Valuations of Life and Health Risks: Evidence from the Indian Labor Market
S.Madheswaran* and K.R.Shanmugam**
*Gokhale Institute of Politics and Economics,
Pune 411 004, India. Email: smadhes@hotmail.com
**Madras School of Economics, Chennai.
Abstract
Development in environment can have a significant impact on it. This is true of
projects in major sectors, including power and energy, industry, transportation, sanitation
and sewage. Exposure to environment contaminants may cause risks to human life and
health. In order to regulate these risks, the government undertakes various projects that
impose costs on society in exchange for reducing the risks. To determine whether a
project is socially desirable, one needs to compare the value of reducing risks to the cost
of such reductions. Several methods have been proposed in the literature for estimating
the implicit prices for life and health. The willingness to pay approach (WTP), however,
has been increasingly considered as the relevant method. The approaches to estimating
WTP to reduce the risks of death and injury fall into four basic categories: wage
differentials between alternative occupations with different statistical risks, contingent
valuation studies, consumer market studies and foregone earnings. This paper deals with
the first category. There are considerable empirical works to analyze the workers
employment decisions in the market with potentially hazardous work. However, most of
the work is related to developed countries. Empirical study on this problem is practically
non-existent in India and perhaps in developing countries, mainly due to data constraints.
In this backdrop, this paper attempts to estimate the values of life and health risk based
on compensating wage differential framework using the primary data from Chennai,
southern part of India. The empirical part of this paper examines the worker’s behavior in
choosing their job risks and the role trade unions in influencing the wage-risk trade-off. It
also analyses the problem of sample selection and its effects on the estimated
compensation for job risks. The empirical results provide a strong support for the
efficiency of labor market in deriving the optimal risk level. They also imply that the
unionists receive a higher compensation for work related risks than the non-unionists.
Allowing for sample selectivity produces a down ward bias in the estimated union wage
differentials for deadly hazards. The value of statistical life is also calculated. The
empirical results show that the calculated value of statistical life is Rs.15.55 million and
Rs.5.49 million and the estimated value of injury is Rs.5598 and Rs.2059 for the union
and non-union sector workers respectively. Comparison of our estimated value of life
with those from developed nations indicates that as expected, our value is lower than the
values from developed nations. The estimated values life and injury can be used to value
reductions in risk of death achieved by industrial safety programs or environmental health
programs. Hence, the study may be useful to policy makers, international agencies and
researchers evaluating health projects in developing nations
Key Words: Compensating wage differentials, hedonic price, Value of life and injury
JEL Classification: J17, J28, J31
Valuations of Life and Health Risk: Evidence from the Indian Labor Market
S.Madheswaran* and K.R.Shanmugam**
*Gokhale Institute of Politics and Economics,
Pune 411 004, India. Email: smadhes@hotmail.com
**Madras School of Economics, Chennai.
1.Introduction
Environmental development can have significant impacts in areas such as power
and energy, industry, transportation, sanitation and sewage. Exposure to environment
contaminants may cause risks to human life and health. In order to regulate these risks,
the government undertakes various projects that impose costs on society in exchange for
reducing the risks. To determine whether a project is socially desirable, one needs to
compare the value of reducing risks to the cost of such reductions.
Several methods have been proposed in the literature for estimating the implicit
prices for life and health. They include cost of illness approach, human capital approach,
insurance approach, court awards and compensation approach and portfolio approach
(Linnerooth, 1979). The willingness to pay (WTP) approach, however, has been
increasingly considered as the relevant method. WTP is typically measured by analyzing
prices paid for goods and services. Prices paid for preventing health and death risks
cannot directly be obtained because prevention of these risks is not directly purchased in
the market. However, there are instances when these prices can be observed or
measured.
There are two principal methodologies for measuring these prices. The first
known as the contingent valuation approach, rests on data generated through
questionnaires ( Alberini et.al 1977, Gerking et.al 1988). In this approach, individuals are
directly asked how much they would be willing to pay to reduce, for instance, their death
risks at work or in traffic accidents. The second is the revealed preference approach. This
method infers the hedonic (that is quality adjusted) value of an environmental good
affecting the value of a market such as air quality. This method relies on property values
and wage data. The approach using wages is popular because the availability of
information on work related environmental risks and associated wages that workers
receive enable the estimation of the market generated wage-risk trade off.
Several empirical studies emerged to estimate the value of life or injury, but most
of them concern developed nations (viscusi, 1993). A study on this topic is practically
scanty in developing countries, mainly due to data constraints. Those valuing the health
impacts of projects in developing countries have two options. First, they can develop
monetary value estimates based on data from developed nations by making appropriate
adjustments (using per capita GDP). This simple transfer mechanism does not take into
account differences in WTP values between different countries due to differences in
factors like living standards, culture and educational attainment. Second, they can rely on
the human capital approach such as loss of earnings. But this approach provides no guide
to action when there are a variety of impacts on unknown value and ignores the quality of
life lengthened. New research on valuing health and death risks in developing countries is
the only way to resolve this problem (Asian Development Bank, 1996).
In the past, India had neglected the environmental consequences of economic
growth. However, in recent years, its approach on environmental problems has changed.
It implements many environmental programs and spends huge amounts on health and
safety programs. Since resources are scarce, it is essential to evaluate these programs and
reallocate funds to achieve maximum net benefit to society. In this context, we estimate
the compensating wage differentials for job related fatal and injury risk using the data
from Indian labor market.. The problem posed by sample selection is also addressed and
its impact on the estimated wage differential is investigated.
The organization of the paper is as follows. Section 2 deals with theoretical and empirical
studies existed in this area. Section 3 gives brief outlines the source of data and
econometric methodology. Empirical results are discussed in Section 4 followed by a
conclusion.
