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PART II BACKGRO~D PAPERS
Income, Opportunities, and the Quality of Life of Urban Residents MARK C. BERGER and GLENN C. BLOMQUIST This paper reports on the economic well-being of urban resi- dents, using estimates of quality of life as well as traditional mea- sures. Traditional measures include household income, the poverty rate, and the unemployment rate, which are reported for residents of central cities, suburbs, small metropolitan areas, and rural areas. These measures are also disaggregated by demographic group for each residential category. Earnings differences across individuals are explained by observable differences in workers, jobs, and locations. Location-specific amenities are shown to give rise to compensating differences in wages and housing prices. Estimating values for such amenities permits comparisons of the quality of life across areas and the augmentation of traditional measures of well-being. Estimates are based on public-use microdata from the 1980 Census of Popula- tion and Housing. CITIES AND ECONOMIC WELL-BEING Cities are monuments to the possibilities of civilized cooperation. The benefits that can be realized by common use of sizable produc- tion resources and synergistic interactions are a powerful force that The authors gratefully acknowledge the helpful comments of John Weicher on an earlier draft of this paper. 67
68 Mark C. Burger and Glenn C. Blom~ui~t draws people together (Milis and Hamilton, 1984:Ch. 1~. The stan- dard of living in the United States Is due in part to the clustering of economic activity. Workers and residents in cities share in these benefits. Nevertheless, there is concern about the economic status of people who live in cities (Tolley et al., 1979~. The concentration of poverty in ghettos and the haunting appearance of abandoned facto- ries are particularly striking. To provide some empirical evidence on the advantages and disadvantages of city life, this study focuses on the well-being of people who work and live in cities, compared with people outside of cities. This paper reviews what ~ known about the economic status of residents of large central cities compared with residents of sum urbs, small metropolitan areas, and rural areas. An ideal measure of economic status would take into account several factors: the future, distinguishing between permanent and temporary situations; the ac- tual decision-making unit, whether independent individuals or close- knit groups; the full resources available, recognizing transactions in kind; the cost of living; and the amenities available, incorporating quality-of-life values (Danziger et al., 1981~. In the absence of an ideal measure, we use a set of measures of economic status to reflect the urban situation. Measures of welI-being for metropolitan areas with populations exceeding 1.5 million are computed from the public-use microdata of the 1980 Census of Population and Housing. Comparisons are made across and within metropolitan areas and across demographic groups by type of area. Emphasis is given to annual money income. A hedon- ic framework of wage determination is offered as an explanation for differences in labor earnings, which account for 70 percent of total national income (Bureau of the Census, 1984:Table 728~. Earnings differences can be attributed to observable differences in the charac- teristics of workers and jobs. Earnings differences also arise because of differences in the amenities available in the area in which the job is located. When these premiums from the labor market are com- bined with the compensation reflected elsewhere, we can estimate differences in the quality of life in various locations. Quality-o£life differences are then used to augment income differences to provide a better measure of differences in the well-being of urban residents.
INCOME, OPPORTUNITIES, AND THE QUALITY OF LIFE TRADITIONAL MEASURES OF WELI~BEING 69 This section provides an overview of some traditional measures of well-being: household income, the poverty rate, the unemployment and employment rates, the manufacturing employment share, and individual income and annual hours worked. These summary mea- sures are all computed from the 1980 Census I-in-l,000 Public Use A Sample. In Tables 1-5, the measures are presented by metropolitan area, location of residence within metropolitan areas, region, family composition, race, and age. Traditional measures of wed-being are useful for describing ur- ban conditions. Household income indicates the amount that can be spent on food, housing, and other categories of consumption. The poverty rate indicates the relative size of the group of people whose money incomes are not adequate to meet basic consumption requirements.' The unemployment rate shows the relative size of the group of people who are not earning income but are looking for work. The employment rate gives the relative size of the group of people who are working. The manufacturing employment share shows the relative size of the local economic base composed of traditional in- dustry. Urban residents are usually considered to be better off when their incomes ~d local employment rates are higher and poverty and unemployment rates are lower. In the past, a high share of manu- facturing employment was considered a good sign, but recent shifts in the economic structure away from manufacturing and toward the service and information sectors have had a negative effect on urban economies based on manufacturing. Large Metropolitan Areas Part A of Table 1 shows traditional measures for the 26 metropoli- tan areas in the United States with populations of 1.5 million or more, according to the 1980 Census. Part B gives summary statistics and correlation coefficients among the various measures. It is apparent iFamilies and unrelated individuals are classified as being above or below the proverty level using an index developed by the Social Security Administra- tion in 1964 and revised by federal interagency committees in 1969 and 1980. The poverty index is based on money income and does not take into account noncash benefits such as food stamps and public housing. The povery thresh- olds are revised annually to reBect the change in the consumer price index. The average poverty threshold for a family of four was $7,412 in 1979.
