A copula-based approach to accommodate residential self-selection effects in travel behavior modeling

Abstract

The dominant approach in the literature to dealing with sample selection is to assume a bivariate normality assumption directly on the error terms, or on transformed error terms, in the discrete and continuous equations. Such an assumption can be restrictive and inappropriate, since the implication is a linear and symmetrical dependency structure between the error terms. In this paper, we introduce and apply a flexible approach to sample selection in the context of built environment effects on travel behavior. The approach is based on the concept of a “copula”, which is a multivariate functional form for the joint distribution of random variables derived purely from pre-specified parametric marginal distributions of each random variable. The copula concept has been recognized in the statistics field for several decades now, but it is only recently that it has been explicitly recognized and employed in the econometrics field. The copula-based approach retains a parametric specification for the bivariate dependency, but allows testing of several parametric structures to characterize the dependency. The empirical context in the current paper is a model of residential neighborhood choice and daily household vehicle miles of travel (VMT), using the 2000 San Francisco Bay Area Household Travel Survey (BATS). The sample selection hypothesis is that households select their residence locations based on their travel needs, which implies that observed VMT differences between households residing in neo-urbanist and conventional neighborhoods cannot be attributed entirely to the built environment variations between the two neighborhoods types. The results indicate that, in the empirical context of the current study, the VMT differences between households in different neighborhood types may be attributed to both built environment effects and residential self-selection effects. As importantly, the study indicates that use of a traditional Gaussian bivariate distribution to characterize the relationship in errors between residential choice and VMT can lead to misleading implications about built environment effects.

Introduction

There has been considerable interest in the land use-transportation connection in the past decade, motivated by the possibility that land-use and urban form design policies can be used to control, manage, and shape individual traveler behavior and aggregate travel demand. A central issue in this regard is the debate whether any effect of the built environment on travel demand is causal or merely associative (or some combination of the two; see Bhat and Guo, 2007). To explicate this, consider a cross-sectional sample of households, some of whom live in a neo-urbanist neighborhood and others of whom live in a conventional neighborhood. A neo-urbanist neighborhood is one with high population density, high bicycle lane and roadway street density, good land-use mix, and good transit and non-motorized mode accessibility/facilities. A conventional neighborhood is one with relatively low population density, low bicycle lane and roadway street density, primarily single use residential land use, and auto-dependent urban design. Assume that the vehicle miles of travel (VMT) of households living in conventional neighborhoods is higher than the VMT of households residing in neo-urbanist neighborhoods. The question is whether this difference in VMT between households in conventional and neo-urbanist households is due to “true” effects of the built environment, or due to households self-selecting themselves into neighborhoods based on their VMT desires. For instance, it is at least possible (if not likely) that unobserved factors that increase the propensity or desire of a household to reside in a conventional neighborhood (such as overall auto inclination, a predisposition to enjoying travel, and safety and security concerns regarding non-auto travel) also lead to the household putting more vehicle miles of travel on personal vehicles. If this self-selection is not accounted for, the difference in VMT attributed directly to the variation in the built environment between conventional and neo-urbanist neighborhoods can be mis-estimated. On the other hand, accommodating for such self-selection effects can aid in identifying the “true” causal effect of the built environment on VMT.

The situation just discussed can be cast in the form of Roy’s (1951) endogenous switching model system (see Maddala, 1983, Chapter 9), which takes the following form:

$$\begin{array}{cc}\hfill & r_q^{\ast }={\beta }^{\prime }{x}_q+{\epsilon }_q\text{,}{r}_q=1\text{if}{r}_q^{\ast }>0\text{,}{r}_q=0\text{if}{r}_q^{\ast }\le 0\text{,}\hfill \\ \hfill & m_{q0}^{\ast }={\alpha }^{\prime }{z}_q+{\eta }_q\text{,}{m}_{q0}=1[r_q=0]m_{q0}^{\ast }\text{,}\hfill \\ \hfill & m_{q1}^{\ast }={\gamma }^{\prime }{w}_q+{\xi }_q\text{,}{m}_{q1}=1[r_q=1]m_{q1}^{\ast }\text{.}\hfill \end{array}$$

