metadata: authors: 0: author: Ospino,Carlos
count: Colombia
docna: 0: docna: Broadband internet, labor demand, and total
            factor productivity in Colombia
docty: Policy Research Working Paper
subtopic: Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Labor Markets,Administrative & Civil Service Reform,Telecommunications Infrastructure,Information Technology,Public Sector Administrative & Civil
            Service Reform,Business Cycles and Stabilization Policies,Democratic Government,Public Sector Administrative and Civil
            Service Reform,De Facto Governments
teratopic: Industry,Science and Technology Development,Rural Development
abstracts: cdata!: This paper studies the relationship
            between information and communication technology, labor
            demand, and total factor productivity in Colombia. It
            estimates total factor productivity for the Colombian
            manufacturing sector using a method that assumes a law of
            motion where total factor productivity evolves according to
            an autoregressive process of order 1 as well as with the
            past use of broadband technologies. Using fixed effects
            models, the paper estimates the effect of broadband use on
            the labor demand of different workers, controlling for total
            factor productivity, capital, and wages. To address the
            potential endogeneity between broadband adoption and labor
            demand, the analysis uses state-industry level variation in
            broadband quality (speed) and intensity of use. The results
            show a positive association of broadband adoption on labor
            demand, suggesting that adoption of information and
            communication technology can offset the employment effects
            of technological growth. Attempts to identify causal effects
            using an instrumental variable approach were inconclusive.
geo_reg_key: 119228,517191
repnme: repnme: Broadband internet, labor demand, and total
            factor productivity in Colombia
display_title: Broadband internet, labor demand,
            and total factor productivity in Colombia
listing_relative_url: /research/2018/01/29532325/broadband-internet-labor-demand-total-factor-productivity-colombia
topicv3: Manufacturing Sectors m1326521 1281,Employment and Wage Data m1327013 2055,Broadband Networks m1327576 291,Labor Demand m1583073 4422,Broadband m1627085 4739
publishtoextweb_dt: 2018-01-24T00:00:00Z
historic_topic: Industry,Science and Technology Development,Rural Development
no_of_pages: 29
model_extractions: WPS8318 Policy Research Working Paper 8318 Broadband Internet, Labor Demand, and Total Factor Productivity in Colombia _Carlos Ospino_ Finance, Competitiveness and Innovation Global Practice Group January 2018
Policy Research Working Paper 8318

# **Abstract**

This paper studies the relationship between information and communication technology, labor demand, and total factor productivity in Colombia. It estimates total factor productivity for the Colombian manufacturing sector using a method that assumes a law of motion where total factor productivity evolves according to an autoregressive process of order 1 as well as with the past use of broadband technologies. Using fixed effects models, the paper estimates the effect of broadband use on the labor demand of different workers, controlling for total factor productivity, capital, and wages. To address the potential endogeneity between broadband adoption and labor demand, the analysis uses state-industry level variation in broadband quality (speed) and intensity of use. The results show a positive association of broadband adoption on labor demand, suggesting that adoption of information and communication technology can offset the employment effects of technological growth. Attempts to identify causal effects using an instrumental variable approach were inconclusive.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at carlos.ospino.h@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Broadband Internet, Labor Demand,

and Total Factor Productivity in Colombia Carlos Ospino<sup>1</sup> JEL codes: D24, L25, L60, O15 Keywords: Total factor productivity, broadband internet, labor demand, production function estimation.

1This work has benefitted from funding by the World Bank’s Latin America and Caribbean’s Chief Economist Office, under the regional study “Digital technology adoption, skills, productivity and jobs in Latin America”. I appreciate the very helpful comments and suggestions from Joana Silva, Mark Dutz, Rita Almeida, Truman Packard, Giulia Lotti and the participants at the author’s workshop at the World Bank. Daniel Espinosa provided much appreciated research assistance to this project. Please send comments and suggestions to <u>carlos.ospino.h@gmail.com</u>
# Introduction

This section of the paper shows the international context of internet use in Colombia.

**Figure 1 OECD Fixed broadband subscriptions per 100 inhabitants, by technology, June 2015**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>05<br>00<br>Chile Mexico OECD Colombia<br>DSL Cable Fibre Satellite Fixed wireless Other<br><!-- End of picture text --> Source: OECD, Broadband Portal, http://www.oecd.org/sti/broadband/oecdbroadbandportal.htm, June 2015.

Figure 1 shows that as of June 2015, Colombia had a total number of internet subscriptions per 100 inhabitants similar to Mexico (11.2), but lower than Chile (14.6). The latter’s is half of the OECD average of 28.8 subscriptions per 100 inhabitants. DSL and cable are the dominant technologies in Colombia and both have the same number of subscriptions (5.2) per 100 inhabitants, despite an ambitious plan to expand the optical fiber network to almost every municipality ((Ministerio de Tecnologías de la Información y las Comunicaciones, 2017).

