S3H Working Paper Series
Number 01: 2016
The Statistical Value of Injury Risk in Construction
and Manufacturing Sector of Pakistan
Ahmad Mujtaba Khan
Asma Hyder
January 2016
School of Social Sciences and Humanities (S3H)
National University of Sciences and Technology (NUST)
Sector H-12, Islamabad, Pakistan
S3H Working Paper Series
Faculty Editorial Committee
Dr. Zafar Mahmood (Head)
Dr. Najma Sadiq
Dr. Sehar Un Nisa Hassan
Dr. Lubaba Sadaf
Dr. Samina Naveed
Ms. Nazia Malik
S3H Working Paper Series
Number 01: 2016
The Statistical Value of Injury Risk in Construction
and Manufacturing Sector of Pakistan
Ahmad Mujtaba Khan Graduate, School of Social Sciences and Humanities, NUST
Email: [email protected]
Asma Hyder Associate Professor, School of Social Sciences and Humanities, NUST
Email: [email protected]
January 2016
School of Social Sciences and Humanities (S3H)
National University of Sciences and Technology (NUST)
Sector H-12, Islamabad, Pakistan
iii
Table of Contents
ABSTRACT………………………………………………………………………………...v
1. INTRODUCTION .............................................................................................................................. 1
2. LITERATURE REVIEW .................................................................................................................... 1
3. DATA ..................................................................................................................................................... 2
4. THEORITICAL MODEL .................................................................................................................. 5
5. EMPIRICAL MODEL ........................................................................................................................ 7
6. RESULTS AND DISCUSSION ........................................................................................................ 7
7. CONCLUSION .................................................................................................................................. 11
REFERENCES .......................................................................................................................................... 12
v
ABSTRACT
Health and safety regulations are one of the important factors of labor market for the policy maker
that needs attention. In developed nations extensive literature is available on compensating wage
differentials and statistical value of injury but unfortunately in developing nations only few such
studies exists. When it comes to Pakistan one or two studies have been carried out on small level.
Therefore, our study contributes in literature by accessing injury risk with occupation and industry
for Pakistan using Labor Force Survey 2013-14. We have targeted five blue collar main occupations
and two industries (Construction and Manufacturing). These occupations and industries have the
highest number of injuries compare to others. In this study we have found that the statistical value
of injury which we get from both occupational and industrial injuries are very small or negligible.
Hence, it does not reflect the wage premium that should be paid to the workers for doing risky jobs.
Keywords: Value of injury, Industry, labor market Condition, Public Policy, Developing Country
1
1. Introduction
Pakistan is populous country and the current population of Pakistan is 182.1 million (UNFPA,
2013). According to the Labor Force Survey 2012-13 the total labor force participation is 59.74
million in which 45.98 million are male and 13.76 million are female workers. Pakistan ranks 146 out
of 187 countries in the 2014 Human Development Index (HDI) with most indicators lower than
most countries in South Asia, and is unlikely to meet the MDGs on several indicators. In 2013
public spending on education was 2.1% of GDP that reflects on the quality, poor teaching and
learning outcomes and inadequate infrastructure. Public spending on health was 1% of GDP in
2013, making Pakistan one of the lowest spenders worldwide (World Bank, 2014).
For estimating the statistical value of injury or life, literature in labor economics report three
different kinds of approaches that have been used by the previous studies. There are three
approaches that are well known in the literature. First approach is used by Viscusi and Aldy (2003)
according to their approach the workers should be given compensation in the form of wages to do
risky jobs. The second approach is used by Blomquist (2004) which deals with observing the
behavior of workers regarding taking risks and measuring its cost. Third is willingness to pay
approach according to WTP approach workers are asked to record their willingness to pay for a
certain amount of reduction in fatal or non-fatal risk. In this study we will be using the first
approach to estimate the wage-risk premium for the workers in the labor market of Pakistan (Rafiq
and Shah, 2010).