The notation 1[r_q = 0] represents an indicator function taking the value 1 if r_q = 0 and 0 otherwise, while the notation 1[r_q = 1] represents an indicator function taking the value 1 if r_q = 1 and 0 otherwise. The first selection equation represents a binary discrete decision of households to reside in a neo-urbanist built environment neighborhood or a conventional built environment neighborhood. $r_q^{\ast }$ in Eq. (1) is the unobserved propensity to reside in a conventional neighborhood relative to a neo-urbanist neighborhood, which is a function of an (M × 1)-column vector x_q of household attributes (including a constant). β represents a corresponding (M × 1)-column vector of household attribute effects on the unobserved propensity to reside in a conventional neighborhood relative to a neo-urbanist neighborhood. In the usual structure of a binary choice model, the unobserved propensity $r_q^{\ast }$ gets reflected in the actual observed choice r_q (r_q = 1 if the qth household chooses to reside in a conventional neighborhood, and r_q = 0 if the qth household decides to reside in a neo-urbanist neighborhood). ε_q is usually a standard normal or logistic error tem capturing the effects of unobserved factors on the residential choice decision.

The second and third equations of the system in Eq. (1) represent the continuous outcome variables of log(vehicle miles of travel) in our empirical context. $m_{q0}^{\ast }$ is a latent variable representing the logarithm of miles of travel if a random household q were to reside in a neo-urbanist neighborhood, and $m_{q1}^{\ast }$ is the corresponding variable if the household q were to reside in a conventional neighborhood. These are related to vectors of household attributes z_q and w_q, respectively, in the usual linear regression fashion, with η_q and ξ_q being random error terms. Of course, we observe $m_{q0}^{\ast }$ in the form of m_q0 only if household q in the sample is observed to live in a neo-urbanist neighborhood. Similarly, we observe $m_{q1}^{\ast }$ in the form of m_q1 only if household q in the sample is observed to live in a conventional neighborhood.

The potential dependence between the error pairs (ε_q, η_q) and (ε_q, ξ_q) has to be expressly recognized in the above system, as discussed earlier from an intuitive standpoint.² The classic econometric estimation approach proceeds by using Heckman’s or Lee’s approaches or their variants (Heckman, 1974, Heckman, 1976, Heckman, 1979, Heckman, 2001, Greene, 1981, Lee, 1982, Lee, 1983, Dubin and McFadden, 1984). Heckman’s (1974) original approach used a full information maximum likelihood method with bivariate normal distribution assumptions for (ε_q, η_q) and (ε_q, ξ_q). Lee (1983) generalized Heckman’s approach by allowing the univariate error terms ε_q, η_q, and ξ_q to be non-normal, using a technique to transform non-normal variables into normal variates, and then adopting a bivariate normal distribution to couple the transformed normal variables. Thus, while maintaining an efficient full-information likelihood approach, Lee’s method relaxes the normality assumption on the marginals but still imposes a bivariate normal coupling. In addition to these full-information likelihood methods, there are also two-step and more robust parametric approaches that impose a specific form of linearity between the error term in the discrete choice and the continuous outcome (rather than a pre-specified bivariate joint distribution). These approaches are based on the Heckman method for the binary choice case, which was generalized by Hay, 1980, Dubin and McFadden, 1984 for the multinomial case. The approach involves the first step estimation of the discrete choice equation given distributional assumptions on the choice model error terms, followed by the second step estimation of the continuous equation after the introduction of a correction term that is an estimate of the expected value of the continuous equation error term given the discrete choice. However, these two-step methods do not perform well when there is a high degree of collinearity between the explanatory variables in the choice equation and the continuous outcome equation, as is usually the case in empirical applications. This is because the correction term in the second step involves a non-linear function of the discrete choice explanatory variables. But this non-linear function is effectively a linear function for a substantial range, causing identification problems when the set of discrete choice explanatory variables and continuous outcome explanatory variables are about the same. The net result is that the two-step approach can lead to unreliable estimates for the outcome equation (see Leung and Yu, 2000, Puhani, 2000).