**Figure 2 OECD fixed broadband penetration (per 100 inhabitants), percentage increase, June 2014‐June 2015, by**

**country**

<!-- Start of picture text --> 12.00<br>10.00<br>8.00<br>6.00<br>4.00<br>2.00<br>0.00<br>Chile Mexico OECD Colombia<br><!-- End of picture text --> Source: OECD, Broadband Portal, http://www.oecd.org/sti/broadband/oecdbroadbandportal.htm, June 2016.

2
Colombia showed a significant increase in fixed broadband penetration. The growth level was almost three times the level of the OECD average and near one and a half times the growth level displayed by Chile. This shows that at the household level, Colombia may still have room to expand its broadband penetration. At the business level, the story is quite different. In 2003, 86% of small and medium size enterprises (SME) and 96% of large ones had internet access (CINTEL) (2003). In 2015, 99% of firms in manufacturing and 99.7% in retail and services used internet (CRC) (2015). This shows that internet access in the last decade has been relatively high in Colombia’s business sector.

This high coverage of internet access poses the challenge of identifying the gains in productivity due to the availability of internet and how it relates to labor demand; I therefore focus on a slightly different question. In the next section, I explore the literature and conclude that the focus, rather than being on internet access per se, should be on high‐speed broadband internet access for firms.

The second section discusses the relevant literature that is related to this paper. The third describes the data used in this analysis. The forth section discusses the results of the ICT module in the Annual Manufacturing Survey for the years 2008‐2014. The fifth section discusses the TFP estimation procedure. The sixth section presents the main results of the paper in terms of the effect of TFP gains due to ICT use on labor demand and wages. The sixth section shows robustness and falsification exercises. The final section concludes with a discussion of the results.

# Literature review

This paper is related to two different branches of the economics literature. The first branch studies the impact of broadband internet and firm productivity ((Fabling, Grimes, & Grimes, 2016). The second branch focuses on the estimation of production functions and total factor productivity. The first contribution of the paper is adding to the scarce empirical evidence about the labor demand predictions of task‐based models of technological change for heterogeneous firms in developing countries, with a special focus on broadband internet. The second one is estimating total factor productivity within the framework of proxy method models of production function estimation proposed by De Loecker and Warzynski (2012) and De Loecker (2013). The innovation is modeling a total factor productivity law of motion that explicitly considers the past adoption of high‐speed broadband internet as a shifter in growth levels. I now discuss each branch of the literature and put the paper’s contribution in context within each one.

The paper’s theoretical underpinnings follow Brambilla (2018). Her model predicts that because of ICT adoption, firms will grow, pay higher wages, become specialized in complex tasks and more skill intensive conditional on skilled labor and ICT being complements. Brambilla (2018) expands traditional task‐based models of technical progress and labor markets to allow firm heterogeneity and wage heterogeneity across firms. In this paper, ICT adoption will be modeled as the adoption of high‐speed broadband internet. Akerman et al. (2015) provide evidence of the skill complementarity of broadband internet adoption. Related to this point, there is also a growing literature about the effect of broadband internet on firm productivity ((Bertschek, Cerquera, & Klein, 2013; Colombo, 3
Croce, & Grilli, 2013; Fabling et al., 2016; Grimes, Ren, & Stevens, 2012; Haller & Lyons, 2015). I now briefly discuss this literature.

Akerman et al. (2015) use Norwegian data and a quasi‐experiment to show that ICTs are innovations biased towards skilled workers. To identify the intent‐to‐treat effects, the authors exploit the staggered roll‐out of broadband internet. To estimate the production function and thus productivity, they use the Levinsohn and Petrin (2003) methodology. The authors find evidence that the availability of broadband is associated with a substantial increase in the output elasticity of skilled labor. They find the opposite effect for unskilled labor.

Fabling et al. (2016) study the effect of ultrafast broadband (UFB) internet on labor demand and productivity in New Zealand between 2010 and 2012. UFB was defined by the government as download speeds of up to 100Mbps and upload speeds of up to 50Mbps. The authors use the proximity to schools as an instrument for broadband availability since the government targeted schools and hospitals in their expansion plans of UFB to reach 80 percent of the population by 2022. The technology through which UFB can be obtained and the authors focus on is (optic) fiber. These authors do not find any statistically significant effects and suggest that most gains from UFB would arise as a result of selection. Their results suggest that gains from UFB would be related to complementary investments by firms.

