between establishments in the group that proxies for the response sample and the group of establishments that proxies for the non-response sample.

**Response bias.** To assess the relevance of response bias, we conducted a parallel pilot in Kenya where we re-interviewed 100 randomly selected establishments with a short version of the questionnaire. For those establishments, we randomly selected three business functions and asked about the presence of the relevant technologies. We estimated a probit model to assess the likelihood of consistent answers between the original and the back-check interviews, controlling for establishment-level fixed-effects. Reporting the use of a technology in the back-check interview is associated with 80.6% of the likelihood of reporting the use of the same technology in the original interview. Conversely, reporting that a technology is not used in the back-check interview, is associated with a 70.7% likelihood of not being reported in the original survey. These estimates do not differ between establishments of different sizes.<sup>13</sup>

**Validation using external sources.** We evaluate the quality and reliability of the data collected by comparing it to external sources in Korea (KED) and Brazil (RAIS). We focus on variables related to establishment size, productivity and technology. Table A.24 shows that the weighted sample averages of the labor variables in the FAT data (number of workers, average wages, share of college workers, share of low- and high-skill workers) are not statistically different from the averages in the universe of firms from the RAIS dataset. In the Brazil matched establishments, we find a strong correlation between FAT measures of log valueadded per worker and the log of average wages from RAIS (See Table A.23). In the Korean matched establishments, we find very high cross-establishment correlations (above 0.93) in the log levels and growth rates of sales and employment, as well as in log labor productivity (0.73).<sup>14</sup> Additionally, the average adoption rate of ERP systems in Korean manufacturing establishments in FAT is similar to Chung and Kim (2021), who used a similar sampling frame (32% vs. 40% in Chung and Kim, 2021), and there is a strong cross-establishment

> 13The re-interviews produced 1,661 answers, 106 interviews times 3 business functions times an average of 5.2 technologies per function. Both the original and back-end interviews in the pilot are conducted by phone by different interviewers. The correlation between the binary responses in survey and pilot is 73% ranging from 65% in business administration to 77% in sales across business functions, and from 85% among the most basic technologies to around 61% in intermediate, and 77% at the most advanced technologies across functions.

> 14In Korea we merge FAT with the Korea Enterprise Data (KED), a leading supplier of business credit reports on Korean businesses. In Brazil, we merge the data with the _Relação Anual de Informações Sociais_ (RAIS), which is an administrative database maintained by the Ministry of Labor providing information on salaries for all formal workers in Brazil. The FAT survey asks about sales and the number of employees for two periods. The most recent year for which the information is available (i.e. the year before the implementation of the survey) and two years before that. For Korea, these reference years are 2019 and 2017.

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