Sample Selection Bias and Heckman Models in Strategic Management Research

Abstract

The use of Heckman models by strategy scholars to resolve sample selection bias has increased by more than 700 percent over the last decade, yet significant inconsistencies exist in how they have applied and interpreted these models. In view of these differences, we explore the drivers of sample selection bias and review how Heckman models alleviate it. We demonstrate three important findings for scholars seeking to use Heckman models: First, the independent variable of interest must be a significant predictor in the first stage of a model for sample selection bias to exist. Second, the significance of lambda alone does not indicate sample selection bias. Finally, Heckman models account for sample-induced endogeneity, but are not effective when other sources of endogeneity are present. Managerial summary: When nonrandom samples are used to test statistical relationships, sample selection bias can lead researchers to flawed conclusions that can, in turn, negatively impact managerial decision-making. We examine the use of Heckman models, which were designed to resolve sample selection bias, in strategic management research and highlight conditions when sample selection bias is present as well as when it is not. We also distinguish sample selection bias, a form of omitted variable (OV) bias, from more traditional OV bias, emphasizing that it is possible for models to have sample selection bias, traditional OV bias, or both. Accurately identifying the type(s) of OV bias present is essential to effectively correcting it. We close with several recommendations to improve practice surrounding the use of Heckman models. Copyright

Influence of mobile phone and internet technology on income of rural farmers: Evidence from Khyber Pakhtunkhwa Province, Pakistan

“…To resolve this type of bias produced by selection errors as soon as conceivable, Heckman's step regression method could be utilized to perform regression management to confirm the influence of crop growers' MPITU on their support and agricultural incomes, recommended via Puhani [91] and Winship and Mare [92]. Heckman's step regression method primarily includes two equations, choice, and outcome [93,94]. First, we used the selection equation to assess how growers choose supporting and agricultural labor.…”