Overall, Lee’s full information maximum likelihood approach has seen more application in the literature relative to the other approaches just described because of its simple structure, ease of estimation using a maximum likelihood approach, and its lower vulnerability to the collinearity problem of two-step methods. But Lee’s approach is also critically predicated on the bivariate normality assumption on the transformed normal variates in the discrete and continuous equation, which imposes the restriction that the dependence between the transformed discrete and continuous choice error terms is linear and symmetric. There are two ways that one can relax this joint bivariate normal coupling used in Lee’s approach. One is to use semi-parametric or non-parametric approaches to characterize the relationship between the discrete and continuous error terms, and the second is to test alternative copula-based bivariate distributional assumptions to couple error terms. Each of these approaches is discussed in turn next.

The potential econometric estimation problems associated with Lee’s parametric distribution approach has spawned a whole set of semi-parametric and non-parametric two-step estimation methods to handle sample selection, apparently having beginnings in the semi-parametric work of Heckman and Robb (1985). The general approach in these methods is to first estimate the discrete choice model in a semi-parametric or non-parametric fashion using methods developed by, among others, Cosslett, 1983, Ichimura, 1993, Matzkin, 1992, Matzkin, 1993, Briesch et al., 2002. These estimates then form the basis to develop an index function to generate a correction term in the continuous equation that is an estimate of the expected value of the continuous equation error term given the discrete choice. While in the two-step parametric methods, the index function is defined based on the assumed marginal and joint distributional assumptions, or on an assumed marginal distribution for the discrete choice along with a specific linear form of relationship between the discrete and continuous equation error terms, in the semi- and non-parametric approaches, the index function is approximated by a flexible function of parameters such as the polynomial, Hermitian, or Fourier series expansion methods (see Vella, 1998, Bourguignon et al., 2007 for good reviews). But, of course, there are “no free lunches”. The semi-parametric and non-parametric approaches involve a large number of parameters to estimate, are relatively very inefficient from an econometric estimation standpoint, typically do not allow the testing and inclusion of a rich set of explanatory variables with the usual range of sample sizes available in empirical contexts, and are difficult to implement. Further, the computation of the covariance matrix of parameters for inference is anything but simple in the semi- and non-parametric approaches. The net result is that the semi- and non-parametric approaches have been pretty much confined to the academic realm and have seen little use in actual empirical application.

The turn toward semi-parametric and non-parametric approaches to dealing with sample selection was ostensibly because of a sense that replacing Lee’s parametric bivariate normal coupling with alternative bivariate couplings would lead to substantial computational burden. However, an approach referred to as the “Copula” approach has recently revived interest in maintaining a Lee-like sample selection framework, while generalizing Lee’s framework to adopt and test a whole set of alternative bivariate couplings that can allow non-linear and asymmetric dependencies. A copula is essentially a multivariate functional form for the joint distribution of random variables derived purely from pre-specified parametric marginal distributions of each random variable. The reasons for the interest in the copula approach for sample selection models are several. First, the copula approach does not entail any more computational burden than Lee’s approach. Second, the approach allows the analyst to stay within the familiar maximum likelihood framework for estimation and inference, and does not entail any kind of numerical integration or simulation machinery. Third, the approach allows the marginal distributions in the discrete and continuous equations to take on any parametric distribution, just as in Lee’s method. Finally, under the copula approach, Lee’s coupling method is but one of a suite of different types of couplings that can be tested.

In this paper, we apply the copula approach to examine built environment effects on vehicle miles of travel (VMT). The rest of this paper is structured as follows. The next section provides a theoretical overview of the copula approach, and presents several important copula structures. Section 3 discusses the use of copulas in sample selection models. Section 4 provides an overview of the data sources and sample used for the empirical application. Section 5 presents and discusses the modeling results. The final section concludes the paper by highlighting paper findings and summarizing implications.