Simultaneous-Equation Systems Involving Binary Choice Variables

Simultaneous-equation systems of continuous variables are well known in the social sciences. Unfortunately, these methods cannot be used when dealing with discrete endogenous variables. Although for discrete types of data a broad range of methods and techniques have been developed, their applicability is restricted to single-equation systems (for example, logit or probit formulations) or to association-type models (log-linear models), mainly because of the lack of suitable estimation methods and computer programs. The basic problem is the multivariate error structure of the endogenous variables. This paper presents a simultaneous-equation system of binary endogenous variables. The model is applied empirically to longitudinal data on mode choice and is compared with the well-known log-linear model.     

Modeling Micro-Spatial Employment Location Patterns: A Comparison of Count and Choice Approaches

This article studies employment location patterns in the Puget Sound Region of Washington State at a micro level of geography. Traditional discrete choice modeling using multinomial logit (MNL) models may be problematic at a micro level of geography due to the high dimensionality of the set of alternative locations and the likely violations of the independence from irrelevant alternatives (IIA) assumption. Count models are free from the IIA assumption and, unlike logit models, actually benefit from large numbers of alternatives by adding degrees of freedom. This study identifies the best-fitting count model as the zero-inflated negative binomial (ZINB) model, because this model more effectively addresses the large number of cells with no jobs and reflects a dual process that facilitates the identification of threshold clustering effects such as those found in specialized employment centers. The estimation and prediction results of ZINB are compared with those of MNL with a random sampling of alternatives estimated on an equivalent data set. The ZINB and MNL models largely agree on major trends, with the ZINB model providing more insightful details, but with less capacity to predict large count situations.     

Telecommunication Media Choice Behavior in Academia: An Austrian-Swiss Comparison

A general framework to analyze communication media choice behavior in the university setting is proposed which integrates a stated preference experimental design procedure into a discrete choice modeling framework. The framework is empirically tested using hypothetical choice experiments in which traditional mail, courier mail, telephone, facsimile, and electronic mail services were choice options to carry out information communication tasks. For this purpose face-to-face interviews were conducted in six universities in Austria and Switzerland. The choice modeling approach developed emphasizes the influence of communication context specific characteristics, individual and organizational characteristics of the communication initiator as well as the individual's perceptions and feelings about the communication media on the formation of preferences. Empirical results are presented using stated preference models of communication media choice behavior for a series of communication situations. Specific emphasis is laid on cross-national differences in choice behavior.     

A Selection Bias Approach to Destination Choice

Research on consumer search behavior commonly envisages destination choice as a two-step process: (1) delineate the search set, and (2) evaluate choices therein. However, much of the empirical work in destination choice—including logit and nested logit formulations—models only the latter, and not the set delineation itself. In the presence of correlation between error terms in set delineation and choice selection, statistical estimators are biased, a problem that Heckman and others have called selection bias. In this paper, an alternative two-stage method is proposed to estimate the parameters of models of set delineation and choice selection. Monte Carlo simulation is used to explore the properties of these two-stage estimators, and to show the magnitude of bias inherent in traditional methods of estimation.     

ON FRONTIER SPECIFICATIONS FOR DISCRETE BINARY CHOICE ANALYSIS

ABSTRACT. In estimating a discrete choice model one is actually estimating the parameters of a conditional indirect utility function. I explore the consequences of recognizing that this function is a maximum-value (frontier) function. I formulate several frontier choice models and, using a pilot empirical study of transportation mode choice, compare the resulting estimates with those of the conventional logit specification. Most strikingly, it appears that the values of time implied by the frontier models are substantially below those of the logit model. This implies that policies designed to improve travel times may be of less value to consumers than is conventionally believed.     

MIGRATION AS A WAY TO DIVERSIFY: EVIDENCE FROM RURAL TO URBAN MIGRATION IN THE U.S.

This paper extends the utility maximization model of migration by introducing income and unemployment‐related uncertainties as determinants of utility, and analyzes the effects of the informational advantages of migrants. The paper maintains that migration would expand an individual's economic choices and opportunities and allow diversification. Consequently, diversification advantages influence the location decisions of migrants, an effect captured by the correlation of incomes at the origin and potential destinations. We use the discrete choice model based on random utility maximization as the framework for our empirical investigation of migration from the United States rural to urban counties. This paper takes advantage of an equivalent relation between the conditional logit model and Poisson regression to study the migration decisions using aggregate data among a large set of spatial alternatives. The results show that the diversification concerns have significant effects on location decisions of the rural‐urban migrants in the United States.     

