Estimating the Effects of Large Shareholders Using a Geographic Instrument
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Abstract Large shareholders may play an important role for firm performance and policies, but identifying this empirically presents a challenge due to the endogeneity of ownership structures. We develop and test an empirical framework that allows us to separate selection from treatment effects of large shareholders. Individual blockholders tend to hold blocks in public firms located close to where they reside. Using this empirical observation, we develop an instrument (the density of wealthy individuals near a firm’s headquarters) for the presence of large, nonmanagerial individual shareholders in firms. These shareholders have a large impact on firms, controlling for selection effects.Keywords:
Endogeneity
Empirical Research
Abstract This article outlines the current situation with respect to ‘endogeneity and structural equation estimation’. It begins by exploring why econometrics, and particularly the treatment of endogeneity, proved so valuable for political scientists. The most prominent methods used to deal with endogeneity are explained. The article also includes a critique of the critical conditions needed to support the use and interpretation of this method and discusses frequently used diagnostics developed to assess the severity of the problems. The estimation method and diagnostics are shown with a running example with data from a previous study of US congressional politics. In many instances, empirical evidence will continue to be the classic observational study, replete with endogeneity problems. Rather than forgoing analyses of these data and questions because of the reservations about a particular method, it is better to use and to improve those methods in the context of these studies and data.
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This paper explicitly models the endogeneity of state life insurance policy in the dynamics of the early American life insurance population. It finds strong evidence of endogeneity and demonstrates that the true effects of public policy on organizational populations are only evident when endogeneity is accounted for.
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Addressing endogeneity can be a challenging task given the different sources of endogeneity and their impacts on empirical results. While premier business journals typically expect authors to rigorously address endogeneity, this expectation is relatively new to many Operations Management (OM) scholars, as exemplified by a recent editorial in Journal of Operations Management that calls for more rigorous treatment for endogeneity. This study serves two purposes. First, we summarize recent OM literature with respect to the treatment for endogeneity by reviewing studies published in leading OM journals between 2012 and 2017. The review provides evidence that endogeneity problems have received increasing attention from OM scholars. However, we also find some common problems that may render the chosen techniques for addressing endogeneity less effective and potentially lead to biased analysis results. Second, since instrumental variable regression is the most prevalent technique for dealing with endogeneity in the OM literature according to our review, we provide an empirical illustration tailored to OM researchers for using instrumental variable regression in the post-design (data analysis) phase. Using variables from a publicly available healthcare dataset, our analysis sheds light on the importance of examining instruments' quality and triangulating results based on more than one test/estimator.
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Endogeneity
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Abstract Chenhall and Moers (European Accounting Review, this issue, pp. 173–195) provide an excellent overview of the econometrics of endogeneity. In response to their discussion I argue that researchers should be courageous enough to set aside endogeneity concerns when their research question is important. Theory does not admit a definite answer to the question whether endogeneity is present in a particular model and econometrics has few technical solutions to offer. Since we cannot be sure endogeneity exists, and if we were to be sure of its existence, there is little we can do about it, researchers are well advised to move on to more serious problems.
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This paper investigates the growing need to control for endogeneity in corporate social responsibility (CSR) research. We modify the theoretical constructs of a neoclassical production function to include CSR and in doing so provide theoretical evidence of endogeneity in the estimation equation. We find that failure to empirically control for endogeneity leads to results that suggest CSR has a negative impact on firm production. However, when controlling for endogeneity through a two‐stage approach, the negative impact disappears. Moreover, we demonstrate the efficacy of using a proven theoretical estimation technique to include CSR and, therefore, mitigate endogeneity concerns.
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In this paper, QUAIDS analysis was carried out to examine the demand system of vegetables in the Philippines, with casting light on the endogenous issues for a more rigorous discussion. As a result of comparing the model controlling for the endogeneity and the model without controlling for the endogeneity, the percent difference between elasticities of two models was shown up to 187%. This result suggests that price and expenditure endogeneities have a significant influence on estimates in the demand system, and the result implies that this may lead to inconsistent estimates when demand analysis does not control for endogeneity.
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Instrumental variable
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Abstract Addressing endogeneity can be a challenging task given the different sources of endogeneity and their impacts on empirical results. While premier business journals typically expect authors to rigorously address endogeneity, this expectation is relatively new to many Operations Management (OM) scholars, as exemplified by a recent editorial in Journal of Operations Management that calls for more rigorous treatment for endogeneity. This study serves two purposes. First, we summarize recent OM literature with respect to the treatment for endogeneity by reviewing studies published in leading OM journals between 2012 and 2017. The review provides evidence that endogeneity problems have received increasing attention from OM scholars. However, we also find some common problems that may render the chosen techniques for addressing endogeneity less effective and potentially lead to biased analysis results. Second, since instrumental variable regression is the most prevalent technique for dealing with endogeneity in the OM literature according to our review, we provide an empirical illustration tailored to OM researchers for using instrumental variable regression in the post‐design (data analysis) phase. Using variables from a publicly available healthcare dataset, our analysis sheds light on the importance of examining instruments' quality and triangulating results based on more than one test/estimator.
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Instrumental variable
Empirical Research
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Based on endogeneity of ownership structure, this paper empirically analyzes the factors resulting in presence of multiple large shareholders and its effects on firm value. It shows that, one hand the poorer investor protection and the more total amount of shareholders, the more possible of presence of multiple large shareholders; on the other hand, this kind of ownership structure benefits firm value and wealth of minority shareholders.
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