Persistent Rural Poverty: Is It Simply Remoteness and Scale?

Transcription

1 Review of Agricultural Economics Volume 29, Number 3 Pages DOI: /j x Persistent Rural Poverty: Is It Simply Remoteness and Scale? Mark D. Partridge and Dan S. Rickman Oakridge, Oregon was a prosperous timber community of about 4,000 people until its last mill closed in Many households now struggle in or just above poverty, though they seem determined to remain in their scenic community. Flourishing Eugene could provide employment opportunities, but being 55 miles away limits the ability of Oakridge s residents to take advantage. (Eckholm) Among Appalachia s problems are that it is too far from big cities to easily attract businesses. (Altman) Despite declines in national poverty measures during the 1990s, high rates of poverty have persisted in several pockets of rural America. The poverty rate exceeded 20% in 356 counties in each of the 1979, 1989, and 1999 censuses of population (Partridge and Rickman, 2005b). Almost 28% of people in completely rural counties were in persistent poverty (PP) counties (U.S. Department of Agriculture). The academic literature lists remoteness and the lack of economic scale as prime suspects for pockets of high rural poverty (Blank; Weber et al.). All else equal, poverty rates fell more in nonmetropolitan counties adjacent to metropolitan areas during the 1990s (Rupasingha and Goetz). Yet, Partridge and Rickman (2005a) did not find that metropolitan adjacency or population affects the probability a previously PP nonmetropolitan county escaped PP status during the 1990s. Distance plays a central role in economic models of agglomeration (see Rosenthal and Strange). Nominal wages are higher in core agglomerated areas, falling with distance from core areas as the agglomeration economies attenuate. Thus, with less labor demand, and likely imperfect labor mobility (Lucas), remoteness could be expected to produce persistent pockets of high rural poverty. Yet, PP areas may be relatively less affected by proximate urban Mark D. Partridge is a professor of AED Economics at The Ohio State University. Dan S. Rickman is a professor of Economics at Oklahoma State University. This paper was presented at the Principal Paper session, Rural Poverty Revisited: A New Look at Place and Policy, Allied Social Sciences Association annual meeting, Chicago, January 5 7, The articles in these sessions are not subject to the journal s standard refereeing process.

2 Proceedings 431 agglomerations. The impoverished in PP areas may be culturally, socially, and economically isolated (Glasmeier and Farrigan; Pickering, Mushinski, and Allen), regardless of geographic proximity to urban agglomerations. This study examines the role of remoteness in nonmetropolitan county PP status. To date, there has been little formal empirical investigation of the nexus between distance from urban agglomeration and rural poverty, and none for rural PP areas. We separately estimate the poverty effects of county distances from successively higher-tiered metropolitan areas for persistent and non-pp nonmetro counties. Rural Distances from Urban Agglomerations and PP Geographic distance can hinder both product and factor market exchange between urban and rural areas, isolating rural businesses and residents from the rewards of urban agglomeration. Theories abound on the reasons for agglomeration. Close proximity lowers transportation costs associated with the delivery of intermediate and final goods, lowers costs of obtaining information about demand and supply conditions, and may foster knowledge spillover between firms. Likewise, Partridge et al. (in press) emphasizes work-commuting opportunities for nonmetro residents from closer proximity to urban areas. Although fully mobile labor would arbitrage away distance-based differentials in labor earnings and poverty (Partridge and Rickman 2006, Ch. 3), distance may limit labor supply responses by rural residents. Information constraints, cultural similarities, the availability of support networks, and low housing costs, often cause the poor not to migrate, or to only migrate to other high-poverty areas (Lucas). Impoverished households in PP counties, however, may face more than geographic isolation. In these counties, formal economic organization may be replaced by informal means such as those based on cultural and social networks (Pickering, Mushinski, and Allen). Potential urban suppliers then may simply be unable to locate sources of demand for their products. Outside businesses also might be reluctant to invest in PP counties because of uncertainty regarding the efficacy of their private and public institutions (Blank). Likewise, cronyism and nepotism, often found in PP counties (Glasmeier and Farrigan), also may reduce outside economic linkages. Income in PP counties may be less likely to trickle down to the impoverished. Restricted political access, democratic failure of institutions, cronyism and nepotism, associated with exclusive social networks, can also make it difficult for the impoverished to improve their standing (Blank; Finneran and Kelly; Glasmeier and Farrigan). Institutional failures then can both limit an area s economic vitality and keep those at the bottom down, making nearby urban agglomerations irrelevant for poverty reduction. Empirical Implementation We define PP counties as those having poverty rates greater than 20% in 1979, 1989, and Following empirical specifications generally found in the poverty literature (e.g., Partridge and Rickman, 2005b, 2006), we employ a

