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Abstract:
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.

Heckman model, statistical significance, delta method, nonparametric

Abstract:
The EU-wide Emission Trading Scheme (ETS), established in 2005, is a key pillar of Europe's strategy to attain compliance with the Kyoto Protocol. Under this scheme, CO2 allowances have thus far been allocated largely free of charge. This paper demonstrates that such cost-free allocation, commonly called grandfathering, implies an increase in electricity prices even when strong competition prevails on electricity markets. As our estimations for Germany's power sector show, these price increases result in substantial windfall profits, giving rise to public skepticism and calls for an auctioning of certificates in the future. While empirical evidence on the ETS' impacts is scant, the findings reviewed here indicate that even in the absence of certificate auctioning, energy-intensive industry sectors, such as primary aluminum production, may suffer heavily from the ETS-induced electricity price increases. We therefore argue that an abrupt transition to a complete auctioning system may endanger the competitive position of energy-intensive industries in Europe, unless all other major industrial and transition countries are integrated into a global emissions trading system.

Grandfathering, auctioning, competition

Abstract:
This study investigates the influence of urban form on automobile travel using travel-diary data from Germany. Two dimensions of car use are considered: the discrete decision to own a car and the continuous decision of distance traveled. Because these decisions are likely to be influenced by factors unobservable to the researcher, we apply censored regression models to evaluate the role of biases emerging from sample selectivity. Unlike much of the literature, we find that urban form variables are a significant determinant of both automobile ownership and use, a finding that holds even after using instrumental variables to control for endogeneity.

urban form, non-work automobile travel, sample selectivity, instrumental

Abstract:
Using a panel of household travel diary data collected in Germany between 1997 and 2005, this study assesses the effectiveness of fuel efficiency improvements by econometrically estimating the rebound effect, which measures the extent to which higher efficiency causes additional travel. Following a theoretical discussion outlining three alternative definitions of the rebound effect, the econometric analysis generates corresponding estimates using panel methods to control for the effects of unobservables that could otherwise produce spurious results. Our results, which range between 57% and 67%, indicate a rebound that is substantially larger than obtained in other studies, calling into question the efficacy of policies targeted at reducing energy consumption via technological efficiency.

Automobile travel, rebound effect, panel models

Abstract:
In addition to efficiency standards and consumer information, car-related taxes constitute one of three pillars of the European Commission's strategy to reduce CO2 emissions from passenger cars. A longstanding question concerns the effectiveness of such taxes in determining the car-purchasing behavior of households. Several recent studies suggest that purchases are primarily determined by retail costs rather than by taxes, the latter of which are typically incurred over the lifetime of the car. Using panel data on new-car registrations in Germany, Europe's largest car market, the present paper addresses this issue with an econometric analysis of the impact of fuel costs and circulation taxes on car market shares. By employing a nested logit model that explicitly recognizes the segmented structure of the car market, the analysis takes account of correlation in unobserved shocks among cars belonging to the same market segment. Moreover, given the panel structure of the data, a fixed effects estimator is employed to control for the influence of unobservable, time- invariant automobile attributes that could otherwise induce biases in the estimated coefficients. Contrasting with much of the evidence garnered to date, the results suggest that circulation taxes and fuel costs significantly determine car market shares, and hence may serve as effective instruments in influencing the composition of the car fleet and associated CO2 emissions.

Fuel tax, circulation tax, car market, Germany, panel data, nested logit model

Abstract:
Focusing on individual motorists in car-owning households in Germany, this analysis econometrically investigates the determinants of automobile travel for non-work service activities against the backdrop of two questions: 1) Does gender play a role in determining the probability of car use and the distance driven? 2) If so, how is this role mitigated or exacerbated by other socioeconomic attributes of the individual and the household in which they reside? Drawing on a panel of data collected between 1996 and 2003, we specify Heckman's sample selection model to control for biases that could otherwise arise from the existence of unobservable variables that determine both the discrete and continuous choices pertaining to car use.The results indicate that although women,on average, undertake more non-work travel than men, they undertake less of such travel by car, implying a greater reliance on other modes. Moreover, employment status, age, the number of children, automobile availability, and the proximity to public transit are all found to have significantly different effects on the probability of non-work car travel between men and women, but - with the exception of automobile availability - not on the distance driven.Taken together, these results suggest that policies targeted at reducing automobile dependency and associated negative externalities such as congestion are unlikely to have uniform effects across the sexes, findings having implications for both policy evaluation as well as travel demand forecasting.

Automobile travel, gender, Heckman model, Monte Carlo simulation

Abstract:
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

Abstract:
As part of its efforts to reach the targets of the Kyoto Protocol, the European Commission is currently considering a new directive to reduce the per-kilometer CO2 emissions of newly registered automobiles. This paper critically assesses this proposal with respect to its economic and technological underpinnings. We argue that the proposal's reliance on targets based on per-kilometer emissions not only conceals the true costs of compliance and thereby stifles informed public discourse, but is also less cost-effective than alternative measures such as emissions trading.We further examine the proposal's underlying assumptions, finding that these misrepresent the current state of automotive technology and therefore may overestimate the feasibility of achieving the suggested emissions targets. Alternative targets are consequently proposed that are argued to more accurately reflect the industry's technological evolution to date.

