|||**Table 6 In**|**put deman**|**d and TFP**|||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|(1)<br>Total em|(2)<br>ployment|(3)<br>Cap|(4)<br>ital|(5)<br>Mate|(6)<br>rials|(7)<br>Ene|(8)<br>rgy|
|TFP|‐0.254***|‐0.317***|‐0.052***|0.026***|‐1.566***|‐1.669***|‐0.156***|‐0.253***|
||[0.008]|[0.006]|[0.013]|[0.008]|[0.012]|[0.009]|[0.013]|[0.010]|
|1.broadband|0.847***|‐0.008|1.372***|0.005|1.353***|0.049***|1.184***|0.032**|
||[0.031]|[0.010]|[0.049]|[0.012]|[0.047]|[0.014]|[0.049]|[0.016]|
|1.BBTechnology|0.440***|‐0.010|0.740***|‐0.010|0.607***|0.027***|0.629***|0.016|
||[0.021]|[0.007]|[0.034]|[0.009]|[0.032]|[0.010]|[0.034]|[0.012]|
|1.broadband#1.BBTechnology|‐0.309***|0.034***|‐0.601***|0.004|‐0.469***|0.033**|‐0.493***|‐0.015|
||[0.032]|[0.010]|[0.051]|[0.013]|[0.049]|[0.015]|[0.051]|[0.017]|
|Constant|4.250***|4.859***|12.972***|12.783***|21.034***|21.339***|12.281***|12.593***|
||[0.044]|[0.028]|[0.069]|[0.037]|[0.066]|[0.041]|[0.069]|[0.048]|
|Observations|55,098|55,098|55,098|55,098|55,098|55,098|55,013|55,013|
|R‐squared|0.103|0.065|0.142|0.030|0.346|0.443|0.170|0.015|
|Plant FE|NO|YES|NO|YES|NO|YES|NO|YES|
|Year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Sector FE|YES|YES|YES|YES|YES|YES|YES|YES|
|City FE|YES|YES|YES|YES|YES|YES|YES|YES|
|model|ols|fe|ols|fe|ols|fe|ols|fe|
|Number of plants||9,917||9,917||9,917||9,910|

Standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 Table 6 shows that there is a negative correlation, for all inputs, between TFP and input demand in OLS estimations. This is true for variable inputs that enter the production function as well as for energy which was not included but has been used by other authors in Colombia (Eslava, Haltiwanger, Kugler, & Kugler (2004)). This same table shows that input demand is higher for firms that have broadband internet and those that have broadband internet technologies. In this case firms that have contracted broadband speeds through broadband technologies (DSL, cable/fiber, wireless) showed a lower input demand than the rest of firms in the OLS estimation. Once I account for firm fixed effects, the correlation between capital and productivity becomes positive and firms that use broadband through broadband technologies have a higher variable input demand. This interaction was chosen as the indicator for broadband use for the remainder of the paper, as it captures the use of higher broadband internet speeds. It is an established fact that high‐speed internet requires technologies such as DSL or optical fiber for its delivery, where regular broadband tends to be more technology neutral (Calvo, 2012).

Table 7 shows the estimation of the TFP policy function through three different methods: Ordinary least squares, fixed effects and dynamic panel models. All estimations show that TFP is a function of past productivity. The coefficient on lagged TFP has the expected positive sign which points to the persistence of TFP growth within firms. Lagged broadband use is not statistically significant and lagged broadband technology is only statistically significant in the fixed effects estimation, which is my preferred specification. In these specifications their interaction is not statistically significant.

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