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Books - Drugs, Crime and Social Isolation
Written by John D. Kasarda   
I wish to acknowledge the superior programming and research assistance of Edward Bachmann, Andrea Bohlig, and Kwok-Fai Ting.
A rapidly expanding literature is targeting the impact of transforming urban economies on joblessness, poverty, and related social problems that are increasingly concentrated in our major cities (for comprehensive reviews, see Jencks and Peterson 1991; Moss and Tilly 1991). Underlying much of this research is the emergence of a large subgroup of inner-city residents who are detached from the formal labor market in far greater numbers and proportions than formerly documented. High rates of joblessness among this subgroup, in turn, have been associated with disproportionately high rates of poverty, school dropout, out-of-wedlock births, and welfare dependency.
When all of these attributes exist concurrently within households, they are purported to be mutually reinforcing, resulting in behavior that substantially diminishes the economic fortunes of affected members (Wilson 1987). Geographic concentration of such severely distressed households further magnifies these problems and accelerates their spread to nearby households through social isolation, peer pressure, and imitative behavior (Martinez-Vazquez and Saposnik 1990; Wilson 1987; Wacquant and Wilson 1989). The upshot is a spiral of negative economic outcomes for the households and their neighborhoods.
This chapter examines the scope and nature of severely distressed households in America's large cities and links their differential growth across cities to changes occurring in the structure of local economies. Severely distressed households are defined as those that simultaneously exhibit five attributes: low income, less than high school education, poor work history, single parenthood, and public assistance dependency. Public-Use Microdata Sample (PUMS) files from the 1970 and 1980 Census of Population and Housing are analyzed to document the size and demographic composition of severely distressed residents in 95 of the largest metropolitan central cities. Utilizing microdata indicators that are analogous to behaviorally based census tract indicators of urban underclass populations (Ricketts and Sawhill 1988), comparisons are made in numbers and composition of this subgroup at the individual and spatially aggregated levels.
Following a discussion of hypothesized causal relations between urban industrial change and economic dislocation, cities with the largest concentrations of severely distressed residents and underclass populations are selected for detailed assessments of their transforming economies. These transformations are benchmarked against those occurring in other large cities where such disadvantaged subgroups have not experienced as much growth. A number of explanations drawing on both demand- and supply-side labor market factors are offered to account for observed differences across these cities in amounts of economic and social dislocation.
Perhaps no social science concept has generated more discussion and controversy in recent years than that of the urban underclass. Some argue that it is little more than new wine in old bottles—a pithy and stigmatizing term for poor or lower-class persons who have always existed in stratified societies (Gans 1990; Jencks 1989; Katz 1989). Others contend that the underclass is a distinct and recent phenomenon that reflects extreme marginalization from mainstream institutions and counterproductive behavior that reached catastrophic proportions in the inner cities by the mid-1970s (Auletta 1982; Glasgow 1980; Nathan 1987; Reischauer 1987; Wilson 1987). Despite the multifaceted and often ambiguous definitions of the urban underclass, almost all the definitions share the notions of weak labor force attachment and persistent low income (Jencks 1989; Ricketts 1990; Sjoquist 1990). Indeed, the first scholar to introduce the term underclass to the literature labeled its members as an emergent substratum of permanently unemployed, unemployables, and underemployed (Myrdal 1962).
Measurement of the size of the underclass is as varied as its definitions. A number of researchers have focused on individual-level indicators of persistent poverty, defined as those who are poor for spells from n to n + x years (Bane and Ellwood 1986; Duncan, Coe, and Hill 1984; Levy 1977) or long-term AFDC (Aid to Families with Dependent Children) recipients (Gottschalk and Danziger 1986). For example, Levy (1977), using the Panel Study of Income Dynamics (PSID, University of Michigan, Ann Arbor, Institute for Social Research) for the years 1967 to 1973, estimated that approximately 11 million Americans were persistently poor for at least five years. When belonging to the underclass is defined as being persistently poor for eight or more years, 6 million people were found to be members (Duncan et al. 1984). This represented approximately one-fifth of the 32 million Americans living in poor households in 1988 (Mincy, Saw-hill, and Wolf 1990).
Another measurement strategy focuses upon the geographic concentration of the poor in urban areas. Using Bureau of the Census tract-level definitions of local poverty areas, Reischauer (1987) reported that, of the nation's population living in such poverty areas, central cities housed over half in 1985, up from just one-third in 1972. Bane and Jargowsky (1988) documented that the number of poor people living in extreme poverty tracts in cities (i.e., census tracts where more than 40 percent of the residents fall below the poverty line) expanded by 66 percent between 1970 and 1980, from 975,000 to 1,615,000. Moreover, just four northern cities (New York, Chicago, Philadelphia, and Detroit) accounted for two-thirds of this increase.
Using an identical definition of extreme poverty tracts, Green (1988) found that 30 large American cities added 527 such tracts between 1970 and 1980. Similarly to Bane and Jargowsky, he discovered that 492 (or 91 percent) of these additional extreme poverty tracts were located in his 15 sampled cities from the Northeast and Midwest. Whereas nearly half (N = 13) of the sampled large cities were in the South, they had a combined increase of only 36 extreme poverty tracts (17 percent), while the 2 large cities of the West, Los Angeles and Phoenix, together added only 9 extreme poverty tracts between 1970 and 1980. Clearly, then, the rise of concentrated poverty appears most severe in the older industrial cities of the North.
Mincy (1988) further documented that concentrated poverty is predominantly a minority problem. His analysis of extreme poverty tracts in the 100 largest central cities in 1980 showed that of the approximately 1.8 million poor people residing in these tracts, fewer than 10 percent were non-Hispanic white (175,178), while nearly 70 percent were black (1,248,151). Nearly all of the remainder were Hispanic.
