Stuart Sweeney

A test for the coincident economic and spatial clustering of business enterprises

Abstract. This paper uses input-output data combined with point process modeling techniques to test whether enterprises linked within nominal buyer-supplier chains have a greater propensity to cluster in space than manufacturing enterprises in general. The methodology controls for the general tendency of firms to seek locations in concentrated agglomerations and isolates the influence of firm interdependence on spatial clustering. Our findings suggest that there is indeed an association between economic linkages and geographic clustering in our study area, but only for some types of economic clusters, mainly those that are comprised mainly of more knowledge-based or technology-intensive sectors. In general, we endeavor to show that spatial analytical methods hold considerable promise for conducting rigorous tests of industrial location questions.

Central-city and suburban migration patterns: is a turnaround on the horizon?

Abstract The huge population losses that characterized many older, larger U.S. cities during the 1960s and 1970s slowed and in some cases ceased during the 1980s and early 1990s. Periodic media reports of neighborhood turnarounds, commercial revitalization, and improvements in housing and the quality of life in selected inner‐city subareas have been taken as signs that central cities are retaining middle‐class residents and even attracting some back from the suburbs. Analysis of metropolitan household migration patterns based on the U.S. Census Bureaus 1980 and 1990 Public Use Microdata Samples and more recent Current Population Surveys shows that the dominant trend in residential movement among most population subgroups is still toward the suburbs. While not discounting reports of central‐city neighborhood turnarounds and selective demographic revitalization, our findings imply that those improvements are limited and that a widespread back‐to‐the‐city movement is not likely in the foreseeable future.

Measuring the Spatial Focus of Migration Patterns

The changing territorial concentration of migration flows is of interest to many geographers, yet we still do not have a widely accepted index of spatial focus. The much used index of migration efficiency has been shown to be an inadequate index of such spatial concentration, and two candidates have been suggested to replace it: the Gini index and the coefficient of variation. Both are examined in this paper, and a comparative assessment is offered. Data from the 1970, 1980, and 1990 censuses are used to illustrate the two measures. An examination of the findings reveals that the coefficient of variation measure indicates higher levels of spatial focus than does the Gini index for states with highly concentrated flows.

Out-Migration, Depopulation, And The Geography Of U.S. Economic Distress

This article uses data from the 1969 to 1999 period to examine the spatial extent and temporal persistence ofU.S. economic distress as viewed by three different indicators: unemployment, low income, and out-migration-induced population decline. The basic unit of analysis is the commuter zone. The shifting geography of distress is summarized for the four census regions and three regions of traditional economic development concern. The research grew out of an effort to assist the U.S. Economic Development Administration (EDA) in a review of criteria used to target development assistance. EDA was concerned that it may be neglecting distress associated with out-migration-induced population decline; that is, that some regions may be deserving of development aid even if their level of distress appears moderate based on the traditional core criteria: low income and high unemployment. The authors address the practical and theoretical issues associated with out-migration-induced population decline as a type of economic distress and comment on the development priorities implied by each of the three indicators.

Population Forecasting with Nonstationary Multiregional Growth Matrices

Though the mathematics of multiregional population projections were defined over twenty years ago, and the methodology has seen some adoption internationally, most practitioners in the United States still use rudimentary cohort component projections techniques. Both the stationarity assumption and the implicit five-year retrospective time scale imposed by the census migration data have probably contributed to the limited use of multiregional projections methods. This paper reviews previous attempts to overcome the stationarity assumption and proposes a decompositional approach using log linear models estimated via the ECM algorithm. The paper discusses the advantages of the decompositional approach and implements the model for intrastate migration in California.

Combining insights from quantile and ordinal regression: Child malnutrition in Guatemala

Chronic child undernutrition is a persistent problem in developing countries and has been the focus of hundreds of studies where the primary intent is to improve targeting of public health and economic development policies. In national level cross-sectional studies undernutrition is measured as child stunting and the goal is to assess differences in prevalence among population subgroups. Several types of regression modeling frameworks have been used to study childhood stunting but the literature provides little guidance in terms of statistical properties and the ease with which the results can be communicated to the policy community. We compare the results from quantile regression and ordinal regression models. The two frameworks can be linked analytically and together yield complementary insights. We find that reflecting on interpretations from both models leads to a more thorough analysis and forces the analyst to consider the policy utility of the findings. Guatemala is used as the country focus for the study.

Robust Point-Pattern Inference from Spatially Censored Data

Administrative data sources are increasingly being used for spatial analysis and policy formation. For example, ‘welfare to work’ programs have stimulated demand for spatial mismatch studies in which ES-202 employment files are used. The increased resolution gained by geocoding the address records in administrative files can be of enormous research value when the process under study resolves over small distances. Yet the resulting point-referenced data are problematic for inferential analysis. In particular, administrative data typically represent a sample of convenience, thus posing serious validity problems for statistical inference. The authors propose a robust estimation method for spatial pattern inference based on spatially censored data. The performance of the estimator is explored with the aid of simulated data and is also demonstrated with ES-202 data from North Carolina.

Growing buildings in corn fields: Urban expansion and the persistence of maize in the Toluca Metropolitan Area, Mexico

Urban growth continues to rise globally, especially in and around small cities and peri-urban areas of the developing world. In Mexico, a culture of maize production still exists alongside rapid urban and industrial growth, which exemplifies a hybridized urban-rural landscape. This paper discusses a study of household land-use and livelihood strategies in the Toluca Metropolitan Area, west of Mexico City, a traditional maize-growing region that has experienced rapid urban growth. Logistic regression combined with ethnographic data illustrate that maize is being abandoned to some extent as producers age and non-farm income sources surge. At the same time, some maize still persists for tradition and security as non-farm income is often volatile. Our results reflect a persistence of maize in peri-urban areas of central Mexico, which should not be ignored by policy and planning.

On the State of the Geography in the U.S. Bureau of Labor Statistics Covered Wages and Employment (ES-202) Series

This article discusses the strengths and weaknesses of the confidential establishment-level Covered Wages and Employment (ES-202) data series for small area economic analyses and other kinds of quantitative geographical research. The article examines the improvements in the geographic identifiers in the file over the past decade based on analysis of confidential micro data for forty-six states. It also examines the extent of spatial censoring in the ES-202 file stemming from two sources: (1) missing or corrupt physical address information in the raw data and (2) failure to attach spatial identifiers (geocode latitude/longitude coordinates) to physical addresses. Results of geocoding tests show that samples of address-matched units from state ES-202 files are likely to be biased along several dimensions. The article argues that a key area of geographical research in the future is methods to address bias in administrative samples. Research along these lines would substantially improve the usefulness of confidential micro data series for regional science research.

Localization and Industry Clustering Econometrics: An Assessment of Gibbs Models for Spatial Point Processes

The objective of this paper is to assess an approach to statistical modeling of point referenced establishment data that permit inclusion of “environmental” or establishment‐specific covariates and specific forms of interestablishment interaction. Gibbs models are used to decompose the conditional intensity of the spatial point process into trend and interaction components. The trend is composed of access measures (primarily different classes of roads) and three different interaction processes are tested: Geyer, area interaction, and Strauss hard core. While the models used have proved to be useful in ecology, we are unaware of any applications to establishment or firm data. In empirical application, the models yield intuitively appealing results for the trend component, and the ability to specify the interaction component gives deeper insights into interestablishment spatial dynamics than any previously published methods.