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This paper evaluates the empirical relevance of a prominent social vulnerability measure to local economic and public health outcomes at two distinct stages of the COVID-19 pandemic. In contrast to traditional least-squares regression, conditional quantile regression offers stronger evidence in support of social vulnerability for predicting the pandemic's uneven economic devastation and death tolls across the United States. An extension of the spatial autoregressive geographically weighted regression model to conditional quantile regression reveals the varying role of social vulnerability within and across broad regions that were exposed to different levels of pandemic impacts. [ABSTRACT FROM AUTHOR]

The study investigated the spatial patterns of fiscal incomes in the United States (US) between 1917 and 2018. The authors examined the spatial shifts in income and population center of gravity and analyzed the role of localities in changes in per capita income using shift-share analysis. The study also calculated the spatial dependence of income and population. The authors analyzed spatial differences using the Hoover index and tried to identify points in time at which economic recessions had a significant impact on spatial processes in the US. The most important result of our research is that, in terms of both spatial dependence and heterogeneity, the New Deal had the greatest impact on spatial processes in the US. No government intervention or market trend since then has had such an impact on spatial processes. [ABSTRACT FROM AUTHOR]

This study evaluated relationships between county-level social vulnerability and broadband access using spatial clustering and regression approaches. County-level broadband availability (Federal Communications Commission [FCC] and Microsoft; 2019–2020), social vulnerability (COVID-19 Community Vulnerability Index [CCVI]; 2020), and primary care access (Area Health Resource File; 2019–2020) data sets were used. Two measures of broadband availability were considered: (1) Microsoft system-reported proportion of county population with broadband and (2) difference in FCC-reported and Microsoft-reported proportions of county population with broadband. Cluster maps were constructed using local Moran's I, and spatial Durbin models were estimated using primary care shortage designation and CCVI themes (socioeconomic status, minority status, housing/transportation/disability, epidemiological risk, health care system, high-risk environment, and population density). Among 3102 counties, county-level broadband coverage varied widely between Microsoft (0.39) and FCC (0.84), with greater coverage in the East and West, and larger discrepancies between FCC and Microsoft data in the South and Appalachia. In spatial regressions, a one-point increase in socioeconomic status vulnerability (0—least; 10—most vulnerable), was associated with a 2.0 percentage point (pp) reduction in broadband access (P < 0.001). Similar inverse relationships were observed with housing, epidemiological, and health care system variables. There were greater divergences between FCC and Microsoft measures with each one-point increase in socioeconomic status (1.4 pp), epidemiological risk (0.6 pp), and health care system (0.7 pp) vulnerability. More vulnerable counties had lower broadband and larger divergences between FCC and Microsoft data. Broadband is necessary for utilizing telehealth services; careful considerations in measuring broadband access can facilitate policies that improve equitable access to care. [ABSTRACT FROM AUTHOR]

This paper empirically investigates the conventional wisdom that urban residents have reacted to the Covid-19 pandemic by fleeing city centres for the suburbs. A conventional panel model of US ZIP code-level data provides mixed evidence in support of a shifting housing preference for more space or neighbourhoods farther from the urban core. Regressions accounting for spatial dependence and spatial heterogeneity show strong support of an urban flight within metro areas, but this local phenomenon is uneven across broad regions of the United States. The finding of geographical disparity underscores both the local as well as the regional nature of housing market conditions. [ABSTRACT FROM AUTHOR]

Widespread flooding can cause major damages and substantial recovery costs. Still, estimates of how susceptible a region is to widespread flooding are largely missing mainly because of the sparseness of widespread flood events in records. The aim of this study is to assess the seasonal susceptibility of regions in the United States to widespread flooding using a stochastic streamflow generator, which enables simulating a large number of spatially consistent flood events. Furthermore, we ask which factors influence the strength of regional flood susceptibilities. We show that susceptibilities to widespread flooding vary regionally and seasonally. They are highest in regions where catchments show regimes with a strong seasonality, that is, the Pacific Northwest, the Rocky Mountains, and the Northeast. In contrast, they are low in regions where catchments are characterized by a weak seasonality and intermittent regimes such as the Great Plains. Furthermore, susceptibility is found to be the highest in winter and spring when spatial flood dependencies are strongest because of snowmelt contributions and high soil moisture availability. We conclude that regional flood susceptibilities emerge in river basins with catchments sharing similar streamflow and climatic regimes. Key Points: We assess (seasonal) probabilities of widespread flooding over the United States using a continuous, stochastic streamflow generatorThe susceptibility to widespread flooding varies seasonally and regionally and is related to the climatic and hydrological regimeRegional flood susceptibility is weakened if river basins span regions with different climate and streamflow regimes [ABSTRACT FROM AUTHOR]