As indicated at the beginning of this chapter, the concept of underclass is typically considered to entail more than poverty, however. It is also posited to incorporate certain behavioral characteristics conflicting with mainstream values: joblessness, out-of-wedlock births, welfare dependency, school dropout, and illicit activities. Attempts have been made to measure the size of the underclass by using multiple "behavioral" indicators derived from census data. Ricketts and Sawhill (1988) measured the underclass as people living in neighborhoods whose residents in 1980 simultaneously exhibited disproportionately high rates of school dropout, joblessness, female-headed families, and welfare dependency. Using a composite definition where tracts must be at least one standard deviation above the national mean on all four characteristics, they found that approximately 2.5 million people lived in such tracts in 1980 and that these tracts were disproportionately located in major cities in the Northeast and Midwest. They reported that in underclass tracts, on average, 63 percent of the resident adults had less than a high school education, 60 percent of the families with children were headed by women, 56 percent of the adult men were not regularly employed, and 34 percent of the households were receiving public assistance. Ricketts and SawhilPs (1988) research also revealed that, although the total poverty population only grew by 8 percent between 1970 and 1980, the number of people living in the underclass areas grew by 230 percent, from 752,000 to 2,484,000.
Hughes (1988) showed an enormous increase between 1970 and 1980 in the isolation and deprivation of ghetto neighborhoods in eight distressed cities. Hughes's mapping of the location and spread of predominantly black census tracts in these cities revealed a substantial growth in the number of poor black neighborhoods that did not border on integrated or nonblack neighborhoods. During the 1970s many predominantly black census tracts became surrounded by other overwhelmingly black census tracts, limiting the potential for contact with nonblack residents by those who resided in increasingly isolated tracts at the ghetto's core.
Hughes (1988) also compared absolute changes between 1970 and 1980 in the number of tracts with high coincident levels of adult male joblessness, mother-only families, and welfare recipiency. He found that these tracts, which he labeled "deprivation neighborhoods," mushroomed over the decade. In Chicago, for example, deprivation neighborhoods increased by 150 percent, from 120 tracts to 299 tracts, while the population living in these tracts expanded by 132 percent, from 445,000 to 1,034,000. Similarly, in Detroit the number of deprivation tracts expanded from 60 to 197 (228 percent), and the population residing in these tracts increased from 193,880 to 708,593. Just as remarkable, the ratio of black nondeprivation tracts to deprivation tracts completely reversed in both cities during the decade; in Chicago from three to two in 1970 to two to five in 1980, and in Detroit from five to two in 1970 to one to four in 1980.
Such location-based aggregate measures have been criticized on the grounds that, aside from race, most urban census tracts are quite heterogeneous along economic and social dimensions. Jencks (1989), for example, observed that, with the exception of tracts composed of public housing projects, there is considerable diversity in resident income, education levels, joblessness, and public assistance recipiency within urban neighborhoods. According to his calculations, even in extreme poverty tracts only about half of all families in 1980 had incomes below the poverty line, and some reported incomes up to four times the poverty level. "As a result, most poor families probably had next door neighbors who were not poor" (Jencks 1989: 15). He further noted Ricketts and Sawhill's (1988) findings that within the worst urban neighborhoods (those they defined as underclass areas) more than half the working-age adults held steady jobs, and only one-third of the households received public assistance. On the other hand, considerable numbers of urban residents who are poor, jobless, and dependent on public assistance live in census tracts where fewer than 20 percent of the families fall below the poverty line (Kasarda 1992).
Jencks (1989) argued that it is only attributes of individuals and not those of their addresses that should matter in measuring the urban underclass. Following this approach, I have utilized the 1970 and 1980 Public-Use Microdata Sample (PUMS) to identify severely distressed households and individuals in America's largest cities.
The 1980 PUMS file identifies metropolitan central cities and provides data for individual housing units and the persons living in them. Both the 5 percent (A file) and the 1 percent (B file) samples are used to obtain a sample of the 100 largest central cities based on their 1980 population. Of these 100 cities, only Amarillo, Corpus Christi, Lincoln, Lubbock, and Montgomery could not be identified. (For these five places, data are not provided for the central cities, only for their metropolitan areas as a whole; therefore, they were removed from the study.) The final sample thus consists of 95 of these cities. All sample counts (that is, households, persons, and children [under 181) are weighted to the population size. This file excludes all persons living in group quarters.'
Five PUMS variables that are consistent with previous indicators are used to define underclass attributes of households and individuals. They are low education, single parenthood, poor work history, public assistance dependency, and poverty. Severely distressed households are those with all five of the following underclass attributes:
Low Education: Both the householder and spouse (if present) did not complete high school.
Single Parenthood: The householder is either single, divorced, widowed, or separated, and young persons under age 18 live in the householder's family.
Poor Work History: Both the householder and spouse (if present) worked less than 26 weeks or usually worked less than 20-hours a week in 1979.
Public Assistance Recipiency: At least one member of the household received public assistance income in 1979.
Poverty: The householder's family income was below poverty in 1979.
It may be argued that the requirement that all five attributes be present for severe distress, while conceptually appropriate, is too restrictive. For example, there may be distressed families or persons in households where children are not present or where the householder or spouse completed high school. For this reason, in the comparative analysis, I identify all households and persons in households with both poor work history and where the householder's family income was below poverty. (Recall that poor work history and poverty are the common threads to virtually all definitions of the urban underclass.)
The first three columns of table 3.1 present, respectively, the number of severely distressed households and numbers of persons and children (under age 18) in these households for the 95 cities that could be identified in the 1980 PUMS files. Columns four and five show the percentage of severely distressed households that are non-Hispanic black and Hispanic, respectively, for each city, whereas columns six through eight describe the percentage of all severely distressed urban households by race/ethnicity found in each city (total households, black households, and Hispanic households, respectively). The cities are ranked by number of severely distressed residents (column 2).
