metadata: authors: 0: author: Cusolito,Ana Paula
1: author: Lederman,Daniel
2: author: Pena,Jorge O.
count: World
docna: 0: docna: The Effects of Digital-Technology Adoption
            on Productivity and Factor Demand : Firm-level Evidence from
            Developing Countries
docty: Policy Research Working Paper
subtopic: ICT Applications,Labor Markets,Plastics & Rubber Industry,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Food & Beverage Industry,Common Carriers Industry,Construction Industry,Business Cycles and Stabilization Policies,General Manufacturing,Educational Sciences
teratopic: Macroeconomics and Economic Growth,Industry,Social Protections and Labor,Information and Communication Technologies,Education
abstracts: cdata!: This paper presents firm-level estimates
            of revenue-based total factor productivity premiums of
            manufacturing firms adopting digital technology in 82
            developing economies over 2002-19. The paper estimates
            productivity using the control function approach and
            assuming an endogenous revenue-based total factor
            productivity process, which is a function of multiple
            firm-choice variables. It estimates the effects of digital
            technology adoption, learning by exporting, and managerial
            experience on revenue-based total factor productivity and
            factor demand. The results reject the 0 hypothesis of an
            exogenous revenue-based total factor productivity process,
            in favor of one in which digital technology adoption, along
            with the other choice variables, affects revenue-based total
            factor productivity and factor demand. The estimated
            premiums are positive for 67.3 (email adoption), 54.6
            (website adoption), 59.4 (learning by exporting), and 60.6
            (managerial experience) percent of the sample. The
            probability-adjusted median (log) revenue-based total factor
            productivity premium associated with email adoption is 1.6
            percent and that of website adoption is 2.2 percent, with
            the latter being higher than the premiums corresponding to
            exporting and managerial experience. On average, changes in
            digital technology adoption, email, and website are labor
            and capital augmenting. The paper also explores the role of
            complementarities among the firm choice variables.
repnme: repnme: The Effects of Digital-Technology Adoption
            on Productivity and Factor Demand : Firm-level Evidence from
            Developing Countries
display_title: The Effects of Digital-Technology
            Adoption on Productivity and Factor Demand : Firm-level
            Evidence from Developing Countries
listing_relative_url: /research/2020/07/32268150/effects-digital-technology-adoption-productivity-factor-demand-firm-level-evidence-developing-countries
topicv3: Manufacturing Sectors m1326521 1281,Education m1327048 470,Jobs m1503931 2670,Digitization m1505678 3078,Labor Demand m1583073 4422
publishtoextweb_dt: 2020-07-23T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,Social Protections and Labor,Information and Communication Technologies,Education
origu: Office of the Chief Economist (MNACE)
no_of_pages: 48
author_keys: 0: author_key: 1003705
1: author_key: 125180
2: author_key: 92277
model_extractions: Policy Research Working Paper 9333 The Effects of Digital-Technology Adoption on Productivity and Factor Demand Firm-level Evidence from Developing Countries _Ana Paula Cusolito Daniel Lederman Jorge Peña_ Middle East and North Africa Region Office of the Chief Economist July 2020
Policy Research Working Paper 9333

# **Abstract**

This paper presents firm-level estimates of revenue-based total factor productivity premiums of manufacturing firms adopting digital technology in 82 developing economies over 2002–19. The paper estimates productivity using the control function approach and assuming an endogenous revenue-based total factor productivity process, which is a function of multiple firm-choice variables. It estimates the effects of digital technology adoption, learning by exporting, and managerial experience on revenue-based total factor productivity and factor demand. The results reject the null hypothesis of an exogenous revenue-based total factor productivity process, in favor of one in which digital technology adoption, along with the other choice variables, affects revenue-based total factor productivity and factor demand. The estimated premiums are positive for 67.3 (email adoption), 54.6 (website adoption), 59.4 (learning by exporting), and 60.6 (managerial experience) percent of the sample. The probability-adjusted median (log) revenue-based total factor productivity premium associated with email adoption is 1.6 percent and that of website adoption is 2.2 percent, with the latter being higher than the premiums corresponding to exporting and managerial experience. On average, changes in digital technology - adoption, email, and website are labor and capital augment ing. The paper also explores the role of complementarities among the firm choice variables.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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://www.worldbank.org/prwp. The authors may be contacted at acusolito@worldbank.org.

_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
The Effects of Digital-Technology Adoption on Productivity and Factor Demand: Firm-level Evidence from Developing Countries<sup>_∗_</sup> Ana Paula Cusolito,<sup>_†_</sup> Daniel Lederman,<sup>_‡_</sup> and Jorge Peña<sup>§</sup> Updated July 30, 2020 _JEL Codes:_ D22, D24, L25, O47. Keywords: productivity, jobs, digital technology, exporting, management.

> _∗_ The authors gratefully acknowledge the support received from the World Bank’s Office of the Chief Economist for the Middle East and North Africa Region (MENA) Region. Cusolito is a Senior Economist in the Finance, Competitiveness and Innovation Global Practice at the World Bank Group. Lederman is Deputy Chief Economist for MENA. Peña is a Professor of Business at the IE University in Madrid, Spain. The opinions expressed in this paper do not represent the views of the World Bank Group, its Board of Directors or the Governments they represent. All errors and omissions are the authors’ responsibility.

> _†_ World Bank Group (email: acusolito@worldbank.org).

> _‡_ World Bank Group (email: dlederman@worldbank.org).

> §IE University (email: jpi@faculty.ie.edu).
# **1 Introduction**

The global digital economy accounted for 15.5 percent of the world’s GDP in 2016 ($11.5 trillion). Yet, not everybody has benefited equally from the arrival of digital technologies. There are still huge disparities across and within countries when it comes to the adoption and usage of digital technologies (Comin and Mestieri 2018). While more than half of the world’s population now has access to the internet, the penetration rate in the least developed countries is only 15 percent, or 1 in 7 individuals (World Development Report 2019).<sup>1</sup> The benefits of adopting digital business solutions like email, launching a business website, or connecting to two-sided digital platforms can be substantial especially for firms (Goldfarb and Tucker 2019). The transfer of information and data over the internet helps reduce production costs and expand demand for a firm’s goods and services and thus for its factors of production. Reductions in search costs enable buyers and sellers of products or services to get better access to the other side of the market, by increasing the speed or efficacy with which firms find workers or input suppliers (De Loecker 2019). Digital business solutions also help expand market opportunities. Reductions in search, transaction, or tracking costs allow firms to overcome geographical barriers, penetrate new markets, and enlarge the volume of trade (World Development Report 2020).

The existing evidence on the impact of digital-technology adoption on productivity and factor demand, however, is surprisingly thin, especially for developing countries. It is even thinner when it comes to quantifying these effects using firm-level data. This paper aims to fill these gaps in the literature. Specifically, we estimate the effects of adopting digital business solutions, namely email to communicate with clients and suppliers and launching a business website, on firm-level revenue-based total factor productivity (TFPR) and demand for labor and capital. We rely on publicly available data from the World Bank’s Enterprise Survey database (WBES), which collects information on sales, factor and input usage, exporting status, managerial experience, and digital-technology adoption at the manufacturing

> 1. https://www.worldbank.org/en/topic/digitaldevelopment/overview.

2
firm level from a sample of 82 developing economies with data from 2002-2019.

To estimate TFPR, we first estimate a log-linearized Cobb-Douglas production function (PF) following Ackerberg, Caves, and Frazer (2015). Although, the Ackerberg, Caves, and Frazer (2015) method, which builds on Olley and Pakes (1996) and Levinsohn and Petrin (2003), assumes an exogenous productivity process, we follow De Loecker (2013) and endogenize TFPR. Thus, TFPR is a function of the adoption of digital business solutions (e.g., email and website) in addition to other firm-choice variables that can also affect firm performance, such as exporting and managerial experience, which have been studied separately in the literature. We validate our data and methodology by replicating the results presented in De Loecker (2013) for the specification that only includes learning-by-exporting effects. The evidence indicates that our estimates of the production function parameters and the coefficients of the endogenous productivity process, covering 82 developing countries, are highly correlated across industries with those reported by De Loecker (2013), for Slovenia.

