Central European University
Department of Economics
Term paper for the course ‘Labor economics’
APPLICABILITY OF THE THEORY OF COMPENSATING
DIFFERENTIALS TO THE RUSSIAN ECONOMY
Professor John Earle. Student Willen Lipatov.
Abstract
The paper shows that the theory of compensating differentials finds an empirical
support in Russia in 1998. Differences in riskiness of work across the regions
significantly influence wages paid. At the same time, differences across five
aggregated sectors are insignificant. The value of life in Russia is estimated to be
lower than in developed countries.
Budapest, 2001
CONTENT
Introduction 3
Hypothesis 4
Data 5
Estimation 6
Industry indicators 9
Value of life 12
Conclusion 13
Bibliography 15
2
Introduction
The theory of compensating differentials has been being tested since long ago. The
first empirical studies were undertaken in the United States; United Kingdom and
other developed countries followed. For a detailed survey of studies undertaken one
can look at Viscusi[5]. Nevertheless, there are hardly any tries to do it in transition
economies, mainly because of major problems with data.
However, the question whether the theory of interest is applicable is of great interest.
In particular, non-applicability would mean that transition undermined basic
mechanism of income – risk tradeoff on the labor market, since the theory finds
strong support in developed economies. The other interesting topic is assessment of
the value of life that is possible only when the theory is valid.
Therefore, despite extremely poor data used to achieve these two goals, the
conclusions are still worth consideration. The tests showed that compensating
differentials existed in Russia in 1998, so the higher fatality risk was associated with
higher wages.
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Hypothesis
The hypothesis to be tested is that riskiness of an occupation influences the wage. In
terms of hedonic wage equation it should mean significantly positive coefficient at the
variable measuring risk.
The project is based on replication of approach presented in the study by Marin and
Psacharopoulus [3], applied to the United Kingdom economy in seventies. The reason
why other, more advanced studies [1, 2, 4] were considered, but not taken as a pattern,
is the poor data. It is hardly sufficient even for a simple model employed here, and
would be irrelevant for more complicated estimations. Nevertheless, the equation
employed in the study [3] was
lnY = f(S, EX, EX2, lnWEEKS, RISK, UNION, OCC, UNION*RISK) + error,
where Y is annual earnings,
S – number of years of schooling,
EX – years of experience in the labor force,
WEEKS – number of weeks worked in the survey year,
RISK – a measure of fatality risk,
UNION – the proportion if the workers in industry covered by the collective
agreement.
OCC – occupation desirability ranking,
In this project, however, the data for variables UNION and OCC was completely
unavailable, so they were omitted. The variables S and EX remained exactly in the
4
same specification. A new variable was added as especially relevant to the Russian
economy. As a result, we arrive at the following equation:
lnY = f(S, EX, EX2, lnWEEK, RISK, DIFFPW, SITE8) + error,
where Y is after-tax wages per hour on the primary job in the last month;
lnWEEK is a number of hours in average workweek;
DIFFPW is a difference in logarithms of Y and wages actually paid on time (Russian
specific variable);
SITE8 – code of region where the survey was conducted (nominal variable);
RISK – number of deaths caused by professional activity per 1000 workers.
Data
The main source of data for this project is Russia Longitudinal Monitoring Survey
(RLMS), round VIII (end 1998 – beginning 1999). The variables Y, SITE8 and EX
were taken directly from it, S was calculated as a sum of years spent at school,
professional courses, PTU, technical (medical, musical etc.) school, university and
graduate study. For constructing variable DIFFPW the amount monthly paid on time
was used.
The other source is Goskomstat site www.gks.ru, from which the variable RISK was
computed across regions. The Russian statistical agency gives death rates (number of
death per 1000 workers) for every region of the country (totally 89). The problem is
that the RLMS gives identifiers only for 8 ‘super-regions’. Accordingly, the death rate
for every ‘super’ were calculated as weighted averages of death rates for every region
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constituting ‘super’. Certainly, only regions that actually participated in the RLMS
were taken into account in averaging1. The final table with average deathrates looks as
following:
Region
Identifiers (SITE8)
Death rate
Moscow, SPetersburg
138-141
0,095
North, NorthWest
1-8, 89-91, 105
0,248
Central, Centralnochernozemn.
