the strange case of the missing west virginia labor force

The Strange Case of the Missing West Virginia Labor Force

STUART DORSEY

ABSTRACT The rate of labor force participation in West Virginia is far below all other states. This study finds that little of the participation rate gap can be attributed to traditional economic or institutional factors. For example, the high long-term unemployment rate in West Virginia accounts for less than 10 percent of the adult male participation gap. Most of the difference is associated with a large Appalachian population, and a high rate of federal disability benefits receipt. The latter, which we argue reflects tastes for nonmarket activities, also is a major factor in low participation rates of West Virginia adult females and teenagers. Unemployment does explain a significant portion of low participation rates for these groups. However, we estimate that a decline in the state’s long-term unemployment rate to the nation’s average would raise its aggregate participation rate by 3.9 percentage points, or just 28 percent of the total gap. It appears that nonparticipating West Virginians are not just “discouraged workers,” and that economic development policies should explore ways to increase aggregate labor supply, as well as labor demand.

Introduction West Virginia has the lowest labor force participation rate in the United

States. This is not by itself remarkable, as the state ranks near the bottom in many economic indicators. However, the size of the difference between West Virginia’s participation rate and the rest of the United States is striking. In 1987 only 51.7 percent of the state’s potential workforce was either working or seeking employment,’ compared with a rate of 65.6 percent in the other states (US. Department of Labor, 1988). West Virginia’s participation rate is nine percentage points below the next lowest, Kentucky’s 60.7 percent, a gap of

Stuart Dorsey is a professor of Economics at Baker University, Baldwin City, Kansas, 66006. He wishes to state that this paper was supported by the Center for Economic Research at West Virginia University. The author wishes to thank Tom Witt, Andrew Isserman, Mary Beth Pudup, Brian Cushing, and participants in the Regional Research Insitute Seminar series, West Virginia University, for their suggestions and assistance.

50 GROWTH AND CHANGE, SUMMER 1991

nearly four standard deviations! (See Appendix for all states’ 1987 participation rates.)

West Virginia’s “participation gap” is robust over time and to different surveys. Consistent with the U.S. Department of Labor’s estimates derived from the Current Population Survey, the Census reported 1970 participation rates of 47.1 percent and 60.3 percent for West Virginia and the U.S., respectively, and a gap of 12.1 points in 1980. Table 1 reveals that the deficit is similar for adult males, adult females, and teenagers.

Despite its persistence and breadth, this phenomenon has not been formally studied. This paper attempts to identify the factors responsible for West Virginia’s weak labor force participation, and determine how much of the participation deficit can be attributed to measurable economic, demographic, and institutional characteristics peculiar to West Virginia.

Unemployment may be an important economic factor. According to the “discouraged worker” hypothesis, a high rate of unemployment lowers participation rates as unemployed workers stop actively seeking work. Indeed, West Virginia’s 1987 unemployment rate of 10.8 percent was below only Louisiana. West Virginia’s high percentage of elderly residents and lower educational attainment are demographic factors which may contribute. Also, the population is largely rural, which may discourage labor force participation of females, a group responsible for the sizable increase in postwar U.S. labor force participation. An institutional factor, the relatively high level of union membership in West Virginia, may lower participation, given organized labor’s support for public policies to discourage labor supply (Anderson, 1987 and Smith and Ehrenberg, 1988).

West Virginia is entirely within Appalachia, raising the possibility of cultural effects. The regional science literature suggests that Appalachian families have a weaker attachment to labor markets, interspersing work for pay with intermittent returns to nonmarket activities, such as “subsistence farming” (Pudup, forthcoming), or leaving jobs in urban centers to return home (White, 1983). Within the context of the economic model of labor supply, Appalachians may have a relatively strong preference for home production.

Why should the results of this study be of concern to anyone outside of West Virginia? Primarily because they speak to labor market policy for economic development. If West Virginia’s participation rate were equal to the national average, its labor force would be 27 percent larger. Obviously, the greater the labor resources of a state or region, the greater its productive potential. A low exogenous participation rate may contribute to depressed economic conditions such as those found in West Virginia. Whether or not weak participation is a drag on economic development depends upon whether it is voluntary (i.e., falls on the aggregate labor supply schedule). If it is instead hidden, involuntary

WEST VIRGINIA LABOR FORCE 51

unemployment, weak participation is an indicator, rather than a cause, of poor economic conditions.