2.Review of Literature
2.1. An Economic Settings for Job Risks and Wages
The causes of accidents (or job risks) are related to technical and human factor; it
has attracted the attention of Psychologists, Sociologists and Engineers for a long period.
In recent years, economists have used their analytical tools to investigate the problem of
employment accidents or risks in a different context. They consider job hazards as a
component of job evaluation system that gives each job rating, which in turn affects the
wages, that is, job risk is taken as one of the determinants of wage differentials. They use
the theory of compensating or equalizing differentials, developed in the context of
hedonic reconstruction of demand theory to analyze the wage-risk relationship. The
theory posits that jobs with more risks require a wage premium to attract workers, other
things being equal. This extra wage or premium is called compensating wage
differentials. However, the required risk premium may be quite different for different
people. Firms attempting to attract workers to hazardous jobs will only offer an adequate
premium to staff those positions with capable employees. This premium will provide a
sufficient inducement for workers most willing to accept risks, while those requiring a
very large wage premium will tend to select safe occupational pursuits. Thus, the
heterogeneity of workers behavior and firms form the basis for labor market equilibrium
and the compensating wage differentials acts as an equalizing or a balancing factor in
keeping the workers in the risky jobs. That is, compensating differentials play a vital role
in allocating labor and resources amongst their various uses.
Suppose there is no compensating differentials, then no worker will choose a
dangerous or an unpleasant work; choosing instead to accept equal wages for
employment in relatively clean, safe and pleasant job. Further, the need to pay
compensation for risky job provides financial incentive for the firms to invest in safety
equipment and other risk reducing controls within the workplace in order to reduce the
wage costs necessary to attract workers. However, making the work place safer typically
involves substantial costs. Hence, a firm has two principal choices. It can simply pay the
workers for incurring or it can reduce its wage costs by making larger safety investments
in the work site. Typically these mechanisms are inter-related. The firm increases its
expenditures on safety until the incremental wage reductions generated by improved
safety no longer exceed the added costs of these improvements. Workers welfare enters
the firm’s calculations through the influence of the risk level on wages. The worker’s
own valuations of risk in effect determine the price the firm must pay if it does not
financially worthwhile to diminish the risk.
Thus, the economic analysis of wage-risk trade off reflected in decisions of
worker and firm suggests that these choices produce powerful incentive for safety. To
the extent that expected costs of industrial accidents to firms reflected in the prevailing
compensating wage differentials for hazardous work, the firm has an incentive to invest
in safety equipment, supervision and training to the socially optimal extent, as the firm
must bear the full social costs of accidents. In recent years, the notion of compensating
wage differentials due to work accident has been generalized in terms of probability. It
considers a situation in which workers face some lotteries on their health status. Thus the
problem of the worker is to choose job risks to maximize his expected utility. Thus, the
problem turns to be one of testing the economic rationality of workers in the markets with
uncertainty. To estimate the compensating differentials for job risks, economists during
the last fifteen years, have, thus, approached the problem from a microanalysis taking an
individual worker as unit of analysis. They have used cross-section data pertain to
individual workers’ wage, job risks and other factors which affect wages. The present
project proposal follows this economic tradition.
One interesting application of this approach is that it can be used to place a money
value on fatal and non-fatal accidents. By accepting wage premium for job hazards,
workers implicitly reveal their value of life and limb. Thus, the estimated wage premium
for job related risk can be used to determine the economic value of saving a human life
and limb.
Since the firm can compensate the workers for risks either through ex ante
compensation (i.e., compensating wage differentials) or ex post compensation (i.e.,
compensation benefits etc.), economists have recently analyzed the role of this expost
component of compensation in affecting the wage-risk trade-off through its financial
mechanism. In India, both ESI scheme and WCA (Workmen Compensation Act) play
vital role in providing compensation for industrial accidents. However, these partial
replacement benefit amounts can’t eliminate all losses in welfare resulting from work
accidents. Hence, the problem of assessing whether these benefits are optimal is
important.
The life-cycle issues such as variations across workers in the potential losses
resulting from death and the discounting problem are also important. Three different
approaches are available in the existing literature to analyze the issues. They are:
discounted life-years-lost approach, life cycle and markov model of the lifetime job
choices. Since no single modeling is dominant in terms of its theoretical and empirical
properties, three approaches are equally plausible. Hence these 3 approaches will be
utilized in the proposed study to assess whether worker’s choice with respect to job risk
is consistent with rational behavior in the market that combines the elements of both
intertemporal choice and uncertainty.
The alternative way is interview or contingent valuation method (CVM). In
this approach, the individuals are asked directly what their trade-offs or how much they
would be willing to pay for a particular risk reduction. In other words, the interviewer
asks individual quite directly what their subjective trade-offs are. The CVM receives
attention in valuing workers life. Viscusi and O’connor (1984) have interviewed the
workers in chemical industry. They ask a series of questions pertaining to risk perception
of the workers. They elicited willingness to accept for non-fatal job risks and estimated
value ranges from $10,000 to $13000. Gerking, Hann and Schulze (1988) are the first
study to examine contingent values of job related fatal accidents and willingness to pay
for avoiding those risks. They have estimated the marginal value of safety by directly
asking respondents about their willingness to substitute money for changes in job related
fatal accidents. They adopt both WTP and WTA principles. He estimated value of life is
$2.66 million in WTP measures and $6.82 million in WTA measures. This method is free
from measurement error in risk-measures and failure to control personal and job
characteristics. However, this method is not free from criticism. It is criticized that: (1)
respondents have no incentive to give thoughtful or honest answers and the process of
thinking about choices involving small probabilities is notoriously difficult; (2)
individuals may misrepresent their preferences if they believe that their responses will
affect the benefits they will receive or taxes they must pay to support a public program to
save lives. Therefore Blomquist (1982) has remarked that contingent values may be
unreliable due to hypothetical or strategic bias.
