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Labor-Market Inequality between Blacks and Whites: How Demography, Geography, and History Matter
Jenny Bourne
Draft 2-14-2015
The presence of racial inequality in labor-market outcomes is well-known.[1] Among primary household earners in the U.S. in 2000, for example, blacks took home on average about 72 percent as much as whites.[2] But the amount of inequality varies across demographic groups and between cities, and the variation can be substantial. For example, married black women are employed at far higher rates and earn more on average than their white counterparts, whereas married black men receive lower wages on average than comparable whites. Single black males are employed at much lower rates than comparable whites; employment rates are more similar for married men. The more education, the greater the wage gap between otherwise similar black and white men. And wages in Detroit and Milwaukee are much closer to racial parity than wages in Atlanta or Washington, D.C., particularly for men. Yet the former rank among the worst and the latter among the best cities for blacks to live, according to a recent survey by Black Enterprise magazine.[3]
These variations occur because the degree of labor-market equality is the result of household optimization rather than the objective. I outline below a basic model of wage determination. I then estimate black-white differences in employment rates and wages for various groups using a Heckman two-step approach on microdata from census year 2000, augmented with city-specific information on residential segregation, the proportion of blacks in the population, urbanization rates, population size, union membership, public school quality, public safety, and black-white incarceration rates.[4] I suggest that rational household decisions, coupled with historical patterns of neighborhood formation, are responsible for observed differences in labor-market inequality between whites and blacks across demographic and geographic subgroups.[5] Awareness of the empirical patterns and the underlying reasons for them is essential for evaluating policies to address racial inequities in the labor market.
THEORETICAL FRAMEWORK
Basic Model
Consider a simple model of the labor market, where wages are a function of supply and demand factors. Utility-maximizing individuals supply labor according to their preferences and the constraints they face; profit-maximizing firms generate labor demand as a function of relative prices and productivity. In short,
w = αw + βwX + εw, (1)
where w is typically cast as the natural log of wages and X denotes a vector of individual and firm characteristics.[6]
Public goods could also appear in X. People might be willing to accept lower wages in exchange for a better public-school system or a lower crime rate, for instance. These relationships are complicated, however, because higher-income neighborhoods can also afford to spend more on local public goods. The sign of a coefficient on a public-good variable in (1) is therefore ambiguous.
Of particular interest is the effect of race on wages. A dummy variable for race could act as a proxy for omitted variables related to preferences of workers or informational signals about productivity, among other things, or it could pick up the presence of labor-market discrimination. Including additional proxies or interactive variables may help differentiate among explanations for labor-market inequality, or for variations in labor-market inequality across subgroups.
Race, Residential Segregation, and Black Representation in the Population
One strand of the literature on inequality estimates the effect on wages associated with the interaction of race and residential segregation. Using 1990 census data on individuals aged 20 to 30 years old, Cutler and Glaeser (1997) found that segregation affected blacks negatively but had little effect on whites.[7]
Spatial mismatch between housing and jobs could explain why blacks living in a segregated area might earn less relative to whites, all else constant, than blacks living in a more integrated area. If some neighborhoods are located far from attractive, high-paying jobs, residents of these areas may rationally choose lower-paying jobs closer to home, or they may drop out of the labor force entirely.[8] Suppose, for example, that blacks and whites live in different neighborhoods and jobs are located primarily in the white neighborhood. Because transportation is costly, we might expect whites to earn higher wages and blacks lower wages in this world as compared to one in which jobs are spread about evenly or people of different races live in integrated neighborhoods. This would imply a negative coefficient on an interactive variable “DIblack” (equal to the degree of residential segregation – or “dissimilarity index” -- times a dummy variable equaling one for black persons).
Other influences could also yield a negative coefficient on “DIblack.” Residential segregation may act as a proxy for labor-market discrimination. Lack of contact between races could lead to statistical discrimination among employers, for example.[9] Or greater residential segregation could imply fewer positive role models for blacks.[10]
Theory is ambiguous as to the expected sign on “DIblack,” however. Preferences could generate a positive coefficient on the interactive variable. This might occur if blacks prefer to live in a racially mixed neighborhood but would be willing to accept more residential segregation if they also receive relatively higher income.[11]
Social networks might matter as well. Neighborhoods could yield a sense of community, thriving internal markets, or superior job contacts.[12] These effects may differ across races.[13] An observed positive coefficient on “DIblack” could reflect better availability and use of social networks for blacks relative to whites in more-segregated cities; an observed negative coefficient could indicate the reverse. Notably, slight differences in initial conditions could lead to large differences in outcomes – if network members have relatively worse starting positions, remaining in a network has comparatively fewer benefits, which could lead to greater network (and labor-market) dropout.[14]
A city is simply a neighborhood writ large. Just as residential segregation could affect contact between races, role-model availability, and social networking, so might the proportion of black persons within a given Metropolitan Statistical Area (MSA).[15] A larger black presence within a city could imply greater familiarity between the races or greater networking opportunities for blacks. This suggests a positive coefficient on an interactive variable “propbblack” (equal to the proportion of city population that is black times a dummy variable equaling one for black persons).
Again, theory is ambiguous. A “blacker” city might also imply greater antagonism between races. Alternatively, non-labor-market benefits for individual blacks associated with a larger percentage of blacks in an MSA could yield a negative coefficient on “propbblack.” That is, blacks might be willing to accept more labor-market inequality if they also have family members close by, a strong religious community, or less of a sense of isolation.
Wage rates are not the only labor-market outcome that matters to people, of course – having a job is a prerequisite to earning wages at all. Spatial mismatch, discrimination, social networks, and tradeoffs could affect relative employment rates as well as wage rates. Including “DIblack” and “propbblack” variables in regressions using employment status as the dependent variable could indicate whether these forces influence the relative probability of employment as well as relative wage.[16]
One final point: interrelationships among residential segregation, urban sprawl, public transportation, and car ownership can complicate matters. Glaeser and Kahn (2003) suggest, for instance, that cities with more sprawl may actually have greater integration yet lower well-being for poor people – particularly poor blacks -- because they cannot afford cars. Stoll et al. (2000) note that many low-skilled jobs in metropolitan areas simply cannot be reached via public transportation. Fortunately, my data allow me to control for both the degree of sprawl in the city of residence and vehicle ownership by the household.[17]
Race and Region of Residence
Patterns of migration throughout the last century, as well as differences in the history of the South and the North, suggest that distinct relationships among residential segregation, black presence in an MSA, and labor-market outcomes might emerge across regions. Ninety percent of blacks lived in the South at the turn of the century, and more than half remained there as of the year 2000.[18] Mass migration northward, typically to urban areas, occurred after both World Wars. Movement north slowed once the civil rights revolution took hold, and migration to and from metro areas has varied across regions over the last decade.[19] Most recently, blacks (particularly college graduates) have begun to move back to the South.[20]
The reasons for migration and neighborhood formation patterns are complex; they include the desire to live near family and friends, different regional conditions for certain types of workers, and institutional constraints such as redlining. Moreover, the relatively longer history in the South of large numbers of blacks living alongside whites could imply greater (or lesser) comparability in the labor market. The huge shifts in the black population over the past several decades thus may mean that cross-sectional relationships among relative employment and wage rates, housing segregation by race, and proportion of MSA residents who are black could differ substantially across regions.
Race, Sex, and Marital Status
Both theory and empirical evidence suggest that sex and marital status matter for labor-force outcomes.[21] Market outcomes may differ for women and men because of differences in non-market activities (housework and childcare, for example), preferences, employer attitudes, or social networks. Occupational segregation might also play a role. Mate selection could mean that married people differ fundamentally from single people: people who are less attractive in the labor market may also be less attractive in the marriage market. What is more, married people arguably make decisions jointly whereas single persons enjoy more autonomy.
One question is whether these factors matter in considering the effects of race in the labor market.[22] Suppose, for example, that more segregation in housing tends to affect black men negatively via greater spatial separation from jobs or inferior networks. Black men able to overcome these obstacles could be more attractive in the marriage market. We might therefore observe different coefficients – even different signs -- on “DIblack” for single and married men.