The final row in table 3.1 shows that the 95 cities had a total of 305,480 severely distressed households in 1980. More than 1,250,000 persons resided in these households, including about 809,700 persons under age 18. Eighty-four percent of the households were black or Hispanic. Consistent with findings on the size and growth of extreme poverty tract and underclass area populations, four cities account for a disproportionate number and share of severely distressed households arid residents—New York City, Chicago, Philadelphia, and Detroit.
These four cities contain 45 percent of the total severely distressed households, including 41 percent of the severely distressed black households and 65 percent of the severely distressed Hispanic households (table 3.1). New York City alone accounts for one-quarter of the total distressed households and 55 percent of all distressed Hispanic households (the latter living largely in the Puerto Rican community). When Los Angeles is included along with these four cities, the percentage of severely distressed households increases to nearly one-half of the 95-city total and nearly three-quarters of all severely distressed Hispanic households.
Hispanics comprise over one-half of the severely distressed households in New York City and over 40 percent of those in Los Angeles (table 3.1). In other large cities with more than 5,000 distressed households, blacks constitute the vast majority. Note, as well, that children constitute the majority of persons in the severely distressed households. For example, of the 293,540 persons in severely distressed households in New York City, 188,060 are persons under age 18.
Appendix table 3.A presents the racial and ethnic composition of severely distressed persons in each of the 95 cities. The racial and ethnic percentages vary substantially across cities, with non-Hispanic white proportions greatest in Spokane, Wichita, and Des Moines (no doubt reflecting the low percentages of nonwhites in these cities) and the non-Hispanic black percentage highest in Jackson, Mississippi, at 100 percent. Cities where blacks comprise over 90 percent of the severely distressed residents include Atlanta, Baton Rouge, Birmingham, Fort Lauderdale, Greensboro, Jackson, Memphis, Mobile, New Orleans, Richmond, and Washington, D.C. Hispanics comprise over 80 percent of severely distressed residents in Albuquerque, El Paso, San Antonio, and Santa Ana. In general, only small numbers of Asians, Pacific Islanders, and American Indians are found in severely distressed urban households, constituting but 1 percent of the severely distressed residents in the 95 cities.
It is informative to compare the counts of severely distressed households and residents that are tabulated using the Ricketts-gawhill (1988) census tract measures and those generated for the same cities using individual-level PUMS indicators that are roughly analogous. Ricketts-Sawhill defined underclass areas as those census tracts for which the value of each of four underclass indicators is greater than or equal to one standard deviation above the 1980 mean. These indicators are:
Female-Headed Families: The proportion of families with children under age 18 that are headed by a woman.
Low Education: The proportion of young persons (ages 16 to 19) not enrolled in school and not high school graduates.
Poor Work History: The proportion of males aged 16 and older who worked less than 26 weeks a year.
Public Assistance Recipiency: The proportion of households or families with public assistance income.
The PUMS indicators used for comparison are the same as those used for severely distressed households, excluding poverty. Thus, to be counted as underclass, the household must simultaneously exhibit the following four characteristics: low education, single parenthood, poor work history, and public assistance dependency, as described previously. Table 3.2 illustrates that in the largest cities the RickettsSawhill (1988) underclass measure generates consistently higher numbers of households and persons than the PUMS measure. Differences are greater for household counts than for person counts, with Detroit exhibiting particularly large differentials. Further analysis revealed that the average size of households in cities qualifying as underclass using the PUMS measure was considerably larger than the average household size in the Ricketts-Sawhill underclass areas. The fact that Ricketts-Sawhill counts of households and persons in underclass areas are greater than the PUMS measures may reflect the social and economic heterogeneity of the underclass areas in these cities.
Appendix table 3.B presents the racial/ethnic comparison of underclass population counts using the Ricketts-Sawhill (1988) and PUMS measures for all 95 cities. With a handful of exceptions, the counts by race and ethnicity generated by Ricketts-Sawhill at the tract level and PUMS at the individual level parallel one another. Not all Ricketts-Sawhill counts are greater than the PUMS counts. Substantial differepces are found for Boston, where PUMS generates nearly 20,000 underclass residents compared to less than 10,000 with census tract measures, as well as for Memphis (10,466 Ricketts-Sawhill versus 28,920 PUMS), Pittsburgh (3,194 Ricketts-Sawhill versus 11,400 PUMS), and Washington, D.C. (18,775 Ricketts-Sawhill versus 29,300 PUMS). Observe that differences in the tabulation of blacks account for most of the discrepancies between the Ricketts-Sawhill and PUMS measures.
To assess the dynamics of severely distressed household and population changes in the five largest cities, the county group sample (5 percent data) of the 1970 PUMS is used to facilitate the comparison of severely distressed households between 1970 and 1980. Since in the 1970 PUMS, central cities cannot be identified, central counties are used as a basis for comparison (for New York City and Philadelphia, city and county boundaries are identical). In addition, poor work history is redefined as both the householder and spouse (if present) working less than 27 weeks in 1979, so that this characteristic is comparable between the 1970 and 1980 PUMS.
Table 3.3 presents the change in the numbers of households, persons, and children under age 18 by race for New York, Chicago, Philadelphia, Detroit, and Los Angeles. New York City exhibited by far the largest growth of severely distressed households, persons, and children, with Hispanics constituting over two-thirds of the increase in that city's severely distressed households and persons and over 90 percent of the increase in children under age 18 in severely distressed households. For Chicago, Philadelphia, and Detroit, blacks constituted the vast majority of increase in severely distressed households, persons, and children. Los Angeles experienced relatively little growth in severely distressed households and persons. Indeed, this city had a net decline in children in severely distressed households, due largely to sharp declines in the number of non-Hispanic white households that are severely distressed. Conversely, there was a considerable rise in the number of Hispanic households, persons, and children in severe distress.
