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Estimated hedonic wage function and value of life in a developing country
Economics Letters 57 (1997) 353–358
Estimated hedonic wage function and value of life in a developing country 1, Jin-Tan Liu *, James K. Hammitt, Jin-Long Liu
Department of Economics, National Taiwan University, 21 Hsu-Chow Road, Taipei (100), Taiwan Received 7 January 1997; received in revised form 3 July 1997; accepted 22 August 1997
Abstract This paper reports the first study of compensating wage differentials for work-related fatalities in a developing country. Using data from the 1982–1986 Taiwan labor surveys, statistically significant compensating wage differentials are found. The implied value of life is US$413 000 (corrected for selectivity bias) and US$461 000 (uncorrected) in 1990 dollars. 1997 Elsevier Science S.A. Keywords: Compensating wage differentials; Value of life JEL classification: J17; J28; J31
1. Introduction The theory of compensating wage differentials was developed more than 200 years ago by Adam Smith. The theory suggests that jobs with greater risks in terms of the probability of fatal or nonfatal accidents should, other things being equal, receive higher wage compensation than less dangerous jobs. Compensating wage differentials rest on the theory of hedonic (quality-adjusted) prices. Rosen (1974) first provided a general discussion of this theory, and Thaler and Rosen (1976) empirically tested Rosen’s framework. Over the past two decades, there have been extensive discussions with a variety of data sets used in the empirical literature 2 . Previous studies, which all use data from developed countries, estimate a hedonic wage function using traditional human capital variables and risk of death and / or injury on the job. Generally, the empirical literature shows that occupational or industry fatality rates enter the wage function with a positive and statistically significant coefficient. However, the value of life estimates vary dramatically across studies, perhaps because of differences *Corresponding author. Tel.: 886-2-3519641, Ext. 520; fax: 886-2-3215704; e-mail:[email protected] 1 Professor, Department of Economics, National Taiwan University and Research Fellow, Institute of Economics, Academia Sinica; Associate Professor, Department of Health Policy and Management and Center for Risk Analysis, Harvard School of Public Health; and Associate Professor, Institute of Industrial Economics, National Central University. 2 See Smith (1974); Dillingham (1985); Fisher et al. (1989), and Viscusi (1993) for reviews of the research in this area and its policy implications. 0165-1765 / 97 / $17.00 1997 Elsevier Science S.A. All rights reserved. PII S0165-1765( 97 )00238-3
J. Liu et al. / Economics Letters 57 (1997) 353 – 358
354
in the definitions of job risk (industry risk or occupational risk) and in risk preferences among samples of workers. Viscusi (1993) reviewed 23 studies and found that the range of value per statistical life estimates was US$0.6 million to US$16.2 million in the United States, United Kingdom, Canada, Australia, and Japan (in 1990 dollars). The purpose of this paper is to test the theory of compensating wage differentials using labor market data from Taiwan from 1982 to 1986. To our knowledge, this is the first study which investigates the wage–risk relationship in a developing country. This paper also provides estimates of the value of life for each of the five years and compares them with estimates for developed countries. The results can be applied to evaluate the benefits of health and safety regulations in developing countries.
2. The hedonic wage model and data sources
2.1. The hedonic wage model The basic framework for hedonic wage analysis requires data on workers’ wages, job risks and other characteristics. The hedonic wage model treats jobs as bundles of risk, working conditions, and other attributes that can occur in various combinations and quantities. The wage that the worker is willing to accept reflects the utility expected from the job characteristics. A worker’s indifference curve represents his tradeoffs between the wage rate and the probability of workplace fatality. Since workplace safety influences firm productivity and costs, the isoprofit curve measures the tradeoffs between wages and job risk for an employer. The hedonic wage function is the envelope of mutual tangencies between worker indifference curves and firm isoprofit curves. Since the selection of a job usually involves selecting a residential location, site characteristics may be important in the hedonic wage model. Thus, the hedonic wage function based on data from national surveys can be specified as follows: 3 ln Wi 5 g(XIi , XJi , XSi ) 1 u i ,
(1)
where ln Wi 5the natural logarithm of the ith individual worker’s wage rate, XIi 5ith individual worker’s characteristics, XJi 5ith individual worker’s job characteristics, XSi 5characteristics of ith individual worker’s residential location, and u i 5random error term. An econometric problem associated with the hedonic wage equation is that the probability of becoming a working person is likely to be correlated with the error term in the wage equation. If so, the conditional expectation of the error term will not be equal to zero. To correct for possible sample selection bias, we employ the hazard technique suggested by Heckman (1979). A probit equation for labor force participation is estimated, then the inverse of the Mills ratio from the probit equation is
3
For a detailed discussion of the importance of site characteristics in the hedonic wage model, see Smith (1983).
