Does Income Distribution Affect U.S. State Economic Growth?*

Abstract. Numerous models propose an income‐distribution/growth linkage, but the empirical evidence is ambiguous and depends on the regression approach. Mixed findings are not unexpected if there are differing short‐ and long‐term responses. Approaches utilizing cross‐sectional variation primarily reflect long‐run effects, whereas those using time‐series variation primarily reveal short‐run effects. This study reconciles these issues using U.S. state data. After allowing for short‐ and long‐run responses and for separate effects between the tails and middle of the distribution, the consistent pattern is the middle‐class share and overall inequality are positively related to long‐run growth. However, the short‐run income‐distribution response is less clear.     

Measuring Impacts of Condition Variables in Rule‐Based Models of Space‐Time Choice Behavior: Method and Empirical Illustration

The use of rule‐based systems for modeling space‐time choice has gained increasing research interests over the last years. The potential advantage of the rule‐based approach is that it can handle interactions between a large set of predictors. Decision tree induction methods are available and have been explored for deriving rules from data. However, the complexity of the structures that are generated by such knowledge discovery methods hampers an interpretation of the rule‐set in behavioral terms with as a consequence that the models typically remain a black box. To solve this problem, this paper develops a method for measuring the size and direction of the impact of condition variables on the choice variable as predicted by the model. The paper illustrates the method based on location and transport‐mode choice models that are part of Albatross model—an activity‐based model of space‐time choice.     

On the Specification of Indirect Utility and Willingness to Pay for Discrete Residential Location Models

In this paper we develop a framework to describe the individual choice of residential location using the microeconomics of discrete choices. The individual is seen as deciding frequency, duration, and location of a set of activities for each potential residential zone, knowing the distribution of goods and activities in space as well as transport costs and travel times. The conditional indirect utility function and its associated willingness to pay function for each zone are obtained, where the roles of accessibility, income, and neighborhood attributes emerge clearly. Zonal utility in discrete location choice models can be specified and interpreted using these functions.     

ANALYZING THE IMPACT OF PUBLIC CAPITAL STOCK USING THE NEG WAGE EQUATION: A SPATIAL PANEL DATA APPROACH

ABSTRACT This paper examines the relationship between the level of public infrastructure and the level of productivity using panel data for the Spanish provinces over the period 1985–2004, a period that is particularly relevant due to the substantial changes occurring in the Spanish economy at that time. The underlying model used for the data analysis is based on the wage equation, which is one of a handful of simultaneous equations which when satisfied correspond to the short‐run equilibrium of New Economic Geography (NEG) theory. This is estimated using various spatial panel models with either fixed or random effects to allow for individual heterogeneity. Using these models, we find consistent evidence that productivity depends directly on the public capital stock endowment of each province, but also there is evidence of negative spillover effects from changes in capital stock in neighboring provinces.     

A Geostatistical Linear Regression Model for Small Area Data. 一种适用于小区域数据的地统计线性回归模型

We present a new linear regression model for use with aggregated, small area data that are spatially autocorrelated. Because these data are aggregates of individual‐level data, we choose to model the spatial autocorrelation using a geostatistical model specified at the scale of the individual. The autocovariance of observed small area data is determined via the natural aggregation over the population. Unlike lattice‐based autoregressive approaches, the geostatistical approach is invariant to the scale of data aggregation. We establish that this geostatistical approach also is a valid autoregressive model; thus, we call this approach the geostatistical autoregressive (GAR) model. An asymptotically consistent and efficient maximum likelihood estimator is derived for the GAR model. Finite sample evidence from simulation experiments demonstrates the relative efficiency properties of the GAR model. Furthermore, while aggregation results in less efficient estimates than disaggregated data, the GAR model provides the most efficient estimates from the data that are available. These results suggest that the GAR model should be considered as part of a spatial analyst's toolbox when aggregated, small area data are analyzed. More important, we believe that the GAR model's attention to the individual‐level scale allows for a more flexible and theory‐informed specification than the existing autoregressive approaches based on an area‐level spatial weights matrix. Because many spatial process models, both in geography and in other disciplines, are specified at the individual level, we hope that the GAR covariance specification will provide a vehicle for a better informed and more interdisciplinary use of spatial regression models with area‐aggregated data.     

