why is u.s. poverty higher in nonmetropolitan than in metropolitan areas?

Why Is U.S. Poverty Higher inNonmetropolitan than in Metropolitan Areas?

MONICA FISHER

ABSTRACT In the U.S., people are more likely to be poor if they live in a nonmetropolitan

(nonmetro) than in a metropolitan (metro) area. A common explanation for this phenomenon is that

nonmetro places offer relatively few economic and social opportunities. This article explores

another plausible explanation, asking if the disproportionate poverty in nonmetro areas partly

reflects attitudes of people with personal attributes related to poverty: they may be attracted to

nonmetro places or otherwise reluctant (or unable) to leave them. To test this hypothesis, data from

nine waves of the Panel Study of Income Dynamics (PSID) are used to track economic well-being

and nonmetro–metro residential choice among a sample of 2,007 low-income householders. A

series of multivariate regression models are estimated in which the dependent variable is a

householder’s income to need (adjusted for spatial cost-of-housing differences), and regressors are

individual attributes, a binary variable for nonmetro residence, and state fixed-effects. Regression

results show that controlling for householder educational attainment reduces the negative associa-

tion between nonmetro residence and income to need; but controlling for unobserved, time-

invariant heterogeneity via individual fixed-effects increases the magnitude of this negative

association. Study findings thus appear to indicate that enduring nonmetro poverty is explained

both by a sorting of low human capital individuals into nonmetro areas and by reduced economic

opportunities in nonmetro compared to metro places.

Introduction

I n the U.S., low-income people are not evenly distributed across the landscape. Povertyrates have long been higher in nonmetropolitan (nonmetro) than metropolitan (metro)

counties. Detailed data starting in the 1960s, when the U.S. embarked on a War on Povertyand official measurement of poverty commenced, are shown in Figure 1. Today, one intwenty metro counties and one in five remote nonmetro counties is classified as a highpoverty county, having a poverty rate of 20 percent or higher. And persistent povertycounties—those with poverty rates of 20 percent or more in each decennial census between

Monica Fisher is an assistant professor in the Department of Agricultural and Resource Economics

at Oregon State University. Her e-mail address is [email protected]. The author would

like to thank Will Masters, Bruce Weber, Thomas Leinbach, and three anonymous reviewers for

valuable comments on an earlier version of this article. The author alone is responsible for any

substantive or analytical errors.

Growth and ChangeVol. 38 No. 1 (March 2007), pp. 56–76

Submitted October 2005; revised December 2005, April 2006; accepted May 2006.© 2007 Blackwell Publishing, 350 Main Street, Malden MA 02148 US and 9600Garsington Road, Oxford OX4, 2DQ, UK.

mailto:[email protected]

1960 and 2000—are overwhelmingly nonmetro. Multivariate statistical analyses furtherdocument a nonmetro welfare disadvantage. Extant research shows the odds of being poorare between 1.2 to 2.3 times higher for people residing in nonmetro compared with metroareas, controlling for individual and family characteristics and, in a few analyses, localcontext variables (see Weber et al. 2005 for a review).

Why is poverty higher in nonmetro than in metro areas? One view, the “structuralcondition hypothesis,” ascribes a causal role to place of residence. From this perspective,otherwise identical individuals will have lower economic well-being in nonmetro comparedwith metro settings because of the spatial distribution of economic and social opportunities(Tickamyer and Duncan 1990; Tomaskovic-Devey 1987). The “residential sorting hypoth-esis,” by contrast, posits that, holding constant human capital attainment, individuals’prospects for economic prosperity are independent of where they live.

The rural poverty literature has emphasized the structural condition hypothesis. Dataindeed confirm that local nonmetro labor markets generally offer fewer job options, andwork tends to be concentrated in minimum wage and part-time jobs offering limitedsecurity and little room for advancement (Gibbs 2001; McKernan et al. 2001). Moreover,

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FIGURE 1. PEOPLE IN POVERTY BY RESIDENCE, 1966–2002.Source: U.S. Bureau of the Census, Current Population Survey, annual March

Supplement.

WHY IS U.S. POVERTY HIGHER IN NONMETRO AREAS? 57

work supports such as job training programs, formal group child care, and public trans-portation tend to be limited or completely absent in nonmetro communities (Colker andDewees 2000; Fletcher et al. 2002).