Global flood models integrate flood maps of constant probability in space, ignoring the correlation between sites and thus potentially misestimating the risk posed by extreme events. Stochastic flood models alleviate this issue through the simulation of flood events with a realistic spatial structure, yet their proliferation at large scales has historically been inhibited by data quality and computer availability. In this paper, we show, for the first time, the efficacy of modeled river discharge reanalyses in the characterization of flood spatial dependence in the absence of a dense stream gauge network. While global hydrological models may show poor correspondence with absolute observed river flows, we find that the rate at which they can simulate the joint occurrence of relative flow exceedances at two given locations is broadly similar to when a gauge‐based statistical model is used. Evidenced over the United States, flood events simulated using observed gauge data from the U.S. Geological Survey versus those generated using modeled streamflows have similar (i) distributions of site‐to‐site correlation strength, (ii) relationships between event size and return period, and, importantly, (iii) loss distributions when incorporated into a continental‐scale flood risk model. Extremal dependence is generally quantified less accurately on larger rivers, in arid climates, in mountainous terrain, and for the rarest high‐magnitude events. However, local‐scale errors are shown to broadly cancel each other out when combined, producing an unbiased flood spatial dependence model. These findings suggest that building accurate stochastic flood models worldwide may no longer be a distant aspiration. Plain Language Summary: Global flood risk is commonly estimated through flood inundation maps with a defined probability of occurrence. These flood simulations have a key drawback in that they fail to capture the spatial patterns exhibited during real flood events, instead modeling the same probability of flooding on every river at once. Solutions which rely on networks of gauged river flow observations will necessarily break down in the majority of the world's regions which lack such a resource. In this paper, we use historic river flows simulated by global rainfall‐runoff models (rather than observed flows) into a statistical model which captures the spatial correlation of flow extremes. If we examine the relative flow exceedance probabilities from these hydrological models rather than the volumetric flow values, flood events are generated which exhibit similar characteristics to those when gauged flow observations are used. Crucially, the simulation‐ and observation‐generated flood events produce near‐identical losses to buildings in the United States. The implications of this are that true stochastic flood risk models, which account for spatial dependence, can proliferate globally via the generation of realistic flood event sets from hydrological models. Key Points: Large‐scale flood hazard models typically neglect to represent the spatial dependence of real flood eventsRelative flow exceedances simulated by the fusion of global hydrological with statistical models reproduce gauge‐driven flood event setsAt the continental scale, key characteristics of a flood risk model are indistinguishable when driven with observed versus modeled flow data [ABSTRACT FROM AUTHOR]

Floods often affect large regions and cause adverse societal impacts. Regional flood hazard and risk assessments therefore require a realistic representation of spatial flood dependencies to avoid the overestimation or underestimation of risk. However, it is not yet well understood how spatial flood dependence, that is, the degree of co‐occurrence of floods at different locations, varies in space and time and which processes influence the strength of this dependence. We identify regions in the United States with seasonally similar flood behavior and analyze processes governing spatial dependence. We find that spatial flood dependence varies regionally and seasonally and is generally strongest in winter and spring and weakest in summer and fall. Moreover, we find that land‐surface processes are crucial in shaping the spatiotemporal characteristics of flood events. We conclude that the regional and seasonal variations in spatial flood dependencies must be considered when conducting current and future flood risk assessments. Plain Language Summary: Floods often affect large regions and cause adverse societal impacts. Regional flood hazard and risk assessments require a realistic representation of spatial flood dependencies to avoid the overestimation or underestimation of regional flood risk. However, it is not yet well understood how the spatial dependence of floods, that is, the degree of co‐occurrence of floods at different locations, varies in space and time and which physical processes influence the strength of this dependence. We identify regions in the United States with seasonally similar flood behavior and analyze processes governing spatial dependence. We find that spatial flood dependence varies regionally and seasonally and is generally strongest in winter and spring and weakest in summer and fall. Moreover, we find that land‐surface processes play a critical role in shaping the spatiotemporal characteristics of flood events. We conclude that to quantify shifts in flood risk in a warming climate one must not only understand land‐surface processes and extreme precipitation increases but also consider the regional and seasonal deviations in spatial flood dependencies. Key Points: We introduce a flood connectedness measure enabling the mapping of spatial flood dependenceThe spatial dependence of floods varies regionally and seasonally and is generally highest in winter and springLand‐surface conditions modulate the direct influence of extreme precipitation upon spatial flood dependence. [ABSTRACT FROM AUTHOR]

