nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2022‒06‒27
four papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Graph-Based Methods for Discrete Choice By Kiran Tomlinson; Austin R. Benson
  2. Joint Location and Cost Planning in Maximum Capture Facility Location under Multiplicative Random Utility Maximization By Ngan Ha Duong; Tien Thanh Dam; Thuy Anh Ta; Tien Mai
  3. Ask a local: improving the public pricing of land titles in urban Tanzania By Martina Manara; Tanner Regan
  4. Preference for Wealth and Life Cycle Portfolio Choice By Campanale Claudio; Fugazza Carolina

  1. By: Kiran Tomlinson; Austin R. Benson
    Abstract: Choices made by individuals have widespread impacts--for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase--moreover, data on these choices are increasingly abundant. Discrete choice models are a key tool for learning individual preferences from such data. Additionally, social factors like conformity and contagion influence individual choice. Existing methods for incorporating these factors into choice models do not account for the entire social network and require hand-crafted features. To overcome these limitations, we use graph learning to study choice in networked contexts. We identify three ways in which graph learning techniques can be used for discrete choice: learning chooser representations, regularizing choice model parameters, and directly constructing predictions from a network. We design methods in each category and test them on real-world choice datasets, including county-level 2016 US election results and Android app installation and usage data. We show that incorporating social network structure can improve the predictions of the standard econometric choice model, the multinomial logit. We provide evidence that app installations are influenced by social context, but we find no such effect on app usage among the same participants, which instead is habit-driven. In the election data, we highlight the additional insights a discrete choice framework provides over classification or regression, the typical approaches. On synthetic data, we demonstrate the sample complexity benefit of using social information in choice models.
    Date: 2022–05
  2. By: Ngan Ha Duong; Tien Thanh Dam; Thuy Anh Ta; Tien Mai
    Abstract: We study a joint facility location and budget planning problem in a competitive market under random utility maximization (RUM) models. The objective is to locate new facilities and make decisions on the budgets (or costs) to spend on the new facilities, aiming to maximize an expected captured customer demand, assuming that customers choose a facility among all available facilities according to a RUM model. We examine two RUM frameworks in the discrete choice literature, namely, the additive and multiplicative RUM. While the former has been widely used in facility location problems, we are the first to explore the latter in the context. We show that, under the additive RUM framework, the resulting cost optimization problem becomes highly non-convex and may have several local optimum solutions. In contrast, the use of the multiplicative RUM brings several advantages to the competitive facility location problem. More precisely, we show that the cost optimization problem under the multiplicative RUM can be solved efficiently by a general convex optimization solver or can be reformulated as a conic quadratic program and handled by a conic solver available in some optimization tools such as CPLEX or GUROBI. Furthermore, we consider a joint location and cost optimization problem and propose three approaches to solve the problem, namely, an equivalent conic reformulation, a multi-cut outer-approximation algorithm, and a local search heuristic. We provide numerical experiments based on synthetic instances of various sizes to evaluate the performances of the proposed algorithms in solving the cost optimization and joint location and cost optimization problems.
    Date: 2022–05
  3. By: Martina Manara; Tanner Regan
    Abstract: Information on willingness-to-pay is key for public pricing and allocation of services but not easily collected. Studying land titles in Dar-es-Salaam, we ask whether local leaders know and will reveal plot owners' willingness-to-pay. We randomly assign leaders to predict under different settings then elicit owners' actual willingness-to-pay. Demand is substantial, but below exorbitant fees. Leaders can predict the aggregate demand curve and distinguish variation across owners. Predictions worsen when used to target subsidies, but adding cash incentives mitigates this. Finally, we demonstrate that leader-elicited information can improve the public pricing of title deeds, raising uptake while maintaining public funds.
    Keywords: property rights, willingness-to-pay, public pricing, local publicly provided goods
    Date: 2022–12
  4. By: Campanale Claudio (Department of Economics, Social Studies, Applied Mathematics and Statistics (ESOMAS) and CERP (CCA) University of Torino, Italy); Fugazza Carolina (Department of Economics, Social Studies, Applied Mathematics and Statistics (ESOMAS) and CERP (CCA) University of Torino, Italy)
    Abstract: Do participation and investment in risky assets increase with wealth? Do the wealthiest households save at higher rates than the median households and is wealth more concentrated than earnings? Based on survey data, this paper shows that this is the case. Moreover, the paper provides a theoretical framework based on an extended version of the life-cycle model of consumption and portfolio choice that enables to explain differences in behavior between the wealthiest and others.
    Keywords: Life-cycle, Portfolio Choice, Preference over Wealth, Wealth Inequality
    JEL: D15 E21 G11
    Date: 2022–06

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