Change of Scale and Forecasting with the Control-Function Method in Logit Models

Endogeneity is a model misspecification that precludes the consistent estimation of the model parameters. The control-function method is the most suitable tool to address endogeneity for several discrete choice models that are relevant in transportation research. However, the estimators obtained with the control-function method are consistent only up to a scale. In this paper, we first depict the determinants of this change of scale by adapting an existing result for omitted orthogonal attributes in logit models. Then, we study the problem of forecasting under these circumstances. We show that a procedure proposed in previous literature may lead to significant biases, and we suggest novel alternatives to be used with synthetic populations. We use Monte Carlo experimentation and real data on residential location choice to illustrate these results. The paper finishes by summarizing the findings of this investigation and suggesting future lines of research in this area.MIT-Portugal Progra

Spatially Correlated Nested Logit Model for Spatial Location Choice

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUGErratum available online: https://doi.org/10.1016/j.trb.2022.06.008[Abstract] Residential location choice is a key component of the models for predicting land-use and transport demand in urban planning. In general, it requires to consider correlation between spatial alternatives. The approach of nested alternatives of the nested logit model has proved highly efficient in this context. This approach incorporates into the nested logit model both spatial and non-spatial correlations due to unobserved variables. The approach of metric extensions to the spatially correlated logit model specifies models for capturing spatial correlations between alternatives without having to design a nested structure. A model combining both approaches is proposed in this research. The spatially correlated nested logit model proposed herein models the correlation between alternatives of the nests of a nested logit model using a metric of spatial correlation between pairs of alternatives. The proposed model improves the properties of the nested logit model without the need of increasing the number of unknown parameters. Our model also improves the properties of a spatially correlated model with the same spatial metric. When needing to incorporate preference heterogeneity into the model, the proposed model is compatible with a mixed specification with random coefficients. The spatially correlated nested logit model was empirically applied to the real case of residential location choice in the city of Santander in Spain. In this empirical context, this model improved the explanatory and predictive power of the models that it combines.The authors acknowledge the financial support provided by the Government of Spain under the projects TRA2012–37659 and RTI2018–097924-B-I00, funded by MCIN/AEI/10.13039/501100011033 and by “ERDFA way of making Europe”. Funding for open access charge: Universidade da Coruña/CISUGhttps://doi.org/10.1016/j.trb.2022.06.00

Parents’ Willingness to Pay for Bilingualism: Evidence From Spain

We analyse parental school preferences in two contiguous provinces of northern Spain, which offer very different school choices since only one of them is bilingual. Basque- Spanish bilingualism affects the school system, labour market, and, consequently, family budgets in the Basque Country. In Cantabria, Spanish is the only official language, and English is the second language typically offered in the school system. Using two discrete choice experiments applied to school choice, we estimate parents’ willingness to pay for different school characteristics in the two areas. We find that the most highly valued school characteristic in both areas is the language of instruction, but in the Basque Country, where the minority language indirectly has a wage premium in the local labour market, its importance relative to other characteristics is more salient.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The authors acknowledge finan- cial support from FEDER/Ministry of Science, Innovation and Universities [MCIN/AEI/10.13039/501100011033 through Grant PID2020-113650RB-I00] and the Basque Government through grant IT1359-19 (UPV/EHU Econometrics Research Group) and Grant IT1336-19 (Bilbao Research Team in Economics)

Incorporating attitudes into the evaluation of preferences regarding agri-environmental practices

Many stated preference studies have shown that individuals’ attitudes play an important role in explaining their behaviour and helping to disentangle preference heterogeneity. When responses to attitudinal questions are introduced into discrete choice models, a suitable approach that corrects for potential endogeneity must be adopted. We use a discrete choice experiment to analyse the preferences of residents regarding the use of agri-environmental practices in the peri-urban area of Milan (Italy). A detailed analysis of these preferences is relevant for policymakers as farmers on the peri-urban fringe are often asked to provide environmental services to urban-dwellers. We apply a latent class model that we extend to include indicators of individuals’ attitudes towards the relationship between agriculture and the environment. Besides the application of the control function approach to deal with endogeneity, our main contribution is the use of a refutability test to check the exogeneity of the instruments in the agri-environmental setting. Our results show that attitudinal indicators help to disentangle the preference heterogeneity and that the respondents’ willingness-to-pay distribution differs according to the indicators’ values

The Role of Customer Investor Involvement in Crowdfunding Success

Entrepreneurs increasingly use reward-based crowdfunding to finance innovation projects through a large number of customer investments. The existing academic literature has predominantly studied the process and the efficiency of these crowd investments. However, we argue that the involvement of customers goes beyond the provision of capital. As investors, customers enter a principal-agent relationship with project creators. As a result, project creators are often confronted with a crowd of customer investors who try to influence the development of product ideas. We show that project creators can actually benefit from this influence, as customer investors partly substitute for the support usually received from conventional investors. Greater involvement of customer investors thus increases the likelihood of funding success. This holds when we control for creator ability and project quality. However, the positive effect of involvement is lower for teams, because customers face lower agency costs when they invest in team projects. We also link the involvement of customer investors during crowdfunding to the crowdsourcing literature and show that its positive effect can be attributed to the elicitation of external information through distant search