COAL MINING AND THE RESOURCE CURSE IN THE EASTERN UNITED STATES

We measure the effect of resource‐sector dependence on long‐run income growth using the natural experiment of coal mining in 409 Appalachian counties selected for homogeneity. Using a panel data set (1970–2010), we find a one standard deviation increase in resource dependence is associated with 0.5–1 percentage point long‐run and a 0.2 percentage point short‐run decline in the annual growth rate of per capita personal income. We also measure the extent to which the resource curse operates through disincentives to education, and find significant effects, but this “education channel” explains less than 15 percent of the apparent curse.     

Evaluation of soluble benzene migration in the Uinta Basin

Field sampling and mathematical modeling are used to study the long‐distance transport and attenuation of petroleum‐derived benzene in the Uinta Basin, Utah. Benzene concentration was measured from oil and oil field formation waters of the Altamont‐Bluebell and Pariette Bench oil fields in the basin. It was also measured from springs located in the regional groundwater discharge areas, hydraulically down‐gradient from the oil fields sampled. The average benzene concentration in oils and co‐produced waters is 1946 and 4.9 ppm at the Altamont‐Bluebell field and 1533 and 0.6 ppm at the Pariette Bench field, respectively. Benzene concentration is below the detection limit in all springs sampled. Mathematical models are constructed along a north–south trending transect across the basin through both fields. The models represent groundwater flow, heat transfer and advective/dispersive benzene transport in the basin, as well as benzene diffusion within the oil reservoirs. The coupled groundwater flow and heat transfer model is calibrated using available thermal and hydrologic data. We were able to reproduce the observed excess fluid pressure within the lower Green River Formation and the observed convective temperature anomalies across the northern basin. Using the computed best‐fit flow and temperature, the coupled transport model simulates water washing of benzene from the oil reservoirs. Without the effect of benzene attenuation, dissolved benzene reaches the regional groundwater discharge areas in measurable concentration (>0.01 ppm); with attenuation, benzene concentration diminishes to below the detection limit within 1–4 km from the reservoirs. Attenuation also controls the amount of water washing over time. In general, models that represent benzene attenuation in the basin produce results more consistent with field observations.     

Developing and Applying a Disaggregated Retail Location Model with Extended Retail Demand Estimations

The spatial interaction model (SIM) is an important tool for retail location analysis and store revenue estimation, particularly within the grocery sector. However, there are few examples of SIM development within the literature that capture the complexities of consumer behavior or discuss model developments and extensions necessary to produce models which can predict store revenues to a high degree of accuracy. This article reports a new disaggregated model with more sophisticated demand terms which reflect different types of retail consumer (by income or social class), with different shopping behaviors in terms of brand choice. We also incorporate seasonal fluctuations in demand driven by tourism, a major source of non‐residential demand, allowing us to calibrate revenue predictions against seasonal sales fluctuations experienced at individual stores. We demonstrate that such disaggregated models need empirical data for calibration purposes, without which model extensions are likely to remain theoretical only. Using data provided by a major grocery retailer, we demonstrate that statistically, spatially, and in terms of revenue estimation, models can be shown to produce extremely good forecasts and predictions concerning store patronage and store revenues, including much more realistic behavior regarding store selection. We also show that it is possible to add a tourist demand layer, which can make considerable forecasting improvements relative to models built only with residential demand.     

Geographic information system‐based evaluation of spatial accessibility to maternal health facilities in Siaya County,Kenya

Maternal mortality is a major problem in middle‐income and low‐income countries, and the availability and accessibility of healthcare facilities offering safe delivery is important in averting maternal deaths. Siaya County, in Kenya, has one of the highest maternal mortality rates in the country—far more than the national average. This study aimed to evaluate geographic access to health facilities offering delivery services in Siaya County. A mixed‐methods approach incorporating geographic information system analysis and individual data from semi‐structured interviews was used to derive travel time maps to facilities using different travel scenarios: AccessMod5 and ArcGIS were used for these tasks. The derived maps were then linked to georeferenced household survey data in a multilevel logistic regression model in R to predict the probability of expectant women delivering in a health facility. Based on the derived travel times, 26 per cent (13,140) and 67 per cent (32,074) of the estimated 46,332 pregnant women could reach any facility within one and two hours, respectively, while walking with the percentage falling to seven per cent (3,415) and 20 per cent (8,845) when considering referral facilities. Motorised transport significantly increased coverage. The findings revealed that the predicted probability of a pregnant woman delivering in a health facility ranged between 0.14 and 0.86. Significant differences existed in access levels with transportation‐based interventions significantly increasing coverage. The derived maps can help health policy planners identify underserved areas and monitor future reductions in inequalities. This work has theoretical implications for conceptualising healthcare accessibility besides advancing the literature on mixed methodologies.     