Social-context variables such as community capacity, local social norms and networks,and the power and motivations of local government also influence the geographic distri-bution of poverty (Blank 2005; Weber et al. 2005). Duncan’s (1999) fieldwork in ruralcommunities of Appalachia and the Mississippi Delta, for example, reveals a rigid two-class system in which the relatively well-off have taken advantage of the local socialstructure to maintain their privileged position and keep the poor marginalized. Rupasinghaand Goetz (2003) use principal components analysis to develop a county-based socialcapital index, combining measures of associational density, political involvement, andresponse rate to the decennial census. They find that nonmetro counties with high socialcapital have lower family poverty rates, all else being equal. In sum, a key explanation forenduring nonmetro poverty is that the local context of many nonmetro areas makes it hardfor people to succeed economically.

This article explores the residential sorting hypothesis of metro–nonmetro differencesin economic well-being, asking whether the disproportionate poverty observed in nonmetrocommunities partly reflects the attitudes of people with personal characteristics related tohuman impoverishment: they may be attracted to nonmetro places or otherwise reluctant(or unable) to leave them. Two studies have explored this contention. Nord (1998) uses1990 census data to examine the effect on the geographic distribution of poverty of thecounty-to-county migration of poor and nonpoor. He finds that, in nonmetro countiesbetween 1985 and 1990, more poor people moved into than out of persistent povertycounties, a pattern that reinforced poverty’s preexisting spatial concentration. Fitchen’s(1995) in-depth interviews with low-income families in upstate New York tell a similarstory. Her case-study community, a rural area facing economic decline, was found to be amigration destination for poor urban families. The lack of jobs in the community did notappear to deter low-income migrants.

Why would people with low-income capacity “choose” to live in or be reluctant toleave nonmetro communities? It is conceivable, as argued by Nord (1998), that individu-als with low education and limited work experience are drawn to places that offer oppor-tunities matching their skills and needs, for example communities with a high share ofentry-level positions and where living costs are low. Low-skill occupations continue tomake up a higher percentage of total jobs in nonmetro areas (42 percent) than in thenation as a whole (35.5 percent) (Gibbs, Kusmin, and Cromartie 2004); perhaps a lackof agglomeration in nonmetro areas attracts such an occupational structure. Studies alsoshow that living costs are substantially lower in nonmetro than in metro areas (e.g. Kurre2003; Nord 2000). Fitchen’s 1995 interviews with poor metro migrants, described above,reveal that the main attraction of her case-study community was its inexpensive rentalhousing. Finally, nonmetro places may appeal to those with low-income capacity becauseof the possibilities for informal work. Studies document a range of informal employmentactivities in nonmetro communities that help the poor weather income shortfalls (e.g.

58 GROWTH AND CHANGE, MARCH 2007

Jensen, Cornwell, and Findeis 1995). In some regions, such work features more promi-nently in the livelihood strategies of nonmetro than of metro residents (Tickamyer andWood 1998).

In this article, data from nine waves of the Panel Study of Income Dynamics (PSID)are used to track economic well-being and nonmetro–metro residential choice among asample of householders. A series of multivariate regression models are estimated toexamine the degree to which the sorting into nonmetro areas of people with low-incomecapacity explains metro–nonmetro differences in economic well-being. The presentarticle complements existing work in several ways: It is the first to employ panel data toexamine the effect of residential selection on the spatial distribution of poverty; Nord(1998) relied on static snapshots of the population from retrospective data. The studyalso uses Fair Market Rent (FMR) data to account for cost-of-housing differences acrossmetro and nonmetro areas. Analysts agree that adjusting for geographic differences inliving costs is critical for obtaining an accurate picture of poverty across regions of thecountry, but researchers rarely make such adjustment. (Exceptions are Jolliffe 2004;Ulimwengu and Kraybill 2004.) Finally, the current study offers another empirical pointin a rather scant literature that asks if the higher risk of poverty in nonmetro versus metroareas partly reflects a concentration in nonmetro places of people with low-incomecapacity.