Or consider this possibility: suppose greater residential segregation or a larger proportion of blacks in a city tends to reduce employment or wage rates for black men relative to white men. To counteract the expected negative relative effect on family income, married black women might seek better-paying jobs than comparable white wives as housing segregation or MSA black presence increases.[23] We could therefore observe opposite signs on a “DIblack” or “propbblack” coefficient for married men and married women.
Residential segregation and black presence in an MSA could affect women and men differently in a more general way. Black women might enjoy relatively better job prospects in segregated (or heavily black) cities if they also form stronger networks in these cities, for example, whereas black men might suffer more from spatial separation between home and work in highly segregated areas. The degree to which whites feel threatened by black population concentration might play out differently for men and women in the labor market. Or stronger family ties for, say, women, in a predominently black community might make seeking better job opportunities elsewhere more costly for women than for men, thus generating different coefficients on a “propbblack” variable for males and females.[24]
Race, Education, Age, and Occupational Category
Education, age, and occupational category are typically included in the vector X in equation (1), often as proxies for productivity. Yet focusing on inequality across educational backgrounds, age groups, and occupational categories could offer useful additional information.
Suppose college graduates experience a larger racial wage gap than high-school graduates, for instance. This might indicate diverse degrees of discrimination for the two groups, or it could imply something about the perceived relative quality of schools attended. City characteristics could interact with educational status as well, perhaps because schooling level helps determine opportunities for migration.
Likewise, differences in labor-market inequality for people of different ages could emerge. Suppose we observe greater racial inequality in wages for older individuals. This could indicate bigger pre-market racial differences – for example, in the relative quality of schools attended – for the older people in a cross-section.[25] Or it might imply that blacks enjoy fewer advancement prospects or greater discrimination on the job as they age. Age could interact with city characteristics as well. For instance, residential segregation could have different labor-market impacts for younger and older individuals because of generational differences in the formation of social networks.
Inequality might differ as well across broad occupational categories. Employers might be less able to discriminate in heavily unionized occupations, for example, or social networks might be better established for whites in the professions. But inequality within a broad occupational category might also reflect occupational segregation, so an investigation of specific occupations by race could be fruitful.
Correcting for Selection Bias
In moving from model to estimation, recall that employment is the essential prerequisite to earning wages. Researchers can only observe actual wages, as Heckman (1979) pointed out in his seminal research, so appropriate estimation includes a variable λ in equation (1) to capture potential selection bias:[26]
w = α + βX + βλλ + ελ. (2)
Although Cutler and Glaeser (1997) analyze the degree of “idleness” present in different races, they do not explicitly account for selection issues in their wage analysis. Instead, they simply exclude individuals who earned nothing. Large racial differences in employment rates suggest at least two things: selection may matter in estimating the racial wage gap, and race-related variables such as residential segregation and percent of the population that is black could influence relative employment rates as well as wages.[27]
DATA
Individual and household data are from the census-2000 5-percent public use micro samples organized at the Minnesota Population Center.[28] I consider only people who identify themselves as black or as white. Although cross-racial differences for other groups are interesting and important, they are beyond the scope of this study.
By focusing my analysis on household heads and their spouses, I also exclude potential wage earners with relatively less attachment to the labor force: children, and adults living with their parents or other adults.[29] Some information for these possible earners remains, because I include a measure of other household income in employment and wage regressions. My results nevertheless pertain only to primary wage earners. Still, it is these individuals who are chiefly responsible for the wellbeing of the household, so my findings on racial inequality for household heads and spouses say something about racial inequality across families as well.
Information on MSA total and black populations, residential segregation as measured by dissimilarity indices (DI), and degree of urban sprawl comes from the U.S. Census Bureau.[30] The total number of MSAs for which these data are available is 206. I obtained MSA unionization rates from the Bureau of Labor Statistics.[31] For a smaller group of 44 MSAs, I also obtain median public school quality, median public safety rating, and the statewide relative black-white incarceration rate for men. [32]
EMPIRICAL FINDINGS
Descriptive Statistics
Table 1 shows average annual earnings, weeks and usual hours worked, and age for employed urban residents by sex, race, and marital status.[33] It also gives average employment rates. Although these figures do not control for underlying differences in individual and MSA characteristics, they are nonetheless striking. Despite comparable mean ages, hours worked, and weeks worked, black male household heads earn only two-thirds as much as white men.[34] Black women are employed at much higher rates than white women; married women earn about the same but single black female household heads earn only 82 percent as much as their white counterparts.[35]
Table 2 reports averages for several variables associated with employed urban household heads and their spouses by sex, race, and region of residence. Black-white ratios by region of these averages reveal several interesting (though not always surprising) results, as do more complex ratios measuring regional differences. Blacks are far more likely to be responsible for grandchildren and to live in the central city, particularly outside the South. Blacks have relatively less education on average, smaller amounts of other household income, and fewer vehicles. A greater proportion of blacks report a work disability, a much larger proportion of blacks work in service or laborer occupations, and a larger proportion of blacks (particularly females outside the South) have less than a high-school education. Blacks (particularly women) work relatively more in public-sector jobs.[36]
Table 2 offers only an “average” picture. Here are some additional details pertaining to MSAs in 2000: the proportion black (propb) in an MSA ranged from 0.5 percent to 26.3 percent outside the South and from 0.5 percent to 45.9 percent in the South. The degree of segregation ranged from 0.198 to 0.846 outside the South and 0.359 to 0.992 in the South. Of the ten MSAs with the largest black population, only one – Dallas – had a DI less than 0.6, which is the threshold commonly used to denote “highly segregated.”[37] The five most segregated urban areas lie outside the South: Detroit, Milwaukee, New York-Northern New Jersey, Chicago-Gary, and Cleveland-Akron.[38]
One individually based statistic is worth noting: the well-known difference in marriage rates between blacks and whites.[39] Table 2 reports figures for employed persons. Among all urban male household heads, 29 percent of whites and 46 percent of blacks were single in 2000. The discrepancy for females is even larger: 34 percent of whites and 67 percent of blacks were single. The percent single is slightly smaller in the South for whites and 6 to 7 percentage points smaller for blacks.
Oaxaca Decompositions
Following Oaxaca (1973), decompositions can help determine the extent to which diverse underlying characteristics explain wage gaps.[40] Table 3 lists variables included; these variables are also used in the race-combined regressions discussed below.[41]
Using regressions for whites as the reference, I estimated the portion of the wage gap attributable to racial differences in observed characteristics. About 95 percent of the gap for single females in 2000 is explained by differences in observed characteristics, 87 percent for single males, and only 74 percent for married males. Married black females in the sample used for the decomposition actually earned slightly more on average than their white counterparts; 48 percent of the tiny difference is explained by disparate observed characteristics.
These results suggest a reasonable next step: combining the races for regression analysis and attempting to ascertain possible sources for the unexplained portion of the wage gaps. I analyze results for both sexes; the inequality investigation for males is arguably more relevant because the gaps for men are either larger or less well-explained than those for women. Recall, however, that “explained” differences could still result from discrimination (perhaps pre-market or non-labor-market) and “unexplained” differences might be due to unmeasured taste or ability dissimilarities.[42]
The following sections outline my findings when the data are partitioned according to marital status, educational background, and city of residence. Table 4 summarizes the signs of coefficients on proportion black and residential segregation by region for stage 1 and stage 2 regressions.[43] I briefly discuss below the results associated with age groups and occupational classifications as well.