Bertschek et al. (2013) do not find a statistically significant effect of broadband on labor productivity, but find positive effects on firm innovation in Germany. Their analysis covers the period 2001 to 2003 when there was already a high penetration rate of broadband use by firms and focus on DSL technology. The authors exploit broadband availability at the postal code level to identify the causal effects.

Colombo et al. (2013) find negligible effects of adopting broadband applications in small and medium enterprises in Italy during the period 1998‐2004. The authors base their analysis on services which require broadband speeds and construct their variable of interest using principal component analysis. Their results point to the complementarity of broadband with other investments at the firm, those related to management practices.

The paper also relates to the literature about production functions and total factor productivity (TFP) estimation ((D. A. Ackerberg, Caves, & Frazer, 2015; De Loecker, 2013; De Loecker & Warzynski, 2012; Gandhi, Navarro, & Rivers, 2016; Levinsohn & Petrin, 2003; Olley & Pakes, 1996). The paper contributes to this literature by explicitly modelling an endogenous total factor productivity process which is governed by past productivity as in D. Ackerberg et al. (2006) and the previous adoption of broadband through broadband technology, namely DSL, cable or optical Fiber. A more detailed discussion about these methods is postponed to the section where the methodology is explained.

# Data description

The main data set is composed of an unbalanced panel of 14,479 plants in the manufacturing sector in Colombia, spanning over 7 years (2008‐2014) of data from the Annual Manufacturing Survey 4
(EAM from its acronym in Spanish.). EAM is a census of manufacturing firms in Colombia that employ at least 10 workers or have an output value about 73,000 USD.<sup>2</sup> It gathers very detailed information about employment levels and costs, asset levels and investment, output and intermediate materials use for production. Starting in the year 2008 it also collects an ICT module where information about internet use and technology can be found. In the sample, 6,999 (48.3%) of the plants have information for every year and were used as a balanced sample to check the robustness of the results. I also use broadband speed data at the state level kindly provided by the Communications Regulation Commission for the years 2008‐2014. The sample used to estimate total factor productivity was a subsample of these firms and it is described in detail in that section.

# ICT use in the Colombian manufacturing sector

In this section I describe the results about ICT use in Colombian firms from the information collected in EAM special modules for the years 2008 through 2014. The pooled sample summary statistics for the five‐year period are discussed. In this section the analysis is broken down by firm size and I report information collected in the ICT module about the use of computers at the firm, internet access, and the type and speed of internet connections.

**Table 1 Distribution of firms by total employment size**

||Full samplep|eriod||
|---|---|---|---|
|Size|Frequency<br>Pe|rcent<br>Cu|m.|
|[0,50]|91,861|72.07|72.07|
|(50,200]|22,776|17.87|89.94|
|(200,1000]|10,382|8.15|98.09|
|>1000|2,438|1.91|100|
|Total|127,457|100||
||Year 200|8||
|Size|Frequency<br>Pe|rcent<br>Cu|m.|
|[0,50]|6,708|69.21|69.21|
|(50,200]|1,884|19.44|88.65|
|(200,1000]|862|8.89|97.54|
|>1000|238|2.46|100|
|Total|9,692|100||

Source: EAM‐DANE 2008‐2014. Author’s calculations.

The top panel in Table 1 shows that 72% of firms in the sample hired up to 50 workers and are considered by most definitions small firms; 18% of firms hired between 50 and 200, 8% between 200 and 1,000 and about 2% of firms hired more than 1,000 workers between 2008 and 2014. It is clear that most firms in the sample are small units of production, and the reader should take this into account when interpreting the results. The bottom panel restricts the sample to the initial year, 2008, where ICT information started to be collected. The main takeaway is the stability in the distribution of firm sizes during the period of analysis.

2 137.2 million COP in the year 2013 and exchange rate of 1,867 COP per USD, which is the average exchange rate for that year.

5
**Table 2 ICT and firm size**

|Size|Total<br>employment|Number of<br>computers<br>|% of<br>workers<br>using<br>computers|Internet<br>use|% of<br>workers<br>use<br>internet<br>for work|Has a<br>website|
|---|---|---|---|---|---|---|
|[0,50]|14.3|3.1|32.2|96.6%|29.7|46.5%|
|(50,200]|91.1|16.6|35.7|99.9%|32.6|77.3%|
|(200,1000]|280.0|98.2|42.3|99.8%|36.3|90.2%|
|>1000|650.1|573.7|55.2|100.0%|47.0|93.7%|
|Total|63.8|30.2|34.5|97.7%|31.4|58.3%|

Source: EAM‐DANE 2008‐2014. Author’s calculations. Each column shows the average value of each variable for each bracket of firm size.

