Regarding explanatory variables motivated by the aforementioned literatures, firm characteristics that may affect a firm’s proclivity to innovate include the following: The Market Structure literature suggests that firm size matters, which we measure by the natural logarithm of the number of permanent and temporary workers and its squared term (to test for a nonlinear relationship). The Global Engagement hypothesis motivates the inclusion of firms’ exporter status, measured by a dummy variable equal to 1 when a firm exports at least 10 percent of its sales; and foreign ownership of the firm, measured by a dummy variable equal to 1 for foreign ownership when more than 10 percent of assets of the firm are owned by foreigners. The surveys also provide information on variables motivated by the Information Spillovers literature, such as whether firms invested in R&D, a dummy variable. Because the literature on Information Spillovers, as well as the Global Engagement hypothesis, has paid much attention to the adoption of foreign technologies, we also use data derived from a question in the surveys that asked managers whether the firm had paid licensing fees during the past two years. This variable is also dichotomous, and the estimates of the effect of R&D and licensing status are thus comparable.

The Global Engagement literature is also related to trade policies, which are observed at the sector-level. We use a composite index of the average applied tariffs and its standard deviation. The index was estimated as the first principal component derived from factor analysis, because the level of the average applied tariff within industries and across countries were highly correlated. Countries with high tariffs in a given sector also tend to have high tariff dispersion. Factor analysis allows for the construction of a single index comprising the information from highly correlated variables. The other trade policy indicator measured at the industry level is the share of tariff lines within each industry that faces one of the so-called “core” non-tariff barriers – see data Appendix. These data were taken from Nicita and Olarreaga (2006).

Country-level variables capture various aspects of the local environment and context. They include an index of infrastructure coverage (from the _World Development Indicators_ (WDI) database), institutional quality (from Kaufmann et al. 2005), and real manufacturing GDP growth (also from WDI). In some models, we also include the level of development (GDP per capita from the Penn World Tables). An important explanatory variable for our analysis, which is related to the Market Structure literature, is a regulatory index capturing the ease of entry. It was calculated from data from the World Bank’s _Doing Business_ database. Due to high correlations among regulatory variables, we again use principal components analysis to calculate a composite index on the quality of governance (including (lack of) corruption, political stability, and rule of law) and the regulatory environment affecting the ease of entry of new firms (including Difficulty of Firing Index, Difficulty of Hiring Index and Days for Starting a Business). We also use patent-counts data from Lederman and Saenz (2005), namely the sum (stock) of 7