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on Discrete Choice Models |
| By: | Salmai Qari; Tobias Börger; Tim Lohse; Jürgen Meyerhoff |
| Abstract: | Defense spending accounts for a large share of the budget in many countries, but the value of the resulting public good - national defense – has so far escaped assessment. Much of the literature has instead considered indirect benefits of defense spending in terms of greater economic growth or technological spillovers. In this paper, we assess the direct welfare effects of defense policy, namely an increase in the security of citizens, by means of a survey-based discrete choice experiment. Drawing on a representative sample of the German population, results suggest substantial willingness to pay for an increase in troop numbers, the establishment of a European army and an improved air defense system. The reintroduction of compulsory military service does not enjoy public support. Results further indicate substantial preference heterogeneity across respondents and policy options which we explore. As such, these findings demonstrate how methods of survey-based, non-market valuation can help to refine research in this area of public policy. |
| Keywords: | public good, national defense, non-market valuation, discrete choice experiment, willingness to pay |
| JEL: | C90 H41 H56 H60 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10872&r=dcm |
| By: | Tanmay Ghosh; Nithin Nagaraj |
| Abstract: | The decision making involved behind the mode choice is critical for transportation planning. While statistical learning techniques like discrete choice models have been used traditionally, machine learning (ML) models have gained traction recently among the transportation planners due to their higher predictive performance. However, the black box nature of ML models pose significant interpretability challenges, limiting their practical application in decision and policy making. This study utilised a dataset of $1350$ households belonging to low and low-middle income bracket in the city of Bengaluru to investigate mode choice decision making behaviour using Multinomial logit model and ML classifiers like decision trees, random forests, extreme gradient boosting and support vector machines. In terms of accuracy, random forest model performed the best ($0.788$ on training data and $0.605$ on testing data) compared to all the other models. This research has adopted modern interpretability techniques like feature importance and individual conditional expectation plots to explain the decision making behaviour using ML models. A higher travel costs significantly reduce the predicted probability of bus usage compared to other modes (a $0.66\%$ and $0.34\%$ reduction using Random Forests and XGBoost model for $10\%$ increase in travel cost). However, reducing travel time by $10\%$ increases the preference for the metro ($0.16\%$ in Random Forests and 0.42% in XGBoost). This research augments the ongoing research on mode choice analysis using machine learning techniques, which would help in improving the understanding of the performance of these models with real-world data in terms of both accuracy and interpretability. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.13977&r=dcm |
| By: | Avner Seror (Aix-Marseille Univ., CNRS, AMSE, Marseille, France) |
| Abstract: | This paper shows how to recover behavioral biases from revealed preference ranking implied by choices. The approach formalizes and unifies well-known behavioral models, including salience thinking, inattention, and logarithmic perception, thereby accounting for many well-documented choice puzzles. I show that this approach provides a way to filter out choice data from behavioral biases explaining rationality breaches before fitting parametric utility models. The approach is applied to workhorse data sets of the literature on choice under risk and scanner consumer choices. |
| Keywords: | Decision Theory, revealed preference, Behavioral Economics |
| JEL: | D91 D11 D81 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:aim:wpaimx:2401&r=dcm |
| By: | Elodie Djemai (PSL, Université Paris-Dauphine, LEDa); Yohan Renard (Universite d’Orleans, Laboratoire d’Economie d’Orleans) |
| Abstract: | We use a randomized experiment to study how a subsidy for a mosquito-repellent ointment to protect from malaria affects uptake, usage, and future demand for the product in Burkina Faso. We randomly vary the subsidy level across enumeration areas and approximately 3, 100 households are randomly allocated to one of the three groups: 0%, 50% of 100% subsidy. Our main results are that subsidies strongly and significantly increase the likelihood of acquiring a jar of mosquito-repellent ointment, and of using it on a regular basis during the rainy season. We do not find any evidence supporting heterogeneous treatment effects based on household characteristics, nor on the use of preventive measures at baseline. |
| Keywords: | Malaria, Behavior, Technology adoption, Price, Africa, Burkina Faso |
| JEL: | I12 O33 H43 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:dia:wpaper:dt202307&r=dcm |
| By: | Shawn Berry |
| Abstract: | The use of online reviews to aid with purchase decisions is popular among consumers as it is a simple heuristic tool based on the reported experiences of other consumers. However, not all online reviews are written by real consumers or reflect actual experiences, and present implications for consumers and businesses. This study examines the effects of fake online reviews written by artificial intelligence (AI) on consumer decision making. Respondents were surveyed about their attitudes and habits concerning online reviews using an online questionnaire (n=351), and participated in a restaurant choice experiment using varying proportions of fake and real reviews. While the findings confirm prior studies, new insights are gained about the confusion for consumers and consequences for businesses when reviews written by AI are believed rather than real reviews. The study presents a fake review detection model using logistic regression modeling to score and flag reviews as a solution. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.11345&r=dcm |