The first column of Table 2 shows the average number of employees in each firm size category, to put into context the variables described. The table also shows that the average number of computers at each firm increases with size as does the share of workers that use computers. Column four shows that the average use of internet is basically universal for firms of all sizes. The final column shows that having a website tends to increase with firm size. In smaller firms about 1 in 3 workers uses a computer while in large firms this ratio is about 1 in 2. In the Colombian manufacturing sector, less than a third of workers uses the internet for their work. This percentage is higher for large firms (those hiring more than 1,000 workers) but is just about 42% of workers. In small firms, just under 28% of workers use the internet in their work. This suggests that the impact of internet use or availability on firms’ productivity would be more likely to be observed in larger firms where internet exposition is greater for workers.

**Table 3 Internet and firm size**

|Size|Modem|ISDN|ADSL|Cable/Fiber|Wireless|Mobile|0‐256<br>Kbps|254‐1024<br>Kbps|1025‐2048<br>Kbps|>= 2049<br>Kbps|
|---|---|---|---|---|---|---|---|---|---|---|
|[0,50]|6.3%|0.8%|25.3%|39.0%|19.7%|6.5%|7.5%|26.9%|38.7%|24.9%|
|(50,200]|2.2%|0.5%|27.0%|51.2%|13.4%|5.6%|2.7%|18.6%|33.4%|45.2%|
|(200,1000]|1.0%|0.3%|13.1%|72.9%|7.7%|5.0%|1.2%|13.7%|24.7%|60.4%|
|>1000|0.2%|1.1%|5.2%|79.2%|7.0%|7.4%|1.2%|7.7%|16.0%|75.0%|
|Total|4.7%|0.7%|23.9%|45.8%|16.9%|6.2%|5.8%|23.4%|35.7%|33.8%|

Source: EAM‐DANE 2008‐2014. Author’s calculations. Each column shows the average value of each variable for each bracket of firm size.

The leading technology in the Colombian manufacturing sector is cable/fiber with just under 46% of firms using this technology to access the internet. The second most popular technology is ADSL, and this is particularly true for smaller firms. 25% for small firms use ADSL while only 5% of firms with more than 1,000 workers use this technology. The most common contracted speed for internet use is 1‐2 Mbps with just under 36% of firms. However, two‐thirds of large firms report internet speeds over 2Mbps.

TFP estimation **Data Cleaning** 6
As discussed above, the sample for the estimation of the production function parameters from which TFP is calculated was different from the main sample. The selection criteria were determined using four items: Value added, prices and input consistency, industry size, and survival. I now discuss each one in detail.

- Value added: firms which reported zero or negative value added were deleted.

- Wage bill and intermediate inputs: I required that both the value of the wage bill and the costs of intermediate materials were strictly lower than the value of output. Firms that did not fulfill this requirement were deleted.

- Industry size: I required that that at least 10 firms were part of an industry at any moment in time to include any firm in that industry.

- Survival: For any firm to be included in the production function estimation sample, it had to report information during four consecutive years.

# **Input selection**

The production function estimation uses capital, labor and intermediate materials as inputs. All monetary variables were deflated using the Producer Price Index which varies by industry class (two digits in ISIC Rev. 3) and the base year is 2013. Capital is defined as the value of fixed capital stock in each year. The stock of capital is constructed recursively using the change in investment levels and assuming a depreciation value of 5% for all capital. �� � ���� ∗ �1 �0.05� � �� . Capital includes three types of assets: Machinery, office equipment and transportation equipment. I have decided to leave out buildings and land to make the analysis comparable across businesses and sectors.<sup>3</sup> Labor includes all personnel, regardless of their skill level or contract type. Finally, intermediate materials include the value of raw inputs, materials and packaging used in the production process.

Total factor productivity was estimated using the GMM methods proposed by De Loecker and Warzynski (2012) and De Loecker (2013). I will refer to this methodology as DLW from now on. A gross output production function was estimated to address the concerns by Gandhi, Navarro, & Rivers (2016) that value‐added production functions may generate too much dispersion in the estimated productivity.<sup>4</sup> In this paper a firm’s broadband use at each moment was used to identify the parameters of the Cobb‐Douglas production function in equation (1), which uses labor, capital and intermediate materials ( �, �, � , respectively). Where ��� is the unobserved total factor productivity and ��� represents measurement error. The assumption is that the demand for inputs differs significantly between firms that decide to use broadband internet and those that do not. All inputs and gross output are expressed in logarithms.

> 3 Eslava et al. (2004) construct capital stock series which include equipment, machinery, buildings and structures, while excluding vehicles and land.

> 4 Gandhi et al. (2013) find that in Colombia, under a value‐added production function results imply that the 95<sup>th</sup> percentile firm would produce more than six times more output whereas under a gross output production function this firm would only produce twice as much.

