An urban general equilibrium model with multiple household structures and travel mode choice

Households in real cities are heterogeneous regarding their size and composition. An aspect usually neglected in urban models used to study economic and policy issues that arise in today's cities. We develop an urban general equilibrium model that takes a more complex household structure explicitly into account. The model is based on the single consumer type model of Anas and Xu (1999) or Anas and Rhee (2006) and treats the interactions of urban product, labor and land markets as well as linkages between city firms and different consumer types living in different household structures. Households differ not only in endowments, preferences and their valuation in regard to different travel modes, but also in size and the composition regarding their members. The implementation of a more complex household structure then allows studying a broad range of further urban economic issues, which treat different household structures differentlyurban economics; general equilibrium; household structure; location choice

How does the household structure shape the urban economy?

Households in real cities are heterogeneous regarding their size and composition. This implies that the household structure -i.e. the (average) household size, the composition, the relative share of different household types, and the number of households - differs across cities. This aspect is usually neglected in urban models used to study economic and policy issues that arise in today's cities. Furthermore, the household structure might change over time. For instance, over the last decades average household size has decreased in many countries. Several implications of this change have been discussed, but usually not in regard to an urban economy with its interdependencies. We develop an applied urban general equilibrium model which explicitly takes the household structure into account and thus allows studying the impacts of changes in the household structure on an urban economy and its spatial pattern. The paper shows that changes in the household structure affect an urban economy in various ways and may contribute to explain economic and spatial effects on cities. Compared to a 'Base City' which reflects the actual household structure in the United States, urban labor force participation, housing demand, rents, wages as well as urban commuting and shopping patterns are considerably affected by, e.g., changes in the average household size in a city. For instance, wage inequality between differently skilled workers rises and extreme cross commuting drops to almost zero when the city turns into a pure 'Singles City'. --General equilibrium,Household structure,Household size,Location,Commuting

An urban general equilibrium model with multiple household structures and travel mode choice

Households in real cities are heterogeneous regarding their size and composition. An aspect usually neglected in urban models used to study economic and policy issues that arise in today's cities. We develop an urban general equilibrium model that takes a more complex household structure explicitly into account. The model is based on the single consumer type model of Anas and Xu (1999) or Anas and Rhee (2006) and treats the interactions of urban product, labor and land markets as well as linkages between city firms and different consumer types living in different household structures. Households differ not only in endowments, preferences and their valuation in regard to different travel modes, but also in size and the composition regarding their members. The implementation of a more complex household structure then allows studying a broad range of further urban economic issues, which treat different household structures differently. --Urban Economics,General Equilibrium,Household structure,Location choice

Income tax deduction of commuting expenses and tax funding in an urban CGE study: the case of German cities

Germany like many other European countries subsidize commuting by granting the right to deduct commuting expenses from the income tax base. This regulation has often been changed and has regularly been under debate during the last decades. The pros (e.g. causing efficiency gains with respect to the spatial allocation of labor) and cons (e.g. causing urban sprawl) are well documented. Nonetheless, there is need for further research. For reasons of tractability the few models applied in the tax deduction related literature are based on restrictive assumptions particularly concerning the design of the income taxation scheme and the structure of households (neglecting household heterogeneity) and, most importantly, they do not integrate labor supply and location decision problems simultaneously. Here, for the first time, those and more features are taken into account in a full spatial general equilibrium simulation approach calibrated to an average German city. This model is applied to calculate the impacts of tax deductions on an urban economy thereby considering different funding schemes. Our results suggest that the tax deduction level currently chosen is below the optimal level in the case of income tax funding. If a change in the tax base occurs, e.g. toward consumption tax or energy tax funding, the optimal size of the subsidy should be even higher. Furthermore, the different policy packages cause a very differentiated pattern regarding welfare distribution, environmental (CO2 emissions) and congestion effects. We also find surprisingly small effects on urban sprawl characterized by suburbanization of residences and jobs, increasing commuting distances and spatial city growth. --urban general equilibrium model,commuting subsidies,income tax deduction

Income tax deduction of commuting expenses and tax funding in an urban CGE study: the case of German cities