J. Liu et al. / Economics Letters 57 (1997) 353 – 358
355
included as an independent variable in the hedonic wage regression. We compare these results with OLS estimates that do not correct for selectivity bias.
2.2. Data sources The data in this study are taken from several sources. Individual characteristics come from the Taiwan Labor Force Survey (TLFS) data tape for each year from 1982 to 1986. The TLFS is a cross-sectional micro data set that covers labor market participants and nonparticipants in Taiwan. Note that the TLFS is not a panel survey; each year has different respondents in the sample. However, the survey is quite useful because each record contains personal characteristics like age, education, job tenure, monthly income, working hours, industry and occupation code, and place of residence. This large micro data set enables us to isolate the role of job risks from traditional human capital variables such as education and experience. Site characteristics are matched using data for 23 locations (7 cities and 16 counties) in Taiwan and are taken from Social Indicators of the Republic of China and Statistical Yearbook of Taiwan Province. Following Smith (1983), we include measures of air pollution, climate, medical facilities, crime rate, and site access to cultural and educational activities. Unpublished fatality data at the three-digit industry level are obtained from the Taiwan Labor Insurance Agency for the corresponding years. The job–risk variable is defined as the total number of compensated work-related deaths divided by the number of employees per industry each year. Obviously, there is measurement error because not all workers face the average risk in each industry; an industry risk variable has been shown to yield higher estimates of the value of life than the occupation risk variable used in earlier studies (Viscusi, 1993). We restrict the sample to the nonagricultural sector and delete dual jobholders, employers, and self-employed workers. Summary statistics for selected variables are presented in Table 1. The average nominal wage, years of education, and proportions of workers who are married and are female increased over the period. The average annual fatality risk, which fell from 3.82 / 10 000 to 2.25 / 10 000, is substantially greater than the average U.S. fatality risk of about 1 / 10 000.4
3. Empirical results The OLS regression and the second step results for the Heckman (1979) procedure to correct for selectivity bias are reported in Table 2. Because of space limitations, we do not report the results of the first stage probit estimation. Both model specifications perform well in all years. The R 2 ranges from 0.46 to 0.53. The signs and magnitudes of the coefficients are as expected. Wages rise at an increasing rate with education, and rise at a declining rate with work experience. Marriage increases earnings, perhaps because motivation and stability increase productivity. The sex dummy variable suggests that males are paid significantly more than females. The coefficients of the occupational 4
The apparent decline in job risk in our sample may be due in part to inter-annual variation in the proportion of sampled workers in each industry.
J. Liu et al. / Economics Letters 57 (1997) 353 – 358
356
Table 1 Means and standard deviation of selected variables Variables
SEX MARRIED EDU EXPER RISK WAGE N
Year 1982
1983
1984
1985
1986
0.638 0.521 9.271 (3.872) 63.943 (78.485) 3.825 (4.123) 55.982 (30.997) 17 250
0.628 0.542 9.512 (3.922) 65.715 (80.962) 2.990 (3.262) 60.369 (36.182) 17 402
0.611 0.547 9.653 (3.908) 64.028 (79.732) 2.881 (7.215) 61.862 (38.370) 18 790
0.610 0.559 9.681 (3.882) 64.040 (78.943) 2.555 (3.503) 64.833 (39.265) 18 635
0.602 0.563 9.706 (3.859) 64.818 (79.697) 2.252 (2.602) 68.773 (41.213) 18 987
Notes: Definitions of variables are: SEX (dummy variable, male51, female50), MARRIED (dummy variable, married51, otherwise50), EDU (years of education), EXPER (experience in current job in months), RISK (annual industry fatalities per 10 000 workers), WAGE (average hourly earnings in New Taiwan dollars). Standard deviations are shown in parentheses.