3 432 Review of Agricultural Economics Table 1. Descriptive statistics and base regression results a (3) (4) (5) (6) (1) (2) PP Non-PP PP Non-PP PP Non-PP Base Base Dist Emp Dist Emp (std dev) (std dev) (t-stats) (t-stats) (t-stats) (t-stats) 1999 poverty rate na na na na (dependent variable) (5.6) (4.1) Lagged 1989 poverty rate (7.3) (5.0) (9.23) (18.31) (9.35) (18.78) 1989 surrounding cty average poverty (6.3) (4.7) (2.12) (4.21) (1.72) (4.21) Dist to nearest MA (kilometers) (40.6) (61.4) (0.28) (5.43) (0.05) (4.56) Incremental dist MA>250k (kilometers) (98.2) (104.2) (0.37) (3.44) (0.47) (3.78) Incremental dist MA>500k (kilometers) (62.9) (68.4) (1.32) (4.32) (1.70) (4.47) Incremental dist MA>1.5m (kilometers) (131.9) (108.5) (1.28) (1.22) (1.50) (1.48) Nearest MA 1990Pop 151, , e 7 1.2e 6 2.8e 7 1.2e 6 (135,741) (135,754) (0.97) (4.26) (0.31) (4.06) Nearest MA Indmix Gr (0.046) (0.065) (2.29) (1.21) Nearest MA Indmix Grth nearest MA dist (2.41) (2.11) % own cty Indmix Grth (0.016) (0.018) (0.35) (3.60) (1.13) (1.10) Dist near MA % own Indmix Grth (1.13) (3.52) own emp growth (19.0) (19.6) (5.25) (1.75) (5.83) (2.03) 1990 county population 18,606 23, e 5 7.0e 7 6.7e 5 1.9e 6 (14,934) (21,614) (2.21) (0.13) (2.38) (0.33) R N 356 1, , ,848 a In parentheses in columns (1) and (2) are standard deviations and absolute values of the robust t-statistics in columns (3) (6). The full set of results is available from the authors. partial adjustment model, in which the current poverty rate is a function of initial characteristics that determine its long-run poverty rate, contemporaneous economic conditions, and the lagged poverty rate. The lagged poverty rate controls for disequilibrium and for any fixed effects that lead to PP rate differentials. Table 1 lists the variables used in the empirical model. The dependent variable is the overall 1999 county person poverty rate. Most of the causal variables are self-explanatory (for details, see Partridge and Rickman 2005b). Except for the job growth variables, the explanatory variables are measured circa 1990 or before. An advantage of including initial conditions is they should avoid any

4 Proceedings 433 reverse causality with current poverty rates. Thus, the following model is estimated for nonmetro counties (county i in state s): (1) POV is1999 = POV is AVGNEIGBORPOV is PROXIMITY is + ECON is1990s + DEMOG is s + ε is1999. AVGNEIGHBOR is the average 1989 poverty rate in contiguous counties (including metropolitan and nonmetropolitan counties) to account for spatial spillover effects. The PROXIMITY vector includes measures of the county s proximity within the urban hierarchy as well as urban spillover. We define an urban center as a metropolitan area (MA). 2 We first include distance to the nearest urban center of any size, measured as the distance from the county s centroid to the centroid of the nearest MA. 3 We also include the incremental distance in kilometers from the county to reach an MA with at least 250,000 people, which is zero if the nearest MA is already over 250, We also include the incremental distances to reach a MA of at least 500,000 and at least 1.5 million. 5 Finally, we include population of the nearest MA, which captures agglomeration or dispersion effects associated with marginal changes in its population. ECON contains measures of job growth and industry restructuring for various periods using place of work county employment growth from the U.S. Bureau of Economic Analysis. 6 Because job growth may be endogenously determined with 1999 poverty rates, we substitute the industry mix variable from shift-share analysis as an exogenous proxy for local labor demand shifts. Industry restructuring is measured as the share of employment that has recently shifted across sectors using a dissimilarity index. In sensitivity analysis, we also interact the distance from the nearest MA with a measure of the own county industry mix job growth, and jointly include a measure of the MA s job growth and its interaction with distance to the MA. The CTY TYPE vector includes the 1990 county population. Besides accounting for thicker labor markets with potentially better employment matches, own population also proxies for county cost of living. The DEMOG vector includes demographic traits commonly believed to be correlated with poverty such as age, race, educational attainment, immigration, and single-headed household status. They are measured at their 1990 levels (or and for the immigration shares). s denotes the state-fixed effect and ε is the error term. 7 State-fixed effects capture specific factors common across counties in each state such as public policies. Empirical Results We compare the determinants of poverty in nonmetropolitan PP counties with those of non-pp nonmetropolitan counties. Columns (1) and (2) of table 1 report the descriptive statistics for the two samples. PP poverty rates were on average over 13% points higher than non-pp rates in 1999 and almost sixteen points higher in The typical PP-county is on average about 87 kilometers from the centroid of their nearest MA, while the corresponding average is about

