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Spatial Mismatch and Social Acceptability
Journal of Urban Economics 50, 474–490 (2001) doi:10.1006/juec.2001.2229, available online at http://www.idealibrary.com on
Spatial Mismatch and Social Acceptability∗ David L. Sjoquist Fiscal Research Program, Georgia State University, Atlanta, Georgia 30303 E-mail: [email protected] Received September 21, 1999; revised March 28, 2001; published online July 18, 2001 Research on spatial mismatch has focused on its effects on job access, but not on the mechanisms through which the effects operate. We explore one such mechanism— namely, that blacks do not search for suburban jobs because they are not socially accepted in the suburbs. Using data from the Greater Atlanta Neighborhood Study, we find that a black person’s perception of his or her acceptance in a job search area has a small, but statistically significant effect on the probability of searching for a job in that area. Limitations associated with our measure of social acceptance suggest that the results should be considered suggestive. © 2001 Academic Press
I. INTRODUCTION Over the past decade or so there has been renewed interest in John Kain’s [19] spatial mismatch hypothesis. This hypothesis has been advanced as an explanation for the poor labor market outcomes of less-skilled inner-city minorities, including such outcomes as lower wage rates (Ihlanfeldt and Sjoquist [13]), reduced employment rates (Ihlanfeldt and Sjoquist [14, 15]), longer spells of unemployment (Rogers [25]), and lower rates of transition from welfare to work (Vartanian [29]). Much (though not all) of the recent evidence shows that reduced access to jobs for less-skilled inner-city minorities leads to reduced labor market outcomes, but this research does not explain why access matters.1 It does not identify the barriers preventing minorities from shifting their labor supply to the suburbs in response to the spatial mismatch. Kain originally gave several possible reasons why distance or inferior access to employment opportunities may reduce the likelihood of minority employment. First, greater distance to jobs either results in higher commuting costs or serves as a proxy for insufficient public transit service to suburban employment ∗ Helpful comments were provided by Keith Ihlanfeldt, Julie Hotchkiss, Mary Beth Walker, Harry Holzer, Sheldon Danziger, Maryann Feldman, John Engberg, Steve Ross, Paul Waddell, and two anonymous referees; Chris Geller provided technical assistance. 1 For recent reviews of the literature, see Kain [20], Ihlanfeldt [10], and Ihlanfeldt and Sjoquist [16].
474 0094-1190/01 $35.00 Copyright © 2001 by Academic Press All rights of reproduction in any form reserved.
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sites (Ihlanfeldt and Young [17]; Holzer and Ihlanfeldt [8]). Second, distance may correlate with reduced information about job opportunities (Ludwig [22]; Ihlanfeldt [12]). Third, suburban employers may be more discriminatory, resulting in reduced minority employment at suburban job sites (Kirschenman and Neckerman [21]; Holzer and Ihlanfeldt [9]). A fourth reason, not specifically mentioned by Kain, is that minorities may believe that suburban job sites provide a more hostile environment in which to work. Blacks may avoid suburban job locations because they believe that, even if hired, they will face a job environment in which coworkers or the customers that they face will be unfriendly, disrespectful, or outright hostile. The possibility that blacks may believe they will not be socially accepted is the focus of this paper. While we have been unsuccessful in finding any literature that directly addresses this issue, Padavic and Reskin [24], Dickson and MacLachlan [4], and Hoffman and Ritchey [7] provide related evidence suggesting that this behavior is possible. The remainder of the paper is organized as follows. Section II presents a simple framework for analyzing this issue and describes the data used in the empirical analysis. The Section III gives the empirical results, and Section IV contains the summary and conclusions. II. FRAMEWORK AND DATA A. Framework Consider a metropolitan area with geographically dispersed job sites and an individual minority, i, who must decide which of these job sites he or she will search. We assume that the probability of searching site j is a function of the expected benefits of employment at that site and the costs of searching that site as compared to other sites. Expected benefits and costs depend upon the following factors: (1) the cost of commuting, which depends on the distance between the job site and the residence, the mode of travel, and the opportunity cost of time; and (2) the wage that an individual expects to earn, which can differ across the job sites. The perceived wage is dependent on the level of relevant information about the job site that the individual has. The individual’s perceived probability of being hired at site j depends on the following factors: (1) the number of relevant positions for which the individual would qualify that the individual believes is available at that site; (2) the expected level of competition for those positions; and (3) the degree of hiring discrimination that the individual expects to confront at the site. The number of perceived relevant positions, as well as the expected level of competition for them, depends on the level and quality of information about opportunities at the site that an individual has. The information, or misinformation, that the individual obtains should depend on the actual number of openings
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at the site, the individual’s ability to gather such information, and the nature of the other sources of job information, which will in turn depend on the individual’s information network. Because we can assume that the duration of an interview is independent of the job sites, the only relevant costs to selecting site j to search are the costs associated with travel. We consider that a racial minority may incur psychic costs associated with being employed at a site because he or she is not socially accepted. This cost is beyond the possible discrimination that the individual might experience in trying to obtain a position. We posit that the expected value of such psychic cost will be directly related to the individual’s beliefs concerning the attitude of employees and customers at that site. The individual will search more than one site if the number of openings that the individual is aware of at site j is fewer than the number of openings for which he or she desires to apply. Also, if the individual thinks that his or her information is imperfect, then he or she may search at more than one site as a way of diversifying risk. Our empirical approach is to estimate a separate linear probability equation for each job site using seemingly unrelated regression and individual-level data. The dependent variable measures whether an individual searched for a job at the site. The independent variables include a measure of the individual’s perception of his or her acceptance at the site and measures of the other factors identified above as being associated with the decision of whether to search a particular site. B. Data The data are from the Greater Atlanta Neighborhood Study (GANS), a survey designed to examine the role of racial attitudes, residential segregation, and the functioning of the labor market in explaining economic inequality in Atlanta.2 The survey was part of a larger project, the Multi-City Survey on Urban Inequality (MCSUI), that examined the causes and consequences of urban inequality in four metropolitan areas: Atlanta, Boston, Detroit, and Los Angeles. We use data only for Atlanta. A stratified random sample of 1,529 adults were interviewed during the summer and fall of 1993; the sampling frame oversampled individuals from lowincome areas. Face-to-face interviews lasting more than 75 minutes on average were conducted with residents from the nine most-central counties in the Atlanta area. Of the respondents, 651 were white, 829 were black, and 49 were of other races. The GANS identified seven areas of employment concentration within the inner-metropolitan region (essentially, the five major urban counties) and for 2
The survey was conducted under contract with Mathematica, Inc. and funded by grants from the Russell Sage Foundation and Ford Foundation.
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each area asked each respondent whether he or she had ever searched for a job in that area. The dependent variable, Sij , equals 1 if individual i searched site j and 0 otherwise. We restrict the sample to those individuals who searched within the past year. Another survey question concerned the respondent’s belief as to whether a black person moving into an area would be accepted. This question was asked for six of the seven areas identified in the search question, excluding only the central business district (CBD).3 The variable that we use to measure social acceptance in a job in a search area, ACC ij , denotes individual i’s perception of the acceptability of a black person moving into area j . ACC ij equals 1 if the respondent said that a black person would be welcomed and 0 if otherwise. However, there are at least four reasons why this variable is not the ideal measure of the degree of social acceptance in a job. First, using this variable requires accepting the premise that if individuals feel less welcome moving into an area, then they will have a greater perceived psychic cost associated with social acceptability in a job in that area. It would be possible for a black person to believe that he or she would be accepted if he or she moved into an area, yet also believe that he or she would not be accepted in a job in that area by either coworkers or the customers that he or she faces. This does not seem reasonable, however. Perception about acceptability in an area (for both a residential and an employment setting) are likely formed from the behavior or attitudes of the area’s residents, rather than employees or customers, because employees and customers are much less visible. And to the extent that white people working or shopping in an area also live in that area (or, at least, black people perceive that they do), then the variable measures blacks’ perception of social acceptability at a job in that area. Even if blacks believe that they will not be accepted as residents, they could still believe that they would be accepted by coworkers and customers in a job in that area. If this, but not the converse, is true, then we would expect the coefficient on ACC to be biased toward 0. Thus our hypothesis could be true, but the coefficient would not confirm it. Second, while the survey question focuses on social acceptance, it is possible that the response reflects expected discrimination at the site. While we use control variables that measure the respondent’s attitudes and opinions regarding discrimination (see below), these are not site-specific variables. Thus a positive coefficient on ACC is consistent not only with the social acceptance hypothesis, but also with an expected discrimination hypothesis; that is, a minority will not search an area because of expected discrimination. We expect that if a black 3 The areas are Midtown (located just north of the CBD), Tri-Cities (located just south of the city), Decatur (located just east of the city), Norcross (located northeast of the city), Roswell/Alpharetta (located north of the city), and Marietta/Smyrna (located northwest of the city). For a map showing these areas, see Sjoquist [26].
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person believes that expected discrimination is high at predominately white job sites, then he or she will believe that racial discrimination is high in general. Thus if ACC is measuring expected discrimination, then we would expect that for sites that are predominately white, ACC will be negatively related to the measures of expected discrimination that are not site-specific. However, we do not observe this, as ACC for predominately white sites is not correlated with our measures of discrimination. While this result supports the position that ACC measures social acceptance and not expected discrimination, we do recognize that ACC may still measure both effects. Third, if there are few black workers employed at a job site, then the information available to inner-city blacks about job openings at the site may be very limited, resulting in only a few black workers searching that job site. So it is possible that ACC could be simply serving as a proxy for the percentage of blacks at the job site (which we cannot measure). In that case, the measured effect of ACC may be the result of a lack of information and not a concern over social acceptance. However, we do have site-specific variables that measure job market information, and thus we do not believe that ACC is measuring a lack of job information (see also Ihlanfeldt [12]). Fourth, search depends on expected commuting distance, not current distance. Expected commuting distance will be less than the current distance if the individual expects to move closer to the job site area after being hired. But if the likelihood of moving into the area is related to social acceptance, then ACC would be positively related with expected commuting costs. The GANS survey also asked respondents to indicate how hard whites were to get along with, which provided a second measure of social acceptability. This variable, denoted ALONGi , is coded on a seven-point Likert scale.4 We use the actual value of the variable, where higher values mean that the respondent believes that whites are harder to get along with. The question refers to whites in general and is not specific to job sites. We argue that blacks who believe that whites are hard to get along with are likely to also believe that they will be less well accepted in a mostly white workplace. So if the racial composition of the residents of a search area reflects blacks’ perceptions of the composition of the area’s employees, ALONGi would measure black workers’ beliefs regarding social acceptability on the job. This leads us to expect that the coefficient on ALONGi will be smaller (negative) for sites where a higher percentage of the 4 The sample was divided into three groups and asked different variations of the question. One group was asked whether whites are hard to get along with, a second group was asked whether white males were hard to get along with, and a third group was asked whether white females were hard to get along with. We combined the responses of all three groups. The difference in mean response between the second and third groups is statistically significant, but the differences for the other two pairs are not. We did run separate regressions for each group; the results are very similar to those using the combined variables, so we report the results using the combined variable.