Modeling Poverty across PlaceThis study tests the hypothesis that the higher risk of poverty in nonmetro places partly

reflects a concentration in nonmetro areas of people with characteristics associated withhuman impoverishment. A series of multivariate regression models are estimated, which arevariants of

y x n si i i i i= + + + +α α α α ε0 1 2 3 . (1)

In equation (1), the dependent variable y is a continuous measure of household incometo need, where income is before-tax money income and need is the Census Bureau’sfamily-size conditioned poverty threshold.1 Explanatory variables are individual-levelfactors x (including the number of household members and presence of a young child inthe household, as well as the householder’s age, race, gender, marital status, education,and current employment status), a binary variable indicating nonmetro residence n, andstate fixed-effects s (to control for state-level differences in expenditure, tax, and welfarepolicy). Finally, e in equation (1) is a random error term assumed uncorrelated with theregressors.

The first regression model to be estimated is an ordinary least squares (OLS) model,which excludes one observed measure of human capital—educational attainment. Thesecond regression model is an OLS model that controls for householder education. Finally,the longitudinal nature of the data is exploited with an individual fixed-effects regressionmodel that controls for unobserved income capacity (at least those attributes that are


individual specific and time invariant).2 The logic of the empirical strategy is as follows: ifpeople with a higher propensity to be poor tend to sort themselves into nonmetro areas(either by remaining in a nonmetro area or by moving to one), then imposing controls forpersonal attributes related to having low income should reduce the absolute value of the(negative) nonmetro effect considerably.3

The proposed empirical approach amounts to an examination of the omitted variablebias that occurs when a researcher omits from equation (1) key factors that are associatedboth with economic well-being and with nonmetro residence. There are two components ofbias: (1) the “true” effect on income to need of the omitted variable(s) and (2) thecorrelation between nonmetro residence and the excluded variable(s). (See Jargowsky 2005for an excellent mathematical exposition of omitted variable bias.) If the bias componentsare either both positive or both negative in sign, then the estimated negative effect ofnonmetro residence on the income-to-need ratio will be understated. If bias componentshave opposite signs, then the measured negative nonmetro effect on income to need will beoverstated.

The first bias component is expected to be positive in sign if human capital character-istics are the omitted variables. It is well documented that human capital is a primarydeterminant of a person’s income potential. Studies show that labor markets rewardcognitive skills, as measured by IQ-type tests like the Armed Forces Qualifying Test orGED examination scores (Cawley et al. 1997; Tyler, Murnane, and Willett 2001). Educa-tion has been found to reduce the number of unemployment spells over the life course(Ashenfelter and Ham 1979; Nickell 1979) and to increase personal earnings(Psacharopoulos and Patrinos 2004). Furthermore, there is evidence that personality andnoncognitive traits such industriousness, motivation, habits, and work attitudes are predic-tors of wages. (See Bowles, Gintis, and Osborne 2001 for a review.)

As for the second bias component, the correlation between omitted variables andplace of residence, there is only limited evidence. Data from the 2000 census indicate ametro–nonmetro gap in educational attainment, which is especially pronounced forcollege completion: 26.6 percent of metro people and 15.5 percent of nonmetro peopleaged 25 and older have a college degree. Whether other income capacity traits (e.g.,personality and noncognitive attributes) are unevenly distributed across the metro–nonmetro landscape has not been studied. It is the correlation between income capabilityand place of residence that is of primary interest in this study because it provides anindication of whether people with a higher propensity to be poor tend to sort themselvesinto nonmetro areas, either by staying in a nonmetro area or by moving to one. The signof this correlation is indeterminate a priori, but insights are gained through the followingexperiments.

One experiment is to compare the coefficient on the nonmetro residence binary variablefrom regressions of income to need on nonmetro residence that first exclude and theninclude human capital variables. Hypothesis 1: there is a concentration of people with loweducational attainment in nonmetro areas. Support for this hypothesis is a finding thatcontrolling for individual educational attainment causes the estimated nonmetro effect


to become smaller in absolute value because such a finding suggests a negative correla-tion between educational attainment and nonmetro residence. This is the first testablehypothesis.