There is little reason for concern if low participation merely reflects discouraged workers. Economic development strategy need focus only on increasing labor demand to levels comparable in high-employment states and, according to the hypothesis, employment and labor force participation would expand elastically. If, on the other hand, unemployment does not explain much of the gap, weak participation is a constraint to economic growth, and policy should focus on encouraging or enabling more of the population to enter the labor force.

Finally, the methodology provides information about the determinants of aggregate state participation rates. Many studies of individual labor force participation decisions are available, but little analysis of statewide participation rates exists. The focus of aggregate participation rate determination shifts away from income and substitution effects and examines the role of regional differences in economic factors, institutions, or preferences.

Labor Force Participation: Concepts and Theory The approach of this paper is to regress the state labor force participation rate

against a set of economic, demographic, institutional, and cultural variables. We then apply the coefficient estimates to evaluate the contribution of each to the West Virginia participation rate gap. For example, given estimates of the marginal effect of unemployment on participation rates across the states, we ask what would be the effect on West Virginia participation if its unemployment rate fell to the U.S. average.

An empirical aggregate labor force participation equation is:

(1) Ns = N(WAGE, INCOME, PERSONAL TAX RATE, PERCENT

PERCENT DISABILITY RECEIPT). HSGRAD, AGE 25-64, PERCENT RURAL, AFDC, AGE < 5, UNION,

The model is based upon the economic theory of labor supply. Individuals do not participate if the value of nonmarket time is high relative to the market wage. Thus factors which affect the net wage, nonmarket productivity, and preferences for leisure influence the decision. An increase in the wage unambiguously raises the likelihood of participation, while greater nonwage income has the opposite effect. A higher marginal personal income tax rate lowers the net wage and participation. Micro-data studies have shown that education raises the likelihood of entrance into the labor market, even given the wage, and that age-participation profiles typically are concave. (See Killingsworth, 1983, for a complete review


of empirical labor supply estimates.) States with a higher percentage of population completing high school, and between age 25 and 64 should have higher participation rates.

Child care and commuting costs have played an important role in female labor force participation studies (Cain, 1966 and Cogan, 1981). Female participation rates are likely to be lower in states with a greater percentage of small children. Also, commuting costs will vary according to population density. A married female living on a ranch in Wyoming faces higher daily travel costs to achieve the same market work opportunities as a housewife in Connecticut. Aid to Families with Dependent Children (AFDC) benefits vary widely across states, and are expected to lower participation rates for low-income single mothers. The income guarantee raises nonlabor income, while the phase-out of benefits as earned income rises imposes a substantial tax on earnings.’

A well-established theoretical result is that union success in raising wages is a function of its ability to restrict labor s u ~ p l y . ~ In addition to the obvious support for the closed shop, casual observation suggests that organized labor supports public policies to restrict or discourage aggregate labor supply (immigration and maximum hours legislation, higher social security and pension benefits, for example). Anderson (1987) has shown that AFDC benefits are higher in states where union membership is larger. We have a measure for AFDC but include the percentage of the workforce belonging to a union as a proxy for other state policies which also may lower aggregate participation. Unions, of course, also may influence labor demand, and possibly unemployment rates, and we address these issues below.

The percentage of the state’s population receiving federal disability benefits varies from 0.6 percent in Alaska to 2.4 percent in West Virginia. This variable controls for participation differences due simply to the mechanics of the disability program. Applicants generally must show that they are unable to do any kind of work to qualify for benefits. In addition, this variable is likely to pick up regional differences in preferences for market work versus nonmarket activities. Disability is an economic, as well as medical concept. Haveman, et. al. (1984) report that the working-age population with some type of work- restricting impairment is over four times the number of disability beneficiaries. While many disability recipients clearly are physically unable to work, marginally impaired workers face a choice between continuing to work or accepting disability benefits and leaving the labor force.

There is evidence that economic factors affect this decision. In the United States the net real replacement rate of disability benefits for the worker with median income rose from 35 to 49 percent between 1968 and 1978 (Haveman, et. al., 1984). Over the same period, the number of beneficiaries doubled. Empirical studies consistently have reported a statistically significant effect of benefits on participation (Parsons, 1980 and Haveman and Wolfe, 1983). These


results imply that workers who have a relative preference for nonmarket activities are more likely to seek benefits. Therefore, differences in disability receipt across states will reflect aggregate differences in preferences for work versus non-market activities.