2. Literature Review
Literature on this topic is nescant especially in developing/underdeveloped countries, mainly
because of non-avaiability of data. However, few studies are prominent in literature and Viscusi and
Aldy (2003) is one of those studies. The study estimated the value of life by using the data on fatality
risk in both occupation and industry for constructing a fatality risk variable. While using Hedonic
wage equation for finding the value of life their results show that for full sample the value of life is
$4.7 million for blue-collar males this value is $7.0 million and for blue-collar females the value of
life is $8.5 million. Kluve and Schaffner (2007) presents the impact of compensations for injury risks
on the gender pay gap. Majority of the Male workers are exposed to most dangerous and risky jobs
as compare to female workers because they always select safer jobs. Thus, the analysis in this article
is the observed gender pay gap due to segregation into more or less dangerous/safer jobs.
2
Some other studies also present the evidence of compensating wage differentials in labor markets
(Ibarraran, 2006; Atkinson and Halvorsen, 1990). In developing countries context few researchers
like Shanmugam (2000) and Madheswaran (2004) for India; Liu and Hammitt (1999) for Taiwan;
Parada-Contzen, Riquelme-Won & Vasquez-Lavin, (2013) for Chile; Polat (2013) for Turkey; Rafiq
and Shah (2010) and Hyder and Behrman (2011) for Pakistan. Most of the studies are based on
Hedonic wage theory and report different value for statistical value of life and injury because of
different model specifications and data.
3. Data
The data used in this study are based on Pakistan Labor Force Survey (LFS) for the year 2012-13
(Pakistan Bureau of Statistic). This survey provides us labor force statistics that are very important
for effective planning, economic growth and human resource development. Pakistan Bureau of
Statistics has been conducting LFS since 1963. We are using the sample of 6,421 individuals from
the LFS survey. LFS is not without limitations and some of the very important information is
missing from the survey, for example the categorization of occupation and industries restrict us only
on two digit industries. Beside, some other information like the nature of injury is not available so
we have to restrict ourselves without estimating the cost that incurred after the injury. Finally we are
only able to work on injury risk or non-fatal risk. Death data are not available, thus our estimates are
again restricted to only non-fatal risk.
The hedonic wage equation estimated in this paper take log of hourly wage as a dependent variable.
The independent variables consists of injury risk or non-fatal injury per 100 workers (both industrial
and occupational risks are used), job training, type of job (permanent or temporary), regional
dummy (urban, rural), provincial dummies, sectorial dummy (private, public), human capital
variables such as age, age square, education, two industrial and broad occupational dummies.
Percentages of data variables are presented in Table 1.
Our sample includes the workers whose age ranges between 14 to 65 years because it is the working
age of an individual. According to theory age has a positive effect on the wage of a worker. Age-
square is a proxy used for experience in the labor market. The expected sign of age-square is
negative. Some studies use the Mincer proxy (Age-schooling-6), but in case of Pakistan there is no
certain age is restricted for school going children. Also every individual do not necessarily get the
3
Table 1: Percentages of Variables
Note: Author’s estimates are based on Labor Force Survey 2012-13 (Pakistan Bureau of Statistic).
Variable (%) Variable (%)
Province
Punjab
Sindh
KPK
Baluchistan
19
47
26
08
Unsafe Acts
Operating Without Authority
Excess Speed
Failure of Safety Device
Unsafe Equipment
Disobeying Instructions
Wrong Order of Supervisor
03
20
17
51
0.2
08
Gender
Male
Female
93
07
Job Status
Permanent
Contract
No Contract
14
61
25
Training
Trained
Not Trained
26
74
Region
Rural
Urban
56
44
Industry
Manufacturing
Construction
34
66
Occupations
Service Shop and Market Sales Workers
Craft and Related Trade Workers
Plant and machine operators
Assemblers Elementary Occupations
05
37
07
51
Education
No Formal Education
Primary to Middle
Matric
Higher Education
55
32
08
05
4
employment after the schooling because of unemployment. The educational level has been divided
into five categories, i.e., no formal education; primary but below middle; middle but below matric;
matriculation and more than matric. Training is expected to have a positive sign as it positively
affects wage of the worker. The more technically trained a worker is the higher will be his/her wages
in our analysis 75% of the workers are not trained and only 25% of them had obtained proper job
training.
The province variable has also been included to see the wage differentials in all the four provinces.
The total sample consists of 19% from Punjab, Sindh with 48% workers, 26% from KPK and only
8% from Baluchistan. Beside residential variables our model specification also include job related
characteristics i.e. sector of employment and job status.
