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The Bivariate Probit Model in Strategy and Management Research: Applications and Potential

Research in Times of Crisis

ISBN: 978-1-80071-798-5, eISBN: 978-1-80071-797-8

Publication date: 20 September 2021


In the early days of the COVID-19 pandemic, an area could only report its first positive cases if the infection had spread into the area and if the infection was subsequently detected. A standard probit model does not correctly account for these two distinct latent processes but assumes there is a single underlying process for an observed outcome. A similar issue confounds research on other binary outcomes such as corporate wrongdoing, acquisitions, hiring, and new venture establishments. The bivariate probit model enables empirical analysis of two distinct latent binary processes that jointly produce a single observed binary outcome. One common challenge of applying the bivariate probit model is that it may not converge, especially with smaller sample sizes. We use Monte Carlo simulations to give guidance on the sample characteristics needed to accurately estimate a bivariate probit model. We then demonstrate the use of the bivariate probit to model infection and detection as two distinct processes behind county-level COVID-19 reports in the United States. Finally, we discuss several organizational outcomes that strategy scholars might analyze using the bivariate probit model in future research.




We are very grateful to Aaron Hill and two anonymous reviewers for clear guidance and constructive feedback on earlier drafts. We are also especially thankful to Michael Howe for insightful comments that helped improve this work. All errors remain our own.


Gong, K. and Johnson, S. (2021), "The Bivariate Probit Model in Strategy and Management Research: Applications and Potential", Hill, A.D., Lê, J.K., McKenny, A.F., O'Kane, P., Paroutis, S. and Smith, A.D. (Ed.) Research in Times of Crisis (Research Methodology in Strategy and Management, Vol. 13), Emerald Publishing Limited, Leeds, pp. 99-122.



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