Marital Status
Table 5 shows average employment and wage gaps between whites and blacks, separately by sex and marital status. At the mean, for example, single black women in the South were employed at nearly the same rate as their white counterparts; outside the South, single women achieved virtual parity in wages.[44] More striking, however, are these four findings: (1) single black male household heads were employed at much lower rates than comparable whites (particularly outside the South) whereas employment rates for married male household heads are more similar across races, (2) married black women were employed at much higher rates than comparable whites, (3) married black women out-earned similar married white women – at the mean, 6.1 percent more outside the South and 2.3 percent more in the South, and (4) black men earn up to 10 percent less on average than comparable white men and the relative wage gap was more than twice as large at the mean for married as compared to single male household heads.[45]
One very tentative explanation for these findings is this: Blacks, especially females, are much more likely to remain single than whites. When they do marry, black women in particular may select a certain (more employable) type of spouse. Alternatively, employers (who may be prejudiced or engage in statistical discrimination, especially against black men) might use marital status as a signal of responsibility or stability. Employment rates thus appear more comparable across race for married men than for single men. Yet black men generally earn less than white men, regardless of marital status. If similar black and white families have like aspirations and desires, a plausible response for black wives is to work and earn more than their white counterparts. But this relatively larger cushion of family income might in turn permit employed married black men to accept a relatively larger wage gap than employed single black men.[46]
Table 5 also offers regression coefficients for both stages separately by sex and marital status. Greater residential segregation reduced black employment rates relative to white rates for all groups except married females in the South.[47] It also lowered black wages relative to white wages for single persons (zero effect for single males outside the South) and married Southern males, but raised relative wages for married black men outside the South and married black women generally.[48] A larger proportion of blacks in the population raised relative employment rates for blacks in the North (or had zero effect in the case of single males) but lowered them in the South. It lowered relative wages for blacks or had zero effect, except for single females outside the South.[49]
These patterns suggest several things. Spatial mismatch, discrimination, and other factors that affect blacks negatively relative to whites manifest themselves primarily via lower relative employment rates. To the extent inferior networks affect relative outcomes, single blacks seem to suffer more. And black couples living outside the South appear to accept segregated neighborhoods in exchange for relatively higher wages. The regional difference in the effect of proportion black could imply that blacks gravitate to Northern cities with better employment prospects but are willing to stay in Southern cities for reasons other than labor-market benefits.[50]
Education
People over age 25 are split (separately by sex) into three groups in Table 6: those with at most a high-school degree, those with some college education, and those with at least a B.A. degree.[51] The averages show that, among males, the largest wage gap corresponds to those with the most education. At the mean, college-educated black men earned 10.6 percent less than comparable white men outside the South; in the South, the figure is 14.5 percent.[52] The average wage gap is larger for men in the South across all educational backgrounds, but the average employment gap is much larger outside the South for men with a high-school degree or less. At the mean, Southern black men with some college and college-educated black men everywhere were employed at higher rates than comparable white men.[53]
Black women on average generally were employed at greater rates than white women, particularly in the South, and the gap increases with education. The least-educated black women earned on average 6.1 percent more than similar white women outside the South; the wage gap is near zero in the South.[54] Interestingly, black women with some college education earned slightly more on average outside the South in 2000 but somewhat less on average in the South relative to comparable white women. College-educated black women earned slightly more on average than similar white women.[55]
The regression coefficients help us detect the sources of these gaps. Perhaps the most notable feature for Southern males is that the negative impact of residential segregation on relative employment and wages is generally stronger with greater educational attainment. Residential segregation actually had a positive effect on relative wages for the least-educated black men outside the South, although it had a negative impact on relative employment. For more-educated men outside the South, the most striking aspect of these regression coefficients is the negative effect of the proportion of blacks in the population -- on relative employment for all men with more than a high-school education and on relative wages for college-educated men. The only significant coefficient in the wage regression for non-Southern men with some college is the intercept term.[56]
A tentative explanation consistent with these patterns is this: Professional contacts yielding higher-paying jobs, as well as greater comfort with diversity, might arise in the South more easily in integrated residential areas. These contacts could be especially important for more-educated men given the significance of historically black colleges and universities in the South.[57] That is, a lack of networks due to matriculation in different institutions could be more easily overcome if people are neighbors. Outside the South, segregated neighborhoods grew up around manufacturing hubs; these industries brought opportunities for less-educated men, black and white.[58] The fortunes of both races rise and fall with economic prospects in these areas, leading to less inequality in wages for the less-educated employed here as compared to men living in less-segregated MSAs outside the South. But spatial mismatch might especially have affected the job prospects of less-educated black men left behind when plants moved out of the central city to the suburbs or overseas, or simply shut their doors, causing greater inequality in employment rates in highly segregated non-Southern MSAs.[59]
Explaining the “propb” coefficients for men outside the South is a bit more difficult. One possibility is that white-collar employers may find it easier to engage in discrimination in cities with a larger proportion of blacks. Another is that non-South MSAs with a greater proportion of educated blacks attract other educated blacks, despite some degree of labor-market inequality between races.
Perhaps the most intriguing pattern for women evident in Table 6 is that greater residential segregation implies higher relative employment for college-educated black women in the South and higher relative wages for these women outside the South, and higher relative wage rates for black women outside the South with at most a high-school degree. The latter parallels the experience for black men. I speculate that the former may be due in part to positive assortative mating plus family dynamics. More highly educated women tend to marry more highly educated men, and most people marry within their own race.[60] We know that more-educated black men earn relatively less than their white counterparts, and that residential segregation widens both the employment and the wage gap for these men in the South. To attain a certain standard of living, then, more-educated black wives living in highly segregated MSAs would be more likely to seek employment and higher wages than similar white wives.
MSAs and Public Goods
This section focuses on 44 MSAs that contain about 21.1 million blacks out of a total 36 million residing in the U.S.[61] The 44 include all cities that ranked high or low in several surveys conducted by Black Enterprise magazine. The magazine’s 2007 survey listed Washington (DC), Atlanta, Houston, Nashville, Dallas, Charlotte, Columbus (OH), Raleigh-Durham, Indianapolis, and Jacksonville as the best cities for blacks. The last three replaced Birmingham, Baltimore, and Memphis from the 2004 survey. Nashville and Columbus replaced Chicago and Philadelphia from the 2001 survey. Cleveland, Detroit, and Milwaukee rank as the three worst cities for blacks.[62]
In the tables below, I report below the results of regressions on the 44 cities together that include the expanded set of independent variables -- median public school quality for the MSA, median public safety rating, and the relative (black-to-white) state incarceration rate for men — and of separate regressions for each MSA.[63] Characteristics such as median school quality and safety undoubtedly indicate something about the attractiveness of an MSA and interact with labor-market outcomes for people regardless of race. Because blacks are more likely to attend public school[64] and live in more dangerous neighborhoods,[65] however, these attributes may be particularly relevant for them. Specifically, we could observe a negative relationship between relative wages and higher-quality public goods if blacks are willing to trade off wage equity for other amenities. One salient community feature is race-specific: relative incarceration rates. Admittedly a crude measure of the “warmth” of racial relationships, nevertheless a large difference in incarceration rates by race could signal a disamenity for blacks. In other words, blacks who live in a state that disproportionately puts them in jail might require compensation in the form of higher wages.
Table 7 gives coefficients for regressions that include blacks and whites from all 44 cities; table 8 offers results for regressions on blacks only. As expected, greater safety corresponds to lower relative (but higher absolute) wages for blacks, and a larger relative incarceration rate yields higher relative and absolute wages for blacks. Better median school quality implies lower absolute wages for black men and lower relative (but higher absolute) wages for black women.[66]
Regional effects of residential segregation and black representation in the population are quite distinct. Greater housing segregation decreases absolute and relative employment rates everywhere, but it increases relative wages (and black male absolute wages) outside the South and decreases relative (and absolute) wages in the South. A larger proportion of blacks in the population increases relative employment outside the South and decreases it in the South, decreases relative wages everywhere, and increases black absolute wages outside the South (and black male absolute wages in the South). As before, these patterns suggest that spatial mismatch, network effects, and other factors negatively affecting blacks relative to whites take their toll mostly on employment rates, that blacks outside the South accept residential segregation if they also gain greater wage parity, and that non-wage benefits of living in a “blacker” city may counterbalance wage inequities for blacks.
