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on Discrete Choice Models |
| By: | Rafi, Arafat Hossain; Jeba, Jebunnesa; tabssum, Tasnim; Khan, Abdul Mahidud |
| Abstract: | Today employees compete for qualified individuals and try to reduce employee turnover as a profit maximizing condition. That is why a proper understanding of employees' demands, including and beyond wage, is critical. The paper examines how various job attributes affect university students’ utility and their tendencies to choose different types of jobs. This study adopted the Discrete Choice Experiment (DCE) to find the Willingness to accept (WTA) among 213 students of Bangladesh University of Professionals (BUP). This study identified four essential job attributes such as monthly wage, job security, working hours and the opportunity of using the knowledge or skills they gained during their bachelor’s or masters and quantify the tradeoff preference among these four attributes. The paper finds that students prefer the public job sector more than the private job, entrepreneurship, and higher study. Having job security increases their utility by 35.8 percent and they require an amount of 16 thousand taka in the absence of job security. Working for long hours such as 46-60 hours and 61-75 hours decreases their utility by 39 percent and 25.2 percent respectively. Moreover, Female students are required more compensation than males for longer working hours whereas male students put more value on high wages. |
| Keywords: | Utility, Preference, Attributes, Discrete Choice Experiment |
| JEL: | D11 D70 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:118424&r=dcm |
| By: | Xiyuan Ren; Joseph Y. J. Chow |
| Abstract: | Estimating agent-specific taste heterogeneity with a large information and communication technology (ICT) dataset requires both model flexibility and computational efficiency. We propose a group-level agent-based mixed (GLAM) logit approach that is estimated with inverse optimization (IO) and group-level market share. The model is theoretically consistent with the RUM model framework, while the estimation method is a nonparametric approach that fits to market-level datasets, which overcomes the limitations of existing approaches. A case study of New York statewide travel mode choice is conducted with a synthetic population dataset provided by Replica Inc., which contains mode choices of 19.53 million residents on two typical weekdays, one in Fall 2019 and another in Fall 2021. Individual mode choices are grouped into market-level market shares per census block-group OD pair and four population segments, resulting in 120, 740 group-level agents. We calibrate the GLAM logit model with the 2019 dataset and compare to several benchmark models: mixed logit (MXL), conditional mixed logit (CMXL), and individual parameter logit (IPL). The results show that empirical taste distribution estimated by GLAM logit can be either unimodal or multimodal, which is infeasible for MXL/CMXL and hard to fulfill in IPL. The GLAM logit model outperforms benchmark models on the 2021 dataset, improving the overall accuracy from 82.35% to 89.04% and improving the pseudo R-square from 0.4165 to 0.5788. Moreover, the value-of-time (VOT) and mode preferences retrieved from GLAM logit aligns with our empirical knowledge (e.g., VOT of NotLowIncome population in NYC is $28.05/hour; public transit and walking is preferred in NYC). The agent-specific taste parameters are essential for the policymaking of statewide transportation projects. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.13159&r=dcm |
| By: | Chakraborty, Debapriya; Bunch , David S.; Brownstone, David |
| Abstract: | The increasing diversity of vehicle type holdings and growing demand for BEVs and PHEVs have serious policy implications for travel demand and air pollution. Consequently, it is important to accurately predict or estimate the preference for vehicle holdings of households as well as the vehicle miles traveled by vehicle body- and fuel-type to project future VMT changes and mobile source emission levels. Leveraging the 2019 California Vehicle Survey data, this report presents the application of a utility-based model for multiple discreteness that combines multiple vehicle types with usage in an integrated model, specifically the MDCEV model. The model results suggest the important effects of household demographics, residence location, and built environment factors on vehicle body type and powertrain choice and usage. Further the predictions associated with changes inbuilt environment factors like population density can inform the design of land-use and transportation policies to influence household vehicle holdings and usage that can in turn impact travel demand and air quality issues in California. View the NCST Project Webpage |
