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on Discrete Choice Models |
| By: | Oleksandr Rossolov; Yusak O. Susilo |
| Abstract: | This paper presents the behavioral study's results on willingness-to-pay the extra money by the customers for e-groceries deliveries based on crowd-shipping. The proposed methodology was tested for Ukraine, i.e., a developing country where the crowd-shipping services are under development conditions. To account for the behavior complexity of the consumers who have not faced the crowd-shipping services in the past, the choice model was enhanced with a latent variable. The findings indicate the revealed readiness of the e-shoppers to pay extra money for crowd-shipping delivery if it provides more flexible and consumer-oriented service. The expected environmental impact of the crowd-shipping delivery was not considered as important by the e-shoppers, which is explained by low concerns about the environment and car-oriented mobility in the considered case study. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.07044&r=dcm |
| By: | David Ronayne (European School of Management and Technology (ESMT Berlin)); Roberto Veneziani (Queen Mary University of London); William R. Zame (University of California at Los Angeles) |
| Abstract: | Abstract: Anscombe and Aumann (1963) offer a definition of subjective probability in terms of comparisons with objective probabilities. That definition – which has provided the basis for much of the succeeding work on subjective probability – presumes that the subjective probability of an event is independent of the prize consequences of that event, a property we term Prize Independence. We design experiments to test Prize Independence and find that a large fraction of our subjects violate it; thus, they do not have subjective probabilities. These findings raise questions about the empirical relevance of much of the literature on subjective probability. |
| Keywords: | subjective probability, choice under uncertainty, online experiments. |
| JEL: | D01 D81 D84 |
| Date: | 2022–06–22 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:940&r=dcm |
| By: | David M\"uller; Emerson Melo; Ruben Schlotter |
| Abstract: | This paper introduces the distributionally robust random utility model (DRO-RUM), which allows the preference shock (unobserved heterogeneity) distribution to be misspecified or unknown. We make three contributions using tools from the literature on robust optimization. First, by exploiting the notion of distributionally robust social surplus function, we show that the DRO-RUM endogenously generates a shock distributionthat incorporates a correlation between the utilities of the different alternatives. Second, we show that the gradient of the distributionally robust social surplus yields the choice probability vector. This result generalizes the celebrated William-Daly-Zachary theorem to environments where the shock distribution is unknown. Third, we show how the DRO-RUM allows us to nonparametrically identify the mean utility vector associated with choice market data. This result extends the demand inversion approach to environments where the shock distribution is unknown or misspecified. We carry out several numerical experiments comparing the performance of the DRO-RUM with the traditional multinomial logit and probit models. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.05888&r=dcm |
| By: | Paolo Verme (World Bank) |
| Abstract: | Poverty prediction models are used by economists to address missing data issues in a variety of contexts such as poverty profiling, targeting with proxy-means tests, cross-survey imputations such as poverty mapping, or vulnerability analyses. Based on the models used by this literature, this paper conducts an experiment by artificially corrupting data with different patterns and shares of missing incomes. It then compares the capacity of classic econometric and machine learning models to predict poverty under these different scenarios. It finds that the quality of predictions and the choice of the optimal prediction model are dependent on the distribution of observed and unobserved incomes, the poverty line, the choice of objective function and policy preferences, and various other modeling choices. Logistic and random forest models are found to be more robust than other models to variations in these features, but no model invariably outperforms all others. The paper concludes with some reflections on the use of these models for predicting poverty. |
| Keywords: | Income modeling, Income Distributions, Poverty Predictions |
| JEL: | D31 D63 E64 O15 |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:inq:inqwps:ecineq2023-642&r=dcm |
| By: | Benjamin Enke; Thomas Graeber; Ryan Oprea |
| Abstract: | We provide experimental evidence that core intertemporal choice anomalies -- including extreme short-run impatience, structural estimates of present bias, hyperbolicity and transitivity violations -- are driven by complexity rather than time or risk preferences. First, all anomalies also arise in structurally similar atemporal decision problems involving valuation of iteratively discounted (but immediately paid) rewards. These computational errors are strongly predictive of intertemporal decisions. Second, intertemporal choice anomalies are highly correlated with indices of complexity responses including cognitive uncertainty and choice inconsistency. We show that model misspecification resulting from ignoring behavioral responses to complexity severely inflates structural estimates of present bias. |
| JEL: | D03 |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31047&r=dcm |
| By: | Nguyen, Hung The; Lam, Bao Quoc; Nguyen, Quyen Le Hoang Thuy To; Nguyen, Phong Thanh; Huynh, Vy Dang Bich |
