Dynamic Discrete Choice Model Research Articles

The Impact of Internship Experience on Schooling and Labour Market Outcomes

Abstract We examine the impact of internship experience during secondary education on students’ schooling and early labour market outcomes by analysing unique, longitudinal data from Belgium. To control for unobservable differences between students with and students without internship experience, we estimate a dynamic discrete choice model. In line with literature on vocational education, we find that internship experience has a positive effect on one’s employment chances up to 5 years after graduation. This positive effect is mainly explained by a positive direct employment effect. Although we also find a positive indirect employment effect through a higher chance to obtain a secondary education qualification, this is largely compensated by negative indirect effects through lower tertiary education attainment. (JEL codes: I21, I26, J21, and J24)

Public-private partnerships: A dynamic discrete choice model for road projects

Public-Private Partnerships (PPPs) are an effective vehicle for delivering critical infrastructure worldwide, particularly road transport assets (e.g., bridges, toll roads and tunnels). However, PPPs have been and continue to be controversial forms of project delivery as there are concerns about their ability to provide taxpayers with value for money (VfM). Current practice to determine VfM focuses on a ‘simplistic’ ex-ante evaluation referred to as the Public Sector Comparator (PSC), aiming to take a life-cycle approach to cost and benefits assessment. However, the complexity of transport projects renders the PSC ineffective and static evaluation of cost and benefits across their life-cycle. Therefore, the PSC cannot accommodate a project's environment's dynamic and changing nature. Acknowledging the limitations of the PSC, we develop and examine a dynamic discrete choice model that can be used to provide a VfM assessment for ‘road projects’. By validating the proposed model using two illustrative cases, the results suggest it can capture a more comprehensive assessment of cost components, functionality, and benefits specific to road projects and quantify the relationship between the consideration of different assessment elements and choice utility. Therefore, utilising the new model to assess VfM can enable policymakers to make more informed decisions about the employment of PPPs.

Spmv Approaches to Dynamic Discrete Choice Models with Limited Transition

Spmv Approaches to Dynamic Discrete Choice Models with Limited Transition

The whole day path planning problem incorporating mode chains modeling in the era of mobility as a service

The growing popularity of Mobility as a Service (MaaS) has led researchers to realize that it can be used for the optimal management of the transportation system of a city. This study is to solve the whole day multimodal path planning problem considering user-specific modal preference, which can be used on the recommended system of the MaaS platform. The mathematical formulations of the whole day multimodal shortest hyperpath problem are proposed while accounting for individual constraints, such as the time window, multicriteria, and park-and-ride demands, to develop customized travel schemes. The primary obstacle involves consideration of the travelers’ modal preferences to develop an optimal whole day journey. The dynamic discrete choice model (DDCM) that accounts for unobservable heterogeneity is proposed to characterize dynamic mode choice behavior and generate a set of feasible mode chains. The conditional choice probability estimator and expectation–maximization (EM) algorithm are used in conjunction to estimate the dynamic model. In this way, the posterior probability can be adapted to the conditional choice probability estimation. The label correcting concept is used to develop a user-constrained shortest hyperpath algorithm that can solve the multimodal shortest hyperpath problem and calculate the Pareto set of the whole day travel schemes according to the feasible mode chains and different constraints. The proposed techniques are verified by utilizing household travel survey data and multimodal network data in Nanjing (China). Two unobserved states of travelers, namely time- and cost-sensitive, are identified based on the BIC value. The adjusted rho-squared value, accuracies of predicted mode chains, along with the aggregate validation results, confirm the model’s effectiveness. The top two resulting hyperpaths are generated for a randomly chosen traveler on the basis of his or her preferences. This study is an important step toward promoting MaaS and improving the sales of MaaS bundles.

Jacob Mincer Award

Jacob Mincer Award

Some remarks on CCP-based estimators of dynamic models

This note provides several remarks relating to the conditional choice probability (CCP) based estimation approaches for dynamic discrete-choice models. Specifically, the Arcidiacono and Miller (2011) estimation procedure relies on the ”inverse-CCP” mapping ψp from CCPs to choice-specific value functions. Exploiting the convex-analytic structure of discrete choice models, we discuss two approaches for computing this mapping, using either linear or convex programming, for models where the utility shocks can follow arbitrary parametric distributions. Furthermore, the ψ function is generally distinct from the ”selection adjustment” term (i.e. the expectation of the utility shock for the chosen alternative), so that computational approaches for computing the latter may not be appropriate for computing ψ.