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72 Mark C. Berger and Glenn C. Blom~uiat from the summary statistics that the measures vary widely across metropolitan areas.2 Somewhat surprisingly, population size is not highly correlated with any of the measures of economic status. Al- though there are several significant correlations among household income, the poverty rate, the unemployment rate, and the employ- ment rate, the poverty rate-unemployment rate correlation is not among them. Metropolitan areas with high unemployment rates do not necessarily have high poverty rates. The unemployment rate, however, is significantly correlated with the manufacturing employ- ment share. This probably reflects the Tong-term structural shift away from goods-producing jobs and the resulting displacement of workers. Central-City, Suburban, Small Metropolitan, and Rural Areas Table 2 presents the measures of economic status for households and persons in and out of metropolitan areas for the entire United States and for the four main Census Bureau regions. Residents of metropolitan areas are broken down further into three groups: residents living in the central city of large (greater than 1.5 million persons) metropolitan areas; those living in the surrounding suburbs; and residents of small (less than 1.5 million persons) metropolitan ar- eas. Looking at averages for the entire United States, nonmetropoli- tan residents have the lowest incomes and employment rate of the four groups, whereas central-city residents of large metropolitan ar- eas have the lowest manufacturing employment share and the highest unemployment and poverty rates. In contrast, suburban residents of large metropolitan areas have the highest household incomes, em- ployment rate, and manufacturing employment share, as well as the lowest poverty and unemployment rates. 2 The household income figures reported in Table 1 are not adjusted for differences in the cost of living because of problems in constructing an acceptable index. Consumer price indexes (CPIs) are reported for 22 of the 26 areas by the Bureau of the Census (1984), and household income can be debated by multiplying it by the average CPI for all areas and dividing by the CPI for the area in question. The cost-of-living factors range from 0.925 for Houston to 1.025 for Atlanta. The correlation between household income and deflated household income is 0.95. However, the CPIs by city are only appropriate for comparisons over time within cities and not across cities at a point in time.
INCOME, OPPORTUNITIES, AND THE QUALIlrY OF LIFE 73 In all four regions, suburban residents are more affluent ac- cording to these traditional measures. Yet the lowest incomes and employment rates and the highest unemployment and poverty rates vary from region to region. In the Northeast and Midwest, central- city residents of large metropolitan areas are the poorest, whereas in the South and West the poorest individuals are those living outside metropolitan areas. Residential Area, Imply, and Race Table 3 gives average household incomes and poverty rates in the different residential locations by faintly composition and race. In every case, suburban residents again have the highest incomes and lowest poverty rates. Nonmetropolitan residents have the lowest in- comes and, except for households headed by white females, they also have the highest poverty rates. Married couples with children have somewhat higher incomes than their counterparts without children, but they also have higher poverty rates. Income levels are substan- tially lower and poverty rates higher for female householders with children than for married couples with children. For perspective, however, it should be noted that there are more than six times as many white married-couple households with children than female- headed households with children. Among blacks the ratio is more than four to one. Summary measures of econorn~c status by race and location of residence are shown in Table 4. White household incomes and em- ployment rates are higher and unemployment and poverty rates lower than those of blacks, regardless of location of residence. In virtually every case the measures for Hispanics fall somewhere between those for blacks and whites. Residential Area, Age, Earninge, and Transfers In Table 5, household income and poverty rates are given by age of the householder and location of residence. Among 25- to 39-year- old householders, central-city residents have the lowest incomes and highest poverty rates. For householders aged 40 and over, it is rural residents who are the least affluent. Again, suburban residents have higher incomes and lower poverty rates than other groups. There does appear to be some tendency toward higher poverty rates and lower incomes among the elderly, but this is not a universal trend.