Individual MSA regressions allow the ranking of MSAs according to the size of average employment and wage gaps. Table 9 shows that Atlanta, for example, ranks 38th for men and 30th for women on the wage gap – black men earn 14 percent less than comparable white men, and black women earn 1.9 percent less than comparable white women. These figures are 19.6 percent and 1.6 percent for those with at least a B.A. degree.[67] Of the ten highest-ranked cities in the Black Enterprise survey for 2007, only Indianapolis and Nashville scored well in terms of wage equity in 2000.[68] The formerly high-ranked cities of Baltimore, Chicago and Philadelphia also ranked fairly well, particularly for women.[69] Houston, Dallas, Charlotte, and Atlanta had large wage gaps – on the order of 12 to 14 percent for men (20 to 25 percent for college-educated men) and 2 to 5 percent for women who were comparable aside from race.[70] Houston and Jacksonville also had hefty employment gaps. In contrast, the lowest ranked MSAs scored very well in terms of wage equity, with black women earning 4 to 10 percent more than comparable white women and black men earning only 0 to 4 percent less than their white counterparts.[71]
Given these figures on wage parity, one might question the survey results. Yet cities, like people, are bundles of characteristics, and wage equity is only one of them. What is more, individual workers can do very little about their skin color and thus are more likely to seek the best living conditions available to them, ceteris paribus, than to minimize the racial wage gap. The gap itself certainly may matter to individuals, both as a matter of fairness and because it might act as a proxy for underlying social conditions; it is certainly a concern for policy makers. But wage gaps might usefully be considered an outcome of household optimization rather than part of the objective function. To the extent people have choices, we might expect to observe greater racial wage inequality in MSAs that offer more of other sorts of amenities – higher real wages, for example, or a higher probability of employment or greater equity in employment rates, or better public schools, or a larger percentage of blacks in the population, or a lower relative incarceration rate. How an MSA ranks to blacks therefore depends on how people value wage parity relative to these other qualities.[72]
Take another look at Table 9 and consider the Black Enterprise survey rankings. As well as ranking labor-market gaps, the table reports various MSA attributes that matter to people, including MSA ranking of real wages for blacks by sex. Cleveland does well on wage gaps but miserably on employment gaps. Milwaukee is at the very bottom of the employment gap ranking; it also has the largest relative incarceration rate aside from Washington, DC. Detroit scores well for real wages and is just below the middle of the employment-gap ranking, but it has the worst public schools and among the worst safety records. Detroit and Milwaukee both have highly segregated housing as well. Detroit and Cleveland are on worst-city list for many surveys, regardless of race.[73] Despite good records on racial wage parity, these cities apparently offer little else to blacks.
What about the highly ranked cities? Atlanta, Washington, Dallas, and Nashville offer fairly high real wages for blacks. Atlanta and Charlotte rank well on the employment gap for both sexes, and Washington, Nashville, Raleigh, and Jacksonville rank highly for one sex. Raleigh has good public schools and safety. Charlotte and Raleigh often score high on surveys for livability, regardless of the race of respondents.[74] Even though racial wage inequity (sometimes quite large) persists in the highly ranked cities, other features can reasonably make them attractive to blacks.
Some cities, by virtue of the statistics, seem to belong on the “best of” list for blacks – Boston and Los Angeles, for example – but are not cited. Yet Boston has a long-held reputation for racism, including virulent fights against busing to integrate schools, the hunt for the non-existent black killer in the Charles Stuart case, and Henry Louis Gates’s recent dustup with the police.[75] Los Angeles is notorious for the 1992 Rodney King incident and subsequent race riots, as well as for purported racism within its police department.[76] That these cities did not make the top ten in livability for black residents is perhaps not that surprising after all.
Age
Segmenting the data by age group highlights some troubling patterns.[77] Outside the South, the largest employment gap occurs among male household heads aged 25 and younger. Residential segregation appears to be a primary factor explaining this pattern, with an additional factor being skin color for single black men. The underlying source may be discrimination, lack of networks, or some other factor; whatever the reason, the size of the employment gap for the youngest black male household heads is notable – and alarming.
Residential segregation also contributes heavily to relatively low employment rates for single black men over age 40 outside the South, again perhaps indicating a lack of job contacts or role models. But more segregation also means higher relative wages for older black males outside the South – once employed, these men experience less wage inequality than similar men living in less-segregated areas. This suggests that black men in their prime working years outside the South face a difficult tradeoff – they can live in highly segregated areas and earn wages fairly comparable to those of whites if they can get a job, or they can live in less-segregated MSAs with more wage inequality but less employment inequality.
One noteworthy pattern associated with “propbblack” is its consistently negative coefficient in the employment regression for persons aged 26-40 living outside the South. For other age groups outside the South, the coefficient is positive or zero. All else constant, non-Southern blacks in the early to middle part of their working years experience much lower employment rates than comparable whites if they live in a “blacker” MSA.
Greater residential segregation in the South yields lower relative wages for blacks in nearly every age group; for one age group, its effect on employment stands out. Generally, Southern residence tends to mitigate the negative effect of residential segregation on relative employment. This is not true, however, for married men and all women of ages 26 to 40. What this may indicate is that old regional patterns may be eroding -- where residential segregation once took its toll primarily on relative employment outside the South and relative wages in the South, the impact may become less regionally distinct in the future.[78]
Occupation
Occupational categories used in the regressions are quite broad and mask possible occupational segregation as a possible source of wage inequity. As Altonji and Blank (1999, p. 3153) note, occupational segregation could be a form of discrimination.
Male professionals exhibit a large racial wage gap at the mean, for example -- 12.9 percent outside the South and 15.1 percent in the South.[79] Thirty-five percent of white men fall into this occupational category, but only 22 percent of black men outside the South and 19 percent in the South. Some of the wage discrepancy is undoubtedly due to a divergence in the type of professional job held: a detailed analysis of occupational codes reveals that white male professionals are much more likely to be lawyers, scientists, engineers, and CEOs, whereas black male professionals are much more likely to be teachers, social workers, therapists, and postmasters.[80] The wage gap for male administrators is likewise attributable in part to the specific occupations blacks and whites have: in this category, blacks are much more likely to be clerks, messengers, postal workers, meter readers, and licensed practical nurses, whereas whites are much more likely to be supervisors, computer software designers, pilots, surveyors, and sales engineers.[81]
For men, one notable feature in separate occupational wage regressions is the unique outcome for service workers.[82] The signs on “propbblack” and “DIblack” are positive and negative, respectively, implying that relative wages for black male service workers are higher when a greater proportion of the population is black but lower when housing is more segregated, ceteris paribus. The opposite is true for all other occupational classifications outside the South and for skilled workers in the South. Residential segregation also has a strong negative effect on relative employment rates for black service workers outside the South.[83]
How can we explain these patterns? As with other occupational groups, white and black service workers hold distinct jobs. Blacks are more likely than whites to be watchmen, nursing aides, housekeepers and butlers, child care workers, and baggage porters, and less likely to be policemen and firefighters. Roughly speaking, the former earn less than the latter.[84] History, plus social and professional networks, may help explain the occupational crowding and thus the regression coefficients. Blacks have long worked in the former group of occupations, and openings for particular jobs could easily become known by word of mouth. So a greater proportion of blacks in the MSA population could reasonably strengthen the networks available to individual blacks for finding these sorts of jobs and therefore lessen inequality. Residential segregation, on the other hand, reduces daily contact between the races; discord thus might more easily arise when the races do meet. Whites in segregated cities might be particularly hostile to entrusting public protection to blacks. In fact, racial tension about hiring and promotion among police and firefighters in highly segregated Northern cities is commonplace.[85] Consequently, more residential segregation could well generate greater racial inequality among service workers.
Racial discrepancies within occupational groups are not as notable for women as for men. More than 90 percent of female workers are in the administrative, professional, or service occupations.[86] Blacks are disproportionately represented as licensed practical nurses, mail and postal clerks, social workers, vocational counselors, nursing aides, and guards. Whites work disproportionately as real estate sales workers, dental hygienists and assistants, advertisers, CEOs, therapists, waitresses, and hairdressers.