5 434 Review of Agricultural Economics 90 kilometers away for non-pp counties. There is evidence that PP counties are more remote only for proximity to MAs greater than 1.5 million. Thus, if PP counties are more adversely affected by remoteness, their marginal effects of distance on poverty must be larger. Columns (3) and (4) report the base regression results for the two samples. The distance variables and the population of the nearest MA do not significantly influence PP-county poverty rates. Conversely, urban proximity has a highly significant impact on non-pp county poverty rates, while non-pp rates are also inversely related to the population of the nearest MA. Although the industry mix employment growth variable is insignificant, the rate of total job growth is both statistically significant and larger in PP counties. 8 This is consistent with Partridge and Rickman (2005b) who found that job growth has almost three times greater poverty reducing impact in PP counties versus non-pp counties. This likely occurs because of limited commuting and migration flows, making long-run labor supply more inelastic in PP counties. Although urban proximity does not appear to directly underlie PP county poverty rates, it may indirectly affect other factors such as job growth, including interacting with how urban job growth affects PP county poverty rates. It could be that PP poverty is only reduced when the nearest MA experiences sufficient job growth such that metropolitan employers are forced to hire disadvantaged workers from more distant PP counties. Even in this case, because of commuting costs, the positive effects of job growth should diminish with distance. To examine this, the models in columns (5) and (6), respectively, add two sets of interactions to the PP and non-pp models. The first set is the industry mix employment growth rate in the nearest MA and its interaction with the rural county s distance to the MA. 9 Second, we add an interaction of distance to the nearest MA with the rural county industry mix growth rate. The results support the hypothesis that new MA jobs spread out and reduce rural poverty. Even though the nearest MA job growth is insignificant at the 10% level in the non-pp sample, job growth in the nearest MA and its interaction with distance to the MA are jointly statistically significant at the 0.1% level in both samples (not shown). The coefficients indicate that greater MA job growth is associated with lower poverty, but this effect attenuates at a greater distance from the MA. However, there is a much greater MA spread effect for the PP counties. In results not shown, replacing the county poverty rate with job growth as the dependent variable, we also found distance to significantly reduce job growth in both PP and non-pp counties, producing an indirect distance poverty link. Summary and Conclusions Combining data from the 1990 and 2000 censuses of population with geographic information system distance measures, we empirically assessed the role of remoteness and scale in rural PP status. We defined remoteness as distances to successively higher-tiered MAs. For PP counties, the primary rural urban linkage we found was that job growth in the nearest MA reduced poverty, in which the antipoverty effect attenuated with greater distance.