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INCOME, OPPORTUNITIES, AND THE QUALITY OF LIFE 81 Among central-city residents the poverty rate is higher for 25- to 3~year-old householders than for 65- to 71-year-old householders. The rest of Table 5 Is devoted to a breakdown of individual income into earnings, transfers, and other income, along with in- formation on annual hours worked. In general, suburbanites have the highest earnings (annual and hourly), other income, and hours worked, whereas transfer income appears to be greatest in the central city. To the extent that, nationally, the poverty rate is highest for central-city residents, there is some evidence that transfer payments are going to those who need them most. As individuals age, it is apparent that transfers and other income partly replace earnings. Among 25- to 39-year-old central-city residents, earnings are 93 per- cent of total income. This percentage drops to 85 percent for 4~ to 64-year-olds, 35 percent for 6~ to 71-year-olds, and 16 percent for those aged 72 and older. Tables 1-5 present traditional measures of economic well-being by metropolitan area, location of residence, region, family compo- sition, race, and age. By looking at several traditional measures and disaggregating them in various ways, it is possible to obtain an overview of the economic weR-being of urban residents. Yet, the traditional measures ignore quality-of-life factors, which can be important components in well-be~ng. As a first step toward incor- porating quality of life into the analysis, the next section develops a framework for explaining earnings differences among urban residents. With this framework, and the estimates that can be obtained, it is also possible to examine the specific factors that contribute to earn- ings and income differences across individuals. The framework and estimates further our ability to explain differences in the economic status of different groups in the urban population. EARNINGS DETERMINATION Ermnework Individuals earn different amounts in the labor market for a variety of reasons. Perhaps the most important cause of observed earnings differences is differences in skills or training. When individ- uals invest in themselves to enhance their future earnings, they are investing in human capital. These investments may take the form of formal schooling, on-thejob training, job searches, or even diet and exercise to improve or maintain health (Becker, 1975; Mincer, 1974~.
82 Mark C. Burger and Glenn C. Blom~ist Controlling for other factors, individuals with greater investments in human capital should have higher earnings. Those with more school- ing or job experience, for example, should earn more than those with less. Even among individuals with the same investments in human capital, however, earnings may differ. For example, employers may find it necessary to pay workers a premium in dangerous or unpleas- ant jobs. These premiums are compensating wage differentials. They exist because jobs have different sets of characteristics, some of which workers find more valuable than others. Workers pay for pleasant job characteristics, such as flexible hours, and receive premiums for unpleasant ones. The magnitude of observed earnings differences be- cause of compensating wage differentials is determined by the tastes of workers, their ability to move from one job to another, and the range of job characteristics offered by employers in the labor market (R. Smith, 1979~. In addition to the characteristics of a job, characteristics associ- ated with the area of a worker's residence may produce compensating wage differentials if enough workers are mobile across areas. Exam- ples of these types of quality-of-life factors are crime, air quality, and climate. If it were assumed that compensation for these amenities and disamenities takes place only in the labor market, then workers in desirable areas would pay for their quality of life through lower earnings. How much compensation of this type occurs in the labor market is determined by the distribution of quality-o£life factors across areas, the tastes and mobility of workers, and the existence of other markets for which compensation may occur (V. Smith, 1983~. Earnings differences may also be caused by other factors. For example, an observed difference in the earnings of two groups may be attributable in part to discrimination in the labor market instead of being explained fully by differences in investment in human capital or other factors. Earnings may differ across jobs because of union- ization, which may alter the workings of the market. Variations in earnings may exist across geographic areas because of cost-of-living differences. These other factors must also be kept in mind when interpreting differences in earnings across the population. Empirical Mode] and Resulte In this section, we estimate a regression mode} that explains aver- age hourly earnings as a function of a number of variables designed to
INCOME, OPPORTUNITIES, AND THE QUALITY OF LIFE 83 capture the effects of human capital investments, job characteristics, and quality-of-life factors. The data on individuals that have been used to estimate the mode} are obtained from the 1-in-1,000 Public Use A Sample of the 1980 Census. Data on job and location charac- teristics are obtained from a variety of sources and are merged with the census data by county, metropolitan area, or industry. Included in the sample are 46,004 individuals living in 253 urban counties in 185 metropolitan areas from which complete data to estimate the mode! are available. These individuals are at least 16 years of age or older; they all reported their 1979 earnings, hours, and weeks; had some wage and salary earnings; and had positive total earnings. The individual characteristics that are incorporated into the estimated wage equation are years of labor market experience (age - schooling - 6~; experience squared; years of schooling; number of children; and dummy variables for race, gender, enrollment in school, marital status, and the presence of health limitations. These variables are included in the mode} alone and are made to interact with one another as appropriate. For example, gender can be made to interact with experience and with experience squared to capture differences between men and women in their profiles of earnings over the life cycle. The variables measuring individual characteristics control for differences in human capital investments and possibly some other factors such as discrimination. The variables designed to capture the