A Geographical Product Differentiation Model of Trade Competition in Third Markets

Product differentiation trade models have been developed in order to account for some contemporary patterns of international trade that are inexplicable under the theory of comparative advantage. This paper presents the case for the geographical differentiation of products. A generalized geographical product differentiation model of trade competition in industrial third markets is outlined within a discrete choice framework. The model treats product price and quality, as determined by country of origin, as distinct variables in export competition. An indirect empirical test of the model's validity, using the cases of export competition between the United States and the Federal Republic of Germany and the United States and Japan, indicates that the generalized model is worthwhile in its ability to account for successful trade competition.     

A Three-State Markov Model of Choice Behavior within Spatial Structures

This paper derives a simple mathematical model of spatial learning and choice by integrating several psychological theories, whose features accord with the results of empirical investigation in a previous paper [2]. A sample of homogeneous decision-makers are assumed to make a succession of selections from a given set of initially unknown spatial alternatives (for example, a set of shopping places for a good). The decision-makers pass through two states of “recognition” and “discrimination” learning, before reaching an equilibrium state. In the equilibrium state, decision-makers have sufficiently learned the attributes of the alternatives so that satisfactory choices are always made. These assumptions permit the derivation of mathematical expressions to answer two questions: first, what proportion of decision-makers will be in the equilibrium state after any given time interval, and second, what proportion of decision-makers will choose any designated spatial alternative after the same time interval. In the second case, the proportion of decision-makers choosing an alternative is predicted from the individuals' perceptions of the attributes of the alternatives. The paper concludes with an evaluation of the model and suggestions for its testing and further development.     

Housing Choice in a Regulated Market: A Nested Multinomial Logit Analysis

The paper integrates several important elements of the institutionalized and regulated nature of the housing market and analyzes the relationship between household type and housing choice in Vienna within a nested multinomial logit framework. In particular, the concept of household specific choice sets is used to account for institutional, informational and income-based constraints of the choice process. Government subsidies (such as housing and rent allowances, non-interest-bearing state loans) are explicitly taken into account in the definition of variables. Housing choice is considered in three stages: the choice of a dwelling unit given dwelling type and residential zone, the choice of a dwelling type given residential zone, and the marginal choice of a residential zone. The coefficients are derived by means of a sequential ML-procedure. The empirical results clearly indicate that demographic variables have significant impacts on housing choice behavior. Income as a single explanatory variable as well as its interacting with size measures and the quality of the house are found to be important criteria for dwelling unit choice, as housing cost variables for dwelling type choice behavior. The dwelling unit and dwelling type choice submodels fit reasonably well, whereas the residential zone model is less successful.     

A CONDITIONALLY PARAMETRIC PROBIT MODEL OF MICRODATA LAND USE IN CHICAGO

Spatial data sets pose challenges for discrete choice models because the data are unlikely to be independently and identically distributed. A conditionally parametric spatial probit model is amenable to very large data sets while imposing far less structure on the data than conventional parametric models. We illustrate the approach using data on 474,170 individual lots in the City of Chicago. The results suggest that simple functional forms are not appropriate for explaining the spatial variation in residential land use across the entire city.     

The Validity of Hierarchical Information Integration Choice Experiments to Model Residential Preference and Choice

Integrated conjoint choice models have recently been introduced in the consumer choice literature as an approach to include a large number of influential attributes in a choice experiment and statistically test its assumed hierarchical structure. In the present article, the validity of integrated conjoint choice models is tested using housing choice as an example. The results of this study suggest that the parameters of housing utility functions derived from integrated conjoint choice models are not statistically different across experiments nor are they different from those derived from nonhierarchical conjoint models. This yields support for the validity of these models. Implications of these findings are discussed.     

Measuring Impacts of Condition Variables in Rule‐Based Models of Space‐Time Choice Behavior: Method and Empirical Illustration

The use of rule‐based systems for modeling space‐time choice has gained increasing research interests over the last years. The potential advantage of the rule‐based approach is that it can handle interactions between a large set of predictors. Decision tree induction methods are available and have been explored for deriving rules from data. However, the complexity of the structures that are generated by such knowledge discovery methods hampers an interpretation of the rule‐set in behavioral terms with as a consequence that the models typically remain a black box. To solve this problem, this paper develops a method for measuring the size and direction of the impact of condition variables on the choice variable as predicted by the model. The paper illustrates the method based on location and transport‐mode choice models that are part of Albatross model—an activity‐based model of space‐time choice.     

Building Spatial Choice Models from Aggregate Data

Abstract Choice model construction is usually based on information about a number of separate choice situations, for which all relevant quantities are known. This paper concerns the case where only higher level, aggregate information is available about the choice results and the prevailing conditions. We demonstrate the applicability of a generic inverse parameter estimation method in estimating a model for grocery store choice. We also propose some enhancements to standard spatial choice models and demonstrate their applicability.     

