Tobit models in strategy research: Critical issues and applications

By Mario Daniele Amore, Samuele Murtinu. Global Strategy Journal, 2019

159 citations recorded by the source corpus.

Research Summary : Tobit models have been used to address several questions in management research. Reviewing existing practices and applications, we discuss three challenges: (a) assumptions about the nature of data, (b) apparent interchangeability between censoring and selection bias, and (c) potential violations of key assumptions in the distribution of residuals. Empirically analyzing the relationship between import competition and industry diversification, we contrast Tobit models with results from other estimators and show the conditions that make Tobit a suitable empirical approach. Finally, we offer suggestions and guidelines on how to use Tobit models to deal with censored data in strategy research. Managerial Summary : Data on strategic decisions often exhibit certain features, such as excess zeros and values bounded within a given range, which complicate the use of linear econometric techniques. Deriving statistical evidence in such instances may suffer from biases that undermine managerial applications. Our study presents an extensive comparison of different econometric models to deal with censored data in strategic management showing the strengths and weaknesses of each model. We also conduct an application to the context of import penetration and industry diversification to highlight how the relationship between these two variables changes depending on the econometric model used for the analysis. In conclusion, we provide a set of recommendations for scholars interested in censored data. We contrast estimates from Tobit and Heckman models, with this latter being the conventional approach to address selection issues. The selection issue we seek to solve with an Heckman model is the one happening between firms that diversify and firms that do not. As long as the zeros are not imputed values to missing data (as in our sample, which excludes observations with missing data in the variable Diversification ), and thus Diversification is always observed, selection bias problems concerning the zeros are unlikely to be present. Our results show that Tobit and Heckman models yield different results with the absolute value of Tobit marginal effect being almost 1.7 times smaller than the Heckman estimate. Even though our empirical analysis employs a relatively large sample, the difference between the two marginal effects is quite large. To understand which method is more suitable, we use the statistical test for corner solutions proposed by Silva et al. The goal of this approach is to compare the conditional expectations of the dependent variable obtained by different models, that is, whether an estimate under an alternative model improves the prediction of the dependent variable obtained by means of the baseline model. Choosing Tobit as the baseline model and Heckman as the alternative model, we run the above test and we do not reject the null hypothesis ( p -value = .99), that is, Tobit is valid.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/866a8d04ebd80e85ec305bafca37ed59e2abda3b