metadata: authors: 0: author: Cirera,Xavier
1: author: Comin,Diego Adolfo
2: author: Vargas Da Cruz,Marcio Jose
3: author: Lee,Kyungmin
4: author: Soares Martins Neto,Antonio
count: Brazil,World
docna: 0: docna: Exporting and Technology Adoption in Brazil
docty: Policy Research Working Paper
theme: Data production, accessibility and use,Business Enabling Environment,Jobs,Public Sector Management,Innovation and Technology Policy,Pandemic Response,Private Sector Development,Disease Control,ICT Solutions,ICT,Data Development and Capacity Building,Human Development and Gender,Job Creation,ICT Policies
abstracts: cdata!: There is limited evidence on the role of
            participating in international trade in the diffusion of
            technologies. This paper analyzes the impact of exporting on
            firms&#x2019; adoption of more sophisticated
            technologies, using a novel dataset, the Firm-level Adoption
            of Technology survey, which includes more than 1,500 firms
            in Brazil. The survey provides detailed information on the
            use of more than 300 technologies, combined with data from
            Brazil&#x2019;s census of formal workers and export data
            from the Ministry of Trade. To address critical endogeneity
            concerns, the analysis applies difference-in-differences
            with multiple periods to examine the effects of entering
            export markets on technology adoption. The findings show
            that exporting has a positive effect on firms&#x2019;
            likelihood of adopting advanced technologies in business
            functions related to business administration, production
            planning, supply chain management, and quality control,
            which are important for managing tasks associated with
            export activities.
display_title: Exporting and Technology Adoption in Brazil
listing_relative_url: /research/2023/05/34067620/exporting-technology-adoption-brazil
topicv3: Firm Innovation, Productivity and Growth
            m1327229 377,Entry and Survival into Exports m1504231 2617
publishtoextweb_dt: 2023-05-24T00:00:00Z
dois: 10.1596/1813-9450-10454
author_keys: 0: author_key: 864597
1: author_key: 914858
2: author_key: 1161834
3: author_key: 1756360
4: author_key: 1826557
model_extractions: Policy Research Working Paper 10454

# Exporting and Technology Adoption in Brazil

_Xavier Cirera Diego Comin Marcio Cruz Kyung Min Lee Antonio Martins-Neto_ Finance, Competitiveness and Innovation Global Practice May 2023
Policy Research Working Paper 10454

# **Abstract**

There is limited evidence on the role of participating in international trade in the diffusion of technologies. This paper analyzes the impact of exporting on firms’ adoption of more sophisticated technologies, using a novel dataset, the Firm-level Adoption of Technology survey, which includes more than 1,500 firms in Brazil. The survey provides detailed information on the use of more than 300 technologies, combined with data from Brazil’s census of formal workers and export data from the Ministry of Trade. To address critical endogeneity concerns, the analysis applies difference-in-differences with multiple periods to examine the effects of entering export markets on technology adoption. The findings show that exporting has a positive effect on firms’ likelihood of adopting advanced technologies in business functions related to business administration, production planning, supply chain management, and quality control, which are important for managing tasks associated with export activities.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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 xcirera@worldbank.org or asmartins@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
# Exporting and Technology Adoption in Brazil

Xavier Cirera<sup>1</sup> , Diego Comin<sup>2</sup> , Marcio Cruz<sup>3</sup> , Kyung Min Lee<sup>1</sup> , and Antonio Martins-Neto<sup>1</sup>

> 2Dartmouth College

## **JEL Codes:** D2, E23, L23, O10, O40

**Keywords:** Technology, International Trade, Adoption and diffusion

> ∗Corresponding author: Xavier Cirera (xcirera@worldbank.org). We thank Martha Denisse Pierola, Santiago Reyes Ortega, and the participants of the Festschrift conference in honor of Professor L. Alan Winters (Economic Policy, Openness, and Development).
# **1 Introduction**

A critical question for economic development is the role of international trade in facilitating the adoption and upgrading of technologies. Participating in international trade can support the diffusion through different channels. Regarding imports, the competitive pressure from increased imports of similar goods can incentivize technology upgrading to diversify to other products but also reduce the rents and push some producers to lower quality segments; thus, disincentivizing innovation and technology adoption. Easier and cheaper access to imports can also facilitate the adoption of new technologies via a reduction in costs and by improving availability of such technologies. In addition, participation in international trade and global value chains (GVCs) can facilitate learning and access to existing technologies via learning from customers in more contested markets or learning from suppliers or buyers.