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population is white, and it will be larger (either less negative or positive) for sites where a lower percentage of the population is white. We have previously suggested that the likelihood of searching a site will depend on the expected level of discrimination in the hiring process. The GANS does not contain a variable that addresses this issue for each site, but it does contain three questions that can be used to measure expected discrimination. For the first variable, denoted DISC1i , respondents were asked whether discrimination hurts the chances of blacks getting good-paying jobs. DISC1i equals 1 if the respondent said that discrimination hurts a lot or some and 0 otherwise. For the second variable, denoted DISC2i , respondents were asked if they had ever experienced workplace discrimination. DISC2i equals 1 if the individual answered yes and 0 if not. We base our use of this variable on the assumption that individuals who have experienced discrimination will have higher expectations of future discrimination. For the third variable, denoted DISC3i , respondents were asked to indicate the degree to which whites discriminate. DISC3i is measured on a seven-point Likert scale.5 We use the actual value of the variable, where higher values mean that a respondent more strongly believes that whites discriminate. The choice of a job site may be related to the individual’s preferences concerning the racial makeup of coworkers. Black workers with stronger preferences for being in racially integrated situations may be more willing or inclined to search at job sites that are more integrated. The GANS survey asked a series of questions concerning the desirability of living in neighborhoods of different racial composition. We used the responses to these questions to develop a variable, denoted RPREF i , to reflect neighborhood racial preference. This variable equals 1 if the individual stated a preference for living in an all black or nearly all black neighborhood and 0 otherwise.6 We expect that RPREF will be negatively related to searches at predominately white sites and positively related to searches at sites with a low percentage of white residents. As with the variable ACC, our use of RPREF infers attitudes regarding a job site from GANS data related to residential settings. It does seem possible that an individual could prefer a black neighborhood but not a black work environment. It is also possible that an individual could prefer an integrated neighborhood but prefer a black work environment. However, this possibility would seem unlikely unless an integrated neighborhood is associated with a preferred neighborhood 5 The sample was divided into three groups and asked different questions. One group was asked whether whites discriminate, a second group was asked whether white males discriminate, and a third group was asked whether white females discriminate. We combined the responses of all three groups. 6 The questions were asked about five neighborhoods with 15 households in each. The five neighborhoods had 15, 11, 7, 3, and 1 black households, the rest being white. If the individual picked the neighborhoods with 15 or 11 black households, then RPREF equaled 1.
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(for example, one with better public services or public education.) Thus, for a predominately white site, we believe that the coefficient is more likely to be biased toward 0 than away from 0. The negative effect of increased minority concentration on white attitudes and behavior toward blacks is well known (Tigges and Tootle [28]). However, research has also found that an increased concentration of blacks leads to an increased ethnocentrism and an increase in prejudice expressed toward whites (Evan and Giles [6]). Thus we might expect that the greater the percentage of blacks within a respondent’s neighborhood, the lower the probability that respondent will search for a job in a predominately white neighborhood. Therefore, we include as a variable the percentage of the respondent’s census tract that is black, denoted PCBLK i . To measure trip time between job sites and the individual’s residence, we use the calculated distances between the centroids of the census tract in which the individual lives and each of the six job sites. These variables, denoted DIST ij , should reflect commuting cost, the travel cost of a job search, and possibly access to job information. To further capture accessibility, we used the variable CARi , which equals 1 if the individual had access to a car when he or she last searched for a job and 0 otherwise. We expect the coefficient on this variable to be positive and of greater importance for the sites that are not as well served by mass transit. Choosing which job sites to search should depend on the individual’s perception of the availability of job openings. The GANS survey asked individuals which of the job sites had the fewest and the most openings for someone without a college degree. Based on these responses, we created two dummy variables, denoted FEW ij and MOST ij . The variable FEW ij MOST ij equals 1 if area j is identified as having the fewest (most) jobs and 0 otherwise.7 The individual’s age, education, gender, and marital status may also affect the search process. Clark [2] found that younger blacks are more likely to move to the suburbs, while Dyer, Vedlitz, and Worchel [5] found that the social distance between blacks and whites decreased with education and increased with age. Thus we would expect older black workers and those with less education to be less likely to search in white communities. We also expect gender to influence the choice of a search site, because females appear to place a greater value on their commuting time (Madden [23]) and have different preferences regarding employment attributes (Agassi [1]). There is also reason to believe that racial discrimination differs by gender; for example, racial differences in earnings are smaller for females than for males. Individuals who are married may be more tied to the labor market and therefore may search more areas. Thus we include variables for age AGE i ; education EDUC i , which 7
has.
Ihlanfeldt [12] used these variables to analyze the level of job information that an individual
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equals 1 if the respondent has completed high school and 0 if not;8 gender SEX i , which equals 1 if male; and marital status MARRi , which equals 1 if married. The choice of search sites may also be affected by the characteristics of the neighborhood in which the individual lives. Wilson [30] and others argue that the concentration of poverty within underclass neighborhoods worsens labor market outcomes among their residents. Early research on this subject has not always found neighborhood characteristics to be of significance (Jencks and Mayer [18]), but Cutler and Glaeser [3] found significant effects. As we have previously noted, job information depends on one’s social network, and the level of social networks may be influenced by the individual’s neighborhood characteristics. Jencks and Mayer [18] cite evidence that social networks of poor families are more geographically restricted than those of affluent families. This suggests that the neighborhood poverty rate may reflect the breadth of social networks. To control for neighborhood characteristics, we included the variable PCPOORi , which is the percentage of poor persons in the respondent’s census tract. The theoretical framework suggests that such factors as availability of public transit at the various sites and the wage rate at site j relative to other sites should influence the choice of sites that are searched. Four of the six job sites that we consider are served by MARTA (Atlanta’s public transit system), although only three by train; a fifth site is served by Cobb Community Transit (CCT), a bus system that links to MARTA. Thus public transit access differs across the job sites, but because for each job site a public transit variable would have to have the same value for all individuals, we cannot use a public transit variable in the regression. Likewise, the wage rate could differ across the sites (Ihlanfeldt [11]), but for any given site, all individuals should face the same wage structure. Our sample was limited to black respondents with a high school degree or less who reported searching within the year prior to the survey, but not necessarily at one of the six sites. The resulting sample size is 116, although with missing values, the regressions were actually estimated with 93 observations. Variable definitions and descriptive statistics are given in Table 1. III. EMPIRICAL RESULTS We are interested in whether the perceived level of acceptance of blacks affects their selection of job search sites. Before preceding with the regression analysis, we present some less formal evidence on the issue. Table 2 presents, for each of the six sites identified in the GANS (column 1), the percentage of blacks who responded that residents in the area would not 8 The analysis is restricted to respondents with a high school degree or less. Unfortunately, for this group the GANS identifies only whether the individual has a high school degree or has not completed high school.
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be upset if a black household moved into the area (column 2), the percentage of blacks and whites that searched that area (columns 3 and 4), and the percentage of residents who are white (column 5), and the mean distance by race (columns 6 and 7), which are variables that help describe the sites. The areas are listed in order of the percentage saying that residents would not be upset. Generally, the percentage of black workers who searched an area decreases as the percentage of blacks who say that blacks would be welcomed decreases. The one job site that does not fit the pattern, Norcross, is not served by public transit, which may explain the unexpectedly smaller percentage who search that area. The simple correlation between these two series is 0.81 and is statistically significant.9 Table 2 supports the hypothesis that the degree of social acceptability affects the choice of search area and thus that concern over social acceptance may be a cause of spatial mismatch and its persistence. However, as we noted above, it is possible that ACC also measures such factors as expected discrimination and not just social acceptability. If black workers are driven by concerns regarding social acceptance and discrimination, then we should expect a racial difference in the patterns of search. The data suggest that blacks and whites search different areas (columns 3 and 4). A chi-squared test leads to a rejection of the hypothesis that the distributions across sites are the same for blacks and whites. Blacks have a higher probability than whites of searching areas located within or south of the central city, while whites have a higher probability than blacks of searching the northern suburban job sites. Given that whites are more likely to live on the north side and blacks on the south side, this pattern is not surprising. But it is also consistent with the lower mean values of ACC for the northern suburbs. There are also differences in the number of areas searched. Whites searched fewer areas than blacks; 55.7% of whites searched only one or two of the seven sites, while 41.9% of blacks searched only one or two sites. Based on a chisquared test, we can reject the possibility that the frequency distributions of the number of sites searched are the same for the two groups. This result is consistent with Stoll’s [27] research on Los Angeles, and is also consistent with the expectation that blacks may have to search more sites to find employment because of employment discrimination and because they search job sites with fewer openings (Ihlanfeldt [12]). To explore the extent to which individual characteristics are associated with racial differences in the number of sites searched, we regressed the number of sites searched against a set of variables that included race, age, gender, education, marital status, availability of a car, residential distance from the CBD, and a set of interaction terms for which we interacted each variable with race. 9
A simple linear regression using the observations in Table 2 yields the following equation: percent searching = 19.6 + 0.392 ∗ percent accept, where the coefficient has a t ratio of 2.79.
