geographic mobility, race, and wage differentials

Journal of Urban Economics 45, 17-46 (1999) Article ID juec.1998.2081, available online at http://www.idealibrary.com on IDEbl@

Geographic Mobility, Race, and Wage Differentials*

Steven Raphael and David A. Riker

Department of Economics, Uniuersiv of California, San Diego, La Jolla, California 92093

Received August 4, 1997; revised January 14, 1998

This paper analyzes the relationship between geographic mobility and earnings. We present an equilibrium search model that yields differences between the reservation wages of mobile and immobile workers. The expected wages of mobile workers exceed those of immobile workers due to partial sorting across high- and low-paying firms. An extension to visibly distinct groups with different immobile proportions yields statistical discrimination against immobile group members. Us- ing combined Displaced Workers Files, we find that mobility positively affects earnings and partially explains racial and ethnic earnings differentials. To test for statistical discrimination, we estimate separate earnings functions for union and nonunion workers. 0 1999 Academic Press

1. INTRODUCTION Minority workers in the United States face several constraints to geo-

graphic mobility. Evidence suggests that African-American and Hispanic workers suffer considerable discrimination in housing markets in both sales and rentals [30]. In addition, African-American applicants are significantly less likely to be approved for home mortgages, even after controlling for past credit histories [24]. Research on the labor market conse- quences of mobility constraints focuses primarily on the effects of racial housing segregation. Spatial mismatch proponents argue that racial segregation spatially isolates minority workers from high-employment areas, creating racial earnings and employment rate differentials [20, 211.

Mobility constraints, however, need not confine minorities to inner-city neighborhoods to generate racial earnings differentials. High mobility costs inhibit migration, weakening the responsiveness of relatively immobile workers to interarea earnings differentials, and thus lowering their local reservation wages. Immobile workers are more likely to accept low-paying job offers and will self-sort into low-paying establishments.

*We would like to thank Julian Betts, John DiNardo, Graham Elliott, Hilary Hoynes, Mark Machina, and Valerie Ramey for helpful comments and suggestions.

17

0094-1190/99 $30.00 Copyright 0 1999 by Academic Press

All rights of reproduction in any form reserved.

18 RAPHAEL AND RIKER

Moreover, to the extent that employers infer average differences in reservation wages by observable traits such as age, race, or gender, employers may statistically discriminate by offering below average wages to relatively immobile groups of workers.

Several studies document the relative immobility of minority workers,’ yet few studies analyze the consequent earnings effects.‘ This paper attempts to document this connection. First we present an equilibrium search model of a local labor market where workers’ heterogeneity with respect to mobility costs and firms’ heterogeneity with respect to productivity yield a nondegenerate distribution of earnings for workers with equivalent skills. In equilibrium, only immobile workers accept employment in low-productivity firms that pay relatively low wages. When firms are unable to infer an applicant’s mobility, a mobility-earnings premium arises due to differences in workers’ reservation wages and their consequent sorting across high- and low-paying firms. When employers are able to infer intergroup differences in mobility costs but not the mobility of each individual, statistical or third-degree wage discrimination occurs.

Using data from the 1986, 1988, and 1990 January Current Population Survey: Displaced Workers Files, we present several estimates of the effect of potential mobility on earnings. First, using a sample of workers displaced by plant closings, we infer relative mobility from workers’ post- displacement migration decisions. To the extent that inherently mobile workers are more likely to move in response to job loss, post-displacement migration proxies for unobserved mobility constraints. In both OLS and 2SLS models, post-displacement migration has a substantial and significant positive effect on pre-displacement earnings. Moreover, controlling for mobility potential causes large declines in residual racial and ethnic earnings differentials.

In a second, complementary estimation strategy, we relate the migration behavior of exogenously displaced workers to demographic characteristics to impute a hypothetical probability-of-moving variable for a representative sample of wage and salary workers. Adding the constructed mobility variable to a standard log-wage regression yields results similar to the pre-displacement earnings model. Potential mobility strongly affects wages and reduces residual racial and ethnic earnings differentials. This sample

‘Ross [27] documents the relative geographic immobility of black workers conditional on job changes. Zax and Kain [31] show that while commute times effect the quit and move decisions of white workers, no comparable effects are found for black workers. Boehm et al. [12] show that both intra- and interregional residential mobility rates are higher for white households. In addition, the relative geographic immobility of minorities is noted in the literature review by Greenwood [18].

‘See [13] for an analysis of relative immobility and changes in the earnings distribution over the 1980s.

MOBILITY, RACE, AND WAGES 19

is then used to estimate separate wage regressions for union and nonunion workers. Several arguments predict a lower mobility-earnings effect in the unionized sector. Since employers in the unionized sector have less discretion over wage setting, statistical or third-degree wage discrimination should be relatively less prominent. Moreover, to the extent that unions direct their organizing efforts at high-paying, high-productivity firms, mobility-earnings effects should be less prominent, since only low-productivity firms pay lower wages and attract immobile workers only. The results from this test are mixed with patterns for female workers that support the prediction and mixed results for male workers.

2. THEORETICAL FRAMEWORK Several theories of racial discrimination provide potential explanations

of the large and persistent racial and ethnic earnings differentials observed in U.S. data. Becker [7] and Arrow [4] present the seminal neoclassical analysis of employers with “tastes” for discrimination. In this model, discriminatory employers hire minority workers only at a wage discount sufficient to compensate for the consequent psychic disutility from hiring minority workers. In a separate literature, employers are modeled as using race to statistically infer unobservable productivity. In statistical discrimination models, employers use mean group productivity differentials [25, 41 or differences in the accuracy of screening devices [l] to assess individual productivity and set wages. High-productivity workers from minority groups with low mean productivity earn less on average than otherwise similar nonminority workers.

Here we present a hybrid model where exogenously given group differences in average geographic mobility permit statistical or, more precisely, third-degree wage discrimination in labor markets. First, we extend the equilibrium search model of Albrecht and Axell [2 ] to the case where workers residing in a local labor market have differential ability to migrate in response to a high-paying external job offer. Relatively immobile, yet otherwise equal, workers have lower reservation wages and lower expected wages conditional on employment. Local firms that vary with respect to productivity face upward-sloping labor supply curves due to differences in the reservation wages of mobile and immobile workers. In equilibrium, firms with productivity above an endogenously determined threshold offer a high wage to all applicants, hiring both mobile and immobile workers. Firms below the threshold offer a low wage, hiring only immobile workers.

Next, we extend the model to the case of two visibly distinct groups of workers that differ with respect to the proportion of their members that are mobile. In equilibrium, immobile workers from the immobile group have a lower reservation wage than immobile workers from the mobile group. Separate firm productivity thresholds arise for each group of


workers, leading to four categories of firms distinguished by productivity and hiring strategies. Statistical wage discrimination occurs in that firms exist that make low-wage offers to immobile group applicants and high- wage offers to mobile group applicants. These results are formalized below.

A . Workers The local economy has L productively homogenous workers. A propor-

tion, p, of workers are mobile-i.e., responsive to outside job offers-and the remaining proportion, 1 - p, are immobile due to prohibitively high relocation costs. Firms in the local economy make two wage offers: a proportion of firms, y , offers the reservation wage of immobile workers, w', while the remaining proportion, 1 - y , offers the reservation wage of mobile workers, wM. Firms are unable to observe mobility status and all searching workers draw one wage offer each period from the local economy. Searching mobile workers receive an outside wage offer net of relocation costs, w H , with probability p . Workers who do not accept an offer receive unemployment benefits, b, in the current period and search again in the following period. It is assumed that the outside wage offer exceeds unemployment benefits, i.e., w H > b. Once a match is made, workers remain in their jobs for as long as they are in the labor force. There is a constant probability of leaving the labor force, S, and each period the labor force is replenished with new entrants, SL. The proportion of new entrants who are mobile equals the proportion mobile in the labor force. Finally, workers are assumed to be risk neutral and maximize expected lifetime income.