A rapidly growing literature has explored the links between trade and innovation as well as technology adoption and upgrading. This literature has explored different channels. The largest share of studies has focused on the impact of imports. Particularly through two specific channels; the impact of imports of intermediate inputs and equipment and the competitive pressure from increasing imports in similar products, such as reductions in tariffs or, more importantly, the China shock. The evidence of these studies is mixed,<sup>1</sup> and emphasizes that the type of market and the type of firm is critical in understanding the impact on technology adoption and innovation from imports.<sup>2</sup> A smaller second set of studies, the focus of this paper, analyzes the impact of exports

> 1Regarding imports, Shu and Steinwender (2019) summarize the empirical evidence. The authors differentiate between the so-called “Schumpeterian” effect, through which increased competition reduces rents and discourages technology upgrading, and the “escape competition” effect, through which some firms use technology upgrading and innovation to upgrade their products and escape import competition. Their synthesis of the evidence suggests that, in general, increases in imports following trade liberalization tend to be positive on innovation, especially in developing countries (Gorodnichenko et al., 2010) and regarding the imported intermediaries channel. However, in general, the effect of import competition is mixed, especially regarding firms in developed economies. For example, looking at the “China shock”, Bloom et al. (2015) find for a sample of firms in 12 European countries that the China competitive pressure positively impacted both technology upgrading and reallocation. In contrast, Autor et al. (2020) using a sample of US firms, find the increased competition from China translated into a reduction of technology patents and R&D.

> 2Two sources of heterogeneity are important when looking at the evidence. First, the type of sector competition affects innovation. Aghion et al. (2005) estimate an inverted U relationship between competition and innovation and how more likely competition will affect firms in neck-to-neck competition sectors positively and negatively in laggards. Second and related, more productive firms are more likely to benefit from the impact of trade on technology adoption. Akcigit and Melitz (2022) develop a model where firms decrease innovation investments when experiencing import shocks. Still, those firms that are better positioned can “escape” this competition by innovating and upgrading. Using data on Indian firms, Bas and Berthou (2016) find that the trade liberalization process in the 1990s shows how only firms in the middle-upper productivity deciles increased technology adoption and the import of capital goods following tariff cuts in intermediaries. Thus, the firm’s productivity level is important for the “escape competition channel” but also the “learning from intermediates” channel.

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on technology upgrading and innovation. Regarding exports, two important channels are at play. First, a scale effect increases the incentives to adopt new technologies. Bustos (2011) shows how tariff reductions in Argentina in the context of MERCOSUR incentivized firms to adopt new technologies given the larger scale and profits. This positive effect, however, concentrated on firms at the top of the productivity distribution. Lileeva and Trefler (2010) analyze the impact of tariff reductions in the U.S. on Canadian plants and show that this had a positive impact on exporters, especially on lower productivity plants that are export entrants. Thus, the positive scale effect can also benefit lower productivity plants, but only if they enter export markets. A second channel is the “learning” channel. Atkin et al. (2017) conduct an experimental design with Egyptian rug producers by randomly assigning export contracts and find an increase in quality and learning for those producers that get the export contract.

In sum, this literature finds that there is a positive “learning” effect, which also applies to imports of intermediates, and a “scale” effect for exporters that increases their incentives to upgrade their technologies. The evidence summarized in Shu and Steinwender (2019) finds some evidence for all these channels, with some of the positive effects concentrated among more productive firms.

Identifying the sign and magnitude of the effects of exporting on technology adoption is challenging for three reasons. First, disentangling the causal direction of these effects is difficult, given that more productive firms tend to export and participate in international markets and, accordingly, are more likely to be technologically sophisticated. In addition, in preparation for exporting, firms may upgrade their technologies to generate competitiveness gains and quality upgrades, allowing them to export. A second challenge is the lack of data on technology use. Most of the evidence focuses on indirect technology measures such as patents or R&D; only Bustos (2011) and Lileeva and Trefler (2010) use direct technology measures. Third, the use of technology is multidimensional in its application to different business functions. Establishments use different technologies for different tasks, and even within the same business function. Thus, the export effects on technology adoption may differ for different tasks and technologies.

