 24
to 28 years old in 1994 and 36 to 40 years old in 2006. For the next cohort,
born in 1961-65 and aged 29 to 33 in 1994 and 41 to 45 in 2006, predicted
earnings were somewhat decreasing over 1994-2006.
For all four older cohorts, born in 1941-60, relative earnings were de-
creasing in 1994-2006 (Figure 5). Older cohorts experienced steeper de-
crease, with the exception of the cohort born in 1941-45. This is a very
small cohort of men born during the World War II, which, first, makes it
7
In this section I only use data from Phase 2 of the RLMS (1994-2006).
14especially sensitive to cohort size effects, and second, makes the estimates
for this cohort less reliable (see Figure 6(h)).
7 Multivariate analysis of the age-earnings associ-
ation
In the previous two sections I analyzed the age-earnings association in a
bivariate setting. Two questions follow from this analysis. First, for most
years in 1991-2006 men’s earnings in Russia after some initial increase start
declining too early, compared to the UK and the US. Second, there is a short-
term change in the shape of the age-earnings profile in the mid-1990s. In the
rest of this paper I focus on the first question and test possible explanations
for the shape of the age-earnings profile in 2006.
Human capital and incentive pay theories suggest several mechanisms
for the age-earnings association. According to the human capital model,
earnings depend on human capital in the form of general education and
specific on-the-job training/experience. The incentive pay theory suggests
that earnings should follow firm-specific experience: the longer a worker is
employed by the firm, the more he or she earns.
In order to test if these explanations can account for the shape of the
age-earnings profiles in Russia, I use the data from the RLMS 2006 and
extend the model 1 adding control variables: education, class8 , industry,
sector of economy (private vs. state), size of the firm, location (Moscow and
St.Petersburg, other cities, countryside), the number of hours worked weekly
and firm-specific experience. Unfortunately, the RLMS does not have data
on on-the-job specific training. To increase the sample size, for this and
all consequent analysis I use not only the cross-sectional RLMS sample for
2006, but also cases from the longitudinal sample for the same year. The
analytic sample size is 2302 observations.
As in model 1, age is entered into the model non-parametrically, as well
as two other continuous predictors: firm-specific experience and the number
of hours worked weekly. All other predictors are categorical and they are
entered parametrically. This yields a semiparametric model:
log earni =b0 + f1 (agei ) + f2 (n.hoursi ) + f3 (f irm.expi ) (6)
+ b1 educi + b2 classi + b3 industryi + b4 statei
+ b5 f irm.sizei + b6 locationi + εi ,
where all parametric terms are entered as unordered factors. Basically,
this is a Mincer-type equation, with three terms entered nonparametrically.
8
For coding class I use the same conversion routine that was used by Gerber and Hout
(1998, 2004). It comes from the original CASMIN project. As in the analysis by Gerber
and Hout, managers were separated from professionals.
15However, contrary to the Mincer-type analysis where the main task is to
establish the rent of return to education, our main task is to see if the
introduction of control variables can change the shape of the age-earnings
profile. Older people have on average lower educational qualifications, more
often reside in the countryside and are employed in the state sector. Will the
shape of the age-earnings profile change if we account for these factors? As
the model is strictly additive (no interaction terms allowed), the age-earnings
profile is modelled to be the same at all levels of the control variables.
The coefficients and standard errors for the parametric terms in the
model are shown in Table 1, and the nonparametric estimates are plotted
in Figure 9.
Let us briefly summarize the effects of control variables on men’s earn-
ings in Russia. Education has an expected effect on earnings. People with
secondary completed and vocational qualifications earn more than people
with primary or incomplete secondary qualifications. Men with a university
degree have even higher earnings.
Managers, higher professionals and self-employed have higher earnings
than other classes. They are followed by lower profesioanls and routine non-
manual employees. Manual workers, both skilled and non-skilled, earn less,
and agricultural workers have the lowest earnings.