7
<!-- Start of picture text --> Vie = Bilit + Bekie + PmMit + Wit + Eit (1)<br>Dit-1<br>Pt<br>Vie = Oe Cit. kit, Mit, Dit) + Wit + Eit (2)<br>Wit<br>Wit = Git. Kit, Mit, Dit)<br>Wit =<br>h(@it-1; B) + Dit-1 + Fit h<br>B= (Bu Bes Bm)<br>Wit = Pe (lit Kit, Mit, Dit) — (Bilit + Bekit + Bmmit)<br>lit-4<br>E git Kit =<br>Mit-1<br>t<br>lit-1 Mit-1<br><!-- End of picture text -->
This paper aims to assess the effect of TFP growth which can be explained by broadband use on labor demand and wages. Since the estimation process imposes a structured autoregressive law of motion on the evolution of TFP which is affected by past broadband use, this exercise can be performed directly by regressing the variables of interest on the estimated TFP. De Loecker (2013) points out that methods which do not consider an endogenous TFP process that depend on past exporting behavior tend to underestimate the relationship between exporting and productivity. Under the assumption that there are similarities in the decision‐making process between the adoption of broadband technology and entering exporting markets, I conjecture that not considering an endogenous TFP process which depends on broadband use is likely to bias the relationship between ICT use and productivity. To assess the nature of the bias I will conduct robustness exercises using the productivity estimator of D. A. Ackerberg et al. (2015) and the De Loecker (2013) procedure which models the productivity law of motion as a function of past exporting status.

Table 4 shows the estimated production function coefficients for each the methods used in this paper. My preferred method’s coefficients for labor and capital fall within the range of values of OLS and fixed effects estimations. Suggesting that both coefficients are biased upward under an OLS estimation. On the contrary, the materials coefficient is higher than either OLS or fixed effects. Table 5 shows that the correlation of TFP across the three methods is very high.

**Table 4 Production function estimated coefficients**

|Method|Labor|Capital|Materials|
|---|---|---|---|
|DLW (Broadband Use)|0.466 ***|0.126 ***|0.529 ***|
|ACF|0.458 ***|0.119 ***|0.539 ***|
|DLW (Exporting Status)|0.408 ***|0.112 ***|0.588 ***|
|OLS|0.492 ***|0.199 ***|0.381 ***|
|Fixed effects|0.373 ***|0.076 ***|0.418 ***|

Author’s estimations. Standard errors for DLW obtained via bootstrapping with 100 repetitions.

**Table 5 Correlation of TFP estimates**

|DLW (Broadband Use)|ACF<br>DLW (Exporting Status)|
|---|---|
|DLW (Broadband Use)<br>1||
|ACF<br>0.8257|1|
|DLW (Exporting Status)<br>0.9779|0.7849<br>1|

Source: EAM‐DANE. Author’s calculations.

DLW estimation is performed under the assumptions that: 1‐Variable input labor demands are a monotonic function of productivity. 2‐Input demand responds differently for firms that use broadband internet and those that do not. 3‐TFP evolves as a function of the previous period productivity and the previous period use of broadband internet. I now provide evidence that these assumptions hold.

9
|||**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.

10
Column (4) shows that all terms of a more flexible, third‐degree polynomial of lagged TFP, are statistically significant. More precisely, that TFP does respond to past broadband use.

**Table 7 TFP policy function estimation**

|VARIABLES|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Lagged TFP|0.904***|0.310***|0.637***|‐1.352***|
||[0.002]|[0.005]|[0.017]|[0.156]|
|Lagged TFP^2||||0.257***<br>[0.023]|
|Lagged TFP^3||||‐0.012***<br>[0.001]|
|Lagged broadband|‐0.003|‐0.003|0.002|‐0.002|
||[0.007]|[0.007]|[0.010]|[0.007]|
|Lagged BBTech|0.002|0.009*|0.010|0.01**|
||[0.005]|[0.005]|[0.006]|[0.005]|
|Lagged broadbandXLagged BBTech|‐0.016**|‐0.004|‐0.040***|‐0.003|
||[0.008]|[0.008]|[0.010]|[0.008]|
|Constant|0.475***|3.182***|1.315***|6.530***|
||[0.010]|[0.024]|[0.221]|[0.331]|
|Observations|44,714|44,714|44,714|44,714|
|R‐squared|0.831|0.094||0.098|
|Plant FE|NO|YES|NO|NO|
|Year FE|YES|YES|YES|YES|
|Sector FE|YES|YES|YES|YES|
|model|ols|fe|gmm|fe|
|Number of nordest|.|9,363|9,363|9,363|

Standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1

# ICT use, productivity and labor demand

In this section I provide evidence of the relationship between productivity, broadband use and labor demand. To do so I estimate longitudinal fixed effects linear models as specified by the following equation:

## ��� ��� ������� �������� ��������Β� ���������Β� ������Β� ����Β� ��� ����

Where interest lies in coefficient �� which captures the elasticity of dependent variable ��� to changes in broadband use. The employment dependent variables are logs of: Total employment, direct employment, outsourced employment, plant laborers, plant professionals, total plant workers, managers, permanent, temporary employment, men and women. This rich range of labor types attempts to identify whether broadband use is related to changes in the labor demand of different workers. METRO, SECTOR and YEAR, are vectors of indicator variables for each 11
metropolitan area, (two‐digit) industry of economic activity and year respectively. Finally, the vector X captures variables which are theoretically related to labor demand. I have included the lagged value of capital and the minimum wage (which is interacted with the share of unskilled labor in industry total employment). All standard errors were two‐way clustered at the state and plant level. The period of analysis is 2008‐2014 given that information on broadband use is only available for this time frame. The estimation sample is restricted to firms that report a consistent industry code during the sample period and therefore the vector of coefficients Β� will not be identified separately form the fixed effect.

The adoption of broadband services may be correlated with unobserved factors, such as the availability of service providers at the plant’s location or the costs of internet services. Under this assumption coefficient �� will be inconsistent to identify the effect of broadband adoption. I instrument broadband use with an interaction of log broadband speeds available for businesses at the state level and the industry average broadband use through broadband technology in the previous year. Therefore, the estimation will pick up the effect of broadband use which can be explained by the variation in broadband speed availability at the industry‐state level. Broadband speed is defined as the download internet speeds weighted by the number of subscriptions each internet service provider reported in each state from 2008 to 2014.

The identification of the effect of broadband use due to broadband service quality use on labor demand relies on the relevance and exclusion restrictions being satisfied by the instrument. The instrument was constructed as the product of two variables: 1‐The average use of broadband at the (three‐digit) industry level in the previous year.<sup>5</sup> 2‐The (weighted<sup>6</sup> ) average download speeds at the state level. The first part of the instruments is relevant since it captures the degree of broadband use in each industry. As Table 15 shows, there are statistically significant differences in labor market variables depending on whether firms belong to an industry where broadband is more intensively used. The second part of the instruments is relevant as it captures a precise definition of broadband internet quality, it is download speeds in the state where the production unit is located.<sup>7</sup> The exclusion restriction of the instrument is given by the fact that the average, three‐digit industry broadband use is predetermined each period. The second part of the instrument exploits the fact that internet quality to businesses is determined by the service provider infrastructure and should only affect labor demand through its effects on broadband adoption and use and not directly. Figure 3 suggests that between 2008 and 2014 the average internet speed increased in Colombia. By 2008 only the darkest areas had internet speeds above 2mbs, while by 2014 some of the white areas had broadband coverage. However, this increase has not radically changed the areas where the highest broadband speeds were available. The Appendix shows the broadband speed information for every

> 5 Recall that in the analysis I include industry dummies at the two‐digit level, so this is a finer measure of industry that is still larger than the firm level.

> 6 The average is weighted by the number of subscriptions each Internet Service Provider has.

> 7 The state level may still be too broad a measure of broadband availability. However, the data provided to us for this research project by Colombia’s national statistics agency did not include a finer geographical identification variable due to data confidentiality concerns.

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year. A violation of the exclusion restriction would suggest firms change labor demand as a response to internet speeds in their state and not because it affects labor demand through its effect on broadband adoption and use.

<!-- Start of picture text --> Figure 3<br>Average Download Speed 2008 Average Download Speed 2014<br>(847,2979] (4663,6717]<br>(582,847] (3821,4663]<br>(492,582] (2650,3821]<br>[248,492] [590,2650]<br><!-- End of picture text --> Table 8 shows some suggestive statistics for firms across two dimensions. The first is whether they were classified as being part of an industry where broadband use was above the manufacturing sector average in the year 2008. The second dimension captures whether the firm currently uses broadband internet. Current use of broadband is related to higher unskilled labor demand, output and productivity. However, there is not any additional correlation due to being part of an intensive broadband use sector. These results suggest a certain degree of complementarity of broadband use with the demand of unskilled labor in Colombia but not with skilled labor. Even in the presence of a positive relationship with output and total factor productivity.