A control-function approach to correct for endogeneity in discrete choice models estimated on SP-off-RP data and contrasts with an earlier FIML approach by Train & Wilson

It is common practice to build Stated Preference (SP) attributes and alternatives from observed Revealed Preference (RP) choices with a view to increasing realism. While many surveys pivot all alternatives around an observed choice, others use more adaptive approaches in which changes are made depending on what alternative was chosen in the RP setting. For example, in SP-off-RP data, the alternative chosen in the RP setting is worsened in the SP setting and other alternatives are improved to induce a change in behaviour. This facilitates the creation of meaningful trade-offs or tipping points but introduces endogeneity. This source of endogeneity was largely ignored until Train and Wilson (T&W) proposed a full information maximum likelihood (FIML) solution that can be implemented with simulation. In this article, we propose a limited information maximum likelihood (LIML) approach to address the SP-off-RP problem using a method which does not need simulation, can be applied with standard software and uses data that is already available for the stated problem. The proposed method is an application of the control-function (CF) method to correct for endogeneity in discrete choice models, using the RP attributes as instrumental variables. We discuss the theoretical and practical advantages and disadvantages of the CF and T&W methods and illustrate them using Monte Carlo and real data. Results show that, while the T&W method may be more efficient in theory, it may however fail to retrieve consistent estimators when it does not account properly for the data generation process if, e.g., an exogenous source of correlation among the SP choice tasks exists. On the other hand, the CF is more robust, i.e. less sensitive, to the data generation process assumptions, and is considerably easier to apply with standard software and does not require simulation, facilitating its adoption and the more extensive use of SP-off-RP data

The Role of Customer Investor Involvement in Crowdfunding Success

Entrepreneurs increasingly use reward-based crowdfunding to finance innovation projects through a large number of customer investments. The existing academic literature has predominantly studied factors that drive crowd investments and whether crowdfunding predicts market success. However, we argue that the involvement of customers goes beyond the provision of capital. As investors, customers enter into a principal– agent relationship with entrepreneurs. Thus, entrepreneurs are often faced with a crowd of customer investors who try to influence product development. We show that entrepreneurs can benefit from this influence, because customer investors provide some of the support usually received from institutional investors. Greater involvement from customer investors thus increases funding success. This holds when we control for creator ability and project quality. The effect is driven by customers’ influence on product development and the reduction in agency costs for prospective customers. We also link the involvement of customer investors during crowdfunding to the crowdsourcing literature and show that its positive effect is augmented by the elicitation of external information through distant search

A Generic Form for Capturing Unobserved Heterogeneity in Discrete Choice Modelling: Application to Neighborhood Location Choice

Discrete choice models and their strength to predict individual choices mostly depend on the quality of datasets that have been used for model generation. However, even the most comprehensive and detailed datasets are not able to observe all factors pertinent to someone’s choice. This issue in the choice modelling literature has been addressed as unobserved heterogeneity, which means that individuals across populations are not affected identically by alternative attributes. Furthermore, such variation in preferences across populations and their sources are not always recognized by researchers. There are different methods to capture unobserved heterogeneity proposed in the discrete choice literature among which the random parameters approach, also referred to as mixed logit models, the latent class approach and the agent effect approach are the most well know methods. The main contribution of this study is to extend the formulation of LC-MMNL model to capture the agent effect by including a random term in the utility function of the model. Three types of models, Mixed Multinomial Logit (MMNL), Latent Class Mixed Multinomial Logit (LC-MMNL) and Agent Effect Latent Class Mixed Multinomial Logit (AGLC-MMNL) have been generated and the results compared. Considering agent effect simultaneously with other sources of unobserved heterogeneity in a latent class context demonstrates improvement in terms of model fit as well as cross section validation. It enables us to generate a latent class model with a larger number of classes explaining more heterogeneity across the population of a neighborhood location choice study. The AGLC-MMNL model is able to detect four distinct classes of individuals in Montreal, exhibiting different behaviours while facing neighborhood location choices in the context of a Discrete Choice Experiment. The classes of the model are able to explain different behaviours of individuals based on their income level, whether they are transit or car oriented, and the importance of privacy to them

Análisis estadístico y modelos econométricos de elección discreta con correlación espacial en transporte y economía urbana: aplicación a los modelos de predicción de la localización residencial