One interesting result, however, is that greater residential segregation lowers relative wages for black female professionals outside the South but raises them for service workers outside the South. The opposite is true in the South. The effects are not large, compared to those for men, except for female Southern service workers. For these women, a greater proportion black also implies lower relative wages. What this may suggest is that black Southern women in service-oriented jobs stay in segregated neighborhoods in heavily black MSAs – and tolerate labor-market inequality – for reasons outside the workplace. Moving could be difficult financially, or amenities like nearby family and friends may compensate for job inequities.
CONCLUSIONS
Recent empirical work indicates that racial wage gaps disappear when measures of ability (typically scores on the Armed Forces Qualification Test) are included.[87] The census does not contain ability measures, so the work presented here cannot address this issue. Yet the sheer range of racial gaps across demographic and geographic groups suggests that observed test scores may not supply all the answers as to why blacks and whites experience different labor-market outcomes.
The research in this paper also reinforces the need to examine employment rates alongside wages to obtain a full picture of racial inequality. Comparable blacks and whites are employed at different rates; what is more, wage regressions conducted on only employed people suffer from selection bias. In virtually every wage regression presented here, the coefficient on the inverse Mills’ ratio is significant.
Two features of an MSA – the proportion of the population that is black and the degree of residential segregation by race – influence racial employment and wage gaps. The effects vary across sex, marital status, region, educational and occupational classes, and age groups. Generally, the “blacker” the MSA, the wider the employment gap in the South and the larger the wage gap regardless of region. This suggests that blacks may trade off labor-market equity for other perceived benefits of MSAs with a relatively larger black population. Greater residential segregation generally decreases relative employment rates, with a larger effect outside the South. It also decreases relative wages in the South. Intriguingly, however, greater residential segregation is associated with higher relative wages for many blacks outside the South, particularly those who are older or less-educated. This may be the product of migration and neighborhood formation patterns, with blacks moving to and remaining in high-wage Northern cities with heavily segregated neighborhoods. Housing segregation is not and was not necessarily perceived positively by blacks, but rather as something acceptable if an MSA offers amenities such as high wages or parity in wages across races.
Breaking down the data by gender and marital status yields particularly interesting results. Racial inequality for single women appears due primarily to differing underlying characteristics for blacks and whites. This does not definitively mean that discrimination is absent in the single-female labor market; rather, observable characteristics explain more for single women than for men (or married women). For married women, the intriguing finding is that black women tend to be employed at much higher rates and earn more than comparable white women. This may result from differences in family situations. One salient disparity for black and white married women is the labor-market position of their spouses, at least in the single-race marriages that form the bulk of the sample. Black men tend to experience lower employment rates and wages than comparable white men. If blacks and whites have similar aspirations, black wives may respond by working relatively more and seeking relatively higher wages.
Educational level also affects labor-market equity. The largest wage gap among men corresponds to those with the most education. The average wage gap is larger for men in the South across all educational levels, but the average employment gap is much larger outside the South for the least-educated men. Residential segregation exacerbates the negative effect on relative employment, although it has a positive effect on relative wages for less-educated black men living outside the South.
Paired-audit studies suggest that discrimination is one reason for inequality in the labor market.[88] This research proposes possible explanations for the unexplained portion of employment and wage gaps, including discrimination, but does not offer a single answer. What it does imply, however, is that labor-market inequality exists in part because people make tradeoffs. Some cities have larger racial wage gaps than others, for instance, because the degree of equality is the result of individual optimization rather than the objective. Blacks may tolerate more inequality in wages if they perceive that a city offers other amenities, like high real wages, good employment prospects, more family and friends, or greater harmony across races.
Recognizing that labor-market racial inequality exists – and differs among demographic and geographic groups -- because of rational tradeoffs made by individuals could help inform policy. Programs designed to reduce racial segregation in housing may have very different effects for women and men, for example; failure to consider this could result in ineffectively targeted policy. What is more, better public goods may offer greater opportunities for private discrimination in the labor market. It may seem odd that cities ranked higher in livability for blacks are characterized by greater wage inequity (which may result from discrimination), but not when one also considers the level of real wages available to blacks, employment prospects, incarceration rates, public school quality, and neighborhood safety.
|TABLE 1: Raw Averages for Employed Urban Household Heads and Spouses by Sex, Race, and Marital Status |
| |White |Black |Black as a Proportion of White |
| | | | | | |
|MALE HOUSEHOLD HEADS/SPOUSES | | | | | |
| Wage |$53,507 |$35,562 | |66% | |
| Employment rate |0.75 |0.73 | |97% | |
| Age |43.36 |42.04 | |97% | |
| Weeks worked |48.31 |46.94 | |97% | |
| Usual hours worked |44.43 |42.35 | |95% | |
|SINGLE MALES | | | | |
| Wage |$41,850 |$31,726 | |76% | |
| Employment rate |0.74 |0.68 | |92% | |
| Age |39.6 |39.86 | |101% | |
| Weeks worked |47.24 |45.98 | |97% | |
| Usual hours worked |43.36 |41.7 | |96% | |
|MARRIED MALES | | | | |
| Wage |$57,456 |$37,724 | |66% | |
| Employment rate |0.75 |0.76 | |101% | |
| Age |44.63 |43.26 | |97% | |
| Weeks worked |48.68 |47.49 | |98% | |
| Usual hours worked |44.79 |42.72 | |95% | |
|FEMALE HOUSEHOLD HEADS/SPOUSES | | | | | |
| Wage |$29,629 |$26,500 | |89% | |
| Employment rate |0.6 |0.68 | |113% | |
| Age |42.76 |40.61 | |95% | |
| Weeks worked |45.34 |44.59 | |98% | |
| Usual hours worked |37.42 |38.55 | |103% | |
|SINGLE FEMALES | | | | |
| Wage |$31,321 |$25,538 | |82% | |
| Employment rate |0.59 |0.66 | |112% | |
| Age |43.1 |40.02 | |93% | |
| Weeks worked |46.16 |44.14 | |96% | |
| Usual hours worked |39.21 |38.53 | |98% | |
|MARRIED FEMALES | | | | |
| Wage |$28,794 |$27,946 | |97% | |
| Employment rate |0.61 |0.71 | |116% | |
| Age |42.59 |41.5 | |97% | |
| Weeks worked |44.94 |45.26 | |101% | |
| Usual hours worked |36.54 |38.58 | |106% | |
| | | | | | |
|Note: All means are weighted by the person weight given in the IPUMS data. Wage, age, weeks worked, and | |
|hours worked are for employed persons represented in wage regressions. “Spouses” refers to spouses of | |
|household heads. | |
|Table 2: Weighted Means | | | |
| |Variable |NON-SOUTH |SOUTH |S/N |NON-SOUTH |SOUTH |S/N |
