Based on the two articles provided summarize and answer the following questions for each article for factors related to pay differences. Use APA format and cite the articles. One page per article 1. What were the main findings in the article? 2. To whic

WORKER LEARNING AND COMPENSATING DIFFERENTIALS

W. KIP VISCUSI and MICHAEL J. MOORE*

The authors hypothesize that in industries with relatively high levels of job-related injury risk, workers with longer job tenure will more clearly appreciate the degree of job risk than will newly hired workers, and will thus be more willing to accept lower wages in return for higher workers' compensation benefits. This hypothesis is confirmed by an analysis of quit behavior using 1981-83 data from the Michigan Panel Study of Income Dynamics and 1981-85 data from the National Institute of Occupational Safety and Health.

IN the standard compensating wagedifferential model, workers value their wage and workers' compensation compo- nents hased on full job risk information. Market forces generate positive wage differentials as ex ante compensation for exposure to relatively high risk. Similarly, market forces generate wage offsets for the increases in ex post risk compensation embodied in workers' compensation bene- fits.

These predictions can be modified to take into account potential imperfections in worker information, as in Viscusi (1979a,b, 1980a,b,d), where the role of learning is incorporated into the worker's decision model. The potential for learning about risks introduces a new market

* W. Kip Viscusi is George G. Allen Professor of Economics, Department of Economics, and Michael J. Moore is Associate Professor of Economics, Fuqua School of Business, Duke University. The first author's research was supported by the endowment of the George G. Allen chair at Duke University, and the second author's research received partial support from the Business Associates Fund at the Fuqua School of Business.

The data and programs used in this study are available on request to Michael J. Moore, Fuqua School of Business, Duke University, Durham, NC 27706.

response through worker quitting after the acquisition of adverse risk informa- tion. In a full information world, after controlling for health status, no unex- pected job risk-quit relationship will be observed. In the more realistic sequential decision model in which there is an opportunity for learning, the acquisition of adverse new information by the worker on the job may lead the worker to quit.

With the exception of the experimental results reported in Viscusi and O'Connor (1984), in which worker responses to alternative chemical labels were moni- tored, tests of the standard compensating differential model and of the learning models have been distinct, as each focuses on a different aspect of labor market behavior. The empirical evidence support- ing compensating risk differentials is substantial: greater job risks boost worker wages, and workers are willing to accept a wage cut in return for higher workers' compensation benefits.' These results are

' See, for example. Smith (1979) and Viscusi (1979a) for analysis of wage-risk tradeoffs. Estimates of wage—workers' compensation tradeoffs appear in Arnould and Nichols (1983), Butler (1983), Dorsey and Walzer (1983), and Viscusi and Moore (1987). In

Industrial and Labor Relations Review, Vol. 45, No. 1 (October 1991). © by Cornell University. 0019-7939/91/4501 $01.00

80

JOB TENURE AND WORKERS' COMPENSATION 81

the main predictions of the standard compensating differential theory, and they continue to hold if learning is introduced. Market tests of the role of worker learning, on the other hand, have focused on two other empirical issues—the effect of injury experiences on workers' risk perceptions and the positive effect of job risks on worker quitting.^

The focus of this paper is broader than that of separate analyses of the wage and quit effects of job risks because we use the relationships typically estimated and tested in the standard compensating differential theory to examine the job risk-learning model as well. In particular, using a large data set on workers in the early 1980s, we evaluate the tradeoffs between wages and workers' compensation benefits and be- tween wages and risks implied by worker quit behavior, and compare these tradeoffs across worker tenure groups.

Younger workers will assess job risk less precisely than more senior workers, since their inf"ormational base for making these judgments is smaller. In multi-period models that incorporate learning and experimentation with risky jobs, workers' reservation wage rates will be less for jobs posing less precisely understood risks, for any given mean level of risk. The empiri- cal prediction is that more senior workers will demand greater compensation for risk because of their more precise judgments.

In addition, workers who are on the quit margin (those who would need only a small inducement to quit their jobs) will have greater subjective risk perceptions than other workers. These workers conse-

' quently will demand greater wage com- pensation for higher risk levels, since they will be comprised disproportionately of workers whose risk beliefs have been adversely affected by on-the-job experi- ences. These workers will also assess a greater chance of receiving workers' com- pensation benefits, so their expected value

Moore and Viscusi (1990) we provide a literature review of estimates of the effects of fatality risks and workers' compensation on wages.

2 See Viscusi (1979a,b, 1980a,b,d, and 1983) and Viscusi and O'Connor (1984).

of higher benefits will be greater. We therefore expect to observe greater wage- risk and wage-workers' compensation tradeoffs for these workers.

Theoretical Eramework

The learning model that we apply here to the wage-workers' compensation trade- off was first introduced in Viscusi (1979a,b, 1980a,b,d).3 Our overall objective is to ex- plore the relationships between the wage- risk tradeoff and the wage-workers' com- pensation tradeoff for new workers and for senior workers on the quit-no quit margin.

The essential ideas can be captured in a two-period model. Let there be two health states: healthy and injured. In the good health state the worker receives a wage rate w, from which he derives utility U^{w). In the injured state the worker receives workers' compensation benefits b, where w > b. This assumption refiects the structure of workers' compensation programs in virtually every state, since benefits are typically two-thirds of the wage or less, except for workers with very low wages. We assume that the worker would rather be healthy than not (that is, f/'(x) > U^{x)), has a higher marginal utility of income when healthy (Ul > U^), and is either risk-averse or risk-neutral (t/^, U%; ^ 0). The worker values payoffs over time using a discount factor p that equals the recipro- cal of 1 plus the interest rate.

Suppose that there are two possible jobs, a risky job and a safe job. We can assume with no loss of generality that the safe job poses no risk of injury.* The safe job offers a payoff WQ forever. The risky job offers the worker an initial perceived probability of not being injured equal to p and a 1-;!? chance of suffering an injury that lasts a single period. If the worker is not injured in period 1, he revises his

' A variant of this analysis without learning appears in Diamond (1977).

One could adopt the assumption that the alternative job poses a known risk of injury without altering the model structure, even in the n-period case. If both jobs are uncertain and there are more than two periods, the model structure becomes more complex. See Viscusi (1979a) for these extensions.

82 INDUSTRIAL AND LABOR RELATIONS REVIEW

assessed probability of not being injured upward to p'^. If he is injured, the assessed probability is p~. It is also possible, as noted by Viscusi (1979a), that workers revise their expectations based on observations of other workers' injury experiences. The revision of workers' probabilistic beliefs follows a standard Bayesian learning process, where

(3) = 0 = pU\w) + i l -

(1) >p>p-

The workers' initial job decision involves a choice between two periods of work on the safe job or initial work on the risky job, after which he can quit if he is injured in period I. As first noted by Viscusi (1979a), the worker's problem mirrors the classic two-armed bandit problem, which de- scribes the optimal sequence of plays on two slot machines. On one machine the probability of success is known, and on the other it is uncertain. Thus, a payoff on the uncertain machine yields information in addition to monetary rewards. Eor this class of two-armed bandit problems, it is shown in Viscusi (1979a,b, 1980a,b,d) that the stay-on-a-winner rule is always opti- mal. The worker will not leave the risky job after a favorable experience in period I. The worker also will not leave the safe job once he starts on it.

