Instrumental variable (IV) estimation

I conducted IV estimation to address the possible endogeniety between the perceived corruption measure and both product and marketing innovation. Prior literature has

acknowledged reverse causality between corruption and measures of innovation (Krammer, 2017,Vial and Hanoteau, 2010). Corrupt public actors impose additional costs on firms commensurate with their ability to pay them (Svensson, 2003). Accordingly, innovating firms face a more severe threat of corruption to the extent that their innovation outputs, such as new products or branding initiatives, are perceived as signals of financial success by corrupt actors (Ayyagari, Demirgüç-Kunt, and Maksimovic, 2014). Furthermore, as alluded to in this paper,

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innovation activities can also leave firms more susceptible to corruption if they require forms of government permission, such as patent protection for product innovations. Corruption and innovation are thought to be jointly influenced by a variety country (e.g. GDP) and industry (e.g.

growth) level factors, which also raises endogeniety concerns.

I instrumented firm-level perceived corruption with the average perception of corruption at the country-region-industry level, following the approach of previous scholars that have corrected for endogeniety when using firm-level corruption measures from the WBES (Fisman and Svensson, 2007; Krammer, 2017). Regions are sub-national geographic areas in a country which can encompass single or multiple cities, provinces, and states. I conducted both two-staged least squares (2SLS) and instrumental variable probit (IV probit) estimations with robust standard errors. Despite the dependent variables being binary, I present only the results of the linear, 2SLS estimations due to space limitations and because the discrete nature of the

endogenous variable violates the IV probit assumption that the endogenous variable should be continuous. Nonetheless, the results from both 2SLS and IV probit were consistent in terms of the sign and significance of the coefficients of interest. A second set of IV probit regressions were also conducted using the log of perceived corruption in order to transform the endogenous variable into one that is continuous, which also produced consistent results.

The assumptions underlying the use of the instrument are confirmed by the data, with country-region-industry perceived corruption being highly correlated with perceived corruption at the firm-level (correlation= 0.3930) but having a low correlation with product innovation (correlation = 0.0192) and comparatively lower correlation with marketing innovation

(correlation =0.1024). The instrument is also found to be a valid determinant of both measures of innovation, having joint F statistics that surpass the suggested threshold of 10 (Stock, Wring, and

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Yogo, 2002) in the regressions explaining product innovation (785.93, p<0.001) and marketing innovation (774.55, p<0.001). As a test of instrument strength, I report the Kleibergen-Paap Walk rk F-statistic since the Cragg-Donald statistic that is commonly reported is not valid when robust standard errors are computed. The F statistic is greater than 20 and significant (p<0.001) for both the regressions predicting product and marketing innovation, indicating that the

instrument is strong and relevant. The Woolridge endogeniety statistics (Woolridge, 1995) reported in the last row of Table 4 indicate that the null hypothesis that firm-level perceived corruption is exogenous is rejected across models at adequate levels of statistical significance (5% or less), except for those predicting product innovation and trademarking, in which there is a lack of significance, and model 10 (which tests the moderating effect of trademarking), in which there is significance at only the 10% level. While instrumenting in cases where endogeniety may not be an issue can result in overestimated standard errors, I choose to present the 2SLS

estimations for these models because (1) endogeniety issues that are uncorrected pose the risk of producing biased estimates, and (2) the 2SLS results can be compared with the main bivariate probit results to ascertain whether standard errors might be overestimated. Consistency between the bivariate probit and 2SLS results indicates that overestimation of standard errors is not an issue.

The results of the 2SLS estimations are presented in the table in Appendix 3d. In Model 9, which predicts product innovation, the coefficient of perceived corruption is negative and significant (β = -0.0270, p<0.05), providing support for Hypothesis 1. In Models 13-15, which predict marketing innovation, the coefficients of perceived corruption are positive and significant (β = 0.0408-0.0471, p<0.01), providing support for Hypothesis 2. Model 7 confirms that

perceived corruption does not have a significant direct effect on trademarking, which is a key

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distinction made from its effect on patenting that is the basis for my argument of its moderating effect. Model 14 confirms that trademarking is positively related to marketing innovation since the coefficient of trademarking is positive and significant (β = 0.241, p<0.001), but Hypothesis 4 cannot be supported by the results since the inclusion of the trademarking and perceived

corruption interaction in Model 15 is not statistically significant.

Estimation using 2SLS provides an advantage over bivariate probit in testing for mediation, allowing for the Baron and Kenney (1986) procedure to be implemented since equations are not estimated simultaneously. While its limitations compared to the bootstrapping method have been previously highlighted, I decided to present the results of this procedure in order to provide an alternative test of mediation than those in the main results. The conditions required for the Baron and Kenney procedure to establish the partial mediation suggested by Hypothesis 3 are met. Firstly, corruption (the independent variable) is negatively associated with patenting (the mediator), as established in Model 6 (β = -0.0576, p<0.001). Secondly, perceived corruption is negatively associated with product innovation (the dependent variable), as

established in Model 9 and previously discussed. Thirdly, patenting has a positive association with product innovation, as established in Model 10 (β = 0.209, p<0.001). Lastly, when comparing Model 9 to Model 11, the coefficient of perceived corruption loses its significance and reduces in magnitude when patenting is included in the regression (β = -0.0135, p>0.1).