2.2. Theoretical Framework
The hedonic approach views that the labor market transactions as a tied sale. On
the one hand, workers sell their services for wages. At the same time, they purchase nonpecuniary work attributes. These attributes may vary from job to job. Hence, the workers
exercise a choice over preferred job attributes by choosing an appropriate job. On the
other hand, firms simultaneously buy the services and characteristics of workers and sell
their attributes of jobs offered to the market. Since the characteristics may differ among
workers, firms have a choice to exercise. An acceptable match occurs when the preferred
choices of both the employer and the employee are mutually consistent. The actual wage,
therefore, embodies a series of implicit prices for both workers characteristics and job
attributes such as pace of work and probability of injury/death accidents. Controlling for
other aspects of a job, one can estimate the wage premium that workers receive for job
related risks. Thus, the observed distribution of wages clear both markets over all worker
characteristics and job attributes. In this sense labor market is viewed as an implicit
market in job and worker attributes.
The purpose of this section is to illustrate the properties of the optimal job choice
of a worker who is choosing from a set of job opportunities that involve the same number
of work hours but have different probabilities of an adverse consequence. Consider a
state dependent utility model. Let Y be the initial asset and W(p) be the schedule of
earnings for the jobs with probability p of an event that leads to ones death or injury.
Therefore, (1-p) is the probability that the worker remains healthy. Suppose U(x) denotes
the utility of being healthy state and V(x) denotes the utility of being injured or dead, for
any given consumption level x which is equal to Y+W(p). It is assumed that the wage
and utility functions are continuous and twice differentiable. Let  be the shadow price of
the good constraint. It is also assumed that the worker would rather be healthy than not
(i.e. U(x) > V(x) >0); the marginal utility of consumption is positive and greater in health
state than in ill health state (i.e., Ux > Vx > 0) and the marginal utility of consumption is
diminishing or the workers are either risk averse or risk neutral (i.e., Uxx , Vxx < 0). The
workers optimal choice among hazardous job alternatives is determined by maximizing
the lagrangian given by
L = (1-p) U(x) + p V(x) +  [ x-Y-W(p)]
(1)
The job with the optimal risk p is determined by solving the following first-order
conditions for a maximum as well as the budget constraint:
Lx = 0 = (1-p) Ux + p Vx +
(2)
Lp = 0 = -U +V - Wp
(3)
 = 0 = x – Y –W(p)
(4)
Solving for Wp produces the result
Wp 
U( x )  V( x )
0
(1  p) U x  pVx
(5)
The necessary condition for an interior maximum is that the marginal increase in earnings
due to increased risk equals the differences in the two state’s utilities divided by the
expected marginal utility of consumption and the resulting value is positive. Notice that
the positive sign of Wp is a result of the nature of the job choice problem. To assure that
a solution to equation (5) is indeed a maximum, the second order condition also must be
satisfied. In mathematical terms, the marginal rate of change of Wp with respect to
further rise in p must be negative or positive, but not too large. Totally differentiating the
first order conditions (2-4) and solving for resultant equations using Cramer’s rule, the
second order condition can be shown as:
Wpp < {-(Wp )2 [(1-p) Uxx + PVxx ] –2 Wp [ Ux – Vx]} {pVx + (1-p) Ux}-1
(6)
In equation (19), the RHS is positive due to plausible restrictions stated above on the
utility functions. Thus, the compensating wage differential result as in (5) implies that
curve relating to W and p must have a positive slope if workers are to be attracted to jobs
along with it. The choice of a job will satisfy the second-order conditions for an optimum
given by (6) if the wage premium per unit of risk with the level of p is constant or
increase at not too great a rate.
Wealth Effect and the Optimal Job Risk
An objection raised against the validity of the theory is that the best jobs in the
society also tend to be the highest paid. To resolve this apparent paradox, the wealth
effect is useful. Because safety is a normal good, those with more assets will choose safer
jobs. Let us investigate the role of worker’s wealth in influencing the optimal job risk
level by totally differentiating the first order conditions, and solving for dp/dY using
Cramers’s rule as:
 [(1  p) U xx  pVxx ]Wp  [ U x  VX ]
dp
(7)

0
2
dY ( Wp )[(1  p) U xx  pVxx ]  2Wp [ U x  Vx ]  Wpp [pVx  (1  p) U x ]
Since numerator is clearly positive, the sign of dp/dY is the same as that of the
denominator. Hazardous jobs will be inferior occupational pursuit as is plausible if :
(Wp )2 [(1-p) Uxx + PVxx ] +2Wp [ Ux – Vx]}+ Wpp [pVx + (1-p) Ux]
< 0
(8)
if equation (8) is solved for W, the condition reduces to equation (6) – the second order
condition for maximum. Consequently, the extent of job hazard one chooses necessarily
decreases with one’s wealth.
2. 3. Empirical Studies
Conceptually, the wage-risk trade-off is interpreted as the amount of wage that a
worker requires for a small amount of additional risk. That is, it measures a required
compensation for an increase in risk. Hence, it is a willingness-to-accept measure.
However, Moore and Viscusi (1988) argue that for sufficiently small changes in risk, the
willingness-to accept risk equals the willingness to pay for a reduction in risk.