We could estimate (1) directly if the unit of observation was the individual. However, since our focus is state participation rates, the wage is not exogenous, and variations in labor demand are relevant. In the short run weak labor demand will lower participation rates as workers move down their supply schedules. Over the long run workers will flow to states where wages are higher, further reducing employment and participation in weak-demand states.

No direct measure of worker productivity differences across states is available; however, educational attainment is a proxy. The higher the tax rate on corporate income, the lower the net return on capital and labor demand. Union organization may have a similar effect, given evidence that union presence on average negatively affects profitability (Ruback and Zimmerman, I984 and Voos and Mishel, 1986).

Solving the aggregate labor demand and supply equations yields a reduced- form participation equation:

(2) N = N (INCOME, PERSONAL TAX RATE, CORPORATE TAX RATE, PERCENT HSGRAD, AGE 25-64, PERCENT RURAL, AFDC, AGE < 5 , UNION, PERCENT DISABILITY RECEIPT).

Equation (2) describes a labor market equilibrium, in which the only unemployment is frictional. It is unlikely, however, that 1987 unemployment rates varying from 2.5 percent to 12.0 percent represent differences in frictional unemployment alone, suggesting that participation and unemployment rates may be inversely related. Time-series evidence generally supports a pro-cyclical aggregate participation rate for adult men and women (de Freitas, 1986, for example). Cross-section studies also find an inverse relation between individual participation decisions and local labor market conditions for men (Parker and Shaw, 1968). To allow for the possibility of differences in effective labor demand, the assumption of labor market equilibrium is relaxed and the long-term unemployment rate (percent unemployed more than 15 weeks) is added to equation (2).

How Well Does the the Participation Rate Model Explain West Virginia?

Table 1 comparisons predict a lower participation rate in West Virginia. Compared with the other states, West Virginia has a significantly higher unemployment rate, union organization, and rural population. A smaller


TABLE 1. WEST VIRGINIA AND AVERAGE STATE CHARACTERISTICS

Average of West Other States Virginia

PARTICIPATION RATE: Adult male Adult female Teenagers

LONG TERM UNEMPLOYMENT RATE:

Adult male Adult female Teenage

UNION

AGE 25-64

PERCENT RURAL

PERCENT HSGRAD

PERCENT DISABILITY RECEIPT

APPALACHIAN POPULATION

NONWAGE INCOME PER CAPITA

AGE < 5

PERSONAL INCOME TAX

CORPORATE INCOME TAX

MAXIMUM AFDC BENEFIT

78.4% 57.9% 56.8%

1.6% 1.6% 4.3%

19.3%

49.8%

35.2%

67.9%

1.4%

6.1%

$2,280

7.9%

4.7%

5.6%

$301

66.9% 40.6% 38.1 %

5.5% 4.0%

12.7%

28.9%

49.0%

63.4%

56.0%

2.4%

100.0%

$1,460

7.0%

4.0%

6.0%

$206

Data Sources. Unemployment and participation rates, 1987, U.S. Department of Labor (1988); per capital nonwage income, 1979, age 25-64, age < 5, rural population, high school graduates, all 1980, U S . Bureau of the Census; personal income tax rate, 1986, Advisory Commission on Intergovernmental Relations; corporate income tax rate, 1983, All States Tar Handbook; union employment, 1982, Industrial Relations Data and Information Services; AFDC benefits and disability beneficiaries, 1987, Social Security Administration; Appalachian population, 1980, Appalachian Regional Commission annual reports.

WEST VlRGiNlA LABOR FORCE 55

TABLE 2. REGRESSION ESTIMATES OF THE ADULT MALE LABOR FORCE PARTICIPATION RATE MODEL

INTERCEPT

LONG-TERM UNEMPLOYMENT RATE

POPULATION CHANGE 1980-87

UNION

PERCENT HSGRAD

AGE 25-64

INCOME

PERSONAL INCOME TAX




R2

N

(a)

79.1

-.353 (1.17)

-.038 (.91)

-.I00 (2.07)

-.079 (1.12)

,464 (2.77)

-.0026 (3.19)

-.075 (.73)

,138 (1.19)

-5.60 (4.46)

-.064 (2.93)

.703

51

(b)

77.37

-.257 (.77)

-.039 (.93)

-. I06 (2.15)

-.058 (.75)

.453 (2.68)

-.0023 (2.55)

-.048 (.43)

.111 ~ 9 0 )

