effects instead of firm fixed effects. Sixth, we explore the possibility that our main results are driven by sector-specific trends related for instance to the commodity boom experienced by Chile over the same period. Finally, we expand the measurement of task content of occupations to allow variation also across sectors.<sup>18</sup> Table 6 reports all the results, with each panel reporting the results for one particular robustness check.<sup>19</sup> For brevity we report only the impacts on firm-level task indexes in Table 6 but we also include in our discussion of results those for the impacts on firm-level occupation shares.<sup>20</sup> Most estimations show a decrease in the abstract task index along with increases in the routine indexes, both cognitive and manual, and in the non-routine manual index.<sup>21</sup> All in all, our main findings reported in Section 6 are robust to changes in the sample size, in the instrumental variable definition, the measurement of task indexes, weighting schemes, and inclusion of sector time trends as control variables. The estimated reduction in the abstract task index and increases in routine-cognitive, routine-manual and non-routine manual indexes reflect adjustments in the occupational composition, where the skilled categories - intensive in abstract and routine-cognitive tasks performed by the complex software - lose share and the unskilled categories gain share in firm total employment. Finally, one could think of two additional threats to our identification strategy. First, the growing use of computers by households in a region may change the quality of the available labor force because workers become more proficient at using computers. Such change in the supply of skilled labor could thus influence the firms’ labor demand. Our assumption is that the growing use of computers by workers in their household does not improve their aptitudes to handle the types of complex software that their firm may adopt. In any case, our region-specific time trends control for potential changes in the quality of a region’s workforce over time. Second, firms could relocate to take advantage of differential reductions in technology prices or even the differential quality of the labor force (via the first threat) across regions. However, in our panel data there is no regional relocation of firms over time.

> 18 We identify in the PIAAC survey three aggregate sectors (primary, manufacturing and services) and we measure the abstract, routine-cognitive, routine-manual, and non-routine- manual task content for the four occupations separately in each of these sectors. Table B8 (Appendix B) reports the average of each task measure for each occupation in each aggregate sector.

> 19 The first-stage coefficients corresponding to the various robustness checks are all positive and significant at standard confidence levels.

> 20 These robustness checks are available from the authors upon request.

> 21 The impacts are significant in most robustness checks with the exceptions being the specification that is estimated ignoring the panel structure of the data where there is no statistically significant change for the abstract and nonroutine-manual indexes.

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