Table 4: Summary of Coefficient Signs
| | | | |HS grad |some |college |
|EMPLOYMENT GAP | |
|propbblack | |
|propbblack |
|and difference-in-difference regression coefficients. For example, the coefficient in stage 1 for single males (Table 5) on “DIblack” is -1.152 |
|and on “DIblacksouth” is 0.804. The first measures the effect of DI on blacks relative to whites outside the South; the sum of the two |
|coefficients measures the effect of DI on blacks relative to whites in the South. |
| |
| | | | | | |
| |FEMALES | |MALES |
| |Single |Married | |Single |Married |
|stage 1 | | | | | |
|black |0.076 |0.541 | |0.333 |0.440 |
|blacksouth |0.301 |ns | |-0.172 |0.125 |
|propbblack |0.195 |1.325 | |ns |0.341 |
|propbblacksouth |-0.710 |-1.797 | |-0.954 |-1.602 |
|DIblack |-0.449 |-0.441 | |-1.152 |-0.784 |
|DIblacksouth |-0.028 |0.680 | |0.804 |0.407 |
| | | | | | |
|MEAN gap (NonSouth) |-0.192 |0.416 | |-0.404 |-0.020 |
|MEAN gap (South) |-0.008 |0.584 | |-0.237 |0.095 |
|Nag. R sqr. |0.619 |0.325 | |0.550 |0.477 |
| | | | | | |
|stage 2 | | | | | |
|black |0.017 |0.013 | |-0.029 |-0.126 |
|blacksouth |0.062 |0.028 | |0.069 |0.176 |
|propbblack |0.108 |ns | |ns |-0.231 |
|propbblacksouth |-0.306 |-0.160 | |ns |0.091 |
|DIblack |-0.045 |0.076 | |ns |0.118 |
|DIblacksouth |-0.042 |-0.052 | |-0.150 |-0.329 |
| |0.002 |0.061 | |-0.029 |-0.078 |
|MEAN gap (NonSouth) | | | | | |
|MEAN gap (South) |-0.013 |0.023 | |-0.047 |-0.100 |
|Adj. R sqr. |0.599 |0.635 | |0.511 |0.468 |
| | | | | | |
|Note: All coefficients are significant at the 10-percent level unless marked "ns." |
|TABLE 6: Regression Coefficients by Sex and Education (age >25) | |
| | | | | | | |
| |FEMALE |MALE |
| |up to |some |college |up to |some |college |
| |HS grad |college |grad + |HS grad |college |grad + |
|STAGE 1 | | | | | | |
|black |0.270 |0.383 |0.368 |0.495 |0.527 |ns |
|blacksouth |0.371 |0.132 |ns |ns |-0.072 |0.698 |
|propbblack |1.242 |0.762 |0.823 |1.394 |-0.368 |-1.744 |
|propbblacksouth |-1.765 |-1.242 |-1.257 |-2.445 |-0.653 |0.825 |
|DIblack |-0.672 |-0.444 |0.000 |-1.388 |-0.865 |0.500 |
|DIblacksouth |0.114 |0.465 |0.537 |0.884 |0.525 |-1.204 |
| | | | | | | |
|MEAN gap (NonSouth) |-0.012 |0.190 |0.469 |-0.226 |-0.054 |0.111 |
|MEAN gap (South) |0.215 |0.429 |0.590 |-0.008 |0.056 |0.096 |
|Nag. R sqr. |0.430 |0.371 |0.373 |0.509 |0.452 |0.454 |
| | | | | | | |
|STAGE 2 | | | | | | |
|black |-0.072 |0.036 |ns |-0.178 |-0.081 |-0.068 |
|blacksouth |0.121 |-0.067 |0.062 |0.235 |0.092 |0.049 |
|propbblack |-0.188 |0.314 |-0.078 |-0.304 |ns |-0.323 |
|propbblacksouth |0.041 |-0.493 |-0.092 |ns |-0.092 |0.350 |
|DIblack |0.240 |-0.096 |0.057 |0.273 |ns |ns |
|DIblacksouth |-0.269 |0.153 |-0.065 |-0.359 |-0.150 |-0.226 |
| | | | | | | |
|MEAN gap (NonSouth) |0.061 |0.012 |0.027 |-0.040 |-0.081 |-0.106 |
|MEAN gap (South) |0.003 |-0.034 |0.023 |-0.054 |-0.093 |-0.145 |
|Adj. R sqr. |0.587 |0.591 |0.554 |0.463 |0.413 |0.372 |
| | | | | | | |
|Note: All coefficients are significant at the 10-percent level unless marked "ns." | |
Table 7: Regression Coefficients for 44-City Regressions
| |MALES |FEMALES |
|stage 1 | | |
|black |1.381 |0.536 |
|blacksouth |-0.806 |0.321 |
|propbblack |1.453 |1.264 |
|propbblacksouth |-2.523 |-1.677 |
|DIblack |-2.499 |-0.861 |
|DIblacksouth |1.939 |0.213 |
|Nag. R sqr. |0.483 |0.435 |
| | | |
|stage 2 | | |
|black |-0.279 |-0.156 |
|blacksouth |0.295 |0.207 |
|propbblack |-0.430 |-0.049 |
|propbblacksouth |0.375 |-0.120 |
|DIblack |0.390 |0.272 |
|DIblacksouth |-0.546 |-0.301 |
|blacksafe |-0.065 |-0.082 |
|blackjail |0.001 |0.002 |
|blackschool |0.000 |-0.012 |
|Adj. R sqr. |0.481 |0.611 |
Table 8: Regression Coefficients, 44-City Regressions on Blacks Only
| |MALES |FEMALES |
|stage 1 | | |
|south |0.442 |0.829 |
|propb |1.687 |1.664 |
|propbsouth |-2.250 |-2.079 |
|DI |-1.326 |-0.378 |
|DIsouth |0.120 |-0.585 |
|Nag. R sqr. |0.429 |0.430 |
| | | |
|stage 2 | | |
|south |0.235 |-0.081 |
|propb |0.162 |0.633 |
|propbsouth |-0.071 |-0.738 |
|DI |0.209 |-0.368 |
|DIsouth |-0.338 |0.303 |
|safety |0.091 |0.104 |
|jail |0.001 |0.002 |
|school |-0.012 |0.024 |
|Adj. R sqr. |0.474 |0.566 |
|Table 9: MSA/CMSA Attributes | | | | | | | |
| |
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[1] For good surveys, see Altonji and Blank (1999) and McCall (2001). Also see Bureau of Labor Statistics (2010) for copious recent statistics based on the Current Population Survey.
[2] By “primary household earners,” I mean persons classified in the census as household heads or spouses of household heads. This figure comes from the 5 percent sample of the U.S. 2000 census. More recent data from the 2008 American Family Survey indicate that this figure has not changed much over the past few years.
[3] Carolyn Brown, “Top Ten Cities for African Americans,” Black Enterprise, 1 May 2007. The magazine also listed Cleveland, Detroit, and Milwaukee among the worst places for blacks to live.
[4] I also point out results obtained from using the 2008 American Family Survey data; these often parallel the findings for the 2000 data, but not always.
[5] I do not exclude discrimination as an explanatory factor for black-white differences in the labor market. But my focus here is variations in these differences across subgroups.
[6] Cain (1986) uses this classic model, for example, and Altonji and Blank (1999) offer an excellent survey of additional literature. The vector X could include, for example, human capital investment (Mincer 1958, Reder 1954), age (Creedy and Hart 1979), other income available to the household, the presence of a work disability, occupational category, union membership, availability of alternative jobs, travel time to work, migration status, and home ownership. The dependent variable could be ln(hourly wages) or ln(annual wages). If w is measured as the natural log of yearly earnings, two “explanatory” factors in X include the number of weeks worked and the number of hours worked per week. I include both full-time and part-time workers, so differences in annual wages between comparable blacks and whites could result from differences in hours worked per week, differences in weeks worked per year, diverse hourly wages, or some combination.
[7] To be precise, their OLS estimates suggest that segregation has no effect on whites, but an instrumental- variables approach yields a small positive effect for whites; they do not “feel comfortable drawing strong conclusions about this effect.” (p. 859) They split their sample into two groups: ages 20-24 and ages 25-30. They conclude that a one-standard-deviation reduction in residential segregation would eliminate one-third of the gap between whites and blacks. Collins and Margo (2000) extend Cutler’s and Glaeser’s work backward to earlier decades. Although urban residential segregation had a strong adverse impact on labor-market and social outcomes of young African-Americans relative to whites during the 1980s, Collins and Margo find little evidence of such an effect for the period 1940 to 1970.
[8] See for example Kain (1968, 1992), Ellwood (1986), Jencks and Mayer (1990), Holzer (1991), McLafferty and Preston (1992), Ihlanfeldt and Sjoquist (1994), Taylor and Ong (1995), Myers and Saunders (1996), Ross (1998), Weinberg (2000), Stoll et al. (2000), Chung et al. (2001), Stoll (2005), and Boustan and Margo (2007).
[9] Neumark (1999) and Ihlanfeldt and Scafidi (2002b) consider this possibility.
[10] See for instance Coleman (1966), Massey et al. (1991), Massey and Denton (1993), O’Regan (1993), Wilson (1987, 1996), Borjas (1995), and Glaeser et al. (1996).
[11] See Ihlanfeldt and Scafidi (2002a), Krysan and Farley (2002), Vigdor (2002), Sethi and Somanathan (2004), Bayer et al. (2004), Adelman (2005), Ananat (2007).
