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The confluence of factors driving urban growth is highly complex, resulting from a combination of ecological and social determinants that co-evolve over time and space. Identifying these factors and quantifying their impact necessitates models that capture both why urbanization happens as well as where and when it happens. Using a database that links five satellite images spanning 1976-2001 to a suite of socioeconomic, ecological and GIS created explanatory variables, this study develops a spatial-temporal model of the determinants of built-up area across a 25,900 square kilometer swath across central North Carolina. Extensive conversion of forest and agricultural land over the last decades is modeled using the complementary log-log derivation of the proportional hazards model, thereby affording a means for modeling continuous-time landscape change using discrete-time satellite data. To control for unobserved heterogeneity, the model specification includes an error component that is Gamma distributed. Results confirm the hypothesis that the landscape pattern surrounding a pixel has a major influence on the likelihood of its conversion and, moreover, that the omission of external spatial effects can lead to biased inferences regarding the influence of other covariates, such as proximity to road. Cartographic and nonparametric validation exercises illustrate the utility of the model for policy simulation. -- Urban growth ; landscape pattern ; satellite imagery ; hazard model ; North Carolina

This paper illustrates two techniques for calculating the statistical significance of the marginal effects derived from Heckman's sample selection model,an increasingly common econometric specification in political science. The discussion draws on an analysis by Sweeney (2003) of the incidence and intensity of interstate disputes. After replicating his results, the paper presents the delta method and the nonparametric bootstrap as alternative techniques for obtaining standard errors of the marginal effects, which themselves are calculated from a transformation of the model parameters.The analysis reveals two variables for which misleading inferences are drawn with respect to the precision of the estimated coefficients in the original study, suggesting that significance testing of the derived marginal effects is warranted.

This paper illustrates two techniques for calculating the statistical significance of the marginal effects derived from Heckman?s sample selection model,an increasingly common econometric specification in political science. The discussion draws on an analysis by Sweeney (2003) of the incidence and intensity of interstate disputes. After replicating his results, the paper presents the delta method and the nonparametric bootstrap as alternative techniques for obtaining standard errors of the marginal effects, which themselves are calculated from a transformation of the model parameters.The analysis reveals two variables for which misleading inferences are drawn with respect to the precision of the estimated coefficients in the original study, suggesting that significance testing of the derived marginal effects is warranted.

This study examines the determinants of urbanized area across a 10,000-mile square swath in central North Carolina, an area undergoing extensive conversion of forest and agricultural land.We model the temporal and spatial dimensions of these landscape changes using a database that links five satellite images spanning 19762001 to a suite of socioeconomic, ecological and GIScreated explanatory variables. By specifying the complementary log-log derivation of the proportional hazards model, we employ a methodology for modeling a continuous time process the conversion of land to impervious surface using discrete-time satellite data. Spatial effects are captured by several variables derived from the imagery that measure the landscape configuration surrounding a pixel. Empirical results confirm the significance of several determinants of urbanization identified elsewhere in the literature, including proximity to roads and population density, but also suggest that the parameterization of these variables is biased when the influence of landscape configuration is unaccounted for. We conclude that the inclusion of spatial pattern metrics significantly improves both the explanatory and predictive power of the estimated model of urbanization.

Drawing on panel data from Germany, this paper estimates the relationship between charging infrastructure and the uptake of electric vehicles (EVs). We specify models with fixed effects and instrumental variables to gauge the robustness of our findings in the face of alternative channels through which endogeneity bias may emerge. We find that charging infrastructure has a statistically significant and positive impact on EV uptake, with the magnitude of the estimate increasing with population density. The evidence further suggests that although the incidence of charging points in Germany far exceeds the European Union's recommended minimum ratio of one point to ten EVs, inadequate infrastructure coverage remains a binding constraint on EV uptake. We use the model estimates to illustrate the relative cost effectiveness of normal and fast chargers by region, which supports a geographically differentiated targeting of subsidies. ; Basierend auf Paneldaten wird in diesem Papier der Zusammenhang zwischen der Ladeinfrastruktur und der Verbreitung von Elektrofahrzeugen in Deutschland untersucht. Wir schätzen Modelle mit fixen Effekten und Instrumentvariablen, um die Robustheit unserer Ergebnisse gegenüber verschiedenen Kanälen zu überprüfen, durch die Endogenitätsverzerrungen entstehen können. Wir finden, dass die Ladeinfrastruktur einen statistisch signifikanten und positiven Einfluss auf die Verbreitung von Elektrofahrzeugen hat, wobei das Ausmaß mit der Bevölkerungsdichte zunimmt. Die Ergebnisse deuten außerdem darauf hin, dass die unzureichende Infrastrukturabdeckung ein Hindernis für die Verbreitung von Elektrofahrzeugen bleibt, obwohl der Ausbau von Ladepunkten in Deutschland das von der Europäischen Union empfohlene Verhältnis von einem Ladepunkt zu zehn Elektrofahrzeugen weit übersteigt. Wir verwenden die Modellschätzungen, um die relative Kosteneffizienz von normalen und schnellen Ladepunkten nach Region zu veranschaulichen, was eine geografisch differenzierte Ausrichtung der Subventionen ermöglichen könnte.