3.1. Construction of Injury Variable:
(a) Industrial Injury Rate
Injury risk variable is the most important variable of this study it shows the risk to which workers
are exposed. The wage equation becomes hedonic wage equation by including injury risk variable.
The method that is used to calculate the incident rate of injury for different industries is adopted by
US Bureau of Labor Statistics. For every industry the incident rate of injury is calculated as:
Industrial Injury Rate= N/H × 200,000
where, N is the total number of injuries within industry and H is the number of total number hours
worked by all the employees within a year and 200,000 is a combine base or scale of total number of
hours worked by 100 workers within a year this technique was also used by (Hersch, 1998). The 2-
digit level industries are given in Annexure A-1 with their coding1 that are used in our analysis.
1 The industrial coding is based on Pakistan Standard Industrial Classification (All Economic Activities) PSIC Rev. 4 2010 (Federal Bureau of Statistics, Ministry of Economic Affairs And Statistics, Government of Pakistan, 2010).
5
(b) Occupational Injury rate
Same procedure has been followed for calculating the occupational injury rate:
Occupational Injury Rate= N/H × 200,000
where, N is the total number of injuries within occupation and H is the number of total number
hours worked by all the employees within a year in the same occupation and 200,000 is a combine
base or scale of total number of hours worked by 100 workers within a year.
The blue-collar sub-occupations are presented in Annexure A-2.
4. Theoretical Model
According to the Hedonic Wage model when the cost of employing a worker increases the demand
for it decreases hence demand for labor is a decreasing function of the cost to employ a labor. These
costs consist of salaries, compensation, medical care, job training, providing safe work environment
etc. For a given level of profit as these costs increases the firms will pay less to their workers. In
labor market the workers chose that wage-risk combination where they are paid the highest wage.
In the Figure (1) suppose we have two workers one is working in high risk job than the other. Let
IC`` be the indifference curve of the worker with greater risk job and IC` be the indifference curve
of the worker with less risky job. When firms provide safe working environment to the workers it
has a cost, so with increase in the level of safety the cost of employing a worker also increases. Thus
demand for labor by the firms is a decreasing function of total cost of employing a worker. Due to
this firms must hire less workers for a given level of profit to provide safer working environment to
the workers. It is shown by the increasing curves OC` and OC`` in Figure (1) called wage risk offer
curves of the two firms. Workers choose the wage risk combinations on the market opportunity
locus denoted by w (f) to maximize their expected utility. Thus for the first worker (worker with high
risk job) the optimal point is the tangency between his indifference curve IC` and the offer curve of
the first firm OC` on the market opportunity locus. The second worker maximizes his expected
utility at a point where his indifference curve IC`` is tangent to OC`` (second firm offer curve) on
the market opportunity locus (Elia and Carrieri, 2009).
6
Figure 1: Wag Risk Trade off with Matching of Workers and Firms
The Hedonic wage function is adopted from the framework provided by (Viscusi, 2003; Elia and
Carrieri, 2009). Suppose that risk has a price in the form of wage premium so workers will be willing
to reduce the probability of injury or death by forgoing some of its wage premium. In this way firms
and workers sets wage-risk combination (w, r) in the implicit labor market. Let’s assume that workers
decision to work in a certain occupation or industry only depends upon the risk they are exposed to
and the wage rate. Consider U (w) the utility function of a healthy worker and V (w) the utility
function of a non-healthy or injured worker at wage w. We also assume that worker likes to be
healthy rather injured U (w) > V (w). In both situations marginal utility of wage is positive U`(w)>0,
V`(w)>0. Now let f be the probability of an accident (fatal, non-fatal) then the expected utility
function of a worker will be:
( ) ( ) ( ) … (1)
By differentiating the above equation (1) with respect to ‘f’ and ‘w’ we show the wage-risk tradeoff:
( ) ( )
( ) ( ) ( ) ( )
IC``
IC`
OC`
OC``
Wage Rate
Risk
w``
w`
r`` r``
w(f)
7
The equation (2) shows that as the level of risk raises the wage rate also increases, that is called
compensating wage differential. The wage risk trade-off has been equated to the differentiation of
the utilities of both kinds by its marginal utility of wages.