Given the difference in opportunities, mobile workers have higher local reservation wages. The reservation wage of mobile workers is that which equates the present discounted value of rejecting all local offers, V M , to the value of accepting the local mobile wage, w M / S . When the local wage offer is rejected, the mobile worker receives unemployment benefits, b, in the current period and with probability 1 - 6 survives to search in the next period. In the following period, the worker receives the outside wage offer with probability p and continues the search process an additional period with probability 1 - p . Hence, the value to a mobile worker of rejecting local wage offers is

V M = b + ( l - S ) p - + ( l - p ) V M . [ wSH 1 Solving for V M and setting equal to the value of accepting a local wage offer gives

W M = ( 1 - a ) b + awH, ( 1 )


where

which is a weighted sum of the unemployment benefits, b, and the outside wage offer, wH.

For immobile workers, the reservation wage, w', equals the wage that equates the value of rejecting all local wage offers below the mobile reservation wage, V', to the value of accepting the immobile wage offer, w'/S. Specifically, the value of rejecting all local offers below w M is

Solving for V', setting equal to the value of accepting the lower wage offer, and substituting Eq. (1) for w M gives the immobile reservation wage

W' = (1 - @ ) b + pwH, (3) where

Similar to w M , w' is a weighted sum of the outside wage offer and unemployment benefits. Since immobile workers cannot migrate, however, the outside wage offer affects w' only indirectly through the probability of receiving a local offer of wM. As a consequence, the equilibrium reservation wage for immobile workers given by Eqs. (3) and (4) places less weight on the outside wage and more weight on the unemployment benefit ( a > p) , which implies that w M > w'.

B. Firms Labor is the only factor of production. All workers are equally produc-

tive within a given firm, but firms vary with respect to labor productivity. Following Albrecht and Axel1 [2], firms have the linear production function Q = AZ, where productivity, A, is uniformly distributed over the interval [w', AH].3 Given the technology, profits for a given wage and A are II = ( A - w)Z(w).

3The reservation wage of immobile workers defines the interval [d, A H ] , which is the productivity range of active firms. Firms are potentially distributed over [ hL, A H ] . Truncating at w r > hL does not change the fact that the parameter A is uniformly distributed.


Given the difference in mobile and immobile reservation wages, local labor supply increases in the wage offered. Let F equal the number of active firms and q equal the number of workers per active firm ( L / F ) . At wage wz, the expected labor supply to a local firm is the sum of new immobile labor market entrants that draw the firm's wage offer (Sq(1 - p)) and all workers that accepted the lower wage in the past and that survive into the current period [(l - S)6q(l - p) + (1 - 6)'q(l - p) + (1 - 6I3q(l - p) + I. Adding the two components and simplifying gives the supply of labor at wz,

Z(w') = q(1 - p). (5)

The supply of labor to firms offering w M can be decomposed into the expected supply from current applicants and the expected supply of past employees surviving into the current period. The expected supply from current applicants is the sum of the expected number of immobile new entrants drawing the firm's offer (Sq(1 - p)), the expected number of mobile new entrants drawing the firm's offer that do not receive a high outside offer (Sqp(1 - p ) ) , and the expected number of searching mobile workers from previous periods drawing the firm's wage offer that do not receive the high outside offer ((1 - 6x1 -p )ySqp( l - p ) + (1 - 8)' (1 - p ) 2 y 2 ~ q p ( 1 - p ) + (1 - 8I3(i - p ) 3 y 3 ~ q p ( 1 - p ) + 1. Summing the components and simplifying the infinite series gives the expected quantity of current hires,

- P) current hires = S q ( 1 - p) +

1 - (1 - 6)(1 - p ) y '

Since the similar quantity of applicants is hired in past periods, adding current hires to the sum of past employees that survive into the current period gives the supply of labor at w M ,

From Eqs. (5) and (6) , it is clear that Z(wM) > Z(w'), implying an upward- sloping supply curve. The greater supply at w M derives from the higher proportion of applicants that accept the wage offer.

Firms choose a wage offer and, in turn, employment level by comparing profits at each point on the labor supply curve. From the profit function, it


follows that firms offer the high wage when

Z ( W " ) - Z ( W ' ) ( A - w') - ( A - w") > (7)

Z(W') h - W "

and offer the low wage otherwise. This condition states that firms offer the high wage when the resulting proportional increase in labor supply exceeds the proportional difference in profits per worker at the two wage offers. Setting the terms in (7) equal and solving for A gives the threshold,

W"Z( w") - W'Z( w') A* =

Z(W") - Z(W') '

at which firms are indifferent between offering the high and low wage. All firms with A > A* offer the high mobile wage, while all firms with A < A* offer the low immobile wage.

C. Equilibrium

threshold productivity equation (8) and rearranging gives Substituting the labor supply values given by Eqs. (5) and (6 ) into the

indicating that A* > w". This is a key aspect of the model. For a range of firms (firms where w " < A < A* 1, employing an additional mobile worker, all else held equal, would increase profits. However, since firms are unable to observe the mobility status of workers, attracting the additional worker requires offering W" to all workers. Here, the marginal cost of attracting an additional worker exceeds the expected reservation wage of the additional worker, who may be immobile, and reduces profits relative to the case where firms directly observe workers' mobility type. This is a common feature of dynamic monopsony models, where the marginal cost curve for labor rises above the labor supply schedule [ll].

Closing the model requires an equilibrium relationship for the proportion of firms, y, offering the immobile wage. Assuming that firm productivity is uniformly distributed over the interval, [w', A H ] , the proportion of firms offering the low immobile wage is given by

Using Eqs. (1)-(4) and (8) for w", w', and A*, the equilibrium proportion can be written in terms of the exogenous parameters of the model.


Given the equilibrium values for w M , w', and y , it can be shown that the expected wage of mobile workers exceeds that of immobile workers. The expected values of the wages earned by mobile and immobile workers conditional on accepting an offer are given by the equations

From Eq. (lo), it follows that wH > E(wlM) > wM > E(wl1) > w'.

D. Extension to Two Groups with Different Mobile Proportions

In the model outlined above, the expected earnings of mobile workers exceed the expected earnings of immobile workers due to the partial segregation of immobile workers in small, relatively less productive firms. The differential arises from a self-selection process whereby low-wage firms make low-wage offers that only immobile applicants accept. In equilibrium, no discrimination exists since firms with a given productivity level offer the same wage to all applicants.

A simple extension of the model to the case of two visibly distinct groups with different mobile proportions illustrates how the group difference results in within-firm discrimination against members of the less mobile group. Assume two groups of workers, indexed by B and W , where the proportion of B workers that are mobile is less than the proportion of W workers mobile, i.e., pB < pw. For simplicity, assume that the distribution across the two groups is stable and reproduced by the infusion of new entrants. From the model above, it follows that the threshold productivity level and the proportion of firms offering the relatively low wage decrease with an increase in the proportion of mobile workers, i.e., dA* /dp < 0 and d y / d p < 0. Moreover, given constant returns to scale and assuming that all workers are perfect substitutes, firm hiring policies for one group of workers are independent of the hiring policies used for the other group. Assuming that firms know the values of pB and pw but do not directly observe individual mobility type, the model of the previous section implies that A; > A&, and therefore, yB > yw. The intuition for the direction of these effects is straightforward. The probability that a randomly chosen applicant from the immobile group of workers will accept a low-wage offer is higher than the similar probability for a randomly chosen applicant from the mobile group. Hence, only the most productive firms with the greatest labor demand offer members of the relatively immobile group the high wage.


From the reservation wage equations (1)-(4), it follows that while the reservation wage of mobile workers is not affected by the proportion of local firms offering the immobile wage, the reservation wage of immobile workers declines with an increase in the proportion of firms offering the immobile wage, i.e., dw'/dy < 0. This is due to the fact that the high outside wage, w H , only indirectly affects the immobile reservation wage through the probability of receiving a wage offer of wM. Hence, it follows that wf = W E = w M and w; < w ; . ~

The equilibrium values of w;, wk, A;, and A'C, define four intervals over the distribution of A that correspond to four distinct employment policies concerning applicants from the two groups of workers. The optimal employment policies conditional on the productivity parameter are

1. for w; I A < w;, offer the wage w; to all applicants, 2. for w k I A < A$, offer w; to B workers and w; to W workers, 3. for A'C, 5 A < A;, offer wj to B workers and w M to W workers,

4. for A; 5 A, offer w M to all workers. and

For firms that employ policies 1 and 4, no discrimination occurs. Policy 1 firms offer the lowest possible wage (w;) to all applicants, although only immobile B workers accept it. Hence, these firms are perfectly segregated by ethnicity and mobility status. Policy 4 firms, the largest and most productive, offer the high wage to everyone and employ workers from all ethnic-mobility groups.

