from the one during the last year covered by the ENDEI 2 survey. While regulations and restrictions on trade policies were prevalent during the ENDEI 1 reference period— especially when the survey was conducted (2013)—they fell off sharply during the last year covered by the ENDEI 2 survey (2016) and dropped even more when it was conducted (2017–18).

This IV strategy rests on the idea that regulations and restrictions on trade policies imposed by the government affect the perceived obstacles to innovation in a heterogenous way for firms of different sectors, regions, and sizes, which in turn may limit the resources they dedicate to innovation activities. In particular, we hypothesize that import barriers on key goods needed to carry out innovation activities are sensitive to regulations and trade policies. We would then expect that changes in trade restrictions in 2016 decreased perceived obstacles to imports of goods for innovation activities, and consequently fostered investments in innovation between 2014 and 2016.

Hence, we proxy a firm’s restrictions on imports in 2014 through the sector-region-size _proportion of firms_ claiming to suffer from import barriers during the ENDEI 1<sup>21</sup> period; we assume that firms belonging to the same group are similarly affected by these barriers. Hence, the variability in our instrument comes from differences in the intensity of perceived obstacles to imports across sector-region-size groups of firms. We then observe how these perceived obstacles relate to the _change_ in innovation investments between 2014 and 2016, given that between these two years many import restrictions were lifted. We expect a positive correlation between the perception of obstacles in the past and the change in innovation investment, given that firms that were more restricted are the ones that increased their investment in innovation activities to a greater extent once these restrictions were removed. The conditional exogeneity assumption is plausible, given that the effect of import restrictions on goods that _are key for innovation_ affects productivity levels precisely through their effects on the firm’s decision to conduct or increase its innovation activities, once these barriers are alleviated. A graphical explanation of the logic of this instrument is presented in figure B.1 in appendix B.

It is important to highlight that the constructed IVs are cross-sectional and are used to instrument the _difference_ in innovation investment between 2014 and 2016 (we are left with only one observation per firm). As a result, the database loses its panel data structure. First differences, however, eliminate the effect of time-invariant omitted covariates.

# **4.3 Measurement Issues**

# **4.3.1. Measuring the Capital Stock**

To estimate equation [1], we needed to produce estimations for the firms’ capital stock because data on this variable are not collected in innovation surveys. Due to serious measurement errors, ENDEI 2 does not provide information about two variables that could have been used as proxy of physical capital stock (which were provided in ENDEI 1):

1. Consumption of energy, gas, and fuel. This variable could have been used as a proxy under the argument that industrial capital stock should increase at a similar rate as the consumption of energy used to operate such equipment (Frank 1959).

> 21 We assume that restrictions present during the ENDEI1 period (2010–12) can represent restrictions present in 2014, given that both the political administration and the trade regulations were the same during both periods.

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