Aggregating such a measure across individuals can provide an estimated value of a
statistical life or injury. (The value of life literature is vast. Various approaches are
discussed in the literature. They include human capital approach, insurance approach,
court awards and compensation approach, portfolio approach and willingness to pay
approach. However, economists have increasingly accepted the willingness-to-pay
approach as the relevant one. There are two principal methodologies for measuring the
Willingness-to-pay prices: Contingent Valuation Techniques and labor market approach
(see Linnerooth, 1979 for a review of these approaches).
The estimated value represents the collective WTP for reducing each member’s
risk by a small amount. The estimated values of life and injury have important
implications for policy analysis of projects involving risks to life and health. Many public
projects have been undertaken to regulate the risks of life and health. The evaluation of
these projects is complicated by the fact that these risks are not traded explicitly in a
market with readily observed market prices. Economists have a strong belief that the
estimated values from the labor market would be one of the best indices to reflect the
value of life or health.
Thaler and Rosen (1976) carried out the first empirical study to estimate the
positive compensating differentials for fatal risks. Consequently, a large number of
studies have been done to estimate the value of life and limb of workers in man countries,
including United States, Britain, Canada, Australia and Japan (See Rosen, 1986 and
Viscusi 1993 for surveys). In most of the studies, the occupational risk variable enters the
earnings equation with a positive and significant coefficient. Rosen (1986) in his survey
article also remarks that “ Virtually all studies undertaken so far have shown that wagerisk gradient is positive. The actual extent to which a degree of risk affects earnings is,
however, a matter of debate. When the risk premiums are converted into the value of a
statistical life/injury, the magnitudes vary dramatically. Table-1 summarizes selected
studies on the implicit value of life and injury. As Dillingham (1985) argues that money
values vary dramatically among these studies due to either specification errors or an
errors in variables.
Table 1: Labor Market Studies on the value of life and injury
Author/Year
Sample
Alberini
et.al
(1997)
Brown (1980)
Primary
survey,
Taiwan, 1992
National
Longitudinal
Survey (1966-71,
1973)
Labor
Survey,
Canada, 1979
Cousineau,
Lacroix
and
Girard (1992)
Dillingham (1985)
French
and
Kendall (1992)
Garen (1988)
Gerking
et.al
(1988)
Hersch
and
Viscusi (1990)
Herzog
and
Schlottman
(1990)
Jones-lee (1976)
Jones-Lee (1989)
Kniesner
Leeth (1991)
and
Source for risk
variable
Acute respiratory
illness
Society
of
actuaries
Value of life
($ Million)
----
Value of injury ($)
0.8
--
Quebec
compensation
board
Bureau of Labor
Statistics
3.6
--
2.5 - 5.3
--
--
38159
PSID, USA, 198182
Mail survey, USA,
1984
Primary survey in
Eugene, 1987
Federal Rail road
administration
injury data
Bureau of Labor
statistics (BLS)
WTP and WTA
change in job risk
Worker’s assessed
injury rate
13.5
21021
3.4(WTP)
8.8(WTA)
--
---
US Census, 1970
BLS
9.1
30781(smokers
92245(seat
belt
users)
--
Mail Survey, UK
Survey on Motor
Vechile accidents,
UK, 1982
2-digit
manufacturing
data, Japan, 1984
Airline safety
WTP
for
risk
reduction
15.6
3.8
---
Year book of labor
statistics
7.6
77547
Industrial accident
data
3.3
8943
0.6
47281
9.7 and 10.3
--
Quality
of
employment
Survey, 1977
CPS
Survey,
USA, 1980
2-digit
manufacturing
data,
Australia,
1984 and
CPS, USA, 1978
Leigh and Folsom
(1984)
PSID, 1974 and
Quality
of
employment
Survey,
USA,
1977
National traumatic
occupation fatality
survey
BLS
39.20
Marin
and
Psacharopoulos
(1982)
Miller and Guria
(1991)
Population Census
and Surveys, UK,
1977
New
Zealand
Survey 1989-1990
Moore and Viscusi
(1988)
Olson (1981)
Thaler and Rosen
(1976)
PSID, USA, 1982
Occupational
Mortality Tables
2.8
--
Series
of
contingent
valuation questions
on traffic safety
BLS
1.2
--
2.5 and 7.3
--
CPS,1978
BLS
5.2
-Survey
of Society
of 0.8
-economic
actuaries
opportunity, USA
Viscusi (1981)
PSID, USA, 1976
BLS
6.5
46200
Viscusi
and Primary Survey in Workers assessed -13890-17761
O’conner (1984)
chemical Industry, injury and illness
USA, 1982
Simon, Cropper, Fourth
1.53-3.58
477-2870
et.al (1999)
Occupational
Wage Survey of
India
Note: Values of life and injury except Alberini e.t.al (1997) were converted in 1990 US dollars. The values
in Alberini’s study related to 1992 US dollars.
Unions play a leading role in the compensating wage differential literature.
Variations in job risk compensation in the union and non-union contexts are also
important in understanding the functioning of the wage setting mechanisms. Many
empirical studies have attempted to distinguish the premium in the union and non-union
jobs. They used either the whole sample method or a split sample method. Firstly, the
estimates of a single equation with a risk and union interaction term would provide the
differential impact of union status on the compensation for job risks. Secondly, the
estimates of separate wage equations for unionist and non-unionists would provide the
union differentials. Until 1981, all studies found that unionized workers obtained
substantially higher premiums for death risks. Examples are Thaler and Rosen (1976) and
Viscusi (1979, 1980). Olson (1981) was the first to estimate a lower risk premium for the
unionists in USA., following which Marin and Psacharopoulos (1982) found a negative
premium for death risks in UK. Since then, various other studies have emerged on one or
the other side of the issue. For example, Ruser (1985) found that the non-union male
workers in the USA received larger wage differentials for injury risks than did union
workers, and female non-union employees had a negative premium. Cousineau et.al
(1992) found a negative but insignificant premium in the non-union jobs in Canada.
