|
on Discrete Choice Models |
| By: | Sophia Möller (University of Kassel, Institute of Economics); Andreas Ziegler (University of Kassel, Institute of Economics) |
| Abstract: | While climate change is widely considered as a major challenge for societies, another pressing global environmental problem, i.e. the loss of biodiversity, is often given less attention despite its strong negative consequences for ecosystems and thus for human life. In light of the strong interconnections between biodiversity loss and climate change, this paper compares the pref-erences and stated willingness to pay (WTP) for biodiversity conservation and climate pro-tection. The empirical analysis is based on data from a broadly representative large-scale com-puter-assisted online survey of more than 9, 000 citizens in Germany in 2021. Our data reveal a strong correlation between the perceived importance of the problems of biodiversity loss and climate change as well as between the WTP for biodiversity conservation and climate protection. However, the average WTP for climate protection is slightly higher than for bio-diversity conservation according to our data. Our econometric analysis with bivariate linear and loglinear regression models as well as Tobit and binary probit models suggests that the main explanatory factors, namely environmental attitudes (i.e. environmental awareness and ecological policy identification) as well as economic preferences (i.e. altruism, trust, and pa-tience) in addition to some socio-economic variables (e.g. equivalized income), are very similar for the WTP for biodiversity conservation and climate protection. However, for many individual characteristics (e.g., ecological policy identification, altruism, trust, patience) that are (statistically) significantly correlated with the WTP for both climate protection and biodiversity conservation, the correlations are significantly stronger for the WTP for climate protection. These estimation results, in combination with a higher average perception in our sample that climate change is an important global environmental problem, could be due to the stronger recognition of climate change and protection in the public debate (e.g., in media coverage) compared to biodiversity loss and conservation. |
| Keywords: | Biodiversity conservation, climate protection, willingness to pay, bivariate econometric models. |
| JEL: | Q57 Q54 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:mar:magkse:202502 |
| By: | Mojtaba Madadi Asl; Kamal Hajian; Rouzbeh Torabi; Mehdi Sadeghi |
| Abstract: | Choice overload occurs when individuals feel overwhelmed by an excessive number of options. Experimental evidence suggests that a larger selection can complicate the decision-making process. Consequently, choice satisfaction may diminish when the costs of making a choice outweigh its benefits, indicating that satisfaction follows an inverted U-shaped relationship with the size of the choice set. However, the theoretical underpinnings of this phenomenon remain underexplored. Here, we present a theoretical framework based on relative entropy and effective information to elucidate the inverted U-shaped relationship between satisfaction and choice set size. We begin by positing that individuals assign a probability distribution to a choice set based on their preferences, characterized by an observed Shannon entropy. We then define a maximum entropy that corresponds to a worst-case scenario where individuals are indifferent among options, leading to equal probabilities for all alternatives. We hypothesized that satisfaction is related to the probability of identifying an ideal choice within the set. By comparing observed entropy to maximum entropy, we derive the effective information of choice probabilities, demonstrating that this metric reflects satisfaction with the options available. For smaller choice sets, individuals can more easily identify their best option, resulting in a sharper probability distribution around the preferred choice and, consequently, minimum entropy, which signifies maximum information and satisfaction. Conversely, in larger choice sets, individuals struggle to compare and evaluate all alternatives, leading to missed opportunities and increased entropy. This smooth probability distribution ultimately reduces choice satisfaction, thereby producing the observed inverted U-shaped trend. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.12721 |
| By: | Ester Sudano |
| Abstract: | We model stochastic choices with categorization, resulting from the preliminary step of grouping alternatives in homogenous disjoint classes. The agent randomly chooses one class among those available, then randomly picks an item within the selected class. We give a formal definition of a choice generated by this procedure, and provide a characterization. The characterizing properties allow an external observer to elicit that categorization is applied. In a more general interpretation, the model allows to describe the observed choice as the composition of independent subchoices. This composition preserves rationalizability by random utility maximization. A generalization of the model subsumes Luce model and Nested Logit. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.03554 |
| By: | Jonathan D. Ketcham; Nicolai V. Kuminoff; Nirman Saha |
| Abstract: | We develop a new revealed preference framework to estimate the value of statistical life (VSL). Our framework starts from a hedonic model of health care in which heterogenous individuals choose how much to spend on medical services that reduce mortality risk. Their choices generate an equilibrium survival function that can be differentiated to recover their marginal willingness to pay for mortality risk reduction. Our IV estimator uses survey data on Americans over age 66, linked to their federal administrative records. The mean VSL is approximately $1 million at age 67 and increasing in health, income, education, and life expectancy. |
| JEL: | Q51 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33165 |
| By: | Tao Sun |
| Abstract: | This paper proposes a Bayesian factor-augmented bundle choice model to estimate joint consumption as well as the substitutability and complementarity of multiple goods in the presence of endogenous regressors. The model extends the two primary treatments of endogeneity in existing bundle choice models: (1) endogenous market-level prices and (2) time-invariant unobserved individual heterogeneity. A Bayesian sparse factor approach is employed to capture high-dimensional error correlations that induce taste correlation and endogeneity. Time-varying factor loadings allow for more general individual-level and time-varying heterogeneity and endogeneity, while the sparsity induced by the shrinkage prior on loadings balances flexibility with parsimony. Applied to a soda tax in the context of complementarities, the new approach captures broader effects of the tax that were previously overlooked. Results suggest that a soda tax could yield additional health benefits by marginally decreasing the consumption of salty snacks along with sugary drinks, extending the health benefits beyond the reduction in sugar consumption alone. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.05794 |
