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on Discrete Choice Models |
| By: | Paramita Sinha; Martha Caulkins; Maureen Cropper |
| Abstract: | Amenities that vary across cities are typically valued using either a hedonic model, in which amenities are capitalized into wages and housing prices, or a discrete model of household location choice. In this paper, we use the 2000 Public Use Microdata Sample (PUMS) to value climate amenities using both methods. We compare estimates of marginal willingness to pay (MWTP), first assuming homogeneous tastes for climate amenities and then allowing preferences for climate amenities to vary by location. We find that mean MWTP for warmer winters is about four times larger using the discrete choice approach than with the hedonic approach; mean MWTP for cooler summers is twice as large. The two approaches also differ in their estimates of taste sorting. The discrete choice model implies that households with the highest MWTP for warmer winters locate in cities with the mildest winters, while the hedonic model does not. Differences in estimates are due to three factors: (1) the discrete choice model incorporates the psychological costs of moving from one’s birthplace, which the hedonic models do not; (2) the discrete choice model allows for city-specific labor and housing markets, rather than assuming a national market; (3) the discrete choice model uses information on market shares (i.e., population) in estimating parameters, which the hedonic model does not. |
| Keywords: | amenity valuation, location choice, hedonic models, value of climate |
| JEL: | Q51 Q54 |
| Date: | 2018–04 |
| URL: | http://d.repec.org/n?u=RePEc:nev:wpaper:wp201804&r=dcm |
| By: | Aguirregabiria, Victor; Gu, Jiaying; Luo, Yao |
| Abstract: | We study the identification and estimation of structural parameters in dynamic panel data logit models where decisions are forward-looking and the joint distribution of unobserved heterogeneity and observable state variables is nonparametric, i.e., fixed-effects model. We consider models with two endogenous state variables: the lagged decision variable, and the time duration in the last choice. This class of models includes as particular cases important economic applications such as models of market entry-exit, occupational choice, machine replacement, inventory and investment decisions, or dynamic demand of differentiated products. The identification of structural parameters requires a sufficient statistic that controls for unobserved heterogeneity not only in current utility but also in the continuation value of the forward-looking decision problem. We obtain the minimal sufficient statistic and prove identification of some structural parameters using a conditional likelihood approach. We apply this estimator to a machine replacement model. |
| Keywords: | Panel data discrete choice models; Dynamic structural models; Fixed effects; Unobserved heterogeneity; Structural state dependence; Identification; Sufficient statistic. |
| JEL: | C23 C25 C41 C51 C61 |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:12930&r=dcm |
| By: | Harbrecht, Alexander; McKenna, Russell; Fischer, David; Fichtner, Wolf |
| Abstract: | This paper presents a stochastic bottom-up model to assess electric vehicles' (EV) impact on load profiles at different parking locations as well as their load management potential assuming different charging strategies. The central innovation lies in the consideration of socio-economic, technical and spatial factors, all of which influence charging behavior and location. Based on a detailed statistical analysis of a large dataset on German mobility, the most statistically significant influencing factors on residential charging behavior could be identified. Whilst household type and economic status are the most important factors for the number of cars per household, the driver's occupation has the strongest influence on the first departure time and parking time whilst at work. An inhomogeneous Markov-chain is used to sample a sequence of destinations of each car trip, depending (amongst other factors) on the occupation of the driver, the weekday and the time of the day. Probability distributions for the driven kilometres, driving durations and parking durations are used to derive times and electricity demand. The probability distributions are retrieved from a national mobility dataset of 70,000 car trips and filtered for a set of socio-economic and demographic factors. Individual charging behaviour is included in the model using a logistic function accounting for the sensitivity of the driver towards (low) battery SOC. The presented model is validated with this mobility dataset and shown to have a deviation in key household mobility characteristics of just a few percentage points. The model is then employed to analyse the impact of uncontrolled charging of BEV on the residential load profile. It is found that the absolute load peaks will increase by up to factor 8.5 depending on the loading infrastructure, the load in high load hours will increase by approx. a factor of 3 and annual electricity demand will approximately double. |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:kitiip:29&r=dcm |
| By: | Bouscasse, H. |