The wage package for the marginal new worker attracted to the firm must satisfy the condition that expected lifetime utility, V, is equal between the two jobs, given the opportunity the worker has to switch from the risky job following an unfavorable period 1 outcome:

(2) V = = pU\w) + {\-p)U\b)

p-U\w) + {\-p-)U\b)].

If we set U^{WQ) equal to zero, with no loss of generality, we have

Not all workers will quit their jobs prior to period 2 after an unfavorable period 1 job experience. The focus here, however, is on the wage-benefit tradeoff of the mar- ginal senior worker relative to the pay pack- age that will attract the worker to the job initially. The marginal senior worker will quit after an adverse experience in period 1, so the wage package {•w,b) sufficient to attract the worker initially must satisfy

(4) y = 0 = pu\w) + (1 - ^

since the last term in equation 5 equals zero after an unfavorable period 1 experience.

The first issue analyzed is the wage- workers' compensation tradeoff that will be reflected in the {w,b) package for new hires. Implicit differentiation of equation 3 yields

(5)

dw _ -

Bb V Ul[p +

The value of dwidb represents the wage offset in response to b for the new hire in a two-period job choice problem. The initial wage package {w,b) will be adequate to retain the worker if his on-the-job experi- ences are favorable.

The worker on the margin at the start of period 2 has an expected utility Z equal to

(6) Z = 0 = p-U\w) + {\-p-)U\b),

since he is indifferent between leaving (where U^{WQ) = 0) and staying on the risky job. The wage-workers' compensa- tion tradeoff for this worker equals

(7) Tr =

One issue that we investigate empirically

JOB TENURE AND WORKERS' COMPENSATION 83

is the relative magnitude of the wage- workers' compensation tradeoffs in equa- tions 5 and 7. Workers who have experi- enced or observed an on-the-job injury should value workers' compensation more highly, since they will assess a higher probability of receiving such benefits than will other workers. For workers with an adverse job experience, the expected amount of workers' compensation bene- fits will be {l-p'*')b. For new hires who plan to quit if their initial period job experience is unfavorable, the discounted expected benefit amount is {l-p)b + ^{l -p){\ -p")b, where these benefits are provided over (l-p) + ^{l-p) periods. The lower expected amount of benefits per period of work for new hires is reflected in a lower expected utility as well, which will influence the compensat- ing differential they are willing to accept for these benefits. In particular, one would expect

Quit New Margin Hire

Tradeoff Tradeoff

(11)

U\w) = -{\-p)U\b) -

db Z,.

or

(9)

ul[p

After some algebraic manipulation, equa- tion 9 reduces to

(10) P'<P^-

Given the restrictions on probabilities outlined in equation 1 above, equation 10 always holds.

The second empirical concern will be the effect of worker learning on the character of the wage premiums com- manded by job risks. From the develop- ment above, equation 4 implies that, for new hires.

Similarly, solving for U^{iu) from equation 6 for workers on the quit margin yields the condition that

(12)

The utility metric defined by setting U^{WQ) equal to zero leads to a negative value of U^{b), implying that the right sides of equations 11 and 12 are positive. The condition that must be met for the risk premium required by senior workers on the margin to be higher than that of new hires is that C/*(w) be greater for this group. Thus, the right side of equation 12 must exceed the expression on the right side of equation 11. This requirement is always satisfied if inequality 1() holds, as is assumed.

In addition to compensation for risk, the overall wage structure of the firm will, of course, also include returns to worker ex- perience and performance. This wage struc- ture can be viewed as defining the pecuni- ary returns to the worker over time. As is standard in agency theory models of wages, it is the new hires and other workers on the margin of leaving the firm who are of great- est concern. By altering the entering wage level, the firm ensures a flow of new work- ers to the firm. Higher wages also diminish the tendency to quit, but since quitters tend to be workers with particularly adverse job experiences, learning-induced quits will continue to occur. Because of the differ- ence in risk perceptions of the potential quitters, both safety and workers' compen- sation should be more highly valued by this group than by the new worker group, which has a lower assessment of the job risk both for the initial period of work and for their expected duration of work at the firm.

The principal predictions that we ex- plore below stem from the effect of worker experiences on risk perceptions. For senior workers on the margin, the wage—workers' compensation tradeoff should be more neg-

84 INDUSTRIAL AND LABOR RELATIONS REVIEW

ative (and less than zero), as indicated by equation 8. This relationship arises be- cause the higher probability these workers attach to receiving benefits as a result of their on-the-job learning about the risks should be reflected in the preferences cap- tured in quit equations. In addition, the compensating differential experienced workers demand for risk will also rise for any given level of perceived risk, to the extent that more experienced workers place less value on future benefits of job exper- imentation because of their more precise evaluations of job risk. Experienced work- ers on the quit margin will also consist dis- proportionately of workers who have had or observed adverse job experiences, boost- ing the required wage-risk tradeoff.

The linkage of the theoretical predic- tions to the empirical model will be fairly direct. Wage and workers' compensation levels are directly observable. We do not have direct measures of workers' subjec- tive probability of being injured for the data set that we will use below, but we do have relatively good data on the fatality rate for the worker's industry. The under- lying assumption is that workers will have higher risk perceptions and will be more likely to acquire adverse job information that will generate quit behavior in indus- tries with high objective measures of risk. The results in Viscusi (1979a) and Viscusi and O'Connor (1984) indicate that the job risk-quit relationship is similar whether one uses objective industry risk variables or subjective risk assessments. We use the objective measure here because our focus on state differences in workers' compensa- tion requires that we use a large national data set for which there are no subjective risk data.

Sample and Empirical Results

Data

The main requirement with respect to the survey data for the study is that they include information on wage rates, quit behavior, the worker's state of residence (to establish matchups to workers' com- pensation benefit formulas), and the

worker's state of residence and industry (to establish matchups with risk data).^ The principal data source we use is the 1981, 1982, and 1983 waves of the University of Michigan Panel Study of Income Dynamics (PSID). The PSID is a broad longitudinal survey data set that contains information pertaining to the characteristics of individuals and their jobs. These data are matched to informa- tion on the risk of an on-the-job fatality provided by the National Institute for Occupational Safety and Health (NIOSH) as part of its National Traumatic Occupa- tional Fatality Project (NTOF). Unlike comparable risk data available from other sources, the NIOSH data represent a census of all occupational fatalities, aver- aged over the years 1981-85. As such, these data are not subject to the sampling error that is present in risk survey data. Furthermore, the NIOSH data vary by both state and industry, making them more comparable to the workers' compen- sation benefit data, which vary primarily by state. This feature provides a better matchup than the matchup possible using other available national survey data, which vary only by industry. The NIOSH data yield over 400 distinct observations of job risk, thus providing one of the most detailed breakdowns of injury risk cur- rently available.^

The workers' compensation data, which are described in detail in Viscusi and Moore (1987), are based on benefits for temporary total disabilities, the injury category under which approximately 65% of total claims fall. Furthermore, in recent years benefit ceilings for temporary total

^ An exact matchup of benefits and risk levels with the worker's state of employment, although desir- able, is not possible with our data, since sample members report only their state of residence. For the majority of cases, however, the state of residence and the state of employment are the same,

** The properties of the NIOSH data are explored in relation to the BLS data in Moore and Viscusi (1988, 1990),

Since the model is based on perceived changes in actual risk, the state-industry risk data will not allow us to distinguish among changes in perceptions, actual risk changes, and changes in risk preferences. We assume that the latter two change slowly, if at all.