Assuming an exogenous TFPR would have implied that digital technologies would have no impact on efficiency or sales. This is not only unrealistic but also, from a methodological point of view, would have invalidated the moment conditions needed to identify the coefficients of the production function. In other words, if TFPR is a function of business digitization that does, in fact, affect factor demand, the estimated production-function elasticities would be biased. The sign of the bias is ambiguous, depending on whether digitization is factor-augmenting or factor-saving. If business digitization is factor-augmenting, then TFPR would be underestimated. If improvements in TFPR are factor-saving, the effect of digitization on TFPR would be overestimated.

There are good reasons to expect that firm TFPR is a function of business digitization, as well as of exporting, as in De Loecker (2013), and managerial experience, as in Bloom and Van Reenen (2007) and Bloom and Van Reenen (2010). Using email to connect with clients or suppliers or having a business website to gain online presence can affect TFPR through different channels. On the demand-side, reductions in search and transaction costs affect 3
firm profitability at the extensive and intensive margins, by facilitating access to new clients or expanding the volume of transactions online. Dynamically, the scale-up of the demand for a firm’s products or services increases profits, allowing it to pay the fixed cost of investing in TFPR-enhancing activities like innovation, managerial upgrading, or technology adoption. On the supply-side, using email to connect with suppliers helps improve production efficiency, enlarging the potential set of input providers in non-relationship specific investments.

The results indicate that estimated TFPR-premiums are positive for 67.3 (email adoption), 54.6 (website adoption), 59.4 (learning by exporting), and 60.6 (managerial experience) percent of the estimation sample, respectively. The probability-adjusted median TFPRpremium associated with email adoption is 1.6 percent and that of website adoption is 2.2 percent. The probability-adjusted median TFPR-premium from getting access to external markets is 1.6 percent, while that of increasing the years of experience of the manager is near zero. Last, on average, changes in digital-technology adoption are labor- and capitalaugmenting. TFPR improvements are also labor-augmenting, while they do not have any impact on the demand for capital. These findings are new to the existing literature.

This paper is related to two strands of research related to the economics of technology adoption. The first one analyzes the impact of digitization on total factor productivity. It is related to the productivity paradox debate, which refers to the global contraction in productivity growth rates, which occurred despite the spectacular technological progress observed in recent decades (Brynjolfsson, Rock, and Syverson 2017; Cusolito and Maloney 2018) . The second strand of research focuses on the creation (or destruction) of jobs brought about by technological change. It is related to the debate about the effects of digitization or robotization on job destruction and skill-biased labor demand (Autor 2015; Autor et al. 2020; Autor and Salomons 2018; Acemoglu and Restrepo 2018, 2019a, 2019b, 2020a, 2020b; World Development Report 2019)). These debates are related because job losses from technology adoption could result from firms’ investments to become more efficient (Autor et al. 2020). The rest of this paper is organized as follows. Section 2, briefly reviews the related 4
literature. Section 3 describes the enterprise data used in the econometric estimations. Section 4 explains the estimation strategy. Section 5 validates the data and methodology by comparing our estimates with those in the existing literature. Section 6 presents the effects on productivity. Section 7 discusses the effects on factor demand. Since the estimation strategy is flexible, allowing for the estimation of a rich set of interactions between the lagged firmchoice variables and TFPR, Section 8 explores the issue of ICT program targeting by showing how the marginal impact of the adoption of digital tools depends on the other explanatory variables. The section concludes.

# **2 Related Literature**

As mentioned, this paper is related to two strands of the literature on technology adoption. One concerns the effect of adoption on productivity. The other is related to the impact of adoption on the demand for factors of production, particularly labor.

## **2.1 Productivity and Technology Adoption Literature**

The productivity paradox debate has recently shifted its focus towards the contribution of digital-technology adoption to productivity. Estimates for developing countries are rare due to data limitations. Recent calculations for the United States show that the sector has been a bright spot in the economy, accounting for 6.5 percent of GDP and 3.9 percent of total employment in 2016 (Barefoot et al. 2018). The new estimates, which ranked the U.S. digital sector just below professional, scientific, and technical services, have encouraged some economists to argue that if the digital economy plays a limited role in advanced economies, we should not expect much for less developed economies, where digital services are less affordable and penetration rates lower.<sup>2</sup>

> 2. Early attempts to explain the productivity paradox have emphasized two hypotheses. The first one relates to the presence of diminishing returns from the digital revolution. Gordon (2015) argues that once firms adjust to the digital electronic wave, by installing new equipment or adopting new business practices, the impact of ICT technologies on productivity began to display diminishing returns. To complement this

5
In a recent influential paper on the United States, Brynjolfsson et al. (2020) argue that in the “discordance between high hopes and disappointing statistical realities, one of the two elements is presumed to be somehow wrong.”<sup>3</sup> However, there are good reasons to be optimistic about the contribution of new technologies, including digital business solutions, to productivity and jobs. These technologies are general purpose technologies (GPTs) that have broad cross-sectoral applications (Jovanovic and Rousseau 2005; Helpman and Trajtenberg 1996). Brynjolfsson, Rock, and Syverson (2017), Syverson (2017), and Brynjolfsson et al. (2020) argue that GPTs have an impact in the economy after firms make the necessary complementary investments or organizational changes needed to take advantage of them. Yet the productivity gains from these investments or restructuring processes do not materialize immediately; it takes time to discover, develop, and implement them (Bresnahan, Brynjolfsson, and Hitt 2002).

Nonetheless, emerging evidence from advanced economies provides room for optimism. Recently, Gal et al. (2019) document that digital adoption in an industry is associated with productivity gains at the firm-level in 20 countries in the European Union and Turkey. Two earlier literature reviews by Syverson (2011) and Draca, Sadun, and Van Reenen (2006) concluded that there is a positive and significant association between ICT and productivity. These findings are, however, in contrast with recent evidence by DeStefano, Kneller, and Timmis (2018) for the United Kingdom, who show that ICT causes increases in firm size (captured by either sales or employment) but not productivity.

While evidence for developing countries is scarce, Hjort and Poulsen (2019) find positive effects of the arrival of internet on firm-level productivity in Africa. World Bank research on Argentina, Brazil, Chile, Colombia, and Mexico concludes that digital technology adoption argument, (Bloom et al. 2020) document that it takes progressively more researchers to generate a unit of TFP. The second hypothesis is related to measurement issues associated with the supply of digital products or services for which the price paid by consumers is zero. Consequently, these transactions are not captured in the data (Mokyr 2014; Hatzius and Dawsey 2015; Byrne, Fernald, and Reinsdorf 2016). However, this hypothesis was challenged by evidence indicating that the size of the productivity slump was unrelated to the spread of digital technologies across countries (Syverson 2017).

> 3. Brynjolfsson, Rock, and Syverson (2017) refers to artificial intelligence, but the argument is equally applicable to other types of general purpose technologies such as digital technologies.

6
offers a pathway to higher productivity (Dutz, Almeida, and Packard 2018). According to the study, the total factor productivity of technology-adopting firms increased in all country studies where data were available, with the finding in Argentina based on labor productivity (Brambilla and Tortarolo 2018; Iacovone and Pereira-López 2018; Almeida et al. 2017; Dutz et al. 2017). However, systematic firm-level evidence for a large sample of developing countries was not available at the time of writing.

# **2.2 Jobs and Technology Literature**

Recent technological innovations have also revamped an old concern related to the tradeoff between efficiency and jobs. This debate is connected to the potential labor-saving and skill-biased effects of technology adoption (Brynjolfsson and McAfee 2014; Frey and Osborne 2017). Evidence about the effect of automation on jobs is primarily available for the United States in general equilibrium, as in Acemoglu and Autor (2011) and Acemoglu and Restrepo (2018, 2019a), and the European Union (Autor and Salomons 2018). For example, Acemoglu and Autor (2011) explore the role of routinization of tasks due to the arrival of ICT technologies in job polarization. The article concludes that job polarization in the United States and the European Union is partly the result of the secular price decline in the real cost of information technologies. This is because routine tasks are characteristic of middle-skilled cognitive and manual jobs, which made them more vulnerable to the effects of technology adoption.

Recent evidence for the United States suggests that automation through the adoption of robotics can displace certain types of jobs (Acemoglu and Restrepo 2018). The estimates imply that one more robot per thousand workers reduces the employment-to-population ratio by about 0.2 percentage point and wages by 0.42 percent. In a follow-up paper, the authors explore the types of workers that have a higher probability of being replaced, concluding that robots primarily replace middle-aged workers between the ages of 21 and 55 (Acemoglu and Restrepo 2019b).