Identification of counterfactuals in dynamic discrete choice models

Dynamic discrete choice (DDC) models are not identified nonparametrically, but the non‐identification of models does not necessarily imply the nonidentification of counterfactuals. We derive novel results for the identification of counterfactuals in DDC models, such as non‐additive changes in payoffs or changes to agents' choice sets. In doing so, we propose a general framework that allows the investigation of the identification of a broad class of counterfactuals (covering virtually any counterfactual encountered in applied work). To illustrate the results, we consider a firm entry/exit problem numerically, as well as an empirical model of agricultural land use. In each case, we provide examples of both identified and nonidentified counterfactuals of interest.

Dynamic Discrete Choice Models with Incomplete Data: Sharp Identification

In many empirical studies, the states that are relevant for economic agents to make decisions may not be included in the data to which researchers have access. This problem often arises in the context of monotone industries. In this paper, we develop the sharp identified sets of structural parameters and counterfactuals for dynamic discrete choice models when empirical data do not cover realizations of relevant states. We use simulation studies to confirm the theoretical property of the sharpness. Applying the proposed method to the annual Toyo Keizai database, we study the behaviors of Japanese firms on foreign direct investments in China without observing the future states after Chinese economy slows down.

Diamonds in the Rough: Leveraging Click Data to Spotlight Underrated Products

Problem Definition. Inspired by a real data set from the Chinese retailer JD.com, we study the click and purchase behavior of customers in an online retail setting by employing a structural estimation approach. In particular, we aim to understand what characteristics of products drive the click versus purchase decisions of customers.With the boom in e-commerce, which has been further fueled due to the current COVID-19 pandemic, an ever increasing number of customers are shopping online. Such a rapid growth in online shopping provides retailers with abundance of data to study customer shopping behavior. For example, online retailers can benefit from a better understanding of the customer search (click) and purchase behavior to improve their operational decisions such as assortment planning. From an academic standpoint, even though there is an extensive body of work on consumer search models, most prior work do not incorporate search sequences and purchase decisions of customers jointly in their analysis. We, however, combine the click and order data to estimate both the click and purchase behavior of customers.Methodology/Results. Using a large data set from JD.com, we propose a structural model to estimate the click and purchase behavior of customers according to a dynamic discrete choice model. In particular, we assume that the customer's utility from each product has an observed and (pre-click) unobserved part (in addition to a random shock). The observed part of the utility is known to the customer prior to the click; however, the unobserved part of the utility can only be learned after the customer clicks on the product. We assume that the customer solves a dynamic program in order to find the optimal search strategy. Due to the curse of dimensionality, we propose a novel value function approximation scheme inspired by the Conditional Choice Probability approach. This, in turn, reduces the estimation into a computationally tractable two-stage process. By combining the click and order data, our proposed structural framework allows us to disentangle and estimate the observed and unobserved parts of product utilities. Our estimation results show that the value of click for customers can be quite significant. This is evidenced by the fact that the unobserved utilities of products vary significantly across products. Most importantly, we are able to identify underrated products which we call diamonds in the rough: these are products with low “face” values (i.e., low observed utilities), but high total utilities due to their high unobserved utilities. Thus, even though such products have a low chance of being clicked (due to their low observed utilities), they have a high chance of being purchased, if clicked.Managerial Implications. Our structural framework provides an online retailer with new tools and insights to better manage the product assortment based on customer click and purchase behavior. In particular, our structural model allows the retailer to disentangle the observed and unobserved parts of product utilities and identify underrated diamond-in-the-rough products. The online retailer can increase her revenue by bringing such products into the spotlight by promoting them on the search page or using tags such as “spotlight product” (similar to “Amazon's Choice” tags on Amazon.com) to entice customers to click on them.