These results on the growth of severely distressed households and individuals are consistent with findings on the dynamics of population growth in extreme poverty and underclass areas, as shown in table 3.4. Jargowsky and Bane (1991) found that two-thirds of the growth of population in extreme poverty tracts in major cities was accounted for by New York, Chicago, Philadelphia, and Detroit. Likewise, a number of analyses by Urban Institute researchers have shown that these same four cities contributed the largest percentage of population growth in underclass areas.
Table 3.4 reveals that the number of extreme poverty tracts increased in New York City between 1970 and 1980 from 73 to 311, in Chicago from 47 to 132, in Philadelphia from 23 to 51, and in Detroit from 23 to 45. Underclass tracts (classified by the Ricketts-Sawhill [1988] indicators) increased in even greater proportions, with huge swells in the population in the extreme poverty and underclass areas of these four cities. In New York City, for example, population in extreme poverty areas rose from just under 300,000 to nearly 1 million between 1970 and 1980 (from 3.8 percent of the city's population to 14.1 percent), and its underclass area population rose from about 95,000 in 1970 to about 421,000 in 1980 (from 1.2 percent of the city's population to 6.0 percent).
Los Angeles, on the other hand, exhibited much lower growth in its extreme poverty areas than the four major frostbelt cities (table 3.4). It did add nine underclass area tracts between 1970 and 1980, contributing an additional 58,000 residents to its underclass total. Still, by 1980 only 3.2 percent of Los Angeles's residents were classified as either residing in extreme poverty areas or underclass areas, compared to Detroit, whose underclass population expanded from 2 percent of the city's total population in 1970 to 13.3 percent in 1980.
Differential changes in the size and racial/ethnic mix of a city (or central county) can heavily influence changes in the number and city percentage of severely distressed households tabulated for each racial/ ethnic subgroup. New York City, for instance, declined by 823,000 residents between 1970 and 1980, but actually lost 1.4 million non-Hispanic whites while adding 176,000 blacks, 204,000 Hispanics, and 190,000 Asians and others. Los Angeles City alone added nearly 400,000 Hispanics during the decade. It is therefore important to control for racial/ethnic change when assessing differential growth rates of severely distressed residents by race and ethnicity. Table 3.5 does this by presenting for the five central counties the percentage of members of each racial/ethnic group that had (1) no distress attributes, (2) poor work history and poverty, and (3) all five distress attributes (severely distressed) in 1970 and in 1980.
For all five central counties, the percentage of total population with no distress attributes declined (table 3.5). This was largely due to a shift in the demographic composition of the cities to those racial/ ethnic subgroups that had smaller proportions of their membership with no distress attributes. In three of the four northern cities, the "other" (predominantly Asian) subgroup showed rising proportions with no distress attributes, whereas the fourth city (Philadelphia) exhibited a decrease. With the exception of New York City, the percentage of blacks with no distress attributes in the largest northern cities declined between 1970 and 1980. The percentage of whites with no distress attributes remained nearly constant in New York and Philadelphia and rose slightly in Chicago and Detroit. In Los Angeles, the proportion of Hispanics and Asians (other) with no distress attributes declined while for non-Hispanic whites and blacks the proportions were fairly stable.
The second set of percentages in table 3.5 describes the change in distribution between 1970 and 1980 of residents by race/ethnicity in each city who had both a poor work history and were in poverty households. All cities exhibited rises in the percentage of persons in households with poor work history and poverty during this period, with sharp increases in all four northern cities. Most of this rise in the northern cities can be accounted for by substantial increases in black and Hispanic households with these characteristics.
In 1980, almost 30 percent of the Hispanic residents in New York City were in households with a poor work history and poverty, and 38 percent of Hispanic residents in Philadelphia lived in such households (table 3.5). Note that there was little increase in the percentages of Los Angeles's Hispanic and black populations residing in households with poor work history and poverty. Los Angeles experienced a decline in the proportion of its white households with these characteristics, while the percentages of this city's black and Hispanic residents who resided in households with poor work history and poverty were stable between 1970 and 1980. The percentage of Los Angeles's Asian (other) residents in households with poor work history and in poverty rose slightly.
The third set of columns in table 3.5 provides the percentage of city residents in each racial and ethnic group who reside in households with all five distress attributes (denoted earlier as the severely distressed). These percentages rose for the total population and for white, black, and Hispanic subgroups within the four northern cities. In Los Angeles, which also had the smallest proportion of residents in households with all five distress attributes in 1970, only the percentage of its Hispanic residents with all five distress attributes increased.
Table 3.6 provides data on the absolute change, percentage change, and percentage distribution shift of residents residing in households with poor work history and poverty between 1970 and 1980 for the five central counties. Despite losing over 800,000 residents during the 1970s, New York City actually added 266,800 people in households with both poor work history and poverty. Almost all of this increase was due to substantial increments in blacks and Hispanics who were poor and not working regularly. This trend is mirrored in the three other large central metropolitan counties in the North. Chicago had a 63 percent increase in the number of blacks who were in households with poor work history and poverty and a more than 200 percent increase in Hispanic residents in such households. Detroit also experienced a substantial percentage increase (71 percent) in its black population residing in households with these characteristics.
The third row of figures in table 3.6 for each city (which also can be calculated from table 3.5) reveals the substantial incremental shift during the 1970s in the percentage of black and Hispanic residents living in households with both poor work history and poverty in the major northern cities. Philadelphia also experienced a large upward shift in its Asian population who were in households with both poor work history and in poverty. Los Angeles, on the other hand, experienced virtually no increase in its minority proportions who were in such households.