Germany like many other European countries subsidize commuting by granting the right to deduct commuting expenses from the income tax base. This regulation has often been changed and has regularly been under debate during the last decades. The pros (e.g. causing efficiency gains with respect to the spatial allocation of labor) and cons (e.g. causing urban sprawl) are well documented. Nonetheless, there is need for further research. For reasons of tractability the few models applied in the tax deduction related literature are based on restrictive assumptions particularly concerning the design of the income taxation scheme and the structure of households (neglecting household heterogeneity) and, most importantly, they do not integrate labor supply and location decision problems simultaneously. Here, for the first time, those and more features are taken into account in a full spatial general equilibrium simulation approach calibrated to an average German city. This model is applied to calculate the impacts of tax deductions on an urban economy thereby considering different funding schemes. Our results suggest that the tax deduction level currently chosen is below the optimal level in the case of income tax funding. If a change in the tax base occurs, e.g. toward consumption tax or energy tax funding, the optimal size of the subsidy should be even higher. Furthermore, the different policy packages cause a very differentiated pattern regarding welfare distribution, environmental (COâ‚‚ emissions) and congestion effects. We also find surprisingly small effects on urban sprawl characterized by suburbanization of residences and jobs and increasing commuting distances.

Why not to choose the most convenient labor supply model? The impact of labor supply modeling on policy evaluation

The public economics, environmental, transportation and urban economics literature applies different labor supply approaches when studying economic or planning instruments. Some studies assume that working hours are endogenous while the number of workdays is given, whereas others model only decisions on workdays. Unfortunately, empirical evidence does hardly exist on account of missing data. Against this background, we provide an assessment of whether general effects of those policies are robust against the modeling of leisure demand and labor supply. We introduce different modeling approaches into a spatial general equilibrium model and discuss how they affect the welfare implication of several policies. We, then, perform simulations and find that in many cases the choice of labor supply modeling not only affects the magnitude of the policy impact but also its direction. While planning instruments are suggested to be quite robust to different labor supply approaches, the way of modeling labor supply may crucially affect the overall welfare implications of economic instruments such as congestion tolls. Based on these findings it becomes clear which labor supply approach is the most appropriate given specific conditions. Our study also emphasizes the need for better micro labor market data that also feature days of sickness, overtime work, the actual number of leave days, part-time work, telecommuting etc

The optimal subsidy on electric vehicles in a metropolitan area - a SCGE study for Germany

Many governments subsidize electric mobility (E-mobility) to increase the share of electric vehicles (EV) in the car fleet. This aims at reducing carbon emissions. Despite that there is not much research on the full economic costs and benefits of this measures. There are only a few Cost Benefit Analyses (CBA). They, however, do not take into account repercussion and substitution effects. We fill this gap in the literature and examine subsidies to EVs in a full spatial general equilibrium model. Since cities are the main area were EVs will be used, we focus on cities and apply a spatial approach. In particular, we ask whether it is optimal to subsidize or tax electric vehicles and, how large, the corresponding optimal rate is. We, first, derive analytically the optimal subsidy in a spatial partial equilibrium model of a city with two zones where commuting, carbon emissions, endogenous labor supply, fuel and power taxes are considered and where we distinguish between fuel vehicles and electric vehicles. There we find that the optimal subsidy rate is the sum of changes in externality costs (emissions + congestion), an opposite tax interaction effect, a redistribution effect between cities inhabitants and absentee landlords and a cost effect due to higher costs of producing travelling with power in comparison to fuel. The latter two effects are usually not considered in CBAs. Second, we extend the model to a full spatial general equilibrium model and employ simulations to calculate sign and size of the optimal subsidy or tax rate. This model is calibrated to a typical German metropolitan area. The results show that electric vehicles should not be subsidized but taxed. The results are robust with respect to changes in the willingness to adopt electric vehicles (EVs), changes in fix costs of EVs, and even if emission of EVs are zero. We change all these parameters to capture extreme and very unlikely behavior such as a very high demand elasticity of EVs with respect to the power tax rate, very low costs and the case that EVs have zero CO2 emissions. Concerning these variables we suggest that EVs should not be subsidized because welfare costs of achieving a small reduction in emissions are very high. We draw the conclusion that E-mobility might only be an efficient policy if it is considered as complement to other policies. This issue is left for future research

Income tax deduction of commuting expenses and tax funding in an urban CGE study: the case of German cities