The literature on high school exit exams has found both positive and negative effects of these high stake exams on high school graduation rates. To this point, the literature has not taken into account the embedded nature of school districts within state education systems. We employ a Bayesian Hierarchical SLX model to account for the hierarchical nature of education data in the United States. Our approach also allows us to account for spatial spillovers that inuence graduation rates across districts and states. Using school district and state-level data for 45 states and 8194 school districts in the U.S. in 2015, we generally find no statistically significant effect of state exit exams on high school graduation rates. Random effect coefficients, however, point towards high school exit exams being negatively associated with graduation rates in a handful of states. [ABSTRACT FROM AUTHOR]

This paper investigates the spatial heterogeneity of landowners' willingness to supply three bioenergy crops: switchgrass, Miscanthus, and willow, in the northeastern United States. Spatial heterogeneity might arise for several reasons. For example, landowners closer to bioenergy processing plants might be more likely to be willing to supply bioenergy crops, and landowners who are more willing to supply bioenergy crops may be spatially clustered because they share similar land attributes, demographics, experiences, and/or values. Using high‐resolution GIS data related to the location of pellet plants utilizing bioenergy crops and survey data related to landowners' characteristics including spatial location, we estimate a spatial probit model to explain the variation in individual‐specific reservation prices (RPs)—the feedstock price at which landowners become willing to supply a bioenergy crop. We find that respondents' RP is lower the closer they live to their nearest pellet plant and spatial dependency is only present for switchgrass supply. We also identify three economic hotspots (areas with high potential supply and low RPs) for each bioenergy crop. We believe that bioenergy supply chains could be developed around these hotspots. Identifying economic hotspots and reservation price for biomass is important in establishing the supply chain for bioenergy. To address this, we apply a spatial econometric model coupled with geospatial hotspot analysis. We identify the price gap currently existing in the bioenergy supply chain, accounting for spatial dependence. Also, we locate potential sites to establish a bioenergy plant, considering biomass yield and price. These results are useful for bioenergy businesses and policymakers aiming to promote bioenergy development. [ABSTRACT FROM AUTHOR]

Gasoline retail prices show sometimes wild, asymmetric fluctuations over time. We explore the impact of spatial dependence on gasoline retail price formation by using for the first time an asymmetric spatial error correction model (ASpECM). We find evidence that the generally assumed symmetric price pattern is fully reversed when we account for spatial spillover effects, indicating that retail prices adjust more rapidly in an upward than in a downward direction. This finding suggests that empirical studies that ignore the role of spatial dependence and local competition may miss an important element of the nature of the gasoline price adjustment mechanism. [ABSTRACT FROM AUTHOR]