THE FUTURE TRAJECTORY OF U.S. CO2 EMISSIONS: THE ROLE OF STATE VS. AGGREGATE INFORMATION*

ABSTRACT This paper provides comparisons of a variety of time‐series methods for short‐run forecasts of the main greenhouse gas, carbon dioxide, for the United States, using a recently released state‐level data set from 1960–2001. We test the out‐of‐sample performance of univariate and multivariate forecasting models by aggregating state‐level forecasts versus forecasting the aggregate directly. We find evidence that forecasting the disaggregate series and accounting for spatial effects drastically improves forecasting performance under root mean squared forecast error loss. Based on the in‐sample observations we attempt to explain the emergence of voluntary efforts by states to reduce greenhouse gas emissions. We find evidence that states with decreasing per capita emissions and a “greener” median voter are more likely to push toward voluntary cutbacks in emissions.     

Income Diffusion and Spatial Econometric Models

This paper considers the development of linked small-area spatial econometric models in which income flows between the areas constitute an important part of model specification. The attributes of income diffusion processes are considered in detail. Both static models and temporal income diffusion models are discussed. The spatial and spatial-temporal autocorrelation functions are derived which provide a measure of the spatial distribution of income. The Green function is also derived as a measure of income impulses through the system of regions. An important aspect of the paper is to relate properties of these functions to key economic parameters in particular propensities to spend locally and to spend nonlocally.     

Sustained Organizational Influence: American Legislative Exchange Council and the Diffusion of Anti‐Sanctuary Policy

Building upon existing literature, we offer a particular model of network policy diffusion—which we call sustained organizational influence. Sustained organizational influence necessitates an institutional focus across a broad range of issues and across a long period of time. Sustaining organizations are well‐financed, and exert their influence on legislators through benefits, shared ideological interests, and time‐saving opportunities. Sustaining organizations' centralized nature makes legislators' jobs easier by providing legislators with ready‐made model legislation. We argue that sustaining organizations uniquely contribute to policy diffusion in the U.S. states. We evaluate this model with a case study of state‐level immigration sanctuary policy making and the role that the American Legislative Exchange Council (ALEC) played in disseminating model legislation. Through quantitative text analysis and several negative binomial state‐level regression models, we demonstrate that ALEC has exerted an overwhelming influence on the introduction of anti‐sanctuary legislative proposals in the U.S. states over the past 7 years consistent with our particular model of network policy diffusion. Implications are discussed.     

Growth and Convergence in a Multiregional Model with Space–Time Dynamics. 多区域时空动态模型的增长与收敛

The goal of this article is to test four distinct hypotheses about whether the relative location of an economy affects economic growth and economic well‐being using an extended Solow–Swan neoclassical growth model that incorporates both space and time dynamics. We show that the econometric specification takes the form of an unconstrained spatial Durbin model, and we investigate whether the results depend on some methodological issues, such as the choice of the time span and the inclusion of fixed effects. To estimate the fixed effects spatial Solow–Swan model, we adjust the Arrelano and Bond (1991) generalized method‐of‐moments (GMM) estimator to deal with endogeneity not only arising from the initial income level, as in the basic model, but also from the initial income levels and economic growth rates observed in neighboring economies.     

Travel-Time Constraints on Destination-Choice Sets

The importance of travel-time constraints in spatial choice is widely recognized in the literature of geography and related disciplines, but little work has been done toward developing operational models of spatial choice wherein these constraints and their effects are made explicit. The purpose of the paper is to test the accuracy of predictions produced by a destination choice model that does not take explicit account of travel constraints under the assumption that observed choices are made from choice sets delineated by a constraint of maximum travel time. Observed choices are generated by simulation from a new random utility model consistent with the constrained nature of individual choice sets. Results show that the characteristics of constraints are a decisive factor in the accuracy of the unconstrained choice model. Choice probabilities of the constrained reality are predicted with a reasonably good accuracy in some instances, but predictions are less impressive, and even poor, in many others.     