Technological progress, private automobiles, efficiency standards

Abstract:
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 1976-2001 to a suite of socioeconomic, ecological and GIS-created 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.

Urbanization, hazard models, satellite imagery

Abstract:
Understanding the determinants of home-efficiency improvements is significant to a range of energy policy issues, including the reduction of fossil fuel use and environmental protection. This paper analyzes retrofit choices by assembling a unique data set merging a nationwide household survey from Germany with regional data on wages and construction costs. To explore the influence of both heterogeneous preferences and correlation among the utility of alternatives, conditional, random parameters, and error components logit models are estimated that parameterize the influence of costs, energy savings, and household-level socioeconomic attributes on the likelihood of undertaking one of 16 renovation options. We use the model coefficients to derive household-specific marginal willingness-to-pay estimates, and with these assess the extent to which free-ridership may undermine the effectiveness of recently implemented programs that subsidize the costs of retrofits.

Heterogenous preferences, residential sector, revealed-preference data

Abstract:
Using a panel of household travel diary data collected in Germany between 1997 and 2005, this study assesses the effectiveness of fuel efficiency improvements by econometrically estimating the rebound effect, describing the extent to which higher efficiency causes additional travel.Following a theoretical discussion outlining three alternative definitions of the rebound effect, the econometric analysis generates corresponding estimates using panel methods to control for the effects of unobservables that could otherwise produce spurious results. Our results, which range between 56% and 66%, indicate a rebound that is substantially larger than obtained in other studies, calling into question the efficacy of recently implemented measures in the European Union targeted at technological innovations in the automotive sector.

Household production, rebound effect, panel models

Abstract:
Focusing on travel survey data from Germany, this paper investigates the determinants of automobile travel, with the specific aim of quantifying the effects of fuel prices and fuel economy. The analysis is predicated on the notion that car mileage is a two-stage decision process, comprising the discrete choice of whether to own a car and the continuous choice of distance traveled. To capture this process, we employ censored regression models consisting of Probit and OLS estimators, which allows us to gauge the extent to which sample selectivity may bias the results. Our elasticity estimates indicate a significant positive association between increased fuel economy and increased driving, and a significantly negative fuel-price elasticity, which ranges between -35% and -41%. Taken together, these results suggest that fuel taxes are likely to be a more effective policy measure in reducing emissions than fuel-efficiency standards.

Automobile travel, rebound effect, two-part model

Abstract:
Identifying the incidence of free-ridership is significant to a range of issues relevant to program evaluation, including the calculation of net program benefits and more general assessments of political acceptability. Estimates of free-ridership in the area of energy policy frequently rely on ex-post surveys that ask program participants whether they would have behaved differently in the absence of program support. The present paper proposes an ex-ante approach to the calculation of the free-rider share using revealed preference data on home renovations from Germany's residential sector. We employ a discrete-choice model to simulate the effect of grants on renovation choices, the output from which is used to assess the extent of free-ridership under a contemporary subsidy program. Aside from its simplicity, a key advantage of the approach is that it bestows policymakers with an estimate of free-ridership prior to program implementation.

Energy efficiency, residential sector, random utility model, discrete choice simulation

Abstract:
Interaction effects capture the impact of one explanatory variable x1 on the marginal effect of another explanatory variable x2. To explore interaction effects, so-called interaction terms x1x2 are typically included in estimation specifications. While in linear models the effect of a marginal change in the interaction term is equal to the interaction effect, this equality generally does not hold in non-linear specifications (AI, NORTON, 2003). This paper provides for a general derivation of marginal and interaction effects in both linear and non-linear models and calculates the formulae of the marginal and interaction eff ects resulting from Heckman’s sample selection model as well as the Two-Part Model, two commonly employed censored regression models. Drawing on a survey of automobile use from Germany, we argue that while it is important to test for the significance of interaction effects, their size conveys limited substantive content. More meaningful, and also more easy to grasp, are the conditional marginal effects pertaining to two variables that are assumed to interact.

Censored regression models, interaction terms

Abstract:
Focusing on individual motorists in car-owning households in Germany, this paper econometrically investigates the determinants of automobile travel with the specific aim of quantifying the effects of fuel prices and person-level attributes on travel conducted over a five-day week and weekend. Our analysis is predicated on the notion that car use is an individual decision, albeit one that is dependent on intra-household allocation processes, thereby building on a growing body of literature that has identified the importance of socioeconomic factors such as employment status, gender, and the presence of children in determining both access to the car and distance driven. To capture this two-stage decision process, we employ the Two-Part Model, which consists of Probit and OLS estimators, and derive elasticity estimates that incorporate both the discrete and continuous choices pertaining to car use. With fuel price elasticity estimates ranging between -0.42 and -0.48, our results suggest raising prices via fuel taxes to be a promising energy conservation and climate protection measure.

Automobile travel, Two-Part Model, interaction effects