-5.37 (4.10)

-.049 (1.58)

.569

50

States’ Points Mean Exp- - WV lained Value

-3.9 1.00

10.3 -.40

-9.6 1.02

11.6 -.67

0.8 .36

820 -1.89

0.7 -.03

-0.2 -.02

-1.0 5.37

-93.7 4.59

c 9.33

56 GROWTH AND CHANGE. SUMMER 1991

percentage of the population is between 25 and 64, fewer have graduated from high school, and the percentage of the population receiving disability benefits is the nation’s highest. Only lower nonlabor income and AFDC benefits would raise the participation rate. The ability of these differences to explain the participation gap depends on the marginal effect of each characteristic. This section presents cross-section estimates of state participation rates, and uses the results to evaluate the contribution of the independent variables to West Virginia’s low participation rate. The model is estimated separately for adult males, adult females, and teenagers, including only the relevant independent variables. For example, AFDC and AGE < 5 are expected to influence female, but not male participation rates.

Table 2 reports estimates of adult male labor force participation rates. According to regression (a), which includes all states, the long-term unemployment rate does not have a statistically significant effect on participation. Note that a negative coefficient would result from workers dropping out of the labor force in discouragement, or from unemployed workers migrating to states with low unemployment rates. Migrants from states where job prospects are poor are more likely to be labor market participants, leaving a disproportionate share of the population in high unemployment states who have opted out of the labor force (e.g., retirees) and therefore, are less concerned about labor market outcomes. The percentage change in state population between 1980 and 1987 is included as a direct control for the effect of migration on participation. States losing population due to weak labor market conditions should have a lower participation rate; however, the population change variable is not significant. There is no evidence that higher unemployment reduces participation either because workers drop out of the labor force or move to high-employment states.

The union coefficient implies that a one-standard deviation increase in the union share of employment (7.5 percentage points) lowers the participation rate .75 percent, on average. This is a reduced-form coefficient, and may reflect union support for public policies that reduce labor supply, or lower profitability, hence labor demand, in union firms. More detailed data are needed to estimate the structural equations which would identify the origin of this effect. We did consider the likelihood that union strength may be correlated with income and unemployment. Excluding the income variable had virtually no effect on the union coefficient. However, when equation (a) is estimated without the long- term unemployment rate, the coefficient on UNION rises to -.145 (t=3.07). The reduced-form coefficient appears to be robust.

The expected negative nonwage income effect and positive age distribution coefficient were found. However, the results indicate no systematic impact of state tax policies on aggregate labor force participation.

The strongest correlate of participation rates is the percentage of population receiving federal disability payments. The eligibility mechanism, under which


workers must withdraw from the labor force to receive benefits, implies a coefficient slightly larger than unity. Yet the disability coefficient is higher by a factor of five!4 This result is robust to the presence or absence of the unemployment, union, or Appalachian population variables. (Excluding Appalachian population raises the disability coefficient to -6.2.)5

Studies by Pudup (forthcoming) and White (1983) imply that West Virginia’s low participation rate may be an Appalachian effect. The Appalachian population coefficient is consistent with the hypothesis that persons in this region have a weaker attachment to the labor market, even controlling for all economic, demographic, and institutional variables.

Of course, West Virginia is virtually 100 percent Appalachian, and the state also has the highest rate of disability receipt. We tested the possibility that the disability and Appalachia coefficients simply reflect an unmeasured West Virginia effect, by excluding the West Virginia observation in the regression reported in column (b). The estimated coefficients are very similar. Interstate variations in disability receipt and Appalachian population are highly negatively correlated with participation even disregarding West Virginia.

Application of the Model to West Virginia The disability and Appalachia variables stand out in Table 2, indicating that

differences in preferences or work attitudes dominate other factors in explaining interstate differences in adult male participation rates. The focus on aggregate participation rates also raises the possibility that union organization can lower participation rates. Of the traditional economic variables, nonwage income has the expected effect, but neither the unemployment rate nor tax policies have a significant impact on adult male participation.