Firms following employment policies 2 and 3 discriminate on the basis of the average mobility differential between the two groups, offering a lower wage to applicants from group B.5 The lower wage offered to B workers is the monopsonist equivalent to third-degree price discrimination in markets with low elasticity of demand by producers with monopoly power [28].6 The largest earnings differences occur in policy 3 firms, since here the expected increase in profits from offering the high wage to W workers, but not to B workers, exceeds the increased wage costs. Hence, in this simple extension,

4Wilson [29] finds substantial racial and ethnic differences in the reservation wages of jobless men residing in poor Chicago census tracts. Average reservation wages for this particular group of workers were $6.00 for black men, $6.20 to $7.20 for Mexican and Puerto Rican men, and $9.00 for white men.

'Legal constraints to within-firm discrimination may be circumvented by job classifications. 'Such an argument is offered as an explanation for the finding that car salesmen make

relatively higher initial and final offers when negotiating with black and female buyers [5]. Black [8] presents a similar model of wage discrimination where nondiscriminating employers exploit the interfirm immobility of blacks that are imposed by search frictions and the unwillingness of some employers to hire black workers.


immobile workers from relatively immobile groups earn less than similar workers from more mobile groups due to two factors: the greater probability of being employed in low-wage, low-productivity firms, and third-degree wage discrimination by employers of moderate productivity and size.

3. EMPIRICAL METHODOLOGY AND DATA DESCRIPTION

The model above suggests two mechanisms by which discrimination in housing markets and the consequent constraints to minority geographic mobility may spill over into the labor market and create racial and ethnic earnings differentials. First, geographically constrained minority workers have relatively low reservation wages and self-sort through the search process into low-paying firms. Second, firms may infer group differences in labor supply elasticities and statistically discriminate against relatively immobile groups. Here, we outline several empirical tests of these hy- potheses.

A . Estimation Strategy The estimation strategy is to use information on the geographic mobility

decisions of workers exogenously displaced from employment as a proxy for mobility costs. We focus on workers displaced by plant closures to eliminate cases of job loss that reflect unobserved worker heterogeneity in prod~ctivity.~ This information is employed in two complementary estimation strategies. First, for the sample of displaced workers we regress pre-displacement earnings on post-displacement mobility. Specifically, we estimate the model

In K = Xi p1 + Moved, pz + q , Moved, = Xial + Ziaz + E, ,

(11)

( 12)

where 1nK is the log of pre-displacement weekly earnings, X , is a set of individual demographic characteristics, Moved, is a dummy variable equal to 1 if the individual moved to look for or take employment after displacement, 2, is a set of instruments, and y and E, are normally distributed, mean-zero error terms. Assuming that the inherently mobile are more likely to move to search for or take employment, the model of the previous section implies a positive relationship between post-displacement mobility and pre-displacement earnings ( pz > 0).

7See Gibbons and Katz [18] for a discussion of the role of employer discretion in layoffs and differences in worker productivity between those displaced by plant closures and those displaced by layoff.


If post-displacement mobility is uncorrelated with the unobserved characteristics that determine wages, Eq. (11) can be estimated by ordinary least squares. Mobility, however, is likely to be correlated with important unobserved variables. For example, more mobile workers may be more motivated or have greater personal assets and better access to capital markets due to relatively high past earnings. Both possibilities suggest a positively biased mobility premium in OLS estimation. On the other hand, post-displacement mobility may reflect out-migration from an unobserv- ably depressed region, imparting a downward bias to the estimated mobility premium using OLS. To account for the possible endogeneity of the mobility outcome, we define a set of instruments based on family and household characteristics and estimate the model of Eqs. (11) and (12) by two-stage least squares.'

In the 2SLS model, all pre-determined variables included in the first- and second-stage regressions ( X i ) have direct effects on earnings through the second-stage equation and indirect effects on earnings through mobility. Substituting Eq. (12) into Eq. (11) gives the reduced form

In K = Xi( P1 + Pz al) + Zi Pz az + q , (13)

where qi = ui + pZq. In Eq. (13), the direct wage effects of Xi are given by P1, while the indirect wage effects operating through mobility are given by Pz al. By comparing coefficient estimates for Xi from the reduced-form equation (1 3) to the comparable second-stage coefficient estimates of the 2SLS model, we can infer the indirect wage effect of each variable. Below, we make such comparisons to assess the indirect mobility effects of race-ethnicity and gender on earnings.

We use two variables as instruments: a variable measuring the number of people in a worker's household and a dummy variable indicating a military spouse. The relationship between mobility and household size is suggested by models of household migration [ 231 that emphasize net family gain rather than net personal gain as being decisive in the household decision to migrate. For workers from relatively large households, household relocation costs will sometimes outweigh the personal labor market gains of migration, possibly preventing a move that would otherwise be optimal in the absence of family ties. Boehm et al. [12] and Hacker [19] provide evidence that household size significantly impedes migration. The second instrument, being a military spouse, should affect potential mobility

'The mobility equation, (121, is specified as a linear probability model to assure that identification hinges on the set of variables excluded from the wage equation rather than an assumption of bivariate normality. As a result, predicted mobility from the first-stage equation will not be binary. We interpret the continuous predicted values for Moved, as measuring continuous variation in mobility potential.


due to the fact that location is exogenously determined through the spouse’s military assignment.

One can argue that household size is directly related to earnings independent of any indirect effects via mobility. Individuals may plan the size of their households-e.g., the number of children-based on earnings potential, yielding reverse causation of wages on household size. This would indicate a positive relationship between earnings and household size in the reduced-form equation (13). In the results below, we present the reduced-form coefficient estimates to facilitate a fuller discussion of the validity of the exclusion restrictions.

A negative association between earnings and having a spouse in the military may be explainable by unobserved skill differentials between military spouses and otherwise observably comparable workers. Alterna- tively, military spouses may have relatively low earnings due to the fact that frequent geographic relocations do not permit the accumulation of job/employer-specific human capital, an important determinant of earnings. In our model estimations below, we attempt to minimize such omitted variables bias by controlling extensively for background and human capital characteristics including age, education, job tenure, occupational and industrial affiliation, and basic demographic characteristics.

In a second set of regressions, we use the mobility data for the sample of exogenously displaced workers to impute potential mobility for a nationally representative sample of wage earners. We then add potential mobility to the specification of a standard log-wage regression. Specifically, for an observation in sample 1 (the displaced workers sample) we observe the vector of variables {Moved,,, Xi,, Z,,}, while for an observation in sample 2 (nationally representative sample of wage earners) we observe the vector of variables {In wz, Xi,, ZJ. Using sample 1, we estimate the equation

Moved,, = X i l a , + Zi,a, + q,, ( 14)

to obtain estimates of the parameters in the vector, a. Next, using sample 2 we estimate the equation

In w, = Xi, p1 + Moved,,& + uiz, (15)

where the line over “Moved” in Eq. (15) indicates the predicted value using the parameter estimates from Eq. (14). When the two samples used to estimate Eqs. (14) and (15) are random samples from the same population, the estimate of p, is equivalent to the split-sample instrumental variables estimator suggested by Angrist and Krueger [3].

The empirical methodology using the split-sample complements the within-sample 2SLS model in several ways. First, the nationally representative sample provides an hourly wage variable at one’s current job rather


than retrospective weekly earnings at a job lost within the past five years. Since weekly earnings are determined by both the rate of pay and hours worked, it is difficult to disentangle a mobility-employment effect from a mobility-earnings effect in the within-sample estimates. The hourly wages observed for the nationally representative sample, however, permit a clear analysis of the relationship between relative mobility and the rate of pay and provides a robustness check to the within-sample results.