5. Empirical Model
To analyze the full data for calculating statistical value of injury we estimate the hedonic wage
equation by regressing log of hourly wage of a worker on the demographic variable, i.e., province
and region, human capital variables, i.e., age, education and experience, industrial dummies and
occupational dummies and injury risk variables using semi log linear model. The hedonic wage
regression equation is given below:
(
) ... (3)
Above equation three has been estimated twice; in first equation occupational injury rate has been
used and in second equation industrial injury rate is used. Where log of hourly wage (hourly wage
variable have been constructed by dividing the weekly wages by the total numbers of hours worked
in that week) of the ith worker.
The value of statistical injury is calculated through the following formula:
SVI = * *2000*1002 ... (4)
where, is the coefficient of the injury risk variable and is the mean wage of all the workers
multiplied by 20003 total numbers of hour worked annually to annualize the value and finally
multiplied by 100 which is the scale of the variable as per 100 worker for the injury risk variable.
6. Results and Discussion
Table 2 presents the estimates for two hedonic wage equations, i.e., for industry injury rate and
occupational injury rate. In the first model our injury variable is based on two-digit industrial injury
2 This formula is also used by (Hersch, 1998). 3 2000 is the per annum average number of hours worked by a worker used globally (Viscusi, 2003).
8
Table 2: Regression Results for Two Hedonic Wage Equations
Variable Model 1 Model 2
Age 0.19*** (0.004)
0.19*** (0.004)
Age Square -0.0002** (0.00005)
-0.0002** (0.00005)
Gender (Reference: Male)
Female -0.326*** (0.039)
-0.310*** (0.038)
Training (Reference: Trained)
Non Trained -0.115*** (0.022)
-0.120*** (0.022)
Education (Reference: No Formal Education)
Primary and Middle 0.062*** (0.016)
0.064*** (0.016)
Matric 0.053** (0.026)
0.052** (0.027)
Above Matric 0.059* (0.032)
0.058* (0.032)
Province (Reference: Punjab)
Sindh -0.153*** (0.017)
-0.155*** (0.017)
KPK -0.362*** (0.023)
-0.362*** (0.024)
Baluchistan 0.274*** (0.037)
0.276*** (0.037)
Region (Reference: Rural)
Urban
0.091*** (0.015)
0.080*** (0.015)
9
Note: The coefficients of the independent variables are presented with robust standard errors in the brackets. *significant at the 10% level, **significant at the 5% level, ***significant at the 1% level
rate. The second model includes the two-digit occupational injury rate beside other control variables.
The results in both equations show that wages increases with increasing age and decreases as the age
increases but it make a parabolic shape for age earning profile. The gender variable shows that male
workers get higher wages in comparison to women. Our training variable is significant but negative,
Table 2: Continued… Variable
Model 1
Model 2
Job Status (Reference: Permanent)
With Contract 0.194** (0.086)
0.196*** (0.084)
Without Contract 0.070 (0.064)
0.081 (0.064)
Industry (Reference: Manufacturing)
Construction 0.377** (0.025)
0.318*** (0.029)
Occupations (Reference: Service Shop and Markets sales workers)
Craft and Related Trade Workers -0.197 (0.132)
-0.318* (0.133)
Plant and Machine Operators and Assembler
-0.175 (0.141)
-0.291* (0.141)
Elementary Occupations -0.514*** (0.133)
-0.591*** (0.131)
Industrial Injury Rate 0.006 (0.005)
-
Occupational Injury Rate
- 0.023*** (0.006)
Constant
3.601*** (0.168)
3.631*** (0.164)
F-Statistic
127.6
135.8
Adjusted R- Square 0.220 0.222
10
which implies that workers who had training are paid higher, compare to non-trained workers
(trained workers are omitted category) or inversely non-train workers are paid 12%4 less in
comparison with trained workers. Educational categories also have positive effect of wages.
The provinces are included in the regression analysis to capture the wage differential between the
provinces all the provinces has been compared to Punjab. Both provincial and urban/rural dummies
are significant showing that residence significantly affects the wages.