Workers, employed in private sector, earn more than workers employed in
state sector. Workers in state-funded industries (education, culture, health,
army, police) and especially in agriculture have lower earnings than in other
industries. In large firms (> 50 workers) employees have higher earnings
than in small firms. People living in towns and cities, and especially in
Moscow and St.Petersburg, enjoy the earnings premium compared to people
living in the countryside.
These results are consistent with the results reported earlier in other
studies (Gimpelson and Kapelyushnikov, 2007; Bian and Gerber, In press).
I plot the nonparametric estimates from model 6 in Figure 9. To produce
predictions from the model, parametrically entered variables were set at
their modal values. As the model is strictly additive, changing values of
other variables does not affect the shapes of nonparametrically estimated
functions. In other words, the number of hours, firm-specific experience and
age were modelled to be associated with earnings in the same way for all
values of parametrically entered variables.
The association between the number of hours worked weekly and earn-
ings is, as we could expect, close to linear. Earnings linearly increase with
the number of hours until the values of 35 hours, then stay at the plateau
until 45 hours, and after that keep linearly increasing. According to the
Russian Labour Code, 40 hours a week is a normal length of the working
week in Russia, and in our sample the modal value is 42 hours.
The association between firm-specific experience and earnings is usually
modelled to be linear: the more years a worker worked for a firm, the higher
16Table 1: Regression estimates from Model 6a
Variable coeff. st.error t p
Education, ref.category:
Secondary incomplete
Secondary completed 0.11 0.05 2.38 0.02
Vocational 0.09 0.04 2.04 0.04
Higher 0.27 0.05 4.98 < 0.01
Class, ref.category:
Ia-IIa. Managers
Ib. Higher professionals -0.04 0.1 -0.38 0.7
IIb. Lower professionals -0.19 0.08 -2.49 0.01
IIIab. Routine non-manual -0.21 0.08 -2.7 < 0.01
IV. Self-employed 0.04 0.11 0.32 0.75
V-VI. Skilled manual and manual
supervisors -0.32 0.07 -4.32 < 0.01
VIIa. Nonskilled manual -0.39 0.07 -5.3 < 0.01
VIIb. Agricultural -0.48 0.11 -4.43 < 0.01
Sector, ref.category:
Private
State -0.17 0.03 -5.21 < 0.01
Industry, ref.category:
Heavy and light industries
Construction and transport 0.03 0.04 0.92 0.36
Agriculture -0.68 0.07 -9.7 < 0.01
State-funded: education, culture,
health, army, police -0.19 0.05 -3.91 < 0.01
Trade and finance -0.07 0.05 -1.35 0.18
Other or no answer -0.14 0.1 -1.47 0.14
Firm size, ref. category:
≤ 50 workers
> 50 workers 0.14 0.04 3.89 < 0.01
no answer 0.02 0.04 0.54 0.59
Location, ref.category:
Countryside
Other cities 0.15 0.03 4.25 < 0.01
Moscow or St.Petersburg 0.65 0.05 13.25 < 0.01
Age nonparametric < 0.01
Number of hours worked weekly nonparametric < 0.01
Firm-specific experience nonparametric 0.01
a Dependent variable = logged monthly earnings, men 22-60 y.o., n = 2302,
R2 = 0.27, RLMS 2006
1710000
10000
9500
9000
8000
Monthly earnings, rub
Monthly earnings, rub
8500
6000
8000
4000
7500
7000
2000
10 15 20 25 30 35 40 45 50 55 60 0 2 4 6 8 10 12 14 16 18 20 22 24 26
Number of hours, worked weekly Firm−specific experience, years
(a) Number of hours worked (b) Firm-specific experience,
weekly, age=40, fexp=3 age=40, hours=40
9000
Monthly earnings, rub
8000
7000
6000
30 40 50 60
Age
(c) Age, hours=40, fexp=3. The dotted
line represents the bivariate age-earnings
profile
Figure 7: Nonparametric terms from model 6: age, the number of hours
worked weekly, and firm-specific experience. Other variables fixed at the
following values: private sector, vocational education, non-skilled manual
workers, working in construction or transport, in a firm with > 50 workers,
living in a city (but not Moscow or St.Petersburg)
18are his or her earnings. Non-parametric estimates reveal that this associa-
tion in Russia is in fact non-linear. Earnings rise linearly with firm-specific
experience for the first 10 years. However, after that any extra years of
firm-specific experience do not produce any earnings premium. Moreover,
after 10 years the estimated function shows some decline. Given that the
data come from the survey conducted in 2006, the results indicate that only
firm-specific experience received since 1996 has a positive effect on earnings.