A second experiment introduces controls for unmeasured income capacity to see whathappens to the nonmetro binary coefficient. If controls for unobserved, time-invariantincome capability are introduced via a fixed-effects specification, the estimated nonmetroeffect may remain unchanged, increase in absolute value, or decrease in absolute value.These possibilities suggest a second testable hypothesis. Hypothesis 2: there is a concen-tration of people with unobserved individual attributes associated with having low incomein nonmetro areas. Evidence in support of this hypothesis comes from a finding that anindividual fixed-effects specification has the effect of reducing the absolute value of thenonmetro binary point estimate. If the estimated nonmetro effect gets smaller in absolutevalue, this indicates that the two bias components are of opposite sign. Thus, unmeasuredindividual attributes that are positively (negatively) associated with income to need arenegatively (positively) correlated with nonmetro residence. The study’s hypotheses aretested below after a discussion of data sources.

Data DescriptionThe main data source is the Panel Study of Income Dynamics (PSID), a longitudinal

survey that has followed a representative sample of approximately five thousand familiesand their descendents since 1968 (see Brown, Duncan, and Stafford 1996; and Hill 1992 fordetailed descriptions of the PSID). The PSID family and individual files contain data on awide range of topics including family structure and demographics, socioeconomic back-ground, geographic mobility, employment, earnings, income, wealth, welfare participation,housework time, health, and food security. Because of the enormous value of nationallyrepresentative longitudinal data on economic and social issues, the PSID is one of the mostwidely used data sets in the world. The PSID data set is particularly useful for the analysesof this article because it provides, for public use, information on nonmetro–metro residencefor certain years.4

The study’s analyses focus on nine waves of the PSID, covering the period 1985 to1993. This analysis period is chosen because it is the only continuous period for which avariable indicating nonmetro–metro residence is available in the PSID. In the PSID, suchinformation is provided for the years 1985–1993, 1999, 2001, and 2003. The householdhead is the appropriate unit of analysis for two key reasons. First, economic well-being ismeasured at the household level in the U.S. Ideally, therefore, one should track householdsover time. However, it is difficult to arrive at a satisfactory definition of a “longitudinalhousehold” because household composition changes considerably even over short periods.(See Duncan and Hill [1985] for a detailed discussion.) The household head should serveas a good proxy for the household because the bulk of household income is earned byhouseholders. A second reason for choosing the householder as the analysis unit is that thePSID provides the most comprehensive information for these household members.


To assemble a sample suitable for empirical analysis, it is necessary to impose severalselection criteria. To retain as large a sample as possible and avoid reducing the sample toa highly selective one, the number of householders is allowed to vary across years; that is,the panel of householders is an unbalanced one. It is necessary, however, that samplehouseholders have at least two years of observations so that controls for individual hetero-geneity can be imposed. Because the focus of the article is poverty, the sample is restrictedto the lower income distribution. Specifically, a householder only enters the sample ifshe/he had household income that was below the U.S. Census poverty threshold in one yearor more during the 1985–1993 period. Other important selection criteria are as follows: foreach analysis year, individual householders only enter the sample if they resided in the U.S.,were part of responding households, and have complete data for all analysis variables. Theconstructed sample consists of 2,007 individuals who were household heads in 1993 and atleast one other year during the period 1985 and 1993 and who were poor in terms of incomein at least one year. The average number of years of the sample householders that make itinto the sample is seven. The total sample size is 15,229 person-years.

The dependent variable for this study is income to need adjusted for spatial housing costdifferences; it has a minimum of -2.07 and a maximum of 109.15.5 The negative values forthe dependent variable are largely explained by household business losses. Adjustment ismade using U.S. Department of Housing and Urban Development Fair Market Rent data,as has been recommended by the National Academy of Sciences Panel on Poverty andFamily Assistance (Citro and Michael 1996).6 Accounting for regional variations in pricesis a critical step in obtaining an accurate picture of metro–nonmetro differences in eco-nomic well-being. Studies show that living costs are considerably lower in nonmetro thanin metro areas, suggesting that current poverty estimates overstate hardship in nonmetrolocations and understate it in metro places (Kurre 2003; Nord 2000).7 Although income toneed should be adjusted for overall cost-of-living differences across metro and nonmetroareas and across regions or states, data for such purpose are currently unavailable (Citro andMichael 1996).