An Empirical Test of Social Choice Theories of Disequilibrium

This paper is an empirical test of 'institution-free' theories of democratic procedures exemplified by Kenneth Arrow and William Riker. 'Social choice' theorists claim that instability is the likely result of a pure majority-rule institution, but such a claim has not been subjected to a great deal of empirical scrutiny. In this paper I test the arguments of social choice theory using the American Continental Congress (1774-89) which was, perhaps, the most democratic legislative institution that has ever existed. I conclude that there is a strong possibility of instability in democratic institutions.     

STRUCTURAL ESTIMATION OF RESIDENTIAL MOBILITY AND HOUSING TENURE CHOICE*

ABSTRACT. This study is devoted to studying households'decisions to move and whether to rent or own after moving. It employs dynamic discrete choice models which condition households'decisions on their circumstances at every point in time during the length of the observation while accounting for individual heterogeneity. The results show that key dynamic elements, captured by means of lagged dependent variables, as well as household heterogeneity, are significant determinants of the tenure choice and mobility decisions. Moreover, homeowners are found to be responsive to housing market conditions when adjusting their housing stock. Housing-price appreciation is found to discourage renters from becoming homeowners. Finally, our results reject the proportional monetary transaction costs specification suggesting that monetary transaction costs might be fixed.     

TRAFFIC CONGESTION,POLYCENTRICITY, AND INTRAURBAN FIRM LOCATION CHOICES: A NESTED LOGIT MODEL FOR THE LOS ANGELES METROPOLITAN AREA

This study empirically investigates traffic congestion effects on agglomeration through the lens of firm location decisions. A discrete choice model is applied to examine new establishments’ location choices within the Los Angeles metropolitan area. Employment centers are defined as the choice set to explore the nature and role of intraurban agglomerations. The results show that metro‐wide congestion negatively affects the location choices of firms in the high‐order office‐related activities, while local congestion have positive impacts on those firms’ location decisions. In contrast, firms in production‐related activities are positively influenced by regional congestion but are negatively affected by local congestion levels.     

From Big Noise to Big Data: Toward the Verification of Large Data sets for Understanding Regional Retail Flows

There has been much excitement among quantitative geographers about newly available data sets, characterized by high volume, velocity, and variety. This phenomenon is often labeled as “Big Data” and has contributed to methodological and empirical advances, particularly in the areas of visualization and analysis of social networks. However, a fourth v—veracity (or lack thereof)—has been conspicuously lacking from the literature. This article sets out to test the potential for verifying large data sets. It does this by cross‐comparing three unrelated estimates of retail flows—human movements from home locations to shopping centers—derived from the following geo‐coded sources: (1) a major mobile telephone service provider; (2) a commercial consumer survey; and (3) geotagged Twitter messages. Three spatial interaction models also provided estimates of flow: constrained and unconstrained versions of the “gravity model” and the recently developed “radiation model.” We found positive relationships between all data‐based and theoretical sources of estimated retail flows. Based on the analysis, the mobile telephone data fitted the modeled flows and consumer survey data closely, while flows obtained directly from the Twitter data diverged from other sources. The research highlights the importance of verification in flow data derived from new sources and demonstrates methods for achieving this.     

Teachers' Attitudes and the Success of School Choice

The 1990s witnessed growing attention to the adoption of choice-based school reforms, particularly charter schooling and school vouchers. Although researchers have approached the school choice debate from theoretical, normative, and empirical directions, little attention has been paid to examining how teachers view school choice or what factors shape their attitudes. This oversight is significant because teachers can impact the success of school choice experiments in many ways, such as through their willingness to launch and staff schools of choice, adopt new practices developed in schools of choice, and support choice-based reform efforts in their unions and communities. Using a survey of Arizona and Nevada teachers, we find that White, experienced, unionized, Democratic educators and those working in a "positive" school environment are less supportive of school choice. Arizona teachers, who live in close approximation of a free market for education, are particularly hostile to choice, but teachers who have had personal contact with a charter school are more supportive. The results indicate that the success of school choice will likely be influenced by the characteristics of the teacher workforce and teachers' familiarity with competition.     

Microeconomic Formulation and Estimation of a Residential Location Choice Model: Implications for the Value of Time

Abstract We formulate a microeconomic model of residential location choice behavior as an aggregate of the individual behaviors of household members, subject to individual time constraints and a common income budget. A simplified version of the model is estimated from stated preference rank‐order data, yielding a function that may be interpretated as a conditional indirect utility function. We consider Box‐Tukey transformations, segmentation by income class, and a consistent treatment of data at different rank depths using the simultaneous mixed‐estimation method. Measures of the household's willingness‐to‐pay (through rents) for reducing travel times to work and study in the short run, are interpreted as subjective values of time and compared with such values derived from mode choice models. Our results are plausible, and consistent with recent findings showing that the short‐run benefits of transport projects derived by transport models are larger than benefits measured at the land use system.