Germany like many other European countries subsidize commuting by granting the right to deduct commuting expenses from the income tax base. This regulation has often been changed and has regularly been under debate during the last decades. The pros (e.g. causing efficiency gains with respect to the spatial allocation of labor) and cons (e.g. causing urban sprawl) are well documented. Nonetheless, there is need for further research. For reasons of tractability the few models applied in the tax deduction related literature are based on restrictive assumptions particularly concerning the design of the income taxation scheme and the structure of households (neglecting household heterogeneity) and, most importantly, they do not integrate labor supply and location decision problems simultaneously. Here, for the first time, those and more features are taken into account in a full spatial general equilibrium simulation approach calibrated to an average German city. This model is applied to calculate the impacts of tax deductions on an urban economy thereby considering different funding schemes. Our results suggest that the tax deduction level currently chosen is below the optimal level in the case of income tax funding. If a change in the tax base occurs, e.g. toward consumption tax or energy tax funding, the optimal size of the subsidy should be even higher. Furthermore, the different policy packages cause a very differentiated pattern regarding welfare distribution, environmental (CO2 emissions) and congestion effects. We also find surprisingly small effects on urban sprawl characterized by suburbanization of residences and jobs, increasing commuting distances and spatial city growth

The economics of workplace charging

To overcome the range-anxiety problem and further shortcomings associated with electric vehicles, workplace charging (WPC) is gaining increasing attention. We propose a microeconomic model of WPC and use the approach to shed light on the incentives and barriers employees and employers face when deciding on demand for and supply of WPC. It is shown that under market conditions there is no WPC contract an employer is willing to offer and at the same time the majority of employees is willing to accept. To overcome the lack of demand or underprovision of WPC we discuss various ‘remedies’, involving subsidies to charging facility costs and adjustments in electricity tariffs or loading technologies. We find that direct subsidies to WPC facilities or subsidies combined with specific energy price policies could be a way to foster WPC provision. In contrast measures on the employee side that may help to stimulate the demand for WPC turn out to be less feasible. Hence, our results suggest that in order to promote WPC it is more promising to support employers in offering WPC contracts than to provide employees an incentive to accept WPC contracts. The study therefore gives a rationale for public initiatives being undertaken to boost WPC provision, as e.g. in the case of the US