The FMR data provide estimates of the cost of gross rent (including utilities) for atwo-bedroom apartment at the 45th percentile of the county or metro area census division.Data are available for 354 metro areas and 2,305 nonmetro counties from 1983 to thepresent. It is necessary to collapse the county-specific FMRs into fewer groups because thePSID public-use data do not contain county identifiers for respondents because of confi-dentiality concerns. Following Jolliffe (2004) and Short (2001), the county-specific FMRsare aggregated into one hundred different price levels. For each state, there is one index formetro counties and one for nonmetro counties (except New Jersey, which has only metrocounties), and there is a separate index for the District of Columbia.8 Spatial housing priceindices are compiled in this manner for each analysis year.

Figure 2, which shows FMRs for metro and nonmetro aggregates for 1985 to 1999,makes clear the need to adjust the income-to-need measure for spatial housing costdifferences. Housing price indices are used to adjust 25 percent of a household’s needthreshold; this is the average percent of total household expenditures spent on housing and


utilities, according to data from the Consumer Expenditure Survey. Thus, it is assumed thatcosts for nonhousing items such as transportation, food, and clothing are, on balance, thesame in nonmetro and metro areas. Some analysts studying metro–nonmetro differences inpoverty have instead used housing costs as a proxy for overall living costs (e.g. Jolliffe2004; Ulimwengu and Kraybill 2004). The latter approach is inappropriate if other house-hold expenditure items are more expensive in nonmetro than metro areas, which is possible.For example, a national survey of 376 supermarkets and 2,002 small groceries found thathouseholds in nonmetro areas face food prices that are 4 percent higher than the pricesfaced by metro households (Mantovani and Daft 1996).

For comparative purposes, Table 1 provides descriptive statistics for the analysis vari-ables for all 1993 PSID-responding households (a nationally representative sample) and forthe subsample. The subsample is intended to be representative of low-income household-ers. Note that sampling weights and variables identifying stratum and sampling errorcomputation units are used to take account of the PSID multistage sampling design anddifferential attrition, and to approximate nationally representative estimates. The test sta-tistics shown in the last column of the table enable hypothesis testing for differences inmeans or differences in proportions. Table 1 indicates that the subsample is economicallydisadvantaged relative to the full sample of PSID householders as measured by income to

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FIGURE 2. FAIR MARKET RENT FOR NONMETRO AND METRO COUNTIES, 1985–1999.Source: Author’s calculations based on U.S. Department of Housing and Urban

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need. Other statistically significant differences are that, compared with householders in thefull PSID sample, subsample householders are more likely to be female; have a youngchild, and live in a nonmetro area; are less likely to be white and employed; and have lowereducational attainment.

ResultsRegression results for three specifications are shown in Table 2. The first two models

treat the data as a cross section and differ on whether educational attainment is excludedor included. The third specification exploits the panel nature of the data; it is an indi-vidual fixed-effects model of income to need. In individual fixed-effects models, time-invariant variables are not included because they are collinear with the person-specificconstant terms. Thus, results for gender, race, and educational attainment (for the PSIDthis information was only collected once during the 1985–1993 period) are not providedfor model 3. The adjusted R-squared values reported at the bottom of Table 2 indicatethat adding controls for educational attainment and unobserved individual heterogeneityimproves model fit considerably. The calculated F-statistics are significant at the 95percent confidence level, providing support for the hypothesis of joint significance of theexplanatory variables. At standard test levels, most of the point estimates are individuallysignificant at the 95 percent confidence level.9 While the magnitudes of variable coeffi-cients differ across specifications, the signs of the point estimates are the same. In addi-tion, the set of statistically significant variables is roughly the same; exceptions are ageand household size, for which statistical significance is reduced in the regressions withmore controls.

The signs of parameter estimates in Table 2 are consistent with prior research. Coeffi-cients for the variables age and age squared in model 1, for example, indicate that age ofthe household head is positively correlated with income to need until the householderreaches the age of 62 years, at which point the correlation becomes negative. Results showhouseholders who are female and whose main race is not white, have lower economicwell-being, all else being equal. Consistent with economic theory and empirical evidence,results for model 2 indicate that education strongly influences income level. For example,evaluated at the sample average for income to need of 1.78, an additional year of educationis associated with a 5 percent increase in income to need. Employed individuals have highereconomic well-being than their counterparts who are unemployed, out of the labor force,retired, or disabled. Consistent with other research, marriage is found to be positivelycorrelated with income to need. Findings also show that households with more membersand with a young child present have lower income to need.