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||**Table 8 Broad**<br>|**band use intensit**<br>|**y and selected outco**<br>|**mes**<br>||
|---|---|---|---|---|---|
|VARIABLES|(1)<br>Laborers|(2)<br>Professionals|(3)<br>Value of production|(4)<br>Output per worker|(5)<br>TFP|
|Intensive & Broadband (1=Yes)|‐0.013|0.017|0.003|0.005|0.008|
||[0.016]|[0.019]|[0.013]|[0.010]|[0.006]|
|Broadband (1=Yes)|0.017***|0.007|0.019**|0.003|0.034***|
||[0.006]|[0.011]|[0.008]|[0.008]|[0.004]|
|Observations|50,466|28,967|52,661|52,616|50,753|
|R‐squared|0.007|0.003|0.009|0.002|0.005|
|Number of plants|9,037|5,915|9,378|9,369|9,139|
|Plant FE|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|
|Sector FE|YES|YES|YES|YES|YES|
|City FE|YES|YES|YES|YES|YES|
|Std. Errors|||Cluster Plant/State|||

Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 I now turn to the main results. Table 9 shows that firms that report using broadband internet appear to have a higher labor demand than those with slower internet access; about 2.2 log points. However, there does not appear to be any relationship between broadband use and outsourced, management and permanent labor demand, which suggests internet use is particularly related to production workers’ labor demand. Temporary labor displays the highest correlation with broadband use. Since TFP has been estimated from a structural model, under the set of assumptions explained before, the results can be interpreted as a causal effect on labor demand.

These results show a negative relationship of productivity gains on labor demand for all types of workers, holding everything else constant. Columns 2 to 9 compare pairs of possible labor substitutes in the production process. Taken at face value, increases in productivity from past adoption of broadband internet would reduce to a higher degree outsourced labor than direct labor, low skilled than high skilled labor demand, production workers relative to managers, and temporary workers relative to permanent ones. These results suggest that labor types which may be easier to adjust, such as outsourced, unskilled and temporary would be the most likely to be substituted by broadband adoption. Interestingly, the size of the effect is similar for men and women, suggesting an absence of gender bias in the substitution between labor and productivity growth.

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||||**Table**|**9 Labor**|**demand a**|**nd Broadb**|**and 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)|0.022***|0.023***|‐0.007|0.021***|0.015**|0.023***|0.006|0.008|0.037**|0.018***|0.021***|
||[0.005]|[0.006]|[0.018]|[0.005]|[0.007]|[0.005]|[0.006]|[0.007]|[0.017]|[0.005]|[0.006]|
|TFP|‐0.332***|‐0.291***|‐0.745**|‐0.341***|‐0.099**|‐0.304***|‐0.195***|‐0.204***|‐0.328***|‐0.253***|‐0.277***|
||[0.056]|[0.049]|[0.324]|[0.078]|[0.049]|[0.055]|[0.037]|[0.039]|[0.083]|[0.041]|[0.051]|
|Observations|50,753|50,255|13,479|48,805|27,910|49,823|48,020|44,098|33,711|49,050|50,186|
|R‐squared|0.071|0.042|0.027|0.046|0.004|0.047|0.021|0.015|0.013|0.029|0.040|
|Number of plants|9,139|9,082|2,716|8,837|5,759|8,989|8,809|8,196|6,593|8,920|9,064|
|Plant FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|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|||||Clu|ster Plant/St|ate|||||
|model||||||fe||||||

Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 Table 10 shows the results of my instrumental variables approach. As discussed, I used the log average download speeds at the state level interacted with the lagged average broadband use at the industry level. While other potential instruments such as the number of broadband subscriptions and the upload speed were available, the preferred specification uses download speed given the results of statistical tests which can be seen in Table 15 and Table 16 in the Appendix.<sup>8</sup> Except for outsourced and professional workers, the first‐stage statistics are well above the conventional levels of statistical significance. This is also true about the weak instruments and weak instrument robust inference statistics.

Correcting for the possible endogeneity in broadband adoption suggests strong positive effects on labor demand of broadband use. The point estimate on total labor demand is around 22 log points, but it is not statistically significant. Lack of significance may be due to the use of two‐way clustered standard errors and the fact that variation of the instrument is at the state and industry level. The fact that most point estimates are strictly greater than zero provides some evidence of a positive effect, which nonetheless is not precisely identified.

Alternatively, given the model assumptions under which TFP was estimated, the underlying labor demand should only be a function of input prices and TFP. Thus, under these assumptions, the effect of broadband adoption should only be observed through its indirect effect on TFP. Therefore, lack of statistical significance in the 2SLS estimation is consistent with this theoretical model.<sup>9</sup>

> 8 See Table 15 which shows the first‐stage regression and Table 16 which shows relevance, and weak inference statistics.

> 9 I thank the participants at the applied microeconomics seminar at Banco de la República in Cali, especially Juan Esteban Carranza, Camila Casas and Salvador Navarro for pointing out this alternative explanation.