To capture the effect of job characteristics our model includes ‘job status’ variables. The estimates
show that contractual jobs pay higher wage premium as compare to permanent jobs and it is
significant as well whereas the non-contractual job dummy is insignificant. The one-digit industrial
dummy is also included in the regression to examine the effect of being in particular industry, the
coefficient show that workers in construction industry receive 30% to 40% higher wages than
manufacturing industry.
Now coming toward injury variable, the estimated coefficient of injury rate variable in model (1) is
positive but not significant even on 10% significance level while the occupational injury risk variable
in model (2) is positive and statistically significant at the 1% significance level this but do not fully
validates the theory of compensating wage differential due to its low coefficient which imply that in
Pakistan workers are compensated very insufficient or little amount for the risk they take at their
work places. The coefficient of occupational injury risk is 0.023, which indicates that 1% increase in
injury risk will bring 2.5% positive change in wage of a worker. While the coefficient of industrial
injury risk is positive but insignificant but still if we consider it the coefficient is 0.006, means that
1% increase in risk of injury will increase the hourly wage of a worker by 0.07%.
Now we calculate the Statistical Value of Injury (per 100 workers) with the formula in equation (3)
SVI = * *2000*100
4 All the dummy coefficients are calculated by the following formula 100( – 1) (Halvorsen and Palmquist, 1980).
11
It is important to note here that SVI1 is based on industry injury rate, the coefficient in this case is
insignificant.
SVI1=0.006*43*2000*100
= 51,600 PKR/100 worker/ year
= 43 PKR/worker/ month
SVI2 is based on occupational injury rate, the coefficient in this case is significant.
SVI2= 0.023*43*2000*100
= 1, 97,800 PKR/100 worker/ year
= 165 PKR/worker/ month
These figure shows that in Pakistan compensating wage differentials are very low and they are not
sufficient5 one reason is due to the unemployment people are forced to do risky jobs without or low
wage premiums. Another reason may be that the blue-collar workers are usually paid flat wage rates.
7. Conclusion
This study incorporates two major industries i.e. construction and manufacturing and blue-collar
occupations to estimate the statistical value of injury for the labor market of Pakistan. The reason
behind selecting these industries and occupation is that it had the highest number of injuries over
the period of one year that means workers in these occupations and industries are exposed to greater
health risks compare to others. And workers in construction industry have higher wages than those
working in manufacturing industry. The estimates do not fully validate the theory6 because these
differentials are of negligible amount that it is not enough to cover the damage to health of the
workers. One reason behind this would be people getting unemployed in Pakistan as unemployment
rate is above 6% so people accept the risky job even if they are not fully compensated for the risk
they take. The results of the study provide a breeding ground for supplementary exploration and
research in this area.
5 Our results are consistent with (Elia and Carrieri, 2009). 6 See (Elia and Carrieri, 2009).
12
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14
Annexure A-1: 2-Digit Level Industries
Manufacturing Industry:
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
Manufacture of food products
Manufacture of beverages
Manufacture of tobacco products
Manufacture of textiles
Manufacture of wearing apparel
Manufacture of leather and related
Manufacture of wood and its products and cork manufacture of articles of straw and plaiting
materials
Manufacture of paper and paper products
Printing and reproduction of recorded
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of basic pharmaceutical products and pharmaceutical preparations
Manufacture of rubber and plastics
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products, except machinery and equipment
Manufacture of computer, electronic and optical products
Manufacture of electrical equipment
Manufacture of machinery and equipment
Manufacture of motor vehicles, trailers and semi-trailers
Manufacture of other transport equipment
Manufacture of furniture
Other manufacturing
Repair and installation of machinery
Construction Industry:
41
42
43
Construction of buildings
Civil engineering
Specialized construction activities
15
Annexure A-2: Blue Collar Occupational Classification
Service Shop and Market Sales Workers:
Personal and Protective Services Workers
Models, Salespersons and Demonstrators
Extraction and Building Trades Workers
Craft and Related Trade Workers:
Metal, Machinery and Related Trades Workers
Precision, Handicraft, Printing And Related Trades Workers
Other Craft and Related Trades Workers
Plant and Machine Operators and Assemblers:
Stationary-Plant and Related Operators
Machine Operators and Assemblers
Drivers and Mobile-Plant Operators
Elementary Occupations:
Sales and Services Elementary Occupations
Laborers in Mining
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