In this paper, we aim to narrow the existing gap in the literature in understanding the relationship between exporting and the technology gap. We use a unique and novel database, the Firm-level Adoption of Technology (FAT) survey and explore the impact of exporting on technology sophistication and the adoption of selected individual technologies. The survey includes more than 1,500 firms in Brazil and provides granular information on the adoption of more than 300 technologies for different business functions as well as participation in international trading activities.

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To address endogeneity concerns, we take advantage of the information collected about the year of adoption of more sophisticated technologies - when adopted - and merge the data with a longitudinal dataset that includes data on export status from Brazil’s Ministry of Trade by firm and year. Moreover, to capture longitudinal information on firms’ number of employees and average wages, we combine the dataset with the census of formal workers in Brazil (RAIS). The combined dataset allows us to use a quasi-experimental design to explore the effect of entering export markets on the adoption of sophisticated technologies.

Advancing our key results, we find that entering export markets increases firms’ likelihood of adopting advanced technologies linked to Business Administration (such as Enterprise Resource Planning (ERP)), Supply Chain Management (such as SRM), and Quality Control. The paper is structured as follows. Section 2 describes the data. Section 3 provides some initial correlations between exporting and technology use. Section 4 describes the methodology used to identify the impact of exporting on technology. Section 5 shows the main results. The last section concludes.

# **2 The data**

## **2.1 The Survey**

The Firm-level Adoption of Technology (FAT) survey collects detailed information for a sample of firms about the technologies each firm adopts and uses to perform key business functions necessary to operate in its respective sector (see Cirera et al. (2020)). The survey is composed of five modules. Module A collects information on the general characteristics of the firm.<sup>3</sup> Module B focuses on technologies used for general business functions regardless of the sector where they operate, and sector-specific business functions (module C) focus on technologies that are relevant only for firms in a given sector.<sup>4</sup> Module D focuses on barriers and drivers of technology adoption, while module E gathers information about the firm’s balance sheet and employment.

> 3The survey is designed, implemented, and weighted at the establishment level. For multi-establishment firms, the survey targets the establishment randomly selected in the sample.

> 4The twelve sectors for which we have developed sector-specific modules are: agriculture and livestock; manufacturing (food processing, wearing apparel, leather and footwear, motor vehicles, and pharmaceuticals); and services (wholesale and retail, financial services, land transport services, accommodation, and health services).

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# **2.1.1 Technology grid**

A critical feature of the survey is how technology is measured. To design modules B and C, the FAT survey relies on a group of technology experts to determine the business functions relevant to the firm and the list of technologies that can be used to implement the key tasks in each function, as described by Cirera et al. (2020). We call the resulting structure the _Technology Grid_ . The grid in FAT has three characteristics. First, it is comprehensive. It includes the main business functions and the full array of technologies in each function, from the most basic to the most advanced technologies available. Second, the business functions and technologies in the grid are relevant to all firms within any given sector. In addition to identifying the key business functions and relevant technologies, technology experts also provided a ranking of the technologies in each business function based on their sophistication. Overall, the FAT survey covers about 300 technologies split into almost 60 business functions, including general business functions (GBF) that apply to all firms, regardless of the sector, and sector-specific business functions (SBFs) applied to agriculture (crops and livestock), manufacturing (food processing, wearing apparel, leather, pharmaceutical, and automotive), and services (retail, accommodation, land transport, banking, and health). Appendix A shows the grid for GBFs and an example of SBFs for the food processing sector.

# **2.1.2 Technology questions**

The survey contains three types of questions about the technologies used by the firm. First, FAT asks whether the firm uses each of the technologies in the grid to conduct the tasks of the particular business function. After determining the technologies that are used by the firm in a business function, the survey asks which of these technologies is the most widely used in the business function. Third, when a firm uses an advanced technology in a given business function, the survey asks how many years the technology has been adopted. This allows us to produce three types of measures of sophistication. One regarding all the technologies that are used, extensive measure (EXT); one regarding the most frequently used technology, intensive measure (INT); and finally, the years of adoption for advanced technologies.