14-38, 67-69, 72, 135, 136, 142-160
0,1353
Volga, VolgoVyatskiy
39-45, 48-51, 70, 100-104, 116-128
0,1262
Northern Caucasus
9, 52-57, 77-83, 129-134, 137
0,1163
Ural
10-13, 46, 47, 106-115
0,1104
Western Siberia
58-65, 71, 84-88
0,14
Eastern Siberia, Far East
66, 73-76, 92-99
0,197
RLMS survey round VIII comprises 8700 observations. First, all observations
corresponding to not working people were eliminated. Then observations with zero
working hours on the primary job, as well as with zero wages were eliminated.
Technically it was necessary to do not to divide by zero and not to take a log of zero.
Statistically it is justified as obvious outliers were eliminated. After such an
adjustment 2538 observations remained.
Estimation
Using the data described the wage hedonic equation was estimated with the help of
statistical package EViews:
1
The map of the survey is available on http://www.cpc.unc.edu/projects/rlms/project/phase2.html
6
LNW = 1.358311107 + 0.0677868371*EDU1 + 0.02199658098*I8YRSWRK +
0.4437301143*DIFFPW - 0.3695981822*LNWEEK + 0.002576878351*SITE8 +
1.37812277*RISK - 0.0004725221389*EX2 - 0.2935844143*GENDER + error
All coefficients are significant, and for more detailed information the estimation
output presented in the appendix A can be checked.
It’s necessary to mention that significant female dummy was included, showing that
on average males were getting 1,35 women’s wage per hour. The variable SITE8 was
included for controlling the regional differences.
The coefficients of variables S, EX and EX2 have ‘classical’ signs, indicating that
there is positive return on education, and wage is increasing concave function of
experience. The marginal effect of education estimated at mean level of wages Y =
3,789 is equal to 0,258. This shows, that additional year of schooling on average
brings about 26 kopecks increase in wage per hour. Marginal effect of experience
estimated at mean value of Y is equal to 3,789(0,022 – 0,000946*EX), which gives
0,083 for persons entering labor force and 0,011 for people with 20 years experience
(median value of the sample).
The variable LNWEEK was included to control for differences in hours worked. The
coefficient obtained shows decreasing marginal reward to work, which is consistent
both with the theory and common sense. The variable DIFFPW was intended to
control for wage arrears, but the sign and value of corresponding coefficient turned
out to be meaningful. Indeed, highly significant positive coefficient indicates that
larger arrears are associated with higher wages. Namely, elasticity of wages with
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respect to accounted/paid wage ratio is equal to 0,44. It means that 1% increase in the
ratio leads to 0,44% in wages.
At last, the most interesting for us RISK variable has significantly positive (even with
1% confidence and White-wash) coefficient. It indicates that the risk premium
estimated at mean value of Y is 5,22. That is, for 0,1% increase of death-rate in a
region the wage is raised by 5,22 rubles per hour. It seems to be a very substantial
amount, if we take into consideration that returns on 13 years of schooling are twice
as low.
Such sensitivity is unlikely to result from individual risk aversion, as people do not
usually move from region to region because they care about 0,0005 difference in
probability to die. It also can hardly be attributed to the profit-maximizing behavior of
firms, as the data does not tell us anything about risks of certain occupations or
industries. The possible explanation goes back to the Soviet era, when wages were
planned, taking into account regional specificities, and riskiness among them.
Transition did not probably destroy this tradition completely, as it is rational and in
line with compensating differentials.
The influence of risk on wages was not significantly different for males and females,
which is illustrated in appendix B1, unlike influences of accounted/paid wage ratio
and experience. Both effects of experience and of arrears were larger for females, as
shown in appendix B2. It seems to be an interesting result, but the discussion of it is
beyond the scope of this paper.