JOB TENURE AND WORKERS' COMPENSATION 85

disabilities and for permanent total dis- abilities have become equal in practically all states.•̂ Fatality benefits also usually equal benefits for the above two catego- ries, although there are some exceptions. This standardization of benefit ceilings allows a more representative measure of ex post accident compensation than was available in most earlier studies. On the other hand, it ignores some other impor- tant aspects of benefit structure, such as differences between ceilings for partial and total disability, waiting periods, and duration. These differences, although im- portant, cannot be captured in a refined fashion, and researchers typically resort to using temporary total disability ceilings or payments as proxies for each state's benefit level.

The workers' compensation benefit lev- els are matched to workers in the PSID by state. They are then used in conjunction with information on the worker's weekly wage, marital status, and family size to determine the weekly benefit for wbich tbe worker qualifies. The benefit variable is computed using the formula b = (2/3 weekly wage) x (I - D) + (benefit maximum) X D, where D = 1 if the worker qualifies for the maximum benefit level, and 0 otherwise. Tbe variable b is tben divided by the worker's after-tax weekly wage to construct tbe wage re- placement rate measure tbat is used in tbe empirical analysis.^

For purposes of estimation, workers wbo report tbeir occupation as farming are excluded from tbe sample, since tbe agricultural sector is excluded from tbe NIOSH data. Also excluded are workers wbose reported hourly wage is below tbe statutory minimum wage, non-beads of

' A detailed exploration of the differences in disability benefits and their interrelationship is provided by Burton and Krueger (1986) and Krueger and Burton (1983), A detailed analysis of the important permanent partial disability compo- nent is provided by Burton (1983), More generally, see Berkowitz and Burton (1987) for an analysis of permanent disability,

^ We assume that benefits are computed based on full-time weekly earnings. This assumption reflects the law in many states, and the actual work week for the majority of the sample.

bousebolds, blacks, workers wbo are over 65 years old or are not in tbe labor force, and cases witb missing data. Tbe sample tbat remains consists of 2,571 observations on 857 workers. The mix of tbe workers in tbe sample follows tbe expected pat- terns. Fifteen percent of tbe sample members are women (FEMALE), 11% are single (SINGLE), and tbe mean number of dependent cbildren is close to 1 (DEPEN- DENTS).

Tbe human capital variables include tbe standard measures. Tbe workers have an average of about 13 years of schooling (EDUCATION), 10 years of experience at tbeir current firm (TENURE), and 18 years of job experience overall (EXPERIENCE). Because of tbe interrelationsbips among tbe various buman capital variables, a worker age measure is not included in tbe analysis. Tbe sample consists primarily of workers in industries in wbicb job hazards are likely to be of consequence. In particular, tbe sample restrictions we bave imposed yield a sample tbat is over 30% unionized (UNION).

Tbe average after-tax real bourly wage equals approximately $7 in 1982 dollars. Tbe tax component of wages was calcu- lated using information on marginal tax rates provided by tbe PSID sample mem- bers. Although most wage equation stud- ies utilize tbe pre-tax wage for simplicity, tbe inclusion of workers' compensation benefits in tbe equation increases tbe importance of using after-tax wages, since tbese benefits bave favorable tax status. Moreover, tbe extent of one's tax savings varies with one's tax bracket and state tax level. Failure to make an adjustment for taxes would tbus distort tbe tradeoff rate between wages and workers' compensa- tion, wbicb is a central empirical concern in tbis paper.

Tbe deatb risk measure (RISK), wbicb was described earlier, implies an average deatb risk of 7.6/100,000 for sample members. Tbis risk level is only sligbtly different from tbe national average NTOF risk measure of 9/100,000, so tbe sample is representa- tive of tbe industry mix captured in tbe NTOF data set. Tbe rougbly 1/10,000 deatb risk level sbould be viewed as a typical risk

86 INDUSTRIAL AND LABOR RELATIONS REVIEW

sample rather than a high-risk sample. We will use the death risk variable as a proxy for the overall job risk, since this measure is available on a state-specific basis, whereas published nonfatal injury data are not readily available.^

The basic workers's compensation vari- able in Table 1 is the real dollar value of the state weekly workers' compensation maximum benefit, WCMAX, which averages $207. Benefits are computed using infor- mation on the worker's state of residence, martial status, number of dependents, and the formula given above. We also compute a benefit variable following Moore and Vis- cusi (1989), using the maximum benefit level in each state as a proxy for expected benefits. Since changes in the maximum influence the distribution of benefits for which each worker can potentially qualify, this variable measures changes in expected benefits when there is wage rate uncer- tainty. To control for the fact that in- creases in the maximum are more highly valued by workers whose wage places them at or above the maximum, we estimate the effect of this variable separately for each class of worker. The relative sizes of the estimated effects provide a check on the plausibility of our results. These results also provide a check on the robustness of the results derived using the actual benefit level in the replacement rate.

As shown in Viscusi and Moore (1987), workers only value accident insurance at positive risk levels. Thus, the appropriate measure of workers' compensation, the weighted weekly benefit level, involves an interaction of the death risk and the benefit level. Insurance benefits are cap- tured by the risk-weighted replacement rate. The benefit level for which the worker qualifies is interacted with the risk variable to create the weighted benefit measure and then divided by the worker's after-tax weekly wage to create the weighted replacement rate variable. This

^ Published single-digit (SIC) injury rates for fatal and nonfatal injuries exhibit a correlation of about ,70 for 1986, significant at the ,10 level. See U,S, Department of Commerce (1989), Tables 681 and 682,

formulation recognizes the fact that the value of the benefit varies with the risk and the fact that workers' compensation benefits are tax exempt. Use of the weighted replacement rate variable is consistent with much of the previous research on the wage effects of workers' compensation, although some studies have entered wages and benefits separately.i°

As Table 1 indicates, the average re- placement rate for the workers in each tenure group is 0.70. This rate is slightly higher than the nominal replacement rate of 0.66 used by most states. Two compet- ing influences lead to this divergence. Since our replacement rate is computed on an after-tax basis, it will tend to be higher than the before-tax nominal rate of 0.66. On the other hand, since many workers' wages put them above the maxi- mum, the observed replacement rate will tend to be lower. The net effect is to yield a rate slightly above the state-mandated nominal rate.