effects of differences in job characteristics are five dummies that control for six broad occupa- tional categories and the unionization rate in the worker's industry. Sixteen quality-o£life factors are also included in the model. Six of these variables control for climatic differences, and six capture differences in environmental quality. Others are dummies for the location of the worker's residence in the central city of the metropoli- tan area or in a county bordering a seacoast or the Great Lakes, the violent crime rate, and the teacher/pupil ratio in the county of residence. The teacher/pupi} ratio is designed to be a measure of the quality of local publicly provided services. Table 6 presents the wage-equation regression estimates, stan- dard errors, and means of the independent variables. The exact functional form that was used was chosen on the basis of the results of a Box-Cox maximum likelihoocI search procedure. It consisted of transforming the hourly wage ~ W) to ~ W ~ - I)/.1 and entering the independent variables in linear form. The parameter estimates pre- sented in Table 6 have been linearized so that they are estimates of
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86 Mark C. Burger awl Glenn C. Blom - ~t the effects on hourly wages of one-unit changes In each variable. For example, the schooling coefficient is .442. It indicates that an extra year of schooling increases the hourly wage by 44 cents, consistent with greater investments in human capital as schooling increases. The experience coefficient is .310; the experience-squared coeffi- cient Is -.005, implying that male earnings rise at a decreasing rate over the career and eventually turn downward at about 31 years of ex- perience. This same pattern is observed in Table 5 if hourly earnings are calculated for the various age groups shown. On-the-job training is accumulated toward the beginning of the career and eventually depreciates. For women, the gender-experience interactions must be taken into account to determine the experience-earnings profile. The negative gender-experience and positive gender-experience squared coefficients indicate that female earnings rise less quickly with experi- ence and are flatter over the life cycle. Women appear to accumulate human capital more slowly than men because of intermittent work histories or discrimination in the provision of on-thejob training opportunities and promotions. To determine the total estimated difference by gender, after con- trolling for other characteristics, one must account for the gender coefficient and the gender interactions. The gender coefficient is -.312, which is the estimated difference in hourly earnings between white, unmarried men and women with no labor market experience. The gender difference for nonwhites and married individuals at var- ious levels of experience can be determined by summing across the appropriate estimated coefficients. Table 3 shows that female-headed households with children had lower income than other types of households. The estimated earnings equation illustrates the adverse effect of children on female earnings. The gender-children coefficient is -.254; that is, each child reduces a woman's wage by 25 cents per hour on average, presumably by restricting the range of accessible jobs in the labor market. Table 4 reveals that white household incomes exceed those of nonwhites. The estimates in Table 6 imply that nonwhite men earn approximately 96 cents per hour less than white men, but nonwhite women earn about 15 cents more (1.102 - .959) per hour than white women. Apparently the higher household income of whites does not exist because white women actually receive higher hourly wages than nonwhite women with similar characteristics. The estimates in Table 6 also illustrate the existence of wage differences that are the result of differences in job characteristics.
INCOME, OPPORTUNITIES,AND THE QUALI=OF LIFE 87 The included occupational categories all earn more than the excluded service occupations category; the differences range from 13 cents per hour for farmers to $2.50 per hour for professionals and managers. Workers in industries that are more extensively unionized also receive higher wages. The coefficients for the quaTity-of-life factors show the compen- sation that takes place in the labor market for differences across urban areas in climate, environmental quality, crime, and so forth. To obtain estimates of the full compensation for these amenity differ- ences, the housing market must be taken into account (see the next section). But labor-market compensation alone is of some interest. According to the estimates for location-specific amenities, shown as the regression coefficients for the last 16 variables in Table 6, lower wages are received in sunny areas, a finding consistent with workers considering sunshine an amenity; higher wages are received in humid and windy areas. Workers also pay implicitly in the labor market for central-city locations, high teacher/pupil ratios, and greater levels of visibility. Compensation is provided for living with more crime and greater quantities of toxic waste. Given the emphasis on the location of a worker's residence, the differences in the wages of workers who reside in the central city and those who reside outside it bear further examination. The central- city coefficient is -.454, which is the estimated effect of living in the central city when the effects of other characteristics are held constant. Thus, workers living in the central city pay for their location through lower wages.3 But the observed characteristics of residents and nonresidents of central cities differ as well. Accordingly, one can estimate the implied difference in the wages of the typical central-city resident and the noncentral-city resident that is due to differences in characteristics, in addition to the "pure" effect of holding characteristics constant. Table 7 presents such estimates, which account for differences in characteristics. The total estimated wage difference produced by differences in characteristics Is quite small (6 cents) compared to the pure effect (45 cents); however, some of the effects of differences in individual characteristics are quite sizable. For instance, central-city residents face much higher crime rates and earn higher wages as a result (33 cents per hour). Other noticeable differences include those attributable to race, gender, and 3An alternative explanation is that unmeasured characteristics of the workers living in central cities or of the jobs they hold lead to lower wages.