Figure 7(c) shows the age-earnings profile, predicted from the model
6. Its shape is very similar to the profile estimated in a bivariate setting,
represented by the dotted line (Figure 3(h))9 . Controlling for education,
firm-specific experience, the number of hours worked and other variables
does not significantly alter the Russian age-earnings profile.
8 Theory of age-based occupational and job seg-
regation
There are two mechanisms that can explain the difference in earnings be-
tween groups of people. First, this is discrimination, when members of two
groups are employed in the same position, but are paid different wages.
The second mechanism is segregation, when members of two groups are un-
equally distributed across occupations or jobs. In this case people are paid
differently because they are doing different jobs, and opportunities for access
to these jobs are unequal for members of two groups.
Both discrimination and segregation are well studied in the context of
gender-based earnings inequality. However, much less attention has been
given in the literature to the age-based discrimination and segregation. In
this paper I leave aside the problem of discrimination and focus on age-based
occupational and job segregation as a possible explanation of the shape of
age-earnings profiles in Russia.
In any country changes in economy and society lead to changes in the
occupational structure. New occupations and jobs constantly appear, and
they are more likely to be taken by younger workers. On the contrary, declin-
ing occupations are mainly occupied by older workers. This is a mechanism
that induces occupational age segregation (MacLean, 2006). As MacLean
notices, the trend opposite to occupational age segregation is the rise of
“credential”, or meritocratic society, where entrance to the most advanta-
geous positions in the labour market depends on educational qualifications
rather than on ascriptive factors.
The theory of age-based occupational segregation suggest a different ex-
planation for the age-earnings association than the human capital theory.
9
Differences in the level of predicted earnings should be disregarded as in model 6 it
depends on the fixed values of the covariates.
19The human capital theory claims that the association between age and earn-
ings is in fact induced by the accumulation of human capital over the life
cycle. It implies that two workers of different age, but with the same amount
of human capital, would have equal earnings. Contrary to this, the theory
of age-based occupational segregation suggests that age affects earnings di-
rectly. In the presence of occupational age segregation, even if younger and
older workers have the same amount of human capital, they will likely oc-
cupy different positions in the labour market and, therefore, have different
earnings.
Let us imagine that younger and older workers are indeed occupation-
ally segregated and occupations and jobs where mainly young people are
employed pay more than occupations and jobs with mainly older employees.
Why do not older workers move to the more lucrative jobs? There may be
several explanations for this.
First, older workers may lack specific human capital. New jobs often
require skills that can be received only if educational qualifications were ob-
tained recently. For instance, computer programmers are likely to be young,
because older workers with engineering degrees were not taught specific skills
and technologies that are necessary for this job.
However, this is not the only possible explanation. Even if specific human
capital of workers of different ages is equal, occupational age segregation can
still be maintained. Older workers may lack incentives to move into new jobs,
even if they know that they are more lucrative than their current positions.
Let us assume that changing a job is a risky decision. It may lead to
gains in earnings, but, on the other hand, a worker can get fired and lose the
new job if he or she does not like it or eventually turns out to lack necessary
qualifications or skills. Younger workers who only enter the labour market
(or even if they have already got some work experience, it is not particularly
large), are more likely to take the risks than older workers. In case they fail
to succeed in the new job, they will lose less then older workers with more
work experience. In other words, the consequences of changing the job
unsuccessfully for older workers would be more negative than for younger
ones. Besides, younger people are likely to be more flexible in terms of
moving to another location and other arrangements if the job requires this.
Yet another mechanism that can help maintain occupational age segre-
gation is hiring practices. Many studies showed that people use their social
networks to learn about new jobs. People tend to maintain social contacts
with individuals of approximately the same age. If new occupations and
jobs were created and initially taken by younger people, then occupational
age segregation can be maintained because information about new jobs will
circulate in networks that also include younger people.