Empirical Results

The empirical tests of the worker learning hypothesis compare the wage- benefit and wage-risk tradeoffs estimated in quit equations across two tenure groups—workers with at least three years of tenure, and those with less than three years. We focus on the quit equation rather than a wage equation because the primary matter of interest is how the wage-risk and wage-benefit preferences of different groups of workers are altered, not how market contracts respond. The cutoff point at three years of tenure is the division used in Kahn's (1987) analysis of the preferences of marginal workers. Furthermore, restricting the newly hired worker sample to two years of tenure or less, as in Viscusi (1980c), yielded very small samples.

Estimates of the parameters of the quit equations for the senior worker group provide information on the behavior of senior workers on the margin, whereas estimates of the parameters of the quit

10 See Worral and Butler (1985),

J O B T E N U R E AND WORKERS' COMPENSATION 87

Table 1. Variable Definitions and Sample Characteristics,

Variable Name

Mean (Standard Deviation)

Variable Definition

EDUCATION Y e a r s of e d u c a t i o n

FEMALE 1 if w o r k e r is f e m a l e , 0 o t h e r w i s e

HEALTH 1 if w o r k e r r e p o r t s t h e p r e s e n c e of a h e a l t h i m p a i r m e n t

LIMITATION t h a t limits t h e a m o u n t of work he can d o , 0 otherwise

DEPENDENTS N u m b e r of d e p e n d e n t c h i l d r e n

SINGLE 1 if w o r k e r has n e v e r b e e n m a r r i e d , 0 otherwise

EXPERIENCE Years w o r k e d for pay since a g e 16

UNION STATUS 1 if worker's j o b is covered by a collective b a r g a i n i n g a g r e e m e n t , 0 otherwise

WAGE W o r k e r ' s a f t e r - t a x h o u r l y w a g e in 1982 d o l l a r s ( G N P d e f l a t o r )

RISK N T O F fatality r a t e v a r i a b l e : n u m b e r of fatalities p e r

1 0 0 , 0 0 0 w o r k e r s , by s t a t e a n d o n e - d i g i t (SIC) i n d u s t r y

WCMAX M a x i m u m b e n e f i t level f o r t e m p o r a r y disability in t h e w o r k e r ' s s t a t e

REPLACEMENT P o r t i o n o f w e e k l y a f t e r - t a x w a g e r e p l a c e d by w o r k e r s ' RATE compensation

d 1 if 2/3 of worker's weekly wage exceeds WCMAX, 0

otherwise

STRONG QUIT 1 if Worker is looking very h a r d for a new j o b , INTENTIONS 0 OTHERWISE

WEAK QUIT 1 if w o r k e r is looking at least s o m e w h a t h a r d for a INTENTIONS new j o b , 0 otherwise ACTUAL QUITS 1 if w o r k e r c h a n g e d j o b s in t h e past year, 0 otherwise

SAMPLE SIZE

Tenure s 3

12,93 (2,61) .

0,11 (0,31)

0,07 (0,26)

1,05 (1,13)

0,09 (0,28)

21,17 (12,15)

0,34 (0,48)

7,11 (2,33)

7,54 (9,81)

210,81 (69,89)

0,70 (0,20)

0,74 (0,44)

0,08 (0,27)

0,14 (0,34)

0,03

2007

Tenure < 3

13,23 (2,35)

0,21 (0,41)

0,05 (0,22)

0,97 (1,09)

0,20 (0,40)

13,61 (10,08)

0,23 (0,42)

6,31 (2,45)

8,43 (9,50)

195,35 (65,50)

0,70 (0,18)

0,66 (0,47)

0,15 (0,36)

0,22 (0,42)

0,10

564

equations for the junior worker group provide information on the preferences of the newly hired worker. Our principal hypothesis is that the wage-benefit trade- offs for the newly hired worker group will be less negative than those of the marginal senior worker group, which will include many workers who have acquired unfa- vorable information about risks on the job. We expect the risk effect to be larger in the senior worker group for the same reason. The self-selection of workers with more extensive learning and more precise risk perceptions into the junior worker group will tend to work against our principal hypotheses.

The quit equations are estimated using three measures of quit behavior. Table 1 defines these variables. If a worker an-

swers "yes" to a question asking whether he is considering looking for a new job, the weak quit intention variable equals one; if "no," it equals zero. A similarly constructed measure of strong quit inten- tions equals one if the worker reports that he is seriously considering a new job and zero otherwise. Finally, the actual quit variable equals one if the worker quit during the year and zero otherwise. This variable pertains only to the 1981 and 1982 data. The quit variables reflect aggregate quit behavior fairly closely, as the average quit rate in manufacturing industries equaled about 1.5% per month in the late 1970s.•' The average value of the weak quit intention variable of 14%, or

' See U,S, Department of Labor (1977),

INDUSTRIAL AND LABOR RELATIONS REVIEW

1.2% per month, roughly equals the observed rate. The actual quit rate in our sample, 4.5% per year, is lower than the aggregate manufacturing rate, as ex- pected, given the broader mix of indus- tries represented in our sample.

The quit equations estimated are of the form

(13) Quit, = [1 + exp - (a'X,

-I- 8^Weighted Replacement Rate,)]

Due to the binary nature of the depen- dent variable in these equations and the pres- ence of the endogenous wage and replace- ment rate variables on the right-hand side of equation 13, nonlinear two stage least squares is used to estimate the parameters of the model.'2 As shown by Amemiya (1985), these estimates are both consistent and asymptotically normal. Instrumental variables include all of the exogenous ex- planatory variables in the quit equations and state dummy variables.'^

Higher worker wages should reduce quitting by increasing the attractiveness of the worker's current job. Quit rates should increase with risk levels if there are learning-induced quits, and higher work- ers' compensation benefits should dimin- ish quitting. The coefficients of interest are 4>q> 7o. and 8̂ , which we will use to calculate the wage—workers' compensation

'^ The SAS procedure SYSNLIN, with the Gauss- Newton Minimization method, is used to estimate the model.

'^ We experimented with the use of age, experi- ence, and tenure variables as instruments. Since these variables are important predictors of the wage, they would serve as useful instruments if they were independent of the error term €^. When experience and tenure variables are added to he vector of instruments, there is no change in point estimates of the coefficients. The estimated standard errors are larger when the age variable is used in the weak quit equation. The main results, particularly those in the actual quit equation, are unaffected. We report results using the age, experience, and tenure variables as instruments.

and wage-risk tradeoffs implied by work- ers' quit behavior.