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TABLE 1 Definitions, Means and Standard Deviations (Blacks with a high school education or less and who searched within the past year)
Variable S1 S2 S3 S4 S5 S6 DIST 1 DIST 2 DIST 3 DIST 4
Definition
DIST 6 ACC 1
R searched Decatur area R searched Tri-Cities area R searched Midtown area R searched Marietta/Smyrna area R searched Roswell/Alpharetta area R searched Norcross area Distance from residence to Decatur Distance from residence to Tri-Cities Distance from residence to Midtown Distance from residence to Marietta/ Smyrna Distance from residence to Roswell/ Alpharetta Distance from residence to Norcross Would be accepted in Decatur
ACC 2
Would be accepted in Tri-Cities
ACC 3
Would be accepted in Midtown
ACC 4
Would be accepted in Marietta/ Smyrna Would be accepted in Roswell/ Alpharetta Would be accepted in Norcross
DIST 5
ACC 5 ACC 6 DISC3 ALONG
FEW 6
Do whites discriminate (1 to 7) Are whites easy to get along with (1 to 7) Discrimination hurts blacks (1 to 4) Age R was discriminated against on a job R says Decatur has fewest jobs R says Tri-Cities has fewest jobs R says Midtown has fewest jobs R says Marietta/Smyrna has fewest jobs R says Roswell/Alpharetta has fewest jobs R says Norcross has fewest jobs
MOST 1 MOST 2 MOST 3
R says Decatur has most jobs R says Tri-Cities has most jobs R says Midtown has most jobs
DISC1 AGE DISC2 FEW 1 FEW 2 FEW 3 FEW 4 FEW 5
Units 1 = yes, 1 = yes, 1 = yes, 1 = yes, 1 = yes, 1 = yes, Miles Miles Miles Miles
0 = no 0 = no 0 = no 0 = no 0 = no 0 = no
Mean
Standard deviation
0.682 0.400 0.536 0.336 0.382 0.291 8.848 9.354 7.572 17.593
0.468 0.492 0.501 0.474 0.488 0.456 5.468 4.761 4.580 5.192
Miles
22.890
3.516
Miles 1 = accepted 0 = not accepted 1 = accepted 0 = not accepted 1 = accepted 0 = not accepted 1 = accepted 0 = not accepted 1 = accepted 0 = not accepted 1 = accepted 0 = not accepted 1 = no, 7 = yes 1 = easy, 7 = hard
20.334 0.956
4.613 0.205
0.798
0.403
0.894
0.308
0.438
0.498
0.248
0.433
0.372
0.485
5.581 4.231
1.957 1.967
1 = a lot, 4 = none years 1 = yes, 0 = no 1 = yes, 0 = no 1 = yes, 0 = no 1 = yes, 0 = no 1 = yes, 0 = no
1.333 33.200 0.241 0.034 0.060 0.213 0.214
0.541 9.368 0.430 0.182 0.238 0.412 0.412
1 = yes, 0 = no
0.282
0.452
1 = yes, 0 = no
0.085
0.281
1 = yes, 0 = no 1 = yes, 0 = no 1 = yes, 0 = no
0.188 0.145 0.325
0.392 0.354 0.470
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david l. sjoquist TABLE 1—Continued
Variable
Definition
MOST 4
R says Marietta/Smyrna has most jobs R says Roswell/Alpharetta has most jobs R says Norcross has most jobs R wants to live in black area Percent black in census tract Is R married R has a high school degree Sex Was car available when R searched Percent poor in census tract
MOST 5 MOST 6 RPREF PCBLK MARR EDUC SEX CAR PCPOOR
Mean
Standard deviation
1 = yes, 0 = no
0.077
0.268
1 = yes, 0 = no
0.077
0.268
0.103 0.431 84.580 0.188 0.846 0.325 0.483 27.862
0.304 0.497 21.098 0.392 0.362 0.470 0.502 17.957
Units
1 = yes, 0 = no 1 = yes, 0 = no Percent 1 = yes, 0 = no 1 = yes, 0 = no 1 = male, 0 = female 1 = yes, 0 = no Percent
In the regression (not reported), race was the only variable that was statistically significant, and it was positive, confirming that blacks search more sites than whites. Distance interacted with race was negative and nearly significant (t ratio of −1.70), which implies that blacks who live further from the CBD search fewer places. Given that most blacks live south of the CBD, greater distance from the CBD implies greater distance from the northern suburban job sites. In addition, age interacted with race was negative and nearly significant (t ratio of −1.74), implying that older blacks search fewer of these sites. TABLE 2 Perceived Reception of Blacks and Job Search Patterns
[1] Job site Decatura Midtowna Tri-Citiesa Marietta/ Smyrnab Norcross Roswell/ Alpharettab a b
[2] Percent saying residents would not be upset
[3] Percent searched job site (blacks)
[4] Percent searched job site (whites)
[5] Percent white residents at site
[6] Mean distance to site in miles (blacks)
[7] Mean distance to site in miles (whites)
95.6 89.5 79.8 43.8
68.2 53.6 40.0 33.6
31.9 29.2 24.3 62.5
59.7 53.8 31.8 79.0
8.8 7.6 9.3 17.6
17.0 15.6 17.8 15.8
37.2 24.7
29.1 38.2
31.9 34.7
80.7 91.9
20.3 22.9
21.3 21.1
Site served by bus and train. Site served by bus.
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We turn now to the regression analysis. One approach is to estimate a single regression in which we treat each respondent’s decision about searching an area as a separate observation.10 As discussed above, we expect the control variables that are not site-specific to have different effects on the probability of search across the various sites. To allow the coefficients on all of the variables that are not site-specific to vary across sites, site dummies and interaction terms should be included. An alternative approach is to estimate separate equations for each site. Given that the choices of search sites are not independent, this approach used seemingly unrelated regression (SUR). The empirical results from the multiequation approach are similar to those obtained using the single equation. SUR estimation results are reported, as this is a richer specification allowing the unobservable variables affecting search probabilities to be correlated across locations. Unweighted linear probability regressions were run for each of the six residential areas identified in the GANS. We restrict the coefficients on the sitespecific variables to be the same for all the regressions, and allow the coefficients on the intercept term and variables that are not site-specific (DISC1, DISC2, DISC3, ALONG, RPREF, PCBLK, SEX, MARR, AGE, EDUC, PCPOOR, and CAR) to differ across the regressions. The results are presented in Table 3. The sign on the coefficients on ACC is positive, as expected, and significant. This result is consistent with the hypothesis that the perception of social acceptance will affect the choice of job search sites and, as noted above, with the hypothesis that expected discrimination is affecting the choice of search sites. However, the effect is not large. For example, calculated at the mean value of S and ACC for Roswell/Alpharetta, the implied elasticity is 0.09. If the value of ACC for Roswell/Alpharetta were to increase to the value for Decatur, then the regression implies that the percentage of black job seekers who would search Roswell/Alpharetta would increase by 9.7 percentage points. This would reduce the difference in the search percentages between the two sites by 32.3%. We also ran regressions in which we used different combinations of variables, in which we ran weighted regressions, in which we restricted the sample to males, and in which we used those who searched within the past 5 years rather than the past year. The results for ACC in each case, as well as for the other regressions discussed below, were basically the same as those reported in Table 3. The coefficient on distance, DIST, is negative, as expected, and significant. The elasticity of S with respect to DIST, calculated at their mean values for Roswell/Alpharetta, is −1.25. It is of course not clear whether distance is measuring commuting cost, search cost, or lack of job information.
10
Thus the number of observations for the regression would be six times the number of respondents in the sample we use.
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david l. sjoquist TABLE 3 Searched Area Regressions: Blacks (Absolute t Statistic) Area
Variable [% white]
Tri-Cities [31.8%]
Midtown [53.8%]
Decatur [59.7%]
Marietta/ Smyrna [79.0%]
Norcross [80.7%]
Roswell/ Alpharetta [91.9%]
IN’CEPT
1.313 (3.34) 0.137 (2.66) −0.021 (4.60) −0.103 (2.28) 0.116 (2.49) −0.016 (0.67) −0.236 (2.71) −0.164 (1.48) 0.002 (0.10) 0.175 (1.76) 0.152 (1.18) −0.013 (2.03) 0.043 (0.42) 0.048 (0.31) −0.046 (0.45) −0.0003 (0.13) −0.004 (1.17)
0.926 (2.29) 0.137 (2.66) −0.021 (4.60) −0.103 (2.28) 0.116 (2.49) −0.009 (0.35) −0.198 (2.23) 0.248 (2.19) 0.053 (1.98) 0.182 (1.79) 0.231 (1.75) −0.018 (2.84) 0.069 (0.66) 0.043 (0.29) 0.077 (0.75) −0.00005 (0.02) −0.006 (0.20)
0.766 (2.19) 0.137 (2.66) −0.021 (4.60) −0.103 (2.28) 0.116 (2.49) 0.020 (0.93) −0.132 (1.74) 0.047 (0.48) −0.031 (1.37) 0.015 (0.17) 0.0002 (0.001) −0.006 (1.10) 0.080 (0.89) 0.083 (0.63) −0.048 (0.54) 0.007 (3.01) −0.007 (2.90)
0.648 (1.52) 0.137 (2.66) −0.021 (4.60) −0.103 (2.28) 0.116 (2.49) 0.011 (0.40) −0.050 (0.54) 0.075 (0.62) 0.020 (0.71) −0.064 (0.60) 0.139 (1.00) −0.002 (0.35) 0.029 (0.26) 0.060 (0.37) −0.028 (0.26) 0.0007 (0.25) −0.001 (0.34)
0.903 (2.26) 0.137 (2.66) −0.021 (4.60) −0.103 (2.28) 0.116 (2.49) −0.002 (0.08) −0.095 (1.08) −0.033 (0.29) 0.040 (1.52) −0.179 (1.80) 0.078 (0.60) −0.009 (1.39) 0.063 (0.61) 0.048 (0.32) −0.125 (1.23) 0.003 (1.18) −0.007 (2.26)
0.503 (1.14) 0.137 (2.66) −0.021 (4.60) −0.103 (2.28) 0.116 (2.49) 0.005 (0.19) −0.034 (0.35) −0.056 (0.45) 0.069 (2.38) −0.018 (0.17) 0.098 (0.68) −0.058 (0.83) −0.013 (0.12) −0.078 (0.48) 0.093 (0.84) 0.005 (1.62) −0.006 (1.62)
ACC DIST FEW MOST ALONG DISC1 DISC2 DISC3 RPREF MARR AGE SEX EDUC CAR PCBLK PCPOOR
System R-squared = 0.260.
To the extent that a respondent searched and found a job and then moved closer to the job site, the coefficient on distance will be negatively biased. We redid the estimation, limiting the sample to those who lived in their current residence before searching; there was essentially no change in the coefficient on DIST.