Identification of Intertemporal Preferences From Choice Set Variation

The paper provides conditions to identify the discount factor(s) and utility function in a infinite-horizon dynamic discrete choice model variation in choice sets over time. We show that if current choices or states are informative about the choice set the agent will face in the future, then the discount factor and utility are identified without any strong normalization. These identification results hold in both the exponential discounting model and, if the choice set provides a form of pre-commitment, the quasi-hyperbolic discounting model. Identifying the discount factor and utility allows us to identify the counterfactual policies which often are the objects of interest in dynamic discrete-choice analysis, but which are not generally identified.

Some Remarks on CCP-based Estimators of Dynamic Models

This note provides several remarks relating to the conditional choice probability (CCP) based estimation approaches for dynamic discrete-choice models. Specifically, the Arcidiacono and Miller [2011] estimation procedure relies on the “inverse-CCP” mapping from CCP’s to choice-specific value functions. Exploiting the convex-analytic structure of discrete choice models, we discuss two approaches for computing this, using either linear or convex programming, for models where the utility shocks can follow arbitrary parametric distributions. Furthermore, the inverse-CCP mapping is generally distinct from the “selection adjustment” term (i.e. the expectation of the utility shock for the chosen alternative), so that computational approaches for computing the latter may not be appropriate for computing inverse-CCP mapping.

Why do life insurance policyholders lapse? The roles of income, health, and bequest motive shocks

AbstractWe present and empirically implement a dynamic discrete choice model of life insurance decisions to assess the importance of various factors in explaining life insurance lapsation. We estimate a model using information on life insurance holdings from the Health and Retirement Study. Counterfactual simulations using the estimates of our model suggest that a large fraction of life insurance lapsations are driven by idiosyncratic shocks, uncorrelated with health, income, and bequest motives, particularly when policyholders are relatively young. As the remaining policyholders get older, however, the role of such independent and identically distributed (i.i.d.) shocks gets smaller, and more of their lapsation is driven by income, health, or bequest motive shocks. As anticipated, income and health shocks are relatively more important than bequest motive shocks in explaining lapsation when policyholders are young, with bequest motive shocks playing a more important role as we age.

Solving dynamic discrete choice models using smoothing and sieve methods

We propose to combine smoothing, simulations and sieve approximations to solve for either the integrated or expected value function in a general class of dynamic discrete choice (DDC) models. We use importance sampling to approximate the Bellman operators defining the two functions. The random Bellman operators, and therefore also the corresponding solutions, are generally non-smooth which is undesirable. To circumvent this issue, we introduce smoothed versions of the random Bellman operators and solve for the corresponding smoothed value functions using sieve methods. We also show that one can avoid using sieves by generalizing and adapting the “self-approximating” method of Rust (1997b) to our setting. We provide an asymptotic theory for both approximate solution methods and show that they converge with N-rate, where N is number of Monte Carlo draws, towards Gaussian processes. We examine their performance in practice through a set of numerical experiments and find that both methods perform well with the sieve method being particularly attractive in terms of computational speed and accuracy.

Admissions Constraints and the Decision to Delay University*

AbstractWe investigate whether delaying entrance into university is affected by restrictions on admissions into competitive programs. Using Danish administrative data, we estimate a dynamic discrete choice model, in which students choose, if admitted, either to enter one of 30 programs or to delay. We use the model to examine delaying choices under different simulated admissions policies. Our experiments suggest that only 28 percent of students who delay do so because of admissions restrictions. Furthermore, although students respond to admissions incentives, our results imply that such policies are unlikely to substantially change the overall distribution of delay.

Semiparametric estimation of dynamic discrete choice models

We consider the estimation of dynamic binary choice models in a semiparametric setting, in which the per-period utility functions are known up to a finite number of parameters, but the distribution of utility shocks is left unspecified. This semiparametric setup differs from most of the existing identification and estimation literature for dynamic discrete choice models. To show identification we derive and exploit a new recursive representation for the unknown quantile function of the utility shocks. Our estimators are straightforward to compute, and resemble classic closed-form estimators from the literature on semiparametric regression and average derivative estimation. Monte Carlo simulations demonstrate that our estimator performs well in small samples.