Table 2 presents estimates of equation 13, using the actual quit variable, for workers in each tenure group. The actual quit variable provides a strong measure of the job satisfaction of workers, since workers are less likely to quit than to merely seek a new job. This measure should consequently reflect most strongly the role of worker learning in affecting the wage-risk and the wage-workers' compensation tradeoffs.

The Table 2 results indicate that wages and job risk characteristics are the most important determinants of workers' quit behavior. For workers with more than three years of tenure, increases in the wage exert significant downward pressure

Table 2. Determinants of Quits: 857 Workers, 1981-1983.

(Standard Errors in Parentheses)

Independent Variable"

EDUCATION

FEMALE

HEALTH LIMITATION

DEPENDENTS

SINGLE

EXPERIENCE

UNION STATUS

In (Weekly Wage)

RISK

WEIGHTED REPLACEMENT

RATE

CONSTANT

Tenure s 3

0.366** (0.167)

-0.832 (0.795) 0.408

(0.641) 0.418**

(0.227) 0.534

(0.671) -0.038* (0.029)

-0.050 (0.568)

-4.111*** (1.430) 0.590***

(0.215) -1.673*** (0.659) 18.029 (7.098)

Tenure £ 5

-0.525*** (0.211)

-0.549 (0.659)

-0.591 (1.632)

-0.306 (0.293) 0.398

(0.670) -0.073* (0.046)

-0.361 (0.557) 3.076**

(1.472) -0.028 (0.144)

-0.141 (0.251)

-12.826 (6.472)

Sources: Michigan Panel Study of Income Dynam- ics, 1981, 1982, 1983; National Institute for Occupa- tional Safety and Health data for 1981-85.

^ Also included as explanatory variables are a Southeast regional dummy variable, a city size variable, and a year dummy variable.

* Statistically significant at the .10 level: ** at the .05 level; *** at the .01 level (one-tailed tests).

JOB TENURE AND WORKERS' COMPENSATION 89

on quits. Similarly, increases in workers' compensation benefits (as captured by tbe weighted replacement variable) decrease quits by workers who have been on the job for three years or more. Furthermore, among senior workers, increases in the risk level have a significant positive effect on quit behavior, as on-the-job experience makes workers more aware of adverse working conditions and also increases the precision of their estimates of the proba- bility of a job-related injury or health problem. This risk effect is the same as that found by Viscusi (1979a). The wage effect, too, mirrors the earnings effect in Viscusi's quit intention equation. The additional effect shown by the workers' compensation variable provides further support for the model of rational worker learning: as workers perceive their jobs to be more dangerous, workers' compensa- tion benefits become more valuable to them and serve to dampen the influence of risk on turnover by mitigating the financial losses associated with an accident.

The remaining variables in the quit equation measure the effect of the work- er's characteristics on quits, holding con- stant the wage, the job risk, and the weighted replacement rate. In most cases, the signs of these variables are theoreti- cally indeterminate. Significant effects are found, however, for education, number of dependents, and work experience in the more senior worker group.

The equation for workers with less than three years of tenure indicates that roughly the same control variables exert a significant effect on worker quits as in the senior group. In one case, however, the effect is opposite that for the older group: education increases quits by the older workers but decreases quits by younger workers. This result could reflect the net effects of a number of influences. Educa- tional attainment may affect one's pros- pects for external mobility in a manner that varies with the extent of one's job-specific experience. Education may also complement specific training, which is accumulated in the early years of a job. This complementarity would tend to re- duce quits initially. The negative educa-

tion effect for junior workers could also reflect firms' greater commitment to pre- serving the match for more educated young workers. As workers acquire more work history, the job-signaling informa- tion content of the education becomes less important to the employer. For senior workers, greater education increases job mobility, with little connection between a worker's education and the firm's desire to retain the worker.

Consistent with our theoretical predic- tions, none of the job risk characteristics variables are statistically significant in the equation for junior workers, because their perceptions of risks and, therefore, their valuations of risk insurance are very imprecise. Indeed, in the early stages of the employment process, workers have been shown by Viscusi (1979a) to show a systematic preference for jobs with charac- teristics that are only poorly understood. This preference is reflected in the lack of an effect of the risk variable and the weighted replacement rate variable.

The wage variable exerts a significant positive effect for the junior workers. This unexpected result could be due to a number of influences. The most obvious explanation, that the causality between quits and wages runs in the opposite direction, not only would have to survive the instrumentation but also would have to provide an explanation of why reverse causality only matters for junior workers. Alternatively, wages could be acting as a proxy for skills that junior workers are offering to different employers. These skills could be observable to both the worker and the firm, unlike those in the signaling context discussed above, but not captured in our data. More able workers and firms would be willing to invest more in the job matching process. Furthermore, in the job matching model of Mortensen (1978), wages are not necessarily nega- tively related to turnover. Rather, the wage acts as a proxy for the match-specific capital; and inclusion of these components of the capital as regressors will eliminate any wage effect. Finally, it could be the case that the expected search costs are lower for high-wage young workers than

90 INDUSTRIAL AND LABOR RELATIONS REVIEW

for those bound by minimum wages, who face large queues for available jobs, thus making the high-wage junior workers more likely to quit.

For the actual quit equation, we thus have reasonably precise estimates of the parameters used to calculate the wage- workers' compensation tradeoff for work- ers with more than three years of tenure. The wage, risk, and workers' compensa- tion variables are all significant at the 1% level or lower for these workers. As expected, variation in benefits does not

cause any significant variation in quits for new hires.

Further support for the learning model is found by comparing the effects of RISK on quit intentions and on actual quits across tenure groups for alternative specifications. Table 3 presents the risk and workers' com- pensation effects for all three measures of quit behavior, as well as the effects of dif- ferent measures of the wage variable (the after-tax weekly wage is always used in the replacement rate variable). In addition to the actual quit variable, we also estimate

Table 3. Wage-Risk and Wage-Replacement Rate Tradeoffs: Summary of Coefficient Estimates. (Standard Errors in Parentheses)

Dependent Variable

Actual Quits (i) ln (Weekly Wage)

TENURE S: 3

TENURE < 3

(ii) Weekly Wage TENURE a S

TENURE < 3

Weak Quit Intentions

(i) ln (Weekly Wage) TENURE a 3 .

