The type of innovation introduced is also expected to matter. When “low-hanging fruits” technologies have been already adopted, the remaining innovations may be expected to yield lower returns (Gordon 2012; Gordon and Sayed 2019).

Understanding the returns to innovation is thus an empirical question. This question has received considerable attention in developed countries, where data are more likely to be available. Evidence from advanced economies, mostly in Europe, points to a positive, albeit somewhat noisy, relationship between (primarily product) innovation and (levels/growth of) productivity (see Hall, 2011 for a review). Evidence from developing countries is more limited and not as conclusive - despite some positive association documented for newly industrialized countries such as the Republic of Korea (Lee and Kang 2007), Malaysia (Hegde and Shapira 2007), and China (FisherVanden et al., 2006). Leveraging cross-country data from East Asia, we want to fill this gap in the literature and offer a consistent assessment of the interaction between productivity and innovation, exploring the complementary role played by the business environment and firms’ characteristics.

A variety of methodologies have emerged to link productivity with innovation, despite limited data on technology adoption at the firm level. For example, some researchers resort to randomized control trials (RCTs) to generate first-hand data on technology adoption and productivity. But their data are of relatively small size and time range making generalization of their analyses difficult (Bloom et al., 2013; Atkin, Chaudhry, Chaudry, Khandelwal, and Verhoogen, 2017; Hardy and McCasland, 2016). Another approach that has gained popularity is to check the effects of innovation on the full-range firm productivity distribution. (Syverson, 2011; Juhász et al.,2020). The firm dynamics embodied in the distribution are critical for understanding differences in productivity across sectors (Hsieh and Klenow, 2009). Yet this method imposes a demanding data requirement for cross-country analyses.

Given the nature of our data, a more suitable empirical strategy is to rely on the Crépon, Duguet, Mairessec (1998, henceforth CDM for short) model which exploits cross-sectional variations and tends to abstract from issues related to the timing of innovation and its contribution to productivity. The CDM model is articulated in three parts and explores: (i) whether and to what extent a firm undertakes R&D investment, as a function of the firm and industry characteristics; (ii) what type of innovation outcomes take place as a function of R&D intensity and other firm and industry characteristics; and (iii) whether and to what extent innovation outcomes affect productivity. The CDM model offers a way to deal with issues related to selection bias (focusing on the few firms reporting positive investments in innovation would skew the sample and possibly bias the results) and endogeneity in the functions of innovation and productivity (due to measurement error or unobservable factors that affect both the productivity of the firms and the choice of inputs used).

We build on the CDM model and explore whether the effects of innovation investments on productivity are heterogeneous and whether there is evidence of spillovers from innovation on productivity. In estimating the relationship between innovation and productivity, we explore the role of other (complementary) factors, including firm-specific factors (e.g., management capabilities, foreign ownership) or business-environment characteristics (e.g., sector financial 3