In this paper we seek to understand the nature of flood spatial dependence over the conterminous United States. We extend an existing conditional multivariate statistical model to enable its application to this large and heterogenous region and apply it to a 40‐year data set of ~2,400 U.S. Geological Survey gauge series records to simulate 1,000 years of U.S. flooding comprising more than 63,000 individual events with realistic spatial dependence. A continental‐scale hydrodynamic model at 30 m resolution is then used to calculate the economic loss arising from each of these events. From this we are able to compute the probability that different values of U.S. annual total economic loss due to flooding are exceeded (i.e., a loss‐exceedance curve). Comparing these data to an observed flood loss‐exceedance curve for the period 1988–2017 shows a reasonable match for annual losses with probability below 10% (e.g., >1 in 10‐year return period). This analysis suggests that there is a 1% chance of U.S. annual fluvial flood losses exceeding $78Bn in any given year, and a 0.1% chance of them exceeding $136Bn. Analysis of the set of stochastic events and losses yields new insights into the nature of flooding and flood risk in the United States. In particular, we confirm the strong relationship between flood affected area and event peak magnitude, but show considerable variability in this relationship between adjacent U.S. regions. The analysis provides a significant advance over previous national flood risk analyses as it gives the full loss‐exceedance curve instead of simply the average annual loss. Plain Language Summary: Traditional flood risk analyses make the assumption that flow probability (the chance that a given river discharge is exceeded) does not vary within river catchments within an event. Real floods, however, do not look like this: In some places flooding is more severe than in others. Over a few tens of kilometers of river assuming the same event return period everywhere is perfectly fine, but over larger areas it breaks down. At national scales traditional risk analyses can only estimate the average annual loss. To estimate the total annual losses that might occur in more extreme flooding years the risk analysis needs to be based on more realistic spatial patterns of flooding. In this paper we use a sophisticated statistical model, based on U.S. Geological Survey river flow data, to simulate 1,000 years of spatially realistic U.S. flooding comprising more than 63,000 individual events. By calculating the damage for each event as a dollar value, we are able to estimate the probability of the United States experiencing particular levels of annual flood losses. We show that there is a 1% chance of U.S. annual fluvial flood losses exceeding $78Bn in any given year, and a 0.1% chance of them exceeding $136Bn. Key Points: 1,000 years of realistic U.S. flood patterns, comprising >63,000 individual events, are simulated using a statistical modelMonetary losses for each event are calculated using a continental hydrodynamic model at 30 m resolutionThe analysis suggests that there is a 1% chance of U.S. annual fluvial flood losses exceeding $78Bn in any given year [ABSTRACT FROM AUTHOR]

Knowledge spillovers are critical for innovation and new value creation in an increasingly knowledge-intensive economy. The substantial scholarly attention on knowledge spillovers has shown that there is a rapid distance decay associated with knowledge spillovers and that there is a positive state border effect. We show that the effects of distance, technology proximity, and the state border effect on knowledge flows are dependent on the size of the regions (MSAs) involved in the knowledge flow. Not accounting for innovation size (innovative communities and social relationships) in the flow of knowledge across origin-destination regions results in aggregation bias in the parameter estimates. Knowledge spillovers are more localized for small innovation MSAs than for large ones. Distance is not as much of a resistance factor in knowledge flow for larger innovation metro areas compared with smaller regions. Spatial origin and destination effects due to technology compatibility of neighboring regions do not affect the knowledge flow among large innovation MSAs, but do have an effect when small MSAs interact with large MSAs. [ABSTRACT FROM AUTHOR]

This study examined spatial dependence in neighborhood change between 1990 and 2010 in the largest 100 metropolitan areas in the U.S. By analyzing neighborhood housing value change, this study found that there is considerable spatial autocorrelation in neighborhood change. Neighborhoods form spatial clusters in neighborhood housing value and its change. The spatial analysis also showed that there was a persistent spatial inequality between the city and suburbs but that this spatial inequality has declined over time. Finally, this study suggests that coordinating community development efforts with surrounding neighborhoods rather than taking isolated actions can result in more successful outcomes. [ABSTRACT FROM PUBLISHER]