Herzog and Schlottmann (1990) showed that the union differentials for deadly hazards
were lower in UK. Some studies in USA also found that the magnitude of union/nonunion risk premium varied when different data sets were used (Freeman and Medoff,
1981) and the sign changed when the measure of risk changed (Dillingham and Smith,
1984).
In theory unions may either raise or lower the risk premium. As a result, the effect
of trade union on risk premium is an empirical question. The rationale for a higher
premium is that unions are better situated to have job hazard information than the
workers themselves. Moreover, they provide a mechanism for voicing their concerns over
the risks. The opponents of this view argue that unions preferences between risk and
reward may be determined by a union voting mechanism that fails to reflect the
preference of the minority of the union members who face the highest risks. Unions may
reallocate the jobs with the highest risk to least senior members. On this view, the market
in unionized workplace fails to reveal the appropriate return to risk although the workers
on the high-risk jobs still share a rent with other unionized workers that is large enough to
keep them in the risky jobs. This perplexing state of affairs is correctly addressed by two
studies. One study (Dillingham and Smith, 1984) concluded that at this point, we do not
know what effect the unions have on the wage-risk trade-off. Another study (Dickens,
1984) remarked that one is forced to conclude that the available data may simple be
inadequate to support investigation of market performance.
3. Data and Methodology
3.1 Source of Data
In order to analyze the workers behavior in choosing their occupational risks, the
micro level workers data were collected through a sample survey in 1993, covering 522
blue collar male workers employed in manufacturing industries in Madras district of
Tamil Nadu, a state in southern part of India. The sampling procedure adopted was the
multi-stage random sampling method. First, Madras district is selected as the study area
as it is one of the major districts (and capital) of Tamil Nadu with large number of
registered factories. In the second stage, blue-collar male employees in manufacture
industries are chosen as they alone face employment death risks in Madras district over
the period from 1987 to 1990. Then, these workers are stratified in to 17 groups
according to their industrial codes at 2 digit National Industrial classification (NIC) level.
Fixing 1 per cent from each stratum, 522 workers are randomly selected for interview on
the basis of four workers from each randomly selected factory. The collected data set
consists of information on workers personal as well as enterprise characteristics.
The data source for the job risk is the Administrative Report of the Chief
Inspector of Factories, Madras. The report provides the data pertaining to the total
number of male workers and the number of death and injury accidents to them on an
annual basis at 2-digit NIC level. Since these risks may vary substantially across years,
particularly when there is a major catastrophe resulting in multiple deaths. Therefore we
computed the average probability of job related fatal risks per 1 lakh workers (RISK) and
the average probability of injury risk per 100 workers (INJURY) over the years 19871990. The computed average measures would eliminate the distorting influence of such
random fluctuations. Then, these risk measures are matched to the workers in the sample
using NIC code. Obviously, there is a measurement problem common to this type of
study because not all the workers in the same industry face the same level of risks. This
problem seems to be most troublesome for white-collar workers since they encounter
much different and much safer working conditions (Garen, 1988). Since this study covers
only blue-collar workers, this problem may not be as serious as in other studies. Besides,
we use a third measure of risk, DANGER which is a subjective variable for whether or
not the worker’s job explore him to dangerous or unhealthy conditions. Measurement of
Variables and definitions used in the analysis are reported in appendix Table-1.
3.2. Econometric Methodology
The existence of risk premium in the union and non-union context is investigated
using the model described below:
I *i  Z'i    i
I =1 if I>0
I =0 if I  0
(9)
ln Yu  R '   X'   U u
(10)
ln Yu  R '   X'   U u
(11)
In the above equations, ln Y is the natural logarithm of after tax hourly earnings.
Subscripts u and n refer to the unionists and the non-unionists respectively. II* represents
the individuals (unobserved) intensity of preference for union membership. I take the
value 1 for a unionist and is 0 otherwise. X and Z are vectors of exogenous variables
other than Risk variables (Risk and Injury). , Uu, Un are random variables which are
assumed to be serially uncorrelated and normally distributed with zero expectation,
constant variance and covariance u; and , and  are parameters to be estimated. In
equation (9), Z is vector of variables assumed to influence the desire to join a trade union
and includes age, age squared and education based on human capital arguments. In
addition, we include the backward class dummy variable to account for differences in
preferences due to the caste system, as well as, a dummy for supervisory status. We also
include the job characteristic variables- whether job has shift hours and has pleasant work
site in order to account for differences in the costs of union membership. The other
variables included are marital status, number of children in the family and firm size.
Since there are economies of scale in both organizing and maintaining a union presence
in a large firm, it is hypothesized that the firm size and collective bargaining will be
positively associated. In equation (10) and (11), X includes all the variables defined in Z,
except marital status, number of children and work force. Estimation of equation (10) and
(11) using OLS leads to inconsistent estimates of the parameters due to non-random
sorting between union and non-union. The consistent estimates can be obtained using
Heckman’s (1979) step procedure. The first stage involves estimating a union
membership equation (9) using a probit method. The estimates are then used to compute
a selectivity variable . In the second stage, the selectivity variable is included in the
appropriate wage equation to control for the potential bias in the earnings equations (10)
and (11). These wage equations are estimated using OLS. The  series for members and
non-members included in their respective wage equations is of the form:
u 
n 
( Z' u )
( Z' u )
(Z' n )
1  ( Z' n )
(12)
(13)
Where  and  are the cumulative normal distribution function and the standard normal
density function respectively, each evaluated at the probit estimate  and Z for each
worker. The coefficient of  can be interpreted as the covariance between the
disturbances in the earnings equations and the disturbances in the union member (choice)
equation.