dummy variables are stable and indicate that managers receive the highest wage and service workers the lowest wage. The coefficients of particular interest in this study, those for fatal job risk, are positive and statistically significant. The OLS estimates are slightly greater than the Heckman-corrected estimates. Table 2 Risk variable estimations in hedonic wage equations Year 1982
1983
1984
1985
1986
0.0121 (16.331) 582 000
0.0096 (10.367) 493 000
0.0029 (7.251) 151 000
0.0074 (9.370) 398 000
0.0123 (11.245) 683 000
Heckman Two-Stage Estimation: RISK 0.0112 0.0086 (15.006) (9.548) Value of Life 539 000 442 000 (1990 US$)
0.0026 (6.684) 135 000
0.0067 (8.715) 360 000
0.0106 (9.989) 589 000
OLS estimation: RISK Value of Life (1990 US$)
Notes: Values of t-ratio are in parentheses. The wage function includes human capital variables, individual characteristics, occupational dummy variables, and site attribute variables. The human capital variables are EDU, EDU squared, EXPER, EXPER squared. Occupational dummy variables are professional, manager, clerk, sales, service, and operator. The transportation occupational dummy variable is omitted. Site attribute variables are number of secondary school students per class in town of residence, population density, unemployment rate in town, criminal offenses in town per 10 000 population, town population per medical doctor, cinema and theatre seats per 1000 population, average temperature, average precipitation, and average suspended particulates in town.
J. Liu et al. / Economics Letters 57 (1997) 353 – 358
357
The risk coefficients can be interpreted as the average willingness to pay for a marginal decrease in mortality risk. These results provide strong evidence of a compensating wage differential for jobs with greater fatality risk in the Taiwanese labor market. The implied values of a statistical life are calculated for each year at the sample mean wage. The figures are obtained by multiplying (1) the risk coefficient, (2) 2000 working hours per year, (3) 10 000 (the denominator of the risk measure) and (4) the average wage rate.5 The estimates, reported in Table 2, range from US$151 000 to US$683 000 for the OLS estimates, and slightly smaller, from US$135 000 to US$589 000, for the Heckman-corrected estimates. The average values for the five years are US$461 000 (OLS) and US$413 000 (Heckman correction). These figures are lower than those found for industrialized countries. Since labor market structures, institutional factors and magnitudes of industrial risk vary across countries, variations in estimates are to be expected. To compare these estimates with estimates for developed countries, we estimated the following regression (OLS) to explain the 17 value of life estimates for which average income and fatality risk are reported in Table 2 of Viscusi (1993) Ln(VOL) 5 10.426 1 0.530 Ln(INCOME) 2 0.2714 (RISK) (1.234)
(0.624)
(2)
(22.890)
R 2 5 0.374 where t-statistics are in parentheses, VOL is estimated value of life, INCOME is annual income, and RISK is annual fatality risk per 10 000 workers. Substituting the five year sample mean income and fatality risk in this equation yields a predicted value of life for Taiwanese workers of $1.4 million, about 3.5 times the estimate of $413 000. (An alternative linear specification yields a predicted value of $3.5 million, 8.5 times our estimate.) The comparatively small value of life estimated from the Taiwan data suggests that there may be structural differences in the value of risk reduction between developing and developed countries, perhaps because information about occupational risks may be less accessible to workers in developing countries. Alternatively, the income elasticity between developing and developed countries may be larger than within developed countries (the income elasticity estimated in Eq. (2) is smaller than expected if safety is a luxury good, however). Overall, the comparison suggests that a simple benefit-transfer equation from the existing literature is inadequate for estimating the value of life in developing countries (Krupnick et al., 1993).