| By: | Yanqin Fan; Yigit Okar; Xuetao Shi |
| Abstract: | This article introduces an iterative distributed computing estimator for the multinomial logistic regression model with large choice sets. Compared to the maximum likelihood estimator, the proposed iterative distributed estimator achieves significantly faster computation and, when initialized with a consistent estimator, attains asymptotic efficiency under a weak dominance condition. Additionally, we propose a parametric bootstrap inference procedure based on the iterative distributed estimator and establish its consistency. Extensive simulation studies validate the effectiveness of the proposed methods and highlight the computational efficiency of the iterative distributed estimator. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.01030 |
| By: | Cuong Le; Tien Mai; Ngan Ha Duong; Minh Hoang Ha |
| Abstract: | We study a competitive facility location problem, where customer behavior is modeled and predicted using a discrete choice random utility model. The goal is to strategically place new facilities to maximize the overall captured customer demand in a competitive marketplace. In this work, we introduce two novel considerations. First, the total customer demand in the market is not fixed but is modeled as an increasing function of the customers' total utilities. Second, we incorporate a new term into the objective function, aiming to balance the firm's benefits and customer satisfaction. Our new formulation exhibits a highly nonlinear structure and is not directly solved by existing approaches. To address this, we first demonstrate that, under a concave market expansion function, the objective function is concave and submodular, allowing for a $(1-1/e)$ approximation solution by a simple polynomial-time greedy algorithm. We then develop a new method, called Inner-approximation, which enables us to approximate the mixed-integer nonlinear problem (MINLP), with arbitrary precision, by an MILP without introducing additional integer variables. We further demonstrate that our inner-approximation method consistently yields lower approximations than the outer-approximation methods typically used in the literature. Moreover, we extend our settings by considering a\textit{ general (non-concave)} market-expansion function and show that the Inner-approximation mechanism enables us to approximate the resulting MINLP, with arbitrary precision, by an MILP. To further enhance this MILP, we show how to significantly reduce the number of additional binary variables by leveraging concave areas of the objective function. Extensive experiments demonstrate the efficiency of our approaches. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.17021 |
| By: | James Bland; Yaroslav Rosokha |
| Abstract: | Estimation of belief learning models relies on several important assumptions regarding measurement errors. Whereas existing work has focused on classical measurement errors, the current paper is the first to investigate the impact of a non-classical, behavioral measurement error—rounding bias. In particular, we design and carry out a novel economics experiment in conjunction with simulations and a meta-study of existing papers to show a strong impact of rounding bias on belief updating. In addition, we propose an econometric technique to aid researchers in overcoming challenges posed by the rounded responses in belief elicitation questions. |
| Keywords: | Rounding Bias, Measurement Errors, Bayesian Updating, Belief Updating, Learning, Conservatism, Base-Rate Neglect, Econometrics, Hierarchical Bayesian Models |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:pur:prukra:1353 |
| By: | Matteo Mogliani; Florens Odendahl |
| Abstract: | The popular choice of using a $direct$ forecasting scheme implies that the individual predictions do not contain information on cross-horizon dependence. However, this dependence is needed if the forecaster has to construct, based on $direct$ density forecasts, predictive objects that are functions of several horizons ($e.g.$ when constructing annual-average growth rates from quarter-on-quarter growth rates). To address this issue we propose to use copulas to combine the individual $h$-step-ahead predictive distributions into a joint predictive distribution. Our method is particularly appealing to practitioners for whom changing the $direct$ forecasting specification is too costly. In a Monte Carlo study, we demonstrate that our approach leads to a better approximation of the true density than an approach that ignores the potential dependence. We show the superior performance of our method in several empirical examples, where we construct (i) quarterly forecasts using month-on-month $direct$ forecasts, (ii) annual-average forecasts using monthly year-on-year $direct$ forecasts, and (iii) annual-average forecasts using quarter-on-quarter $direct$ forecasts. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.06092 |
| By: | John Eric Humphries; Scott T. Nelson; Dam Linh Nguyen; Winnie van Dijk; Daniel C. Waldinger |
| Abstract: | Recent research has documented the prevalence and consequences of evictions in the United States, but our understanding of the drivers of eviction and the scope for policy to reduce evictions remains limited. We use novel lease-level ledger data from high-eviction rental markets to characterize key determinants of landlord eviction decisions: the persistence of shocks to tenant default risk, landlords' information about these shocks, and landlords' costs of eviction. Our data show that nonpayment is common, is often tolerated by landlords, and is often followed by recovery, suggesting that landlords face a trade-off between initiating a costly eviction or waiting to learn whether a tenant can continue paying. We develop and estimate a dynamic discrete choice model of the eviction decision that captures this trade-off. Estimated eviction costs are on the order of 2 to 3 months of rent, and the majority of evictions involve tenants who are unlikely to pay going forward. As a result, while commonly-proposed policies can generate additional forbearance for tenants, they do not prevent most evictions. Compared to policies that create delays in the eviction process, increasing filing fees or providing short-term rent subsidies are more likely to prevent evictions of tenants who would resume paying. |
| JEL: | G51 L51 L85 R31 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33155 |