4. Empirical Results
Probit Estimates of Union Membership:
The maximum likelihood probit estimates of the union membership equation and
the corresponding marginal effects are reported in Table 2. The maximized value of the
log-likelihood function and chi-square statistic for testing the null hypothesis that all the
slope coefficients in the model are zero. We reject the null hypothesis that the
explanatory variables have no effect. The computed Pseduo R-square can be interpreted
as r-square in OLS equation. The computer Pseduo R-square value is 60 percent. This
implies that equation fits well overall. The impact of education on union status is clear
attractive. The propensity to become a unionist is positively and significantly associated
with schooling attainment. The coefficient of age and its square term are significant and
it reveals that age has an inverted U shaped effect. The workers belonging to a backward
community and those employed in pleasant work sites are less likely to be unionist. The
employee living with spouse and those with shift work hours are marginally more likely
to join the unions. As expected, the firm size has a positive and significant on the
probability of membership. The variables indicating number of children and supervisory
status have least impact on the union membership.
Table 2: Compensating Wage Differentials: Estimates of Union Choice and Wage
Equation
Variables
Mean (S.D.)
Constant
Probit Estimates for Union Status
Wage Equation
(Total Sample)
Coefficients
-4.1944 (-2.59)
Marginal Effects
-1.6551
-0.7562(-2.935)
Education
9.98 (2.46)
0.1206 (4.51)
0.0476
0.0341(6.808)
Age
34.14 (6.69)
0.1143(1.23)
0.0451
0.0779(5.567)
Age Square
--
-0.0012(-1.02)
-0.0005
-0.0007(-3.708)
BC
0.64(0.48)
-0.2154(-1.69)
-0.0850
0.0548(2.233)
Supervisor
0.27(0.44)
0.0288(0.87)
0.0114
0.1352(4.553)
Shift
0.41(0.49)
0.6358(4.97)
0.2509
0.0286(1.135)
Pleasant
0.52(0.49)
-0.4285(-3.48)
-0.1691
-0.0153(-0.651)
Married
0.81(0.39)
0.5240(2.47)
0.2068
--
Number of
1.37(1.16)
0.1292(1.82)
0.0509
--
Children
Work Size
90.96(27.66)
Risk
10.44(9.26)
0.0016(0.933)
Injury
7.29(16.54)
0.0031(2.033)
Risk X Union
--
0.0149(6.399)
Risk X Injury
--
0.0005(0.293)
No. of Obs.
Log-likelihood
Pseduo R2
%corr. Predicted
R-Ssquare
F-Value
Value of life:
Union (Rs. In
Millions
Value of life Nonunion (Rs. In
Millions)
Value of Injury:
Union (in Rs.)
Value of Injury
Non-Union (in Rs.
522
0.0026(3.24)
522
-290.02
0.602
55.63%
0.0010
-----
--
522
---0.603
70.68
17.49
1.70
3816
3286
Note : Figures in parentheses indicates t-values
Estimates of Earnings Function:
In order to compare, we present the estimated coefficients of the earnings
equation for the whole sample in column 5 of Table-4. In this specification, we allow
union status to interact with risk variables. The results show that the age earnings profile
exhibits an inverted U shape as expected in the human capital theory with a maximum at
age 56 years (by letting W/age=0). The return to education is about 3.4 percent.
Backward community workers and supervisors tend to earn more wages while the nonmonetary job attributes show little effect on wages. Before considering the effects of risk
and unions. We turn to separate estimates for members and non-members of the union.
The results are reported in Table 3. There are significant differences between two groups,
which is evident from the significant F-value (chow test). First, let us consider the OLS
estimation results without the selectivity term. The return to education is 1.5 percent in
the union jobs and 4.8 percent in the non-union jobs. The unionists have a more hamped
age earnings profile with a peak at 64 years, while the non-unionist show a clear nonlinear relationship between age and earnings with a maximum at 47 years. Supervisory
status and community dummy variables influence only the non-union earnings
significantly. Both union and non-union wages are insensitive to job attributes. In
selectivity corrected estimates, the effects of human capital variables are strongly evident
only in non-union context. We now turn to the effects of risk and union variables that are
of direct interest. In the OLS regression for the Whole sample (Table), the estimated
coefficients on the union and fatal risk interaction term is positive and statistically
significant and larger than the coefficient of RISK itself, suggesting that hazard
compensation occur mostly in the union context. The union interaction with INJURY is
not significant, whereas INJURY is statistically significant at 5 percent level. It implies
that unions are less worried about minor injuries. The estimated union wage effect is not
similar across risk levels. It is 15.5 percent (=0.0149 x 10.44 x100) for fatal risk and 0.3
percent (=0.0005 x 7.29 x100) for non-fatal risk. These results may be biased since we do
not take in to account the sample selection effect of the union membership. In the split
sample specification (Table 3), the risk coefficients are positive and statistically
significant at 5 percent level in both OLS and selectivity corrected estimates except
INJURY in non-union jobs. These results imply the existence of positive wage
compensation for employment related accidental risks in the Indian labor market.