4. Conclusion This paper provides estimates of the marginal value of job hazards for the Taiwanese labor market from 1982 to 1986. We find evidence of compensating wage differentials for industrial risk in Taiwan. The implied value of statistical life averages US$413 000 (corrected for selectivity bias) and 5
The values are adjusted to 1990 using the Taiwan GDP deflator and converted to U.S. dollars at the 1990 exchange rate of 27.1075 New Taiwan dollars per U.S. dollar.
358
J. Liu et al. / Economics Letters 57 (1997) 353 – 358
US$461 000 (uncorrected) in 1990 dollars, smaller than the value estimated from developed country data. The estimate may be useful for evaluating the benefits of regulations that reduce the risk of death in developing countries.
References Dillingham, A.E., 1985. The influence of risk variable definition on value-of life estimates. Economic Inquiry 24, 277–294. Fisher, A., Chestnut, L.G., Violette, D.M., 1989. The value of reducing risks of death: a note on new evidence. Journal of Policy Analysis and Management 8, 88–100. Heckman, J.J., 1979. Sample selection bias as a specification error. Econometrica 47, 153–161. Krupnick, A., Harrison, K., Nickell E., Toman, M., 1993. The benefits of ambient air quality improvements in central and eastern Europe: a preliminary assessment. Resources For the Future, Discussion Paper ENR93-19, Washington, D.C. Rosen, S., 1974. Hedonic prices and implicit markets. Journal of Political Economy 82, 34–35. Smith, R.S., 1974. Compensating wage differentials and public policy: review. Industrial Labor Relations Review 32, 339–352. Smith, V.K., 1983. The role of site and job characteristics in hedonic wage models. Journal of Urban Economics 13, 296–321. Thaler, R., Rosen, S., 1976. The value of saving a life: evidence from the labor market. In: Terleckyj, N. (Ed.), Household Production and Consumption. Columbia University Press, New York. Viscusi, W.K., 1993. The value of risks to life and health. Journal of Economic Literature 31, 1912–1946.
Estimated hedonic wage function and value of life in a developing country 1, Jin-Tan Liu *, James K. Hammitt, Jin-Long Liu
Department of Economics, National Taiwan University, 21 Hsu-Chow Road, Taipei (100), Taiwan Received 7 January 1997; received in revised form 3 July 1997; accepted 22 August 1997
Abstract This paper reports the first study of compensating wage differentials for work-related fatalities in a developing country. Using data from the 1982–1986 Taiwan labor surveys, statistically significant compensating wage differentials are found. The implied value of life is US$413 000 (corrected for selectivity bias) and US$461 000 (uncorrected) in 1990 dollars. 1997 Elsevier Science S.A. Keywords: Compensating wage differentials; Value of life JEL classification: J17; J28; J31
1. Introduction The theory of compensating wage differentials was developed more than 200 years ago by Adam Smith. The theory suggests that jobs with greater risks in terms of the probability of fatal or nonfatal accidents should, other things being equal, receive higher wage compensation than less dangerous jobs. Compensating wage differentials rest on the theory of hedonic (quality-adjusted) prices. Rosen (1974) first provided a general discussion of this theory, and Thaler and Rosen (1976) empirically tested Rosen’s framework. Over the past two decades, there have been extensive discussions with a variety of data sets used in the empirical literature 2 . Previous studies, which all use data from developed countries, estimate a hedonic wage function using traditional human capital variables and risk of death and / or injury on the job. Generally, the empirical literature shows that occupational or industry fatality rates enter the wage function with a positive and statistically significant coefficient. However, the value of life estimates vary dramatically across studies, perhaps because of differences *Corresponding author. Tel.: 886-2-3519641, Ext. 520; fax: 886-2-3215704; e-mail:[email protected] 1 Professor, Department of Economics, National Taiwan University and Research Fellow, Institute of Economics, Academia Sinica; Associate Professor, Department of Health Policy and Management and Center for Risk Analysis, Harvard School of Public Health; and Associate Professor, Institute of Industrial Economics, National Central University. 2 See Smith (1974); Dillingham (1985); Fisher et al. (1989), and Viscusi (1993) for reviews of the research in this area and its policy implications. 0165-1765 / 97 / $17.00 1997 Elsevier Science S.A. All rights reserved. PII S0165-1765( 97 )00238-3
J. Liu et al. / Economics Letters 57 (1997) 353 – 358
354
in the definitions of job risk (industry risk or occupational risk) and in risk preferences among samples of workers. Viscusi (1993) reviewed 23 studies and found that the range of value per statistical life estimates was US$0.6 million to US$16.2 million in the United States, United Kingdom, Canada, Australia, and Japan (in 1990 dollars). The purpose of this paper is to test the theory of compensating wage differentials using labor market data from Taiwan from 1982 to 1986. To our knowledge, this is the first study which investigates the wage–risk relationship in a developing country. This paper also provides estimates of the value of life for each of the five years and compares them with estimates for developed countries. The results can be applied to evaluate the benefits of health and safety regulations in developing countries.
