The Heterogeneous Impacts of R&D on Innovation in Services Sector: A Firm-Level Study of Developing ASEAN

Identifying the determinants of firms’ investment in knowledge, this study first explores the heterogeneous impacts of research and development (R&D) on product, process, organization, and marketing innovation. Second, it examines if there exists a complementary (substitute) relation in terms of firms’ preference between four types of innovation. Studying 1500 firms of seven developing economies of the Association of Southeast Asian Nations (ASEAN), we applied the least absolute shrinkage and selection operator (LASSO), a machine learning-based regression, to identify key predictors likely to influence firms’ R&D propensity and intensity. Estimating the knowledge function, we found—in line with LASSO—that medium-sized firms, human capital (training) and credit facilities favorably affect firms’ decision to invest in R&D. Contrarily, the impact is adverse if the first or main product generates firms’ large share of revenue, a unique finding not captured by previous studies. The marginal effects of four univariate probit models indicate that firms’ investment in R&D translates into innovation. However, the application of the Geweke–Hajivassiliour–Keane (GHK)-simulator based multivariate probit, which considers simultaneity of firms’ innovation decisions that univariate probit ignores, suggests that the relationship between different types of innovation is complementary. Firms’ strategy to adopt a particular type of innovation is influenced by other types. This led to the estimation of R&D’s impact on technological and nontechnological innovation, which shows that while firms innovate both types, there is a skewed link between nontechnological innovation and the services sector.

Z-Tests in Multinomial Probit Models under Simulated Maximum Likelihood Estimation: Some Small Sample Properties

SummaryThis paper analyzes small sample properties of several versions of z-tests in multinomial probit models under simulated maximum likelihood estimation. Our Monte Carlo experiments show that z-tests on utility function coefficients provide more robust results than z-tests on variance covariance parameters. As expected, both the number of observations and the number of random draws in the incorporated Geweke-Hajivassiliou-Keane (GHK) simulator have on average a positive impact on the conformities between the shares of type I errors and the nominal significance levels. Furthermore, an increase of the number of observations leads to an expected decrease of the shares of type II errors, whereas the number of random draws in the GHK simulator surprisingly has no significant effect in this respect. One main result of our study is that the use of the robust version of the simulated z-test statistics is not systematically more favorable than the use of other versions. However, the application of the z-test statistics that exclusively include the Hessian matrix of the simulated loglikelihood function to estimate the information matrix often leads to substantial computational problems.