Second, for the national sample, we observe variables not available for displaced workers that permit a test for third-degree wage discrimination. Comparing the mobility premium among union workers to that among nonunion workers provides a weak test of statistical discrimination. Several factors suggest that the mobility premium should be less prominent among unionized workers. To the extent that unions standardize internal wage structures, employers are unable to set compensation taking into account group differences in potential m~bili ty.~ Hence, while immobile workers may still be more likely to accept employment in relatively low-paying firms within the unionized sector, the lack of within-firm statistical discrimination should lower the overall mobility premium. Such a conjecture is consistent with the relatively strong preference for collective bargaining exhibited by African-American workers [151. Alternatively, to the extent that unions focus their organizing efforts on large, high-wage employers that, according to the model, are less likely to discriminate due to greater labor demand, statistical discrimination will be less prominent in the union sector. Here, however, a lower mobility premium among union workers may be the result of which firms are targeted for organization drives.

In terms of the parameters of the model, both arguments suggest that pz in an earnings regression for union workers should be lower than the comparable parameter estimate from the earnings regression for nonunion workers. To test this hypothesis, we use the split-sample model to estimate separate earnings functions for union and nonunion workers.

B. The Data

The data for this paper come from the 1986, 1988, and 1990 January Current Population Survey: Displaced Workers Files. In addition to the standard monthly labor force survey, the Displaced Workers files include an additional set of questions asked of all workers 20 years of age or older

'Several researchers attribute the lower variance of union wages to the ability of unions to attach wages to jobs rather than individuals. For evidence of relative earnings equality among union workers, see [lo], [141, and [161.


that involuntarily lost a job over the previous five years. The supplements include a set of question concerning aspects of the pre-displacement job, post-displacement search and geographic mobility decisions, and post-displacement employment outcomes.

We use two subsamples from the combined 1986-1988-1990 file. Of the workers identified as displaced, we select a subsample of civilian workers between 20 and 65 years of age that lost jobs due to the closure or relocation of a plant or company. Restricting the sample to workers that lost jobs through plant closures eliminates job loss that is nonrandom with respect to worker productivity [171. For the within-sample 2SLS model, we further restrict the displaced worker subsample to pre-displacement full- time (usually worked 35 hours per week or more) workers in an attempt to isolate a mobility-earning effect. Moreover, the sample is further re- stricted to workers for whom pre-displacement weekly earnings are observed. In the split-sample regressions we include all full-time civilian wage and salary workers between 20 and 65 years of age in outgoing rotation groups with either an observable hourly wage or observable weekly earnings and hours worked.”

To gauge the comparability of workers displaced by plant closures to the average wage and salary worker, Table 1 presents a comparison of mean characteristics for wage and salary workers self-identifying as displaced from a full-time job by plant closures in the 1986, 1988, and 1990 surveys to those for all full-time wage and salary workers from the combined outgoing rotation groups of the three files. The later sample, when weighted, presents nationally representative estimates of the variable means for full-time wage and salary workers 20-65 years of age. With the exception of the variables “Age,” “Education,” and “Persons in household,” all variables listed in the table are dummy variables equal to 1 if the variable label applies to the observation. It should be noted that for displaced workers all personal characteristics (except occupation) describe the respondent at the survey date rather than at the time of displacement. While many of the personal characteristics are unchanging (race, gender), most of the family and household variables may change from the time of

We use the 1986, 1988, and 1990 Displaced Workers file rather than the later files for several reasons. After 1992, the supplemental survey is administered to workers that have lost jobs during the three years rather than five years prior to the survey date. Blanchard and Katz [9] show that the effects of regional employment shocks dissipate after five to seven years, largely through interregional labor migration. To capture long-term adjustments, we use the earlier surveys. In addition, in the later files (1992, 1994). we observe very few displaced military spouses, a key instrument in the analysis.

10


TABLE 1 Mean Characteristics of the Combined Displaced Workers Sample and a

Representative Sample of Wage and Salary Workersa

Variables

Personal characteristics Age Education Black Hispanic Female Veteran Married Spouse in armed forces Persons in household

Executive, admin. & managerial Professional specialty Technicians & related support Sales Administrative support Private household Protective service Other service Precision prod., craft & repair Machine ops., assemb. inspect. Transportation, material moving Handlers, equipment cleaners Farming, forestry, & fisheries

Occupation

N

Workers displaced by plant closure

All wage and salary workers

38.2 12.3 0.11 0.08 0.39 0.19 0.62 0.004 3.06

0.11 0.05 0.03 0.11 0.12 0.00 0.01 0.07 0.17 0.18 0.06 0.06 0.02 7178

37.6 13.2 0.11 0.08 0.43 0.18 0.64 0.003 3.13

0.13 0.13 0.04 0.10 0.18 0.00 0.02 0.08 0.13 0.09 0.05 0.04 0.01

35,100

"Calculated from the Current Population Survey: January Displaced Workers Files, 1986, 1988, and 1990. The first sample includes all workers in the three surveys that are 20-65 years of age and who had been permanently displaced from a full-time job due to a plant closure during the previous five years. These figures are weighted using the CPS final weights. The second sample includes all full-time wage and salary workers between 20 and 65 in the outgoing rotation groups of the samples. These figures are also weighted.

displacement. As we are unable to observe these variables at the time of displacement, we can only note this qualification.

Displaced workers are quite similar to the average wage and salary worker, suggesting that displacement through plant closure occurs randomly. While, on average, a displaced worker is slightly older, slightly less educated, slightly less likely to be married, and less likely to be a female,


the racial and ethnic compositions of the two samples as well as the proportion of veterans are nearly identical. However, noticeable differences between the two samples exist in the occupational distribution. Displaced workers are less likely to be in the “Professional specialty” category (0.05 vs 0.13) and more likely to be machine operators, assem- blers, or inspectors (0.18 vs 0.09). Outside of these two categories, the occupational distributions are similar.

Table 2 summarizes post-displacement mobility outcomes for workers displaced by plant closings. The CPS supplement asks displaced workers if they moved to a different city or county to look for work or take a different job since displacement. The figures in Table 2 provide the proportion of displaced workers that report moving for all workers, workers by gender, race, and ethnicity, and workers stratified by the values of the instrumental variables outlined in the previous section. A large percentage of workers relocate to seek or take a new job (approximately 18%). This figure most likely understates mobility, since workers need only be displaced five years prior to be designated as displaced, and workers displaced in later years have yet to fully react to the job loss. There are large interracial and intergender mobility differences. Black and Hispanic workers are less mobile than white workers, both overall and within

TABLE 2 Proportion of Sample Permanently Displaced by a Plant or Establishment Closing

Who Moved to a Different County or City to Search For or Take Another Job“

Total Men Women

Total Race/ethnicity

White Black Hispanic

Spouse in armed forces No spouse in armed forces Above average household size Below average household size

0.177 (0.004)

0.188 (0.005) 0.122 (0.012) 0.138 (0.015) 0.069 (0.039) 0.178 (0.004) 0.171 (0.007) 0.180 (0.005)

0.213 (0.006)

0.224 (0.007) 0.167 (0.020) 0.157 (0.021) 0.803 (0.398)b 0.213 (0.006) 0.209 (0.010) 0.216 (0.008)

0.129 (0.006)

0.139 (0.006) 0.073 (0.013) 0.107 (0.022) 0.055 (0.036)b 0.130 (0.006) 0.109 (0.009) 0.138 (0.007)

“Standard errors are in parentheses. Calculated from a sample of workers drawn from the Current Population Survey: January Displaced Workers files, 1986, 1988, and 1990. The sample includes all respondents age 20-65 from the three surveys that were displaced from a wage and salary job due to a plant closure five years prior to the survey dates. All figures are weighted.

bThere are 40 female observations in the displaced by plant closure sample with spouses in the armed forces and 2 male observations.


gender. Moreover, the standard errors for the proportions suggest that the racial and ethnic mobility differentials are statistically significant. Finally, women are less likely to move than men across all groups.

The designated instrumental variables (“Spouse in the armed forces” and “Household size”) are clearly related to the mobility decision. Both men and women residing in households of above average size are less likely to move than workers in households of below average size. Since there are very few male displaced workers with military spouses, the estimate of the proportion that move has a large standard error and is unreliable. Nonetheless, women married to men in the armed forces are considerably less likely to move in search of employment than women not married to armed forces personnel.

