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on Discrete Choice Models |
| By: | Liu, Pengcheng; Xie, Qing; You, Yi; Dong, Qingqing |
| Abstract: | Consumers are presented with increasingly difficult choice tasks and are experiencing more choice overload during the decision-making process. Based on the emotion-imbued choice model and incorporating subjective state consequences into the framework of experienced utility, this research constructed a systematic scale to measure choice overload in several decision-making stages. This research conducted three experiments using liquid milk as a consumption product to test whether choice overload would be influenced by increasing the number of attributes, adding similar options, and information nudges, and whether this effect would be heterogeneous in consumer characteristics. Results indicate that more attributes and the addition of similar options would increase the perceived difficulty of choice and result in negative emotions, while information nudges might lessen choice overload and help consumers make decisions. Besides, consumers’ pursuit of maximization also determines their perceived choice overload; maximizers are more likely to experience choice overload than satisficers. |
| Keywords: | Consumer/Household Economics, Food Consumption/Nutrition/Food Safety |
| Date: | 2024–08–07 |
| URL: | https://d.repec.org/n?u=RePEc:ags:cfcp15:344272 |
| By: | Halkos, George; Zisiadou, Argyro; Aslanidis, Panagiotis-Stavros; Koundouri, Phoebe |
| Abstract: | The Black Sea region faces pressures on ecosystem services (ES) due to invasive species, waste, eutrophication, and biodiversity loss. We apply a stated preference technique, i.e. a choice experiment (CE), aiming to compare three hypothetical scenarios regarding the welfare impact of ES on citizens’ lives in terms of willingness-to-pay (WTP). Initially, the distributed questionnaires underwentan econometric pre-test regarding the orthogonality of all CE scenarios in R-studio. Questionnaire distribution occurred from 29/05/2023 to 21/11/2023 with a total number of 375 responders from the three pilot sites: Turkey, Romania, and Georgia. The highest WTP occurred in Turkey (56.72€) for all scenarios followed by Georgia (49.04€), and Romania (47.96€). Moreover, the greater WTP value is demonstrated by Scenario C (25.51€) followed by Scenarios B (25.17€) and Scenario A (25.11€). Interesting socioeconomic characteristics derived from Cross-Tabulation Analysis that notably cannot impact the WTP are income, gender, and age. Furthermore, marital status and education might affect the WTP only in Romania, however, this is not demonstrated in Turkeyor Georgia. Interestingly, the higher level of education in Romania is linked to lower WTP, nevertheless, education typically relates to environmental sensitivity. Another aspect is that occupation can change responders’ WTP in Romania and Georgia, but not in Turkey. In essence, the economic valuation of ES through CE methodology can offer policymaking insights into Blue Growth initiatives. |
| Keywords: | ecosystem management; human impacts; valuation studies; choice experiment; stated preferences; blue economy;sustainable development goals. |
| JEL: | Q0 Q01 Q1 Q5 Q50 Q51 R10 R11 R14 |
| Date: | 2024–08–15 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:121733 |
| By: | de Bresser, J.;; Knoef, M.;; van Ooijen, R.; |
| Abstract: | There is rising interest in combined insurance products to finance long-term care (LTC) and retirement income. We analyze the market for life care annuities, which combine life annuities and LTC insurance, and examine how reverse mortgages can extend the accessibility of these retirement finance products. These combined products offer several benefits, such as reducing adverse selection, enabling consumption smoothing, and enhancing financial well -being at advantaged ages, while not breaking the role of housing as a savings commitment. To explore the preferences for combined products, we conducted a discrete choice experiment in a large representative household panel in the Netherlands, including individuals aged 40 to 66. We found that 40% would want to buy LTC-only annuities – which pay out between 500 and 1250 euros per month when having LTC needs – at market prices regardless of whether the payment vehicle is a monthly premium or a reverse mortgage. Reverse mortgages as a mode of payment increases the desired demand for more expensive life care annuities by 8 %-points. Further, the accessibility of life care annuities increases considerably when home equity can be used as a funding source. |
| Keywords: | long-term care; life care annuities; reverse mortgages; discrete choice experiment; saving motives; health expectations; |
| JEL: | D14 I13 J14 J18 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:yor:hectdg:24/11 |
| By: | Adan L. Martinez Cruz; Yadira Elizabeth Peralta Torres (Division of Economics, CIDE); Valeria Garcia Olivera |
| Abstract: | The travel cost (TC) method models the number of trips to a recreation site as a function of the costs to reach that site. The single site TC equation is particularly vulnerable to endogeneity since travel costs are chosen by the visitor. This paper suggests a control function approach that breaks the correlation between travel costs and the error term by plugging inferred omitted variables into the TC equation. Inference of omitted variables is carried out on an endogenous free, stated preference equation that, arguably, shares omitted variables with the TC equation. By revisiting the TC and contingent valuation (CV) data analyzed by Fixand Loomis (1998), this paper infers the omitted variables from the CV equation via a finite mixture specification -an inference strategy whose justification resembles the use of heteroscedastic errors to construct instruments as suggested by Lewbel (2012). Results show that not controlling for endogeneity in this particular case produces an overestimation of welfare measures. Importantly, this infer and plug-in strategy is pursuable in a number of contexts beyond recreation demand applications. |
| Keywords: | Travel cost method, endogeneity, stated preference responses, control function |
| JEL: | Q26 C26 C29 |
| Date: | 2024–02 |
| URL: | https://d.repec.org/n?u=RePEc:emc:wpaper:dte632 |
| By: | Sergiy Tkachuk; Szymon {\L}ukasik; Anna Wr\'oblewska |