Actions and Outcomes: The Evaluative Function of Moral Emotions

Results from 10 empirical studies and 1 review article are described and can be summarized as follows: Only moral emotions represent an evaluation of person's behavior, whereas non-moral emotion provide information about outcomes. Positive moral emotions (e.g. pride, respect) signal that a person's (self or other) behavior was right, whereas negative moral emotions (e.g., guilt, indignation) signal that a person's behavior was wrong. These evaluations and signals are elicited by judgments of ought, goal attainment and effort (see Heider, 1958). Some moral emotions (e.g., shame or admiration) are also elicited by judgments on a person's ability. A person's responsibility (Weiner, 1995, 2006) and the perceived morality of a person's behavior (i.e., with regard to rightness and wrongness) represent further cognitive antecedents of moral emotions. Some moral emotions (e.g., regret, sympathy) are also influenced by a person's empathy (see Paulus, 2009) towards others. There are specific moral emotions that are closely connected to help-giving (e.g., sympathy), whereas other moral emotions are more closely related to reward (e.g., admiration) or punishment (e.g., anger). With regard to the cognitive effort underlying emotions, moral emotions require more cognitive effort (i.e., longer reaction times) than non-moral emotions.:Danksagung Inhaltsverzeichnis 1 Einleitung 1 2 An Introduction to Moral Emotions: Summary of Published Articles 3 2.1 A First Empirical Analysis of Moral Emotions 3 2.1.1 Towards a Classification of Moral Emotions 4 2.1.2 Cognitive Antecedents of Moral Emotions 5 2.1.3 Empirical Data 7 2.1.3.1 Positive Moral Observer Emotions 7 2.1.3.2 Negative Moral Observer Emotions 8 2.1.3.3 Positive Moral Actor Emotions 9 2.1.3.4 Negative Moral Actor Emotions 9 2.1.4 Cluster Analyses of Moral Emotions 10 2.1.5 Conclusions 12 2. 2 Integrating Moral Emotions in the Context of Attributional Theories 13 2.2.1 Metaphorical Backgrounds of Moral Emotions 13 2.2.2 Moral Emotions as ‘Stop vs. Go - Signals\' 15 2.3 Open Questions 16 2.3.1 Distinguishing Moral from Non-Moral Emotions 16 2.3.2 Controllability 16 2.3.3 Ability as a Further Antecedent Condition Eliciting Moral Emotions 16 2.3.4 Behavioral Consequences of Moral Emotions 17 2.3.5 Personality 17 2.3.6 Cognitive Effort 18 3 On Distinguishing Moral from Non-Moral Emotions. 19 3.1. Abstract 19 3.2 Introduction and Theoretical Background 20 3.2.1 Identifying Moral Emotions: Cognitive Antecedents 21 3.2.1.1 Agency 21 3.2.1.2 Moral Standards 22 3.2.1.3 Effort 23 3.2.2 Moral vs. Non-Moral Emotions 24 3.2.2.1 Non-Moral Emotions 25 3.2.2.2 Emotions with Both Moral and Non-Moral Qualities 25 3.2.2.3 Discordant Emotions 26 3.2.4 Aims and Expectations 27 3.3 Study 1 28 3.3.1 Method 28 3.3.1.1 Participants 28 3.3.1.2 Experimental Design 29 3.3.1.3 Materials and Procedure 29 3.3.1.4 Data Analysis 30 3.3.2 Results 31 3.3.2.1 Positive Moral Observer Emotions 31 3.3.2.2 Positive Non-Moral Observer Emotions 32 3.3.2.3 Negative Moral Observer Emotions 35 3.3.2.4 Negative Non-Moral Observer Emotions 36 3.3.3 Discussion of Study 1 37 3.4 Study 2 41 3.4.1 Method 41 3.4.1.1 Participants 41 3.4.1.2 Experimental Design 42 3.4.1.3 Materials and Procedure 42 3.4.1.4 Data Analysis 42 3.4.2 Results 43 3.4.2.1 Positive Moral Actor Emotions 43 3.4.2.2 Positive Non-Moral Actor Emotions 43 3.4.2.3 Negative Moral Actor Emotions 44 3.4.2.4 Negative Non-Moral Actor Emotions 48 3.4.3 Discussion of Study 2 48 3.5 General Discussion 51 3.5.1 The Signal-Function of Moral Emotions 54 3.5.2 Limitations and Implications for Future Research 55 4 The Who and Whom of Help-Giving: An Attributional Model Integrating the Help-Giver and the Help-Recipient. 58 4.1. Abstract 58 4.2 Introduction and Theoretical Background 59 4.2.1 Responsibility, Moral Observer Emotions and Help-Giving 62 4.2.2 Responsibility, Moral Actor Emotions and Help-Giving 63 4.2.3 Responsibility and Deservingness …………………………………………...... 65 4.2.4 Personal Characteristics of the Help-Giver 65 4.2.5 Aims and Expectations 66 4.3 Method 67 4.3.1 Participants 67 4.3.2 Experimental Design 67 4.3.3 Materials and Procedure 68 4.3.4 Data Analysis 70 4.4 Results 71 4.4.1 Manipulation Checks 71 4.4.2 From Thinking to Feeling 72 4.4.3 From Thinking to Acting 72 4.4.4 From Feeling to Acting 73 4.4.5 Moral Emotions as Mediators between Thinking and Acting 74 4.4.5.1 Moral Actor Emotions 74 4.4.5.2 Moral Observer Emotions 76 4.4.6 Empathy and Help-Giving 77 4.4.7 A Comprehensive Model 78 4.5 Discussion 83 4.5.1 Responsibility and Feelings of the Moral Actor 83 4.5.2 Moral Actor Emotions and Help-Giving 83 4.5.3 Responsibility and Help-Giving 84 4.5.4 Responsibility of the Person in Need and Moral Observer Emotions 85 4.5.5 Moral Observer Emotions and Help-Giving 85 4.5.6 Moral Emotions as Mediators between Cognitions and Help-Giving 85 4.5.7 Stable Characteristics of the Moral Actor 87 4.5.8 A Comprehensive Model of Help-Giving 87 4.5.9 Recommendations for Future Research 89 5 Moral Emotions: Cognitive Basis and Behavioral Consequences. 