In the next section I test whether the theory of age-based occupational
and job segregation can help us explain the association between age and
earnings in Russia.
209 Age-based job segregation and age-earnings pro-
files in Russia
In all countries occupational structure changes with time. Russia, however,
is a country where changes in the economic structure happened in a very fast
way. In 1991 most of the Russian economy was owned or controlled by the
state and the labour market was organized in a very different way compared
to market economies. By 1998, less than 40% of workers in Russia were
employed in the state sector (Trud, 1999). Privatization and other market
reforms induced creation of many occupations and jobs that did not exist
in the USSR. Most of this jobs were created in private sector, where, as we
have seen before, employees earn on average more than in state sector.
New jobs that appeared in the course of the market reforms could have
been taken mainly by younger workers, while older employees were more
likely to stay in old and often economically unsuccessful enterprises. If
this is the case, then the difference in the shapes of age-earnings profiles
between Russia and Western countries should be explained by higher age-
based occupational and job segregation in Russia that is an effect of rapid
market reforms.
Unfortunately, it is not easy to test this hypothesis with the RLMS
data. In order to test how age-segregated are jobs in Russia, we need data
on several key variables. First, this is detailed information on employees’
occupations. Second, we need to know whether workers are employed in
state or private sectors of economy. People in the same occupation may
have very different job conditions and earnings, depending on the sector
where they are employed. (Compare, for instance, an electronics engineer in
a former Soviet state enterprise, which is hardly surviving in the new mar-
ket economy, and an engineer working for a successfull new private firm).
Ideally, we would like to differentiate between former state enterprises that
were privatized in the course of the market reforms, and new private firms
that were created after the collapse of the socialist economy (Clarke and
Kabalina, 2000, stress the importance of this distinction). Third, workers
can be segregated across industries, as well as across occupations and sec-
tors. As shown in the previous analysis, firms in some industries pay higher
earnings than in others.
The RLMS does contain data on occupation, sector of economy and in-
dustry. However, it is impossible to distinguish between privatized and new
private enterprises or get any information on how successful on the market
are the firms where respondents are employed. The major problem, however,
is the sample size. If we conduct the analysis at the detailed occupational
level we would have a very small number of cases in many occupations,
which makes it impossible to find out how age-segregated separate occupa-
tions are. If we add two other variables, sector of economy and industry, to
21define jobs rather than occupations, the problem of having a small sample
size deteriorates.
To conduct the best possible test with the present data, I do the fol-
lowing. As in the previous analysis, I combine occupation and employment
status to define 8 classes, according to the modified EGP class schema. Sec-
tors of economy are defined as a dummy variable (state vs. private). I
estimate the model with three predictors: age, class and sector of economy,
and allow for nonparametric interactions between age and the other two
predictors.
log earni =f1 (agei ) + b1 classi + b2 statei (7)
+ f2 (agei )classi + f3 (agei )statei ,
Effectively, the aim of this model is to estimate separate age-earnings
profiles for occupational classes employed in private and state sectors of
economy. Ideally, we would like to distinguish between more narrowly de-
fined groups, have more detailed occupational classification and control not
only for sector of economy, but for industry and location. However, as men-
tioned before, the RLMS sample size does not allow to conduct a more
detailed analysis.
Another reason for not including more covariates in the model is that
it does not aim to control for confounding factors in order to estimate a
causal effect of age on earnings (it would be impossible anyway due to un-
observed heterogeneity). Our goal is to see if age-earnings profiles remain
the same within occupational classes and sectors of economy, within making
any claims about causal effects.
Before interpreting the results of model 7, let us explore descriptive
statistics. Table 2 shows the distribution of people between classes in private
and state sectors. As in all previous analysis, the sample includes only male
workers between 22 and 60 years old. In both sectors manual classes pre-
dominate. The proportion of lower professionals and routine non-manual
workers is higher in state sector compared to private sector. Managers,
higher professionals, self-employed and agricultural workers have a smaller
number of observations compared to other classes.