7
While evidence for developing countries is thin, the recent World Development Report (World Development Report 2019) shows that the variance of the labor-saving effect is so large that it is hard to conclude that robots will indeed decrease the net demand for labor. Furthermore, as highlighted by Acemoglu and Restrepo (2018, 2019a, 2019b), at the aggregate level, the job displacement effects will push wages down and help introduce new tasks that are labor-intensive.

Evidence about firm- and country-level job effects from technology adoption are only available for a handful of middle-income countries. A World Bank study (Dutz, Almeida, and Packard 2018), which summarizes findings for Argentina, Brazil, Colombia, Chile, and Mexico, shows that across these economies except Brazil, ICT adoption by firms is associated with increases in total employment and in employment of low-skilled labor (Brambilla and Tortarolo 2018; Dutz, Almeida, and Packard 2018; Iacovone and Pereira-López 2018; Almeida et al. 2017; Dutz et al. 2017). This paper advances the literature by providing evidence about the effect of digital-technology adoption on factor demand across a large sample of formal manufacturing enterprises in developing countries and by identifying the channels through which factor demand is affected. The two channels are factor-saving productivity improvements and scale effects, which reflects the impact of digital-technology adoption on a customer base.

# **3 Data**

The empirics rely on panel data of manufacturing firms from the World Bank Enterprise Survey Database (WBES). The estimation sample covers 82 countries from a maximum sample of 90 countries in the six regions where the World Bank operates: Europe and Central Asia - ECA (30), Sub-Saharan Africa - SSA (27), Latin America and the Caribbean - LAC (18), East Asia and Pacific - EAP (6), South Asia - SA (6), and Middle East and North Africa - MENA (3).

8
The survey is nationally representative of the formal private sector. It is built based on a stratified random sampling frame designed by the WBES team. Three variables are used to construct the strata: firm size, sector, and geographic area within a country. Under the WBES sampling framework, firms are divided into three categories according to their size: small, medium-sized, and large. Small firms are those with 5-19 full-time employees; mediumsized firms have 20-99 full-time employees; and the large ones have more than 99 full-time employees. The industries are classified according to the ISIC Revision 3.1 classification at 2-digits. The regions within a country are defined by the WBES team. The database also includes sampling weights that can be used to mimic nationally representative samples in the empirics.

The WBES collects data on a broad range of variables related to firm production, performance, and the business environment in which firms operate. It has information on variables related to production such as sales, capital, labor, materials, investment, exports, and manager’s education, among others. Due to the lack of information on prices at the firm-level, we use the consumer price index from the World Bank’s World Development Indicators to deflate sales, capital, materials, and investment, thus transforming nominal values into 2010dollar values. Firms’ labor is equal to the number of permanent employees that work for the firm. The survey collects data on the percentage of firms’ sales that are exported. Last, a firm’s managerial capability is measured by the number of years of experience of the manager. The novelty of the WBES is that it also collects information on technology adoption at the firm-level. Thus, at every wave, firms are asked whether they use a business email to communicate with clients and suppliers and whether they have a business website in order to carry out their operations. We exploit this variation in time, across countries, sectors, and firms to estimate the effects of digital business solutions on productivity and factor demand.

To construct the estimation sample, we first compiled all the WBES waves available from 2002-2019. This creates a sample of 145,626 observations, which corresponds to 118,868 firms, operating in the manufacturing or service industries. Table 6 in section A of the Appendix 9
provides detailed information about this sample across countries and years. After this, we drop firms for which we cannot identify the sector in which they operate. This give us a sample of 131,347 observations.

If we further restrict this sample to manufacturing industries, which is the focus of our analysis, we end up with a sample of 74,723 observations corresponding to 59,820 firms. Of these firms, 79.4 percent appear only once in the database; 17.0 percent appear twice; 3.0 percent appear three times; 0.5 percent appear four times; and 0.1 percent appear five times. Table 7 in section A of the Appendix displays detailed information about this sample across countries and years. A common feature of many firm-level databases from developing countries is the presence of missing values for variables needed to measure firm performance (e.g., labor, sales, capital, and materials, and investment). For example, in our sample, labor is the variable with the least proportion of missing values (2.3 percent), followed by sales (14.2 percent), materials (31.8 percent), capital (32.8 percent), and investment (58.2 percent).

To maximize sample size, correct selection in misreporting, and gain efficiency, we impute data for sales, labor, capital, materials, and investment using the largest WBES database available, which contains 131,347 observations, and a pseudo-Gibbs sampler (Lee and Carlin 2010; Van Buuren, Boshuizen, and Knook 1999).<sup>4</sup> The explanatory variables used for imputation include email adoption, website adoption, export status, managerial experience proxied by a dummy variable that identifies firms with managers with above-median years of experience. It also controls for country, industry, and survey year. We do not impute data for email adoption, website adoption, export status, and managerial experience as we are interested in understanding their effect on TFPR. Table 8 in section A of the Appendix presents summary statistics of the main variables with and without imputation. As can be observed, the imputation method performs well, as there are not statistically significant differences in the statistics across sample groups.

> 4. The only observations that were not included in the imputation method were those that did not report any sector activity.

10
<!-- Start of picture text --> 120.0%<br>100.0% 91.9% 95.6%<br>78.3%<br>80.0%<br>65.3%<br>58.7%<br>60.0% 53.2%<br>40.0%<br>20.0%<br>0.0%<br>EAP ECA LAC MENA SA SSA<br><!-- End of picture text --> <!-- Start of picture text --> 90.0%<br>80.2%<br>80.0% "<br>72.9%<br>70.0%<br>60.0% 54.3%<br>50.0% 44.9%<br>39.8%<br>40.0% 37.4%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>EAP ECA LAC MENA SA SSA<br><!-- End of picture text -->
# **4 Methodology**

The estimation strategy proceeds in two stages. The first focuses on the estimation of TFPR; the second on the estimation of factor demand.

## **4.1 Estimating the productivity premium from digital-technology adoption**

The productivity variable to be estimated is revenue-based total factor productivity (TFPR). We estimate this measure, instead of physical TFP, because the WBES does not collect information on prices. Thus, in order to construct proxy variables for output and inputs in comparable units across countries and over time, we use country deflators like the consumer price index. Our measure of TFPR thus captures variations in prices and efficiency. It is therefore a measure of firm profitability.

To estimate TFPR, we first estimate a log-linearized Cobb-Douglas production function (PF), assuming that the PF elasticities vary at the 2-digit sector level. The estimation method follows Ackerberg, Caves, and Frazer (2015), who rely on the control function approach (CFA) to allow for endogeneity of factor choices and materials usage to make productivity observable. Since the WBES follows a sub-sample of firms interviewed to construct the panel, the data do not capture firm entry-exit dynamics. As a result, we could not control for selection in factor choices and materials usage.

While the Ackerberg, Caves, and Frazer (2015) method assumes an exogenous productivity process, we follow De Loecker (2013) and endogenize it. Thus, in our specification, TFPR is a function of the adoption of digital business solutions (e.g., email and website) as well as exporting status and managerial experience. Assuming an exogenous TFPR process, by contrast, would have implied that digital business solutions would have no impact on 12
efficiency or sales. This is not only unrealistic, but also would have invalidated the moment conditions needed to identify the coefficients of the production function, as the productivity shock would not have been orthogonal to factor choices. In other words, if TFPR is a function of digitization, the PF elasticities will be biased. The sign of the bias is ambiguous, depending on whether digitization is factor-augmenting or factor-saving. If business digitization is factor-augmenting, then TFPR would be underestimated. By contrast, if TFPR is factor-saving, TFPR will be overestimated.

There are important reasons to make TFPR a function of business digitization. Using email to connect with clients and suppliers or having a business website to gain online presence can affect TFPR through various channels. On the demand-side of the market for an enterprise’s goods and services, reductions in search and transaction costs affect firm profitability at the extensive and intensive margins, by facilitating access to new clients or expanding the volume of transactions online. Dynamically, the scale-up of the demand for a firm’s products or services increases profits, allowing it to pay the fixed cost of investing in TFPR-enhancing activities like innovation, managerial upgrading, or technology adoption (Bustos 2011). On the supply-side, using email to connect with suppliers helps improve production efficiency, by enlarging the potential set of input providers in non-relationship specific investments. Alternatively, it reduces the number of suppliers in relationship-specific investments but enlarges the fraction of repeated interactions, thus addressing contract incompleteness and guaranteeing access to specific assets needed to produce more sophisticated goods (Aral, Bakos, and Brynjolfsson 2018). Because adoption of digital business solutions is not exogenous, we lagged the corresponding variables used to estimate their effects on TFPR.