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The literature suggests that females may be less willing to search at sites that are a long distance from where they live. Our data do not provide support for this position. We included a variable created by interacting SEX and DIST, but the resulting coefficient was insignificant. The availability of public transit is a fixed effect that should be reflected in the intercept. However, it is possible that the effect of DIST might be affected by the availability of mass transit at a site. Individuals who rely on mass transit are unlikely to search a site not served by mass transit, regardless of distance (except if within walking distance). We estimated regressions equivalent to those reported in Table 3, but allowed the coefficient on DIST to differ across regressions. The six coefficients on DIST were all negative and did not significantly differ from each other. However, the coefficients on DIST were statistically significant for Decatur, Tri-Cities, and Marietta/Smyrna and were nearly significant for Midtown. For Norcross, an area unserved by public transit, and for Roswell/Alpharetta, an area served only by bus, DIST had very large standard errors. We explored two possible explanations for the lack of significant coefficients on distance for Norcross and Roswell/Alpharetta, but found no support for either explanation. The first explanation is based on the argument that, regardless of distance, those reliant on public transit will not search a site without good public transit access, while distance should matter at the margin for those with a car. We estimated the same regressions using just those observations for which the respondents reported having access to a car, but the results did not change. The coefficients on DIST were still insignificant for Norcross and Roswell/Alpharetta. We also included DIST interacted with CAR; none of the interaction terms was significant for any of the sites, while the coefficients on DIST did not change. A second possible explanation focused on the fact that Norcross and Roswell/Alpharetta are the two sites farthest from the CBD and thus farthest from where the vast majority of blacks live. Therefore, the distance may be so great that it does not matter at the margin for the typical black job seeker. To investigate this, we interacted DIST with a dummy variable that equaled 1 if distance was greater than x miles, where x took alternative values between 15 and 20 miles. If individuals had a distance threshold, then the interaction term should be negative and large. But the coefficients on the interaction terms were small and statistically insignificant. The coefficients on FEW and MOST (the job information variables) are expected to be negative and positive, respectively. The results are consistent with that expectation. The elasticities, calculated at the means, are −0.076 for FEW and 0.023 for MOST. We had hypothesized that the variables DISC1, DISC2, DISC3, and ALONG would show coefficients that are negative for areas with a large percentage of white residents and that increase as the white percentage decrease. However,
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in general, these variables did not perform as expected. The coefficients on ALONG are insignificant for all of the equations. The coefficients on DISC1 are negative and significant for two of the three areas with the fewest whites but insignificant for the other areas. The coefficients on DISC2 are insignificant except for one site, Midtown, for which the coefficient is positive. DISC3 is positive and significant for Midtown and Roswell/Alpharetta; a negative sign was expected for Roswell/Alpharetta. The lack of expected results for the discrimination variables are not driven by multicollinearity. The largest correlation coefficient between the three variables is 0.11, and estimating regressions by separately including each of the three variables did not produce different results. Respondents who stated a preference for living in a black neighborhood (reflected in RPREF) were expected to be less likely to search in those areas with predominately white populations. The coefficients are, as expected, negative for the three areas with the highest white population and positive for the three sites with fewer whites, but the results are only marginally significant at best. The coefficients on PCBLK are insignificant for all equations except Decatur, for which it is positive. These results do not fit the hypothesis that blacks from mainly black communities are more likely to search in black areas. The coefficients on CAR are all insignificant, suggesting that the availability of a car does not affect the choice of which areas to search. This result is surprising. We expected that the lack of a car would result in the individual searching areas more accessible to mass transit and that the availability of a car would result in more searches at sites at greater distances. Contrary to expectation, the coefficients on variables related to individual characteristics (AGE, SEX, MARR, and EDUC) were, in general, insignificant. The coefficients on the percentage of poor (PCPOOR), which was included to control for neighborhood effects, are significant for only two sites. Because our arguments imply that black behavior toward the choice of search locations should differ from white behavior, comparing the regression results for blacks with equivalent regression results for whites would be of interest here. Regressions for whites, excluding variables that we believe apply only to blacks (i.e., variables associated with expected social acceptance and discrimination) were estimated but are not reported here.11 There is consistency across the two sets of coefficients. The coefficient on DIST is negative and significant for 11 Some of the questions asked of blacks were not asked of whites; for example, the questions on which ACC is based. The question asked of whites concerning their preferences regarding the racial composition of the neighborhood was not the same as for blacks. Whites were asked about their level of comfort with a neighborhood of varying racial composition. To create the variable RPREF for whites that is similar, but not identical, to the one used for blacks, we coded RPREF as 1 if the white respondent stated that he would be uncomfortable living in a neighborhood with less than a majority of whites and 0 otherwise.
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whites but is somewhat smaller than for blacks, −0.018 versus −0.021. As in the regressions for blacks, MOST is positive and significant, although for whites FEW is unexpectedly positive and significant. The other control variables are generally insignificant, as in the black equations. IV. SUMMARY AND CONCLUSIONS The relatively poorer labor market outcomes for less-skilled inner-city minorities have been attributed to the existence of spatial mismatch, but there has been little discussion in the literature regarding the mechanism through which a lack of access to jobs results in the reduced labor market performance. This paper has suggested that one of the possible reasons why blacks may not hold jobs in the suburbs is that they believe they may not be socially accepted there. Therefore, they do not search suburban job sites. We explored this hypothesis using data from the GANS, considering the choice of search sites by less-skilled black respondents across six identified areas in the Atlanta region. We find that our measure of a black respondent’s perception of the acceptance of blacks in an area is a statistically significant factor in determining the probability of searching for a job in that area, although the effect appears to be small. We also found that distance between a respondent’s residence and the job site, as well as the respondent’s perception about the availability of jobs, affect the choice of job search location in the expected direction. Because of the limitations of the analysis, as we have previously explained, our results should be taken as suggestive. In particular, our measure of social acceptance refers to social acceptance in the residential community, not the job site. It may also reflect other factors, such as expected discrimination at the job site. Clearly, additional research using better measures of social acceptance should be carried out. Furthermore, there is a need for additional research on the entire question of the process through which job access affects labor market outcomes. REFERENCES 1. J. B. Agassi, “Comparing the Work Attitudes of Men and Women,” Lexington Books, Lexington, MA (1982). 2. W. A. V. Clark, Residential segregation in American cities: A review and interpretation, Population Research and Policy Review, 5, 95–127 (1986). 3. D. M. Cutler and E. M. Glaeser, Are ghettos good or bad?, Quarterly Journal of Economics, 112, 827–872 (1997). 4. J. P. Dickson and D. L. MacLachlan, Social distance and shopping behavior, Journal of the Academy of Marketing Science, 18, 153–161 (1990). 5. J. Dyer, A. Vedlitz, and S. Worchel, Social distance among racial and ethnic groups in Texas: Some demographic correlates, Social Science Quarterly, 70, 607–616 (1989). 6. A. S. Evans, Jr. and M. W. Giles, Effects of percent black on blacks’ perceptions of relative power and social distance, Journal of Black Studies, 17, 3–14 (1986).
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7. C. A. Hoffman and P. N. Ritchey, Assessing the differences in distance of interstate migration, Sociological Focus, 25, 241–255 (1992). 8. H. J. Holzer and K. R. Ihlanfeldt, Spatial factors and the employment of blacks at the firm level, New England Economic Review (Federal Reserve Bank of Boston), May/June Special Issue, 65–82 (1996). 9. H. J. Holzer and K. R. Ihlanfeldt, Customer discrimination and employment outcomes for minority workers, Quarterly Journal of Economics, 113, 835–867 (1998). 10. K. R. Ihlanfeldt, “Job Accessibility and the Employment and School Enrollment of Teenagers,” Upjohn Institute, Kalamazoo, MI (1992). 11. K. R. Ihlanfeldt, Intra-urban wage gradients: Evidence by race, gender, occupational class, and sector, Journal of Urban Economics, 32, 70–91 (1992). 12. K. R. Ihlanfeldt, Information on the spatial distribution of job opportunities within metropolitan areas, Journal of Urban Economics, 41, 218–242 (1997). 13. K. R. Ihlanfeldt and D. L. Sjoquist, The impact of job decentralization on the economic welfare of central city blacks, Journal of Urban Economics, 26, 110–130 (1989). 14. K. R. Ihlanfeldt and D. L. Sjoquist, Job accessibility and racial differences in youth employment rates, American Economic Review, 80, 267–276 (1990). 15. K. R. Ihlanfeldt and D. L. Sjoquist, The effect of job access on black and white youth employment: A cross-sectional analysis, Urban Studies, 28, 255–265 (1991). 16. K. R. Ihlanfeldt and D. L. Sjoquist, The spatial mismatch hypothesis: A review of recent studies and their implications for welfare reform, Housing Policy Debate, 9, 849–892 (1998). 17. K. R. Ihlanfeldt and M. Young, Intra-metropolitan variations in wage rates: The case of Atlanta fast-food restaurant workers, Review of Economics and Statistics, 76, 425–433 (1994). 18. C. Jencks and S. E. Mayer, The social consequences of growing up in a poor neighborhood, in “Inner-City Poverty in the United States” (L. E. Lynn and M. B. H. McGeary, Eds.), National Academy Press, Washington, DC (1990). 19. J. F. Kain, Housing segregation, Negro employment, and metropolitan decentralization, Quarterly Journal of Economics, 82, 175–197 (1968). 20. J. F. Kain, The spatial mismatch hypothesis: Three decades later, Housing Policy Debate, 3, 371–460 (1992). 21. J. Kirschenman and K. M. Neckerman, ‘We’d love to hire them, but. . .’: The meaning of race for employees, in “The Urban Underclass” (C. Jencks and P. E. Peterson, Eds.), The Brookings Institution, Washington, DC (1991). 22. J. Ludwig, Information and inner city educational attainment, Economics of Education Review, 18, 17–30 (1999). 23. J. F. Madden, Why women work closer to home, Urban Studies, 18(1), 181–194 (1981). 24. I. Padavic and B. F. Reskin, Men’s behavior and women’s interest in blue-collar jobs, Social Problems, 37, 613–628 (1990). 25. C. Rogers, Job search and unemployment duration: Implications for the spatial mismatch hypothesis, Journal of Urban Economics, 42, 109–132 (1997). 26. D. L. Sjoquist, “The Atlanta Paradox,” Russell Sage Foundation, New York (2000). 27. M. A. Stoll, Spatial job search, spatial mismatch, and the employment and wages of racial and ethnic groups in Los Angeles, Journal of Urban Economics, 46, 129–155 (1999). 28. L. M. Tigges and D. M. Tootle, Underemployment and racial competition in local labor markets, The Sociological Quarterly, 34, 279–298 (1993). 29. T. P. Vartanian, “Large City and Neighborhood Effects on AFDC Spells: A Test of the Spatial Mismatch and Social Isolation Hypothesis,” Ph.D. dissertation, University of Notre Dame, Notre Dame, IN (1992). 30. W. J. Wilson, “The Truly Disadvantaged,” University of Chicago Press, Chicago (1987).
