There is a positive and significant wage premium associated with technology adoption, especially for sector-specific business functions (figure 4.9).8 For instance, an SBF index that is 1 percent higher at the intensive margin is associated with 0.27 percent higher monthly wages. The results are significant even when controlling for other important firms characteristics. Panels b, c, and d show the coefficients of a similar exercise, now disaggregated by broad sectors. Panel b shows that the premium is larger for firms in agriculture, but not significant for GBFs at the intensive margin. In contrast, the premium is smaller for services and not significant at the extensive margin for both GBFs and SBFs. Firms with more sophisticated technologies pay higher wages. Given the existence of a premium linked to the adoption of more sophisticated technologies, another important question is whether higher-paid individuals capture most of the premium with respect to those at the bottom of the distribution: that is, whether technology is associated with within-firm wage inequality. Recent literature has underlined the importance of within-firm variation in explaining earnings variance (see Song et al. 2018). For instance, Alvarez et al. (2018) document a significant decrease in earnings inequality in Brazil from 1992 to 2012 and find that within-firm variance accounts for 40 percent of the total decline in inequality. To test for the relationship between technology adoption and wage inequality, the authors used the matched database in the state of Ceará in Brazil and constructed, for each establishment, a measure of wage inequality based on the ratio of the 90th to 10th percentiles log wage differential. Figure 4.10 reports the coefficients of regressions of the logarithm of wage inequality (90/10 log wage differential) on the logarithm of the FIGURE 4.10 Technology Sophistication Contributes to Wage Inequality within Firms
Coefficient
0.2
0.1
0 GBF EXT
GBF INT
SBF EXT
SBF INT
Source: Original figure based on Firm-level Adoption of Technology (FAT) survey data and RAIS data. Note: The figure uses data from the state of Ceará in Brazil. EXT = extensive margin; GBF = general business function; INT= intensive margin; SBF = sector-specific business function. RAIS is a matched employer-employee database covering formal firms and formal workers in Brazil.
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Bridging the Technological Divide