LUTI模型原理、实现及应用综述

经济全球化背景下可持续发展已成为当今世界发展的主题。快速城镇化下的中国,城市空间的可持续性发展提上日程。为此,期待有力的、可操作的工具用于定量模拟城市空间演化过程、预测不同政策下城市空间的发展趋势,为城市化持续健康发展提供决策支持。本文分析了LUTI模型的基本原理、构建思路、实现框架和建立中国城市LUTI模型面临的问题,并回顾了国内外利用LUTI模型模拟城市空间演化过程,以辅助表明城市空间决策的研究现状,希望能为中国的城市空间可持续发展模拟分析研究提供参考。     

Dynamic Models in Space and Time

This paper presents a first‐order autoregressive distributed lag model in both space and time. It is shown that this model encompasses a wide series of simpler models frequently used in the analysis of space‐time data as well as models that better fit the data and have never been used before. A framework is developed to determine which model is the most likely candidate to study space‐time data. As an application, the relationship between the labor force participation rate and the unemployment rate is estimated using regional data of Germany, France, and the United Kingdom derived from Eurostat, 1983–1993.     

A Simple(R) Model to Predict the Source of Dietary Carbon in Individual Consumers

Quantitative individual human diet reconstruction using isotopic data and a Bayesian approach typically requires the inclusion of several model parameters, such as individual isotopic data, isotopic and macronutrient composition of food groups, diet‐to‐tissue isotopic offsets and dietary routing. In an archaeological context, sparse data may hamper a widespread application of such models. However, simpler models may be proposed to address specific archaeological questions. As a consequence of the intake of marine foods, individuals from the first century ad Roman site of Herculaneum showed well‐defined bone collagen radiocarbon age offsets from the expected terrestrial value. Taking as reference these radiocarbon offsets and using as model input stable isotope data (δ¹³C and δ¹⁵N), the performance of two Bayesian mixing model instances (routed and concentration‐dependent model versus non‐routed and concentration‐independent) was compared to predict the carbon contribution of marine foods to bone collagen. Predictions generated by both models were in good agreement with observed values. The model with higher complexity showed only a slightly better performance in terms of accuracy and precision. This demonstrates that under similar circumstances, a simple Bayesian approach can be applied to quantify the carbon contribution of marine foods to human bone collagen.     

Latent trajectory models for space‐time analysis: An application in deciphering spatial panel data

This article introduces latent trajectory models (LTMs), an approach often employed in social sciences to handle longitudinal data, to the arena of GIScience, particularly space‐time analysis. Using the space‐time data collected at county level for the whole United States through webpage search on the keyword “climate change,” we show that LTMs, when combined with eigenvector filtering of spatial dependence in data, are very useful in unveiling temporal trends hidden in such data: the webpage‐data derived popularity measure for climate change has been increasing from December 2011 to March 2013, but the increase rate has been slowing down. In addition, LTMs help reveal potential mechanisms behind observed space‐time trajectories through linking the webpage‐data derived popularity measure about climate change to a set of socio‐demographic covariates. Our analysis shows that controlling for population density, greater drought exposure, higher percent of people who are 16 years old or above, and higher household income are positively predictive of the trajectory slopes. Higher percentages of Republicans and number of hot days in summer are negatively related to the trajectory slopes. Implications of these results are examined, concluding with consideration of the potential utility of LTMs in space‐time analysis and more generally in GIScience.     

THE DAY AFTER THE DISASTER: FORCED MIGRATION AND INCOME LOSS AFTER HURRICANES KATRINA AND RITA

A model of post‐disaster migration responses and income consequences poses that damage severity and individual resilience affect moving decisions. Forced moves are linked to little resilience relative to damage incurred and post‐move income reductions. The empirical analysis analyzes households affected by hurricanes Katrina and Rita. Using American Community Survey data, unobserved heterogeneous income damages are framed as treatment, with the moving decision being the treatment decision. An endogenous switching regression addresses self‐selection issues. The results suggest that movers encountered double victimization: (1) they were forced to move and their income declined; (2) low‐income households were more severely affected than the average.     

The Location Choice of Employment‐based Immigrants among U.S. Metro Areas*

Abstract. This paper examines the initial location choice of legal employment‐based immigrants to the United States using Immigration and Naturalization Service data on individual immigrants, as well as economic, demographic, and social data to characterize the 298 metropolitan areas we define as the universal choice set. Focusing on interactions between place characteristics and immigrant characteristics, we provide multinomial logit model estimates for the location choices of about 38,000 employment‐based immigrants to the United States in 1995, focusing on the top 10 source countries. We find that, as groups, immigrants from nearly all countries are attracted to large cities with superior climates, and to cities with relatively well‐educated adults and high wages. We also find evidence that employment‐based immigrants tend to choose cities where there are relatively few immigrants of nationalities other than their own. However, when we introduce interaction terms to account for the sociodemographic characteristics of the individual immigrants, we find that the estimated effects of location destination factors can reverse as one takes account of the age, gender, marital status, and previous occupation of the immigrants.