| Abstract: | In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecasting and fine-tune inventory management. This paper presents an innovative application of Generative Adversarial Networks (GANs) to generate synthetic retail transaction data, specifically focusing on a novel system architecture that combines consumer behavior modeling with stock-keeping unit (SKU) availability constraints to address real-world assortment optimization challenges. We diverge from conventional methodologies by integrating SKU data into our GAN architecture and using more sophisticated embedding methods (e.g., hyper-graphs). This design choice enables our system to generate not only simulated consumer purchase behaviors but also reflects the dynamic interplay between consumer behavior and SKU availability -- an aspect often overlooked, among others, because of data scarcity in legacy retail simulation models. Our GAN model generates transactions under stock constraints, pioneering a resourceful experimental system with practical implications for real-world retail operation and strategy. Preliminary results demonstrate enhanced realism in simulated transactions measured by comparing generated items with real ones using methods employed earlier in related studies. This underscores the potential for more accurate predictive modeling. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.03655 |
| By: | Langevin, R.; |
| Abstract: | Finite mixtures are often used in econometric analyses to account for unobserved heterogeneity. This paper shows that maximizing the likelihood of a finite mixture of parametric densities leads to inconsistent estimates under weak regularity conditions. The size of the asymptotic bias is positively correlated with the degree of overlap between the densities within the mixture. In contrast, I show that maximizing the max-component likelihood function equipped with a consistent classifier leads to consistency in both estimation and classification as the number of covariates goes to infinity while leaving group membership completely unrestricted. Extending the proposed estimator to a fully nonparametric estimation setting is straightforward. The inconsistency of standard maximum likelihood estimation (MLE) procedures is confirmed via simulations. Simulation results show that the proposed algorithm generally outperforms standard MLE procedures in finite samples when all observations are correctly classified. In an application using latent group panel structures and health administrative data, estimation results show that the proposed strategy leads to a reduction in out-of-sample prediction error of around 17.6% compared to the best results obtained from standard MLE procedures. |
| Keywords: | panel data; Finite mixtures; EM algorithm; CEM algorithm; K-means; healthcare expenditures; unobserved heterogeneity; |
| JEL: | C14 C23 C51 I10 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:yor:hectdg:24/16 |
| By: | Ugo Bolletta; Laurens Cherchye; Thomas Demuynck; Bram De Rock; Luca Paolo Merlino |
| Abstract: | We propose a method to identify individuals’ marriage markets assuming that observed marriages are stable. We aim to learn about (the relative importance of) the individual’s observable characteristics defining these markets. First, we use a nonparametric revealed preference approach to construct inner and outer bound approximations of these markets from observed marriages. We then use machine learning to estimate arobust boundary between them (as a linear function of individual characteristics). We demonstrate the usefulness of our method using Dutch household data and quantify the trade-off between the characteristics such as age, education and wages defining individuals’ marriage markets. |
| Keywords: | marriage market, identification, revealed preferences, machine learning, support vector machine (SVM). |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:eca:wpaper:2013/376857 |
| By: | Hyoji Choi (Inha University); Frank Neffke (Harvard University); Donghyeon Yu (Inha University); Bogang Jun (Inha University) |
| Abstract: | This study explores how the relatedness density of amenities influences consumer buying patterns, focusing on multi-purpose shopping preferences. Using Seoul¡¯s credit card data from 2018 to 2023, we find a clear preference for shopping at amenities close to consumers¡¯ residences, particularly for trips within a 2 km radius, where relatedness density significantly influences purchasing decisions. The COVID-19 pandemic initially reduced this effect at shorter distances but rebounded in 2023, suggesting a resilient return to pre-pandemic patterns, which vary over regions. Our findings highlight the resilience of local shopping preferences despite economic disruptions, underscoring the importance of amenity-relatedness in urban consumer behavior. |
| Keywords: | Resilience, Consumption behavior, Relatedness, COVID-19 |
| JEL: | D12 O18 R12 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:inh:wpaper:2024-4 |
| By: | L. Sanna Stephan |
| Abstract: | Dyadic network formation models have wide applicability in economic research, yet are difficult to estimate in the presence of individual specific effects and in the absence of distributional assumptions regarding the model noise component. The availability of (continuously distributed) individual or link characteristics generally facilitates estimation. Yet, while data on social networks has recently become more abundant, the characteristics of the entities involved in the link may not be measured. Adapting the procedure of \citet{KS}, I propose to use network data alone in a semiparametric estimation of the individual fixed effect coefficients, which carry the interpretation of the individual relative popularity. This entails the possibility to anticipate how a new-coming individual will connect in a pre-existing group. The estimator, needed for its fast convergence, fails to implement the monotonicity assumption regarding the model noise component, thereby potentially reversing the order if the fixed effect coefficients. This and other numerical issues can be conveniently tackled by my novel, data-driven way of normalising the fixed effects, which proves to outperform a conventional standardisation in many cases. I demonstrate that the normalised coefficients converge both at the same rate and to the same limiting distribution as if the true error distribution was known. The cost of semiparametric estimation is thus purely computational, while the potential benefits are large whenever the errors have a strongly convex or strongly concave distribution. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.04552 |
| By: | Brian Jabarian |
| Abstract: | In this article, we explore the transformative potential of integrating generative AI, particularly Large Language Models (LLMs), into behavioral and experimental economics to enhance internal validity. By leveraging AI tools, researchers can improve adherence to key exclusion restrictions and in particular ensure the internal validity measures of mental models, which often require human intervention in the incentive mechanism. We present a case study demonstrating how LLMs can enhance experimental design, participant engagement, and the validity of measuring mental models. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.12032 |