The coefficient of RISK indicates the effect of a unit increase in RISK, for a rise
in the annual death risk by 1/100000 (0.00001). Its effect on the value of the logarithm of
wage equals 0.0124. Evaluating at the mean level of wage, this would give an estimated
trade-off 0.0771 between hourly wage and fatality rate in the union jobs. Multiplying by
2000 hours to annualize the figure and by one lakh to reflect the scale of risk variable
yield a trade-off Rs.15.55 million and Rs.5.49 million per statistical life in the union
and non-union sector respectively. Using the same terminology one can estimate the
implicit values for all specifications. The estimated values of life and injury in the union
and non-union jobs are shown at the bottom of Tables 2 and 3. There are variations in the
values between union and non-union jobs and between OLS and selectivity corrected
estimates. These results indicates that the significance of the effect of union status and the
self-selection problem. Hence, the estimated values of life and limb without considering
the selection problem may provide misleading results for making relevant policy.
Table 3: Estimates of Earnings function
Dependent variable: ln(after tax hourly wage rate)
Variables
Union
Non-Union
Mean (SD) Uncorrected Corrected
Mean(SD) Uncorrected Corrected
Constant
--0.2764
1.5320
--0.9817
-1.1944
(-0.69)
(2.22)
(-2.86)
(-2.70)
Education
10.35
0.0158
0.0065
9.57
0.0476
0.0521
(2.47)
(2.56)
(1.67)
(2.39)
(6.08)
(5.27)
Age
35.52
0.0641
0.0031
32.62
0.0815
0.0931
(6.20)
(2.98)
(1.99)
(6.89)
(4.34)
(3.86)
2
Age
1299.81
-0.0004
-0.0002
1111.38
-0.0008
-0.0009
(462.36)
(1.80)
(1.73)
(490.24)
(-3.18)
(-3.09)
BC
0.63
0.0466
0.0798
0.67
0.0802
0.0712
(0.48)
(1.59)
(2.23)
(0.47)
(2.04)
(1.74)
Supervisor
0.31
0.0602
0.0811
0.22
0.2486
0.2497
(0.46)
(1.73)
(1.97)
(0.42)
(5.20)
(5.27)
Shift
0.52
0.0057
0.1117
0.29
0.0232
0.0544
(0.50)
(0.19)
(2.24)
(0.45)
(0.57)
(0.94)
Pleasant
0.43
0.0044
0.0736
0.62
-0.0181
-0.0376
(0.50)
(0.15)
(1.82)
(0.49)
(-0.47)
(-0.82)
Risk
10.19
0.0124
0.0125
10.72
0.0065
0.0064
Injury

(9.71)
10.04
(9.47)
--
(6.18)
0.0047
(4.68)
--
(6.50)
0.0045
(4.38)
-0.3122
(-3.39)
0.639
46.64
274
15.55
R2
-0.615
F
-46.90
N
274
274
Value of
-15.42
life (Rs.in
Million)
Value of
-5847
5598
Injury (in
Rs.)
Note: Figures in parentheses indicates t-values
(8.74)
4.26
(11.90)
--
(2.84)
0.0022
(1.38)
--
--248
--
0.480
24.46
248
5.58
(2.84)
0.0024
(1.50)
0.0814
(0.74)
0.481
22.00
248
5.49
--
1888
2059
Job Risk Equation Estimates
A separate account is made in this section to test whether the optimal job risk
would necessarily decrease with the workers wealth. The OLS estimates of the job risk
equations are reported in Table 4. The explanatory variables included are: (a) the
variables which affect earnings such as education, age, union status and occupational
dummies; (b) the variables denoting the non-labor income and the value of assets
including the house owned; (c) proxies for the degree of risk aversion. Since the
measures of the stability of worker's life style are inversely correlated with the degree of
risk aversion, the following proxy measures of stability are included: the number of
dependents (DC), the marital status and dummy capturing the employment status of the
spouse; and (d) the industrial dummies to capture the differences in production process
which presumably influence the safety levels of the firms. The human capital variables
are expected to have a negative relation with job risk variables. On the contrary to the
expectation, they have the opposite sign, but not statistically significant at 5 percent level.
BC is included to test the hypothesis that the workers belonging to backward class are
discriminated in terms of the riskiness of their jobs. Such a discrimination hypothesis is
not supported by the result. As expected, UNION influences risk negatively. But its
impact on INJURY is positive and significant. Though the results are puzzling, they
imply that the unions play a significant role in reducing the fatal risks and they are least
worried about injuries. As expected, DC, SPOUSE and MARRIED are negatively
associated with risks. However, these results are not supported by t-values. Occupation
dummies have positive impact on risk variables, while the industrial dummies show
negative impact. Notably most of them are statistically significant at 5 percent level.
INCOME and ASSET influence the job risks negatively as expected, but they are not
statistically significant. The elasticity’s of fatal and injury risks with respect to non-labor
income are estimated as 0.02 and 0.06 respectively. The respective elasticity’s with
respect to ASSET are 0.02 and 0.004. For comparative purpose, the maximum likelihood
estimates of the logit parameters pertaining to the DANGER perception variable is
depicted in Column 3 of Table-4. The personal (SCHOOL, AGE) and social
characteristics variables (DC, MARRIED, SPOUSE) are having least impact on danger
perception. UNION is positively related with DANGER and statistically significant at 1
percent level, indicating that the union workers are capable of identifying the hazards
they face since they are having the provision of job hazard information through collective
bargaining. The negative impact of ASSISTANT indicates that the assistants are not
capable of identifying the risks or they are safer. The final matter of empirical interest is
the influence of ASSET and INCOME. As expected, both are negative and INCOME is
statistically significant at 1 percent level, confirming the result that the optimal job risk
would necessarily decrease with workers’ wealth.