Figures 8, 9 and 10 present boxplots showing the age distributions across
classes and sectors of economy. The width of the boxes is proportional to
the square root of the number of observations in the sample for each class
within sectors.
Even at the rather agrregate level of occupational classes, there is some
evidence of occupational age segregation. Employees in state sector are on
average older than in private sector (Figure 10). As could be expected,
managers (i.e., senior officials, departmental heads and general managers)
tend to be older than other employees in both sectors of economy. Higher
22Table 2: Desriptive statistics for classes in private and
state sectorsa
Private State
Class n % n %
Ia-IIa. Managers 70 4.7 28 3.4
IIa. Higher professionals 37 2.5 39 4.8
IIb. Lower professionals 143 9.7 127 15.5
IIIab. Routine non-manual 128 8.6 151 18.4
IV. Self-employed 53 3.5
V-VI. Manual supervisors
and skilled manual 501 33.8 194 23.6
VIIa. Non-skilled manual 512 34.6 255 31.1
VIIb. Agricultural 38 2.6 26 3.2
Total 1482 100 820 100
a Men, 22-60 y.o., RLMS 2006
Private sector
60
50
Age
40
30
s
l
f.
.
ed
l
l
al
ra
of
ua
ua
er
o
u
tu
pr
pr
oy
ag
an
an
an
ul
pl
er
er
an
m
m
m
ic
em
h
w
n−
r
M
d
d
ig
Ag
Lo
lle
lle
lf−
H
no
ki
i
Se
Sk
e
−s
tin
on
ou
N
R
Class
Figure 8: Age differences between classes, private sector, RLMS 2006. The
width of the boxes is proportional to the square root of the number of
observations in groups
23State sector
60
50
Age
40
30
s
l
f.
.
l
al
l
ra
of
ua
ua
er
ro
u
tu
pr
ag
an
an
an
rp
ul
er
an
m
m
m
he
ric
w
n−
M
d
d
ig
Ag
Lo
lle
lle
H
no
ki
i
Sk
e
−s
tin
on
ou
N
R
Class
Figure 9: Age differences between classes, state sector, RLMS 2006. The
width of the boxes is proportional to the square root of the number of
observations in groups
60
50
Age
40
30
Private State
Sector of the economy
Figure 10: Age differences between sectors of economy, RLMS 2006. The
width of the boxes is proportional to the square root of the number of
observations in groups. Notches represent confidence intervals for medians
24professionals in private sector are surprisingly young, with median age under
30 years. In state sector, however, they are much older. This suggests that
higher professionals employed in state and private sectors in fact belong to
different occupations. In both sectors routine non-manual workers tend to
be considerably younger than other classes (except for higher professionals in
private sector). Both skilled and non-skilled manual workers have relatively
high median age, especially in state sector.
Let us now turn to the results from model 7. As the model is semipara-
metric and includes interactions, the estimates should be analyzed visually.
Figures 10 and 11 present the age-earnings profiles predicted for particu-
lar classes and sectors of economy. Figure 10 shows profiles for workers
employed in private sector, and Figure 11 shows predictions for state sector.
The figures should be read in the following way. Solid lines represent
estimated age-earnings profiles, with 95% confidence bands (dashed lines).
The dotted line represents the bivariate age-earnings profile for all observa-
tions (the same as in Figure 3(h)). All figures are plotted on the same scale
for earnings so that predicted earnings in different classes and sectors could
be visually compared.
Even brief visual examination of the profiles shows that their shapes vary
across occupational classes and sectors.
The shape of the profile for managers differs radically from other classes.
Young managers both in state and private sectors have the highest predicted
earnings, then their earnings decline until 40 years, increase somewhat from
40 to 50 years and drop after that. Note that as there are not so many
managers in the sample, especially young ones, the standard errors for pre-
dictions are quite large.
