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.     

DISCRETE SPATIAL CHOICE MODELS FOR AGGREGATE DESTINATIONS*

ABSTRACT. In problems of spatial choice, the choice set is typically more aggregated than the one considered by decision-makers, often because choice data are available only at the aggregate level. These aggregate choice units will exhibit heterogeneity in utility and in size. To be consistent with utility maximization, a choice model must estimate choice probabilities on the basis of the maximum utility within heterogeneous aggregates. The ordinary multinomial logit model applied to aggregate choice units fails this criterion as it is estimated on the basis of average utility. In this paper, we derive and discuss a model which utilizes the theory underlying the nested logit model to estimate the appropriate maximum utilities of aggregates. We also demonstrate that the aggregate alternative error terms are asymptotically Gumbel, thereby relaxing the assumption of extreme value distributed error terms. This is accomplished with help from the asymptotic theory of extremes.     

Disaggregate Migration Behavior and the Volume of Interregional Migration

Nested multinomial logit models are used to investigate migration behavior during the 1971–74 period for a large sample of the population of Ecuador. The nested form of the model makes it possible to test hypotheses about the importance of destination characteristics in conditioning the odds for out-migration. Our empirical results indicate that the odds for migration from each origin are conditioned by the expected utilities of the available set of destinations, as well as characteristics of the origins and the personal characteristics of potential migrants. The association between destination characteristics and the frequency of out-migration allows the total volumes of migration to be adjusted to interregional differences in place-specific utilities.     

SOME EVIDENCE THAT WOMEN ARE MORE MOBILE THAN MEN: GENDER DIFFERENCES IN U.K. GRADUATE MIGRATION BEHAVIOR

ABSTRACT.  In this paper we employ dichotomous, multinomial and conditional logit models to analyze the employment-migration behavior of some 380,000 U.K. university graduates. By controlling for a range of variables related to human capital acquisition and local economic conditions, we are able to distinguish between different types of sequential migration behavior from domicile to higher education and on to employment. Our findings indicate that U.K. female graduates are generally more migratory than male graduates. We suggest that the explanation for this result lies in the fact that migration can be used as a partial compensation mechanism for gender bias in the labor market.     

Calibration and Test of a Discrete Choice Model with Endogenous Choice Sets

Conventional discrete choice models assume implicitly that the choice set is independent of the decisionmaker's preferences conditional on the explanatory variables of the models. This assumption is implausible in many choice situations where the decisionmaker selects his or her choice set. This paper estimates and tests a discrete choice model with endogenous choice sets based on Horowitz' theoretical work. To calibrate the model, a new probability simulator is introduced and a sequential estimation procedure is developed. The model and calibration methods are tested in an empirical application as well as Monte Carlo simulations. The empirical results are used to test the theory of endogenous choice sets and to examine the differences between the new model and a conventional choice model in parameter estimates and predicted choice probabilities. The empirical results strongly suggest that ignoring the endogeneity of choice sets in choice modeling can have serious consequences in applications.     

Gaussian Process Regression and Bayesian Model Averaging: An Alternative Approach to Modeling Spatial Phenomena

Gaussian Process Regression (GPR) is a nonparametric technique that is capable of yielding reliable out‐of‐sample predictions in the presence of highly nonlinear unknown relationships between dependent and explanatory variables. But in terms of identifying relevant explanatory variables, this method is far less explicit about questions of statistical significance. In contrast, more traditional spatial econometric models, such as spatial autoregressive models or spatial error models, place rather strong prior restrictions on the functional form of relationships, but allow direct inference with respect to explanatory variables. In this article, we attempt to combine the best of both techniques by augmenting GPR with a Bayesian Model Averaging (BMA) component that allows for the identification of statistically relevant explanatory variables while retaining the predictive performance of GPR. In particular, GPR‐BMA yields a posterior probability interpretation of model‐inclusion frequencies that provides a natural measure of the statistical relevance of each variable. Moreover, while such frequencies offer no direct information about the signs of local marginal effects, it is shown that partial derivatives based on the mean GPR predictions do provide such information. We illustrate the additional insights made possible by this approach by applying GPR‐BMA to a benchmark BMA data set involving potential determinants of cross‐country economic growth. It is shown that localized marginal effects based on partial derivatives of mean GPR predictions yield additional insights into comparative growth effects across countries.     

Representing the Shape and Orientation of Destinations in Spatial Choice Models

This paper illustrates the importance of spatial unit shape and orientation in spatial choice analysis. It also examines the implications of assuming spatial units to be dimensionless points in this context. Through theory and simulation experiments, it is shown how these aspects cannot be ignored and how the ordinary multinomial logit model applied at the spatially aggregate level is particularly vulnerable to such oversights. The aggregated spatial logit model in the end is recommended as a general formulation which addresses many of these fundamental issues of interest to geographers and regional scientists.     

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.     