The study’s first hypothesis is that householders with low educational attainment tendto sort themselves into nonmetro areas. One way to test this hypothesis is through changein the coefficient on nonmetro residence when controls for educational attainment areintroduced; that is, compare models 1 and 2 in Table 2. Model 1 shows a point estimateof -0.16 for nonmetro residence. Evaluated at the sample average for income to need of1.78, this result indicates that a householder living in a nonmetro area has income to


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need that is 9 percent lower than a similar household head residing in a metro area. Thisgap in economic well-being between nonmetro and metro residents is not insubstantialgiven that the income-to-need measure has been adjusted for spatial housing price dif-ferences. Model 2, which controls for householder educational attainment, shows thatincome to need is 7 percent lower for householders in nonmetro compared with metroareas. Thus, controlling for householder education does not eliminate the metro incomepremium, but it does reduce it by about 30 percent. In tandem, results for models 1 and2 suggest that one reason economic well-being is lower in nonmetro than in metro areasis that there is a relative concentration of people with low educational attainment innonmetro places.

The second study hypothesis is that people with unobserved attributes related to havinglow income tend to sort themselves into nonmetro localities. Model 3 controls for unob-served income capacity (at least that which is time invariant) by including individualconstant terms for each householder. If unobserved income capability is negatively corre-lated with nonmetro residence, then controlling for individual heterogeneity should reducethe absolute value of the nonmetro coefficient. Results for model 3 show that introductionof individual fixed-effects actually leads to an increase in the metro income premium: Thenonmetro coefficient indicates a householder living in a nonmetro area has income to needthat is 15 percent lower than a householder residing in a metro place. This finding, ratherthan lending support to the residential sorting hypothesis, provides indirect evidence infavor of the structural condition hypothesis—that otherwise identical individuals will havelower economic well-being in nonmetro compared with metro settings because of thespatial distribution of economic opportunities. Another plausible interpretation is thatequally able workers earn lower wages in rural than in urban areas because of a lack ofagglomeration economies in rural places.

Several other model specifications are examined. These exploit more fully the panelnature of the data, allowing different residential changes to have various effects onincome to need. Categorical variables indicating types of moves (nonmetro to metro ormetro to nonmetro) and types of stays (remained in a nonmetro area or remained in ametro area) are substituted for the nonmetro binary variable.10 During the analysis period,there were 166 metro-to-nonmetro moves and 143 nonmetro-to-metro moves. Using theparameter estimates from the categorical residential mobility variables, one can answertwo separate questions: (1) What happens to the economic well-being of a metro house-holder who moves to a nonmetro place compared with the well-being of a similar metrohouseholder who stays in an metro place? (2) What happens to nonmetro householderswho move to a metro area compared to otherwise similar householders who remain in anonmetro area?

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nonmetro–metro moves have a positive association with income to need, but the variable isnot statistically significant at standard test levels. It may be that it takes time for trans-planted nonmetro workers to catch up with other metro residents; that is, rather than animmediate income-level increase as a result of a nonmetro–metro move, migrants mayexperience income gains over time.

Regression results indicate that income to need is associated with moving from a metroto a nonmetro area. Model 4 shows that metro-to-nonmetro migrants see their income-to-need ratio fall by 25 percent (the coefficient divided by average income to need) immedi-ately after such a move. This effect changes with controls for householder educationalattainment and unobservables. In particular, model 6 shows that metro-to-nonmetromigrants have income to need which is 30 percent lower than comparable individuals whoremain in metro places. This finding is consistent with results reported in Table 2, suggest-ing that otherwise similar individuals will fare worse economically in a nonmetro than ametro area.