Since WBES data are not census data, a key question is whether to do weighted estimations by using sampling weights to estimate the coefficients of the production function and TFPR. Following Cameron and Trivedi (2005), sampling schemes such as stratification lead to the conditional density of any variable in the sample differing from that in the population.

13
However, if stratification is purely exogenous, such that it does not take into consideration the dependent variable to stratify the sample, then the estimated parameters are consistent, regardless of differences between the estimation sample and the true underlying population. By contrast, under pure endogenous sampling, the marginal distribution of the dependent variable in the sample differs from that in the population, and as a result, the estimated coefficients are inconsistent. Since firms’ sales have not been used to stratify the WBES, we do not use country-specific weights for the estimation of the coefficients of the PF. Last, following the literature on PF estimation using the CFA, we bootstrapped the standard errors using 100 replications and country and year to construct the strata.

After estimating the PF elasticities, we use equation 4.1 to estimate TFPR. Then, with unbiased estimates of TFPR at the firm-level in hand, we pool all the observations and run an OLS regression of unbiased-TFPR on digital business solutions (e.g., email and website) to estimate the marginal effects of digitization on TFPR. The OLS coefficients are mathematically equivalent to the weighted average of the estimated coefficients obtained from the PF estimation, where the Markov coefficients vary at the sector-level (see Appendix B for the proof).

We assume homogeneous effects of digital-technology adoption on TFPR (instead of sector effects) for two reasons. First, the type of digitization we are interested in falls under the category of general-purpose technologies (instead of sector-specific technologies). The second reason is to gain efficiency and increase the degrees of freedom in the estimation, as several sectors have few observations once we lagged the explanatory variables used to endogenize TFPR to control for endogeneity. Provided we focus the interpretation of the results (inference) on the entire sample, our approach eliminates imprecisions coming from making estimations with small sub-samples.

Our empirical strategy has three stages. Stages 1 and 2 are the standard Control Function Approach stages, with the difference that we extend Ackerberg, Caves, and Frazer (2015) and De Loecker (2013) and endogenize TFPR as a function of four firm-choice variables, 14
including the adoption of digital tools (website and email), exporting status and managerial experience. In the third stage, we recover the weighted average email and website marginal effects on TFPR at the firm-level. The following sub-sections provide further details about the specifications estimated in each stage.

# **4.1.1 TFPR estimation: First stage of the CFA**

We first estimate a log-linearized Cobb-Douglas production function at the sectoral level: ln ( _Yijct_ ) = _aj_ + _bj_ ln ( _Lijct_ )+ _cj_ ln ( _Kijct_ )+ _dj_ ln ( _Mijct_ )+ln ( _TFPRijct_ )+ _Dc_ + _Dt_ + _eijt,_ (4.1) _Yijct_ , _Lijct_ , _Kijct_ , and _Mijct_ refer to output, labor, capital, and materials used by firm _i_ , which operates in sector _j_ of country _c_ , at time _t_ . _eijct_ is an i.i.d error term that captures unanticipated shocks to production or measurement error. _Dc_ and _Dt_ are country fixedeffects and time fixed-effects, respectively. Since productivity, _TFPRijct_ , is unobservable, we follow Ackerberg, Caves, and Frazer (2015) and use materials to make it observable: where _Xijct_ is the set of control variables that can affect TFPR (e.g., exporting status, managerial experience). Since materials are a strictly monotonic function of TFPR, we can invert function _h_ ( _·_ ), and express TFPR as a function of labor, capital, materials, digital business solutions and other determinants of firm performance: Inserting equation (4.3) into (4.1) yields: 15
Equation (4.4) can be estimated by OLS. We approximate function _h_ ( _·_ ) using a third degree polynomial on labor, capital, and materials. Following Ackerberg, Caves, and Frazer (2015), in the first step we cannot identify the coefficients of the PF. However, we can remove the estimated error term, and use output minus its predicted value to estimate the TFPR process and use the productivity shock for the moment conditions used to estimate the elasticities of the PF.

# **4.1.2 TFPR estimation: Second stage of the CFA**

As mentioned, the Ackerberg, Caves, and Frazer (2015) CFA relies on an exogenous Markovian TFPR process to estimate the PF elasticities: Following De Loecker (2013), the standard CFA can be extended by endogenizing TFPR as a function of digital business solutions, or any firm choice variable. Moreover, we adopt a flexible functional approach, which allows the marginal effects of digital business solutions to vary with a firm’s initial level of TFPR. Formally, to control for the endogeneity of email and website adoption, we lagged these variables, as well as those included in _Xijct_ . The resulting estimation equation is: 16
where Ψ is a function that includes _Emailijct−_ 1, _Websiteijct−_ 1, _Exportijct−_ 1, and _Managerialijct−_ 1 as free-standing variables, as well as all the possible interaction terms with all the arguments of function Ψ. The term _εijct_ is by assumption uncorrelated with any lagged choice variable because the latter are in the firm’s information set. This forms the basis for the identification of the labor, capital, and material elasticities in the final stage of the Ackerberg, Caves, and Frazer (2015) procedure. Thus, the PF elasticities are estimated using the following moment conditions: The Ackerberg, Caves, and Frazer (2015) approach uses a value-added (instead of output) PF to estimate TFPR. It is intentionally done in this way to avoid estimating the elasticity corresponding to materials and therefore address the concern that lagged materials is not a valid instrument. Bond and Söderbom (2005) argue that materials are a flexible input, which implies that it does not follow an auto-regressive process. To explore this issue, we estimated an AR (1) model for materials and found that it is equal to 0.86. We prefer this approach instead of the value-added approach, as the latter implicitly assumes an output elasticity with respect to materials equal to 1. The coefficients of the production functions are thus estimated by minimizing the sample analogue of equation (4.7) using GMM.

# **4.1.3 TFPR estimation: Estimating global average digital business solution marginal effects**

With unbiased estimates of TFPR in hand, we pool all the observations and estimate equation (4.6) using OLS. Appendix B shows that the estimated coefficients in the whole sample are a weighted average of the coefficients obtained across subsamples.

17
# **4.2 Estimating the Effects on Labor and Capital Demand**

Recent technological innovations have revamped an old concern about productivity-driven displacement of jobs and shifts of labor demand towards skilled workers. New theoretical models developed to understand the potential effects of automation on jobs, as well as the channels through which it operates, depart from the standard factor-augmenting technological change approach. Instead, they propose a new framework, where robots compete against workers, and thus machines replace tasks previously performed by humans (Acemoglu and Restrepo 2018, 2019b, 2020a; Autor and Salomons 2018). However, the displacementinduced contraction in wages dynamically creates incentives for the introduction of new tasks, in general equilibrium, where labor has a comparative advantage relative to technology, the so-called reinstatement effect. This new setup thus enables researchers to think about new forces at work, which have opposing effects on labor demand in general equilibrium.

Our estimation framework with enterprise panel data is, by definition, partial equilibrium. But the estimation framework is flexible and allows for the estimation of the effects of the choice variables on both TFPR and factor demand. As mentioned, the latter effect has two channels, the factor-augmenting or saving effect as well as a scale effect. This allows for a direct test of labor- (and capital-) saving hypothesis. Since our TFPR measure confounds both prices and efficiency, our productivity-driven effect is not fully comparable to the displacement effect cited in the literature (Acemoglu and Restrepo 2018, 2019a). This is because the price-related component of this effect could be labor-augmenting, as efficiency gains are passed-through onto product prices. That is, efficiency gains resulting in price reductions can increase product demand. The efficiency-related component of this effect could be labor-saving, just like the displacement effect cited in the literature. The scale effect, however, is unambiguously labor-augmenting. It is associated with an expansion in firms’ profits due to a reduction in marginal costs or the scale-up of demand for a firm’s output as digitization allows firms to reach a larger potential customer base. Thus, to estimate the factor demand effects from digitization (as well as that of exporting and managerial 18
experience), we estimate the following equation: where ∆ln ( _FPijc_ ) stands for changes in the use of factors of production, labor and capital.

# **5 Data and Estimation Validation**

To validate the estimations of the effect of digital technology adoption on TFPR when applied to the WBES database, we estimate the same specification as the baseline specification reported in De Loecker (2013), which relies on data from Slovenia. This involves the estimation of a value-added Cobb-Douglas production function on labor, capital, and productivity, where the latter is assumed to be an endogenous process of learning by exporting. Table 1 presents the results from the production function elasticities, while Table 2 displays the median learning-by-exporting effects on TFPR.