Spatial Mismatch and Social Acceptability∗ David L. Sjoquist Fiscal Research Program, Georgia State University, Atlanta, Georgia 30303 E-mail: [email protected] Received September 21, 1999; revised March 28, 2001; published online July 18, 2001 Research on spatial mismatch has focused on its effects on job access, but not on the mechanisms through which the effects operate. We explore one such mechanism— namely, that blacks do not search for suburban jobs because they are not socially accepted in the suburbs. Using data from the Greater Atlanta Neighborhood Study, we find that a black person’s perception of his or her acceptance in a job search area has a small, but statistically significant effect on the probability of searching for a job in that area. Limitations associated with our measure of social acceptance suggest that the results should be considered suggestive. © 2001 Academic Press
I. INTRODUCTION Over the past decade or so there has been renewed interest in John Kain’s [19] spatial mismatch hypothesis. This hypothesis has been advanced as an explanation for the poor labor market outcomes of less-skilled inner-city minorities, including such outcomes as lower wage rates (Ihlanfeldt and Sjoquist [13]), reduced employment rates (Ihlanfeldt and Sjoquist [14, 15]), longer spells of unemployment (Rogers [25]), and lower rates of transition from welfare to work (Vartanian [29]). Much (though not all) of the recent evidence shows that reduced access to jobs for less-skilled inner-city minorities leads to reduced labor market outcomes, but this research does not explain why access matters.1 It does not identify the barriers preventing minorities from shifting their labor supply to the suburbs in response to the spatial mismatch. Kain originally gave several possible reasons why distance or inferior access to employment opportunities may reduce the likelihood of minority employment. First, greater distance to jobs either results in higher commuting costs or serves as a proxy for insufficient public transit service to suburban employment ∗ Helpful comments were provided by Keith Ihlanfeldt, Julie Hotchkiss, Mary Beth Walker, Harry Holzer, Sheldon Danziger, Maryann Feldman, John Engberg, Steve Ross, Paul Waddell, and two anonymous referees; Chris Geller provided technical assistance. 1 For recent reviews of the literature, see Kain [20], Ihlanfeldt [10], and Ihlanfeldt and Sjoquist [16].
474 0094-1190/01 $35.00 Copyright © 2001 by Academic Press All rights of reproduction in any form reserved.
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sites (Ihlanfeldt and Young [17]; Holzer and Ihlanfeldt [8]). Second, distance may correlate with reduced information about job opportunities (Ludwig [22]; Ihlanfeldt [12]). Third, suburban employers may be more discriminatory, resulting in reduced minority employment at suburban job sites (Kirschenman and Neckerman [21]; Holzer and Ihlanfeldt [9]). A fourth reason, not specifically mentioned by Kain, is that minorities may believe that suburban job sites provide a more hostile environment in which to work. Blacks may avoid suburban job locations because they believe that, even if hired, they will face a job environment in which coworkers or the customers that they face will be unfriendly, disrespectful, or outright hostile. The possibility that blacks may believe they will not be socially accepted is the focus of this paper. While we have been unsuccessful in finding any literature that directly addresses this issue, Padavic and Reskin [24], Dickson and MacLachlan [4], and Hoffman and Ritchey [7] provide related evidence suggesting that this behavior is possible. The remainder of the paper is organized as follows. Section II presents a simple framework for analyzing this issue and describes the data used in the empirical analysis. The Section III gives the empirical results, and Section IV contains the summary and conclusions. II. FRAMEWORK AND DATA A. Framework Consider a metropolitan area with geographically dispersed job sites and an individual minority, i, who must decide which of these job sites he or she will search. We assume that the probability of searching site j is a function of the expected benefits of employment at that site and the costs of searching that site as compared to other sites. Expected benefits and costs depend upon the following factors: (1) the cost of commuting, which depends on the distance between the job site and the residence, the mode of travel, and the opportunity cost of time; and (2) the wage that an individual expects to earn, which can differ across the job sites. The perceived wage is dependent on the level of relevant information about the job site that the individual has. The individual’s perceived probability of being hired at site j depends on the following factors: (1) the number of relevant positions for which the individual would qualify that the individual believes is available at that site; (2) the expected level of competition for those positions; and (3) the degree of hiring discrimination that the individual expects to confront at the site. The number of perceived relevant positions, as well as the expected level of competition for them, depends on the level and quality of information about opportunities at the site that an individual has. The information, or misinformation, that the individual obtains should depend on the actual number of openings
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at the site, the individual’s ability to gather such information, and the nature of the other sources of job information, which will in turn depend on the individual’s information network. Because we can assume that the duration of an interview is independent of the job sites, the only relevant costs to selecting site j to search are the costs associated with travel. We consider that a racial minority may incur psychic costs associated with being employed at a site because he or she is not socially accepted. This cost is beyond the possible discrimination that the individual might experience in trying to obtain a position. We posit that the expected value of such psychic cost will be directly related to the individual’s beliefs concerning the attitude of employees and customers at that site. The individual will search more than one site if the number of openings that the individual is aware of at site j is fewer than the number of openings for which he or she desires to apply. Also, if the individual thinks that his or her information is imperfect, then he or she may search at more than one site as a way of diversifying risk. Our empirical approach is to estimate a separate linear probability equation for each job site using seemingly unrelated regression and individual-level data. The dependent variable measures whether an individual searched for a job at the site. The independent variables include a measure of the individual’s perception of his or her acceptance at the site and measures of the other factors identified above as being associated with the decision of whether to search a particular site. B. Data The data are from the Greater Atlanta Neighborhood Study (GANS), a survey designed to examine the role of racial attitudes, residential segregation, and the functioning of the labor market in explaining economic inequality in Atlanta.2 The survey was part of a larger project, the Multi-City Survey on Urban Inequality (MCSUI), that examined the causes and consequences of urban inequality in four metropolitan areas: Atlanta, Boston, Detroit, and Los Angeles. We use data only for Atlanta. A stratified random sample of 1,529 adults were interviewed during the summer and fall of 1993; the sampling frame oversampled individuals from lowincome areas. Face-to-face interviews lasting more than 75 minutes on average were conducted with residents from the nine most-central counties in the Atlanta area. Of the respondents, 651 were white, 829 were black, and 49 were of other races. The GANS identified seven areas of employment concentration within the inner-metropolitan region (essentially, the five major urban counties) and for 2
The survey was conducted under contract with Mathematica, Inc. and funded by grants from the Russell Sage Foundation and Ford Foundation.
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each area asked each respondent whether he or she had ever searched for a job in that area. The dependent variable, Sij , equals 1 if individual i searched site j and 0 otherwise. We restrict the sample to those individuals who searched within the past year. Another survey question concerned the respondent’s belief as to whether a black person moving into an area would be accepted. This question was asked for six of the seven areas identified in the search question, excluding only the central business district (CBD).3 The variable that we use to measure social acceptance in a job in a search area, ACC ij , denotes individual i’s perception of the acceptability of a black person moving into area j . ACC ij equals 1 if the respondent said that a black person would be welcomed and 0 if otherwise. However, there are at least four reasons why this variable is not the ideal measure of the degree of social acceptance in a job. First, using this variable requires accepting the premise that if individuals feel less welcome moving into an area, then they will have a greater perceived psychic cost associated with social acceptability in a job in that area. It would be possible for a black person to believe that he or she would be accepted if he or she moved into an area, yet also believe that he or she would not be accepted in a job in that area by either coworkers or the customers that he or she faces. This does not seem reasonable, however. Perception about acceptability in an area (for both a residential and an employment setting) are likely formed from the behavior or attitudes of the area’s residents, rather than employees or customers, because employees and customers are much less visible. And to the extent that white people working or shopping in an area also live in that area (or, at least, black people perceive that they do), then the variable measures blacks’ perception of social acceptability at a job in that area. Even if blacks believe that they will not be accepted as residents, they could still believe that they would be accepted by coworkers and customers in a job in that area. If this, but not the converse, is true, then we would expect the coefficient on ACC to be biased toward 0. Thus our hypothesis could be true, but the coefficient would not confirm it. Second, while the survey question focuses on social acceptance, it is possible that the response reflects expected discrimination at the site. While we use control variables that measure the respondent’s attitudes and opinions regarding discrimination (see below), these are not site-specific variables. Thus a positive coefficient on ACC is consistent not only with the social acceptance hypothesis, but also with an expected discrimination hypothesis; that is, a minority will not search an area because of expected discrimination. We expect that if a black 3 The areas are Midtown (located just north of the CBD), Tri-Cities (located just south of the city), Decatur (located just east of the city), Norcross (located northeast of the city), Roswell/Alpharetta (located north of the city), and Marietta/Smyrna (located northwest of the city). For a map showing these areas, see Sjoquist [26].
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person believes that expected discrimination is high at predominately white job sites, then he or she will believe that racial discrimination is high in general. Thus if ACC is measuring expected discrimination, then we would expect that for sites that are predominately white, ACC will be negatively related to the measures of expected discrimination that are not site-specific. However, we do not observe this, as ACC for predominately white sites is not correlated with our measures of discrimination. While this result supports the position that ACC measures social acceptance and not expected discrimination, we do recognize that ACC may still measure both effects. Third, if there are few black workers employed at a job site, then the information available to inner-city blacks about job openings at the site may be very limited, resulting in only a few black workers searching that job site. So it is possible that ACC could be simply serving as a proxy for the percentage of blacks at the job site (which we cannot measure). In that case, the measured effect of ACC may be the result of a lack of information and not a concern over social acceptance. However, we do have site-specific variables that measure job market information, and thus we do not believe that ACC is measuring a lack of job information (see also Ihlanfeldt [12]). Fourth, search depends on expected commuting distance, not current distance. Expected commuting distance will be less than the current distance if the individual expects to move closer to the job site area after being hired. But if the likelihood of moving into the area is related to social acceptance, then ACC would be positively related with expected commuting costs. The GANS survey also asked respondents to indicate how hard whites were to get along with, which provided a second measure of social acceptability. This variable, denoted ALONGi , is coded on a seven-point Likert scale.4 We use the actual value of the variable, where higher values mean that the respondent believes that whites are harder to get along with. The question refers to whites in general and is not specific to job sites. We argue that blacks who believe that whites are hard to get along with are likely to also believe that they will be less well accepted in a mostly white workplace. So if the racial composition of the residents of a search area reflects blacks’ perceptions of the composition of the area’s employees, ALONGi would measure black workers’ beliefs regarding social acceptability on the job. This leads us to expect that the coefficient on ALONGi will be smaller (negative) for sites where a higher percentage of the 4 The sample was divided into three groups and asked different variations of the question. One group was asked whether whites are hard to get along with, a second group was asked whether white males were hard to get along with, and a third group was asked whether white females were hard to get along with. We combined the responses of all three groups. The difference in mean response between the second and third groups is statistically significant, but the differences for the other two pairs are not. We did run separate regressions for each group; the results are very similar to those using the combined variables, so we report the results using the combined variable.