| Keywords: | Social and Behavioral Sciences, Vehicle Choice, Vehicle Miles Traveled, Joint Discrete Choice Model |
| Date: | 2023–09–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt3jj3v7pw&r=dcm |
| By: | Andrén, Daniela (Örebro University School of Business); Laitila, Thomas (Örebro University School of Business); Ryen, Linda (University Health Care Research Center, Faculty of Medicine and Health); Vimefall, Elin (Örebro University School of Business) |
| Abstract: | This article investigates how the Swedish population values a reduction in the number of suicides in relation to other life-saving interventions within the health care sector. An online discrete choice experiment was conducted with a sample of 1000 Swedish members of the web panel Userneeds to elicit the relative importance placed on reducing the number of suicides in comparison to deaths due to pancreatic cancer, breast cancer, and acute heart attack. The choice set consisted of three attributes: number of lives saved, cause of death, and age group affected. We found that respondents valued saving lives by suicide prevention lower than saving lives from pancreatic cancer, breast cancer, or acute heart attack. |
| Keywords: | relative value of suicide prevention; health care; priority setting; saving lives; discrete choice experiment; pancreatic cancer; breast cancer; or acute heart attack. |
| JEL: | D61 I10 I12 I18 |
| Date: | 2023–09–26 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:oruesi:2023_010&r=dcm |
| By: | Francesco Jacopo Pintus (University of Padova) |
| Abstract: | Drawing upon an extensive body of valuation literature focused on water quality, I conduct a meta-analysis benefit transfer exercise with the aim of quantifying the Willingness to Pay (WTP) for an enhancement in drinking water quality among households directly exposed to Perfluoroalkyl Substances (PFAS) over recent decades in Italy. My analysis comprises a metadata compilation encompassing 72 WTP estimates extracted from 39 previous valuation studies conducted in advanced economies. The transfer of values is realized estimating a meta regression model (MRM) which includes both study design and socio-economic explanatory variables, according to the Weak Structural Utility Theoretic approach. To determine the most suitable MRM specification, I engage in a comparative evaluation of various model configurations, assessing their predictive performance in terms of transfer errors and explanatory capability. The mean transfer error and the adjusted R-squared of the preferred MRM are in line with previous published meta-analysis and equal respectively to 0.665 and 0.607. Furthermore, the parameters estimated within the model align with both intuitive expectations and economic theory. As a result of the benefit transfer process, I estimate an annual WTP of e250.80 per household for improved drinking water quality within the PFAS-affected area, and an aggregate value of social benefits from PFAS decontamination of around e12 million. |
| Keywords: | WTP; Meta-Analysis; Benefit Transfer; PFAS; Drinking Water. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:pad:wpaper:0308&r=dcm |
| By: | Phu Nguyen-Van; Thierry Blayac; Dimitri Dubois; Sebastien Duchene; Bruno Ventelou; Marc Willinger |
| Abstract: | This paper studies the behavioral and socio-demographic determinants of reported compliance with prophylactic measures against COVID-19: barrier gestures, lockdown restrictions and mask wearing. The study contrasts two types of measures for behavioral determinants: experimentally elicited preferences (risk tolerance, time preferences, social value orientation and cooperativeness) and stated preferences (risk tolerance, time preferences, and the GSS trust question). Data were collected from a representative sample of the metropolitan French adult population (N=1154) surveyed during the first lockdown in May 2020, and the experimental tasks were carried out on-line. The in-sample and out-of-sample predictive power of several regression models - which vary in the set of variables that they include - are studied and compared. Overall, we find that stated preferences are better predictors of compliance with these prophylactic measures than preferences elicited through incentivized experiments: self-reported level of risk, patience and trust are predicting compliance, while elicited measures of risk-aversion, patience, cooperation and prosociality did not. |
| Keywords: | COVID-19, individual preferences, social preferences, elicited preferences, stated preferences |
| JEL: | C90 D90 I18 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:drm:wpaper:2023-27&r=dcm |