TENURE < 3

(ii) Weekly Wage TENURE a 3

TENURE < 3

Strong Quit Intentions

(i) ln (Weekly Wage) TENURE a 3

TENURE < 3

(ii) Weekly Wage TENURE a 3

TENURE < 3

Sources: See notes to Table 2. ' Estimates would not converge. * Statistically significant at the .10 level;

Wage

-4.111*** (1.430) 3.076**

(1.472)

-0.012** (0.005) 0.004**

(0.002)

-2.246*** (0.573) 0.458

(0.681)

-0.005** (0.002) 0.04E-3

(1.58E-3)

-2.104** (0.646) 3.077**

(1.472)

-0.005** (0.002)

a

** at the .05 level;

Risk

0.590*** (0.215)

- 0 . 0 2 8 (0.144)

0.650*** (0.245) 0.003

(0.115)

0.077* (0.051)

- 0 . 0 3 1 (0.057)

0.072* (0.048)

- 0 . 0 0 9 (0.053)

0.085** (0.052)

-0.028 (0.144)

0.087** (0.050)

a

Weighted Replacement Rate

-1.673*** (0.659)

-0.141 (0.251)

-1.819*** (0.690)

-0.130 (0.221)

-0.122* (0.083) 0.042

(0.076)

- 0 . 1 1 5 * (0.079) 0.014

(0.072)

- 0 . 1 1 1 * * (0.087)

- 0 . 1 4 1 (0.251)

-0.112* (0.083)

a

*** at the .01 level (one-tailed testsV

JOB TENURE AND WORKERS' COMPENSATION 91

equation 13 using measures of weak and strong quit intentions as dependent vari- ables. As the results in Table 3 indicate, the findings in Table 2 are quite robust over the different specifications. Both higher wages and increases in the weighted replacement rate significantly reduce quits and quit in- tentions for workers in the more senior ten- ure groups. Eor workers with less than three years' tenure, actual quits are positively and significantly related (as before) to wages; nei- ther risk nor the weighted replacement rate shows any systematic effects on quits or quit tendencies for these workers.

To evaluate the robustness of our results, particularly with respect to the specifications used in some of" our previ- ous work, we also estimated the quit equations using the benefit measure of Moore and Viscusi (1989), which analyzed the effects of workers' compensation on job fatalities. As noted above, the numera- tor of the weighted replacement rate variable in this case uses the maximum benefit payment for the worker's state as the numerator of the replacement rate. To account for the fact that changes in the benefit maximum will be more highly valued by the workers whose wage exceeds the maximum, we estimate 8̂ separately for each worker group (that is, for those workers whose current weekly wage places them above or below the maximum). This comparison is accomplished using the dummy variable D, defined earlier. The variable D is also treated as endogenous in estimating the quit equations. An addi- tional test of the plausibility of our results is the prediction that the estimate of 8̂ , given D = 1, should be more negative than its estimated value when D = 0.

Use of the benefit maximum as the numerator could introduce some error into our measure of the replacement rate, since it overstates the replacement rate for workers whose wages are low enough to place them below the maximum in their state. In Moore and Viscusi (1989), how- ever, we argue that changes in the maximum will be valued by all workers because weekly wages are not known with certainty a priori. An increase in the maximum will therefore increase expected

benefits for all workers, with the extent of the increase being felt most strongly by workers whose wage places them above the maximum. Einally, since benefit ceil- ings are one of the primary policy instru- ments available for altering benefit levels, direct estimates of the effect of changes in the maximum are most relevant for policy purposes.'"*

The results of this estimation, summa- rized in Table 4, essentially replicate those reported in Table 3 in terms of sign and statistical significance. The quit behavior of workers with more than three years of ten- ure is systematically related to both the risk level and the risk-weighted value of work- ers' compensation benefits. Increases in risk lead to significantly more quits, and to in- creased intentions to quit. This effect is once again reduced by insurance for financial losses and medical costs associated with an injury that are embodied in the workers' compensation program. Quits and quit in- tentions of workers with less than three years' tenure, on the other hand, are not significantly related to either of these forces.

A further test of the plausibility of the model compares the coefficients on the weighted replacement rate variables for workers whose wage places them above or below the benefit maximum. If workers are uncertain about their future wage, then each worker will attach some likeli-. hood to the possibility that the wage at the time of an injury will exceed the benefit maximum. Workers whose wages cur- rently exceed the maximum will attach a greater probability to this outcome and will, therefore, value changes in the benefit maximum more highly. As a consequence, the estimated replacement rate effects should be more negative for workers whose current weekly wage ex- ceeds the benefit maximum.

Our results support this prediction for the

'̂ A further reason for use of the maximum is that it allows direct comparisons with our other published studies on this subject (Moore and Viscusi 1990), An earlier version of this paper used the benefit maxima rather than the replacement rate as the benefit measure. The signs, significance levels, and magni- tudes of the key coefficients in the wage and quit equations mirror those reported here.

92 INDUSTRIAL AND LABOR RELATIONS REVIEW

Table 4. Wage-Risk and Wage-Benefit Tradeoffs: Summary of Coefficient Estimates Using Alternative Benefit Variable. (Standard Errors in Parentheses)

Dependent Variable

Weak Quit Intentions TENURE a 3

TENURE < 3

Strong Quit Intentions

TENURE a 3

TENURE < 3

Actual Quits"

TENURE a 3

TENURE < 3

In (Wage)

-2.022*** (0.680)

1.193 (1.025)

-2.200*** (0.750)

-0.422 (0.904)

-0.070** (0.042)

0.151 (0.106)

Independent

Risk

0.266** (0.115)

-0.062 (0.087)

0.411*** (0.159)

0.042 (0.086)

5.73E-3*** (2.32E-3)

-2.28E-3 (5.58E-3)

Variable

Weighted Replacement Rate

X d

-0.579** (0.290)

0.091 (0.124)

-1.028** (0.464)

- 0 . 1 2 9 (0.176)

-8.45E-3*** (2.94E-3)

-3.09E-3 (6.82E-3)

X (l-d)

-0.205** (0.101)

- 0 . 1 0 1 (0.094)

-0.290** (0.139)

- 0 . 0 2 6 (0.072)

a

a

Sources: See notes to Table 2. " Not significantly different from adjacent estimates. * Statistically significant at the .10 level: ** at the .05 level; *** at the .01 level (one-tailed tests).

senior worker group in two of three cases. For both quit intention variables, the weighted replacement rate effects are neg- ative and significant whether the wage ex- ceeds or falls below the maximum. Further- more, the estimated coefficient is always more negative than the corresponding esti- mate for low-wage workers. In the actual quit equation estimated for more senior workers, the two estimates are not signifi- cantly different from each other. When re- stricted to equality, they are negative and significant. Consistent with our principal hy- pothesis, the estimated wage-benefit trade- offs are less negative for the junior worker group. This pattern obtains for both low- and high-wage workers, regardless of the quit variable used. These results, and the general robustness of the results in Table 3, indicate that the hypothesis withstands a va- riety of changes in specification.

Tests of Worker Learning-Wage Benefit Tradeoffs

The model predicts that the wage- benefit tradeoff should be more negative

for senior workers on the quit-no quit margin than for the new hires. In an important sense, the detailed calculations are unnecessary, since none of the trade- offs for the junior worker groups are based on coefficients that differ signifi- cantly from zero. Nonetheless, point esti- mates will indicate whether the hypotheses hold for the data in our sample. Using the results in Table 3, the tradeoffs can be calculated directly.