ConclusionIn this article data from nine waves of the Panel Study of Income Dynamics (PSID)

were used to test the hypothesis that the higher incidence of poverty in nonmetro comparedwith metro America is partly explained by a sorting into nonmetro areas of people withpersonal attributes associated with human impoverishment. The study’s hypotheses aretested indirectly with a series of multivariate regression models in which the dependentvariable is a householder’s income to need and explanatory variables are individual char-acteristics and place-level factors, including whether the county of residence is nonmetro-politan. A base regression model which excludes controls for householder educationalattainment and for unobserved individual heterogeneity shows that nonmetro householdershave income to need that is 9 percent lower than metro householders. Controlling foreducational attainment has the effect of reducing the nonmetro effect to 7 percent. Bycontrast, a third regression model that introduces controls for unmeasured income capacityvia an individual fixed-effects specification shows a metro-nonmetro gap in economicwell-being of 15 percent. Taken together, study findings suggest enduring nonmetro povertyis explained both by a sorting of low human capital individuals into nonmetro areas and byreduced economic opportunities in nonmetro compared to metro places. This conclusion,however, must be qualified given that the empirical approach of this study is very indirect.Migration modeling in future work will be important to directly test the self-sortinghypothesis.

Results stimulate several questions that warrant investigation. First, what unobservedincome capacity variables are being controlled for in the individual fixed-effects models?Study findings may indicate that people who “choose” nonmetro living have a strongerwork ethic, are more motivated, or have higher individual social capital (e.g., socialnetworks, social skills). Qualitative studies may prove useful for gaining clues on this issue.A second question is why do people choose nonmetro living given its reduced possibilities


for economic well-being? Are people drawn to nonmetro places because of lower livingcosts (beyond housing costs), possibilities for self-employment, quality-of-life factors, orother reasons?

Finally, why is income to need lower in nonmetro places? Do place-level factors such asa community’s level of social capital, job mix, job growth, and availability of work supportsplay important roles in the geographic distribution of poverty (e.g. Cotter 2002; Partridgeand Rickman 2005; Rupasingha and Goetz 2003)? To assess the relative importance ofplace-level and individual-level factors in a longitudinal framework is an important area forfuture research on nonmetro poverty; such research requires access to confidential datawith identification codes for respondents’ places of residence. Future empirical work canimprove the design of anti-poverty policy, providing insights on what combinations ofhuman-capital and community-strengthening policies are most likely to reduce nonmetropoverty and its unfavorable consequences.

NOTES1. The study’s unit of analysis is the household, which includes persons related by blood, marriage,

adoption, and unrelated long-term cohabitors. A household can also be a single person living alone

or a person sharing a dwelling unit with a nonrelative. It should be noted that the household is a

broader analysis unit than the family unit used by the U.S. Census Bureau for determining poverty

thresholds.

2. Fixed-effects models are commonly used to analyze panel data. In this specification, individual-

varying, time-invariant (e.g. gender or “motivation”) and time-varying, individual-invariant (e.g.

interest rates) omitted variables are assumed to be constant and enter as binary variables in the

regression equation (Hsiao 1986).

3. The empirical approach is similar to that of Glaeser and Maré (2001) who examined whether the

observed wage premium in large cities reflects that “more-able” workers choose city living.

4. The main national surveys used for poverty research are the PSID, the Current Population Survey

(CPS), the Survey of Income and Program Participation (SIPP), the National Longitudinal Survey

of Youth (NLSY), and the National Survey of America’s Families (NSAF). The CPS, similar to

the PSID, provides public-use access to data on metro–nonmetro residence.

5. Requiring all sample members to have at least one year in poverty has the effect of reducing the

positive skewness of the dependent variable’s distribution.

6. For discussion of the rationale for using FMR data, see Citro and Michael (1996). For discussion

of some shortcomings of using FMR data for living cost adjustment see Short (2001).

7. The FMR spatial cost-of-housing adjustment of the dependent variable also acts as a de facto

inflation adjustment.

8. An obvious limitation of this approach is that within a given state, metro areas with very different

rental housing prices are grouped together. For example, for NewYork state, Buffalo and NewYork

City have the same FMR index.

9. Standard errors reported in Table 2 and Table 3 use the Huber/White heteroskedasticity-consistent

estimator of variance (Huber 1967, White 1980).

10. This approach is similar to Freeman (1984) who studied the wage premium of union workers.


11. Parameter estimates and goodness-of-fit measures are invariant to the excluded category. Only

parameter estimates for the move/stay binaries change when the excluded category is alternately

“stayed in a nonmetro area” or “stayed in a metro area.”

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