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||**Table 1**|**0 2SLS**|**estimati**|**on Labor**|**demand**|**and Bro**|**adband u**|**se**||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||(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)|0.217|0.017|1.672*|0.182|‐1.217|0.166|‐0.100|‐0.011|0.607|‐0.047|0.361|
||[0.293]|[0.396]|[1.001]|[0.393]|[2.076]|[0.393]|[0.409]|[0.415]|[0.812]|[0.285]|[0.339]|
|TFP|‐0.304***|‐0.250***|‐0.698*|‐0.316***|‐0.001|‐0.280***|‐0.157***|‐0.174***|‐0.316***|‐0.213***|‐0.267***|
||[0.060]|[0.060]|[0.360]|[0.093]|[0.133]|[0.068]|[0.050]|[0.053]|[0.080]|[0.048]|[0.056]|
|Observations|44,816|44,396|11,597|43,030|24,517|43,970|42,347|38,880|29,570|43,266|44,298|
|R‐squared|0.009|0.035|‐0.668|0.012|‐0.599|0.017|0.009|0.012|‐0.077|0.021|‐0.082|
|Number of plants|8,853|8,802|2,574|8,549|5,503|8,703|8,522|7,909|6,354|8,629|8,777|
|Plant FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|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)|12.73|12.01|4.126|13.42|2.841|12.39|12.77|11.02|9.504|13.96|13.25|
|Robust standard errors in brackets||||||||||||
|*** p<0.01, ** p<0.05, * p<0.1||||||||||||

# Robustness exercise

In this section I use a balanced panel sample to assess the degree to which sample selection could change the previous results. The balanced panel has a slightly higher share of large firms than the unbalanced panel in the main exercise, but the size distributions are not too different from one another.

**Table 11 Size distribution in the balanced panel.**

||Full sampl|e period|
|---|---|---|
|Size|FrequencyPe|rcent<br>Cum.|
|[0,50]|42,241|70.19<br>70.19|
|(50,200]|10,868|18.06<br>88.25|
|(200,1000]|5,509|9.15<br>97.41|
|>1000|1,561|2.59<br>100|
|Total|60,179|100|
||Year|2008|
|Size|FrequencyPe|rcent<br>Cum.|
|[0,50]|5,863|68.2<br>68.2|
|(50,200]|1,699|19.76<br>87.96|
|(200,1000]|802|9.33<br>97.29|
|>1000|233|2.71<br>100|
|Total|8,597|100|

Using a balanced panel does not change the main results, so there should be little concern about the effect of sample selection in the OLS regression. The fact that the balanced sample is comprised of about 70% of plants in the period is also reassuring. When I instrumented broadband on the balanced panel, the instrument loses its relevance, making the comparison between the two samples difficult. This suggests, however, that entry and exit dynamics could be related to broadband quality in a way that is systematically different for firms that remain in the sample since 2008 and those that enter or leave. Therefore, the 2SLS results in the main section should be 16
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
factor explaining the adoption of broadband internet, it was considered a suitable candidate for an instrument.

In this paper, I proposed and estimated total factor productivity under the assumption that it evolves according to a process which depends on past broadband adoption. I estimated the effect of broadband adoption on labor demand for different worker types. I find positive correlations between broadband adoption and labor demand of most worker types of about 2%. The instrumental variable estimation showed a positive but statistically insignificant effect of broadband use on total labor demand. The fact that I used two‐way clustered standard errors could have affected the statistical significance of the test. Alternatively, lack of statistical significance could come from the fact that broadband should only affect labor demand through its direct effect on TFP growth. In Table 8, I showed a positive relationship between broadband use and total factor productivity, which suggests a positive complementarity between broadband use and productivity. To further asses the possible skill complementarity of broadband, I interacted total factor productivity and broadband adoption. Table 17 in the Appendix shows that labor demand for unskilled labor is negatively correlated to productivity increases even further for firms that use broadband, regardless of the method used to estimate TFP. Table 18 shows that for exporting firms although unskilled labor demand is negatively related to TFP growth, the value of production and output per worker are positively related to TFP growth.

The paper contributes by providing evidence about the effect of ICT adoption which is associated with productivity growth on labor demand. The lack of significance in the instrumental variable estimations suggests that other identification strategies are needed to capture the causal effect of broadband use on labor demand. Particularly, since the instrument appears to be highly sensitive to entry and exit dynamics. Although several possible instruments were available, download speed appears to be a strong predictor of broadband adoption to a greater extent than the number of subscriptions or upload speed, but more work should be conducted in determining the appropriate instruments to pin down the causal effects of broadband adoption on labor demand and productivity.

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