# **2.1.3 Summary technology sophistication index**

As an aggregate indicator to measure sophistication we use a simple cardinal index. Based on the experts’ assessment, we order the technologies in each function _f_ according to their sophistication, and assign each a rank _rf ∈_ 1 _,_ 2 _, ..., Rf_ , from least to most advanced. Because several technologies may have the same sophistication, the highest rank in a function _Rf_ may 4
be smaller than the number of possible technologies _Nf_ .<sup>5</sup> We define the relative rank of a technology as _r_ ˆ _f_ = _Rrff−−_ 11<sup>.Notethat</sup><sup>_r_ˆ</sup><sup>_f∈_[0</sup><sup>_,_1].Thetechnologysophisticationofbusiness</sup> function _f_ in firm _j_ is a monotonic increasing function of the relative rank of _the most widely used technology_ of firm _j_ in function _f_ (ˆ _rf,j_ ). For example, our baseline sophistication measure is Since our baseline sophistication measure is linear, it displays constant increments in sophistication as we move up in the rank. For example, a firm that uses ERP for production planning, the frontier technology has a score of 5, while one that uses specialized planning software would have an index of 4. A priori, the sophistication measures could also be concave or convex in the rank, reflecting diminishing or increasing marginal increments in sophistication as the rank increases. In Cirera et al. (2020), we show how this simple index is robust to alternative cardinalizations.<sup>6</sup> but we use the index only in the descriptive statistics section, moving to adoption of specific advanced technologies in the empirical section.

# **2.2 Sample**

We use an original sample of about 1,500 establishments from Brazil. The data includes information from formal establishments in agriculture, manufacturing, and services with at least five employees. Table 1 contains detailed information for our sample, disaggregated at narrowly defined industries. For instance, in manufacturing, a large share of establishments are in food processing and wearing apparel, whereas in the services sectors, most establishments are in wholesale and retail. Data were collected face-to-face in 2019 for the state of Cear ́a. For the states of S ̃ao Paulo and Paran ́a, interviews were carried during 2022.

In addition to detailed information on the technology used for each business function, the FAT survey also includes information on several firms’ characteristics, which we use to control for other covariates likely to explain differences in technology adoption. For example, other than firms’ size, region, and sector, the database includes information on managers’ and workers’ education, the use of formal incentives and performance indicators, and in-

> 5In a small number of business functions, the technologies covered are used in various subgroups of tasks. For example, in the body pressing and welding functions of the automotive sector, the survey differentiates between technologies used for pressing skin panels, pressing structural components and welding the main body. In cases like this, we construct ranks of technologies for each subgroup of tasks within the business function, and then aggregate the resulting indices by taking simple averages across the task groups.

> 6The non-uniqueness of latent cardinal variables associated with an ordinal rank such as _r_ ˆ _f_ is common in many economic applications such as measures of institutional quality, quality of education, well-being, trust, social norms, and sophistication of management practices, to name a few. However, it is critical to demonstrate that these indices and results are robust to alternative plausible cardinalizations of the ordinal rankings they measure.

5
Table 1: Sample distribution by sector

|**Sector**|**Frequency**|**Share**|
|---|---|---|
|Agriculture|65|4.2%|
|Livestock|31|2.0%|
|Food Processing|211|13.8%|
|Apparel|167|10.9%|
|Motor vehicles|77|5.0%|
|Pharmaceuticals|8|0.5%|
|Wholesale or retail|319|20.8%|
|Financial services|4|0.3%|
|Land transport|18|1.2%|
|Health services|15|1.0%|
|Other Manufact.|263|17.2%|
|Other Services|353|23.1%|
|**Total**|1531|100%|

novation practices, among others. Table 2 offers a description of the information available in the database and presents the main differences between exporters and non-exporters. For instance, the first four lines describe the gap between exporters and non-exporters for the logarithm of the four technological indexes. Non-exporters show, on average, 11% to 22% lower indexes, are also significantly smaller, interact less with multinational enterprises (MNEs), and receive less government support. Moreover, fewer non-exporters use formal incentives and performance indicators. The gap is also large for managers with a college degree, experience in large companies, or experience abroad. Finally, exporters are more likely to innovate and show a larger share of R&D employees.