[12] See Glazer and Moynihan (1963), Wilson (1987), Granovetter (1995), Borjas (1995), and Calvó-Armengol and Jackson (2004).
[13] See Moore (1990), Hanson and Pratt (1991), Massey and Shibuya (1995), Fernandez-Kelly (1995), Waldinger (1996), Korenmen and Turner (1996), Elliott and Sims (2001), Mouw (2002), Parks (2004), and Christie-Mizell (2006). The effects could differ across genders as well. See, for example, Montgomery (1991).
[14] Calvó-Armengol and Jackson (2004) focus on this possibility in their study of employment rates.
[15] McCall (2001) notes this possibility as well.
[16] Bruekner and Zenou (2003) link unemployment to housing discrimination, for instance.
[17] The data also include a variable indicating transport time to work. Unfortunately, many of these values are missing; they also appear to be rounded numbers where they do exist.
[18] See and .
[19] On the latest patterns of migration, see “Recent Black Migration Change,” .
[20] See “The New Great Migration,” .
[21] See for example Becker (1981), Lundberg and Pollak (1996), Blau et al. (2000), and Hersch and Stratton (2002).
[22] Cutler and Glaeser (1997) control for gender but do not explicitly examine whether segregation could yield different results for married and single persons. Although they do look at the effect of segregation on single motherhood, they do not separate individuals by gender and marital status in earnings regressions. Their results (fn. 19, p. 844) show similar coefficients on “DIblack” for men and women when they run separate regressions by gender.
[23] Less than 1 percent of married couples in my sample are mixed-race. Among married black females, only 2.6 percent are married to white males. Only 6.5 percent of married black males have white spouses. Average spousal wages are highest for all-white couples ($60,554) and lowest for all-black couples ($48,051). For mixed-race couples, average spousal wages are $56,871. Interestingly, the average wage for the spouse of the household head (who is usually, but not always, the female) is highest in mixed-race couples. These figures do not control for education or other influential factors, of course. Future research will focus on households and families as well as individuals.
[24] McCall (2001) discusses the literature in this area.
[25] See for example O’Neill (1990), Juhn et al. (1991), and Aronson (1998).
[26] The Heckman procedure first estimates a probit model of the form D = aðd + bðdY + eðd, where D equals 1 for workers and 0 otherwise, and Y includes relevant exogenous variables from (1) plus at least one additional iden= αd + βdY + εd, where D equals 1 for workers and 0 otherwise, and Y includes relevant exogenous variables from (1) plus at least one additional identifying variable. The predicted values from the probit regression can then be used to construct an inverse Mills ratio λ to correct for selection bias in equation (1). The resulting coefficients on X are unbiased, although standard errors must be corrected for heteroskedasticity.
[27] Heckman et al. (2000) document a 23 percent withdrawal rate for blacks in 1990 as compared to a 10 percent rate for whites. These authors also point out that measures of black economic progress must account for selective dropout as well as narrowing racial wage gaps. They, along with Butler and Heckman (1977) and Brown (1984), find that measures of black progress that focus exclusively on the wage gap overestimate the gains that blacks have made. Chung et al. (2001) investigate the selection issue in the context of transportation.
[28] In places, I also indicate results stemming from the use of the 2008 American Family Survey. I updated all information for these observations except the dissimilarity index discussed in the next note; here, I had to use the 2000 measures. Because these data are not all from the same year and do not constitute as large a sample, the 2008 results should be considered preliminary.
[29] Note that those coded as household heads by definition are not housed in group quarters, such as prisons.
[30] I use the residential dissimilarity index between blacks and whites for each metropolitan standard area to measure DI. This index indicates the proportion of blacks (or whites) that would need to move across census tracts to obtain a perfectly even proportion of black residents throughout the entire metropolitan area. The DI data come from , selecting table black2000_metro20003.xls. Other researchers have analyzed different demographic groups; see. for example, McLafferty and Preston (1992). I report results only for individuals living in areas with known DI. This includes about 90 percent of blacks -- the U.S. Census Bureau reports an African-American population of just over 36 million in the year 2000. Of these, about 31.5 million live in an area for which the Bureau also reports the DI. Including individuals for whom DI is not reported does not change my results materially.
I obtained the data indicating degree of sprawl for each metro area from . The percentage of MSA population that is black came from . I refer to all urban areas as MSAs although technically some are called C(onsolidated)MSAs.
[31] Statistics on union membership come from and .
[32] Male prison population by race and state is recorded at . I obtained state population by race at . The website allows one to obtain data on neighborhood public-school quality and crime rates within a 5-mile radius. I calculated median figures for each MSA represented in my data. The website reports using sources from the U.S. Bureau of the Census, the U.S. Department of Justice, the National Center for Education Statistics, and the U.S. Geological Service, among others.
[33] All means and regressions are weighted using the person weights given in the Public Use Microsample (PUMS). I originally included observations from non-urban areas in the analysis. Although the results did not differ qualitatively from the findings presented here, I decided to focus on metropolitan areas in part because most blacks live in urban areas and because many policy efforts tend to focus on cities and surrounding suburbs.
[34] The wage ratio is consistent with what Altonji and Blank (1999), p. 3146, calculate from the March 1996 Current Population Survey data. Employment rates for all groups have declined recently, with the largest decline associated with black men. Bureau of Labor Statistics (August 2010), p. 1.
[35] Wage and employment gaps are roughly the same in the 2008 data, although the average ages are higher and average hours are smaller. Weeks worked are reported only in intervals for the 2008 data but appear similar to those for 2000. Bureau of Labor Statistics (2010), p. 3, indicates that the racial wage gap continues to be more pronounced for men.
[36] The 2008 averages are comparable, save one: blacks owned more vehicles on average than whites.
[37] The cities are New York, Chicago-Gary, Washington-Baltimore, Atlanta, Philadelphia, Detroit, Los Angeles, Houston, Dallas, and New Orleans.
[38] The nation’s capital – and the capital of the Union during the Civil War – had a DI of 0.625. By comparison, the DIs in the three Confederate capitals were 0.550 (Montgomery), 0.562 (Richmond), and 0.339 (Danville). Not everyone agrees with the rankings of segregation produced by the DI measure. See, for example, Quinn and Pawasarat (2003).
[39] Statistics on marriage rates by race calculated from Survey of Income and Program Participation data can be found at .
[40] Detailed results are available from the author.
[41] I could include occupational variables in the first-stage regression if individuals who were not working reported their usual occupation (if they had one). The coding in the PUMS data typically lists “unreported occupation” for those not working, however. What is more, including the occupational variables caused the iterations in the first step to exceed the maximum allowed by the software for the size of data sets used. I therefore decided to include occupational categories as right-hand-side variables in the second but not the first stage. Occupation is arguably determined jointly with education and wages, rather than “causing” wages. Essentially, then, I assume that people select the optimal occupation among the choices available to them and I focus on wages as the endogenous variable.
[42] Altonji and Blank (1999), p. 3156, stress this point. Discrimination can influence human capital acquisition, for instance, as Coate and Lowry (1993) discuss. Lundberg and Startz (1996) note that past discrimination and pre-market discrimination can have feedback effects. And costly search can complicate matters, as Black (1995) and Bowles and Eckstein (1998) emphasize.
[43] Signs on coefficients other than ones related to the racial gap generally correspond to expectations. For instance, having more children at home generally reduces the employment rate for women but increases it for men. Hotz and Miller (1988) document the negative effect of children on female labor force participation. Greater household income aside from the individual’s wages tends to reduce employment rates for both sexes, reduce male wages, but to increase female wages. This suggests that the income effect matters for labor supply, but that positive assortative mating may complicate matters. Vehicle ownership affects males positively but has little effect on females. Homeownership has a positive effect on wages, all else constant, and persons who do not live in the center city do better wagewise than their counterparts downtown. The coefficient on lambda is often negative (except for highly educated males), meaning that unobserved factors tending to increase the probability of participation also tend to decrease wage income. The coefficients on public-sector employment, urbanization, and size of population are typically positive.
