interpreted as being driven mainly by entry and exit dynamics in the manufacturing sector. Further work to account for these dynamics would be an interesting extension to the current results.

**Table 12 Labor demand and Broadband use**

|VARIABLES|(1)<br>Total|(2)<br>Direct|(3)<br>Outsourced|(4)<br>Laborer|(5)<br>Professional|(6)<br> Production|(7)<br> Management|(8)<br> Permanent|(9)<br> Temporary|(10)<br>Women|(11)<br>Men|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Broadband (1=Yes)|0.019***|0.021***|‐0.012|0.017**|0.018*|0.022***|0.004|0.011|0.020|0.015***|0.018***|
||[0.006]|[0.007]|[0.022]|[0.007]|[0.009]|[0.007]|[0.007]|[0.008]|[0.019]|[0.005]|[0.006]|
|TFP|‐0.276***|‐0.238***|‐0.580**|‐0.305***|‐0.076*|‐0.269***|‐0.176***|‐0.176***|‐0.289***|‐0.220***|‐0.230***|
||[0.052]|[0.045]|[0.272]|[0.084]|[0.040]|[0.060]|[0.031]|[0.039]|[0.076]|[0.035]|[0.046]|
|Observations|34,602|34,232|10,511|33,236|19,944|33,944|32,839|30,384|24,095|33,488|34,282|
|R‐squared|0.060|0.033|0.019|0.040|0.003|0.042|0.019|0.014|0.010|0.025|0.032|
|Number of plants|6,363|6,322|2,248|6,150|4,180|6,256|6,160|5,751|4,830|6,225|6,323|
|Plant FE|NO|NO|NO|NO|NO|NO|NO|NO|NO|NO|NO|
|Year FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Sector FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|City FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Std. Errors|||||Clus|ter Plant/St|ate|||||
|model||||||fe||||||

Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1

**Table 13 14 2SLS estimation Labor demand and Broadband use**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Total|Direct|Outsourced|Laborer|Professional|Production|Management|Permanent|Temporary|Women|Men|
|Broadband (1=Yes)|‐0.121|‐0.399|1.296|‐0.086|‐2.233|‐0.095|‐0.232|‐0.572|0.601|‐0.319|‐0.045|
||[0.247]|[0.417]|[1.082]|[0.365]|[1.926]|[0.371]|[0.382]|[0.530]|[0.816]|[0.356]|[0.320]|
|TFP|‐0.263***|‐0.199***|‐0.637**|‐0.293***|0.060|‐0.257***|‐0.155***|‐0.127**|‐0.322***|‐0.190***|‐0.224***|
||[0.060]|[0.063]|[0.316]|[0.102]|[0.128]|[0.075]|[0.054]|[0.065]|[0.091]|[0.056]|[0.052]|
|Observations|34,602|34,232|10,511|33,236|19,944|33,944|32,839|30,384|24,095|33,488|34,282|
|R‐squared|0.033|‐0.143|‐0.407|0.029|‐2.067|0.026|‐0.027|‐0.205|‐0.082|‐0.069|0.028|
|Number of plants|6,363|6,322|2,248|6,150|4,180|6,256|6,160|5,751|4,830|6,225|6,323|
|Plant FE|NO|NO|NO|NO|NO|NO|NO|NO|NO|NO|NO|
|Year FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Sector FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|City FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Errors|||||Clu|ster Plant/St|ate|||||
|Instrument|||||Downloa|d speed at st|ate level|||||
|Weak identification test (Kleibergen‐||||||||||||
|Paap rk Wald F statistic)|6.114|5.409|2.662|6.852|4.456|6.803|5.672|6.131|5.684|5.201|7.062|

Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1

# Discussion

Colombia is a country where businesses have a relatively high level of internet access. However, there is a great amount of heterogeneity regarding internet speed. Considering that the identification strategy proposed for the paper relies on the geographical nature of internet rollout to identify the causal effect of ICT on labor demand which is mediated through productivity gains, a finer measure of internet access was considered. The measure combines broadband speeds (greater than 1Mbps) and broadband technology use (DSL, cable/fiber, wireless). Figure 4 and Figure 5 showed that this measure displayed both time and industry variation, making it a good candidate to model productivity gains due to broadband use. Since broadband download speed might be a key 17