19
Table 1: WEBS-De Loecker Comparison: Production Function Elasticities

|SectorDescrition||WBES|||De Loecke|r|
|---|---|---|---|---|---|---|
|p|_L_|_K_|_K/L_|_L_|_K_|_K/L_|
|Food & beverages|0.933|0.241|0.258|0.810|0.131|0.162|
|Textiles|0.925|0.206|0.223|0.562|0.165|0.294|
|Garments|0.911|0.251|0.276|0.833|0.152|0.182|
|Leather|0.735|0.364|0.495|0.542|0.356|0.657|
|Wood|0.868|0.160|0.184|0.885|0.063|0.071|
|Publishing, printing and|0.978|0.262|0.268|0.603|0.337|0.559|
|reproduction|||||||
|Chemicals|1.038|0.205|0.197|0.601|0.274|0.456|
|Rubber & plastics|1.071|0.204|0.190|0.669|0.142|0.212|
|Other non-metallic|0.974|0.254|0.261|0.614|0.255|0.415|
|products|||||||
|Basic metals|1.202|0.198|0.165|0.751|0.042|0.056|
|Fabricated metal prods.|1.097|0.184|0.168|0.666|0.194|0.291|
|Machinery and|0.991|0.225|0.227|0.700|0.199|0.284|
|equipment|||||||
|Electrical machinery|1.102|0.230|0.209|0.558|0.223|0.400|
|Furniture|0.877|0.307|0.350|0.709|0.146|0.206|

Notes. Table 1 presents the production function elasticities from estimating a value-added log-linearized Cobb-Douglas production function following De Loecker (2013). In this paper, value-added is a function of labor and capital. The estimating method is based on the Control Function approach by Ackerberg, Caves, and Frazer (2015). However, it departures from the latter by assuming an endogenous Markovian productivity process, which is a function of learning by exporting. WBES data covers a sample of 7,916 manufacturing enterprises from 82 developing countries during the period 2002-2019; while De Loecker (2013) study focuses on 7,915 manufacturing firms in Slovenia during the period 1994-2000. Data for WBES come from the World Bank, while data fromDe Loecker (2013) come from the Slovenian Central Statistical Office. The correlation coefficient between the K-to-L estimated ratio using the WBES and De Loecker (2013) database is 0.55. It is also statistically significant at the 5 percent level.

Using the production function elasticities from Table 1, we calculate sector-specific factor intensities, defined as the capital-to-labor PF elasticity ratio, and examine the pairwise correlations between the results obtained using the WBES database and those from De Loecker (2013). We found a correlation coefficient of 0.55 between factor intensities, which is significant at the 5 percent level. The correlation coefficient between median productivitypremium from exporting is 0.36. This is high given that we only have 15 observations and there is a lot of cross-country variation in the WBES database.

20
Table 2: WBES-De Loecker Comparison: Non-Parametric Estimates of Exporting on TFPR (in <u>percent)</u>

|SectorDescrition|Median Productivity Premium from Exporting|
|---|---|
|p|WBES<br>De Loecker|
|Food & beverages|5.953<br>2.280|
|Textiles|4.949<br>1.980|
|Garments|3.696<br>1.660|
|Leather|-1.577<br>1.830|
|Wood|7.186<br>1.920|
|Publishing, printing and|5.732<br>4.880|
|reproduction||
|Chemicals|6.541<br>3.930|
|Rubber & plastics|6.122<br>4.500|
|Other non-metallic products|5.246<br>2.730|
|Basic metals|5.141<br>3.190|
|Fabricated metal products|6.071<br>3.320|
|Machinery and equipment|4.218<br>3.450|
|Electrical machinery|3.687<br>4.640|
|Furniture|1.862<br>1.990|

Note: Table 2 presents the median TFPR-premium from exporting following De Loecker (2013) method. The latter is based on the estimation of a value-added log-linearized Cobb-Douglas production function based on the Control Function approach by Ackerberg, Caves, and Frazer (2015) and assuming an endogenous (cubic) Markovian (AR 1) productivity process, which is a function of learning by exporting. WBES data covers a sample of manufacturing enterprises from 82 developing countries during the period 2002-2019; while De Loecker (2013) study focuses on 7,915 manufacturing firms in Slovenia during the period 1994-2000. Data for WBES come from the World Bank, while data from De Loecker (2013) come from the Slovenian Central Statistical Office. The correlation coefficient is 0.36.

# **6 Nonparametric Estimates of the Digital-Technology Adoption Effect (DAE) on TFPR**

This section presents the semi-parametric estimates of the digital adoption effects (e.g., email and website), using the approach presented in section 4. Table 3 reports the median effects, the percentage of the estimation sample with positive marginal effects, and the F-test associated with each variable of interest. Column (1) displays the results from estimating an endogenous TFPR process that is a function of learning by exporting, as in De Loecker (2013). Column (2) reports the results from estimating an endogenous TFPR process that is a function of the adoption of digital business solutions, namely email and website. Column 21
(3) presents the results from estimating an endogenous TFPR process that is a function of managerial experience. Column (4) shows the most complete specification that includes four choice variables, business web site, business email, exporting, and managerial-experience Column (1) reports a probability-adjusted expected median productivity premium from exporting of 1.68 percent, with the entire sample of firms displaying positive marginal effects from exporting. This is calculated as the sample probability of becoming an exporter times the estimated marginal productivity effect (0.3 times 0.056). As in De Loecker (2013), we reject the null hypothesis of an exogenous productivity process, in favor of a specification with learning by exporting effects. Column (2) shows a positive productivity premium from email adoption for almost 50 percent of the estimation sample. The probability-adjusted premium is almost negligible. The probability-adjusted median TFPR-premium from website adoption is also close to zero, with 22.57 percent of the estimation sample showing a positive impact. The large proportion of firms displaying negative marginal effects could mirror the same measurement problem associated with estimating the effects of process innovation on productivity. If innovation (in this case digital technology adoption) is cost saving and the demand for the good a firm sells is not sufficiently price responsive, then TFPR can decrease when digitization-triggered cost reductions are passed-through onto prices (see the literature review by Hall and Monhen (2013)). As with the first specification, we reject an exogenous productivity process in favor of a specification, where digital technology adoption affects firm performance. Column (3) shows a positive managerial-experience premium for all firms with more educated managers. The median premium effect is 0 percent and the F-test rejects an exogenous TFPR process. However, these three model specifications can yield biased estimates because they omit the other firm-choice variables. Therefore, our preferred specification reported under column 4 includes all four choice variables simultaneously.