Biomass is low-carbon energy and has tremendous potential as an alternative to fossil fuels. However, the significant role of biomass in future low-carbon energy portfolio depends heavily on its consumption. The paper presents a first attempt to examine the spatial-temporal patterns of biomass consumption in the United States (US), using a novel method-spatial Seemingly Unrelated Regression (SUR) model, in order to strengthen the link between energy planning and spatial planning. In order to obtain the robust parameters of spatial SUR models and estimate the parameters efficiently, an iterative maximum likelihood method, which takes full advantage of the stationary characteristic of maximum likelihood estimation, has been developed. The robust parameters of models can help draw a proper inference for biomass consumption. Then the spatial-temporal patterns of biomass consumption in the US at the state level are investigated using the spatial SUR models with the estimation method developed and data covering the period of 2000–2012. Results show that there are spatial dependences among biomass consumption. The presence of spatial dependence in biomass consumption has informative implications for making sustainable biomass polices. It suggests new efforts to adding a cross-state dimension to state-level energy policy and coordinating some elements of energy policy across states are still needed. In addition, results consistent with classic economic theory further proves the correctness of applying the spatial SUR models to investigate the spatial-temporal patterns of biomass consumption. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing, also known as fracking, has been increasing exponentially across the United States, which holds the largest known shale gas reserves in the world. Studies have found that the high-volume horizontal hydraulic fracturing process (HVHFP) threatens water resources, harms air quality, changes landscapes, and damages ecosystems. However, there is minimal research focusing on the spatial study of environmental and human risks of HVHFP, which is necessary for state and federal governments to administer, regulate, and assess fracking. Integrating GIS and spatial kernel functions, we study the presently operating fracking wells across the state of Pennsylvania (PA), which is the main part of the current hottest Marcellus Shale in US. We geographically process the location data of hydraulic fracturing wells, 2010 census block data, urbanized region data, railway data, local road data, open water data, river data, and wetland data for the state of PA. From this we develop a distance based risk assessment in order to understand the environmental and urban risks. We generate the surface data of fracking well intensity and population intensity by integrating spatial dependence, semivariogram modeling, and a quadratic kernel function. The surface data of population risk generated by the division of fracking well intensity and population intensity provide a novel insight into the local and regional regulation of hydraulic fracturing activities in terms of environmental and health related risks due to the proximity of fracking wells. [ABSTRACT FROM AUTHOR]

Spatially structured discrete data arise in diverse areas of application, such as forestry, epidemiology, or soil sciences. Data from several binary variables are often collected at each location. Variation in distributional properties across the spatial domain is of interest. The specific application that motivates our work involves characterizing historical distributions of two species of Oak in the Driftless Area in the Midwestern United States. Scientists are interested in understanding the patterns of interaction between species, as well as their relationships to spatial covariates. Accounting for spatial dependence is not only of inherent interest but also reduces prediction mean squared error, and is necessary for obtaining appropriate measures of uncertainty (i.e., standard errors and confidence intervals). To address the needs of the application, we introduce a centered bivariate autologistic model, which accounts for the statistical dependence in two response variables simultaneously, for the association between them and for the effect of spatial covariates. The model proposed here offers a relatively stable large-scale model structure, with model parameters which can be interpreted in the usual sense across levels of dependence. Since the model allows for separate dependence parameters for each variable, it offers, in essence, the equivalent of a model with a non-separable covariance function. The flexible model framework permits straightforward generalizations to structures with more than two variables, a temporal component, or an irregular lattice domain. Supplementary materials accompanying this paper appear on-line. [ABSTRACT FROM AUTHOR]

Weather and its fluctuations have been found to influence the consumption of negative hedonic goods. However, such findings are of limited use to health marketers who cannot control the weather, and hence, its effects. The current research utilizes data obtained at the zip-code level to study geographical variations in the effect of weather on tobacco consumption across the entire continental United States. The results allow health marketers to identify areas that will be most responsive to marketing efforts aimed at curtailing negative hedonic consumption and thus implement more effective, region-specific initiatives. [ABSTRACT FROM PUBLISHER]

Organic agriculture has expanded greatly over the past decades, but the rate of conversion has not been evenly distributed across the United States. Measures of spatial concentration such as local Moran's I show that the highest rates of conversion are clustered in the Western United States, especially California, Washington, and Oregon, but also on the East Coast in New England. The influence of several variables on the spatial distribution of organic conversion is first explored through ordinary least squares regression analysis and then through a more localized technique called geographically weighted regression (GWR). Of the analyzed factors, share of existing organic farms, prevalence of full-time operators, and average farm size were found to be significant determinants of organic agriculture conversion rates. Furthermore, results show that spatial dependence is highly influential on the distribution of farms that are converting to organic production, suggesting the existence of relevant agglomeration effects. The GWR model suggests significant variation in the relationship between average farm size and conversion rates: The relationship is negative in most of the country and positive only in the Northeast and parts of the Western United States. These results highlight the need to consider local models in conjunction with global regression techniques for a better understanding of the spatial relationship between conversion to organic production methods and potential determinants. [ABSTRACT FROM AUTHOR]

Abstract: We estimate the effect of proximity on residential property values in US cities using a hedonic housing price model with spatial autocorrelation. Estimates based on all 1990 and 2000 Census block groups within five miles of every NFL, NBA, MLB, and NHL facility in the US suggest that the median house value in block groups is higher in block groups closer to facilities, suggesting that positive externalities from professional sports facilities may be capitalized into residential real estate prices. The existence of external benefits may justify some of the large public subsidies for construction and operation of professional sports facilities. [Copyright &y& Elsevier]