In examining the hypothesized causes of the differential rise of joblessness, poverty, and related problems of the inner city, much attention has been given to changing urban labor markets. Efforts to model and assess the implications of these labor market changes have taken both labor demand and labor supply approaches. Moss and Tilly (1991) have provided a thorough literature review and appraisal of the evidence and issues raised by these studies; I only note the highlights here, emphasizing changing labor demands.
The basic argument on the demand side is that there has been a dramatic decline in the demand for lower-skilled labor in particular industries, occupations, and locations. As a result, both skill and spatial mismatches have emerged between urban resident groups and job opportunity structures. Blacks and Hispanics have educational distributions skewed toward the bottom end, whereas demand for poorly educated residents has been slackening in recent decades. These minorities have very low proportions who have the higher educations that are increasingly required for employment in new urban growth industries. Not only have industries with higher education hiring requirements been replacing those with low education requirements in the central cities, but even within traditional urban industries there has been a shift away from the less educated (Katz and Murphy 1990). As a result, jobless rates of poorly educated inner-city residents have skyrocketed since 1970, regardless of race (Kasarda 1989, 1990).
At the same time that poorly educated people have remained confined in the central city, jobs appropriate to their skill levels have dispersed to the suburbs. Farley (1987), Hughes (1989), Price and Mills (1985), Vrooman and Greenfield (1980), and Welch (1990) document the negative earnings and employment consequences that lower-skilled job dispersion has had for inner-city minorities.
Wilson (1987) provided a cogent description of the impact of urban economic transformations on black male joblessness and its relationship to a range of other problems that characterize the ghetto poor. He focused on the loss of manufacturing and other low-skill jobs from the central cities and the resulting economic and social dislocations. Modeling the precise effects of urban industrial shifts during the 1970s and 1980s on wages and employment of black and white males, Bound and Holzer (1991) found that one-third to one-half of the employment decline of less-educated young blacks in the 1970s can be accounted for by these shifts, especially manufacturing job loss.
As observed earlier in this chapter, four cities (New York, Chicago, Detroit, and Philadelphia) had particularly large increases in their severely distressed populations and residents of extreme poverty tracts and underclass areas between 1970 and 1980. This increase is consistent with prior studies cited (Ricketts and Sawhill 1988; Bane and Jargowsky 1988) that showed that the bulk of increases in extreme poverty and underclass area populations were in the largest industrial cities of the Northeast and Midwest. In this section, I examine industrial transformations and other economic base changes in these cities and compare them to changes occurring in four of the largest cities in the South and West (Atlanta, Dallas, Los Angeles, and Phoenix).
Table 3.7 provides an overall comparison of change in economic activity within the four frostbelt and four sunbelt cities (central counties) between 1970 and 1980 and between 1980 and 1987, based on changes in jobholder earnings (in constant dollars) by place of work. It should be reiterated that these data are for central counties that—as in the case of Chicago (Cook County)—are substantially larger than the central cities. Nevertheless, the devastation of the economies of the major northern cities during the 1970s and (with the exception of Detroit) their recovery in the 1980s may be observed in the top panel of table 3.7. Earnings in New York City alone declined by nearly $12 billion between 1970 and 1980 and rose by nearly $22 billion during the 1980-87 upswing in that city's economy. The four sunbelt cities did not experience these dramatic swings in economic activity, with fairly stable growth throughout the 1970s and 1980s. Note, in particular, the significant percentage growths in their economies during the 1970s.
Table 3.8 illustrates how the changes in jobholders' total earnings were distributed among eight industrial sectors. Manufacturing earnings were particularly hard hit in the four largest northern central counties during the 1970s and continued to spiral downward during the 1980s. Construction earnings and retail trade earnings were also substantially diminished between 1970 and 1980 in the northern cities. Only service sector earnings were robust in these cities during the 1970s, led by substantial increases in earnings in business, legal, and health services. The fiscal crisis of New York City may also be observed with its nearly $2 billion drop in earnings in government between 1970 and 1980.
The recovery of the economies of major cities of the North in the 1980s was led by strong growth in their financial, real estate, and service sectors. New York City alone experienced a $19 billion increase in earnings of workers in finance, insurance, real estate, and other service industries (table 3.8).
The four sunbelt cities were resilient in their earnings across industries during the 1970s. Los Angeles, especially, experienced powerful growth in manufacturing earnings between 1970 and 1980, in contrast to the cities of the North. Table 3.9 further highlights the robustness of the sunbelt cities compared to the frostbelt cities by presenting percentage changes in earnings for the central counties by industry. Whereas New York City and Philadelphia each lost approximately 40 percent of their jobholder earnings base in manufacturing industries during the 1970s, jobholder earnings in these same industries increased by 33 percent in Dallas and 62 percent in Phoenix. Clearly, there were sharp contrasts between the economies of the frost-belt and sunbelt cities throughout the 1970s, when severely distressed populations differentially expanded in the frostbelt cities.
Table 3.10 depicts the changing industrial composition of the cities, as evidenced by the percentage distributions of workers' earnings by industry, for 1970, 1980, and 1987. Whereas manufacturing and trade showed considerable proportional declines in frostbelt cities, finance, insurance, and real estate (F.I.R.E.) and services expanded. By 1987, 54 percent of all earnings in New York City were accounted for by jobholders in the F.I.R.E. and service sectors. In Philadelphia, 43 percent of all earnings were in these two predominantly white-collar sectors. In contrast, declines in manufacturing earnings proportions did not tend to be as large in the sunbelt cities and the overall transition of these cities to service sector industries not so marked. That is, by 1987 none of the four sunbelt cities had 40 percent or more of their earnings accounted for by jobholders in the F.I.R.E. and service sectors.
