estimated in several contexts, including developing countries.<sup>7</sup> Although this literature has reached consensus on the relevance of investments in innovation in terms of firm upgrading, there is less clarity about what factors affect the rate of return.

When trying to understand what factors affect these returns, it is very important to consider the systemic nature of innovation. In this view, innovation is considered to be the result of the interaction of many socioeconomic actors whose behaviors cannot be separated from other contextual characteristics, such as regulations, social norms, political values, and collective priorities (Lundvall 1992, Nelson 1993). This approach focused initially on innovation at the national level, through so-called National Innovation Systems, but later expanded to consider the concept of _systems of innovation_ at regional levels (Cooke 2001, Cooke, Uranga, and Etxebarria 1997) and sectoral levels (Malerba 2002). The bottom line of this literature is that external knowledge boosts firms’ _learning capabilities_ , which, in turn, positively affect the return of private investment in innovation.

In addition, a more scattered literature analyzing macro-micro linkages on innovation also provides some insights on what factors could affect firms’ returns to R&D through their capacity to convert opportunities into economic rewards (that is, how they could enhance firms’ _business capabilities_ ). This strand of literature has examined how productivity or innovation reacts to macroeconomic uncertainty (Bernanke 1983, Bloom 2009, Acemoglu et al. 2003); competition (Aghion et al. 2005); institutional quality (Acemoglu 2003); and demand-pull factors (Adner 2002).

In this paper, based on the _knowledge expanded production function_ , we bring together different contributions from these strands of literature, including qualitative insights, to analyze the role of contextual complementary factors and firms’ capacity to enhance the returns of investment in innovation. We organize this body of literature into three groups: those studies that discuss contextual aspects that enhance firms’ technological learning capabilities (that is, knowledge complementary factors); studies that discuss the role of contextual factors affecting firms’ capacity of doing business or fully appropriating the rewards of their assets (that is, market complementary factors); and studies discussing the role of micro heterogeneity in taking advantage of contextual opportunities (that is, firms’ innovative capacity).

7 Jefferson et al. (2006) used panel data from China to estimate the impact of R&D in terms of productivity and profitability. The paper establishes a lag structure to offset simultaneity biases and correct for endogeneity. It finds that R&D has positive effect on both profitability and productivity. Antoncic et al. (2007) used a structural equation framework on cross-sectional data for Slovenia and Romania to test a hypothesis about the positive impact of organizational support and alliances on innovativeness and, in turn, a positive impact of innovation on firms’ performance (measured by growth, profitability, and wealth). They find empirical support for their hypotheses. Benavente (2006) applied an adapted version of the CDM framework using Chilean cross-section data. They find that neither R&D nor innovation results (share of sales of new products—so-called innovative sales) have an effect on productivity (measured as value added per worker). Crespi and Zuniga (2012) applied the CDM framework on micro data for six Latin American countries. They find that greater investment in R&D leads to a higher probability of having at least one process or product innovation. In addition, results show a positive impact of technological innovation on productivity (log of sales per employee) for all countries except Costa Rica. Moreover, they find that the magnitude of the results is very heterogeneous. Crespi, Tacsir, and Vargas (2016) used 2010 World Bank Enterprise Survey firm-level data to analyze 17 Latin American countries. Results show that investment in R&D per worker increases the probability that the firm will innovate, and that this translates into a strong increase in labor productivity (measured as log of sales per employee). These results are robust to five different measures of innovation: innovation of product or process, product innovation, process innovation, innovative sales (share of sales of new products), and filing for intellectual property rights. In Argentina, the CDM framework was first used by Chudnovsky, López, and Pupato (2004) and then by Arza and López (2010). Both papers show that investment in R&D boosts firms’ labor productivity.

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