In order to construct our instrument _ITint,j ∗ ITHHuses,t_ , we take the IT-intensity classification used in Bloom et al. (2012b) and O’Mahony and Van Ark (2003), which is based on Stiroh (2002) and interact this sectoral variable with the change in household IT-use at the state level.<sup>4</sup> The use of this second measure is broadly based on Akerman et al. (2015), who take advantage of broadband availability roll-out for Norway as a measure for IT adoption in order to overcome the endogeneity problem. To construct this variable we use two alternative sources. The first one is the 2000 and 2010 Population and Housing Censuses, while the second one is the Module on Information Technology Availability and Use in Households (MODUTIH 2010 and 2013).<sup>5</sup> An advantage of our instrument is that the sectoral IT intensity is based on U.S. data, which is by definition exogenous to the decision of Mexican firms and a better measure of “technological characteristics” at the industry level. Additionally, in our robustness tests, we consider an alternative measure of sectoral IT intensity based on the 1999 Mexico’s Economic Census in which we calculate both the share of investment in computer equipment over total investment in fixed assets, as well as the share of computer equipment assets on total assets.

# **4 Data and Descriptive Statistics**

## **4.1 Firm-level IT use**

Data regarding IT use were obtained from Mexico’s National Survey on Information Technologies 2009 and 2013. This survey was designed by the National Science and Technology Council (CONACYT) and conducted by the National Institute of Statistics and Geography (INEGI). It includes detailed information on IT use at the firm-level that, to the best of our knowledge, has not been previously used for research purposes.

We constructed a panel including firms that appeared in both surveys. Due to the sample design of this survey we were able to build a panel of 719 firms.<sup>6</sup> Additionally, firms from the trade, minining and

> 4Considering that these classifications are based on ISIC rev.3.1 we used the correspondences tables between ISIC rev 3.1 and ISIC 4 and then we used the NAICS-ISIC 4 correspondence table. As there are different levels of aggregation in this process, we also tested an IT intensity variable based on the sectoral descriptions rather than the classification codes, which proved to be more highly correlated with our endogenous variable (IT use).

> 5Considering that for some states there are great differences between the urban and rural sectors, different versions of these indicators were analyzed restricting the sample to localities with more than 15,000 inhabitants (urban).

> 6The ENTIC surveys were designed to gather information for firms with more than ten employees from the manufacturing, construction, services, electricity, mining, transportation and communications sectors according to the NAICS 2007 classification. The sample design is probabilistic and stratified by employment level. The sample is statistically representative at the national level by economic sector. Due to this probabilistic design it is easier to find that big firms are surveyed in both waves (2009 and 2013).

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