4. EMPIRICAL RESULTS

A . Within-Sample Results with the Displaced Workers Subsample

Table 3 presents the results from several pre-displacement log weekly earnings regressions. The first three columns report results excluding occupation and industry, the next three columns add occupational dummies to the specification, and the final three models add both occupation and industry dummies. For each set of regressions, three models are presented: an OLS regression on a mobility dummy variable indicating a post-displacement move and a standard set of human capital controls, the reduced-form model given by Eq. (13) using the instrumental variables listed in Table 2, and the 2SLS model given by Eqs. (11) and (12). In addition to the variables listed, the specification of each regression includes all of the variables listed in the first half of Table 1, job tenure and job tenure squared, a set of year of displacement dummies, and dummy variables indicating the year of the survey. The coefficient estimates for the set of human capital controls are similar to standard findings in the literature [22, 261 and are suppressed here to conserve space. For the 2SLS models, test statistics and P-values are provided for tests of the collective significance of the instruments in the first-stage regressions and for tests of the overidentifying restrictions. The first-stage results from the 2SLS model are presented in Appendix Table Al.”

Before discussing the results in Table 3, a brief discussion of the mobility differential estimates from the first-stage results in Appendix

The exclusion of observation with missing pre-displacement weekly earnings accounts for 11

the difference in sample size between Tables 1 and 3.

TAB

LE 3

O

LS a

nd 2

SLS

Reg

ress

ions

of P

re-D

ispl

acem

ent E

arni

ngs

on O

bser

ved

Post

-Dis

plac

emen

t M

obil

ity

for W

orke

rs D

ispl

aced

by

Pla

nt C

losu

res“

No

occu

pati

on o

r in

dust

ry d

umm

ies

Con

trol

ling

for o

ccup

atio

n C

ontr

ollin

g fo

r ind

ustr

y &

occ

upat

ion

Red

uced

R

educ

ed

Red

uced

O

LS

fo

rm

ZSLS

O

LS

form

ZS

LS

OL

S fo

rm

ZSLS

1.19

3 M

oved

0.

080

-

1.42

4 0.

076

-

1.19

1 0.

066

Bla

ck

-0.1

57

-0.1

54

- 0.

054

-0.1

25

-0.1

23

- 0.

043

-0.1

07

-0.1

05

- 0.

029

His

pani

c - 0.

059

- 0.

054

0.00

8 - 0

.058

- 0.

054

- 0

.004

Fem

ale

- 0.

354

- 0.

363

- 0.

257

- 0.

336

- 0.

342

- 0.

275

- 0.

304

- 0.

309

- 0.

249

-

(0.0

15)

(0.6

39)

(0.0

14)

(0.5

72)

(0.0

15)

(0.5

76)

(0.0

21)

(0.0

21)

(0.0

57)

(0.0

20)

(0.0

20)

(0.0

50)

(0.0

19)

(0.0

19)

(0.0

47)

(0.0

24)

(0.0

24)

(0.0

47)

(0.0

24)

(0.0

23)

(0.0

42)

(0.0

23)

(0.0

23)

(0.0

41)

b

b

(0.0

13)

(0.0

13)

(0.0

50)

(0.0

13)

(0.0

13)

(0.0

36)

(0.0

14)

(0.0

13)

(0.0

34)

z (0

.004

) (0

.004

) (0

.004

) U

? - 0

.048

- 0.

044

0.00

3 2 F

forc

es

(0.0

86)

(0.0

84)

(0.0

82)

2 R

2 0.

307

0.30

6 0.

175

0.35

4 0.

353

0.23

4 0.

385

0.38

5 0.

254

E

-

-0.0

14

-

-

-

-

-

-0.0

15

- 0.

027

- 0

.018

0.02

0

Hou

seho

ld si

ze

Spou

se in

arm

ed

-

0.00

8 -

-

-

-

-

N

7140

71

40

7140

71

40

7140

71

40

7140

71

40

7140

;a

F-st

atis

ticb

(P

-val

ue)

(0.0

14)

(0.0

14)

(0.0

15)

Ove

rid.

test

C

(P-v

alue

) (0

.070

) (0

.081

) (0

.139

)

4.18

1

2.19

1

-

-

-

-

-

-

4.29

9

3.04

3

4.25

7

3.31

3 -

-

-

-

-

-

‘Sta

ndar

d er

rors

are

in p

aren

thes

es. E

ach

spec

ific

atio

n in

clud

es a

con

stan

t ter

m, a

ge, a

ge s

quar

ed, j

ob te

nure

, job

ten

ure

squa

red,

edu

cati

onal

att

ainm

ent,

mar

ital

and

vet

eran

sta

tus,

year

of

disp

lace

men

t, an

d th

e ye

ar o

f sur

vey.

In re

gres

sion

s co

ntro

lling

for

occ

upat

ion,

14

occu

patio

nal

cate

gori

es a

re c

ontr

olle

d fo

r. In

reg

ress

ions

con

trol

ling

for

indu

stv,

22

indu

stv

cate

gori

es a

re c

ontr

olle

d fo

r.

bThe

F-s

tati

stic

test

s th

e co

llect

ive

sign

ific

ance

of

the

excl

uded

iden

tifyi

ng in

stru

men

ts in

the

fir

st-s

tage

mob

ility

regr

essi

on.

“Thi

s is

an F

-sta

tistic

for

the

ove

ride

ntif

ying

rest

rict

ions

tes

t of

Bas

man

n [6

].


Table A1 is needed. In all three specifications, the racial and gender mobility differentials are large and statistically significant. The black-white differential in the probability of moving in response to job loss is approximately 7.3, 7, and 6.5% in the mobility models excluding industry and occupation, the model controlling for occupation, and the model controlling for both industry and occupation, respectively. Note these are nearly identical to the mean black-white mobility differential presented in Table 2. The comparable Hispanic-white mobility differentials are 4.6, 4.5, and 4%, while the comparable male-female differentials are 7.3, 5.4, and 4.9%. Hence, even after controlling extensively for background human capital characteristics, black, Hispanic, and female workers are considerably less likely to move in response to job loss. This is an important finding in that it is a precondition for the indirect wage effects outlined in Eq. (13).

Turning to the results in Table 3, for all three specifications workers that moved post-displacement had considerably higher log weekly earnings pre-displacement. The coefficient on the mobility dummy is positive and significant at 1% with implied earnings effects ranging from nearly 7% in the specification controlling for industry and occupation (exp(0.066) - 1) to slightly larger than 8% in the regression excluding industry and occupation controls (exp(0.080) - 1). In the reduced-form models, the instrument “Household size” has a statistically significant negative effect in all specifications, while the other instrument “Spouse in armed forces” is statistically insignificant. Note the negative effect of household size is inconsistent with the hypothesis of reverse causation between earnings and household size. In all of the OLS and reduced-form results, there are large and statistically significant race/ethnicity and gender earnings differentials ranging between 10 and 15% for the black-white differential, 5 6 % for the His- panic-white differential, and 30-40% for the gender earnings differential.

In the 2SLS results, the coefficients on the fitted mobility variable are larger and statistically significant at the 5% level for all three specifications. While the coefficients on the variable “Moved” in the 2SLS models are not directly comparable to the OLS results,“ a rough comparison can be made by calculating the average fitted values of “Moved” for movers and stayers from the first-stage estimation, taking the differences between the averages, and then multiplying by the coefficient estimate on “Moved” in the second-stage regression. Such calculations imply mobility earnings premiums of 9.5% in the model excluding industry and occupation, a

l2 Using the first-stage regressions to fit values for “Moved” converts the discrete mobility variables into a continuous probability.


premium of 8.7% in the model controlling for occupation, and 9.7% in the model controlling for both industry and occupation. In all three models, the 2SLS mobility premium estimates exceed the point estimates from the OLS models.

The estimated effects of “Black,” “Hispanic,” and “Female” decline substantially when the 2SLS structure is imposed on the reduced form, indicating indirect effects of race, ethnicity, and gender through mobility. In all specifications, the coefficient on “Black” in the 2SLS model drops to nearly one-third the value of the coefficient estimate in the reduced-form model. For example, in the model controlling for industry and occupation, the coefficient on “Black” drops from - 0.105 in the reduced-form model to - 0.029 in the 2SLS model. In all models, the estimated Hispanic-white earnings differential decline from approximately 5% to 0 when the 2SLS structure is imposed. Moreover, the decline in the coefficient on the “Female” dummy variables ranges from approximately 30% in the models excluding industry and occupations controls (from - 0.363 to - 0.257) to approximately 20% in the models controlling for occupation and industry (from -0.309 to -0.249).