Table 4: Ordinary Least Squares and Logit Estimates of Job Risk Equations
Variables
OLS Estimates
Logit Estimates
Dep Var: Risk
Dep.Var: Injury
(Dep.Var: Danger)
Constant
5.0902 (0.56)
-21.8500(-1.117)
0.6299(0.16)
Education
0.2245(1.49)
0.6220(1.90)
-0.0849(-1.11)
Age
0.2297(0.45)
1.0313(0.93)
0.1054(0.50)
2
Age
-0.0011(-0.16)
-0.0132(-0.91)
-0.0014(-0.52)
BC
-0.2003(-0.29)
-0.2414(-0.16
0.5532(1.59)
Union
-0.4917(-0.69)
5.9345(3.83)
1.5656(3.89)
DC
-0.2378(-0.65)
-0.3801(-0.47)
-0.2013(-1.05)
Married
-1.2162(-1.05)
-1.1083(-0.44)
-0.0162(-0.02)
Spouse
0.2705(0.23)
4.2378(1.69)
0.5277(0.95)
Work size
0.0019(1.49)
0.0040(1.43)
0.0021(1.16)
Private
-0.8651(-0.86)
1.2776(0.58)
0.5803(1.06)
Supervisor
2.6334(2.60)
3.2462(1.47)
0.8498(1.44)
Machinist
3.5820(4.04)
4.2056(2.17)
0.1198(0.26)
Turner
5.6138(1.79)
-8.9770(-1.32)
0.1037(0.30)
Assistant
3.6847(2.88)
2.7649(0.99)
-1.2408(-2.38)
Income
-0.0010(-1.07)
-0.0023(-1.11)
-0.0011(-3.03)
Asset
-0.00001(-0.28)
-0.0000(-0.59)
-0.14E-5(0.69)
Ind-1
-14.4111(-9.24)
-9.9514(-2.92)
0.1004(0.06)
Ind-2
-13.0975(-13.13)
-6.3253(-2.91)
-1.5582(-3.50)
Ind-3
-8.6012(-7.89)
-8.3512(-3.51)
-1.2418(-2.41)
2
R
0.3997
0.1990
-F
17.5920
14.100
-Log-likelihood
---128.332
Chi-square
--97.82
Pseduo R2
--0.1304
N
522
522
522
Note: Figures in parentheses indicates t-values
5.Conclusion
In this paper attempt has been made to test the predictions of the competitive
wage theory and assess the role of trade unions in determining the wage premiums for job
related death and injury risks for Indian workers. Since union status is not exogenous, we
have applied the standard Heckman’s two step procedure to correct the self-selection
bias. The results show no evidence of selectivity bias in the non-union earnings equation.
But we have observed a strong evidence of selectivity bias in the union context. The
results imply that on an average, the union male worker employed in a manufacturing
factory receives approximately Rs.155.50 in annual wages for an increase in the risk of
deadly hazards at work by a probability of 0.00001 and Rs.55.98 for a rise in the risk of
injury by 0.01. The respective values for the non-union workers are Rs.54.90 and
Rs.20.59. Since union and non-union compensation vary widely, the imperfect
information is probably the most often cited justification for government interference in
the matter of job safety. It may take the role of providing necessary risk details and
adequate compensation for employment risks. The empirical results provide the estimated
value of statistical life is Rs.15.55 millions and 5.49 millions and the estimated value of
injury is Rs.5598 and Rs.2059 for the union and non-union sector workers respectively.
The value of life here represents the rate for very small risks, not the amount that worker
will pay for certain life extension. Besides, life is priceless and no money can compensate
a person for his life. A comparison of our estimates with those from developed nations
(given in Table-1) indicates that our value is lower than the values from developed
nations. In view of this, our estimates seem reasonable. These results also have important
implications for policy analyses of projects involving risks to life and health. This value
can be used to value reductions in risk of death achieved by industrial safety programs or
environmental health programs. It should be, however, noticed that this study is not free
from limitations. Estimates of this study may be biased due to the fact that it fails to
include the impact of insurance benefit variables and life cycle issues such as age related
differences in value of life and discounting problem.
Appendix Table1. Measurement of Variables and its Definition
Variables
Definition
Risk
Job related fatal risks per 1 lakh workers
Injury
Danger
Education
Age
BC
Married
Spouse
DC
Union
Job related non-fatal risks per 100 workers
Job hazard perception=1 if job exposes the worker to
dangers; 0 otherwise
Education (in completed years)
Worker’s age in years
Worker’s community=1 if he belongs to backward
community; 0 otherwise
Marital Status=1 if married; 0 otherwise
Employment of spouse=1 if the spouse is employed; 0
otherwise
The number of dependent children, aged 0-16
Union status=1 if worker is a member of union; 0
otherwise
Work Size
Supervisor
Machinist
Assistant
Turner
Security
Pleasant
Decision
Irregular
Private
Income
Asset
Ind-1
Ind-2
Ind-3
Wage
Total work force of the firm where he works
If worker is a supervisor=1; 0 other wise
If worker is a machinist=1; 0 otherwise
If worker is an assistant=1; 0 otherwise
If worker is turner=1; 0 otherwise
If workers job provides security=1; 0 otherwise
Condition of work site: if workers job has pleasant=1; 0
other wise
Workers decision on the job: if worker is the decision
maker=1; 0 otherwise
Irregular work hours: if the worker has shift hour
works=1; 0 otherwise
If the worker’s employment is in private sector=1; 0
otherwise
Non labor income of the respondent
The value of the property owned by the respondent
including the house
If the industry is manufacture of rubber, plastic,
petroleum and coal products=1; 0 otherwise
If the industry is manufacture of machinery, machine
tools and parts=1; 0 other wise
If the industry is manufacture of transport equipment and
parts=1; 0 otherwise
Natural logarithm of after tax hourly wage rate
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