workers, conditional on output; and output shifts to the firms that become relatively more productive due to their relatively more intense employment of both ICT and skilled workers. For skilled workers, while these three substitution effects work in their favor, a fourth substitution effect, namely the possibility of ICT replacing skilled labor, may work against them. On the other hand, ICT adoption may support increased skilled employment inasmuch as they are net complements at the level of task performance. So while the overall effects of ICT adoption on employment are theoretically ambiguous, sufficiently strong output effects can increase both skilled and unskilled employment, while likely substitution effects can increase the ratio of skilled to unskilled labor. Finally, wages may increase for both skilled and unskilled workers due to rent-sharing wage schedules.

These theoretical perspectives are a substantial aid in delineating the relevant empirical and policy issues surrounding the extent and incidence of recent and forthcoming technology-driven economic growth. For example, according to Pagés (2010), there has been a lack of dynamism in the creation and expansion of productive firms and the exit of unproductive firms within Latin American and Caribbean countries relative to advanced economies. Nonetheless, there likely remains substantial potential for the within-firm channel of productivity gains from technology adoption to be a driver of broader income and aggregate productivity growth, and for such effects to ultimately be magnified by spurring more inter-firm dynamism.<sup>3</sup> The objective of this paper is to provide a better empirical understanding of the impact of digital technology adoption in Brazil on jobs, wage levels and wage inequality economy-wide and across skill levels and industry groups. Our analysis is enabled by novel data that detail the fact that in Brazil the internet was rolled out in a staggered fashion over time from 2000 to 2014 across different municipalities. We are able to categorize our analyses by the industry groups that are primary (agriculture, fishing and mining), secondary (manufacturing) and tertiary (including finance & insurance, real estate, transport & storage, utilities, construction, hospitality, wholesale & retail and public administration), as well as disaggregated by employment-skill terciles. We use comprehensive matched employee-employer longitudinal data and quantify aggregate effects of the introduction of internet access in municipalities on local industry clusters and labor markets.

> 3 In a related study, Song, Price, Guvenen and Bloom (2016) construct a matched employer-employee data set for US firms. They show that virtually all of the rise in earnings dispersion among workers is accounted for by increasing dispersion in average wages paid by the employers of these individuals – namely the increase in individual inequality can be accounted for mainly by increases in cross-firm inequality, while the overall earnings dispersion within firms did not increase. Ongoing work of theirs is exploring the extent to which the increase in cross-firm inequality in the US is driven by growing productivity differential across firms versus increased sorting (that firms over time have been employing workers from narrower skills groups, with some firms paying much higher average wages than before because their average worker quality has increased). We intend to replicate the Song et al. approach for Brazil as part of a separate research project.

4