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go Mark (I. Burger arid Glenr' O. Blomqpuist marital status, a pattern that reflects differences in the demographic makeup of the central-city and noncentral-city populations. QUA[ITY-OF-LIFE COMPARISONS Economic Status and Quality of Life In the preceding sections, we presented an economic mode] of wage determination that explains differences in wages. Schooling, experience, occupation, unionization, and other job-related charac- teristics were shown to be determuning factors of wage differences. In addition, we found that wages are also affected by sunshine, the crime rate, the teacher/pupil ratio, and other amenities of the area in which the job is located. Taken as a group these results demonstrate that workers pay attention to amenities and that amenity levels affect labor earnings and thus income.4 The results suggest that we can infer from the relationship between wages and amenities the values people place on amenities. These quality-of-life values can then be used, along with traditional measures of economic status, to reflect more fully the well-being of urban residents in various locations. Our measure of quality of life thus augments traditional measures such as household money income. Labor Markets, Housing Markets, and Quality of Life Our approach to measuring the value of the quality of life in different locations is based on the notion that people choose the amenity "bundle" they desire by locating in areas with the amenities they want. They also pay for those amenities in observable markets. If the trade-off were solely between wages and amenities, one would expect workers who live in areas with high amenity levels to earn less. In other words, those workers pay for amenities through a corresponding reduction in wages. The difference in wages for similar workers in similar jobs but in different locations could be attributed to the difference in amenities. These wage differences would measure the value of the quality of life in different locations. In Hoehn et al. (1987), we develop a more comprehensive frame- 4In their book on urban amenities, Diamond and Tolley (1982) conclude that amenities strongly shape economic activity. One impact is their effect on wages.
INCOME, OPPORTUNITIES, AND THE QUALITY OF LIFE 91 work that incorporates this notion of implicit markets for amenities. The framework is a hedonic mode! of interregional wages, rents, and amenity values. The mode} expands the principle of compensating differences to allow for trade-offs between housing prices (or rents) and amenities, as well as between wages and amenities. The results of the housing hedonic regression for the areas and amenities corre- sponding to those in the wage hedonic regression reported in Table 6 are shown in Table 8. Housing prices are also affected by amenities factors such as sunshine, violent crime, and the teacher/pupi! ratio. In the context of the housing market alone, one might expect to find a trade-off in the form of higher housing prices for more amenities. Our more comprehensive model, which allows for compensation in multiple markets, shows that the value of amenities is the sum of partial compensations in the housing and labor markets. For an amenity, even though the sum must be positive, it is not necessary that the housing price differential be positive and the wage differential be negative. The requirement is only that the sum of the housing price differential and the (negative of the) wage differential be positive. Because the mode! considers geographic city size, population city size, agglomeration effects, and the costs of production for firms, as well as residential location and utility for individuals, one clifferentia] may be negative as long as it is offset by the compensation implied by the other differential. The full amenity values, based on the impact of amenities on both wages and housing prices, are used to calculate a quality-of-life index for metropolitan areas. Quality of Life In Metropolitan Areas There are noticeable differences in amenities across urban areas. as there are in income and employment. The mean, standard devi- ation, minimum, and maximum for each of the 16 amenities in our mode! are shown in Table 9. Considerable variation is evident; for example, precipitation ranges from 4 to 67 inches per year, violent crime ranges from 63 to 1,650 crimes per 100,000 people per year, and the number of Superfund sites ranges from 0 to 9 per county. We cart sum the impacts on wages and housing prices to obtain the full amenity values after the linearized amenity coefficients in the wage and hedonic regressions are converted to annual values per household. The amenity values are calculated as follows:
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INCOME, OPPORTUNITIES, AND THE QUALITY OF LIFE AVi = (HCi) (12) - (WCi) (1.54) (37.85) (42.79), 95 where AVi is the amenity value for amenity i, HCi is the linearized housing coefficient, 12 is the number of months per year, WCi is the linearized wage coefficient, and 1.54, 37.85, and 42.79 are the sample means for workers per household, hours per week, and weeks per year, respectively. The marginal amenity values for each amenity are shown in the last column of Table 9. The interpretation is that people value a change in an amenity at the amount shown. For example, a reduction in violent crime from 536 to 535 crimes per 100,000 people per year is valued at $1.03 per household per year. The aggregate value of all amenities in an urban area forms the quaTity-of-life index (QOLI). The index values are calculated as follows: 16 QOLIj = ~ AViSij i=1 j= 1,. . .,m, where QOLIj is the quality-of-life index for area j, AVi is the amenity value for amenity i, Sij is the quantity of amenity i in area j, and m is the number of areas being ranked. Quality-of-life index values for 24 selected large metropolitan areas are shown in Table 10. All of the metropolitan areas for which the traditional measures of economic status were given in Table 1 are included, except for Boston and Miami, which were excluded because of incomplete data. The values are taken from a study by Berger et al. (1987) that estimates the quality of life for 185 metropolitan areas. Given that our bundle of climatic, urban, and environmental amenities represents quality of life, the QOLI measures the value of differences in quality of life among urban areas. The difference between the quality of life in Denver and the quality of life in St. Louis is valued at $2,188 (1,197.96 + 990.10) per year per household. This value is approximately 10 percent of the average household income for the metropolitan areas covered in Table 1. Table 11 reports the rankings of the 24 large metropolitan areas based on quality of life, household income, poverty rate, and unem- ployment rate. There is no strong relationship between quality of life and any of the other measures. In fact, quality-of-life consid- erations can change our comparisons of areas based on traditional economic measures. In Table 12, the QOLl is added to household income to produce a quality-o£life adjusted household income for the 24 metropolitan areas included in Table 10. Although the rankings
96 Mark C. Bearer arid Glenn C. Blom~uist TABLE 10 Quality-of-Life Index Values for Large Metropolitan Areas Metropolitan Areaa (1980 SMSAs) Quality-of-Life b Index (1979 dollars) Denver-Boulder, Colo. San Diego, Calif. Phoenix, Ariz. Anaheim-Santa Ana-Garden Grove, Calif. Nassau-Suffolk, N.Y. Los Angeles-Long Beach, Calif. Tampa-St. Petersburg, Fla. San Francisco-Oakland, Calif. Ri~rerside-San Bernardino-Ontario, Calif. Philadelphia, Pa.-N.J. Washington, D.C.-Md.-Va. Newark, N.J. Atlanta, Ga. Seattle-Everett, Wash. Cleveland, Ohio Pittsburgh, Pa. New York, N.Y.-N.J. Minneapolis-St. Paul, Minn.-Wis. Dallas-E`ort Worth, Tex. Baltimore, Md. Chicago, Ill. Houston, Tex. Detroit, Mich. St. Louis, Mo.-Ill. 1,197.96 980.83 870.69 803.49 687.80 667.64 191.57 139.55 135.46 9.21 5.08 -11.48 -25.74 -124.18 -190.62 -330.90 -369.20 -372.20 _399.70 -422.70 -822.80 948.40 -968.00 _990.10 aListed are 24 standard metropolitan statistical areas (SMSAs) with a 1980 population exceeding 1.5 million. The 1980 definition of an SMSA is used. Boston, Mass., and Miami, Fla., are omitted because sufficient data were not available to estimate the parameters for the quality-of-life index (QOLI). The mean QOLI for the 24 SMSAs is -11.95. b The differences in index values represent the~ annual premiums households are willing to pay for differences in amenities in different metropolitan areas. The values reported are taken from a study by Berger et al. (1987) that ranks 185 metropolitan areas by quality of life. produced by household income and quaTity-of-life adjusted house- hold income are similar, there are noticeable differences for cities with extreme QOLl values. For instance, Denver-Boulder has the 11th highest household income, but the 4th highest QOLI-adjusted household income because of its high quality of life. San Diego and Phoenix also move up the ladder from 18th and 19th to 13th and 14th, respectively, after adjusting for their QOI`I values. On the other hand, Detroit and St. I,ouis drop from 5th and 16th to 10th