| Abstract: | Mode choice depends on observable characteristics of the transport modes and of the decision maker, but also on unobservable characteristics, known as latent variables. By means of an integrated choice and latent variable (ICLV) model, which is a combination of structural equation model and discrete choice model, it is theoretically possible to integrate both types of variables in a psychologically and economically sound mode choice model. To achieve such a goal requires clear positioning on the four dimensions covered by ICLV models: survey methods, econometrics, psychology and economics. This article presents a comprehensive survey of the ICLV literature applied to mode choice modelling. I review how latent variables are measured and incorporated in the ICLV models, how they contribute to explaining mode choice and how they are used to derive economic outputs. The main results are: 1) the latent variables used to explain mode choice are linked to individual mental states, perceptions of transport modes, or an actual performed behaviour; 2) the richness of structural equation models still needs to be explored to fully embody the psychological theories explaining mode choice; 3) the integration of latent variables helps to improve our understanding of mode choice and to adapt public policies. |
| Keywords: | MODE CHOICE;SURVEY;INTEGRATED CHOICE;LATENT VARIABLE MODEL;STRUCTURAL EQUATION MODELLING;BEHAVOURAL THEORIES;ECONOMIC OUTPUTS |
| JEL: | C25 D9 R41 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:gbl:wpaper:2018-07&r=dcm |
| By: | Sarantis Tsiaplias (Melbourne Institute: Applied Economic & Social Research and Department of Economics, The University of Melbourne) |
| Abstract: | Conditions are derived for relating household well-being functions to household utility. In particular, an isomorphic relationship between the equivalent incomes stemming from subsistence-based utility functions and well-being functions is established. This allows estimates from standard models of well-being based on a CDF (eg. probit and logit models) to be given a formal welfare interpretation. New measures of the welfare distortion due to unobserved heterogeneity are also derived. An Australian household-level dataset is used as a case study for exploring the proposed measures of distortion. The results indicate that the failure to account for unobserved heterogeneity produces significant welfare distortions (primarily in the form of under-compensation). A unique welfare sensitivity curve is also estimated that indicates the presence of non-linearities that impair the typically monotonic relationship between household income, the household’s capacity to adjust its income and its marginal utility of consumption. The results are significant for better understanding the welfare implications of tax and transfer policies. |
| Keywords: | Household expenditure, social welfare, consumption, heterogeneity, equivalence scales, income |
| JEL: | D12 D10 C33 C35 D60 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:iae:iaewps:wp2017n21&r=dcm |
| By: | Engström, Emma (Folksam Research); Algers, Staffan (CTS - Centre for Transport Studies Stockholm (KTH and VTI)); Beser Hugosson, Muriel (CTS - Centre for Transport Studies Stockholm (KTH and VTI)) |
| Abstract: | Dedicated to show climate leadership, Sweden has committed to cut 70% of greenhouse gas emissions in the domestic transport sector by 2030 as compared to levels in 2010 (except flights). The aim of this study was to quantify car type choice among private buyers and individuals with cars provided as a fringe benefit, and to investigate the impacts of retrospective policy scenarios using Sweden as a case study. Models were developed using revealed preferences data relating to car attributes and buyer socioeconomics. The company car type choice model reflected both company policy restrictions and employee preferences. The results indicated that range and safety were crucial factors for the widespread introduction of electric cars and plug-in hybrids. Company car owners were more inclined to choose cars with climate friendly fuels than private buyers. Average CO2 emissions per car were however similar in the two groups, which might relate to a stronger preference for heavier and larger cars among company car holders, in combination with the weights-based ‘Clean car’ definition in Sweden. A ‘Clean car’ restriction was company policy for 7.5% of employees, among whom the share of diesel cars was 88%. Policy scenario modeling results further indicated that the impact of recent climate and transport policies has been small: the most notable effect was a policy of reduced fringe benefits taxation on alternative fuels, worth up to €1,100 annually, which resulted in 0.7 % lower average CO2/km per car. For private buyers, a ‘Super Clean Car’ premium, worth ca € 2,000 – € 4,000, had a 0.4 % effect on the average emissions per car, according to models. This effect was twice as high as that for a five year tax-exemption for ‘Clean cars’, worth ca €200 annually for private buyers. Apparently, in order to substantially change the fleet of new cars in Sweden there is a need for tougher transport policies related to climate change mitigation. |
| Keywords: | Public transport; bus; demand model; fares; frequencies; supply; optimization; urban; welfare |
| JEL: | R41 R42 R48 |
| Date: | 2018–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:ctswps:2018_009&r=dcm |