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population is white, and it will be larger (either less negative or positive) for sites where a lower percentage of the population is white. We have previously suggested that the likelihood of searching a site will depend on the expected level of discrimination in the hiring process. The GANS does not contain a variable that addresses this issue for each site, but it does contain three questions that can be used to measure expected discrimination. For the first variable, denoted DISC1i , respondents were asked whether discrimination hurts the chances of blacks getting good-paying jobs. DISC1i equals 1 if the respondent said that discrimination hurts a lot or some and 0 otherwise. For the second variable, denoted DISC2i , respondents were asked if they had ever experienced workplace discrimination. DISC2i equals 1 if the individual answered yes and 0 if not. We base our use of this variable on the assumption that individuals who have experienced discrimination will have higher expectations of future discrimination. For the third variable, denoted DISC3i , respondents were asked to indicate the degree to which whites discriminate. DISC3i is measured on a seven-point Likert scale.5 We use the actual value of the variable, where higher values mean that a respondent more strongly believes that whites discriminate. The choice of a job site may be related to the individual’s preferences concerning the racial makeup of coworkers. Black workers with stronger preferences for being in racially integrated situations may be more willing or inclined to search at job sites that are more integrated. The GANS survey asked a series of questions concerning the desirability of living in neighborhoods of different racial composition. We used the responses to these questions to develop a variable, denoted RPREF i , to reflect neighborhood racial preference. This variable equals 1 if the individual stated a preference for living in an all black or nearly all black neighborhood and 0 otherwise.6 We expect that RPREF will be negatively related to searches at predominately white sites and positively related to searches at sites with a low percentage of white residents. As with the variable ACC, our use of RPREF infers attitudes regarding a job site from GANS data related to residential settings. It does seem possible that an individual could prefer a black neighborhood but not a black work environment. It is also possible that an individual could prefer an integrated neighborhood but prefer a black work environment. However, this possibility would seem unlikely unless an integrated neighborhood is associated with a preferred neighborhood 5 The sample was divided into three groups and asked different questions. One group was asked whether whites discriminate, a second group was asked whether white males discriminate, and a third group was asked whether white females discriminate. We combined the responses of all three groups. 6 The questions were asked about five neighborhoods with 15 households in each. The five neighborhoods had 15, 11, 7, 3, and 1 black households, the rest being white. If the individual picked the neighborhoods with 15 or 11 black households, then RPREF equaled 1.
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(for example, one with better public services or public education.) Thus, for a predominately white site, we believe that the coefficient is more likely to be biased toward 0 than away from 0. The negative effect of increased minority concentration on white attitudes and behavior toward blacks is well known (Tigges and Tootle [28]). However, research has also found that an increased concentration of blacks leads to an increased ethnocentrism and an increase in prejudice expressed toward whites (Evan and Giles [6]). Thus we might expect that the greater the percentage of blacks within a respondent’s neighborhood, the lower the probability that respondent will search for a job in a predominately white neighborhood. Therefore, we include as a variable the percentage of the respondent’s census tract that is black, denoted PCBLK i . To measure trip time between job sites and the individual’s residence, we use the calculated distances between the centroids of the census tract in which the individual lives and each of the six job sites. These variables, denoted DIST ij , should reflect commuting cost, the travel cost of a job search, and possibly access to job information. To further capture accessibility, we used the variable CARi , which equals 1 if the individual had access to a car when he or she last searched for a job and 0 otherwise. We expect the coefficient on this variable to be positive and of greater importance for the sites that are not as well served by mass transit. Choosing which job sites to search should depend on the individual’s perception of the availability of job openings. The GANS survey asked individuals which of the job sites had the fewest and the most openings for someone without a college degree. Based on these responses, we created two dummy variables, denoted FEW ij and MOST ij . The variable FEW ij MOST ij equals 1 if area j is identified as having the fewest (most) jobs and 0 otherwise.7 The individual’s age, education, gender, and marital status may also affect the search process. Clark [2] found that younger blacks are more likely to move to the suburbs, while Dyer, Vedlitz, and Worchel [5] found that the social distance between blacks and whites decreased with education and increased with age. Thus we would expect older black workers and those with less education to be less likely to search in white communities. We also expect gender to influence the choice of a search site, because females appear to place a greater value on their commuting time (Madden [23]) and have different preferences regarding employment attributes (Agassi [1]). There is also reason to believe that racial discrimination differs by gender; for example, racial differences in earnings are smaller for females than for males. Individuals who are married may be more tied to the labor market and therefore may search more areas. Thus we include variables for age AGE i ; education EDUC i , which 7
has.
Ihlanfeldt [12] used these variables to analyze the level of job information that an individual
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equals 1 if the respondent has completed high school and 0 if not;8 gender SEX i , which equals 1 if male; and marital status MARRi , which equals 1 if married. The choice of search sites may also be affected by the characteristics of the neighborhood in which the individual lives. Wilson [30] and others argue that the concentration of poverty within underclass neighborhoods worsens labor market outcomes among their residents. Early research on this subject has not always found neighborhood characteristics to be of significance (Jencks and Mayer [18]), but Cutler and Glaeser [3] found significant effects. As we have previously noted, job information depends on one’s social network, and the level of social networks may be influenced by the individual’s neighborhood characteristics. Jencks and Mayer [18] cite evidence that social networks of poor families are more geographically restricted than those of affluent families. This suggests that the neighborhood poverty rate may reflect the breadth of social networks. To control for neighborhood characteristics, we included the variable PCPOORi , which is the percentage of poor persons in the respondent’s census tract. The theoretical framework suggests that such factors as availability of public transit at the various sites and the wage rate at site j relative to other sites should influence the choice of sites that are searched. Four of the six job sites that we consider are served by MARTA (Atlanta’s public transit system), although only three by train; a fifth site is served by Cobb Community Transit (CCT), a bus system that links to MARTA. Thus public transit access differs across the job sites, but because for each job site a public transit variable would have to have the same value for all individuals, we cannot use a public transit variable in the regression. Likewise, the wage rate could differ across the sites (Ihlanfeldt [11]), but for any given site, all individuals should face the same wage structure. Our sample was limited to black respondents with a high school degree or less who reported searching within the year prior to the survey, but not necessarily at one of the six sites. The resulting sample size is 116, although with missing values, the regressions were actually estimated with 93 observations. Variable definitions and descriptive statistics are given in Table 1. III. EMPIRICAL RESULTS We are interested in whether the perceived level of acceptance of blacks affects their selection of job search sites. Before preceding with the regression analysis, we present some less formal evidence on the issue. Table 2 presents, for each of the six sites identified in the GANS (column 1), the percentage of blacks who responded that residents in the area would not 8 The analysis is restricted to respondents with a high school degree or less. Unfortunately, for this group the GANS identifies only whether the individual has a high school degree or has not completed high school.
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be upset if a black household moved into the area (column 2), the percentage of blacks and whites that searched that area (columns 3 and 4), and the percentage of residents who are white (column 5), and the mean distance by race (columns 6 and 7), which are variables that help describe the sites. The areas are listed in order of the percentage saying that residents would not be upset. Generally, the percentage of black workers who searched an area decreases as the percentage of blacks who say that blacks would be welcomed decreases. The one job site that does not fit the pattern, Norcross, is not served by public transit, which may explain the unexpectedly smaller percentage who search that area. The simple correlation between these two series is 0.81 and is statistically significant.9 Table 2 supports the hypothesis that the degree of social acceptability affects the choice of search area and thus that concern over social acceptance may be a cause of spatial mismatch and its persistence. However, as we noted above, it is possible that ACC also measures such factors as expected discrimination and not just social acceptability. If black workers are driven by concerns regarding social acceptance and discrimination, then we should expect a racial difference in the patterns of search. The data suggest that blacks and whites search different areas (columns 3 and 4). A chi-squared test leads to a rejection of the hypothesis that the distributions across sites are the same for blacks and whites. Blacks have a higher probability than whites of searching areas located within or south of the central city, while whites have a higher probability than blacks of searching the northern suburban job sites. Given that whites are more likely to live on the north side and blacks on the south side, this pattern is not surprising. But it is also consistent with the lower mean values of ACC for the northern suburbs. There are also differences in the number of areas searched. Whites searched fewer areas than blacks; 55.7% of whites searched only one or two of the seven sites, while 41.9% of blacks searched only one or two sites. Based on a chisquared test, we can reject the possibility that the frequency distributions of the number of sites searched are the same for the two groups. This result is consistent with Stoll’s [27] research on Los Angeles, and is also consistent with the expectation that blacks may have to search more sites to find employment because of employment discrimination and because they search job sites with fewer openings (Ihlanfeldt [12]). To explore the extent to which individual characteristics are associated with racial differences in the number of sites searched, we regressed the number of sites searched against a set of variables that included race, age, gender, education, marital status, availability of a car, residential distance from the CBD, and a set of interaction terms for which we interacted each variable with race. 9
A simple linear regression using the observations in Table 2 yields the following equation: percent searching = 19.6 + 0.392 ∗ percent accept, where the coefficient has a t ratio of 2.79.
