Economics Letters 57 (1997) 353–358

Estimated hedonic wage function and value of life in a developingcountry

1 ,*Jin-Tan Liu , James K. Hammitt, Jin-Long LiuDepartment 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 AdamSmith. The theory suggests that jobs with greater risks in terms of the probability of fatal or nonfatalaccidents should, other things being equal, receive higher wage compensation than less dangerousjobs. 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) empiricallytested Rosen’s framework. Over the past two decades, there have been extensive discussions with a

2variety of data sets used in the empirical literature . Previous studies, which all use data fromdeveloped countries, estimate a hedonic wage function using traditional human capital variables andrisk of death and/or injury on the job. Generally, the empirical literature shows that occupational orindustry 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:liujt@gate.sinica.edu.tw1Professor, 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 Schoolof Public Health; and Associate Professor, Institute of Industrial Economics, National Central University.

2See Smith (1974); Dillingham (1985); Fisher et al. (1989), and Viscusi (1993) for reviews of the research in this area andits policy implications.

0165-1765/97/$17.00 1997 Elsevier Science S.A. All rights reserved.PII S0165-1765( 97 )00238-3

354 J. Liu et al. / Economics Letters 57 (1997) 353 –358

in the definitions of job risk (industry risk or occupational risk) and in risk preferences among samplesof workers. Viscusi (1993) reviewed 23 studies and found that the range of value per statistical lifeestimates 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 labormarket data from Taiwan from 1982 to 1986. To our knowledge, this is the first study whichinvestigates the wage–risk relationship in a developing country. This paper also provides estimates ofthe 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 developingcountries.

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 andother characteristics. The hedonic wage model treats jobs as bundles of risk, working conditions, andother attributes that can occur in various combinations and quantities. The wage that the worker iswilling to accept reflects the utility expected from the job characteristics. A worker’s indifferencecurve represents his tradeoffs between the wage rate and the probability of workplace fatality. Sinceworkplace safety influences firm productivity and costs, the isoprofit curve measures the tradeoffsbetween wages and job risk for an employer. The hedonic wage function is the envelope of mutualtangencies between worker indifference curves and firm isoprofit curves.

Since the selection of a job usually involves selecting a residential location, site characteristics maybe important in the hedonic wage model. Thus, the hedonic wage function based on data from national

3surveys can be specified as follows:

ln W 5 g(X , X , X ) 1 u , (1)i Ii Ji Si i

where ln W 5the natural logarithm of the ith individual worker’s wage rate, X 5ith individuali Ii

worker’s characteristics, X 5ith individual worker’s job characteristics, X 5characteristics of ithJi Si

individual worker’s residential location, and u 5random error term.i

An econometric problem associated with the hedonic wage equation is that the probability ofbecoming 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 sampleselection bias, we employ the hazard technique suggested by Heckman (1979). A probit equation forlabor force participation is estimated, then the inverse of the Mills ratio from the probit equation is

3For 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 withOLS 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 theTaiwan Labor Force Survey (TLFS) data tape for each year from 1982 to 1986. The TLFS is across-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, jobtenure, monthly income, working hours, industry and occupation code, and place of residence. Thislarge micro data set enables us to isolate the role of job risks from traditional human capital variablessuch as education and experience.

Site characteristics are matched using data for 23 locations (7 cities and 16 counties) in Taiwan andare taken from Social Indicators of the Republic of China and Statistical Yearbook of TaiwanProvince. 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 LaborInsurance Agency for the corresponding years. The job–risk variable is defined as the total number ofcompensated 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 theoccupation risk variable used in earlier studies (Viscusi, 1993). We restrict the sample to thenonagricultural sector and delete dual jobholders, employers, and self-employed workers.

Summary statistics for selected variables are presented in Table 1. The average nominal wage, yearsof 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

4than the average U.S. fatality risk of about 1 /10 000.

3. Empirical results

The OLS regression and the second step results for the Heckman (1979) procedure to correct forselectivity bias are reported in Table 2. Because of space limitations, we do not report the results of

2the first stage probit estimation. Both model specifications perform well in all years. The R rangesfrom 0.46 to 0.53. The signs and magnitudes of the coefficients are as expected. Wages rise at anincreasing rate with education, and rise at a declining rate with work experience. Marriage increasesearnings, perhaps because motivation and stability increase productivity. The sex dummy variablesuggests that males are paid significantly more than females. The coefficients of the occupational

4The apparent decline in job risk in our sample may be due in part to inter-annual variation in the proportion of sampledworkers in each industry.