6 Proceedings 435 Greater distance also reduced own-county job growth in both samples. Larger scale, measured by the beginning of period population, was associated with lower rates of poverty among PP counties. Yet, distances to successively higher-tiered MAs were themselves insignificant determinants of poverty for PP counties but were significant for non-pp counties. Thus, the usual benefits of close proximity such as lower costs of information and trade, and greater access to urban amenities and services did not apply to PP counties. Cultural and social isolation, in addition to other forms of economic isolation, may underlie the lack of direct importance of distance for PP counties. The importance of nearby MA employment growth and initial own-county scale suggests the potential benefit of a growth-pole economic development strategy for reducing poverty in PP counties. The insignificance of the distance variables themselves suggests the need to better integrate the economies in PP counties with the urban hierarchy, and perhaps the need to better tie the fortunes of the impoverished with their local economies through increasing social capital. Endnotes 1 USDA defines PP counties as having poverty rates above 20% in each of 1969, 1979, 1989, and Our findings were essentially unchanged when using alternative definitions of extreme poverty. Unless otherwise indicated, the variables are drawn from 1990 and 2000 Census SF3 files available online at at American Fact Finder. The distance variables are derived using geographical information systems. 2 Nonmetropolitan counties are defined based on the 2000 Bureau of Economic Analysis s classification. 3 The population-weighted county centroids are from the U.S. Census Bureau, which are also used to calculate the population-weighted centroids of multi-county MAs. The size categories are based on initial 1990 MA population. 4 For example, for a rural county 50 kilometers from the nearest MA of (say) 125,000 population and 90 kilometers to a MA of 400,000, the incremental distance to the nearest MA of at least 250,000 would be 40 kilometers (90 50). 5 For a county whose nearest MA is equal to or larger than its own size class, the incremental value is zero; if the county s nearest MA is already over 500,000, then the incremental values for the at least 250,000 and at least 500,000 categories are both zero. 6 Experimentation revealed that five-year (e.g., ) measures were superior to those from other periods, which were often highly insignificant. 7 We also allow for spatial clustering of the residuals that may affect the t-statistics. Using the Stata cluster command, we assume that county residuals are correlated within their economic region, but independent of county residuals in other regions. We use the U.S. Bureau of Economic Analysis s 179 economic regions to denote functional economic areas. Partridge and Rickman (2005b) report that their nonmetro poverty results were unaffected by instead using spatial econometric approaches. 8 One possible reason for the lower precision of the PP industry mix growth rate coefficients is the PP sample is much smaller and the industry mix growth rate is a proxy subject to measurement error. 9 The industry mix employment growth in the nearest MA is used to mitigate any endogeneity that may arise if we used actual job growth. Likewise, experimentation suggested that ten-year employment growth is appropriate when considering MAs. References Altman, D. The Disaster Behind the Disaster. NY Times, September 18, Available at Blank, R. Poverty, Policy, and Place: How Poverty and Policies to Alleviate Poverty are Shaped by Local Characteristics. Inter. Reg. Science Rev. 28(October 2005): Eckholm, E. Rural Oregon Town Feels Pinch of Poverty. NY Times, August 20, Available at

7 436 Review of Agricultural Economics Finneran, L., and M. Kelly. Social Networks and Inequality. J. Urban Econ. 53(March 2003): Glasmeier, A.K., and T.L. Farrigan. Poverty Sustainability and the Culture of Despair: Can Sustainable Development Strategies Support Poverty Alleviation in America s Most Environmentally Challenged Communities? The Annals of the Amer. Academy of Pol. and Soc. Science 590(November 2003): Lucas, R.E.B. The Effects of Proximity and Transportation on Developing Country Population Migrations. J. of Econ. Geog. 1(July 2001): Partridge, M.D., R. Bollman, M.R. Olfert, and A. Alasia. Riding the Wave of Urban Growth in the Countryside: Spread, Backwash, or Stagnation. Land Econ. in press. Partridge, M.D., and D.S. Rickman. Why Some US Metropolitan Counties Moved Out of Persistent High-Poverty Status in the 1990s. App. Econ. Letters 12(June 2005a): High-Poverty Nonmetropolitan Counties in America: Can Economic Development Help? Inter. Reg. Science Rev. 28(October 2005b): The Geography of American Poverty: Is There a Role for Place-Based Policies? Kalamazoo, MI: The W.E. Upjohn Institute for Employment Research, 2006:52. Pickering, K., D. Mushinski, and J.C. Allen. The Role of Social Capital in Poverty Alleviation in Native American Reservation Communities. Working Paper No , Rural Poverty Research Center, Oregon State University, OR, February, Rosenthal, S.S., and W.C. Strange. The Determinants of Agglomeration. J. of Urban Econ. 50(April 2001): Rupasingha, A., and S.J. Goetz. The Causes of Enduring Poverty: An Expanded Spatial Analysis of the Structural Determinants of Poverty in the U.S. Rural Development Paper No. 22, Northeast Regional Center for Rural Development, University Park, PA, U.S. Department of Agriculture. Rural Income, Poverty, and Welfare: Rural Poverty. Washington DC. Available at accessed on September 18, Weber, B., L. Jensen, K. Miller, J. Mosely, and M. Fisher. A Critical Review of Rural Poverty Literature: Is There Truly a Rural Effect? Inter. Reg. Science Rev. 28(October 2005):