Table 3.11, parts A and B (the frostbelt and sunbelt cities, respectively) highlight the industry-selective nature of employment change in the central cities and the suburban rings of the four frostbelt and four sunbelt metropolitan areas for the periods 1967-77 and 1977-87. Between 1967 and 1987, Chicago lost 60 percent of its manufacturing jobs, Detroit 51 percent, New York City 58 percent, and Philadelphia 64 percent. In absolute numbers, New York City's manufacturing employment declined by 520,000 jobs and Chicago's by 226,000 jobs. During this same pericd, New York City added over 110,000 manufacturing jobs in suburban rings and Chicago 34,000, all between 1967 and 1977. Retail trade was also hard hit in northern cities between 1967 and 1977 and only modestly recovered in the following 10 years. The big burst of employment in the northern cities was in their service sectors, with New York City adding 286,000 such jobs between 1977 and 1987. (Some of this increase is artificial, owing to definitional changes in the industries included in the Census of Selected Services for those two dates.)
Suburban ring employment growth in northern metropolitan areas was very strong, with the exception of manufacturing in Detroit and Philadelphia. Trade and services in the suburbs added hundreds of thousands of jobs in each of the four northern major metropolitan areas.
Miming to the sunbelt cities, table 3.11, part B, reveals that their central cities did not suffer nearly so severely with manufacturing and trade job losses during the 1967-77 period; in fact, all four cities added manufacturing jobs between 1977 and 1987, along with experiencing growth in their trade and service sectors. In Dallas-Fort Worth, Los Angeles, and Phoenix, central-city growth in trade and services was substantial. The suburban rings of all sunbelt cities continued to blossom, with accelerated job growth in the 1980s compared to the 1970s. The main difference between frostbelt and sunbelt metropolitan areas in terms of employment change, then, involves the dramatically divergent employment opportunities in the central cities, especially in manufacturing.
I now turn to characteristics of the subareas of the eight central cities. Selected data are presented for these cities' low poverty areas (where less than 20 percent of the households fall below poverty) as contrasted with areas within each city where 20 percent or more and 40 percent or more of the households fall below poverty. Table 3.12, part A, illustrates subarea characteristics within the four northern cities; part B presents the same data for the four sunbelt cities. Observe in part A the sharp declines in the ratios of employed persons aged 16 and older per 100 persons in the subareas who are unemployed or out of the labor force (OLF, over age 16) as one moves from nonpoverty subareas to increasingly intense poverty subareas. Note also the high percentage of poverty-area workers who were still employed in manufacturing as late as 1980. Of those jobholders in extreme poverty areas, 27 percent were employed in manufacturing in both Chicago and Detroit, 24 percent in New York City, and 22 percent in Philadelphia. Note that the percentage residing in extreme poverty areas who are employed in manufacturing is greater than the percentage both for the city total and in nonpoverty areas of cities for all northern cities with the exception of Detroit, which is approximately equal. Two other important factors should be noted: the high percentage of persons in the poverty areas who have not completed high school and the similarly large percentage who reside in households with no vehicle available. As jobs with lower education requirements have dispersed from the central cities to the suburbs, many of these people have been left behind without transportation to these jobs.
Although poverty and extreme poverty areas are not as substantial in the sunbelt cities, table 3.12, part B, shows that many of their residents experience somewhat similar plights. Poverty areas in these cities do, however, tend to have lower percentages of workers employed in manufacturing and higher percentages who have worked in the previous year. They also tend to have more households with automobiles available.
The importance of skills mismatches and, to a lesser extent, spatial mismatches in major cities of the Northeast and Midwest is highlighted in tables 3.13 and 3.14. Table 3.13 presents changes between 1970 and 1980 in the number of jobs held in the four largest northern cities in terms of the actual educational level of city jobholders. Most striking is the fact that northern cities experienced not only substantial declines in jobs held by those who did not complete high school but also considerable declines in jobs held by those with only a high school degree. At the same time, the number of city jobs held by those with higher education mushroomed.
The four cities that accounted for the lion's share of the increases in concentrated poverty populations during the 1970s (New York, Chicago, Detroit, and Philadelphia) also experienced the lion's share of declines in jobs held by high school dropouts and by those with only high school degrees. In 1980 there were 496,229 fewer workers in New York City who did not have a high school degree than there were in 1970 (table 3.13). The number of workers holding only a high school degree (12 years of schooling completed) also decreased by 99,874 during the decade, while the number of city jobholders with some college attendance or a college degree increased by 164,640 and 307,201, respectively. Similar patterns were exhibited by the other large northern cities, with major increases in jobs held by those with education beyond high school and even greater declines in employment by those with a high school degree or less.
Portions of the decrease in city jobs occupied by those without high school degrees and growth in number of jobs held by those with higher education reflect improvements in the overall educational attainment of the city labor force, including blacks, during the 1970s. These improveinents, however, were not nearly so great as the concurrent upward shifts in the educational levels of city jobholders. As a result, much of the job increase in the "some college" or "college-graduate" categories for each city was absorbed by suburban commuters, while many job losses in the "less-than-high-school-completed" or "highschool-only" categories were absorbed by city residents. Moreover, general improvements in city residents' educational levels meant that less-educated jobless blacks fell further behind in the hiring queue (Lieberson 1980). Particularly affected were those large numbers of urban blacks who had not completed high school, especially younger ones. For city black youth, school dropout rates ranged from 30 percent to 50 percent during the 1970s and early 1980s, with case studies of underclass neighborhoods and schools suggesting even higher dropout rates among the most impoverished (Hess 1986; Kornblum 1985).
Table 3.13 also illustrates the structural dilemma facing sizable portions of the black urban labor force. It compares the 1980 educational distributions of those employed by city industries, including the self-employed, with the educational distributions of all out-of-school black males aged 16-64 and out-of-school black males aged 16-64 who are not working. The educational disparities between black residents and jobs are dramatic. Despite educational gains, black urban labor remains highly concentrated in the education category where city employment has rapidly declined since 1970—the category in which people have not completed high school—and is greatly underrepresented in the educational attainment categories where city employment is rapidly rising, especially the category of "college graduate." As late as 1980, the modal education-completed category for out-ofschool black male residents in the four cities was less than 12 years.
