Addressing unobserved endogeneity bias in accounting studies: control and sensitivity methods by variable type

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Peel, Michael John (2014)

Together with their associated statistical routines, this paper describes the control and sensitivity methods that can be employed by accounting researchers to address the important issue of unobserved (omitted) variable bias in regression and matching models according to the types of variables employed. As with other social science disciplines, an important and pervasive issue in observational (non-experimental) accounting research is omitted variable bias (endogeneity). Causal inferences for endogenous explanatory variables are biased. This occurs in regression models where an unobserved (confounding) variable is correlated with both the dependent (outcome) variable in a regression model and the causal explanatory (often a selection) variable of interest. The Heckman treatment effect model has been widely employed to control for hidden bias for continuous outcomes and endogenous binary selection variables. However, in accounting studies, limited (categorical) dependent variables are a common feature and endogenous explanatory variables may be other than binary in nature. The purpose of this paper is to provide an overview of contemporary control methods, together with the statistical routines to implement them, which extend the Heckman approach to binary, multinomial, ordinal, count and percentile outcomes and to where endogenous variables take various forms. These contemporary methods aim to improve causal estimates by controlling for hidden bias, though at the price of increased complexity. A simpler approach is to conduct sensitivity analysis. This paper also presents a synopsis of a number of sensitivity techniques and their associated statistical routines which accounting researchers can employ routinely to appraise the vulnerability of causal effects to potential (simulated) unobserved bias when estimated with conventional regression and propensity score matching estimators.
  • References (24)
    24 references, page 1 of 3

    Abadie, A., Angrist, J. and Imbens, G., 2002. Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings. Econometrica, 70 (1), 91-117.

    Abrate, G., Capriello, A. and Fraquelli, G., 2011. When quality signals talk: evidence from the Turin hotel industry. Tourism Management, 32 (4), 912-921.

    Abreu, M., Faggian, A. and McCann, P., 2014. Migration and inter-industry mobility of UK graduates. Journal of Economic Geography. Early (online), 29 January 2014. Available from: [Accessed 3 May 2014].

    Ahmed, A., Billings, B., Morton, R. and Harris, M., 2002. The role of accounting conservatism in mitigating bondholder-shareholder conflicts over dividend policy and in reducing debt costs. Accounting Review, 77 (4), 867-890.

    Aldamen, H., Duncan, K., Kelly, S., McNamara, R. and Nagel, S., 2012. Audit committee characteristics and firm performance during the global financial crisis. Accounting and Finance, 52 (4), 971-1000.

    Altonji, J., Todd, E., Elder, T. and Taber, C., 2005. Selection on observed and unobserved variables: assessing the effectiveness of Catholic schools. Journal of Political Economy, 113(1), 151-184.

    Amemiya, T., 1981. Qualitative response models: a survey. Journal of Economic Literature, 19 (4), 1483- 1536.

    Ammann, M., Hoechle, D. and Schmid, M., 2013. Is there really no conglomerate discount? Journal of Business Finance & Accounting, 39 (1&2), 264-288.

    Andini, C., 2010. Within-groups wage inequality and schooling: further evidence for Portugal. Applied Economics, 42 (28), 3685-3691.

    Angrist, J., 2001. Estimation of limited endogenous variable models with dummy endogenous regressors: simple strategies for empirical practice. Journal of Business and Economic Statistics, 19 (1), 2-16.

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