In the quit equations, the wage-benefit tradeoff is computed as the negative of the ratio of the partial effect of a dollar increase in workers' compensation bene- fits on quit intentions, -(dQ/db), to the effect of a dollar increase in wages on the same dependent variables, {dQ/dw), where b and w denote the benefit and the wage:

(14) ola; _ -dQ/db

db ~ dQ/dw •

To evaluate this expression, the quit equation given by equation 13 above is rewritten as

JOB TENURE AND WORKERS' COMPENSATION 93

(15) Q = [1 + exp - {a'X + + -ij, + \pR)],

where w denotes the wage, p the death risk, H the hours worked per week, and R the replacement rate.'^ Letting P ( 0 denote the right-hand side of this equa- tion, the partial effect of an increase in the wage on quits and quit intentions equals

= p(Q) (l-P(Q)) (4),// +

(20)

dw

The replacement rate can be written as a function of the weekly wage, the dummy variable D, the benefit ceiling WCMAX, and the tax rate:

(2/3) {\-D)wH

Differentiating this expression with re- spect to the wage then yields the expres- sion

(18) dw

-(m)wH]— - DWCMAX] dw

The first term in this expression will always equal zero, since variation in the weekly wage will affect the variable D only when the worker just qualifies for the maximum, at which point the first part of this term equals zero. Inserting the non- zero portion of equation 18 into equation 16 yields the expression

(19) a Q ^ dw

Again using equation 15 and the defini- tion of the replacement rate R = bl w{\—t)H, the effect of an increase in benefits on quit behavior equals

'^ Note that we now use p to denote the risk of an injury, whereas the theoretical model used p to denote the probability of no injury.

db

The wage-benefit tradeoff then equals the negative of the ratio of equation 20 to equation 19,

db

Similar calculations yield the expression for the wage-risk tradeoff

(22) ^ = dp

Table 5 summarizes the median wage- benefit tradeoffs, and also the median wage- risk tradeoffs computed using the coeffi- cient estimates in Table 3. We report medians to control for outlier problems associates with computing the sample means.

In every case considered, as predicted by the theory, the median wage-benefit tradeoffs are more negative (and less than zero) for the senior workers. For the median senior worker in the quit equa- tion, the wage-benefit tradeoff equals

Table 5. Wage-Benefit and Wage-Risk Tradeoffs: Median Estimated Effects.

Quit and Wage Variables

a 3 years 3 years

Wage-Benefit Tradeoffs Actual Quits

ln (Weekly Wage) (x 10-') Weekly Wage (x 10-')

Weak Quit Intentions ln (Weekly Wage) (X 10"^) Weekly Wage (x 10"')

Strong Quit Intentions ln (Weekly Wage) (X 10"') Weekly Wage (x 10"')

- 3 . 5 - 3 . 6

- 6 . 6 - 7 . 6

- 6 . 4 - 7 . 4

Wage-Risk Tradeoffs Actual Quits

In (Weekly Wage) (x 10"^) Weekly Wage (x 10 "2)

Weak Quit Intentions ln (Weekly Wage) (X 10"^) Weekly Wage (x 10 "2)

Strong Quit Intentions ln (Weekly Wage) (X 10-^) Weekly Wage (x 10 "2)

22.5 12.2

- 4 . 4 - 6 . 3

0.4 1.7

5.3 12.0

- 0 . 2 48.9

5.3 a

25.5 37.9

- 2 . 6 -20.8

25.5 a

Sources: See notes to Table 2.

94 INDUSTRIAL AND LABOR RELATIONS REVIEW

- . 3 5 cents per dollar of henefits, com- pared to a positive wage-benefit tradeoff of .53 cents per dollar of benefits for junior workers. In addition to the absolute differences observed in the quit equations and the similar differences in the weak and strong quit intentions equations, the wage-benefit tradeoffs for the senior workers are all based on coefficient esti- mates that are statistically significant, whereas those tradeoffs estimated for the junior workers represent a combination of coefficients, none of which are signifi- cantly different from zero. Finally, the wage-benefit tradeoffs for the senior workers are quite stable across all six equations. Thus, in addition to the ob- served differences in the tradeoffs be- tween the more and less senior workers, the tradeoffs estimated for the senior worker group are also much more precise. We conclude that the predictions of the model are supported and appear to be robust across a variety of specifications of the dependent variables.

The wage-risk tradeoff results summa- rized in the second panel of Table 5 are less consistent with the learning model in which experienced workers have higher risk assessments. If workers were fully informed and all workers' perceptions were identical, the wage-risk tradeoffs would not vary by tenure group. In almost every instance, however, there is evidence of a larger positive wage premium for risk in the case of less experienced workers on the quit margin. Once again, however, few of the coefficient estimates in the junior worker group are significant, so this result is not particularly troublesome. Moreover, since the risk variable enters the equation directly and through the expected replace- ment rate variable, it may be difficult to reliably estimate the effects of these correlated variables.

Due to the insignificance of the coeffi- cients in the junior worker equations, the most important information to be gained from these calculations lies not in the com- parison of effects, but in the policy implica- tions of the estimates for the senior worker group. Most important, the results support the view of the labor market as an efficient

sorter of workers that compensates workers for exposure to risk and subsidizes injury insurance through wage reductions.

We can also use these results to estimate whether benefit levels are adequate, using the procedure in Viscusi and Moore (1987). In this framework, the observed tradeoff will equal the ratio —pl(\ —p), where p is the probability of an accident. For the fatality risk data, this ratio equals about - 1/10,000. The estimated tradeoffs between benefits and wages in the actual quit equations, when both wages and benefits are put on a weekly basis, are many times greater than this figure.'^ Thus, since the observed tradeoff exceeds the optimal tradeoff, it would appear that benefit levels are too low. Since we do not include a nonfatal risk variable in the quit equations, however, the observed rate of tradeoff might reflect instead the rate that is optimal for risks of all types of injuries, which would be much closer to the observed tradeoff.

The same coefficients can also be used to calculate the value of life and the degree of risk aversion implied by our estimates. Us- ing the estimates in the strong quit equa- tion, the estimated value of life implied by the estimates ranges between 1 and 4,25 million dollars.'^ This range is lower than the estimates in the wage equations esti- mated by Moore and Viscusi (1989) using the NTOF data, and is similar to many es- timates found in the literature. Since the wage-risk tradeoffs are not as stable as the wage-benefit tradeoffs across equations within the senior worker group, the value- of-life calculations are not as meaningful as the calculations for assessing benefit opti- mality.

Finally, since we know that workers will give up approximately .004 dollars in weekly wages for a one-dollar increase in benefits, we can compute the degree of

"'The estimated tradeoff of ,0035 dollars per hour multiplied by 40 hours yields a weekly wage decrease of 14 cents corresponding to a $1 benefit increase,

' ' For example, the wage-risk tradeoff of ,017, when multiplied by 2,000 hours, an inflation factor of 1,25, and 100,000 (the risk scaling factor that yields one statistical life), equals 4,25 million.