Programa Oficial de Doutoramento en Enxeñaría Civil. 5011V01[Resumen] En esta tesis se propone un nuevo modelo de elección discreta entre alternativas de naturaleza geográfica, el modelo spatially correlated nested logit (SCNL). En este tipo de elecciones se prevé un elevado número de alternativas y la presencia de correlación, al menos espacial, entre ellas. La literatura actual en modelos de elección discreta considera dos enfoques para este tipo de elecciones, que son analizados en esta tesis. El modelo propuesto combina ambos enfoques sin añadir parámetros desconocidos, presenta una estructura matemática cerrada y es compatible con especificaciones mixtas que permitan variaciones en los gustos de quienes deciden. El modelo SCNL se formula y analiza en esta tesis, donde también se aplica a un caso real, para modelizar la elección de localización residencial en contexto urbano, que es un problema clave en planificación del transporte y economía urbana. Los resultados empíricos con el nuevo modelo mejoran significativamente los obtenidos con los modelos actuales. Además, se propone una métrica espacial para zonificaciones del espacio con diferentes tamaños y formas irregulares, muy habituales en áreas administrativas de ciudades europeas. Los modelos especificados con la métrica espacial propuesta obtuvieron resultados empíricos significativamente mejores que con el resto de métricas espaciales consideradas.[Resumo] Nesta tese proponse un novo modelo de elección discreta entre alternativas de natureza xeográfica, o modelo spatially correlated nested logit (SCNL). Neste tipo de elección espérase un elevado número de alternativas e a presenza de correlación, polo menos espacial, entre elas. A literatura actual sobre modelos de elección discreta considera dous enfoques para este tipo de eleccións, que se analizan nesta tese. O modelo proposto combina ambos enfoques sen engadir parámetros descoñecidos, presenta unha estrutura matemática pechada e é compatible con especificacións mixtas que permiten variacións nos gustos de quen decide. O modelo SCNL formúlase e analízase nesta tese, onde tamén se aplica a un caso real, para modelizar a elección da localización residencial nun contexto urbano, que é un problema clave na planificación do transporte e na economía urbana. Os resultados empíricos co novo modelo melloran significativamente os obtidos cos modelos actuais. Ademais, proponse unha métrica espacial para zonificacións do espazo con diferentes tamaños e formas irregulares, moi común en áreas administrativas das cidades europeas. Os modelos especificados coa métrica espacial proposta obtiveron resultados empíricos significativamente mellores que co resto de métricas espaciais consideradas.[Abstract] A new model of discrete choice between alternatives of a geographical nature is proposed in this thesis, the spatially correlated nested logit (SCNL) model. This type of choices are generally characterized by the existence of a high number of alternatives and the presence of correlation, at least spatial, between them. The current literature on discrete choice models considers two approaches for this type of choice, which are analyzed in this thesis. The proposed model combines both approaches without adding unknown parameters, it presents a closed mathematical structure and it is compatible with mixed specifications that allow variations in the tastes of those who decide. The SCNL model is formulated and analyzed in this thesis, where it is also applied to a real case, to model the choice of residential location in an urban context, which is a key problem in transport planning and urban economics. The empirical results with the new model significantly improve those obtained with the current models. In addition, a spatial metric for spatial zoning with different sizes and irregular shapes is proposed, very common in administrative areas of European cities. The models specified with the proposed spatial metric obtained empirical results that were significantly better than those obtained with the rest of the spatial metrics considered

Assessment and correction of endogeneity problems in discrete choice models

PhD ThesisThe term endogeneity is used when there is a correlation between one or more observed explanatory variables (independent variables) and the error term of an econometric model. Endogeneity is considered a practically inevitable phenomenon in econometric modelling, as there are many potential causes behind it: omitted variables, measurement or specification errors, simultaneous estimation and self-selection. The problem is that it may give rise to inconsistent parameter estimates, and if its effects are not considered when estimating a model, the analyst may come to wrong forecasts and conclusions. Correcting for endogeneity has been widely addressed in the linear models (LM) literature, but LM have a limited scope in certain areas. This is particularly the case in planning and social evaluation of transport projects, where Discrete Choice Models (DCM), which are highly non-linear, play a fundamental role. Unfortunately, DCM are not often corrected for endogeneity, so a gap has been identified in the state of knowledge that this thesis intends to close. Thus, the general aim of this Ph.D. dissertation is to develop a set of guidelines that allow for the assessment and correction of endogeneity problems in DCM. We establish conclusions of a theoretical, empirical and methodological nature. In the first instance, it is desired to determine adequate instrumental variables for endogeneity correction in transport modelling and measure the impact of this correction on strategic modal split models. We can reduce the errors associated with the estimation of DCM, improve its forecasting capabilities, and achieve consistent parameters resulting in corrected estimates of model valuation measures, such as the subjective value of time (SVT). Furthermore, we formulate an empirical methodology, supported by Monte Carlo simulation, to predict using DCM corrected for endogeneity with a new and more adequate version of the CF method. We also define guidelines to clarify under what conditions discrete indicators work (or not) when DCM are corrected for endogeneity using the MIS method. Finally, we structure a methodology to detect weak DCM instruments based on what has been proposed for linear model