While these coefficients are by themselves interesting, our main purpose is to use them to identify the determinants of the West Virginia participation rate gap. Column (c) lists the difference between the mean value of the other 50 states and the West Virginia value for each independent variable. Column (d) is the product of this difference times the regression coefficient in column (b). Under the assumption that the underlying structure of participation rate determination in West Virginia is not different from the rest of the US., the figures in the last column represent what the participation rate would be if West Virginia had the mean value of each independent variable.6 For example, West Virginia’s union employment percentage is 9.6 points higher than the sample mean. If each percentage-point increase in union employment share lowers participation by .lo6 points, this factor by itself “explains” 1.02 percentage points of the gap. The same methodology attributes one point of the deficit to relatively high unemployment (although the confidence interval about this


estimate is high) and .36 points to a smaller share of the population at prime working ages. However, these factors are offset by an income effect: West Virginia’s lower per capita nonwage income suggests that its participation rate should be almost two points higher.

The most important factor in this accounting exercise is federal disability receipt. Almost half of the adult male participation rate gap of 11.5 points is attributable to this variable. Nearly as much is accounted for by the Appalachia effect. Even excluding West Virginia, states with a relatively large Appalachian population experience lower participation rates, and of course West Virginia’s population is nearly 100 percent in Appalachia. Almost all of the 9.33 points of the deficit accounted for by the regression model is due to these variables.

Notice the use of the phrase “accounts for,” rather than “explains.” Disability receipt does not by itself reduce participation; rather it proxies for factors which simultaneously increase the likelihood of receiving payments and reduce labor supply. While a plausible case can be made that this latent variable is preferences for market versus nonmarket activites, it is only that-a plausible case. Nevertheless, the weight of the disability variable suggests that cultural factors dominate economic variables or institutions in causing the lower West Virginia participation rate for adult males.

Results for Females and Teenagers The participation rate for adult females was 17.3 points lower in West

Virginia than the other states in 1987. Table 3 analyzes this gap. The regression model adds variables which are expected to affect primarily secondary workers- AFDC benefits, percentage of population under 5, and rural population. With two exceptions, the estimates are similar to the male results. The union coefficient is smaller and not statistically significant, while unemployment has a large impact. The latter is consistent with well-established larger female wage elasticities: higher unemployment reduces the expected wage, causing females to withdraw from the labor force. The income and age distribution effects also are larger for females. However, neither AFDC benefits, child population, nor rural population has a signficant effect on female participation rates.

The disability coefficient is very close to the male estimate, while the Appalachia effect is nearly doubled. Therefore, disability receipt and Appala- chian population also account for most of the weak participation of West Virginia females. Unlike adult males, however, about one-fourth of the gap is attributable to the depressed West Virginia labor market.

Table 4 reports the results of a similar methodology for all teenagers.’ The regression model excludes age distribution and high school graduate percentage and adds the percentage of teenagers attending school, the most likely alternative

TABLE 3. REGRESSION ESTIMATES OF ADULT FEMALE LABOR FORCE PARTICIPATION RATE MODEL

States’ Points

- WV plained Mean EX-

(a) (b) Value

INTERCEPT

LONG-TERM FEMALE UNEMPLOYMENT RATE


UNION

PERCENT HSGRAD

AGE 25-64

INCOME

PERSONAL INCOME TAX


PERCENT DISABILITY RE- CEIFT


AGE < 5

AFDC BENEFITS

RURAL POPULATION

R2

N

33.1

-1.98 (2.30)

-.020 (.26)

-.044 (.49)

-.057 (1.12)

1.08 (3.07)

-.0047 (2.94)

-.172 (1.10)

.068 (.38)

-5.62 (1.97)

-.118 (3.74)

(.33)

.003 (.64)

.027 (1.02)

-.369

.793

51

31.8

-1.96 (2.87)

-.022 (.271

-.045 (.SO)

-.050 (.38)

1.08 (3.04)

-.0046 (2.61)

-.I62 (.W .056

(.29)

-5.48 (1.85)

-.I 1 I (2.43)

-.341 (.30)

.004 (55)

.028 (1.01)

,719

50

-2.4

10.3

-9.6

11.6

0.8

820

0.7

-0.2

- 1 .o

-93.7

0.9

95

-28.2

4.70

-.23

.43

-.58

.86

-3.77

-.11

-.01

5.48

10.4

-.3 1

.38

-.79

C16.45


TABLE 4. REGRESSION ESTIMATES OF TEENAGE LABOR FORCE PARTICIPATION RATE MODEL

(a) (b) States' Points Mean Explained wv

Value

Intercept

LONG-TERM TEENAGE UN- EMPLOYMENT RATE


UNION

SCHOOL ATTENDANCE %

PERCENT NONWHITE

INCOME

PERSONAL INCOME TAX


AFDCBENEF'ITS



R2

N

82.1

-1.07 (2.67)