22
|earning by Exporting, and Managerial<br>Probability-adjusted Efects<br>(1)<br>(2)<br>(3)<br>(4)|0.0168<br>0.0168<br>100<br>59.4<br>4.362***<br>8.193***|-0.0007<br>0.010<br>49.538<br>67.3<br>9.685***<br>5.629***|-0.00285<br>0.022<br>22.578<br>54.6<br>4.882***<br>3.246**|0.0005<br>0.0005<br>83.0<br>60.6<br>2.26*<br>7.493***<br>0.877<br>0.886<br>0.890<br>0.887<br>11.753***<br>6.77***<br>16<br>5) and endogenizing the (cubic) Markovian (AR 1) productivity<br>fects represent weighted average of the efects estimated at the<br>ects. Productivity determinants have been instrumented with a<br>atistics are used to evaluate the null hypothesis of an exogenous<br>oduct in international markets; “email adoption” takes value 1 if<br>experience” is measured by number of years of experience of the<br>ved after the productivity premium efects were calculated. We<br> the frst quartile minus 2.5 times the interquartile range (IQR)|
|---|---|---|---|---|
|ated Median Productivity Premium: Digital-Technology Adoption, L<br>(log)-Productivity<br>Premium<br>Endogenous Markov Specifcation<br>(1)<br>(2)<br>(3)<br>(4)|Median TFPR Efect<br>(MPE)<br>0.056<br>0.036<br>% of obs. with MPE >0<br>100<br>59.4<br>F-test<br>4.362***<br>8.193***|Median TFPR Efect<br>(MPE)<br>-0.001<br>0.015<br>% of obs. with MPE >0<br>49.538<br>67.3<br>F-test<br>9.685***<br>5.629***|Median TFPR Efect<br>(MPE)<br>-0.057<br>0.044<br>% of obs. with MPE >0<br>22.578<br>54.6<br>F-test<br>4.882***<br>3.246**|Median TFPR Efect<br>(MPE)<br>0.001<br>0.001<br>% of obs. with MPE >0<br>83.0<br>60.6<br>F-test<br>2.26*<br>7.493***<br>0.877<br>0.886<br>0.890<br>0.887<br>11.753***<br>6.77***<br>7,9<br>he results from estimating equation 4.6 using the Control Function approach by Ackerberg, Caves, and Frazer (201<br>ction of digital-technology adoption, learning by exporting, and managerial experience. The estimated marginal e<br> pool specifcation used to recover the coefcients from equation 4.6 controls for sector, country, and time fxed ef<br> for endogeneity. Standard errors have been bootstrapped using 100 replications and country-year strata. The F-st<br>inst an alternative hypothesis of an endogenous process. The “exporting status” takes value 1 if the frm sells a pr<br>nnect with clients and suppliers; “website adoption” takes value 1 if the frm has a business website; “managerial <br>ect of experience is for frms with managers with above median years of experience (17 years). Outliers were remo<br>ervations whose corresponding productivity premiums is higher than “U” or lower than “L”, where U is defned as <br>hird quartile plus 2.5 times IQR.|
|Table 3: Estim<br>Experience<br>Productivity<br>Determinants|Exporting<br>status|Email<br>Adoption|Website<br>Adoption|Managerial<br>Experience<br>_R_2<br>F-Total<br>_N_<br>Note: Table 3 presents t<br>process to make it a fun<br>sectorial level. Thus, the<br>one-period lag to control <br>productivity process aga<br>the frm uses email to co<br>manger.The reported ef<br>defne outliers those obs<br>and L is defned as the t|

23
The results of the preferred model indicate that the omission of any of these variables changes the results. Figure 6.1 displays the corresponding kernel densities for the TFPRpremium associated with email adoption (panel a), website adoption (panel b), learning by exporting (panel c), and accumulation of managerial experience (panel d) after removing outliers. There are two kernels in each panel. One represents the distribution of the TFPR-premium for the partial model and the other one for the complete model. The (log)TFP-premiums are positive for 67.3 (email adoption), 54.6 (website adoption), 59.4 (learning by exporting), and 60.6 (managerial experience) percent of the estimation sample, respectively. The probability-adjusted median TFPR-premium associated with email adoption is 1.6 percent and that of website adoption is 2.2 percent. The probability-adjusted median TFPR-premium from getting access to external markets is 1.6 percent, while that of increasing the years of experience of the manager is near zero.

24
<!-- Start of picture text --> Complete model: Median=0.044, 54.6% over 0<br>Partial model: Median=-0.057, 22.6% over 0<br>"=<br>o<br>ro)<br>2s<br>8<br>8<br>°<br>-2 -1 te) 1 2<br>Marginal effect WEB<br>——— Complete model ——— Partial model<br><!-- End of picture text --> <!-- Start of picture text --> Complete model: Median=0.015, 67.3% over 0<br>Partial model: Median=-0.001, 49.5% over 0<br>N<br>0<br>><br>a<br>82<br>°o<br>-5 to) 5<br>Marginal effect EMAIL<br>———Complete model ——— Partial model<br><!-- End of picture text --> <!-- Start of picture text --> Complete model: Median=0.036, 59.4% over 0<br>Partial model: Median=0.056, 100.0% over 0<br>9;<br>8<br>3<br>o<br>-1 -5 oO 5 1<br>Marginal effect EXP<br>——— Complete model ———— Partial model<br><!-- End of picture text --> <!-- Start of picture text --> Complete model: Median=0.001, 60.6% over 0<br>Partial model: Median=0.001, 82.9% over 0<br>8<br>8<br>g<br>a°<br>8<br>o<br>-.01 -.005 f) .005 01 .015<br>Marginal effect MANG<br>——— Complete model ———— Partial model<br><!-- End of picture text -->
they operate. As discussed in the methodological section, we work with a specification that assumes two different channels: (i) a productivity-driven effect and (ii) a scale effect. Since our TFPR measure confounds both prices and efficiency, our productivity-driven effect is not fully comparable to the displacement effect cited in the literature (Acemoglu and Restrepo 2018, 2019b). This is because the price-related component of this effect could be laboraugmenting if the efficiency gains are passed-through onto prices, and the price reduction can increase product demand. The scale effect is labor-augmenting. It is associated with an expansion in firms’ profits due to a reduction in marginal costs or the scale-up of demand as digitization allows firms to reach a larger potential customer base.

# **7.1 on Jobs**

Table 4 presents the results from estimating equation 4.8, the effects of digital-technology adoption on jobs, for each of the endogenous TFPR specifications we estimated in the previous section (Table 3 columns 1-4). Unfortunately, due to WBES limitations in questionnaire design, we cannot measure the impact of digital-technology adoption on the skill composition of labor in a straightforward manner, especially across regions. This is because there are discrepancies in the questionnaires across countries and time. For example, while some surveys collect information on high-school education, others do it for college graduates. Further, the type of information collected is not independent of the level of development of the country. The WBES questionnaires for upper middle-income countries focus on college graduates, whereas those for low-income countries collect information on the share of high-school graduates. Column (1) displays estimated labor-demand effects, when assuming an endogenous TFPR process that is a function of learning by exporting. Column (2) excludes exporting effects and assumes an endogenous TFPR process that is a function of digitization. Column (3) assumes an endogenous TFPR process that is a function of managerial experience. The most complete specification is the one displayed in Column (4), which shows the effects of changes in digitization, learning by exporting, and accumulation of managerial experience 26
on labor demand.

Table 4: Estimates of the Digital-Technology Adoption Effects on Jobs

|VariableofInterest|||WB|ES||
|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|
||Coef.|0.341|||0.303|
|Change in Export Status|St.Dev|(0.082)|||(0.066)|
||T-test|[4.167]|||[4.578]|
|ChaneinEmail|Coef.||0.240||0.220|
|g <br>Adti|St.Dev||(0.077)||(0.068)|
|opon|T-test||[3.109]||[3.212]|
|ChaneinWebsite|Coef.||0.227||0.215|
|g <br>Adti|St.Dev||(0.046)||(0.040)|
|opon|T-test||[4.919]||[5.418]|
|ChiM’|Coef.|||-0.539|0.004|
|ange n anagers<br>i|St.Dev|||(0.175)|(0.001)|
|experence|T-test|||[3.074]|[2.981]|
||Coef.|0.083|0.062||0.034|
|Change in TFPR|St.Dev|(0.022)|(0.021)||(0.015)|
||T-test|[3.841]|[2.955]||[2.293]|
|_R_<sup>2</sup>||0.073|0.081|0.053|0.105|
|_N_|||7,9|26||

Note: Table 4 presents the results from estimating equation 4.8 for the pool sample. For each of the estimated specifications, we use changes in estimated TFPR assuming the same corresponding specification as in Table 3. The estimation controls for sector, country, and time fixed effects. The “exporting status” takes value 1 if the firm sells a product in international markets; “email adoption” takes value 1 if the firm uses email to connect with clients and suppliers; “website adoption” takes value 1 if the firm has a business website; “managerial experience” takes value 1 if the firm has a manager with years of experience above the country-median. “Employment” measures full-time employees; “Exporting status” takes value 1 if the firm sells a product in international markets; “Email adoption” takes value 1 if the firm uses email to connect with clients and suppliers; “Website adoption” takes value 1 if the firm has a business website; “Manager’s experience” takes value 1 if the firm has a manager with years of experience above the country-median.

Table 4 shows that changes in digital-technology adoption, exporting, and accumulation of managerial experience have positive and statistically significant effects on jobs. For our preferred specification, which is the one displayed in Column 4, the largest effect comes from exporting (30 percent, approximately), followed by digitization (21 percent for each variable), and managerial experience (0.1 percent). Interestingly, in all the specifications, the TFPRrelated effect is positive and statistically significant, meaning that TFPR improvements are labor-augmenting. However, this does not necessarily means that the effect is positive for all the sectors, as Table 3 displays pooled regressions, which are a weighted-average of the sector-specific ones. Sector-specific regressions, which are available upon request, show that 27
the positive TFPR effect is mainly explained by sectors like garments and fabricated metals. This contrasts with other sectors such as chemicals, where the estimated effects are negative.