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TABLE 1 Definitions, Means and Standard Deviations (Blacks with a high school education or less and who searched within the past year)
Variable S1 S2 S3 S4 S5 S6 DIST 1 DIST 2 DIST 3 DIST 4
Definition
DIST 6 ACC 1
R searched Decatur area R searched Tri-Cities area R searched Midtown area R searched Marietta/Smyrna area R searched Roswell/Alpharetta area R searched Norcross area Distance from residence to Decatur Distance from residence to Tri-Cities Distance from residence to Midtown Distance from residence to Marietta/ Smyrna Distance from residence to Roswell/ Alpharetta Distance from residence to Norcross Would be accepted in Decatur
ACC 2
Would be accepted in Tri-Cities
ACC 3
Would be accepted in Midtown
ACC 4
Would be accepted in Marietta/ Smyrna Would be accepted in Roswell/ Alpharetta Would be accepted in Norcross
DIST 5
ACC 5 ACC 6 DISC3 ALONG
FEW 6
Do whites discriminate (1 to 7) Are whites easy to get along with (1 to 7) Discrimination hurts blacks (1 to 4) Age R was discriminated against on a job R says Decatur has fewest jobs R says Tri-Cities has fewest jobs R says Midtown has fewest jobs R says Marietta/Smyrna has fewest jobs R says Roswell/Alpharetta has fewest jobs R says Norcross has fewest jobs
MOST 1 MOST 2 MOST 3
R says Decatur has most jobs R says Tri-Cities has most jobs R says Midtown has most jobs
DISC1 AGE DISC2 FEW 1 FEW 2 FEW 3 FEW 4 FEW 5
Units 1 = yes, 1 = yes, 1 = yes, 1 = yes, 1 = yes, 1 = yes, Miles Miles Miles Miles
0 = no 0 = no 0 = no 0 = no 0 = no 0 = no
Mean
Standard deviation
0.682 0.400 0.536 0.336 0.382 0.291 8.848 9.354 7.572 17.593
0.468 0.492 0.501 0.474 0.488 0.456 5.468 4.761 4.580 5.192
Miles
22.890
3.516
Miles 1 = accepted 0 = not accepted 1 = accepted 0 = not accepted 1 = accepted 0 = not accepted 1 = accepted 0 = not accepted 1 = accepted 0 = not accepted 1 = accepted 0 = not accepted 1 = no, 7 = yes 1 = easy, 7 = hard
20.334 0.956
4.613 0.205
0.798
0.403
0.894
0.308
0.438
0.498
0.248
0.433
0.372
0.485
5.581 4.231
1.957 1.967
1 = a lot, 4 = none years 1 = yes, 0 = no 1 = yes, 0 = no 1 = yes, 0 = no 1 = yes, 0 = no 1 = yes, 0 = no
1.333 33.200 0.241 0.034 0.060 0.213 0.214
0.541 9.368 0.430 0.182 0.238 0.412 0.412
1 = yes, 0 = no
0.282
0.452
1 = yes, 0 = no
0.085
0.281
1 = yes, 0 = no 1 = yes, 0 = no 1 = yes, 0 = no
0.188 0.145 0.325
0.392 0.354 0.470
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david l. sjoquist TABLE 1—Continued
Variable
Definition
MOST 4
R says Marietta/Smyrna has most jobs R says Roswell/Alpharetta has most jobs R says Norcross has most jobs R wants to live in black area Percent black in census tract Is R married R has a high school degree Sex Was car available when R searched Percent poor in census tract
MOST 5 MOST 6 RPREF PCBLK MARR EDUC SEX CAR PCPOOR
Mean
Standard deviation
1 = yes, 0 = no
0.077
0.268
1 = yes, 0 = no
0.077
0.268
0.103 0.431 84.580 0.188 0.846 0.325 0.483 27.862
0.304 0.497 21.098 0.392 0.362 0.470 0.502 17.957
Units
1 = yes, 0 = no 1 = yes, 0 = no Percent 1 = yes, 0 = no 1 = yes, 0 = no 1 = male, 0 = female 1 = yes, 0 = no Percent
In the regression (not reported), race was the only variable that was statistically significant, and it was positive, confirming that blacks search more sites than whites. Distance interacted with race was negative and nearly significant (t ratio of −1.70), which implies that blacks who live further from the CBD search fewer places. Given that most blacks live south of the CBD, greater distance from the CBD implies greater distance from the northern suburban job sites. In addition, age interacted with race was negative and nearly significant (t ratio of −1.74), implying that older blacks search fewer of these sites. TABLE 2 Perceived Reception of Blacks and Job Search Patterns
[1] Job site Decatura Midtowna Tri-Citiesa Marietta/ Smyrnab Norcross Roswell/ Alpharettab a b
[2] Percent saying residents would not be upset
[3] Percent searched job site (blacks)
[4] Percent searched job site (whites)
[5] Percent white residents at site
[6] Mean distance to site in miles (blacks)
[7] Mean distance to site in miles (whites)
95.6 89.5 79.8 43.8
68.2 53.6 40.0 33.6
31.9 29.2 24.3 62.5
59.7 53.8 31.8 79.0
8.8 7.6 9.3 17.6
17.0 15.6 17.8 15.8
37.2 24.7
29.1 38.2
31.9 34.7
80.7 91.9
20.3 22.9
21.3 21.1
Site served by bus and train. Site served by bus.
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We turn now to the regression analysis. One approach is to estimate a single regression in which we treat each respondent’s decision about searching an area as a separate observation.10 As discussed above, we expect the control variables that are not site-specific to have different effects on the probability of search across the various sites. To allow the coefficients on all of the variables that are not site-specific to vary across sites, site dummies and interaction terms should be included. An alternative approach is to estimate separate equations for each site. Given that the choices of search sites are not independent, this approach used seemingly unrelated regression (SUR). The empirical results from the multiequation approach are similar to those obtained using the single equation. SUR estimation results are reported, as this is a richer specification allowing the unobservable variables affecting search probabilities to be correlated across locations. Unweighted linear probability regressions were run for each of the six residential areas identified in the GANS. We restrict the coefficients on the sitespecific variables to be the same for all the regressions, and allow the coefficients on the intercept term and variables that are not site-specific (DISC1, DISC2, DISC3, ALONG, RPREF, PCBLK, SEX, MARR, AGE, EDUC, PCPOOR, and CAR) to differ across the regressions. The results are presented in Table 3. The sign on the coefficients on ACC is positive, as expected, and significant. This result is consistent with the hypothesis that the perception of social acceptance will affect the choice of job search sites and, as noted above, with the hypothesis that expected discrimination is affecting the choice of search sites. However, the effect is not large. For example, calculated at the mean value of S and ACC for Roswell/Alpharetta, the implied elasticity is 0.09. If the value of ACC for Roswell/Alpharetta were to increase to the value for Decatur, then the regression implies that the percentage of black job seekers who would search Roswell/Alpharetta would increase by 9.7 percentage points. This would reduce the difference in the search percentages between the two sites by 32.3%. We also ran regressions in which we used different combinations of variables, in which we ran weighted regressions, in which we restricted the sample to males, and in which we used those who searched within the past 5 years rather than the past year. The results for ACC in each case, as well as for the other regressions discussed below, were basically the same as those reported in Table 3. The coefficient on distance, DIST, is negative, as expected, and significant. The elasticity of S with respect to DIST, calculated at their mean values for Roswell/Alpharetta, is −1.25. It is of course not clear whether distance is measuring commuting cost, search cost, or lack of job information.
10
Thus the number of observations for the regression would be six times the number of respondents in the sample we use.
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david l. sjoquist TABLE 3 Searched Area Regressions: Blacks (Absolute t Statistic) Area
Variable [% white]
Tri-Cities [31.8%]
Midtown [53.8%]
Decatur [59.7%]
Marietta/ Smyrna [79.0%]
Norcross [80.7%]
Roswell/ Alpharetta [91.9%]
IN’CEPT
1.313 (3.34) 0.137 (2.66) −0.021 (4.60) −0.103 (2.28) 0.116 (2.49) −0.016 (0.67) −0.236 (2.71) −0.164 (1.48) 0.002 (0.10) 0.175 (1.76) 0.152 (1.18) −0.013 (2.03) 0.043 (0.42) 0.048 (0.31) −0.046 (0.45) −0.0003 (0.13) −0.004 (1.17)
0.926 (2.29) 0.137 (2.66) −0.021 (4.60) −0.103 (2.28) 0.116 (2.49) −0.009 (0.35) −0.198 (2.23) 0.248 (2.19) 0.053 (1.98) 0.182 (1.79) 0.231 (1.75) −0.018 (2.84) 0.069 (0.66) 0.043 (0.29) 0.077 (0.75) −0.00005 (0.02) −0.006 (0.20)
0.766 (2.19) 0.137 (2.66) −0.021 (4.60) −0.103 (2.28) 0.116 (2.49) 0.020 (0.93) −0.132 (1.74) 0.047 (0.48) −0.031 (1.37) 0.015 (0.17) 0.0002 (0.001) −0.006 (1.10) 0.080 (0.89) 0.083 (0.63) −0.048 (0.54) 0.007 (3.01) −0.007 (2.90)
0.648 (1.52) 0.137 (2.66) −0.021 (4.60) −0.103 (2.28) 0.116 (2.49) 0.011 (0.40) −0.050 (0.54) 0.075 (0.62) 0.020 (0.71) −0.064 (0.60) 0.139 (1.00) −0.002 (0.35) 0.029 (0.26) 0.060 (0.37) −0.028 (0.26) 0.0007 (0.25) −0.001 (0.34)
0.903 (2.26) 0.137 (2.66) −0.021 (4.60) −0.103 (2.28) 0.116 (2.49) −0.002 (0.08) −0.095 (1.08) −0.033 (0.29) 0.040 (1.52) −0.179 (1.80) 0.078 (0.60) −0.009 (1.39) 0.063 (0.61) 0.048 (0.32) −0.125 (1.23) 0.003 (1.18) −0.007 (2.26)
0.503 (1.14) 0.137 (2.66) −0.021 (4.60) −0.103 (2.28) 0.116 (2.49) 0.005 (0.19) −0.034 (0.35) −0.056 (0.45) 0.069 (2.38) −0.018 (0.17) 0.098 (0.68) −0.058 (0.83) −0.013 (0.12) −0.078 (0.48) 0.093 (0.84) 0.005 (1.62) −0.006 (1.62)
ACC DIST FEW MOST ALONG DISC1 DISC2 DISC3 RPREF MARR AGE SEX EDUC CAR PCBLK PCPOOR
System R-squared = 0.260.
To the extent that a respondent searched and found a job and then moved closer to the job site, the coefficient on distance will be negatively biased. We redid the estimation, limiting the sample to those who lived in their current residence before searching; there was essentially no change in the coefficient on DIST.