JOB TENURE AND WORKERS' COMPENSATION 95

risk aversion exhibited by the workers in our sample. If benefits were to rise hy two dollars per week, wages would fall by about $.01, for an annual decline of 50 cents. The discounted expected value of this two-dollar benefit increase, at an annual risk level of 1/10,000 and with 52 weeks per year and a real discount^ate of 5%, is 21 cents. Thus, the implication is that workers are risk averse, and are willing to sacrifice one dollar of wages for about 42 cents in expected insurance benefits.

Conclusion

The results in this paper and in other recent studies suggest that compensating differentials for risk should be viewed as a broader issue than the standard wage-risk tradeoff literature implies. Wages and workers' compensation serve as comple- mentary compensation mechanisms, with wages providing ex ante risk compensa-

tion and workers' compensation providing ex post earnings replacement. Each of these earnings components reduces worker quitting, and workers accept a lower wage in return for higher workers' compensation benefits. This tradeoff re- flects worker preferences for different forms of risk compensation.

The results presented here utilize these wage and quit relationships to explore the differences in the wage-workers' compen- sation tradeoff for workers on the quit margin. Estimated tradeoffs indicate that workers on the quit margin place a much higher relative value on workers' compen- sation than do new hires. Moreover, greater job risks lead to significant in- creases in quits and quit intentions for workers on the quit margin but have no effect on the quit behavior of junior workers. These fmdings are consistent with a model in which worker quits are induced in part by learning about risks on the job.

REFERENCES

Amemiya, Takeshi, 1985, Advanced Econometrics. Cambridge, Mass,: Harvard University Press,

Arnould, Richard J,, and Len M, Nichols, 1983, "Wage-Risk Premiums and Workers' Compensa- tion: A Refinement of Estimates of Compensating Wage Differential," Joumal of Political Economy, Vol, 91, pp, 332-40,

Berkowitz, Monroe, and John F, Burton, Jr, 1987, Permanent Disability Benefits in Workers' Compensa- tion. Kalamazoo, Mich,: LIpjohn Institute,

Burton, John F,, Jr, 1983, "Compensation for Permanent Partial Disabilities," In John D. Worral, ed,. Safety and the Work Force: Incentives and Disincentives in Compensation. Ithaca, N,Y,: ILR Press, pp, 18-60,

Burton, John F,, Jr,, and Alan B, Krueger, 1986, "Interstate Variations in the Employers' Cost of Workers' Compensation, with Particular Reference to Connecticut, New Jersey, and New York," In James R, Chelius, ed,. Current Issues in Workers' Compensation. Kalamazoo, Mich,: LIpjohn Institute,

Butler, Richard J, 1983, "Wage and Injury Rate Response to Shifting Levels of Workers' Compen- sation," In John D, Worral, ed,. Safety and the Work Force: Incentives and Disincentives in Compensation. Ithaca, N,Y,: ILR Press, pp, 61-86,

Butler, Richard J,, and John D, Worral, 1983, "Workers' Compensation: Benefit and Injury Claims Rates in the Seventies," Review of Economics and Statistics, Vol, 65, No, 4, pp, 580-99,

Diamond, Peter, 1977, "Insurance Theoretic Aspects of Workers' Compensation," In Peter Diamond, ed,. Natural Resources, Uncertainty, and General Equilibrium Systems. New York: Academic Press, pp, 67-89,

Dorsey, Stuart, and Norman Walzer, 1983, "Work- ers' Compensation, Job Hazards, and Wages," Industrial and Labor Relations Review, Vol, 36, No. 4, pp, 642-54,

Ehrenberg, Ronald G, 1989, "Workers' Compensa- tion, Wages, and the Risk of Injury," In John F, Burton, Jr,, ed,. New Perspectives on Workers' Compensation. Ithaca, N,Y,: ILR Press, pp, 71-96,

Kahn, Shulamit, 1987, "Occupational Safety and Worker Preferences: Is There a Marginal Worker?" Review of Economics and Statistics, Vol. 69, No, 2, p p , 262-68,

Krueger, Alan B,, and John F, Burton, Jr, 1990, "The Employers' Cost of Workers' Compensation Insurance: Magnitudes, Determinants, and Public Policy," Review of Economics and Statistics, Vol. 72, No, 2, pp, 228-40,

Moore, Michael J,, and W, Kip Viscusi, 1988. "Doubling the Estimated Value of Life: Results Using New Occupational Fatality Data," Joumal of Policy Analysis and Management, Vol, 7, No, 3, p p , 476-90,

1989, "Promoting Safety Through Workers' Compensation: The Efficacy and Net Wage Costs

96 INDUSTRIAL AND LABOR RELATIONS REVIEW

of Injury Insurance," Rand Journal of Economics, pp, 499-515,

_, 1990, Compensation Mechanisms for Job Risks: Wages, Workers' Compensation, and Product Liability. Princeton, N,J,: Princeton University Press,

Mortensen, Dale, 1978, "Specific Capital and Labor Turnover," Bell Journal of Economics, Vol, 9, No, 2, pp, 572-86,

Smith, Robert S, 1979, "Compensating Differentials and Public Policy: A Review," Industrial and Labor Relations Review, Vol, 32, pp, 339-52,

U,S, Department of Labor, 1977, Handbook of Labor Statistics. Bulletin No, 1966, Washington, D,C,: U,S, GPO,

tJ,S, Department of Commerce, 1990, Statistical Abstract of the United States. Washington, D,C,: U,S, GPO,

Viscusi, W, Kip, 1979a, Employment Hazards: An Investigation of Market Performance. Cambridge, Mass,: Harvard University Press,

1979b, "Job Hazards and Worker Quit Rates: An Analysis of Adaptive Worker Behavior," Intemationai Economic Review, Vol, 20, No, 1, pp, 29-58,

1980a, "A Theory of Job Shopping: A

Bayesian Perspective," Quarterly Journal of Econom- ics, Vol, 94, pp, 609-14,

_, 1980b, "Imperfect Job Risk Information and Optimal Workman's Compensation Benefits," Journal of Public Economics, Vol, 14, No, 3, pp, 319-37,

_, 1980c, "Sex Differences in Worker Quitting," Review of Economics and Statistics, Vol, 62, No, 3, pp, 388-98,

_, 1980d, "Self-Selection, Learning Induced Quits, and the Optimal Wage Structure," Interna- tional Economic Review, Vol, 21, No, 3, pp, 529-46,

Viscusi, W, Kip, and Michael J, Moore, 1987, "Workers' Compensation: Wage Effects, Benefit Inadequacies, and the Value of Health Losses," Review of Economics and Statistics, Vol, 69, No, 2, pp, 249-61,

Viscusi, W, Kip, and Charles O'Connor, 1984, "Adaptive Responses to Chemical Labeling: Are Workers Bayesian Decision Makers?" American Economic Review, Vol, 74, pp, 942-56,

Worrall, John D,, and Richard J, Butler, 1985, "Work Injury Compensation and the Duration of Nonwork Spells," Economic Journal, Vol, 95, pp, 714-24,