METROPOLITAN OUTMIGRATION PATTERN OF CANADIAN LABOUR FORCE ENTRANTS, 1971–76

This paper analyses the 1971 -76 metropolitan out-migration pattern of Canadian males in the labour force entrance age group. Migration is conceptualized within a three-level choice framework, and statistical inference is based on a multinomial logit model. It was found (1) that the propensity to outmigrate and the destination choice pattern vary substantially among the 23 metropolitan areas; (2) that 83 per cent of the variation in the destination choice probabilities of metropolitanward migrants can be explained by only five variables (log of distance, cultural dissimilarity, temperature, employment growth, and population size); and (3) that housing conditions are essentially the results (rather than the causes) of intermetropolitan migration .     

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.     

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.     

THE JOINT CHOICE OF AN INDIVIDUAL'S OCCUPATION AND DESTINATION*

ABSTRACT This study examines the relationship between an individual's occupation choice and destination choice. It portrays the relationship as an interaction between the supply of occupational skills by individuals and demand by different labor‐market regions. The unusual merger of a multinomial logit model of occupational choice and the conditional logit model of destination choice in a simultaneous equation framework requires derivation of a unique variance–covariance matrix. Results indicate strong association between supply of (migration) and demand for (industry mix) an individual's occupational skills. These effects are especially strong for destinations experiencing slow economic growth, while relatively unimportant for high‐growth locations.     

The Determinants of Regional Freight Transport: A Spatial,Semiparametric Approach

In the context of modeling regional freight the four‐stage model is a popular choice. The first stage of the model, freight generation and attraction, however, suffers from three shortcomings: first of all, it does not take spatial dependencies among regions into account, thus potentially yielding biased estimates. Second, there is no clear consensus in the literature as to the choice of explanatory variables. Second, sectoral employment and gross value added are used to explain freight generation, whereas some recent publications emphasize the importance of variables which measure the amount of logistical activity in a region. Third, there is a lack of consensus regarding the functional form of the explanatory variables. Multiple recent studies emphasize nonlinear influences of selected variables. This article addresses these shortcomings by using a spatial variant of the classic freight generation and attraction models combined with a penalized spline framework to model the explanatory variables in a semiparametric fashion. Moreover, a Bayesian estimation approach is used, coupled with a penalized Normal inverse‐Gamma prior structure, to introduce uncertainty regarding the choice and functional form of explanatory variables. The performance of the model is assessed on a real‐world example of freight generation and attraction of 258 European NUTS‐2 level regions, covering 25 European countries.     

THE AGGREGATED SPATIAL CHOICE MODEL VS. THE MULTINOMIAL LOGIT: AN EMPIRICAL COMPARISON USING MIGRATION MICRODATA

Frequently, in spatial interaction analysis, researchers are forced to use destinations that are zonal aggregates of the ‘real’ destinations perceived by the participants in the interaction process. Previous simulation studies demonstrated that, under certain circumstances, the aggregated spatial choice model can outperform the popular ordinary multinomial logit model, both in explanatory power and predictive ability. In this paper, the two models are compared with interprovincial migration microdata for the time period 1990-91, obtained from the 1991 Canadian census. Since this is not meant to be a migration study, the analysis is limited to out-migrants from Ontario. The results indicate that, at least with the data used, the multinomial logit model performed reasonably well. The paper, however, highlights some practical advantages that can accrue from the use of the aggregated model. Dans l'analyse de l'interaction à référence spatiale, il arrive fréquemment que les chercheurs soient contraints de se servir de destinations qui regroupent les?vraies?destinations perçues par les participants dans le processus d'interaction. Les études en simulations antérieures ont démontré que, dans certaines circonstances, le modèle groupé des choix à référence spatiale peut donner de meilleurs résultats que le modèle ordinaire commun du logit multinomial, sur le plan de la capacité d'explication comma sur celui de la valeur de prévision. Dans la présente étude, on fait la comparaison entre les deux modèles en utilisant les microdonnées sur l'émigration interprovinciale en 1990-91 obtenues par le recensement de 1991. L'analyse ne porte que sur les émigrants de l'Ontario puisqu'elle ne vise pas particulièrement l'étude de l'émigration. D'après les résultats, le modèle employant le logit multinomial fonctionne relativement bien, au moins avec les données utilisées. Toutefois, l'étude souligne certains avantages pratiques pouvant inciter à se servir davantage du modèle groupé.     

Modeling Strategies for Categorical Data: Examples from Housing and Tenure Choice

Models to investigate categorical data can be divided into preprocessing, limited parameterization, and formal logit models. To illustrate the advantages of preprocessing and limited parameterization models they are applied to a data set of tenure and type of housing choice before the data are examined with hierarchical logit and nested logit models. The preprocessing approaches are useful in selecting optimal subsets of independent variables with respect to the dependent variable. The ease of application and interpretation of a limited parameterization approach extends the clarity of the results from the preprocessing approaches. Because some variables are only relevant at specific levels of other independent variables, nonstandard (nested) logit models are necessary to understand the nested relationships.     

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.     