-. 104 (1.20)

,097 (.92)

-.I44 (.65)

-215 (3.85)

-.0042 (2.63)

-.418 (1.83)

.415 (1.62)

-.003 (.36)

-3.33 (1.52)

-. 173 (3.76)

.789

51

83.3

-1.51 (2.62)

-. 127 (1.41)

,088 (.83

.042 ( ~ 4 )

-.225 (3.97)

.0048 (2.82)

-.515 (2.08)

.517 (1.87)

-.004 (.44)

-3.58 (1 6 3 )

-.220 (3.37)

.765

50

-8.4

10.3

-9.6

-2.3

12.0

820

0.7

-0.2

95

-1.0

-93.7

12.68

-1.31

-.84

-.10

-2.7

-3.94

-.36

-.10

-.38

3.58

20.6

c 27.1


to employment for teenagers. Nonwhite population is added because black teenagers, unlike black adults, are much less likely to enter the labor market. The U.S. participation rates for black and white teenagers were 41.6 percent and 57.7 percent, respectively, in 1987 (U.S. Department of Labor, 1988).

Similar to adult females, the long-term unemployment rate has a large negative impact on teenage participation. This result is consistent with a high opportunity cost of time for teenagers-if employment prospects are dim, younger workers can choose to enroll in school-and also with micro-data studies which show a much greater unemployment elasticity for teenagers than adult males (de Freitas, 1986). Nonlabor income and the proportion of nonwhite population have large and significant negative coefficients. However, there is no evidence that union strength reduces participation rates for teenagers.

The disability effect again is quite important, especially when the West Virginia observation is excluded. Note that the mechanical correlation between disability and labor force withdrawal should be lessened for teenagers, since variations in this variable primarily will reflect adult disability. On the other hand there will be an income effect for 16-17 year olds, who receive benefits as dependents of disabled adults. The persistence of this effect for teenagers is expected if disability is a proxy for region-specific tastes for labor market participation. Also note that the Appalachian population variable has its greatest effect for teenagers. The model accounts for a 27 percentage point participation deficit, while the actual deficit is just 18.7 points. In other words, given the high teenage unemployment rate, disability rate, and Appalachian population, we might expect West Virginia’s teenage labor force participation rate to be even lower.

Conclusion This paper began with an empirical puzzle: why is the labor force participa-

tion rate in West Virginia so low? The results of our attempt to explain the gap are primarily negative-very little can be attributed to measurable economic or demographic factors. Of the traditional economic variables, only nonwage income per capita has a statistically significant coefficient, but this factor predicts greater participation in West Virginia. Moreover, almost all of the model’s explanatory power comes from disability receipt and Appalachian population, two variables which proxy for unidentified latent variables.

Yet the information is valuable. First, we are able to reject the hypothesis that depressed labor market conditions are the primary cause of weak participation in West Virginia. The state’s high long-term unemployment rate accounts for just 8.7 percent and 27 percent of adult male and female participation rate gaps, respectively (if we count the statistically insignificant male coefficient).


Most of the teenage gap is due to higher unemployment. But even including teenagers, if all West Virginia’s long-term unemployment rates were equal to the other states’, its overall participation rate would rise by just 3.9 points, only 28 percent of the the total gap. The implication that low labor force participation contributes to, rather than simply reflects, poor economic performance is consistent with the state’s low participation rate in the late 1970s when its unemployment rate was low.

Second, the results suggest that cultural differences in the population explain much of the variation in aggregate labor force participation. Even excluding West Virginia, disability receipt had the most explanatory power in the empirical model. The most plausible explanation is that preferences for nonmarket activities increase disability receipt, while simultaneously lowering participation rates. This interpretation is suggested by numerous studies which have indicated that disability receipt is influenced by economic factors. The results also confirm weaker attachment of the Appalachian population to formal labor markets.

The policy implication is that economic development strategies should seek to promote labor supply, as well as demand. Our results contradict the notion that West Virginia’s small labor force, relative to its population, is an illusion created by high unemployment, and that the participation rate gap represents an extra 200,000 workers who are willing and able to enter the labor force if effective labor demand were comparable to other states. Clearly, raising employment opportunities should be the top policy objective, given the state’s high unemployment rate, especially in the short run. But our results suggest that as labor demand expands, weak labor force participation will constrain further West Virginia economic development. Policies to encourage more of the population to enter the labor force will be needed to raise the state’s long-term economic growth rate.