A test of the statistical significance of these changes (the significance of the indirect mobility effects) is to reestimate the reduced-form models constraining the coefficients on race/ethnicity and gender to the values from the second stage of the 2SLS model. An F-test between the constrained and unconstrained versions of the reduced-form equation provides a test for significant indirect mobility effects. For all three reduced- form models presented in Table 3, such tests overwhelmingly reject the parameter constraints at the 1% level of confidence, indicating that observed racial and gender earnings differentials are due in part to differential geographic mobility. An alternative test would constrain the race, ethnicity, and gender coefficients in the 2SLS models to the parameter estimates from the corresponding reduced-form results and then con- struct an R e s t of the constrained model against the unconstrained model. For all three of the models in Table 3, this test fails to reject to parameter constraints at the 10% level. This is due to the fact that the standard errors in the 2SLS models are considerably large than in the reduced-form models. Hence, the results from these tests provide partial support for the contention that systematic differentials in geographic mobility partially explain racial and gender earnings differential^.'^

130ne contention to the results given in Table 3 is that they are overly restrictive in forcing similar coefficients by gender and that the model should be estimated separately for men and women. Appendix Table A2 provides models estimated separately by gender, while Appendix Table A3 provides the corresponding first-stage mobility results. For the most part, the findings do not differ substantially, although standard errors are higher and significance levels are lower.


Concerning the exclusion restriction used to identify the 2SLS models, the F-statistics from the first-stage regressions reject the hypothesis that the coefficients on the two instruments are zero. Moreover, both instruments are independently significant at the 5% level. Both household size and having a spouse in the armed forces have the expected negative effects on mobility. Finally, while the test for the overidentifying restriction marginally rejects the restriction for the specification excluding industry and occupation and the specification including occupation dummies but excluding industry (P-values of 0.07 and 0.08, respectively), the test fails to reject the restriction at the 10% level of confidence for the model controlling for both industry and occupation.

In sum, results from the within-sample estimates indicate a substantial mobility premium in the pre-displacement earnings distribution. In all three models, 2SLS estimation, which permits race and gender to have indirect effects on earnings via mobility, leads to large reductions in the point estimates of the direct effects of race and gender on earnings. The statistical significance of the changes in the estimated direct effects, however, varies across tests.

B. Split-Sample Results for a National Sample of Wage and Salary Workers While the results from the previous section provide evidence of a

mobility-earnings premium, using weekly earnings as a dependent variable may confound a mobility-earnings effect with a mobility-employment effect. To the extent that immobile workers are more likely to be trapped in depressed local labor markets, the positive effects found above may simply reflect differences in average work hours between mobile and immobile workers. While restricting the sample to full-time workers partially mitigates this problem, hours may still vary considerably.

Here we present comparable results for a nationally representative sample of workers for whom hourly wages are observable. The sample includes full-time wage and salary workers in the combined outgoing rotation groups from the 1986, 1988, and 1990 files with usable information for either hourly earnings or weekly earnings and hours worked. Again, we use the mobility equation estimates from the displaced workers sample to impute a probability-of-moving variable for the outgoing rotation group sample. The same exclusion restrictions used to identify the within-sample 2SLS models are needed here to avoid perfect collinearity among the explanatory variables.

Table 4 presents log-wage regression results for all workers and by gender. For each sample, we present results omitting occupation and industry controls, controlling for occupation, and controlling for industry and occupation. Within each of the three sets, two regressions are presented: the equivalent to the reduced-form regression given by Eq. (13)


TABLE 4 Log-Wage Regression Results for Split Sample, Reduced Form, and

Controlling for Imputed Mobility"

A. All workers Imputed mobility

Black

Hispanic

Female

Household size

Spouse in armed

R2 B. Men

Imputed mobility

Black

Hispanic

Household size

forces

Spouse in armed

R2

Imputed mobility

Black

Hispanic

Household size

forces

C. Women

Spouse in armed

R2 forces

No occupation or industry dummies

2.335 (0.202)

(0.008) (0.017)

(0.010) (0.018) -0.256 -0.104 (0.004) (0.013) - 0.021 (0 .oo 1)

-0.112 (0.036) 0.371 0.371

1.834 (0.277)

- 0.148 - 0.072 (0.011) (0.017) - 0.098 - 0.032 (0.014) (0.017) - 0.016 (0.002)

-0.141 (0.140) 0.341 0.341

2.611 (0.263)

(0.010) (0.029)

(0.015) (0.021)

-

-0.118 0.041

- 0.071 0.025

-

-

-

-

-

-

-0.093 0.162

-0.044 0.080

- 0.030

-0.101

-

(0.002) -

(0.036) 0.321 0.319

Controlling for Controlling for industry occupation & occupation

- 0.069 (0.008) - 0.052 (0.009)

-0.245 (0.005)

-0.016 (0.002>

-0.105 (0.034) 0.445

- 0.096 (0.011) - 0.075 (0.012)

-0.012 (0.002)

-0.231 (0.133) 0.408

- 0.044 (0.010) - 0.027 (0.0 14) - 0.025 (0.002) - 0.087 (0.033) 0.416

1.913 (0.194) 0.057

(0.016) 0.024

(0.013) -0.156 (0.010) -

0.444

1.351 (0.271)

(0.015)

(0.017)

- 0.052

- 0.029

-

0.408

- 0.072 (0.007) - 0.047 (0.009)

-0.219 (0.005)

-0.014 (0.001) - 0.082 (0.032) 0.480

- 0.092 (0.010) - 0.064 (0.013)

-0.012 (0.002) - 0.207 (0.129) 0.442

1.716 (0.189) 0.031

(0.014) 0.015

(0.012) -0.149 (0.009) -

0.479

1.435 (0.299)

(0.013)

(0.016)

- 0.058

- 0.023

-

0.440

1.883 (0.249) (0.227)

(0.028) (0.009) (0.025)

(0.019) (0.013) (0.017) - 0.021 (0.002) - 0.064 (0.032)

0.413 0.463 0.462

- 2.201

0.178 -0.054 0.130

0.075 -0.030 0.052

- -

- -

"The total sample includes 35,100 observation, 19,734 of which are males and 15,366 are females. Standard errors are in parentheses. Each specification includes a constant term, age, age squared, educational attainment, marital and veteran status, union status, eight dummies indicating census region of residence, a central-city dummy, a dummy for residence in a non-central-city portion of a PMSA, and dummies for the year of survey. In regressions controlling for occupation, 14 occupational categories are controlled for. In regressions controlling for industry, 22 industry categories are controlled for. Year of displacement dummies is included in the first-stage mobility regression used to impute mobility. For the imputed mobility values, the year of displacement dummies is evaluated at the means for the sample of displaced workers.


and the structural regression on the imputed mobility variable and the set of human capital controls. In addition to the variables used in the within- sample specifications, eight dummy variables indicating census region of residence, central-city, and balance-of-PMSA dummy variables, and a union dummy variable are added to the baseline specification^.'^

In all specifications and in all subsamples, imputed mobility has a strong and significant effect on hourly wages. The regression results by gender indicate a relatively larger mobility-earnings effect for females. The coefficient estimates on the imputed mobility variable in Table 4 imply earnings premia for a five percentage point difference in imputed mobility that range between 8 and 12% for the full sample, 7 and 10% for the male sample, and 10 and 14% for the female sample. In all models, imposing structure on the reduced-form causes large reductions in the estimated racial and gender wage differentials. Looking first at the pooled sample results, controlling for mobility eliminates the residual black-white and Hispanic-white earnings differentials observed in all three reduced-form specifications. In addition, the estimated negative effect of “Female” drops by nearly 60% in the models excluding occupation and industry dummies and by approximately 25% in the model controlling for occupation and industry.

Similar changes occur in the models estimated separately by gender. For men, imposing structure on the reduced-form causes declines in the estimated black-white earnings differential ranging from 30 to 50% and declines in the residual Hispanic-white earnings differential of approximately two-thirds in all specifications. For women, controlling for mobility reverses the signs of the black-white and Hispanic-white earnings differential. Hence, for all workers and by genders, race and ethnicity appear to have strong indirect effects on wages via mobility differentials.