2. The hedonic wage model and data sources
2.1. The hedonic wage model The basic framework for hedonic wage analysis requires data on workers’ wages, job risks and other characteristics. The hedonic wage model treats jobs as bundles of risk, working conditions, and other attributes that can occur in various combinations and quantities. The wage that the worker is willing to accept reflects the utility expected from the job characteristics. A worker’s indifference curve represents his tradeoffs between the wage rate and the probability of workplace fatality. Since workplace safety influences firm productivity and costs, the isoprofit curve measures the tradeoffs between wages and job risk for an employer. The hedonic wage function is the envelope of mutual tangencies between worker indifference curves and firm isoprofit curves. Since the selection of a job usually involves selecting a residential location, site characteristics may be important in the hedonic wage model. Thus, the hedonic wage function based on data from national surveys can be specified as follows: 3 ln Wi 5 g(XIi , XJi , XSi ) 1 u i ,
(1)
where ln Wi 5the natural logarithm of the ith individual worker’s wage rate, XIi 5ith individual worker’s characteristics, XJi 5ith individual worker’s job characteristics, XSi 5characteristics of ith individual worker’s residential location, and u i 5random error term. An econometric problem associated with the hedonic wage equation is that the probability of becoming a working person is likely to be correlated with the error term in the wage equation. If so, the conditional expectation of the error term will not be equal to zero. To correct for possible sample selection bias, we employ the hazard technique suggested by Heckman (1979). A probit equation for labor force participation is estimated, then the inverse of the Mills ratio from the probit equation is
3
For a detailed discussion of the importance of site characteristics in the hedonic wage model, see Smith (1983).
J. Liu et al. / Economics Letters 57 (1997) 353 – 358
355
included as an independent variable in the hedonic wage regression. We compare these results with OLS estimates that do not correct for selectivity bias.