[44] By 2008, blacks’ position relative to whites worsened – the mean wage gap was 7.7 percent outside the South and 6.5 percent in the South.
[45] These four held in 2008 as well, although married black women eaned only 2.6 percent more on average than their white counterparts outside the South and the same in the South.
[46] These are speculations only; I have not accounted for interracial marriage or marriage length, nor have I analyzed household-level (as opposed to individual-level) data. As indicated earlier, however, more than 99 percent of married couples had husband and wife of the same race. I do control for other household income in both stages of the regressions, but only for the given year. Because employment rates tend to be lower for black men than for white men, the “other income” variable controls only partially for overall family earnings capacity – that is, black wives might work and earn more than comparable white wives, even controlling for other family income in a given year, because they reasonably expect more spells of unemployment for spouses over time. Married black men in turn might accept a relatively larger wage gap, even controlling for other family income in a given year, because they expect their wives to work and earn relatively more over several years.
[47] Single females were also an exception in 2008.
[48] In 2008, greater segregation affected relative wages positively only for married black women.
[49] A larger proportion black lowered relative employment and wages across the board in 2008.
[50] By 2008, whatever benefits seemed to be associated with living in a “blacker” city had largely evaporated for blacks.
[51] I omit younger people because they have not had as much time to finish their schooling. Including them does not materially change the analysis.
[52] The gap is larger for 2008 data – 16.7 and 23.1 percent, respectively. Bound and Freeman (1992) also found that young black college graduates did substantially worse than their white counterparts.
[53] Relative rates were no longer higher by 2008, but the gap in the South was still much smaller than outside the South.
[54] The figures are 6.7 percent and 1 percent for 2008.
[55] By 2008, this was no longer true: blacks always earned less than comparable whites, regardless of region.
[56] The results are mixed for the 2008 data, perhaps because the DI measure pertains to an earlier year.
[57] According to the website , 23 percent of African-Americans earning B.A.s attended a historically black college or university.
[58] See Taeuber and Taeuber (1965) and Farley (1968).
[59] Zax and Kain (1996) found that blacks are more likely to quit when an employer moves out of the area. Glaeser and Kahn (2001) document the movement out during the mid-twentieth century; more recent movements are discussed in Persky and Wiewel (2000). An interactive map at , based on Bureau of Labor Statistics data, shows how jobs have migrated over the last few years. The nation’s mayors report that, in 2001, Detroit, Cleveland, Chicago, St. Louis, and Greensboro each lost over 10,000 jobs, mostly due to cutbacks in manufacturing plants. .
[60] I noted the tiny proportion of interracial marriage earlier in this paper. In my sample, 90 percent of white female college graduates are married to men with at least some college education; the figure is 80 percent for blacks. Ninety-two percent of white females with education beyond the B.A. are married to men with at least some college education, as are 82 percent of black females.
[61] These MSAs contain a balance of MSAs inside and outside the South, most with significant numbers or proportions of blacks, many of which are highly segregated.
[62]
[63] Including city fixed effects is another possibility, although doing so says little about exactly what city characteristics matter. I do not have the computing capacity to do this.
[64] Major differences exist in the racial/ethnic composition of private school enrollments compared with public school enrollments in 2007-08. Whites made up a greater share of private school enrollment than of public school enrollment (75 vs. 56 percent), while the opposite was true for Blacks (10 vs. 17 percent). .
[65] In 2007, the percentage of children in an “always safe” neighborhood was highest for whites, then “other races,” Hispanics, and blacks (57, 52, 51, and 47 percent, respectively). Black children were most likely to live in neighborhoods reported to be never or sometimes safe. About four of every ten black children lived in such neighborhoods in 2007. Nearly one in five Hispanic children and “other race” children lived in unsafe neighborhoods, whereas less than ten percent of white children lived in such neighborhoods. .
[66] Recall the earlier discussion about the ambiguous sign on public goods: greater income could signify a larger ability to pay for public goods, or it could represent a compensatory wage premium in cities with low-quality public goods. Where greater safety or school quality corresponds to higher absolute wages, the former effect appears to dominate.
[67] Atlanta ranked 33rd for men and 14th for women by 2008, with wage gaps of 20.9 percent for men and virtually zero for women. Full regression results by MSA are available from the author.
[68] Indianapolis maintained its ranking in 2008 but Nashville’s position worsened, falling below the median. Columbus and Jacksonville ranked reasonably well in 2008 – 14 for men and 21 for women in Columbus, 5 for men and 31 for women in Jacksonville.
[69] By 2008, Philadelphia fell to 35th for men, with an wage gap of 21.7 percent.
[70] By 2008, wage gaps for men ranged from 20.9 to 26 percent.
[71] Results are more variable for 2008: Wages were close to parity for both men and women in Detroit. Black women in Cleveland earned 3.6 more on average than comparable white women, but black men earned 16.7 percent less than comparable whites. Black women and men both earned relatively less in Milwaukee in 2008: 7.7 percent and 6.1 percent at the mean.
[72] I have implicitly assumed that people are mobile, and the evidence indicates that blacks may be even more mobile than whites. See Frey et. al (2005), for example. Yet moving can be costly, and inadequate schooling or other human-capital acquisition might make re-location difficult. Some inequality might therefore persist, simply because individuals are stuck. Altonji and Blank (1999, p. 3153) mention this, referring to Bound and Freeman (1992) and Bound and Holzer (1996). Also see Huffman and Feridhanusetyawan (2007).
[73] See for example . In 2008, Detroit dropped to 30th in rank for the employment gap for men; Cleveland improved to 26th in rank.
[74] See for example , .
[75] For an evaluation of current opinion, see Beth Potier, Has Boston shed its racist reputation? : Panel tackles racism and segregation in Boston, Harvard Gazette (November 2002) ,
[76] See for example the testimony in Marshall v. Gates, 44 F. 3d 722 (!995).
[77] Detailed results are available from the author.
[78] Preliminary results using 2008 data support this contention.
[79] The gaps are larger in 2008, at 19 and 25.4 percent, respectively. Detailed results are available from the author.
[80] The Occupational Employment Statistics (OES) survey conducted by Bureau of Labor Statistics in 2000 reported an average annual wage of $104,630 for CEOs, $91,320 for lawyers, and in the $50-70,000 range for scientists and engineers. In contrast, average wages for teachers, social workers, and therapist were in the $30-40,000 range. Postmasters earned on average $46,260. .
[81] Twenty-three percent of white males outside the South are in administrative occupations and 24 percent in the South; among black males, the figures are 21 percent and 20 percent. Among skilled workers, blacks are disproportionately telecom installers, bakers, and in the South concrete workers; whites are disproportionately tool and die makers, cabinetmakers, small engine repairmen, and locksmiths. These workers constitutes 13 to 18 percent of the male work force. Laborers are about the same percentage of the white male work force, but laborers make up one-quarter of employed blacks. Among laborers, blacks are disproportionately bus and cab drivers, garbage and recycling workers, and parking lot attendants, whereas whites are operating engineers, typesetters, excavators and loaders, and ship crew workers. Not much has changed over the past few years – more recent statistics are available in Bureau of Labor Statistics (2010).
[82] Service workers constitute only 7 to 8 percent of white male workers but 15 to 18 percent of black male workers.
[83] The results for 2008 are somewhat different, again perhaps due to the use of an outdated DI measure.
[84] Data are located at .
[85] A federal judge recently ruled that New York City intentionally discriminated against blacks in the written application test in United States, et al. v City of New York, for example. Discussion of the case appears at . A similar case is pending in New Jersey over state exams for police, and the case of Ricci v. DeStefano concerned possible reverse discrimination against Hartford firefighters. For documents and discussion, see .
[86] About 40 percent are in administration, 38 percent of whites and 27 percent of blacks in professions, and about 12 percent of whites and 23 percent of blacks in service occupations.
[87] See Neal and Johnson (1996), Altonji and Blank (1999), and Bollinger (2003).
[88] See, for example, Turner et al. (1991), Bertrand and Mullainathan (2004).
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