Mark D. Partridge

Regional Computable General Equilibrium Modeling: A Survey and Critical Appraisal

Regional computable general equilibrium (CGE) models have grown in popularity in recent years as an alternative method to examine regional economies and regional policy issues. However, the contribution of regional CGE models has yet to be assessed. Therefore, this paper surveys the literature related to regional CGE modeling. The survey includes a discussion of the contributions that CGE models have made to regional economic analysis. In addition, the basic approach of regional CGE modeling is outlined, and the key features of existing regional CGE models are detailed. The paper also appraises of the current state of the art of regional CGE modeling and suggests future research directions.

The waxing and waning of regional economies: the chicken–egg question of jobs versus people

Abstract A central question in urban and regional economics is whether people follow newly created jobs into regions, or whether jobs follow newly arrived migrants. This study revisits the issue by constructing structural vector autoregression (SVAR) models for the 48 contiguous states. The SVAR models contain long-run identifying restrictions based on a simple labor-market model. The empirical results suggest that labor-demand shocks are generally more important than migration labor-supply shocks, although labor-supply innovations in total account for a majority of state employment fluctuations. Thus, it is slightly more likely that people are following jobs. Yet, the relative importance of demand and supply shocks greatly varies by period and region.

Voting with Their Feet: Jobs versus Amenities

The determinants of rural and urban community population change over the period 1991-2001 are investigated at a very fine level of disaggregation for Canada. The study examines the influence of local amenities, economic factors, and agglomeration economies on population growth for age cohorts starting from the very young to the elderly. Motivated by the objective of assessing the overall jobs versus people question in economic development, the emphasis is on estimating the relative contribution of groupings of variables in explaining the variations in population change rather than the contribution of individual variables. Results indicate that rural and urban populations are influenced to differing degrees by amenity, economic, and urban scale groupings of variables and that there are variations among age cohorts in both urban and rural areas. While economic variables are the most influential in population change for all rural cohorts, their contribution somewhat diminishes with age. In urban areas, amenity, and economic variable groupings have approximately equal importance across all cohorts. For the key young adult cohort, the economic grouping is clearly the most influential in rural areas, while it is a close second to amenities in urban areas. Copyright 2007 Blackwell Publishing.

A Century of Research on Rural Development and Regional Issues

Rural North America has undergone a major economic transformation over the past century due to labor-saving technological progress, reductions in transport costs, and rising household incomes. The results are greater rural economic diversity, selected rural population decline, increased rural--urban interdependence, emergent exurban areas, and amenity-led rural growth. We summarize key research insights and provide a selected review of the economics literature over the past 100 years with a focus on this economic transformation of rural places, its implications for rural communities, and key modeling innovations and applications. The many important contributions by agricultural economists are highlighted. Copyright 2010, Oxford University Press.

The Causes of Regional Variations in U.S. Poverty: A Cross-County Analysis

The persistence of poverty in the modern American economy, with rates of poverty in some areas approaching those of less advanced economies, remains a central concern among policy makers. Therefore, in this study we use U.S. county-level data to explore potential explanations for the observed regional variation in the rates of poverty. The use of counties allows examination of both nonmetropolitan area and metropolitan area poverty. Factors considered include those that relate to both area economic performance and area demographic composition. Specific county economic factors examined include economic growth, industry restructuring, and labor market skills mismatches. Copyright 2000 Blackwell Publishers

The Geographic Diversity of U.S. Nonmetropolitan Growth Dynamics: A Geographically Weighted Regression Approach

Spatial heterogeneity is introduced as an explanation for local-area growth mechanisms, especially employment growth. As these effects are difficult to detect using conventional regression approaches, we use Geographically Weighted Regressions (GWR) for non-metropolitan U.S. counties. We test for geographic heterogeneity in the growth parameters and compare them to global regression estimates. The results indicate significant heterogeneity in the regression coefficients across the country, most notably for amenities and college graduate shares. Using GWR also exposes significant local variations that are masked by global estimates suggesting limitations of a one-size-fits-all approach to describe growth and to inform public policy. (JEL R11, R23)

Riding the Wave of Urban Growth in the Countryside: Spread, Backwash, or Stagnation?

The advisability of an urban-centered growth strategy to reap the benefits of urban agglomeration economies is much debated. Rural areas benefit when the growth “spreads” to the hinterlands, especially within daily commuting distance. Yet, in distant-peripheral locations, urban growth may create a “backwash” as households relocate to the urban center. This study examines spread vs. backwash, as separate from long-run, distance-from-urban-center trend effects, using a novel Canadian GIS database. The unique nation-wide approach yields a spread and backwash rural-growth topography that varies by distance from the urban center, by urban population vs. income growth, and by size of rural community. (JEL R11, R14)

An SVAR Model of Fluctuations in U.S. Migration Flows and State Labor Market Dynamics

Large internal migration flows are typically viewed as evidence of flexible U.S. labor markets adjusting to asymmetrical regional demand shocks. Yet, amenity-induced migration flows suggest that they may not necessarily facilitate adjustment to demand shocks and instead may be destabilizing. This paper employs a structural vector autoregression model with long-run identifying restrictions to account for both labor-demand and labor-supply shocks in examining the role of migration in U.S. regional labor-market fluctuations. The results reveal that less than one-half of innovations in state migration flows are responses to labor-demand shocks. It is not until the third period that migrants fill a majority of demand-induced jobs in a typical state, while it takes about 7 to 8 years for migration flows to fully adjust to labor-demand shocks. The extent of the migration response also has implications for how much state and local economic development policies benefit original residents.

Distance from Urban Agglomeration Economies and Rural Poverty

Despite strong national economic growth and significant poverty reduction during the late 1990s, high poverty persisted in remote rural areas. This study uses a geographical information system county database to examine the nexus between rural U.S. poverty and remoteness. We find that poverty rates increase with greater rural distances from successively larger metropolitan areas (MAs). We explain this outcome as arising from the attenuation of urban agglomeration effects at greater distances and incomplete commuting and migration responses to lower labor demand in rural areas. One implication is that remote areas may particularly experience greater reductions in poverty from place-based economic development policies.

CAN GEOGRAPHICALLY WEIGHTED REGRESSIONS IMPROVE REGIONAL ANALYSIS AND POLICY MAKING

Policy design in a regional context requires explicit recognition of spatial heterogeneity in community characteristics as well as in the heterogeneity of how these characteristics impact the target variables. By providing only a “global” measure for the entire space, standard approaches such as ordinary least squares or (most) spatial econometric models tend to compromise spatial heterogeneity in favor of average estimates and efficiency. More assessment is needed of whether the gains of simplicity and statistical efficiency offset the losses from ignoring spatial heterogeneity. Using data for about 1,900 rural Canadian communities as a backdrop, the authors address this issue using a geographically weighted regression approach. The authors find that for about two-thirds of the variables, standard approaches would have significantly understated the spatial differences in the impact of selected variables. Standard analysis would not have uncovered this information, suggesting that subsequent policy inferences would be poorly suited to many local settings.