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The literature suggests that females may be less willing to search at sites that are a long distance from where they live. Our data do not provide support for this position. We included a variable created by interacting SEX and DIST, but the resulting coefficient was insignificant. The availability of public transit is a fixed effect that should be reflected in the intercept. However, it is possible that the effect of DIST might be affected by the availability of mass transit at a site. Individuals who rely on mass transit are unlikely to search a site not served by mass transit, regardless of distance (except if within walking distance). We estimated regressions equivalent to those reported in Table 3, but allowed the coefficient on DIST to differ across regressions. The six coefficients on DIST were all negative and did not significantly differ from each other. However, the coefficients on DIST were statistically significant for Decatur, Tri-Cities, and Marietta/Smyrna and were nearly significant for Midtown. For Norcross, an area unserved by public transit, and for Roswell/Alpharetta, an area served only by bus, DIST had very large standard errors. We explored two possible explanations for the lack of significant coefficients on distance for Norcross and Roswell/Alpharetta, but found no support for either explanation. The first explanation is based on the argument that, regardless of distance, those reliant on public transit will not search a site without good public transit access, while distance should matter at the margin for those with a car. We estimated the same regressions using just those observations for which the respondents reported having access to a car, but the results did not change. The coefficients on DIST were still insignificant for Norcross and Roswell/Alpharetta. We also included DIST interacted with CAR; none of the interaction terms was significant for any of the sites, while the coefficients on DIST did not change. A second possible explanation focused on the fact that Norcross and Roswell/Alpharetta are the two sites farthest from the CBD and thus farthest from where the vast majority of blacks live. Therefore, the distance may be so great that it does not matter at the margin for the typical black job seeker. To investigate this, we interacted DIST with a dummy variable that equaled 1 if distance was greater than x miles, where x took alternative values between 15 and 20 miles. If individuals had a distance threshold, then the interaction term should be negative and large. But the coefficients on the interaction terms were small and statistically insignificant. The coefficients on FEW and MOST (the job information variables) are expected to be negative and positive, respectively. The results are consistent with that expectation. The elasticities, calculated at the means, are −0.076 for FEW and 0.023 for MOST. We had hypothesized that the variables DISC1, DISC2, DISC3, and ALONG would show coefficients that are negative for areas with a large percentage of white residents and that increase as the white percentage decrease. However,
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david l. sjoquist
in general, these variables did not perform as expected. The coefficients on ALONG are insignificant for all of the equations. The coefficients on DISC1 are negative and significant for two of the three areas with the fewest whites but insignificant for the other areas. The coefficients on DISC2 are insignificant except for one site, Midtown, for which the coefficient is positive. DISC3 is positive and significant for Midtown and Roswell/Alpharetta; a negative sign was expected for Roswell/Alpharetta. The lack of expected results for the discrimination variables are not driven by multicollinearity. The largest correlation coefficient between the three variables is 0.11, and estimating regressions by separately including each of the three variables did not produce different results. Respondents who stated a preference for living in a black neighborhood (reflected in RPREF) were expected to be less likely to search in those areas with predominately white populations. The coefficients are, as expected, negative for the three areas with the highest white population and positive for the three sites with fewer whites, but the results are only marginally significant at best. The coefficients on PCBLK are insignificant for all equations except Decatur, for which it is positive. These results do not fit the hypothesis that blacks from mainly black communities are more likely to search in black areas. The coefficients on CAR are all insignificant, suggesting that the availability of a car does not affect the choice of which areas to search. This result is surprising. We expected that the lack of a car would result in the individual searching areas more accessible to mass transit and that the availability of a car would result in more searches at sites at greater distances. Contrary to expectation, the coefficients on variables related to individual characteristics (AGE, SEX, MARR, and EDUC) were, in general, insignificant. The coefficients on the percentage of poor (PCPOOR), which was included to control for neighborhood effects, are significant for only two sites. Because our arguments imply that black behavior toward the choice of search locations should differ from white behavior, comparing the regression results for blacks with equivalent regression results for whites would be of interest here. Regressions for whites, excluding variables that we believe apply only to blacks (i.e., variables associated with expected social acceptance and discrimination) were estimated but are not reported here.11 There is consistency across the two sets of coefficients. The coefficient on DIST is negative and significant for 11 Some of the questions asked of blacks were not asked of whites; for example, the questions on which ACC is based. The question asked of whites concerning their preferences regarding the racial composition of the neighborhood was not the same as for blacks. Whites were asked about their level of comfort with a neighborhood of varying racial composition. To create the variable RPREF for whites that is similar, but not identical, to the one used for blacks, we coded RPREF as 1 if the white respondent stated that he would be uncomfortable living in a neighborhood with less than a majority of whites and 0 otherwise.
spatial mismatch and social acceptability
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whites but is somewhat smaller than for blacks, −0.018 versus −0.021. As in the regressions for blacks, MOST is positive and significant, although for whites FEW is unexpectedly positive and significant. The other control variables are generally insignificant, as in the black equations. IV. SUMMARY AND CONCLUSIONS The relatively poorer labor market outcomes for less-skilled inner-city minorities have been attributed to the existence of spatial mismatch, but there has been little discussion in the literature regarding the mechanism through which a lack of access to jobs results in the reduced labor market performance. This paper has suggested that one of the possible reasons why blacks may not hold jobs in the suburbs is that they believe they may not be socially accepted there. Therefore, they do not search suburban job sites. We explored this hypothesis using data from the GANS, considering the choice of search sites by less-skilled black respondents across six identified areas in the Atlanta region. We find that our measure of a black respondent’s perception of the acceptance of blacks in an area is a statistically significant factor in determining the probability of searching for a job in that area, although the effect appears to be small. We also found that distance between a respondent’s residence and the job site, as well as the respondent’s perception about the availability of jobs, affect the choice of job search location in the expected direction. Because of the limitations of the analysis, as we have previously explained, our results should be taken as suggestive. In particular, our measure of social acceptance refers to social acceptance in the residential community, not the job site. It may also reflect other factors, such as expected discrimination at the job site. Clearly, additional research using better measures of social acceptance should be carried out. Furthermore, there is a need for additional research on the entire question of the process through which job access affects labor market outcomes. REFERENCES 1. J. B. Agassi, “Comparing the Work Attitudes of Men and Women,” Lexington Books, Lexington, MA (1982). 2. W. A. V. Clark, Residential segregation in American cities: A review and interpretation, Population Research and Policy Review, 5, 95–127 (1986). 3. D. M. Cutler and E. M. Glaeser, Are ghettos good or bad?, Quarterly Journal of Economics, 112, 827–872 (1997). 4. J. P. Dickson and D. L. MacLachlan, Social distance and shopping behavior, Journal of the Academy of Marketing Science, 18, 153–161 (1990). 5. J. Dyer, A. Vedlitz, and S. Worchel, Social distance among racial and ethnic groups in Texas: Some demographic correlates, Social Science Quarterly, 70, 607–616 (1989). 6. A. S. Evans, Jr. and M. W. Giles, Effects of percent black on blacks’ perceptions of relative power and social distance, Journal of Black Studies, 17, 3–14 (1986).
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7. C. A. Hoffman and P. N. Ritchey, Assessing the differences in distance of interstate migration, Sociological Focus, 25, 241–255 (1992). 8. H. J. Holzer and K. R. Ihlanfeldt, Spatial factors and the employment of blacks at the firm level, New England Economic Review (Federal Reserve Bank of Boston), May/June Special Issue, 65–82 (1996). 9. H. J. Holzer and K. R. Ihlanfeldt, Customer discrimination and employment outcomes for minority workers, Quarterly Journal of Economics, 113, 835–867 (1998). 10. K. R. Ihlanfeldt, “Job Accessibility and the Employment and School Enrollment of Teenagers,” Upjohn Institute, Kalamazoo, MI (1992). 11. K. R. Ihlanfeldt, Intra-urban wage gradients: Evidence by race, gender, occupational class, and sector, Journal of Urban Economics, 32, 70–91 (1992). 12. K. R. Ihlanfeldt, Information on the spatial distribution of job opportunities within metropolitan areas, Journal of Urban Economics, 41, 218–242 (1997). 13. K. R. Ihlanfeldt and D. L. Sjoquist, The impact of job decentralization on the economic welfare of central city blacks, Journal of Urban Economics, 26, 110–130 (1989). 14. K. R. Ihlanfeldt and D. L. Sjoquist, Job accessibility and racial differences in youth employment rates, American Economic Review, 80, 267–276 (1990). 15. K. R. Ihlanfeldt and D. L. Sjoquist, The effect of job access on black and white youth employment: A cross-sectional analysis, Urban Studies, 28, 255–265 (1991). 16. K. R. Ihlanfeldt and D. L. Sjoquist, The spatial mismatch hypothesis: A review of recent studies and their implications for welfare reform, Housing Policy Debate, 9, 849–892 (1998). 17. K. R. Ihlanfeldt and M. Young, Intra-metropolitan variations in wage rates: The case of Atlanta fast-food restaurant workers, Review of Economics and Statistics, 76, 425–433 (1994). 18. C. Jencks and S. E. Mayer, The social consequences of growing up in a poor neighborhood, in “Inner-City Poverty in the United States” (L. E. Lynn and M. B. H. McGeary, Eds.), National Academy Press, Washington, DC (1990). 19. J. F. Kain, Housing segregation, Negro employment, and metropolitan decentralization, Quarterly Journal of Economics, 82, 175–197 (1968). 20. J. F. Kain, The spatial mismatch hypothesis: Three decades later, Housing Policy Debate, 3, 371–460 (1992). 21. J. Kirschenman and K. M. Neckerman, ‘We’d love to hire them, but. . .’: The meaning of race for employees, in “The Urban Underclass” (C. Jencks and P. E. Peterson, Eds.), The Brookings Institution, Washington, DC (1991). 22. J. Ludwig, Information and inner city educational attainment, Economics of Education Review, 18, 17–30 (1999). 23. J. F. Madden, Why women work closer to home, Urban Studies, 18(1), 181–194 (1981). 24. I. Padavic and B. F. Reskin, Men’s behavior and women’s interest in blue-collar jobs, Social Problems, 37, 613–628 (1990). 25. C. Rogers, Job search and unemployment duration: Implications for the spatial mismatch hypothesis, Journal of Urban Economics, 42, 109–132 (1997). 26. D. L. Sjoquist, “The Atlanta Paradox,” Russell Sage Foundation, New York (2000). 27. M. A. Stoll, Spatial job search, spatial mismatch, and the employment and wages of racial and ethnic groups in Los Angeles, Journal of Urban Economics, 46, 129–155 (1999). 28. L. M. Tigges and D. M. Tootle, Underemployment and racial competition in local labor markets, The Sociological Quarterly, 34, 279–298 (1993). 29. T. P. Vartanian, “Large City and Neighborhood Effects on AFDC Spells: A Test of the Spatial Mismatch and Social Isolation Hypothesis,” Ph.D. dissertation, University of Notre Dame, Notre Dame, IN (1992). 30. W. J. Wilson, “The Truly Disadvantaged,” University of Chicago Press, Chicago (1987).