To test the significance of these changes, we estimated earnings equations controlling for mobility but constraining the coefficient estimates on race, ethnicity, and gender in the pooled regression and on race and ethnicity in the gender-specific regressions to the respective coefficient estimates from the corresponding reduced-form models. For all subsamples and all specifications, simple F-tests reject the parameter constraints at the 1% level of confidence. Comparable tests that constrain the coefficients in the reduced-form model to the coefficient estimates from the structural model similarly reject the parameter restrictions at the 1% level in all models.

The estimated effects of the two instrumental variables in the reduced- form equations have the hypothesized signs and are, for the most part,

Job tenure is omitted since it is not reported for observations in the outgoing rotation 14

groups.


statistically significant in all regressions. Again, the negative coefficients on household size are inconsistent with the hypothesis that households with higher earnings can afford larger families, thus suggesting that household size is a valid instrument for mobility. Being a military spouse has a significant negative effect in all of the pooled regressions, in all of the regressions estimated separately for females, and in two of the reduced- form models for males.

C. A Weak Test of Statistical Discrimination A possible indirect test for wage discrimination against immobile work-

ers compares the mobility-earnings premia for union and nonunion workers. To the extent that employer discretion in wage setting is limited by collective bargaining, firms will be constrained from discriminating on the basis of mobility-induced differences in reservation wages. In other words, the coefficient on the imputed mobility variable should be smaller for union workers than for nonunion workers. To test this hypothesis, Table 5 presents mobility premium estimation results from separate regressions for union and nonunion workers. The table suppresses all other parameter estimates as they do not differ substantially from the results in Tables 3 and 4. The table presents the estimated mobility-earnings effects for all workers, male workers, and female workers by union status using the three specifications in Tables 3 and 4.

The results of this test are mixed. While the mobility premium for union workers is greater than the mobility premium for nonunion workers in all specifications for the pooled sample, the difference is statistically significant for the specification excluding occupation and industry only. For the male sample, the point estimate for union workers exceeds that for nonunion workers in the first specification only. For the female sample, the mobility-earnings effect for union workers exceeds that of nonunion workers for all specifications.

In sum, the split-sample results support the mobility-earnings hypothesis advanced here. Potential mobility has a strong effect on the wages of both male and female workers. Moreover, controlling for potential mobility explains a large share of the racial/ethnic earnings differentials observed in standard OLS wage regressions. The separate estimates for union and nonunion workers, however, yield mixed results with relatively stronger union mobility premia for female workers and mixed results for male workers.

5. CONCLUSION Constraints to geographic mobility affect earnings through many chan-

nels. The geographically immobile may, on average, become trapped in


TABLE 5 OLS Regression Coefficients on Imputed Mobility Variables When Wage Regressions

are Estimated Separately for Union and Nonunion Workersa

Controlling for No occupation or Controlling for industry and

Sample industry dummies occupation occupation

Total sample Nonunion

Union

Male sample Nonunion

Union

Female sample Nonunion

Union

2.389 (0.231) 1.513

(0.408)

1.710 (0.331) 1.513

(0.474)

2.703 (0.291) 1.722

(0.611)

1.821 (0.219) 1.558

(0.39 7)

1.046 (0.321) 1.559

(0.471)

2.218 (0.272) 1.530

(0.602)

1.641 (0.214) 1.604

(0.39 2)

1.147 (0.356) 1.941

(0.524)

1.922 (0.248) 1.278

(0.544)

“Standard errors are in parentheses. Each specification includes a constant term, age, age squared, educational attainment, marital and veteran status, dummy variables for black and Hispanic workers, union status, year of survey, eight dummies indicating census region of residence, and dummies indicating residence in a central-city or in the non-central-city balance of a PMSA. In regressions controlling for occupation, 14 occupational categories are controlled for. In regressions controlling for industry, 22 industry categories are controlled for.

areas of weak labor demand, leading to below average earnings. The effect of mobility costs on reservation wages may lead to partial sorting across high- and low-paying firms by mobility type, yielding an observable wage differential. Alternatively, to the extent that firms infer broad differences in mobility costs from observable personal characteristics, employers may discriminate statistically against relatively immobile workers. All of these avenues suggest possible points of spillover between housing and labor markets and may provide useful paths for future research concerning observed racial and ethnic earnings differentials.

We have demonstrated a strong relationship between potential mobility and earnings. The point estimates from our various tests show a strong effect of mobility type on earnings that partially explains observed black-white and black-Hispanic earnings differentials.


APPENDIX

TABLE A1 First-Stage Mobility Regression Results from the 2SLS Models Presented in Table 3’

Variables

Age

Age‘

Years of schooling

Job tenure

Job tenure’

Black

Hispanic

Female

Married

Veteran

Household size

Spouse in armed

R2 F-statb (P-value)

forces

No occupation or industry dummies

Controlling for occupation

Controlling for industry and occupation

0.0008 (0.0025) - 0.0001 (0.0000) 0.0063

(0.00 19) - 0.0064 (0.001 7) 0.0001

(0.0000) - 0.0725 (0.0161) - 0.0464 (0.0190) - 0.0726 (0.0101) - 0.0247 (0.0104) 0.0340

(0.0131)

(0.0033)

(0.0669) 0.066 4.257

(0.014)

- 0.0079

- 0.1 150

- 0.0000 (0.0025) - 0.00004 (0.00002) 0.0065

(0.0021) - 0.0061 (0.0017) 0.0001

(0.0000) - 0.0695 (0.0162) - 0.0448 (0.0190) - 0.0542 (0.01 11) - 0.0230 (0.0104) 0.0341

(0.0131) - 0.0081 (0.0033) - 0.1 120 (0.0667) 0.073 4.299

(0.014)

- 0.0004 (0.0026)

(0.00003) 0.0071

(0.0022) - 0.0063 (0.001 7) 0.0001

(0.0000) - 0.0649

- 0.00004

(0.0161) -0.0415 (0.0190) - 0.0489 (0.0 1 13)

(0.0104) 0.0343

(0.0131)

(0.0033)

(0.0666) 0.082 4.181

(0.015)

-0.0253

- 0.0080

-0.1074

aThere are 7140 observations. Standard errors are in parentheses. Each specification includes a constant term, dummy variables for year of displacement, and the year of the survey. In regressions controlling for occupation, 14 occupational categories are controlled for. In regressions controlling for industry, 22 industry categories are controlled for.

bThe F-statistic tests the collective significance of the two instrumental variables, “Spouse in armed forces” and “Household size.”


TABLE A2 OLS and 2SLS Regressions of Pre-Displacement Earnings on Observed Post-Displacement

Mobility, by Gender’

No occupation Controlling Controlling for industry or for & industry dummies occupation occupation

OLS ZSLS OLS ZSLS OLS ZSLS

A. Men Moved

Black

Hispanic

R2 F-statistic (P-value) Overid. testC (P-value)

B. Women Moved

Black

Hispanic

R2 F-statistic (P-value) Overid. testC (P-value)

0.079 (0.019)

-0.197 (0.029) - 0.082 (0.032) 0.228 -

-

0.078 (0.025)

(0.028)

(0.036) 0.231

-0.145

- 0.052

-

-

1.749 (1.072)

(0.085)

(0.073) 0.096 1.895

(0.150) 0.102

(0.749)

1.293 (0.735)

(0.078) 0.017

(0.064) 0.141 3.034

(0.048) 4.824

(0.028)

- 0.089

- 0.004

- 0.031

0.072 (0.019)

-0.152 (0.029) - 0.070 (0.031) 0.273 -

-

0.078 (0.024)

(0.027)

(0.034) 0.300

-0.116

- 0.057

-

-

1.476 (0.959)

(0.068)

(0.065) 0.139 1.883

(0.152) 0.104

(0.746)

1.034 (0.646)

(0.071)

(0.056) 0.216 3.093

(0.045) 5.057

(0.025)

- 0.076

- 0.007

- 0.023

- 0.004

0.064 1.694 (0.018) (1.201)

-0.135 -0.057 (0.028) (0.075) - 0.054 0.010 (0.031) (0.071) 0.311 0.136

1.42 1 (0.242) 0.160

(0.688)

-

-

0.070 1.113 (0.024) (0.631) - 0.096 0.001 (0.026) (0.068) - 0.047 0.005 (0.034) (0.054) 0.338 0.233

3.372 (0.035) 3.238

(0.072)

-

-

“The sample size for men is 4290 while the sample size for women is 2850. Standard errors are in parentheses. Each specification includes a constant term, age, age squared, job tenure, job tenure squared, educational attainment, marital and veteran status, year of displacement, and the year of survey. In regressions controlling for occupation, 14 occupational categories are controlled for. In regressions controlling for industry, 22 industry categories are controlled for.

bThe F-statistic tests the collective significance of the excluded identifying instruments in the first-stage mobility regression.