Strategic voting and constituency context: Modelling party preference and vote in multiparty elections

We examine the role of strategic motivations in mediating the relationship between underlying political preferences and vote choice, in a multiparty, single member, simple plurality system, and examine the role of constituency context in determining the scope for strategic voting. Political preference data from the British Election Panel Survey, 1997–2001, were modelled with mixed multinomial logit models. Latent variables were used to model the stable party political traits underlying observed preferences, allowing correlation between choices and so avoiding the restrictive assumption of independence from irrelevant alternatives. Ranked approval ratings were used to characterize the underlying political preferences in the presence of insincere voting. From these models we estimate that approximately 9% of votes cast may have been affected by strategic factors. In keeping with ‘Duvergers law’, the smallest of the three main parties, the Liberal Democrats, were found to be most affected by strategic voting.     

Exposing Unobserved Spatial Similarity: Evidence from German School Choice Data

In a spatial context, flexible substitution patterns play an important role when modeling individual choice behavior. Issues of correlation may arise if two or more alternatives of a selected choice set share characteristics that cannot be observed by a modeler. Multivariate extreme value (MEV) models provide the possibility to relax the property of constant substitution imposed by the multinomial logit (MNL) model through its independence of irrelevant alternatives (IIA) property. Existing approaches in school network planning often do not account for substitution patterns, nor do they take free school choice into consideration. In this article, we briefly operationalize a closed‐form discrete choice model (generalized nested logit [GNL] model) from utility maximization to account for spatial correlation. Moreover, we show that very simple and restrictive models are usually not adequate in a spatial choice context. In contrast, the GNL is still computationally convenient and obtains a very flexible structure of substitution patterns among choice alternatives. Roughly speaking, this flexibility is achieved by allocating alternatives that are located close to each other into nests. A given alternative may belong to several nests. Therefore, we specify a more general discrete choice model. Furthermore, the data and the model specification for the school choice problem are presented. The analysis of free school choice in the city of Dresden, Germany, confirms the influence of most of the exogenous variables reported in the literature. The estimation results generally indicate the applicability of MEV models in a spatial context and the importance of spatial correlation in school choice modeling. Therefore, we suggest the use of more flexible and complex models than standard logit models in particular. En un contexto espacial, los patrones sustitución flexible juegan un papel importante en el modelamiento del comportamiento de las decisiones individuales. Varios problemas de correlación pueden presentarse si dos o más alternativas de elección comparten características no observables por el modelador. Los modelos de valor extremo (multivariate extreme value‐MEV) ofrecen la posibilidad de relajar la propiedad de sustitución constante (constant substitution) presente en los modelos logit multinomiales (multinomial logit‐MNL), a través de su propiedad de independencia de alternativas irrelevantes (Independence of irrelevant alternatives property ‐IIA). A menudo, los enfoques existentes en la planificación de redes escolares no toman en consideración los patrones de sustitución y de libre elección de escuela. En este artículo, los autores presentan brevemente el funcionamiento de un modelo de elección discreta (discrete choice model) para la maximización de utilidad o modelo logit anidado generalizado (generalized nested logit model‐GNL) para dar cuenta de la autocorrelación espacial. Los autores sostienen que modelos demasiado simples y restrictivos no suelen ser adecuados en un contexto de elección espacial. En contraste el modelo GNL es conveniente en términos de su computación y obtiene una estructura muy flexible de los patrones de sustitución entre las alternativas de elección. En términos generales, esta flexibilidad se logra mediante la asignación (o anidación) de las alternativas cercanas en el espacio (una alternativa puede pertenecer a varios nidos). Por lo tanto, los autores presentan un modelo de elección discreta más general. El estudio presenta además datos y la especificación del modelo para un caso de elección de escuela concreto: el análisis de libre elección de escuela en la ciudad de Dresden, Alemania. El análisis confirma la influencia de la mayoría de las variables exógenas presentes en la literatura. Los resultados de la estimación demuestran en términos generales la aplicabilidad de los modelos MEV en un contexto espacial y la importancia de la autocorrelación espacial en el modelado de elección de escuela. Los autores concluyen sugiriendo el uso de modelos más flexibles y complejos que los modelos utilizados habitualmente, en particular los modelos logit estándar. 从空间视角看,灵活的替代模式在个人行为选择建模中发挥着重要作用。当存在两个或两个以上备选方案集具有共性且无法被建模者观察到时,就可能出现相关性问题。多元极值模型（MEV）通过不相关的替代属性（IIA）实现了对多元logit模型（MNL）中常数限制的松弛替代。现有校园网络规划方法通常无法解释替代模式,而且没有考虑到自由择校因素。本文简要地建立一个封闭离散选择模型（广义嵌套（GNL）模型）,从效用最大化角度来解释空间相关性。此外分析还表明,非常简单的约束模型通常不具有足够的空间选择情境。相比之下,GNL模型计算便捷,且可以在各选择方案中获得非常灵活的替代模式。大致而言,这种灵活性大体是通过与住处位置距离上彼此靠近的替代选择分配而获得,一个给定的选择可能属于不同的住处。因此,我们给出了一个更一般的离散选择模型。此外,还给出了针对择校问题的数据和模型设定。基于德国德累斯顿市自由择校分析,证实了已有研究中多数外生变量的影响。估计结果证实了MEV模型在空间分析中的适用性以及择校模型中空间相关的重要性,并建议使用更加灵活和复杂的模型而不是标准的logit模型。     

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.     

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.