2.2. Data sources The data in this study are taken from several sources. Individual characteristics come from the Taiwan Labor Force Survey (TLFS) data tape for each year from 1982 to 1986. The TLFS is a cross-sectional micro data set that covers labor market participants and nonparticipants in Taiwan. Note that the TLFS is not a panel survey; each year has different respondents in the sample. However, the survey is quite useful because each record contains personal characteristics like age, education, job tenure, monthly income, working hours, industry and occupation code, and place of residence. This large micro data set enables us to isolate the role of job risks from traditional human capital variables such as education and experience. Site characteristics are matched using data for 23 locations (7 cities and 16 counties) in Taiwan and are taken from Social Indicators of the Republic of China and Statistical Yearbook of Taiwan Province. Following Smith (1983), we include measures of air pollution, climate, medical facilities, crime rate, and site access to cultural and educational activities. Unpublished fatality data at the three-digit industry level are obtained from the Taiwan Labor Insurance Agency for the corresponding years. The job–risk variable is defined as the total number of compensated work-related deaths divided by the number of employees per industry each year. Obviously, there is measurement error because not all workers face the average risk in each industry; an industry risk variable has been shown to yield higher estimates of the value of life than the occupation risk variable used in earlier studies (Viscusi, 1993). We restrict the sample to the nonagricultural sector and delete dual jobholders, employers, and self-employed workers. Summary statistics for selected variables are presented in Table 1. The average nominal wage, years of education, and proportions of workers who are married and are female increased over the period. The average annual fatality risk, which fell from 3.82 / 10 000 to 2.25 / 10 000, is substantially greater than the average U.S. fatality risk of about 1 / 10 000.4
3. Empirical results The OLS regression and the second step results for the Heckman (1979) procedure to correct for selectivity bias are reported in Table 2. Because of space limitations, we do not report the results of the first stage probit estimation. Both model specifications perform well in all years. The R 2 ranges from 0.46 to 0.53. The signs and magnitudes of the coefficients are as expected. Wages rise at an increasing rate with education, and rise at a declining rate with work experience. Marriage increases earnings, perhaps because motivation and stability increase productivity. The sex dummy variable suggests that males are paid significantly more than females. The coefficients of the occupational 4
The apparent decline in job risk in our sample may be due in part to inter-annual variation in the proportion of sampled workers in each industry.
J. Liu et al. / Economics Letters 57 (1997) 353 – 358
356
Table 1 Means and standard deviation of selected variables Variables
SEX MARRIED EDU EXPER RISK WAGE N
Year 1982
1983
1984
1985
1986
0.638 0.521 9.271 (3.872) 63.943 (78.485) 3.825 (4.123) 55.982 (30.997) 17 250
0.628 0.542 9.512 (3.922) 65.715 (80.962) 2.990 (3.262) 60.369 (36.182) 17 402
0.611 0.547 9.653 (3.908) 64.028 (79.732) 2.881 (7.215) 61.862 (38.370) 18 790
0.610 0.559 9.681 (3.882) 64.040 (78.943) 2.555 (3.503) 64.833 (39.265) 18 635
0.602 0.563 9.706 (3.859) 64.818 (79.697) 2.252 (2.602) 68.773 (41.213) 18 987
Notes: Definitions of variables are: SEX (dummy variable, male51, female50), MARRIED (dummy variable, married51, otherwise50), EDU (years of education), EXPER (experience in current job in months), RISK (annual industry fatalities per 10 000 workers), WAGE (average hourly earnings in New Taiwan dollars). Standard deviations are shown in parentheses.
dummy variables are stable and indicate that managers receive the highest wage and service workers the lowest wage. The coefficients of particular interest in this study, those for fatal job risk, are positive and statistically significant. The OLS estimates are slightly greater than the Heckman-corrected estimates. Table 2 Risk variable estimations in hedonic wage equations Year 1982
1983
1984
1985
1986
0.0121 (16.331) 582 000
0.0096 (10.367) 493 000
0.0029 (7.251) 151 000
0.0074 (9.370) 398 000
0.0123 (11.245) 683 000
Heckman Two-Stage Estimation: RISK 0.0112 0.0086 (15.006) (9.548) Value of Life 539 000 442 000 (1990 US$)
0.0026 (6.684) 135 000
0.0067 (8.715) 360 000
0.0106 (9.989) 589 000
OLS estimation: RISK Value of Life (1990 US$)
Notes: Values of t-ratio are in parentheses. The wage function includes human capital variables, individual characteristics, occupational dummy variables, and site attribute variables. The human capital variables are EDU, EDU squared, EXPER, EXPER squared. Occupational dummy variables are professional, manager, clerk, sales, service, and operator. The transportation occupational dummy variable is omitted. Site attribute variables are number of secondary school students per class in town of residence, population density, unemployment rate in town, criminal offenses in town per 10 000 population, town population per medical doctor, cinema and theatre seats per 1000 population, average temperature, average precipitation, and average suspended particulates in town.
