Correcting for endogeneity due to omitted attributes in discrete-choice models: the multiple indicator solution

Guevara, C. Ángelo; Polanco, Daniel

doi:10.1080/23249935.2016.1147504

Cited by 35 publications

(30 citation statements)

References 44 publications

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“…Given the definitions of the two indicators, it is expected for them to be correlated due to the fact that they are both influenced by the (omitted) school composition. This justifies their use in the MIS approach per Guevara (2015) and Guevara and Polanco (2016).…”

Section: Utonomous Regionsmentioning

confidence: 81%

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School Choice across Different Regions of Spain

Vega-Bayo¹,

Mariel²

2018

HPE-RPE

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This paper analyses the characteristics that lead parents to choose a particular school for their children in different Spanish regions. This choice is studied taking into account several school and family characteristics. We estimate the probability of parents choosing a particular school using a conditional logit model for different Spanish regions with data from PISA 2012. The main results indicate that the characteristics with the biggest influence on school choice are public/private, location, and reputation. As expected, some characteristics, like a school being private, and its socioeconomic composition, become more relevant as family wealth and parents' education level increases.

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Section: Utonomous Regionsmentioning

confidence: 81%

“…According to Guevara (2015) and Guevara and Polanco (2016), the MIS approach takes advantage of the existing correlation between two indicators; this correlation is assumed to be due to the omitted variable.…”

Section: Estimation Methodologymentioning

confidence: 99%

School Choice across Different Regions of Spain

Vega-Bayo¹,

Mariel²

2018

HPE-RPE

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“…iii. Guevara and Polanco (2016) extend the Wooldridge (2010) MIS method (for linear models) to discrete choice models. iv.…”

Section: Discussionmentioning

confidence: 99%

How risky are uninsured drivers?

Yarmukhamedov

2018

Journal of Transportation Safety & Security

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Obtaining insurance is expected to have disincentive effects on driver behavior, and as such on accident probabilities due to the adverse selection and=or moral hazard effects, though empirical studies provide mixed evidence. The risks of an accident incurred by uninsured drivers are, however, indeterminate as well, because of the unavailability of individual level data on uninsured drivers. Therefore, the purpose of this article is to empirically analyze whether insured or uninsured drivers pose greater accident risks. It uses unique individual level data on privately owned insured and uninsured passenger vehicles in Sweden for the period 2006 to 2010. The results show that the accident probability of uninsured drivers is 34 percentage points higher than for insured drivers, with uninsured drivers age 25 to 34 years posing the highest risk of an accident. This implies that uninsured drivers are a high-risk group and a significant threat to traffic safety. Possible solutions and policy implications are discussed at the end of the article.

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“…Guevara, 2015). In this article we illustrate and assess the applicability of the Multiple Indicator Solution (MIS) method (Guevara and Polanco, 2016) to address the problem of endogeneity due to omitted crowding in public transportation choice models.…”

mentioning

confidence: 99%

“…Wooldridge (2010) applies the MIS method to linear models; where he proposes using the 2SLS method for the second stage. Guevara and Polanco (2016) adapted and applied the MIS method to Logit models by considering the control-function approach (Heckman, 1978;Petrin and Train, 2010;Guevara andBen-Akiva 2006, 2012;Guevara, 2015;Fernández et. al, 2016) in the second stage of the MIS method.…”

mentioning

confidence: 99%

Correcting for endogeneity due to omitted crowding in public transport choice using the Multiple Indicator Solution (MIS) method

Guevara

Tirachini

Hurtubia

et al. 2020

Transportation Research Part A: Policy and Practice

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Crowding levels are very relevant for the analysis and evaluation of the performance of public transport as they strongly affect the level of service and the overall perceived quality of the system. However, crowding is not an easy variable to measure and, hence, demand models often tend to ignore or use abstract proxies for it. In this paper we assess the Multiple Indicator Solution (MIS) method in a Stated Preference (SP) experiment where crowding conditions were displayed to the respondent, but are artificially omitted in the estimation to cause endogeneity. Results show strong evidence that the MIS method can be used to control for a wide range of omitted attributes in SP data. We also discuss the potential application of this approach to Revealed Preferences (RP) models of public transport by asking suitable post-trip questions to users. Two MIS variations were applied to this SP case study and both provided outcomes that were superior to those of the curtailed model. We enrich the analysis with the aid of Monte Carlo simulation. Results suggest that potential problems may arise in the presence of neglected interactions and if indicators are only weakly correlated with the omitted attribute. For the SP case study analysed, only the former issue seems to play a role in the results. The article finishes by discussing the implications of these findings for the correction of endogeneity on SP and RP data on public transport.

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Correcting for endogeneity due to omitted attributes in discrete-choice models: the multiple indicator solution

Cited by 35 publications

References 44 publications

School Choice across Different Regions of Spain

School Choice across Different Regions of Spain

How risky are uninsured drivers?

Correcting for endogeneity due to omitted crowding in public transport choice using the Multiple Indicator Solution (MIS) method

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