91 5.1 Abstract 91 5.2 Introduction and Theoretical Background 92 5.2.1 Cognitive Antecedents of Moral Emotions 94 5.2.2 Behavioral Consequences of Moral Emotions 97 5.2.3 Combining Cognitive Antecedents and Behavioral Consequences 98 5.2.4 Aims and Expectations 100 5.3 Study 1 100 5.3.1 Method 101 5.3.1.1 Participants 101 5.3.1.2 Experimental Design 102 5.3.1.3 Materials and Procedure 102 5.3.1.4 Data Analysis 103 5.3.2 Results 103 5.3.2.1 Manipulation Checks 103 5.3.2.2 Eliciting Moral Cognitions and Moral Actor Emotions 104 5.3.2.3 Predicting Moral Actor Emotions from Cognitions: Attained Goals 109 5.3.2.4 Predicting Moral Actor Emotions from Cognitions: Non-Attained Goals 111 5.3.2.5 Predicting Moral Actor Emotions from Cognitions: Mediation Analyses. 113 5.3.3 Discussion of Study 1 116 5.3.3.1 Cognitive Antecedents as Elicitors of Moral Cognitions and Moral Actor Emotions 116 5.3.3.2 Moral Cognitions Eliciting Moral Actor Emotions. 119 5.4 Study 2 121 5.4.1. Method 123 5.4.1.1 Participants 123 5.4.1.2 Experimental Design 123 5.4.1.3 Materials and Procedure 124 5.4.1.4 Data Analysis 125 5.4.2 Results 125 5.4.2.1 Manipulation Checks 125 5.4.2.2 Eliciting Moral Cognitions, Moral Observer Emotions and Sanctioning Behavior 126 5.4.2.3 Predicting Moral Observer Emotions from Cognitions: Attained Goals 132 5.4.2.4 Predicting Moral Observer Emotions from Cognitions: Non-Attained Goals 134 5.4.2.5 Predicting Moral Observer Emotions from Cognitions: Mediation Analyses 135 5.4.2.6 Predicting Sanctioning Behavior from Cognitions: Attained Goals 138 5.4.2.7 Predicting Sanctioning Behavior from Cognitions Non-Attained Goals 139 5.4.2.8. Predicting Sanctioning Behavior from Moral Observer Emotions: Attained Goals 139 5.4.2.9 Predicting Sanctioning Behavior from Moral Observer Emotions: Non-Attained Goals 140 5.4.2.10 Predicting Sanctioning Behavior from Cognitions and Moral Observer Emotions: Mediation Analyses 141 5.4.2.11 Predicting Sanctioning Behavior from Cognitions and Emotions: a Comprehensive Cognition Emotion Action Model 144 5.4.3 Discussion of Study 2 146 5.4.3.1 Cognitive Antecedents as Elicitors of Moral Cognitions, Moral Observer Emotions, Reward and Punishment 146 5.4.3.2. Moral Cognitions Eliciting Moral Observer Emotions 150 5.4.3.3 Relationships between Moral Cognitions, Moral Observer Emotions, Reward and Punishment 151 5.5 General Discussion 155 5.5.1 Moral Cognitions 155 5.5.2 Moral Emotions 155 5.5.3 Sanctioning Behavior 157 5.5.4 Limitations and Recommendations for Future Research. 159 6 Moral vs. Non-moral Emotions: Further Differentiation Based on Cognitive Effort 160 6.1 Abstract 160 6.2 Introduction and Theoretical Background 161 6.2.1 The Cognitive Effort Underlying Moral vs. Non-Moral Emotions 162 6.2.2 Cognitive Effort and Balance Theory 163 6.2.3 Aims and Expectations 166 6. 3 Method 168 6.3.1 Participants 168 6.3.2 Experimental Design 168 6.3.3 Materials and Procedure 169 6.3.4 Data Analysis 170 6.4 Results 171 6.4.1 Frequency of Choice 171 6.4.1.1 Moral Cognitions 171 6.4.1.2 Positive Emotions 172 6.4.1.3 Negative Emotions 174 6.4.2 Reaction Times 175 6.4.2.1 Moral Cognitions 174 6.4.2.2 Moral and Non-Moral Emotions 176 6.5 Discussion 180 6.5.1 Shortcomings and Issues for Future Research 183 7 Summary and Future Prospects 185 7.1 A Classification of Moral and Non-Moral Emotions 185 7. 2 Moral Emotions and Subsequent Behaviors 189 7.3. Moral Emotions and Personality 190 7.4 Cognitive Effort and Moral Emotions 192 7.5 An Empirically Based Definition of Moral Emotions 194 8 Zusammenfassung und Ausblick 195 8.1 Eine Klassifikation moralischer und nicht-moralischer Emotionen 195 8.2. Moralische Emotionen und nachfolgende Verhaltensweisen 200 8.3. Moralische Emotionen und Persönlichkeit 202 8.4 Kognitive Beanspruchung und moralische Emotionen 203 8.5 Eine empirisch fundierte Klassifikation moralischer Emotionen 205   Appendix A 207 Appendix B 208 Appendix C 209 Appendix D 210 Literaturverzeichnis 211 Tabellenverzeichnis 239 Abbildungsverzeichnis 240 Erklärung zur Selbstständigkeit LEBENSLAU