J. Liu et al. / Economics Letters 57 (1997) 353 – 358
357
The risk coefficients can be interpreted as the average willingness to pay for a marginal decrease in mortality risk. These results provide strong evidence of a compensating wage differential for jobs with greater fatality risk in the Taiwanese labor market. The implied values of a statistical life are calculated for each year at the sample mean wage. The figures are obtained by multiplying (1) the risk coefficient, (2) 2000 working hours per year, (3) 10 000 (the denominator of the risk measure) and (4) the average wage rate.5 The estimates, reported in Table 2, range from US$151 000 to US$683 000 for the OLS estimates, and slightly smaller, from US$135 000 to US$589 000, for the Heckman-corrected estimates. The average values for the five years are US$461 000 (OLS) and US$413 000 (Heckman correction). These figures are lower than those found for industrialized countries. Since labor market structures, institutional factors and magnitudes of industrial risk vary across countries, variations in estimates are to be expected. To compare these estimates with estimates for developed countries, we estimated the following regression (OLS) to explain the 17 value of life estimates for which average income and fatality risk are reported in Table 2 of Viscusi (1993) Ln(VOL) 5 10.426 1 0.530 Ln(INCOME) 2 0.2714 (RISK) (1.234)
(0.624)
(2)
(22.890)
R 2 5 0.374 where t-statistics are in parentheses, VOL is estimated value of life, INCOME is annual income, and RISK is annual fatality risk per 10 000 workers. Substituting the five year sample mean income and fatality risk in this equation yields a predicted value of life for Taiwanese workers of $1.4 million, about 3.5 times the estimate of $413 000. (An alternative linear specification yields a predicted value of $3.5 million, 8.5 times our estimate.) The comparatively small value of life estimated from the Taiwan data suggests that there may be structural differences in the value of risk reduction between developing and developed countries, perhaps because information about occupational risks may be less accessible to workers in developing countries. Alternatively, the income elasticity between developing and developed countries may be larger than within developed countries (the income elasticity estimated in Eq. (2) is smaller than expected if safety is a luxury good, however). Overall, the comparison suggests that a simple benefit-transfer equation from the existing literature is inadequate for estimating the value of life in developing countries (Krupnick et al., 1993).
4. Conclusion This paper provides estimates of the marginal value of job hazards for the Taiwanese labor market from 1982 to 1986. We find evidence of compensating wage differentials for industrial risk in Taiwan. The implied value of statistical life averages US$413 000 (corrected for selectivity bias) and 5
The values are adjusted to 1990 using the Taiwan GDP deflator and converted to U.S. dollars at the 1990 exchange rate of 27.1075 New Taiwan dollars per U.S. dollar.
358
J. Liu et al. / Economics Letters 57 (1997) 353 – 358
US$461 000 (uncorrected) in 1990 dollars, smaller than the value estimated from developed country data. The estimate may be useful for evaluating the benefits of regulations that reduce the risk of death in developing countries.
References Dillingham, A.E., 1985. The influence of risk variable definition on value-of life estimates. Economic Inquiry 24, 277–294. Fisher, A., Chestnut, L.G., Violette, D.M., 1989. The value of reducing risks of death: a note on new evidence. Journal of Policy Analysis and Management 8, 88–100. Heckman, J.J., 1979. Sample selection bias as a specification error. Econometrica 47, 153–161. Krupnick, A., Harrison, K., Nickell E., Toman, M., 1993. The benefits of ambient air quality improvements in central and eastern Europe: a preliminary assessment. Resources For the Future, Discussion Paper ENR93-19, Washington, D.C. Rosen, S., 1974. Hedonic prices and implicit markets. Journal of Political Economy 82, 34–35. Smith, R.S., 1974. Compensating wage differentials and public policy: review. Industrial Labor Relations Review 32, 339–352. Smith, V.K., 1983. The role of site and job characteristics in hedonic wage models. Journal of Urban Economics 13, 296–321. Thaler, R., Rosen, S., 1976. The value of saving a life: evidence from the labor market. In: Terleckyj, N. (Ed.), Household Production and Consumption. Columbia University Press, New York. Viscusi, W.K., 1993. The value of risks to life and health. Journal of Economic Literature 31, 1912–1946.