Those out-of-school black males who are jobless display an even more mismatched educational distribution in relation to the city job structures. For example, in Philadelphia, whereas 44.5 percent of all black males who were out of school had fewer than 12 years of education in 1980, 60.1 percent of black males out of school and jobless had not completed high school (table 3.13). More than 50 percent of jobless black males in all cities had completed less than 12'years of schooling. Comparing these figures with the percentage of city jobs filled in 1980 by those with less than a high school degree and with changes in city jobs occupied by the poorly educated between 1970 and 1980 reveals the substantial educational disparity faced by urban blacks in general and blacks not at work in particular.
This educational disparity between city jobs and black residents poses a serious structural impediment to major improvements in urban black employment prospects. It may be that any individual black male who has not completed high school can secure employment in the city—some vacancies almost always exist, even in declining employment sectors. But, given the demographic-employment distributions shown, if large portions of out-of-work urban blacks all sought the jobs available, they would simply overwhelm vacancies at the lower end of the educational continuum.
The structural mismatch between city jobs and black labor that is displayed at the higher-education end of the continuum helps explain why policies based primarily on urban economic development have had limited success in reducing urban black joblessness. Most blacks simply lack the education that would enable them to participate in the new growth sectors of the urban economy. Whereas city jobs taken by college graduates have skyrocketed, the percentage of urban black males who have completed college remains extremely small. For those who are out of work, the disparity at the higher-education end is even greater.
If a skills mismatch is at the heart of the joblessness problem, we should see corresponding rises in the unemployment rates of poorly educated white city residents as well as blacks over the last decades.
Table 3.14 documents that this is indeed the case. The jobless rates by education levels of out-of-school black and white males (aged 16-64) for the largest central cities and suburban rings in the Northeast and Midwest were constructed by pooling their data within four separate three-year time periods between 1968 and 1988. Pooling of the within-region city and suburban data for three-year time intervals (1968-70, 1976-78, 1980-82, and 1986-88) enabled the generation of sufficient sample sizes to obtain more reliable estimates of jobless rates by race, education, and intrametropolitan residential location.
Results show that for every time period and residential location, blacks have higher rates of joblessness than whites. This supports long-standing arguments that race is a critical variable in accounting for joblessness (Ellwood 1986; Holzer 1989). Results also show that, for both whites and blacks, there is a strong relationship during each time period between education completed and joblessness. For the 1986-88 period, the lowest percentage of central-city residents not working who had not completed 12 years of education was 35.5 for whites in the Northeast, and the lowest percentage for suburban-ring residents was 24.7 for whites in the Midwest (table 3.14). This confirms the instrumental role of human capital factors in metropolitan employment and the serious handicap of a limited education, regardless of race and residential location.
Apropos the skills mismatch thesis, jobless rates of central-city whites who have not completed high school have monotonically risen during the 1968-88 pooled time periods (table 3.14). In fact, increases in white male joblessness since 1976 among the least educated' are greater than those for their black male counterparts in both northeastern and midwestern central cities. These results were replicated for non-Hispanic whites, with sharp increases in city jobless rates of those without a high-school degree during the past decade. The post-1982 economic recovery experienced by most of the cities in table 3.8 thus bypassed both poorly educated blacks and whites, lending empirical credence to the skills mismatch argument.
Apropos the spatial mismatch thesis, note that jobless rates for white and black males who did not complete 12 years of education and who resided in the suburban rings declined after 1982, although only among blacks are the declines substantial (from 40.7 to 32.5 between the 1980-82 and 1986-88 intervals—table 3.14). During the same period, jobless rates for the least-education black males in mid-western suburbs declined from 42.2 to 30.5.
Whereas suburban residential selectivity may account for some of the decline in joblessness among poorly educated suburban black males during the 1980s, such declines are consistent with the contention that less-skilled blacks have better employment prospects in the suburbs than in the central cities. In this regard, not only are jobless rates among the least-educated blacks and whites consistently lower in the suburban rings than in the central cities, but also the absolute gap in the percentage of jobless people between the least educated central-city residents has widened during the past two decades. Since residential segregation in these suburbs tends to be nearly as great as in the central cities, especially for the least educated blacks, segregation per se would not appear to be the pivotal factor explaining city-suburban jobless differences (Jencks and Mayer 1990; Massey and Denton 1987). Growth in low-skill job opportunities in closer proximity to poorly educated black suburban residents would seem to be a more plausible explanation of the somewhat lower suburban rates.
This relative accessibility explanation is supported by comparative journey-to-work times of poorly educated black and white jobholders residing and working in the central cities and suburbs of major metropolitan areas in 1980. Table 3.15 shows these commuting times, by means of transport, for black and white workers without a high school degree for three residence-workplace categories—live in the city and work in the city; live in the suburbs and work in the suburbs; and commuters between cities and suburbs. (Commuting times are shown for the largest metropolitan area in each of the four census regions for which suburban boundaries could be determined from the PUMS.)
Poorly educated black workers who live and work in the city or who commute across city-suburb boundaries have consistently higher one-way journey-to-work times than those who live and work in the suburbs, regardless of means of transport. In the northern metropolitan areas of Chicago and Philadelphia, the accessibility advantage of poorly educated blacks living and working in the suburbs is particularly striking, compared to living and working in the city.
Table 3.15 also highlights the greater accessibility poorly educated whites have to their workplaces compared to poorly educated blacks. For almost all residence-workplace categories across the four metropolitan areas, blacks have longer journey-to-work times than whites. Again, the black accessibility disadvantage is especially acute in the two major northern metro areas.