INCOME, OPPORTUNITIES, AND THE QUALITY OF LIFE TABLE 11 Comparisons of Rankings of Metropolitan Areas by Alternative Measures of Economic Status Metropolitan Area (1980 SMSAs) Quality of Life (ranked highest to lowest) Household Poverty Income Rate (ranked (ranked highest lowest to lowest) to highest) Unemploy- ment Rate (ranked lowest to highest) Den~rer-Boulder, Colo. San Diego, Calif. Phoenix, Ariz. Anaheim-Santa Ana-Garden Grove, Calif. Naseau-Suffolk, N.Y. Los Angeles-Long Beach, Calif. Tampa-St. Petersburg, Fla. San Francisco-Oakland, Calif. Ri~rerside-San Bernardino- Ontario, Calif. Philadelphia, Pa.-N.J. Washington, D.C.-Md.-Va. Newark, N.J. Atlanta, Ga. Seattle-Everett, Wash. Cleveland, Ohio Pittsburgh, Pa. New York, N.Y.-N.J. Minneapolis-St. Paul, Minn.-Wis. Dallas-Ft. Worth, Tex. Baltimore, Md. Chicago, Ill. Houston, Tex. Detroit, Mich. St. Louis, Mo.-Ill. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 2 3 13 24 7 22 21 1 6 1? 14 20 23 9 15 12 10 4 5 16 12 9 3 20 21 12 18 22 4 15 23 2 6 9 24 5 17 19 16 12 7 11 12 10 12 9 10 23 22 4 17 8 18 20 21 16 4 1 4 5 1 24 19 NOTE: SMSAs = standard metropolitan statistical areas. 97 and 20th, respectively, after adjusting their household incomes for low measured quality-of-life values. Similar quality-of-life adjusted household incomes could be cal- culated for those living in the central city or suburbs, in large or small metropolitan areas, or outside metropolitan areas. As an illustration, the household income figures in Table 2 for those living in metropoli- tan areas with populations greater or less than 1.5 million can be adjusted using QOI`T figures from Table 10 and the study by Berger et al. (1987~. The average household income of those living in large (greater than 1.5 million population) metropolitan areas is $21,846, whereas for small (less than 1.5 million population) metropolitan areas it is $19,574. The average QOLl for large metropolitan areas is -$12; for small areas, it is $308, thus producing quality-of-life adjusted household incomes of $21,834 in large areas and $19,882 in
98 Mark C. Bcrycr and Gler~n C. Blom~ui~t small areas. On average, the quality-of-life value is higher in small areas, and although this offsets somewhat the income advantage of large areas, quality-of-life adjusted income is still higher in large metropolitan areas. Finally, in Table 13 we present rank correlations between the al- ternative measures of economic status. The quality-of-life index is not highly correlated with any of the alternative measures of economic status, including quaTity-of-life income. Quality-of-life adjusted in- come and household income are highly correlated as expected. The poverty and unemployment rates are more highly correlated with quaTity-o£life adjusted income than with unadjusted income. From the observed correlations, it is apparent that quality of life a(lds an- other dimension to comparisons of the economic well-being of urban residents. TABLE 12 Comparison of Metropolitan Areas Based on Income and Quality of Life QOLI + Household Household Income, QOLI, Income, Metropolitan Area 1979 (S) 1979 ($) 1979 (S) Washington, D.C.-Md.-Va. 27,295 5 27,300 Anaheim-Santa Ana-Garden Grove, Calif. 26,434 803 27,237 Nassau-Suffolk, N.Y. 25,997 688 26,685 Denver-Boulder, Colo. 22,664 1,198 23,862 Houston, Tex. 24,607 -948 23,659 San Francisco-Oakland, Calif. 23,151 140 23,291 Newark, N.J. 23,251 -11 23,240 Seattle-Everett, Wash. 23,075 -124 22,951 Minneapolis-St. Paul, Minn-Wis. 23,032 -372 22,660 Detroit, Mich. 23,288 -968 22,320 Los Angeles-Long Beach, Calif. 21,639 668 22,307 Chicago, Ill. 23,017 -823 22,194 San Diego, Calif. 21,114 981 22,095 Phoenix, Ariz. 20,874 871 21,745 Cleveland, Ohio 21,461 -191 21,270 Baltimore, Md. 21,657 -423 21,234 Atlanta, Ga. 21,189 -26 21,163 Dallas-Ft. Worth, Tex. 21,318 -400 20,918 Philadelphia, Pa.-N.J. 20,239 9 20,248 St. Louis, Mo.-Ill. 21,225 -990 20,235 Pittsburgh, Pa. 20,275 -331 19,944 Riverside-San Bernardino- Ontario, Calif. 19,504 135 19,639 New York, N.Y.-N.J. 19,142 -369 · 18,773 Tampa-St. Petersburg, Fla. 16,812 192 17,004 NOTE: QOLI = quality-of-life index.