‘This is an F-statistic for the overidentifying restrictions test of Basmann [6].


TABLE A3 First-Stage Mobility Results by Gender from the 2SLS Models Presented in Table 4“

No occupation or Controlling for Controlling for industry industry dummies occupation and occupation

Variables Men Women Men Women Men Women

Age

Age‘

Years of schooling

Job tenure

Job tenure‘

Black

Hispanic

Married

Veteran

Household size

Spouse in armed

R2 F-statb (P-value)

forces

0.0029 (0.0037)

-0.0001 (0.0000) 0.0076

(0.0026) - 0.0089 (0.0023) 0.0001

(0.0000) - 0.0608 (0.0239)

(0.0262)

(0.0151) 0.0358

(0.0148)

(0.0043) 0.1489

(0.2866) 0.061 1.895

(0.150)

-0.0422

- 0.0049

-0.0081

-0.0021 (0.0034) - 0.00002 (0.00004) 0.0037

(0.0030)

(0.0026) 0.00003

(0.00009~

(0.0208)

(0.0271)

(0.0139) 0.0931

(0.0649)

(0.0051)

(0.0623) 0.058 3.034

(0.048)

- 0.0035

- 0.0885

- 0.0533

- 0.0470

- 0.0088

-0.1103

0.0020 (0.0037)

-0.0001 (0.0000) 0.0068

(0.0029) - 0.0084 (0.0023) 0.0001

(0.0000) - 0.0508 (0.0241)

(0.0261)

(0.0151) 0.0364

(0.0148)

(0.0043) 0.1300

(0.2860) 0.069 1.883

(0.152)

- 0.0405

- 0.0087

- 0.0082

- 0.0024 (0.0034) - 0.00001 (0.00004) 0.0049

(0.0033) - 0.0029 (0.0026) 0.00002

(0.00009~ -0,0919 (0.0209)

-0.0517 (0.0271)

(0.01 39) 0.1031

(0.0647) - 0.0090 (0.0052)

-0.1093 (0.0621) 0.070 3.093

(0.046)

- 0.0401

0.0019 (0.0037)

-0.0001 (0.0000) 0.0082

(0.0028) - 0.0086 (0.0022) 0.0001

(0.0000) - 0.0449 (0.0241)

(0.0261)

(0.0151) 0.0364

(0.0148)

(0.0043) 0.1393

(0.2845) 0.083 1.421

(0.242)

- 0.0360

-0.0161

- 0.0070

- 0.0027 (0.0035) - 0.00001 (0.00004) 0.0045

(0.0033) - 0.0029 (0.0026) 0.00003

(0.00009~

(0.0209) - 0.0872

- 0.0462 (0.0271)

(0.0140) 0.1214

(0.0648)

(0.0051)

(0.06 19) 0.086 3.372

(0.035)

- 0.0384

-0.0103

-0.1027

“The sample size for males is 4290 and the sample size for females is 2850. Standard errors are in parentheses. Each specification includes a constant term, dummy variables for year of displacement, and the year of the survey. In regressions controlling for occupation, 14 occupational categories are controlled for. In regressions controlling for industry, 22 industry categories are controlled for.

bThe F-statistic tests the collective significance of the three instrumental variables, “Spouse in armed forces,” “Two-earner couple,” and “Household size.”

REFERENCES

1. D. J. Aigner and G. J. Cain, Statistical theories of discrimination in labor markets,

2. J. Albrecht and B. Axell, An equilibrium model of search unemployment, Journal of Industrial and Labor Relations Review, 30, 175-187 (1977).

Political Economy, 92, 824-840 (1984).


3. J. Angrist and A. Krueger, Split-sample instrumental variable estimates of the return to schooling, Journal of Business and Economic Statistics, 13, 225-235 (1995).

4. K. Arrow, The theory of discrimination, in “Discrimination in Labor Markets” (0. Ashenfelter and A. Rees, Eds.), Princeton University Press, Princeton, NJ (1973).

5. I. Ayres and P. Siegelman, Race and gender discrimination in bargaining for a new car, American Economic Review, 85, 304-321 (1995).

6. R. Basmann, On finite sample distributions of generalized classical linear identification test statistics, Journal of the American Statistical Association, 55, 650-659 (1960).

7. G. S. Becker, “The Economics of Discrimination,” University of Chicago Press, Chicago, IL (1957).

8. D. A. Black, Discrimination in an equilibrium search model, Journal of Labor Economics, 13, 309-324 (1995).

9. 0. J. Blanchard and L. F. Katz, Regional evolution, Brookings Papers on Economic Activity, 1, 1-75 (1992).

10. F. E. Block and M. S. Kuskin, Wage determination in the union and non-union sectors, Industrial and Labor Relations Review, 31, 183-192 (1978).

11. W. M. Boa1 and M. R. Ransom, Monopsony in the labor market, Journal of Economic Literature, 35, 86-112 (1997).

12. T. Boehm, H. Herzog, and A. M. Schlottmann, Intra-urban mobility, migration, and tenure choice, Review of Economics and Statistics, 73, 59-68 (1991).

13. J. Bound and H. J. Holzer, Demand shifts, population adjustment, and labor market outcomes during the 1980s, NBER working paper 5685, National Bureau of Eco- nomic Research, 1996.

14. G. Duncan and D. E. Leigh, Wage determination in the union and non-union sectors: A sample selectivity approach, Industrial and Labor Relations Review, 34, 24-34 (1980).

15. H. S. Farber, The determination of the union status of workers, Econometrica, 51,

16. R. Freeman, Unionism and the dispersion of wages, Industrial and Labor Relations

17. R. Gibbons and L. Katz, Layoffs and lemons, Journal of Labor Economics, 9, 351-380

18. M. Greenwood, Research on internal migration in the United States: A survey, Journal of

19. R. S. Hacker, Mobility and regional economic downturns, unpublished manuscript. 20. H. J. Holzer, The spatial mismatch hypothesis: What has the evidence shown?, Urban

21. J. F. Kain, The spatial mismatch hypothesis: three decades later, HousingPolicy Debate, 3,

22. A. Krueger, How computers have changed the wage structure: Evidence from microdata,

23. J. Mincer, Family migration decisions, Journal of Political Economy, 86, 749-773 (1978). 24. A. H. Munnell, G. M. Tootell, B. Browne, E. Lunn, and J. McEneaney, Mortgage lending

in Boston: Interpreting HMDA data, American Economic Review, 86, 25-53 (1996). 25. E. S. Phelps, The statistical theory of racism and sexism, American Economic Review, 62,

26. S. Raphael and D. Toseland, Skills, skill breadth, and wage determination, unpublished

1417-1437 (1983).

Review, 34, 3-23 (1980).

(1991).

Economic Literature, 13, 397-433 (1975).

Studies, 28, 105-122 (1991).

371-462 (1991).

1984-1989, Quarterly Journal of Economics, 108, 35-78 (1993).

659-661 (1972).

manuscript.


27. S. L. Ross, Racial differences in residential and job mobility: Evidence concerning the spatial mismatch hypothesis, Journal of Urban Economics, 43, 112-135 (1998).

28. J. Tirole, “The Theory of Industrial Organization,” MIT Press, Cambridge, MA (1988). 29. W. J. Wilson, “When Work Disappears: The World of the New Urban Poor,” Knopf,

30. J. Yinger, “Closed Doors, Opportunities Lost: The Continuing Costs of Housing Discrim-

31. J. S. Zax and J. F. Kain, Commutes, quits, and moves, Journal of Urban Economics, 29,

New York (1996).

ination,” Russell Sage Foundation, New York (1995).

153-165 (1991).

geographic mobility, race, and wage differentials

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