Home About us Contact
|
Home About us Contact | ||
|
|
||
County-level Data (county-level + data)
Selected AbstractsWAL-MART, LEISURE, AND CULTURECONTEMPORARY ECONOMIC POLICY, Issue 4 2009ART CARDEN This essay contributes to the debate about the alleged spillover effects associated with Wal-Mart's growth. Combining county-level data on Wal-Mart entry and location from 1985 through 1998 with individual-level data on leisure activities, we estimate a positive relationship between Wal-Mart penetration and participation in activities involving inputs that can be bought at Wal-Mart. The relationship between Wal-Mart penetration and activities that do not involve inputs that can be bought at Wal-Mart is negative in most cases but may be positive or zero for "cultural" activities such as attending classical music concerts and visiting art galleries. The evidence is consistent with the thesis that deeper Wal-Mart penetration expands consumption possibilities.(JELA13, D00, C12, Z11, Z13) [source] STRUCTURAL COVARIATES OF U.S. COUNTY HOMICIDE RATES: INCORPORATING SPATIAL EFFECTS,CRIMINOLOGY, Issue 3 2001ROBERT D. BALLER Spatial analysis is statistically and substantively important for macrolevel criminological inquiry. Using county-level data for the decennial years in the 1960 to 1990 time period, we reexamine the impact of conventional structural covariates on homicide rates and explicitly model spatial effects. Important findings are: (1) homicide is strongly clustered in space; (2) this clustering cannot be completely explained by common measures of the structural similarity of neighboring counties; (3) noteworthy regional differences are observed in the effects of structural covariates on homicide rates; and (4) evidence consistent with a diffusion process for homicide is observed in the South throughout the 1960,1990 period. [source] Change in the Concentration of Employment in Computer Services: Spatial Estimation at the U.S. Metro County LevelGROWTH AND CHANGE, Issue 1 2007DONALD GRIMES ABSTRACT This article models the concentration of computer services activity across the U.S. with factors that incorporate spatial relationships. Specifically, we enhance the standard home-area study with an analysis that allows conditions in neighboring counties to affect the concentration of employment in the home county. We use county-level data for metropolitan areas between 1990 and 1997. To measure change in employment concentration, we use the change in location quotients for SIC 737, which captures employment concentration changes caused by both the number of firms and the scale of their activity relative to the national average. After controlling for local demand for computer services, our results support the importance of the presence of a qualified labor supply, interindustry linkages, proximity to a major airport, and spatial processes in explaining changes in computer services employment concentration, finding little support for the influence of cost factors. Our enhanced model reveals interjurisdictional relationships among these metro counties that could not be captured with standard estimates by state, metropolitan statistical area (MSA), or county. Using counties within MSAs, therefore, provides more general results than case studies but still allows measurement of local interactions. [source] Predicting Patterns of Mammography Use: A Geographic Perspective on National Needs for Intervention ResearchHEALTH SERVICES RESEARCH, Issue 4 2002Julie Legler Objective. To introduce a methodology for planning preventive health service research that takes into account geographic context. Data Sources. National Health Interview Survey (NHIS) self-reports of mammography within the past two years, 1987, and 1993,94. Area Resource File (ARF), 1990. Database of mammography intervention research studies conducted from 1984 to 1994. Design. Bayesian hierarchical modeling describes mammography as a function of county-level socioeconomic data and explicitly estimates the geographic variation unexplained by the county-level data. This model produces county use estimates (both NHIS-sampled and unsampled), which are aggregated for entire states. The locations of intervention research studies are examined in light of the statewide mammography utilization estimates. Data Extraction. Individual level NHIS data were merged with county-level data from the ARF. Principal Findings. State maps reveal the estimated distribution of mammography utilization and intervention research. Eighteen states with low mammography use reported no intervention research activity. County-level occupation and education were important predictors for younger women in 1993,94. In 1987, they were not predictive for any demographic group. Conclusions. Opportunities exist to improve the planning of future intervention research by considering geographic context. Modeling results suggest that the choice of predictors be tailored to both the population and the time period under study when planning interventions. [source] THE EFFECTS OF TRADE WITH DEVELOPING COUNTRIES ON THE REGIONAL DEMAND FOR SKILL IN THE U.S.: EVIDENCE FROM COUNTY DATA,JOURNAL OF REGIONAL SCIENCE, Issue 3 2009Ivan T. Kandilov ABSTRACT Using county-level data from the 1980s and 1990s and a county-level trade measure that incorporates the county's industrial mix and patterns of international trade across industries, I provide new evidence that trade with developing countries raises the demand for skill and the skill premium in the U.S. Consistent with Heckscher,Ohlin, I find that trade driven by differences in factor endowments has an economically significant impact on local labor markets. The evidence suggests that when trade with developing countries rises, counties with higher skill endowment and greater employment in industries with larger trade shares experience greater relative demand for high-skilled labor. [source] The Causes of Regional Variations in U.S. Poverty: A Cross-County AnalysisJOURNAL OF REGIONAL SCIENCE, Issue 3 2000William Levernier 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. [source] | ||||||||||