# **7.2 Effects on Capital**

Table 5 reports the results for demand for capital. For the variable of interest, the results are similar to those reported in Table 4. That is, changes in digital-technology adoption, (both email and website), exporting status, and managerial experience have a positive and statistically significant effect on changes in the demand for capital. The largest effect is observed for email adoption (57 percent), followed by exporting (35 percent), and website adoption (17 percent, approximately) (Table 4, column 4). In contrast, changes in TFPR have no statistically significant effect in any specification.

Table 5: Estimates of the Digital-Technology Adoption Effects on Capital

|VariableofInterest|||WB|ES||
|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|
||Coef.|0.418|||0.349|
|Change in Export Status|St.Dev|(0.115)|||(0.103)|
||T-test|[3.623]|||[3.380]|
|ChiEil|Coef.||0.594||0.566|
|ange n ma<br>Adotion|St.Dev||(0.137)||(0.131)|
|p|T-test||[4.340]||[4.322]|
|ChaneinWebsite|Coef.||0.207||0.171|
|g <br>Adti|St.Dev||(0.065)||(0.062)|
|opon|T-test||[3.199]||[2.736]|
|ChiM’|Coef.|||0.009|0.008|
|ange n anagers<br>i|St.Dev|||(0.004)|(0.003)|
|experence|T-test|||[2.498]|[2.425]|
||Coef.|-0.003|-0.026|0.009|0.074|
|Change in TFPR|St.Dev|(0.076)|(0.075)|(0.076)|(0.05)|
||T-test|[0.045]|[0.346]|[0.120]|[1.488]|
|_R_<sup>2</sup>||0.254|0.266|0.252|0.265|
|_N_|||7,9|26||

Note: Table 5 presents the results from estimating equation 4.8 for the pool sample. For each of the estimated specifications, we use changes in estimated TFPR assuming the same corresponding specification as in Table 3. The estimation controls for sector, country, and time fixed effects. The “exporting status” takes value 1 if the firm sells a product in international markets; “email adoption” takes value 1 if the firm uses email to connect with clients and suppliers; “website adoption” takes value 1 if the firm has a business website; “managerial experience” takes value 1 if the firm has a manager with years of experience above the country-median. “Capital” measures the replacement value of the firm’s assets; “Exporting status” takes value 1 if the firm sells a product in international markets; “Email adoption” takes value 1 if the firm uses email to connect with clients and suppliers; “Website adoption” takes value 1 if the firm has a business website; “Manager’s experience” takes value 1 if the firm has a manager with years of experience above the country-median.

28
# **8 Program Targeting and Complementarities among TFPREnhancing Investments**

A fundamental question that emerges from the analysis is how governments can use the previous findings to guide the design of public programs aimed at fostering digital-technology adoption. Governments are often concerned with “targeting”: that is, identifying the types of firms that can benefit the most from a specific policy. Targeting is important when public resources are limited. Targeting is not trivial as there is heterogeneity in firms’ attributes and performance, even within narrowly defined industries (Syverson 2014).

Another relevant policy question is related to the existence of potential complementarities between productivity-enhancing investments (e.g., upgrading for exporting, improving managerial capabilities, adopting complementary business solutions). This is because complementarities can make multiple-treatment business support programs more effective than those that provide only one arm of support. For example, recent firm-level evidence on digital-technology adoption shows the importance of making complementary investments and organizational innovations to help adopting firms take advantage of their newly adopted digital business solutions (Brynjolfsson et al. 2020; Brynjolfsson, Rock, and Syverson 2017; Bresnahan, Brynjolfsson, and Hitt 2002).

Panels (a) and (b) of Figure 8.1 show the (log)TFPR-premium from email and website adoption for the typical firm. Based on the estimation sample, the typical firm does not export, does not have a business website, and has a manager with 17 years of experience. Both panels in Figure 8.1 show that for the typical firm, the benefits from digital-technology adoption are decreasing in TFPR. Thus, fostering email or website adoption by laggard firms will deliver higher aggregate productivity gains than promoting adoption by leaders. However, if email or website adoption is coupled with other digital-business solutions and 29
<!-- Start of picture text --> Fa<br>Es ° _<br>79839° S385 SANS Nine naesewe ss Taak Seed Rew ae ae ree<br>doen<br>1<br>—tmal (no managerial experiance) Email (median managerial experience)<br>—SFrmad and Pxpor (median managerial experiance) SF mail and Website (median managerial rxperioner)<br>Email, Website, anc Export (median managerial experience)<br><!-- End of picture text --> <!-- Start of picture text --> € °° beeen ene eens es enns oem e amg eS eee nye anne eeenseeanseee<br>S02E 9999 S699 anad AANA i a wee SSES RRR Rees Saree<br>Wogiinon<br>:<br>—Werbsite (manager no experience) —Website (median manager)<br>——Website and Exporter (median manager} —Website and Email (median manager)<br>——Website, Email and Exporter (median manager)<br><!-- End of picture text -->
and launching a business website on TFPR and factor demand. The data and methodology appear to be consistent with the existing literature that focuses only on learning by exporting effects. The empirical strategy builds on the Control Function approach and thus controls for the endogeneity of input choices. In addition, we assume an endogenous productivity process that is a function of firm digitization, learning-by-exporting, and managerial experience. At the time of writing, this paper is the only study that utilizes a large sample of enterprises from across the developing world and simultaneously studies the impact of more than one choice variable on both TFPR and factor demand.

The resulting evidence suggests that digital-technology adoption affects manufacturing firm performance in developing countries. However, the productivity-premium from email and website adoption varies across firms, as do the effects of exporting and managerial experience. Nonetheless, estimates of the median effect of digital technology adoption on TFPR indicate that the expected economic magnitudes (probability-adjusted) of these effects are potentially larger for digital-technology adoption than for exporting or enhancing managerial capabilities. Moreover, there is evidence of complementarities among these choice variables when it comes to their impact on TFPR. Finally, on average and contrary to most of the evidence found in the literature, we do not find a digitization-driven displacement effect on jobs or capital. By contrast, digital technology adoption seems to increase firms’ demand for labor and capital. Last but not least, the evidence from the rich set of interactions suggests that program targeting in developing economies can yield substantial aggregate TFPR gains relative to random treatment selection.

# **References**

Acemoglu, Daron, and David H Autor. 2011. “Skills, tasks and technologies: implications for employment and earnings.” _Handbook of labor economics_ 4:1043–1171.

31
- Acemoglu, Daron, and Pascual Restrepo. 2018. “The race between man and machine: implications of technology for growth, factor shares, and employment.” _American Economic Review_ 108 (6): 1488–1542.

- ~~.~~ 2019a. “Automation and new tasks: how technology displaces and reinstates labor?”

- _Journal of Economic Perspectives_ 33 (2): 3–30.

- ~~.~~ 2019b. “Demographics and automation.” _National Bureau of Economic Research_

- Working Paper No. 24421.

- ~~.~~ 2020a. “Robots and jobs: evidence from US labor markets.” _Journal of Political_

- _Economy_ 128 (6): 2188–2244.

- ~~.~~ 2020b. “Unpacking skill bias: automation and new tasks.” _AEA Papers and Proceed-_

- _ings_ 110 (356–361).

- Ackerberg, Daniel A, Kevin Caves, and Garth Frazer. 2015. “Identification properties of recent production function estimators.” _Econometrica_ 83 (6): 2411–2451.

- Alloway, Tracy. 2015. “Goldman: how ’grand gheft auto’ explains one of the biggest mysteries of the US economy.” _Bloomberg Business._

- Almeida, Rita, Ana M Fernandes, Mariana Viollaz, and Rita Kullberg Almeida. 2017. “Does the adoption of complex software impact employment composition and the skill content of occupations? Evidence from Chilean firms.” _World Bank Group Policy Research_ Working Paper No. 8110.

- Aral, Sinan, Yannis Bakos, and Erik Brynjolfsson. 2018. “Information technology, repeated contracts, and the number of suppliers.” _Management Science_ 64 (2): 592–612.

- Asker, John, Allan Collard-Wexler, and Jan De Loecker. 2014. “Dynamic inputs and resource (mis) allocation.” _Journal of Political Economy_ 122 (5): 1013–1063.

32
- Autor, David H. 2015. “Why are there still so many jobs? The history and future of workplace automation.” _Journal of Economic Perspectives_ 29 (3): 3–30.