Despite steady increases in fuel economy, CO2 emissions from road transportation in Germany are on the rise, increasing by nearly 4% since 2009. This study analyzes the impact of different policy levers for bucking this trend, focusing specifically on the role of fuel prices and features of the built environment. We estimate two multinomial logit models, one addressing work-related tours and the other non-work related tours. Both models consider two interrelated dimensions of travel on the extensive margin: mode choice and tour complexity. We use the model estimates to predict outcome probabilities for different levels of our policy variables. Our results suggest significant effects of the built environment – measured by bike path density, urbanization, and proximity to public transit – in discouraging car use and increasing tour complexity. Fuel prices, by contrast, appear to have little bearing on these choices. ; Trotz stetiger Verbesserungen der Kraftstoffeffizienz sind die CO2-Emissionen im Verkehrssektor in Deutschland seit 2009 um ca. 4 Prozent gestiegen. Die vorliegende Studie analysiert die Auswirkungen verschiedener politischer Handlungsoptionen, um diesem Trend entgegen zu wirken, mit besonderem Fokus auf der Rolle des Kraftstoffpreises sowie infrastrukturellen Begebenheiten. Wir schätzen zwei multinomial logit Modelle, zum einen bezüglich arbeitsbezogener Touren und zum anderen bezüglich nicht-arbeitsbezogener Touren. Beide Modelle betrachten jeweils zwei zueinander in Wechselbeziehung stehende Merkmale der individuellen Mobilitätsentscheidung: den Transportmodus und die Tourkomplexität. Darüber hinaus verwenden wir die Schätzergebnisse, um Wahrscheinlichkeiten der einzelnen Entscheidungen bei variierenden infrastrukturellen Begebenheiten vorherzusagen. Unsere Ergebnisse legen nahe, dass eine höhere Dichte an Fahrradwegen, eine stärkere Urbanisierung, sowie mehr Nähe zum öffentlichen Nahverkehr die Autonutzung signifikant reduzieren und die Tourkomplexität signifikant erhöhen. Hingegen scheinen Kraftstoffpreise nur einen geringen Effekt auf diese Entscheidungen zu haben.

Using household travel diary data collected in Germany between 1997 and 2012, we employ an instrumental variable (IV) approach that enables us to consistently estimate both fuel price and efficiency elasticities at once. The aim is to gauge the relative impacts of fuel economy standards and fuel taxes on distance traveled. We find that the magnitude of the elasticity estimates are statistically indistinguishable: higher fuel prices reduce driving by the same degree as higher fuel efficiency increases driving. This finding indicates an offsetting effect of fuel efficiency standards on the effectiveness of fuel taxation, calling into question the efficacy of the European Commission's current efforts to legislate CO2 emissions limits for new cars given prevailing high fuel taxes. The ecological implications of this legislation for emissions reductions is explored through a simple numerical simulation using the econometric estimates.

Sample selection models, variants of which are the Heckman and Heckit models, are increasingly used by political scientists to accommodate data in which censoring of the dependent variable raises concerns of sample selectivity bias. Beyond demonstrating several pitfalls in the calculation of marginal effects and associated levels of statistical significance derived from these models, we argue that many of the empirical questions addressed by political scientists would – for both substantive and statistical reasons – be more appropriately addressed using an alternative but closely related procedure referred to as the two-part model (2 PM). Aside from being simple to estimate, one key advantage of the 2 PM is its less onerous identification requirements. Specifically, the model does not require the specification of so-called exclusion restrictions, variables that are included in the selection equation of the Heckit model but omitted from the outcome equation. Moreover, we argue that the interpretation of the marginal effects from the 2 PM, which are in terms of actual outcomes, are more appropriate for the questions typically addressed by political scientists than the potential outcomes ascribed to the Heckit results. Drawing on data from the Correlates of War database, we present an empirical analysis of conflict intensity illustrating that the choice between the sample selection model and 2 PM can bear fundamentally on the conclusions drawn.

Sample selection models, variants of which are the Heckman and Heckit models, are increasingly used by political scientists to accommodate data in which censoring of the dependent variable raises concerns of sample selectivity bias. Beyond demonstrating several pitfalls in the calculation of marginal effects and associated levels of statistical significance derived from these models, we argue that many of the empirical questions addressed by political scientists would – for both substantive and statistical reasons – be more appropriately addressed using an alternative but closely related procedure referred to as the two-part model (2 PM). Aside from being simple to estimate, one key advantage of the 2 PM is its less onerous identification requirements. Specifically, the model does not require the specification of so-called exclusion restrictions, variables that are included in the selection equation of the Heckit model but omitted from the outcome equation. Moreover, we argue that the interpretation of the marginal effects from the 2 PM, which are in terms of actual outcomes, are more appropriate for the questions typically addressed by political scientists than the potential outcomes ascribed to the Heckit results. Drawing on data from the Correlates of War database, we present an empirical analysis of conflict intensity illustrating that the choice between the sample selection model and 2 PM can bear fundamentally on the conclusions drawn. ; Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich. - This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.

Using household travel diary data collected in Germany between 1997 and 2012, we employ an instrumental variable (IV) approach to estimate fuel price and efficiency elasticities. The aim is to gauge the relative impacts of fuel economy standards and fuel taxes on distance traveled. We find that the magnitude of the elasticity estimates are statistically indistinguishable: higher fuel prices reduce driving by the same degree as higher fuel efficiency increases driving. This finding indicates an offsetting effect of fuel efficiency standards on the effectiveness of fuel taxation, calling into question the efficacy of the European Commission's current efforts to legislate CO2 emissions limits for new cars given prevailing high fuel taxes.