8
Nevertheless, males and females subsamples were considered, the results are
represented in appendices CMale and CFemale. Basically, they remained unchanged.
The experience variable for males became insignificant that indicates the
consequences of transition (a lot of experienced professionals lost their jobs and had
to take unqualified work). Also the risk variable is significant only with 7%
confidence.
Industry indicators
Being aware of imperfections of data for the RISK variable, an attempt was made to
include another indicator of risk, this time measured across industries. The Russian
statistical agency provides data for death rates for 5 aggregated industries in 1999:
manufacturing, agriculture, forestry, transport and construction. The problem is that
RLMS doe not give any hint in which industry an individual works. However, there is
ILO classification of occupations contained in the survey. From this classification
every observation was associated to some industry with the use of the following
scheme:
Industry
ILO code
Manufacturing 121,1222,123, 1312, 2145-7, 241, 3112-7, 4131,2, 711, 72, 81, 82, 9311, 932
Agriculture
113,1221,1311, 32, 611-3,5, 62, 833, 9211,3
Forestry
1221,1311,3211,3212, 614, 9212
Transport
1226, 1316, 2144, 314, 4133, 511, 831,2,4, 933
Construction
1223, 1313, 712-4, 9312,3
Other
111,112, 114,1224-25, 27-29, 1314-15, 17-19, 211,212, 213, 2141-3,8,9, 22,
23, 242-6, 3111,8,9, 312, 313, 315, 322-324, 33, 34, 411,412, 414, 419, 42,
9
512-6, 52, 73, 74, 91, 0
Then the risk indicators taken from Goskomstat are:
Industry total
males
females
Manufacturing
0.137
0.222
0.022
Agriculture
0.199
0.296
0.027
0.21
0.245
0.059
Transport
0.151
0.208
0.03
Construction
0.291
0.377
0.036
Other
0.134
0.22
0.021
Forestry
The ‘other’ category was used because many occupations didn’t fit any of industries
(a major part of services, for example). The values of death rates for this category is
estimated intuitively to be 0.01 below average. The constructed variable was called
IND.
This time the equation estimated had a form of
LNW = 1.723544988 + 0.05707124226*EDU1 + 0.0223825037*I8YRSWRK 0.0004807891561*EX2 + 0.4408332668*DIFFPW - 0.3693461084*LNWEEK +
0.00260083928*SITE8 + 1.413290956*RISK - 2.112218096e-05*JOB 0.3330831896*GENDER - 0.7165202745*IND + error,
where variable JOB is ILO classification code of the occupation of the observed
individual. It was introduced to control for differences in occupations. It turned out to
be significantly negative, which goes well with expectations about the sign: larger
numbers in ILO classification roughly correspond to less skilled workers.
10
The coefficient of IND is insignificant, however, which can be explained by large
errors in constructing data required. For more detailed information about the
regression output appendix D can be consulted.
The estimations of other coefficients didn’t change much that proves validity of the
model as a whole. The construction of an aggregate variable for risk as geometrical
mean of RISK and IND proved to be invalid, as the corresponding coefficient was
insignificant. From this it could be concluded that the model without IND fits data
better.
Interestingly enough, however, the difference in the influence of IND on wages for
males and females is significant, so the two sub-samples were considered separately.
The results are shown in Appendices Emale and Efemale correspondingly.
For males sub-sample all the coefficients are significant, but the coefficient of
MALEIND variable (measure of industry risk for males) has a ‘wrong’ sign. It can be
again attributed to the data imperfections and is not worth paying attention to. For
females sub-sample the controlling variable for occupations is significant only at 6%
level of confidence. The coefficient of FEMIND (measure of industry risk for
females) is significant and unbelievably large. So, although there is an evidence of
different influence of industry risks on males versus females wages, the magnitude of
the influence itself is undetermined, as the data is unreliable.