INCOME, OPPORTUNITIES, HIND THE QUALIlrY OF LIFE TABLE 13 Rank Correlations of Rankings by Alternative Measures of Economic Status 99 Quality- of-Life Quality Household Poverty Unemploy- Adjusted Measure of Life Income Rate ment Rate Income Quality of life -- -.080 .119 .262 .253 Household income -- .654 .357 .921 Poverty rate -- .162 .673 Unemployment rate -- .469 Quality-of-life adjusted income -- CONCLUSIONS The focus of this paper has been on the economic well-being of urban residents. We have compared the economic status of people living in large central cities to that of people living in suburbs, small metropolitan areas, and rural areas. Data from the 1980 Census of Population and Housing facilitated an in-depth inquiry for various subnational categories and groups, but it precluded a longitudinal study that might identify trends. The use of several measures of well-being somewhat mitigates the shortcomings of each, but such measures as annual household income fail to reflect relevant noncash transfers, wealth, and quality of life. In this paper, we develop a methodology to adjust for differences in quality of life. Using the 1980 Census, we compute for metropolitan areas with more than 1.5 million residents the average household income, poverty rate, unemployment rate, employment rate, and manufac- turing employment share. These measures range from $16,812 to $27,295, from 5.1 percent to 15.8 percent, from 3.2 percent to 11.6 percent, from 49.2 percent to 69.6 percent, and from 5.1 percent to 30.1 percent,respectively. The findings were several: (1) population is not correlated with any of the other measures; (2) the poverty rate and the unemployment rate are not significantly correlated; and (3) the manufacturing employment share of an area and its unemploy- ment rate are positively correlated. Further computations were made for traditional measures by population size of area of residence and by demographic group. Na- tionally, suburbanites in large metropolitan areas are more affluent than residents of large central cities, small metropolitan areas (less than 1.5 million population), or rural areas, and this dominance
100 Mark a. Beeper and Glenn C. Blom~uist pervades all measures and groups. Which area's residents are the poorest, according to traditional measures, depends on the region being considered. Central-city residents are the poorest group in the Northeast and Midwest, but rural residents are the poorest in the South and West. Poverty is not peculiar to New York or De- troit inner-city neighborhoods. Among white, black, and Hispanic married-couple households, those living in rural areas are the least affluent. The same is true among black and Hispanic households headed by women. When grouped by age, central-city residents who are 25 to 39 years of age are the poorest of all age groups, but for those people over 40 years of age, rural residents are again the poorest. Analysis based on a hedonic framework of wage determination demonstrates that differences in a major source of incomewages- can be explained by observable differences in the characteristics of workers, jobs, and job locations. For example, the higher central- city crime rate is a factor that has increased wages in the central city relative to wages outside it. On average, however, central-city residents earn less. Indeed, the crime rate and other amenity factors induce compensating differences in wages across urban areas and also compensating differences in housing prices. The compensating differences can be combined to obtain a full amenity value that, in turn, can be used to create a quaTity-of-life index. Comparisons across large metropolitan areas show that rankings based on quality of life are not correlated with rankings based on traditional measures of well-being. The quality-of-life premium is added to household income for each of the large metropolitan areas to obtain a quality- of-life adjusted income. The adjustment changes the ranking for areas with extremely high or extremely low quality-of-life values. The adjustment also illustrates how traditional measures can be modified to reflect well-being more comprehensively. REFERENCES Becker, Gary S. 1975 Truman Capital, 2d ed. Chicago: University of Chicago Press. Berger, Mark C., Glenn C. Blomquist, and Werner Waldner 1987 A revealed-preference ranking of quality of life for metropolitan areas. Social Scicnec Quarterly 68(Dec.) :761-778. Bureau of the Census 1983a Census of Population, 1980. Genera] Population Characteristics: Uruted States Summary (PC80-1-B1~. Washington, D.C.: U.S. Department of Commerce.
INCOME, OPPORTUNITIES, AND THE QUALIlrY OF LIFE 101 1983b Census of Population and Housing, 1980: Public-Use Microdata Sample A. Washington, D.C.: U.S. Department of Commerce. 1984 Statistical Abstract of the United States: 1985. lO5th ed. Washington, D.C.: U.S. Department of Commerce. Danziger, Sheldon, Robert Haveman, and Robert Plotnick 1981 How income transfer programs affect work, savings, and the income distribution: A critical review. Journal of Economic Literature 19:975- 1028. Diamond, Douglas B., and George S. Tolley, eds. 1982 The Economics of Urban Amcnitic~. New York: Academic Press. Hoehn, John P., Mark C. Berger, and Glenn C. Blomquist 1987 A hedonic model of interregional wages, rents, and amenity values. Journal of Regional Scicnec 27:605-620. Mills, Edwin S., and Bruce W. Hamilton 1984 Urbar: Economics, 3rd ed. Glenview, Ill.: Scott, Foresman & Co. Mincer, Jacob 1974 Schooling, Expenenec, and Earrungs. New York: National Bureau of Economic Research. Smith, Robert S. 1979 Compensating wage differentials and public policy: A review. Indw- trial and Labor Relations Review 32:339-352. Smith, V. Kerry 1983 The role of site and job characteristics in hedonic wage models. Journal of Urban Economics 13:296-321. Tolley, George S., Philip E. Graves, and John L. Gardner 1979 Urban Growth in a Market Economy. New York: Academic Press.
