# **Box 1. Which firm characteristics are important to explain innovation performance across developing East Asian countries?**

To assess how propensity to innovate varies across different firm characteristics (besides sector of operations), a logistic model is estimated where the dependent variable (“innovative company”) is a dummy variable that takes the value of one when the firm has introduced innovation and zero when it has not; all types of innovation outputs mapped in the ES data set are modeled. Firm age, size (measured in terms of number of employees), ownership, export status, city size and sector are included as explanatory variables. Parameters are estimated by maximum likelihood using sampling weights, and standard errors are clustered by country.

Table A2 in the annex shows – for each type of innovation output - the odds ratio of each firm characteristic. Results suggest that the role played by firm characteristics varies according to type of innovation outputs. The fact that the odds ratio of the firm age variable is less than one suggests that older firms are less prone to innovate; this is valid for all types of innovation. Firm size is an important characteristic regardless of the type of innovation output; in this case, for instance, the odds of introducing product or process innovation is 3.23 times greater for large firms than for micro firms (see column 1). Export status is another firm characteristic to influence innovation performance; the odds of introducing product innovation is 1.46 times higher for exporters than for non-exporter firms (see column 2). However, if the analysis is restricted to firms that report product innovation (see column 3), being an exporter reduces the probability of introducing product innovation that is new to the market. Foreign owned firms are surprisingly less prone to innovate when compared to domestic companies: in the case of process innovation for instance, foreign companies are 31.6 percent less likely to innovate than domestic peers (column 4). Size of locality also plays an important role: being located in large cities increases the probability of innovating; for certain types of innovation activities (precisely for process, organizational and marketing innovation) this association seems to be nonlinear. Sector of activity also matters: all else equal, other service sector companies present the highest probability to introduce a product innovation (2.415 times higher) when compared to the baseline category (wholesale and retail).

Innovation inputs - defined as the set of investments and activities that firms carry out to develop their capabilities to innovate (Cirera, Lopez-Bussols and Muzi, 2016) - represent another relevant dimension to be considered when assessing innovation performance at the firm level. ES data cover a limited set of innovation inputs: R&D activities; purchase and/or licensing of (patented and non-patented) technology; and, formal training for development of innovative products/processes.

R&D activities represent the most commonly discussed innovation input in the literature; see for instance, Mohnen and Hall (2013) for a recent review of studies across OECD countries. ES data for the selected set of developing East Asian countries under analysis show that R&D intensity (measured as the total R&D expenditures per worker) does not vary too much with income level, a result that is driven by Indonesia which presents a low level of (average log) R&D intensity (Figure 13). The linear association (between R&D intensity and GDP per capita) would be strongly positive if Indonesia is excluded. On the other hand, R&D incidence decreases sharply with income level, a result that is robust to the exclusion of Indonesia. When taken together, these results suggest that - at least for this selected set of developing East Asian countries, and when taking Indonesia out of the analysis - a smaller fraction of firms undertake R&D as income level increases, while R&D expenditures (among these firms conducting R&D) increases across the income span. Overall, this result is consistent with findings from Cirera and Maloney (2017) using 14