356 J. Liu et al. / Economics Letters 57 (1997) 353 –358

Table 1Means and standard deviation of selected variables

Variables Year

1982 1983 1984 1985 1986

SEX 0.638 0.628 0.611 0.610 0.602MARRIED 0.521 0.542 0.547 0.559 0.563EDU 9.271 9.512 9.653 9.681 9.706

(3.872) (3.922) (3.908) (3.882) (3.859)EXPER 63.943 65.715 64.028 64.040 64.818

(78.485) (80.962) (79.732) (78.943) (79.697)RISK 3.825 2.990 2.881 2.555 2.252

(4.123) (3.262) (7.215) (3.503) (2.602)WAGE 55.982 60.369 61.862 64.833 68.773

(30.997) (36.182) (38.370) (39.265) (41.213)N 17 250 17 402 18 790 18 635 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 per10 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 workersthe lowest wage.

The coefficients of particular interest in this study, those for fatal job risk, are positive andstatistically significant. The OLS estimates are slightly greater than the Heckman-corrected estimates.

Table 2Risk variable estimations in hedonic wage equations

Year

1982 1983 1984 1985 1986

OLS estimation:RISK 0.0121 0.0096 0.0029 0.0074 0.0123

(16.331) (10.367) (7.251) (9.370) (11.245)Value of Life 582 000 493 000 151 000 398 000 683 000(1990 US$)

Heckman Two-Stage Estimation:RISK 0.0112 0.0086 0.0026 0.0067 0.0106

(15.006) (9.548) (6.684) (8.715) (9.989)Value of Life 539 000 442 000 135 000 360 000 589 000(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. Thetransportation occupational dummy variable is omitted. Site attribute variables are number of secondary school students perclass 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, averageprecipitation, 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 inmortality risk. These results provide strong evidence of a compensating wage differential for jobs withgreater fatality risk in the Taiwanese labor market. The implied values of a statistical life arecalculated for each year at the sample mean wage. The figures are obtained by multiplying (1) the riskcoefficient, (2) 2000 working hours per year, (3) 10 000 (the denominator of the risk measure) and (4)

5the average wage rate. The estimates, reported in Table 2, range from US$151 000 to US$683 000 forthe OLS estimates, and slightly smaller, from US$135 000 to US$589 000, for the Heckman-correctedestimates. The average values for the five years are US$461 000 (OLS) and US$413 000 (Heckmancorrection). These figures are lower than those found for industrialized countries. Since labor marketstructures, institutional factors and magnitudes of industrial risk vary across countries, variations inestimates are to be expected.

To compare these estimates with estimates for developed countries, we estimated the followingregression (OLS) to explain the 17 value of life estimates for which average income and fatality riskare reported in Table 2 of Viscusi (1993)

Ln(VOL) 5 10.426 1 0.530 Ln(INCOME) 2 0.2714 (RISK) (2)

(1.234) (0.624) (22.890)

2R 5 0.374

where t-statistics are in parentheses, VOL is estimated value of life, INCOME is annual income, andRISK is annual fatality risk per 10 000 workers. Substituting the five year sample mean income andfatality 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 valueof $3.5 million, 8.5 times our estimate.)

The comparatively small value of life estimated from the Taiwan data suggests that there may bestructural 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 developingcountries. Alternatively, the income elasticity between developing and developed countries may belarger than within developed countries (the income elasticity estimated in Eq. (2) is smaller thanexpected if safety is a luxury good, however). Overall, the comparison suggests that a simplebenefit-transfer equation from the existing literature is inadequate for estimating the value of life indeveloping countries (Krupnick et al., 1993).

4. Conclusion

This paper provides estimates of the marginal value of job hazards for the Taiwanese labor marketfrom 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

5The values are adjusted to 1990 using the Taiwan GDP deflator and converted to U.S. dollars at the 1990 exchange rate of27.1075 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 countrydata. The estimate may be useful for evaluating the benefits of regulations that reduce the risk of deathin 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.