Several theories suggest that states' choices of constitutional rules are at least partially a function of neighboring constitutions. This paper provides the first analysis of spatial dependence of specific provisions within state constitutions in the United States. The analysis effectively makes constitutional rules endogenous, contributing to a relatively underdeveloped branch of constitutional economics. By employing a series of probit estimations of nineteen specific constitutional rules, I find evidence of spatial dependence in state constitutions. Specifically, the presence of specific constitutional constraints pertaining to term limits, supreme court justice selection, recall, home rule, direct democracy, constitutional amendment by convention, balanced budget requirements, tax and expenditure limits, line item veto, victims' bill of rights, health and welfare, right to privacy, environmental protection, sex discrimination, abortion, and official language all exhibit some evidence spatial dependence. [ABSTRACT FROM AUTHOR]

Urban distinctiveness occurs in both technological and geographic space. This article explores spatial associations in the locational distribution of subcategories of patents across U.S. metropolitan areas. I converted patent counts to location quotients and used nonspatial methods to compare concentration levels of patents (Gini coefficients) and to identify groups of patents that tend to colocate (principal components analysis). The results show considerable variation in concentration levels and that nine groupings, entitled 'technology components,' account for almost 68 percent of the variance in the distribution of the subcategories. Spatial analysis permits the exploration of spatial dependencies in each 'technology component.' The results identify distinctive inventive regions that are termed inventive megaregions. [ABSTRACT FROM AUTHOR]

Many researchers advocate active local government responses to poverty and other economic disparities. In doing so, they raise a generally unexplored question: can local governments themselves influence poverty net of other determinants? This study extends past research in two ways by (1) analyzing the poverty-reducing role of county governments and (2) evaluating new relationships pertaining to the comparative influence of government capacity and specific policies. The authors assess the degree to which county government capacity and economic development policies relate to disparities in job growth, individual and child poverty, and household income. The empirical analysis is based on a unique set of primary and secondary data on county governments for the post-2000 period. County government capacity as measured by county centralization and autonomy from upper-level government is related to economic growth and poverty reduction. By contrast, policy variables have little consistent association with economic disparities. [ABSTRACT FROM PUBLISHER]

The last decade has seen max-stable processes emerge as a common tool for the statistical modeling of spatial extremes. However, their application is complicated due to the unavailability of the multivariate density function, and so likelihood-based methods remain far from providing a complete and flexible framework for inference. In this article we develop inferentially practical, likelihood-based methods for fitting max-stable processes derived from a composite-likelihood approach. The procedure is sufficiently reliable and versatile to permit the simultaneous modeling of marginal and dependence parameters in the spatial context at a moderate computational cost. The utility of this methodology is examined via simulation, and illustrated by the analysis of United States precipitation extremes. [ABSTRACT FROM AUTHOR]

This article develops a growth-based regionalisation of the United States using both principal components and cluster analyses to endogenously sort states into regional transition clubs with somewhat uniform annual rates of per capita real GSP growth in the period 1977–2004. I correlate the principal components with annual growth in per capita real GDP and assess spatial dependencies in the identified transition clubs. Growth variability in the transition clubs is compared with growth variability in BEA and Census regions. Results show a large Coastal transition club that contains most of the New England, Middle Atlantic, and South Atlantic census regions. California, Minnesota, and Arizona belong to this grouping. A second growth transition club titled Eastern Interior groups most of the states in the East North Central and the East South Central census regions. A Western Interior region groups states in the southern Great Plains. Coefficients of variation show that these three large interstate groupings grow more uniformly compared with associated census and BEA regions. Growth in western states is idiosyncratic. Most western states group with one or two related states, California and Arizona associate with the Coastal club, Nevada does not pigeonhole well with other states, and Alaska's annual growth trajectory is unique. Este artículo desarrolla una regionalización de los Estados Unidos basada en crecimiento usando componentes principales y análisis de cluster para agrupar los estados en grupos de transición regional según tasas anuales de crecimiento más o menos uniformes en el producto estatal bruto (GSP) real per capita en el periodo 1977–2004. Correlaciono los componentes principales con el crecimiento anual en el producto interior bruto (GDP) real per capita y evalúo dependencias espaciales en los grupos de transición identificados. La variabilidad de crecimiento en los grupos de transición se compara con la variabilidad de crecimiento en regiones BEA (Bureau of Economic Analysis) y Censuales. Los resultados muestran un grupo de transición grande en la Costa que contiene la mayoría de regiones censuales de New England, Atlántico Medio, y Atlántico Sur. California, Minnesota, y Arizona pertenecen a este grupo. Un Segundo grupo de transición de crecimiento llamado Interior Este aglutina la mayoría de estados de las regiones censuales Central Norte Oriental y Central Sur Oriental. Una región Interior Occidental agrupa los estados de las Grandes Llanuras del Sur. Los coeficientes de variación muestran que estos tres grandes grupos interestatales crecen más uniformemente en comparación con las regiones censuales y BEA asociadas. El crecimiento en los estados occidentales es idiosincrásico. La mayoría de los estados occidentales se agrupan con uno o dos estados relacionados, California y Arizona se asocian con el grupo de la Costa, Nevada no se clasifica bien con ningún otro estado, y la trayectoria de crecimiento anual de Alaska es única. [ABSTRACT FROM AUTHOR]