Linear IV regression estimators for structural dynamic discrete choice models

In structural dynamic discrete choice models, unobserved or mis-measured state variables may lead to biased parameter estimates and misleading inference. In this paper, we show that instrumental variables can address such measurement problems when they relate to state variables that evolve exogenously from the perspective of individual agents (i.e., market-level states). We define a class of linear instrumental variables estimators that rely on Euler equations expressed in terms of conditional choice probabilities (ECCP estimators). These estimators do not require observing or modeling the agent’s entire information set, nor solving or simulating a dynamic program. As such, they are simple to implement and computationally light. We provide constructive arguments for the identification of model primitives, and establish the estimator’s consistency and asymptotic normality. Four applied examples serve to illustrate the ECCP approach’s implementation, advantages, and limitations: dynamic demand for durable goods, agricultural land use change, technology adoption, and dynamic labor supply. We illustrate the estimator’s good finite-sample performance in a Monte Carlo study, and we estimate a labor supply model empirically for taxi drivers in New York City.

Dynamic decisions under subjective expectations: A structural analysis

We study dynamic discrete choice models without assuming rational expectations. Agents’ beliefs about state transitions are subjective, unknown, and may differ from their objective counterparts. We show that agents’ preferences and subjective beliefs are identified in both finite and infinite horizon models. We estimate the model primitives via maximum likelihood estimation and demonstrate the good performance of the estimator by Monte Carlo experiments. Using the Panel Study of Income Dynamics (PSID) data, we illustrate our method in an analysis of women’s labor participation. We find that workers do not hold rational expectations about income transitions.

Conflicting consequences of price-induced product lifetime extension in circular economy: The impact on metals, greenhouse gas, and sales of air conditioners

This study examined the effects of product price increases on metal consumption, greenhouse gas (GHG) emissions, and product sales via changes in the product replacement decisions of consumers. A dynamic discrete choice model was applied to air conditioners (ACs) in Japan to determine these effects when product price is increased by from 5% to 30% in 5% increments. To illustrate, a 5% increase in air conditioners was shown to reduce consumption of iron, copper, and aluminum by six, one, and two thousand tons, respectively, while increasing GHG emissions by 506 kt-CO2 eq. and decreasing product sales by 56 billion Japanese yen. Thus, product price increases for ACs led to a reduction in material consumption while decreasing product sales. The effect on GHG emissions depends on the level of the product price increase. These interactions suggest that consumers will use products longer so long as the value of the product's enhanced durability and longevity matches the increased price of the product. Efforts to improve product durability and longevity, however, will require that AC companies explore new business models in order to cope with the anticipated decrease in product sales as the transition to a circular economy continues.

A DYNAMIC DISCRETE CHOICE MODEL OF REVERSE MORTGAGE BORROWER BEHAVIOR

AbstractUsing unique data on reverse mortgage borrowers in the Home Equity Conversion Mortgage (HECM) program, we semiparametrically estimate a dynamic discrete choice model of borrower behavior. Our estimator is based on a new identification result we develop for models with multiple terminating actions. We show that the per‐period utility functions and discount factor are identified without restrictive, ad hoc identifying restrictions that lead to incorrect counterfactual implications. Our estimates provide insights about factors that influence HECM refinance, default, and termination decisions and allow us to quantify the trade‐offs involved for proposed program modifications, such as income and credit requirements.

Tour-based mode choice modelling as the core of an activity-based travel demand modelling framework: a review of state-of-the-art

ABSTRACT The paper presents a critical review of the methodological approaches used in tour-based mode choice models within the activity-based modelling frameworks. Various components of the activity-based models, such as activity type choice, activity location choice, and activity duration have already matured significantly. However, the mode choice component is often simplified in many ways. Both trip-based and tour-based approaches are used in many cases. However, the tour-based approach is considered to be the most relevant to the activity-based modelling framework. This paper presents a synthesis of the strengths and weaknesses of existing tour-based mode choice models. The previous studies on tour-based mode choice models are grouped into seven categories, ranging from simplified main tour mode to complex dynamic discrete choice models. Besides, challenges with data-hungry models, simulation-based models and static models are discussed elaborately. In conclusion, it proposes a few methodological suggestions for researchers and practitioners for finding an appropriate mode choice modelling framework for activity-based models. In addition, the paper also provides a guideline on how to incorporate automated vehicles and Mobility-as-a-Service within the framework of tour-based mode choice models.