The Chief Marketing Officer Matters!Marketing academics and practitioners alike remain unconvinced about the chief marketing officer's (CMO's) performance implications. Whereas some studies propose that firms benefit financially from having a CMO in the C-suite, other studies conclude that the CMO has little or no effect on firm performance. Accordingly, there have been strong calls for additional academic research regarding the CMO's performance implications. In response to these calls, the authors employ model specifications with varying identifying assumptions (i.e., rich data models, unobserved effects models, instrumental variable models, and panel internal instruments models) and use data from up to 155 publicly traded firms over a 12-year period (2000-2011) to find that firms can indeed expect to benefit financially from having a CMO at the strategy table. Specifically, their findings suggest that the performance (measured in terms of Tobin's q) of the sample firms that employ a CMO is, on average, approximately 15% greater than that of the sample firms that do not employ a CMO. This result is robust to the type of model specification used. Marketing academics and practitioners should find the results intriguing given the existing uncertainty surrounding the CMO's performance implications. The study also contributes to the methodology literature by collating diverse empirical model specifications that can be used to model causal effects with observational data into a coherent and comprehensive framework.

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This paper has two main contributions. Firstly, we introduce a new approach, the latent instrumental variables (LIV) method, to estimate regression coefficients consistently in a simple linear regression model where regressor-error correlations (endogeneity) are likely to be present. The LIV method utilizes a discrete latent variable model that accounts for dependencies between regressors and the error term. As a result, additional ‘valid’ observed instrumental variables are not required. Furthermore, we propose a specification test based on Hausman (1978) to test for these regressor-error correlations. A simulation study demonstrates that the LIV method yields consistent estimates and the proposed test-statistic has reasonable power over a wide range of regressor-error correlations and several distributions of the instruments. Secondly, the LIV method is used to re-visit the relationship between education and income based on previously published data. Data from three studies are re-analyzed. We examine the effect of education on income, where the variable ‘education’ is potentially endogenous due to omitted ‘ability’ or other causes. In all three applications, we find an upward bias in the OLS estimates of approximately 7%. Our conclusions agree closely with recent results obtained in studies with twins that find an upward bias in OLS of about 10% (Card, 1999). We also show that for each of the three datasets the classical IV estimates for the return to education point to biases in OLS that are not consistent in terms of size and magnitude. Our conclusion is that LIV estimates are preferable to the classical IV estimates in understanding the effects of education on income. Copyright Springer Science + Business Media, Inc. 2005instrumental variables, latent instruments, testing for endogeneity, mixture models, identifiability, estimating the return to education,

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Market response models based on field-generated data need to address potential endogeneity in the regressors to obtain consistent parameter estimates. Another requirement is that market response models predict well in a holdout sample. With both requirements combined, it may seem reasonable to subject an endogeneity-corrected model to a holdout prediction task, and this is quite common in the academic marketing literature. One may be inclined to expect that the consistent parameter estimates obtained via instrumental variables (IV) estimation predict better than the biased ordinary least squares (OLS) estimates. This paper shows that this expectation is incorrect. That is, if the holdout sample is similar to the estimation sample so that the regressors are endogenous in both samples, holdout sample validation favors regression estimates that are not corrected for endogeneity (i.e., OLS) over estimates that are corrected for endogeneity (i.e., IV estimation). We also discuss ways in which holdout samples may be used sensibly in the presence of endogeneity. A key takeaway is that if consistent parameter estimates are the primary model objective, the model should be validated with an exogenous (rather than endogenous) holdout sample.