Several econometric issues within the field of education finance that have not been fully explored to date are addressed. Focusing on demographic factors and per-pupil expenditures in the United States, an econometric model that incorporates spatial analysis is developed and a unique framework for analyzing the impact of demographics on local public demand is introduced. The results show that ethnic diversity has a negative impact on per-pupil spending, while the elderly have a positive impact. [ABSTRACT FROM AUTHOR]

Endogenous growth theory places spatial knowledge spillovers at the center of national technological progress. Advances in spatial autocorrelation and regression analyses provide methods to assess the influence of these spillovers on the geographical distribution and growth of invention. This article investigates interstate inequality and convergence in per capita patenting in the United States in the period 1963–2003. We analyze both the complete forty years and trends in ten-year intervals. Moran's I reveals spatial dependence in patenting levels and growth, and LISA cluster maps identify regional groupings of leading and trailing states. Our regression results show that both regional effects and spatial spillovers influence convergence rates, which were low and steady in the thirty years before 1993. In the subsequent decade, patenting expansion concentrated in a few states, inequality increased, and divergence ensued. Western states, in general, and the Pacific Northwest, in particular, increasingly dominate patent growth. Rank order correlation analyses show that convergence before 1993 was driven by catch-up and not by leapfrogging. A final regression analysis shows that patent growth rates in the 1993–2003 interval were higher in more rural states and in those with high proportions of payrolls generated by high-technology manufacturing and producer services industries. States in the South significantly lagged. Our results support the hypothesis that creative skilled professionals seek to reside in states that offer both well-paying jobs in high-technology manufacturing and producer services sectors and easy access to rural outdoor recreation and leisure amenities. [ABSTRACT FROM AUTHOR]

Developments in the provision and quality of digital data are creating possibilities for spatial and temporal measurement of the properties of socioeconomic systems at finer levels of granularity. In this article, we suggest that the “lifestyles” datasets collected by private sector organizations in the U.K. and the U.S. provide one such prospect for better inferring the structure, composition, and heterogeneity of urban areas. Using a case study of Bristol, U.K., we compare the patterns of spatial dependence and spatial heterogeneity observed for a small-area (“lifestyles”) income measure with those of the census indicators that are commonly used as surrogates for it. This leads first to an exploration of spatial effects using geographically weighted regression (GWR) and then to a specification of spatial dependence using a spatially autoregressive model. This analysis extends our understanding of the determinants of hardship and poverty in urban areas; urban policy has hitherto used aggregate, outdated, or proxy measures of income in an insufficiently critical manner, and techniques for measuring spatial dependence and heterogeneity have usually been applied at the regional, rather than intra-urban, scales. The consequence is a limited understanding of the geography and dynamics of income variations within urban areas. The advantages and limitations of the data used here are explored in the light of the results of our statistical analysis, and we discuss our results as part of a research agenda for exploring dependence and heterogeneity in research focusing on the intra-urban geography of deprivation. [ABSTRACT FROM AUTHOR]