- Autor, David H, David Dorn, and Gordon H Hanson. 2015. “Untangling trade and technology: evidence from local labour markets.” _The Economic Journal_ 125 (584): 621– 646.

- Autor, David H, and Anna Salomons. 2018. “Is automation labor share-displacing? Productivity growth, employment, and the labor share.” _The Brookings Institution_ 49 (1): 1– 87.

- Autor, David, David Dorn, Lawrence F Katz, Christina Patterson, and John Van Reenen. 2020. “The fall of the labor share and the rise of superstar firms.” _The Quarterly Journal of Economics_ 135 (2): 645–709.

- Barefoot, Kevin, Dave Curtis, William Jolliff, Jessica R Nicholson, and Robert Omohundro. 2018. “Defining and measuring the digital economy.” _US Department of Commerce Bureau of Economic Analysis_ 15.

- Bloom, N, C Jones, J Van Reenen, and M Webb. 2020. “Are ideas getting harder to find?” _American Economic Review_ 110 (4): 1104–1144.

- Bloom, Nicholas, and John Van Reenen. 2007. “Measuring and explaining management practices across firms and countries.” _The Quarterly Journal of Economics_ 122 (4): 1351– 1408.

- ~~.~~ 2010. “Why do management practices differ across firms and countries?” _Journal of_

- _Economic Perspectives_ 24 (1): 203–24.

- Bond, Stephen, and Måns Söderbom. 2005. “Adjustment costs and the identification of Cobb Douglas production functions.” _Institute for Fiscal Studies_ IFS Working Papers W05/04.

33
- Brambilla, Irene, and Darío Tortarolo. 2018. “Investment in ICT, productivity, and labor demand: the case of Argentina.” _World Bank Group Policy Research_ Working Paper No. 8325.

- Bresnahan, Timothy F, Erik Brynjolfsson, and Lorin M Hitt. 2002. “Information technology, workplace organization, and the demand for skilled labor: firm-level evidence.” _The Quarterly Journal of Economics_ 117 (1): 339–376.

- Brynjolfsson, Erik, Avinash Collis, Erwin Diewert, Felix Eggers, and Kevin J Fox. 2020. “Measuring the impact of free goods on real household consumption.” _AEA Papers and Proceedings_ 110:25–30.

- Brynjolfsson, Erik, and Andrew McAfee. 2014. _The second machine age: work, progress, and prosperity in a time of brilliant technologies._ WW Norton & Company.

- Brynjolfsson, Erik, Daniel Rock, and Chad Syverson. 2017. “Artificial intelligence and the modern productivity paradox: a clash of expectations and statistics.” _National Bureau of Economic Research_ The Economics of Artificial Intelligence: An Agenda:23–57.

- Bustos, Paula. 2011. “Trade liberalization, exports, and technology upgrading: evidence on the impact of MERCOSUR on Argentinian firms.” _American Economic Review_ 101 (1): 304–40.

- Byrne, David M, John G Fernald, and Marshall B Reinsdorf. 2016. “Does the United States have a productivity slowdown or a measurement problem?” _The Brookings Institution:_ 109–182.

- Cameron, A Colin, and Pravin K Trivedi. 2005. _Microeconometrics: methods and applications._ Cambridge University Press.

34
- Comin, Diego, and Bart Hobijn. 2010. “An exploration of technology diffusion.” _American Economic Review_ 100 (5): 2031–59.

- Comin, Diego, and Martí Mestieri. 2018. “If technology has arrived everywhere, why has income diverged?” _American Economic Journal: Macroeconomics_ 10 (3): 137–78.

- Cusolito, Ana Paula, and William F Maloney. 2018. _Productivity revisited: shifting paradigms in analysis and policy._ Edited by License: Creative Commons Attribution CC BY 3.0 IGO. World Bank Group.

- De Loecker, Jan. 2013. “Detecting learning by exporting.” _American Economic Journal: Microeconomics_ 5 (3): 1–21.

- ~~.~~ 2019. “Digital platforms: a technical note.” _World Bank Group._

- DeStefano, Timothy, Richard Kneller, and Jonathan Timmis. 2018. “Broadband infrastructure, ICT use and firm performance: evidence for UK firms.” _Journal of Economic Behavior & Organization_ 155:110–139.

- Draca, Mirko, Raffaella Sadun, and John Van Reenen. 2006. “Productivity and ICT: a review of the evidence.” _Centre for Economic Performance_ Discussion Papers dp0749.

- Dutz, Mark A, Rita K Almeida, and Truman G Packard. 2018. _The jobs of tomorrow: technology, productivity, and prosperity in Latin America and the Caribbean._ Edited by License: Creative Commons Attribution CC BY 3.0 IGO. World Bank Group.

- Dutz, Mark A, Lucas Ferreira Mation, Stephen D. O’Connell, and Robert D Willig. 2017. “Economywide and sectoral impacts on workers of Brazil’s internet rollout.” _World Bank Group Policy Research_ Working Paper No. 8042.

35
- Frey, Carl Benedikt, and Michael A Osborne. 2017. “The future of employment: how susceptible are jobs to computerisation?” _Technological forecasting and social change_ 114:254– 280.

- Gal, Peter, Giuseppe Nicoletti, Theodore Renault, Stéphane Sorbe, and Christina Timiliotis. 2019. “Digitalisation and productivity: in search of the holy grail–firm-level empirical evidence from EU countries.” _OECD Economics Department_ Working Paper No. 1533.

- Goldfarb, Avi, and Catherine Tucker. 2019. “Digital economics.” _Journal of Economic Literature_ 57 (1): 3–43.

- Gordon, Robert J. 2015. “Secular stagnation: a supply-side view.” _American Economic Review_ 105 (5): 54–59.

- ~~.~~ 2017. _The rise and fall of American growth: the US standard of living since the civil_

- _war._ Vol. 70. Princeton University Press.

- Hall, B, and P Monhen. 2013. “Innovation and productivity: an update.” _Eurasian Business Review:_ 47–65.

- Hatzius, Jan, and Kris Dawsey. 2015. “Doing the sums on productivity paradox v2. 0.” _Goldman Sachs US Economics Analyst_ 15/30.

- Helpman, Elhanan, and Manuel Trajtenberg. 1996. “Diffusion of general purpose technologies.” _National Bureau of Economic Research_ Working Paper No. 5773.

- Hjort, Jonas, and Jonas Poulsen. 2019. “The arrival of fast internet and employment in Africa.” _American Economic Review_ 109 (3): 1032–79.

- Iacovone, Leonardo, and Mariana Pereira-López. 2018. “ICT adoption and wage inequality: evidence from Mexican firms.” _World Bank Group Policy Research_ Working Paper No. 8298.

36
Jovanovic, Boyan, and Peter L Rousseau. 2005. “General purpose technologies,” 1:1181–1224.

- Lee, Katherine J, and John B Carlin. 2010. “Multiple imputation for missing data: fully conditional specification versus multivariate normal imputation.” _American Journal of Epidemiology_ 171 (5): 624–632.

- Levinsohn, James, and Amil Petrin. 2003. “Estimating production functions using inputs to control for unobservables.” _The Review of Economic Studies_ 70 (2): 317–341.

- Mokyr, Joel. 2014. “Secular stagnation? Not in your life.” _Geneva Reports on the World Economy:_ 83–89.

- Olley, G. Steven, and Ariel Pakes. 1996. “The dynamics of productivity in the telecommunications equipment industry.” _Econometrica_ 64 (6): 1263–1297.

- Syverson, Chad. 2011. “What determines productivity?” _Journal of Economic Literature_ 49 (2): 326–65.

- ~~.~~ 2014. “The importance of measuring dispersion in firm-level outcomes.” _IZA World_

- _of Labor:_ 1–53.

- ~~.~~ 2017. “Challenges to mismeasurement explanations for the US productivity slow-

- down.” _Journal of Economic Perspectives_ 31 (2): 165–86.

- Van Buuren, Stef, Hendriek C Boshuizen, and Dick L Knook. 1999. “Multiple imputation of missing blood pressure covariates in survival analysis.” _Statistics in Medicine_ 18 (6): 681–694.

- World Development Report. 2019. _The changing nature of work._ Edited by License: Creative Commons Attribution CC BY 3.0 IGO. World Bank Group.

37
World Development Report. 2020. _Trading for development in the age of global value chains._ Edited by License: Creative Commons Attribution CC BY 3.0 IGO. World Bank Group.

38
39