| By: | Philipp Lergetporer; Katharina Wedel; Katharina Werner |
| Abstract: | We study how beliefs about the automatability of workers’ occupation affect labor-market expectations and willingness to participate in further training. In our representative online survey, respondents on average underestimate the automation risk of their occupation, especially those in high-automatability occupations. Randomized information about their occupations’ automatability increases respondents’ concerns about their professional future, and expectations about future changes in their work environment. The information also increases willingness to participate in further training, especially among respondents in highly automatable occupation (+five percentage points). This uptick substantially narrows the gap in willingness to train between those in high- and low-automatability occupations. |
| Keywords: | automation, further training, labor-market expectations, survey experiment, information |
| JEL: | J24 O33 I29 D83 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10862&r=dcm |
| By: | Amanda Agan; Bo Cowgill; Laura Gee |
| Abstract: | Correspondence audit studies have sent almost one-hundred-thousand resumes without informing subjects they are in a study - increasing realism, but without being fully transparent. We study the potential trade-offs of this lack of transparency by running a hiring field experiment with recruiters in a natural setting. One group of recruiters is told they are screening for an employer, and another is told they are part of an academic study. Job applicants' gender is randomly assigned. When subjects are told they are in an experiment, callback rates and willingness-to-pay for male candidates decline relative to female candidates (with no detectable change for female candidates). This suggests that telling subjects they are in an experiment would underestimate gender inequality. |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:feb:natura:00781&r=dcm |
| By: | Ben Aoki-Sherwood; Catherine Bregou; David Liben-Nowell; Kiran Tomlinson; Thomas Zeng |
| Abstract: | A common theory of choice posits that individuals make choices in a two-step process, first selecting some subset of the alternatives to consider before making a selection from the resulting consideration set. However, inferring unobserved consideration sets (or item consideration probabilities) in this "consider then choose" setting poses significant challenges, because even simple models of consideration with strong independence assumptions are not identifiable, even if item utilities are known. We consider a natural extension of consider-then-choose models to a top-$k$ ranking setting, where we assume rankings are constructed according to a Plackett-Luce model after sampling a consideration set. While item consideration probabilities remain non-identified in this setting, we prove that knowledge of item utilities allows us to infer bounds on the relative sizes of consideration probabilities. Additionally, given a condition on the expected consideration set size, we derive absolute upper and lower bounds on item consideration probabilities. We also provide algorithms to tighten those bounds on consideration probabilities by propagating inferred constraints. Thus, we show that we can learn useful information about consideration probabilities despite not being able to identify them precisely. We demonstrate our methods on a ranking dataset from a psychology experiment with two different ranking tasks (one with fixed consideration sets and one with unknown consideration sets). This combination of data allows us to estimate utilities and then learn about unknown consideration probabilities using our bounds. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.11016&r=dcm |
| By: | Joseph Y. Halpern; Evan Piermont |
| Abstract: | We show that it is possible to understand and identify a decision maker's subjective causal judgements by observing her preferences over interventions. Following Pearl [2000], we represent causality using causal models (also called structural equations models), where the world is described by a collection of variables, related by equations. We show that if a preference relation over interventions satisfies certain axioms (related to standard axioms regarding counterfactuals), then we can define (i) a causal model, (ii) a probability capturing the decision-maker's uncertainty regarding the external factors in the world and (iii) a utility on outcomes such that each intervention is associated with an expected utility and such that intervention $A$ is preferred to $B$ iff the expected utility of $A$ is greater than that of $B$. In addition, we characterize when the causal model is unique. Thus, our results allow a modeler to test the hypothesis that a decision maker's preferences are consistent with some causal model and to identify causal judgements from observed behavior. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.10937&r=dcm |
| By: | Jochmans, Koen |
| Abstract: | We consider point estimation and inference based on modifications of the profile likelihood in models for dyadic interactions between n agents featuring agent-specific parameters. The maximum-likelihood estimator of such models has bias and standard deviation of order n-1 and so is asymptotically biased. Estimation based on modified likelihoods leads to estimators that are asymptotically unbiased and likelihood ratio tests that exhibit correct size. |
| Keywords: | Asymptotic bias; Dyadic data; Fixed effects ; Undirected random graph |
| JEL: | C23 |
| Date: | 2024–01–24 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:129030&r=dcm |
| By: | Alfaro, Laura; Chen, Maggie; Chor, Davin |
| Keywords: | Agribusiness, Agricultural Finance, International Relations/Trade |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iats22:339437&r=dcm |