Regional science has a variety of characteristics that distinguish it from its sister social sciences: it works the boundaries of economics, geography, planning studies, and sociology; space is its focus; spatial dependence is a core concern; comparative advantage remains a key concept for understanding how space will develop. Facing new and old challenges from global competition, lagging regions of the U.S., and especially in the rural South, will need new ideas from regional science on how to fit into the emerging economy in ways that promote higher incomes across all deciles of the income distribution. [ABSTRACT FROM AUTHOR]

This study tests for fiscal interdependence in the provision of a local public good -- libraries in Los Angeles County. The authors present evidence that the reaction of libraries to fiscal externalities is dependent on the level of government producing the good. The authors find that the city-nm libraries respond to neighboring output but the county system largely does not. [ABSTRACT FROM AUTHOR]

On April 1, 1990, the decennial census for the United States will be conducted by the U.S. Bureau of the Census. By December 31, 1990, the Census Bureau is specified by law to submit state population counts for the purpose of reapportionment of the U.S. House of Representatives, and by March 31, 1991, to submit small-area population counts for the purpose of redistricting. Census counts are used in a variety of other ways: for revenue-sharing formulas between different levels of government, for demographic projections, as a base for morbidity and mortality statistics, and so forth. Inaccurate census counts should be cause for concern for the whole nation. It is universally acknowledged that certain groups of people (e.g., young black males, illegal aliens, etc.) are harder to count than others. If the hard-to-count groups are distributed in equal proportions throughout the United States, there would be far less controversy over what to do about the uncounted people. As it is, many large American cities such as Chicago, Detroit, New York, and Los Angeles feel they are losing federal funds because their cities contain larger numbers of the groups that are less well counted. And certain states such as New York and California feel they are underrepresented in Congress, to the benefit of Midwestern states such as Indiana and Iowa. Census undercount is defined simply as the difference between the true count and the census count, expressed as a percentage of the true count. Small-area estimation of this undercount is considered here, using empirical Bayes methods based on a new and, it is argued, more realistic model than has been used before. Grouping of like subareas from areas such as states, counties, and so on into strata is a useful way of reducing the variance of undercount estimators. By modeling the subareas within a stratum to have a common mean and variances inversely proportional to their census counts, and by taking into account sampling of the areas (e.g., b [ABSTRACT FROM AUTHOR]

Past studies have rather consistently shown a significant positive association between firearm ownership and suicide rates (particularly firearm-related) in the United States. However, the impact of spatial dependence of suicide rates has not been considered in the existing research that took an ecological studies approach. To bridge this gap in the literature, we estimated and compared the association using Ordinary Least Square (OLS), spatial autoregressive (SAR), and hierarchical spatial autoregressive (HSAR) regression models. The outcome variable was the average age-adjusted and smoothed suicide rate (all, firearm, and non-firearm) at the county level in the United States between 2008 and 2014. The covariates included the state-level firearm ownership and several key demographic, geographic, religious, psychopathological, and suicide-related variables at both the state and county levels. Our main findings were: 1) the spatially-informed models (SAR and HSAR) were significantly outperformed the OLS model. The SAR lag model was a better choice than the SAR err model in fitting our data. However, the HSAR model was not statistically better than the SAR model; 2) based on the results from the three final SAR lag models, firearm ownership was significantly associated with firearm suicide rates, but not with all or non-firearm suicide rates, and such relationships were also observed in the SAR lag models using contiguity-based spatial weight matrix; and 3) The final SAR lag models explained 82.6%, 84.9%, and 76.1% of the total variance in all, firearm, and non-firearm suicide rates, respectively. In conclusion, the SAR lag model provided more robust evidence supporting the positive relationship between firearm suicide rate and firearm ownership. Results from this study confirmed that access to household firearms was a significant risk factor of firearm suicide in the United States and hence means reduction should be included in the suicide prevention strategy in the United States. In the future, the effect of state firearm laws and regulations on suicide rate should also be investigated using spatially informed models to gain insights on the connection among firearm ownership, firearm laws and regulations, and population-based suicide rates. • State-level firearm ownership was significantly associated with county-level firearm suicide rates. • State-level firearm ownership was not significantly associated with county-level all or non-firearm suicide rates. • Access to household firearms was a significant risk factor of firearm suicide in the United States. • Means reduction should be included in the suicide prevention strategy in the United States. [ABSTRACT FROM AUTHOR]