The nature of these policies will require a better understanding of why labor force participation is low in Appalachia. Are there impediments - educational, informational, or institutional- not captured by our aggregate model? Or do nonparticipants simply prefer nonmarket activities? What is the nature of these activities, i.e. what are nonparticipating West Virginia adult males doing? An economist is loathe to attribute such an interesting economic phenomenon to “tastes and preferences.” However, these results suggest that the most fruitful research of the West Virginia and Appalachian labor market will be achieved by attempting to look inside the “black box” of tastes and preferences, along with studying competing nonmarket activities.

Finally, the focus on aggregate participation rates has proven to be useful. Micro-data studies, typically of females, focus on income and substitution effects of wage changes and are useful for predicting responses to changes in welfare programs and income tax rates. In a typical study, for example, Reimers ( I 985) estimates a linear probability model of labor force participation for married


women as a function of husband’s wage, other family income, age of children, education, maximum AFDC benefit, health status, race, and urban/rural residence. Our focus allowed an evaluation of the effects of aggregate characteristics, highlighting disability receipt and Appalachian population. In addition, we found that a high level of union membership may lower the participation rates of adult males in general. Further research is needed to determine if this result is robust, and if it originates in the supply or demand side.

NOTES 1. The U.S. Department of Labor defines the participation rate as the percentage of the

work-eligible population that is either employed or actively seeking work. The military, children under the age of 16, and institutionalized persons are excluded from the work-eligible population.

2. The AFDC variable is the maximum benefit for a family of three. This is a superior measure to the average benefit received, which obviously depends upon mother’s wage income, hence participation decision.

3. Unions will be more successful at raising wages when the demand for union labor is higher and more inelastic. Restricting the ability of employers to substitute nonunion workers for union labor will make the demand for union labor more inelastic. See “How Unions Achieve Their Objectives” in Smith and Ehrenberg (1988).

4. A one-point increase in the population approved for benefits will lower the participation rate by l/P’ points, where P’ is the percentage of the population in the potential labor force. Nationwise, P’ is approximately 76%. This mechanical effect probably is overstated, however, as some workers who obtain benefits would have dropped out for other reasons.

5. Another interpretation is that disability proxies for poor health: for each person receiving disability, there may be another five whose health is poor enough to cause withdrawal from the labor force, even though they are not eligible for benefits. This seems unlikely as health does not have a strong effect on participation, except for retirement-age individuals. In 1986 while 1.8% of the potential labor force was receiving federal disability payments, only an additional 0.7% reported illness or disability as the reason for nonparticipation.

6. This calculation follows standard procedure for accounting for wage differences by sex or race: given the determinants of wages, what part of the wage gap can be attributed to differences in mean worker attributes? Another portion of the gap is attributed to differences in wage coefficients, given mean attributes. In this case, since we cannot estimate the participation rate model separately for West Virginia, we must assume the same structure and are able only to calculate the portion of the gap attributable to differences in characteristics.

7. The Bureau of Labor Statistics does not report teenage participation rates separately for males and females at the state level.


APPENDIX Labor Force Participation Rates, All Workers, 1987

Nevada Alaska Vermont New Hampshire Minnesota Connecticut Utah District of Columbia Colorado Mary land Kansas South Dakota Wisconsin Nebraska Texas North Dakota Wyoming Virginia Rhode Island North Carolina Delaware Iowa Montana Hawaii Massachusetts

72.7 72.1 71.5 71.3 70.7 69.9 69.7 69.7 69.6 69.6 69.2 69.0 68.9 68.7 68.7 68.6 68.6 68.2 68.1 68.1 67.7 67.7 67.5 67.4 67.3

Georgia Missouri California Oregon Washington Indiana New Jersey Illinois Maine South Carolina Michigan Oklahoma Ohio Arizona New Mexico Tennessee Alabama Florida Mississippi New York Louisiana Pennsylvania Kentucky West Virginia

67.0 67.0 66.9 66.6 66.4 66.1 66.1 65.6 65.4 65.4 65.3 65.1 64.4 64.2 63.6 63.4 62.4 62.3 61.3 61.7 60.9 60.8 60.7 51.7

Source: U S . Department of Labor. 1987. Geographic Profile of Employment and Unemployment, Washington, DC. U.S. Government Printing Office.

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