| Abstract: | Career path choice was an important decision in each individual life. Industry 4.0 has brought new challenges to a career path with much emphasis on smart instead of hard work. Globalization has also challenged Confucian culture in Vietnam and led to the change in the criteria of career path choice. The objective of this research was to explore and rank the criteria for career path choice in the context of Ho Chi Minh City, Vietnam. The innovative grey decision-making method, namely Grey Analytical Hierarchy Process (GAHP), was employed to reach the research objectives. The data of 30 experts who were researchers and practitioners in human resources were purposively selected to be used for the analysis. Research results showed that the hierarchical model of career path choice in the Vietnamese context had three levels, in which the first level is the goal level. The second level included three main criteria with the priority orders as follows: (1) work diversity, (2) job prospects, and (3) family preference. Further, the ranking was made with thirteen sub-indicators in the third level of the hierarchical model. The findings reflected the great changes in career path choice criteria, switching to work diversity from traditionally Confucian values with much reliance on family preference and job prospects in the public sector. This implied a proper performance of competency-based education for employees to meet the labor market demand of the private sector. |
| Keywords: | Career path, decision making, Ho Chi Minh City, grey system theory, grey AHP, industry 4.0, Vietnam |
| JEL: | C6 D14 I31 L8 O14 |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:116683&r=dcm |
| By: | Balié, Jean |
| Keywords: | Environmental Economics and Policy |
| Date: | 2022–08–15 |
| URL: | http://d.repec.org/n?u=RePEc:ags:cfcp22:330870&r=dcm |
| By: | Marcus C Christiansen |
| Abstract: | A common problem in various applications is the additive decomposition of the output of a function with respect to its input variables. Functions with binary arguments can be axiomatically decomposed by the famous Shapley value. For the decomposition of functions with real arguments, a popular method is the pointwise application of the Shapley value on the domain. However, this pointwise application largely ignores the overall structure of functions. In this paper, axioms are developed which fully preserve functional structures and lead to unique decompositions for all Borel measurable functions. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.07773&r=dcm |
| By: | Qingyi Wang; Shenhao Wang; Yunhan Zheng; Hongzhou Lin; Xiaohu Zhang; Jinhua Zhao; Joan Walker |
| Abstract: | Classical demand modeling analyzes travel behavior using only low-dimensional numeric data (i.e. sociodemographics and travel attributes) but not high-dimensional urban imagery. However, travel behavior depends on the factors represented by both numeric data and urban imagery, thus necessitating a synergetic framework to combine them. This study creates a theoretical framework of deep hybrid models with a crossing structure consisting of a mixing operator and a behavioral predictor, thus integrating the numeric and imagery data into a latent space. Empirically, this framework is applied to analyze travel mode choice using the MyDailyTravel Survey from Chicago as the numeric inputs and the satellite images as the imagery inputs. We found that deep hybrid models outperform both the traditional demand models and the recent deep learning in predicting the aggregate and disaggregate travel behavior with our supervision-as-mixing design. The latent space in deep hybrid models can be interpreted, because it reveals meaningful spatial and social patterns. The deep hybrid models can also generate new urban images that do not exist in reality and interpret them with economic theory, such as computing substitution patterns and social welfare changes. Overall, the deep hybrid models demonstrate the complementarity between the low-dimensional numeric and high-dimensional imagery data and between the traditional demand modeling and recent deep learning. It generalizes the latent classes and variables in classical hybrid demand models to a latent space, and leverages the computational power of deep learning for imagery while retaining the economic interpretability on the microeconomics foundation. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.04204&r=dcm |
| By: | Anna Naszodi |
| Abstract: | We apply a pseudo panel analysis of survey data from the years 2010 and 2017 about Americans' self-reported marital preferences and perform some formal tests on the sign and magnitude of the change in educational homophily from the generation of the early Boomers to the late Boomers, as well as from the early GenerationX to the late GenerationX. In the analysis, we control for changes in preferences over the course of the survey respondents' lives. We use the test results to decide whether the popular iterative proportional fitting (IPF) algorithm, or its alternative, the NM-method is more suitable for analyzing revealed marital preferences. These two methods construct different tables representing counterfactual joint educational distributions of couples. Thereby, they disagree on the trend of revealed preferences identified from the prevalence of homogamy by counterfactual decompositions. By finding self-reported homophily to display a U-shaped pattern, our tests reject the hypothesis that the IPF is suitable for constructing counterfactuals in general, while we cannot reject the applicability of the NM. The significance of our survey-based method-selection is due to the fact that the choice between the IPF and the NM makes a difference not only to the identified historical trend of revealed homophily, but also to what future paths of social inequality are believed to be possible. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.05895&r=dcm |