The profiles for higher and lower professionals demonstrate another pat-
tern. In private sector, their predicted earnings increase from 22 to 30 years,
then remain stable until about 48 years and then somewhat decrease. This
is the pattern that is consistent with the shape of the age-earnings profile in
Britain and the US. Moreover, predicted earnings of professionals in state
sector hardly decrease at all in older age. In both private and state sectors
standard errors for predictions for professionals are quite large. We can eas-
ily draw a straight line within the confidence bands, so it is not possible to
reject the hypothesis that earnings of professionals do not change at all over
the life cycle, at least with the limited RLMS sample.
On the contrary, routine manual workers in both sectors have even
stronger age-earnings association than in the bivariate profile (compare the
shapes of solid and dotted lines in figures ?? and ??). This is not the case
for self-employed, although because of large standard errors for predictions
making any conclusions about the age-earnings association for self-employed
is hard.
Earnings of skilled manual workers and supervisors in private sector start
declining only in the age of about 50 years, and in state sector show little
25Monthly earnings, rub
Monthly earnings, rub Monthly earnings, rub Monthly earnings, rub
ers
4000 6000 8000 10000 12000 14000 16000
4000 6000 8000 10000 12000 14000 16000 4000 6000 8000 10000 12000 14000 16000 4000 6000 8000 10000 12000 14000 16000
30
30
30
30
40
40
40
40
Age
Age
Age
Age
50
50
50
50
(a) Managers
(e) Self-employed
(c) Lower professionals
60
60
60
60
(g) Non-skilled manual work-
26
Monthly earnings, rub
Monthly earnings, rub Monthly earnings, rub Monthly earnings, rub
ers
4000 6000 8000 10000 12000 14000 16000
4000 6000 8000 10000 12000 14000 16000 4000 6000 8000 10000 12000 14000 16000 4000 6000 8000 10000 12000 14000 16000
30
30
30
30
40
40
40
40
Age
Age
Age
Age
50
50
50
50
60
(h) Agricultural workers
(b) Higher professionals
60
60
60
(f) Skilled manual workers
(d) Routine non-manual work-
dotted line represents the age-earnings profile for all observations
Figure 11: Private sector, age-earnings profiles, men, RLMS 2006. TheMonthly earnings, rub Monthly earnings, rub Monthly earnings, rub
4000 6000 8000 10000 12000 14000 16000 4000 6000 8000 10000 12000 14000 16000 4000 6000 8000 10000 12000 14000 16000
30
30
30
40
40
40
Age
Age
Age
Monthly earnings, rub
4000 6000 8000 10000 12000 14000 16000
50
50
50
(a) Managers
30
(c) Lower professionals
60
60
60
(e) Skilled manual workers
40
Age
27
Monthly earnings, rub Monthly earnings, rub Monthly earnings, rub
ers
ers
4000 6000 8000 10000 12000 14000 16000 4000 6000 8000 10000 12000 14000 16000 4000 6000 8000 10000 12000 14000 16000
50
30
30
30
60
(g) Agricultural workers
40
40
40
Age
Age
Age
50
50
50
line represents the age-earnings profile for all observations
(b) Higher professionals
60
60
60
(f) Non-skilled manual work-
(d) Routine non-manual work-
Figure 12: State sector, age-earnings profiles, men, RLMS 2006. The dottedevidence of any decline. On the contrary, earnings of non-skilled manual
workers in private sector start declining in about 30 years and decline even
faster than in the averaged age-earnings profile. In state sector the decline
begins later (in about 37 years), but is also noticeable. Earnings of agricul-
tural workers in both sectors also decline in accordance with the averaged
pattern, although due to their small number in the sample the decline is not
statistically significant, according to the conventional criteria.
The results of the analysis show that the observed decline in men’s earn-
ings in Russia after 35 years old is largely driven by the decline in four
classes: managers, routine non-manual, non-skilled manual and agricultural
workers. As the number of observations for managers and agricultural work-
ers in the sample is small, they could hardly affect the averaged pattern in
a strong way.
In the analysis that will follow, I will try to demonstrate that this pattern
can be explained by higher occupational age segregation within these four
classes.
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Brunello, G. 2007. “The Effects of Cohort Size on European Earnings.”
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Fox, J. 2000a. Multiple and Generalized Nonparametric Regression. Thou-
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