Today's inner-city economies may be considered to comprise three components: (1) the mainstream economy, consisting of traditional and newer-employing institutions ranging from manufacturing and trade to the full complement of blue- and white-collar service industries; (2) the underground economy, composed of drug trade, prostitution, and other illicit activities; and (3) the welfare economy, based on a variety of cash and in-kind public assistance transfers.
Within the mainstream economy, the functional transformation of cities from centers of goods processing to centers of information processing and the suburbanization of lower-skill jobs resulted in both skill and spatial mismatches for large numbers of inner-city minorities lacking the education to take advantage of new urban growth industries or unable to relocate near appropriate suburban jobs. These minorities, especially blacks and certain Hispanic groups such as Puerto Ricans, also appear to lack the social networks and familial and economic solidarity to overcome their structurally disadvantaged positions in transforming urban economies. As a result, with the deterioration of their traditional blue-collar employing bases in the inner cities, they have increasingly relied on the two surrogate economies (the underground and the welfare economies) to stay afloat.
Given rising formal-sector skills limiting their employment in new growth sectors as well as low hourly wages for jobs for which they are qualified, many of the disadvantaged see themselves better off in the underground economy where incomes are actually or perceived to be higher. The underground economy also provides substantially more temporal flexibility and personal autonomy than working in mainstream institutions. This may be particularly important to lifestyle choices of teenagers and young adults.
Partly in response to, but also reenforcing, inner-city joblessness and poverty were certain public assistance programs introduced in the 1970s. These programs were guided by the reasonable principle that public assistance should be targeted to areas where the needs are the greatest as measured by such factors as job loss, poverty rate, and persistence of unemployment. The idea was that the most distressed areas should receive the largest allocation of government funds for subsistence and local support services for the economically displaced. Although these policies unquestionably helped relieve pressing problems such as the inability of the unemployed to afford private-sector housing or to obtain adequate nutrition and health care, they did nothing to reduce the skills or spatial mismatch between the resident labor force and available urban jobs. In fact, spatially concentrated assistance may have inadvertently increased the mismatch and the plight of educationally disadvantaged residents by binding them to inner-city areas of severe blue-collar job decline and to areas that, by program definition, are the most distressed.
For those individuals with some resources and for the fortunate proportion whose efforts to break the bonds of poverty succeed, spatially concentrated public assistance will not impede their mobility. But for many inner-city poor without skills and few economic options, local concentrations of public assistance and community services can be "sticking" forces. Given the lack of skills of these individuals, the opportunity cost of giving up their in-place assistance if they were to move would be too high. They may see themselves as better off with their marginal but secure in-place government assistance than taking a chance and moving in search of a minimum wage, entry-level job, often in an unknown environment.
The spatial confluence of blue-collar job decline in the 1970s and 1980s with rising illicit activities and welfare dependency in the inner cities has generated a powerful spatial interaction of the three. Associated with this interaction, a plethora of concentrated social problems has further aggravated the predicament of people and neighborhoods in distress, such as high rates of family dissolution, out-ofwedlock births, school dropout, joblessness, and violent crime. Negative stereotyping and distancing by outsiders (often with racial connotations) has resulted in further spatial and social isolation of the severely, distressed from mainstream institutions, magnifiying their dilemma.
No straightforward policy prescriptions exist for ameliorating this predicament. In previous papers (Kasarda 1985, 1989, 1990), I have outlined a variety of proposals that, in combination, would help to reduce the skill and spatial mismatches documented in this chapter. These include (1) educational upgrading and vocational training programs in the inner city; (2) computerized job-opportunity information networks; (3) partial underwriting of more distant job searches by the ghetto unemployed; (4) tax incentives to promote affordable housing construction in the suburbs by the private sector; (5) need-based temporary relocation assistance for ghetto unemployed once a job has been secured; (6) housing vouchers as opposed to additional spatially fixed public housing complexes in the inner city; (7) stricter enforcement of fair housing and fair hiring laws; (8) public-private cooperative efforts to van-pool unemployed inner-city residents to suburban businesses facing labor shortages; and (9) a thorough review of all spatially targeted low-income public assistance programs to ensure that they are not inadvertently anchoring the ghetto poor in areas where there are few prospects for permanent or meaningful employment.
I am not as sanguine as I once was of the prospects of such policies significantly reducing inner-city joblessness and revitalizing the ghettos. I am far less sanguine about the prospects for more radical prescriptions—such as a Marshall Plan to rebuild the ghettos—since political and economic realities, in all likelihood, preclude such massive government intervention. Simply put, the will of most Americans for such major intervention does not exist.
I am further tempered by the limits of government to affect what many believe to be at the root of the urban underclass dilemma—attitudes, of those outside as well as within the ghettos. Government cannot legislate away discriminatory stereotyping and other racist views held by many outsiders. Positive attitudinal changes will likely occur only as outsiders come to believe through actions of the ghetto poor that, despite their social and economic disadvantages, they subscribe to traditional American values of strong families, individual initiative, self-sufficiency, responsibility, discipline, and normative order, and that they eschew self-destructive behaviors.
Government cannot weave such values back into ghetto communities nor dictate behavior of their residents. Almost by definition, the moral authority for these tasks must come from within the community, from charismatic leadership and voluntary and religious organizations like the traditional black Protestant church or the Nation of Islam, with its emphasis on the family, disciplined economic self-support, and antidrug, anticrime programs. In the end, restoring strong internal normative order, responsibility, family cohesiveness, and racial/ethnic economic solidarity may be the only real option for the ghetto poor to break out of the cycle of poverty, revitalize their neighborhoods, and enjoy the benefits heretofore restricted to the social and economic mainstream.
The Severely Distressed in Economically Transforming Cities 95
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Our valuable member John D. Kasarda has been with us since Monday, 10 June 2013.