Network effects and spatial autoregression in mode choice models: Three essays in urban transportation economics

Network analysis in transportation economics has traditionally focused on congestion as a negative externality stemming from supply-side capacity constraints. In my first paper paper, an analytical mode choice model is developed to examine the demand-side network effects. The assumption behind the approach is that, because of social network effects, the utility of people taking the mode increases with its mode share. It is found that social network effects change the modal aggregate demand curve for the mode to an inverted u-shape. This result has far-reaching policy consequences, since multiple equilibria become a possibility, causing positive externalities and path-dependency.;Transportation planners have always been aware of positive network effects in public transit use, which can be attributed to the fact that people choose transit, because other people already take it. In my second essay, I employ a spatially autoregressive mode choice mode to econometrically test for the existence of social network effects. It is found that the coefficient estimate for transit use network effects is positive and significantly different from zero. Furthermore, if social network effects are not included, it can be shown that an omitted variable bias is introduced into the model, which can lead to a systematic error in travel forecasts.;The third essay explains municipal differences in bicycle mode share with social network effects. Using data from the nation-wide travel behaviour survey, Mobility in Germany 2002, a binary logistic regression model was developed to identify in how much a city-specific \u27\u27biking culture\u27\u27 has an impact on the city\u27s bike modal split. To avoid endogeneity of the biking culture variable, a social network effects instrument was developed. It was found that not only bicycle infrastructure, but also social network effects change municipal bike mode share. Further results were that work/educational and leisure trips depend less on social network effects than other trip purposes. The outcome of this research has significant policy implications, such as, that transportation planners can target biking culture in a city as a mean to improve bike mode share

Climate-friendly technologies in the mobile air-conditioning sector: A patent citation analysis

The development of climate-friendly technologies and its diffusion across countries is of key importance to slow climate change. This paper considers technologies in the mobile air-conditioning (MAC) sector which is a major contributor of fluorinatedgreenhouse gas emissions. Using patents as an indicator of innovations and patent citations as a proxy for knowledge flows the inducement of new environmental and non-environmental technologies and its diffusion within and across countries and withinand across patent applicant- and firm-types is analyzed. We find that most environmental patents originate from Germany and the US and are filed by individuals rather than firms. Most knowledge flows take place within countries. Regarding cross-countryflows most environmental knowledge diffuses from French and German patents, which is likely to be a result of regulatory activities in Europe and intensified research on environmentally benign MAC systems. Yet, this exchange of knowledge is not very intensive and stable, so that the impact of EU regulations on US and Japanese patenting behaviour remains fairly weak.Environmental innovation, patent, count data models

The Determinants of Environmental Innovations and Patenting: Germany Reconsidered

This paper provides new evidence on the objectives and determinants of different typesof innovations and patents, environmental as opposed to other innovations and patents,and different variants of environmental innovations and patents. We investigate howfirm-specific and sector-specific driving forces differ by innovation type. Moreover, weoutline the functions that different innovation types have for environmental and innovationpolicies. We find that eco-innovators put relatively more attention to cost reduction, inparticular the reduction of energy and resource costs, compared to other innovators.Cost pressure and reliable, predictable and strict framework conditions of environmentalpolicy turns out to be an important driver for more incremental, firm-level eco-innovationscontributing to the diffusion of principally known technologies among firms. By contrast,more far-reaching patented eco-innovations are driven by the opportunity to create newmarkets and by government subsidies.Environmental innovation, patent, discrete choice models

Correcting Sample Selection in FARS Data to Estimate Seatbelt Use

In this paper, we use 2006 FARS data to estimate seatbelt use in the United States. We apply a method to correct the FARS data for sample selection bias introduced by Levitt and Porter (2001), as well as discuss the advantages of using FARS data for seatbelt analysis. Furthermore, based on assumptions of independence for seatbelt choice, we establish a lower and upper bound for seatbelt usage rates, and that once we correct for sample selection bias, the seatbelt usage estimates from the corrected FARS emerge at least as a comparable alternative to NOPUS estimates. This implies that researchers can use corrected FARS to complement NOPUS, thus being able to utilize the rich cross-sectional details available in FARS data to analyze various relevant research questions

Determinants of Seat Belt Use: Regression Analysis with FARS Data Corrected for Self-Selection

We develop a methodology to use FARS data as an alternative to NOPUS in estimating seat belt usage. The advantages of using FARS over NOPUS are that (i) FARS is broader because it contains more variables relevant for policy analysis, (ii) FARS allows for easy multivariate regression analysis, and, finally, (iii) FARS data is more cost-effective. Methodology: We apply a binary logit model in our analysis to determine the likelihood of seat belt usage given various occupant, vehicle, and built environment characteristics. Using FARS data, we derive coefficient estimates for categories such as vehicle occupants\u27 age and night time seat belt use that observational surveys like NOPUS cannot easily provide. Results: Our results indicate that policies should focus on passengers (as opposed to drivers), male and young vehicle occupants, and that law enforcement should focus on pick-up trucks, rural roads, and nights. We find evidence that primary seat belt laws are effective. Conclusions: Although this is primarily a methodological paper, we present and discuss our results in the context of public policy so that our findings are relevant for road safety practitioners, researchers, and policymakers

Are Travel Demand Forecasting Models Biased because of Uncorrected Spatial Autocorrelation? By

ABSTRACT: This paper discusses spatial autocorrelation in mode choice models, including what kind of bias it introduces and how to remedy the problem. The research shows that a spatially autocorrelated mode choice model, not uncommon because of, in terms of transit characteristics homogeneous neighborhoods, systematically overestimates transit trips from suburban transit-unfriendly areas and underestimates transit trips in the transit-friendly city center. Adding a spatial lag term into the model specification avoids the bias, however, it also changes sampling approaches, requires higher quality household forecast data and complicates forecastin

Testing for Spatial Equilibrium Using Happiness Data

Happiness data are rarely used in regional and urban analysis, but it is a prime data set for testing the assumption of spatial equilibrium, the key assumption in the field of urban economics. In this paper, we explore the relationship between regional happiness and one-year lagged change in population growth rates for the nine census regions in the United States using data on reported well-being from National Opinion Research Center\u27s annual General Social Survey. We observe that, while there is evidence of spatial disequilibrium during recessions and in the long run, happier regions generally experience higher population growth rates indicating a movement (or tendency) toward spatial equilibrium

Unpacking Preference: How Previous Experience Affects Auto Ownership in the United States

As environmental concerns mount alongside increasing auto dependence, research has been devoted to understanding the number of automobiles households own. The 2000 US census public use micro sample is used to demonstrate the importance of preference formation in auto ownership by studying auto ownership among recent movers. Using a multinomial probit model, the paper demonstrates that residents in the US transit cities who moved from major metropolitan areas are more likely to own fewer vehicles than counterparts who moved from smaller metropolitan areas and non-metropolitan areas. It is concluded that these results are due to learned preferences for levels of car ownership. Once the self-reinforcing 'cultural knowledge' of living without cars is lost, it could be difficult to regain. A focus on children and young adults, familiarising them with alternatives to the car may be an important approach to developing collective preferences for fewer cars.