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Value of life
Since the theory of compensating differentials is supported, we are able to calculate
the value of life in Russia. Just as in the article, it is equal to 1000 * Y (dlnY/dRISK),
evaluated at the means of Y. We take the coefficient from the most complete equation
(represented in Appendix F):
LNW = 1.911680346 + 0.05717096486*EDU1 + 0.01724346371*I8YRSWRK 0.0004483781282*EX2 + 0.3918445718*DIFFPW - 0.3733768822*LNWEEK +
0.002635627428*SITE8 + 1.468386116*RISK - 2.361770183e-05*JOB 0.8595151231*GENDER - 1.220156672*IND + 2.587242416*(GENDER*IND) +
0.007216082746*(GENDER*I8YRSWRK) + 0.1167141898*(GENDER*DIFFPW)
Although it considers data on industry risks that proved to be unreliable, the
coefficient at RISK is higher than in other representations. As John E. Caren claims,
the estimations of this coefficient are subject to downward simultaneity bias. So, this
biggest estimation is justified to be used in assessment of the value of life.
It should be noted, too, that the estimation of the RISK coefficient is very robust: it
does not vary significantly over the number of specifications presented here. So, the
value of life in Russia is 5562 * hours worked in the last 30 days estimated at the
mean * 12 months = 5562*166*12 = 11 080 006 rubles or $ 382 069. This is much
lower than in any developed countries, as expected.
We believe that it happens mostly because of lower per capita income. However, the
difference is possible to be enhanced by the lower risk aversion of Russian workers,
weaker safety regulations or poorer information about risks available for workers. The
magnitude of the difference is of great interest. It is not that huge, especially if
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adjusted for PPP. So, it is natural to suggest that the risk aversion of the worker in
Russia and in, say, US, does not differ substantially.
For more precise calculation of the value of life, the measure of non-fatal accident
risk should also be included in the estimation to control for its influence on the wage.
This is pointed out in Vscusi . Omitting of non-fatal risks leads to the upward bias of
the estimation of fatality risk coefficient. The number of other biases are discussed by
Viscusi, but the possible ways to eliminate them require much more refined data,
which is unavailable so far. Still, the present estimation can be considered to be a first
approximation for the value of life in Russia.
Conclusion
The study undoubtedly showed that the theory of compensating differentials is
applicable to Russian economy. The coefficient of regional risk is significantly
positive and robust in different specifications of hedonic wage equation. We can
deduct that transition did not only undermine basic trade-off between income and risk,
but even preserved a mechanism of realization of this trade-off in-built in the planned
economy.
The estimated value of life is 11 mln rubles, which is a very substantial sum for an
average Russian. Still, the study taken as a pattern for this paper estimated the value
of life in the UK at $2,8 mln, which is seven times higher. Taking into account
differences in GDP per capita, it does not make a big surprise.
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Thus, the two conclusions of the paper appear to be plausible. For future research in
this field it would be interesting to look at the same results if non-fatal risks are
included. It is also possible to compare the results for different years of transition.
Finally, it would be great to get some improvements to the data.
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Bibliography
1. Richard J. Arnolds, Len M. Nichols “Wage–Risk Premiums and Worker’s
Compensation: Refinement of Estimates of Compensating Wage Differentials”,
Journal of Political Economy, April 1983, #91(3)
2. John E. Caren “Compensating Wage Differentials and the Endogeneity of Job
Riskiness”, Review of Economics and Statistics, February 1988, #70(1)
3. Alan Marin, George Psaharopoulos “The Rewards for Risk in the Labor Market:
Evidence from the UK and Reconciliation with Other Studies”, Journal of Political
Economy, August 1982, #90(4)
4. Michael Moore, W. Kip Viscusi “The Quantity Adjusted Value of Life”,
Economic Inquiry, July 1988, #26(3)
5. W. Kip Viscusi “The Value of Risks to Life and Health”, Journal of Economic
Literature, December 1993
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