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Table 2: Differences between exporters and non-exporters

|||Non-exporter||Exp|orter|
|---|---|---|---|---|---|
||Mean|Std. Dev.|Mean|Std. Dev.|Diference|
|GBF EXT|1.10|0.26|1.30|0.19|0.18***|
|GBF INT|0.84|0.30|1.10|0.24|0.22***|
|SBF EXT|1.00|0.34|1.20|0.37|0.19***|
|SBF INT|0.66|0.37|0.77|0.41|0.11***|
|Number of employees|108.33|334.58|674.35|1480.48|566.01***|
|Multinational|0.03|0.17|0.15|0.36|0.13***|
|Interaction with MNEs|0.51|0.50|0.86|0.35|0.35***|
|Government support|0.14|0.35|0.32|0.47|0.17***|
|Financial constraints|0.19|0.39|0.20|0.40|0.01|
|Family company|0.097|0.30|0.15|0.36|0.05*|
|Formal incentives|0.54|0.50|0.62|0.49|0.08*|
|Performance indicators|0.40|0.37|0.66|0.36|0.26***|
|Manager’s with college|0.56|0.50|0.74|0.44|0.17***|
|Manager’s experience (years)|24.36|11.51|27.02|14.50|2.66**|
|Experience in large company|0.30|0.46|0.49|0.50|0.19***|
|Studied abroad|0.12|0.32|0.28|0.45|0.16***|
|Share of college-educated employees|0.12|0.15|0.18|0.23|0.07***|
|Share of R&D employees|0.002|0.01|0.007|0.01|0.01***|
|Innovation|0.26|0.44|0.60|0.49|0.34***|

Note: Table shows descriptive statistics and differences by exporter status. First rows present the logarithm of the technology indexes including GBF (EXT and INT) and SBF (EXT and INT). The last column is the coefficient of a simple regression of trade status on the variable. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

# **3 Descriptive statistics**

To begin exploring the relationship between exporting and the adoption of advanced technologies, we perform some correlations between trade status and the technology index. Figure 1 shows the coefficient estimates of regressing the different aggregate technology indices on dummies to capture trading status, controlling for sector, dummies for firms’ size and age, and additional control variables. The indices include the extensive measure (EXT) and the intensive measure (INT) for both general business functions (GBFs) and sector specific business function (SBF). The estimates show a positive correlation between exporter status and technology sophistication for all indices.

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<!-- Start of picture text --> 4<br>15<br>~c<br>Bo)<br>ar)<br>£<br>o<br>fe}<br>oO<br>.25<br>0<br>Exporter<br>[__] GBFEXT [4 GBFINT [__] SBFEXT [GG SBF INT<br><!-- End of picture text --> <!-- Start of picture text --> Business Admin.<br>Quality control Production Planning<br>Payment Sourcing<br>4<br>5<br>Sales Marketing<br><!-- End of picture text --> <!-- Start of picture text --> Business Admin.<br>Quality control Production Planning<br>Payment Sourcing<br>Sales Marketing<br><!-- End of picture text -->
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control, and fabrication (only available for firms in manufacturing). The list of technologies for each business function includes: (i) specialized software and ERP for Business Administration; (ii) specialized software and ERP for Production Planning; (iii) non-integrated and integrated Supplier Relation Management (SRM) for Supply Chain Management; (iv) Customer Relationship Management software (CRM) and Big data Analytics or Machine learning algorithms for Marketing; (v) computer numerical controlled machine, robots, and advanced manufacturing for Fabrication; (vi) online sales and electronic orders integrated to specialized supply chain management systems for Sales Methods; (vii) online or electronic payment through a bank wire and online payment through platform for Payment Methods; and, (viii) statistical process control with software monitoring and data management and automated systems for inspection for Quality Control. For instance, in the case of business administration, we have information on whether firms adopted specialized software or ERP and, more importantly, the date on which the firm adopted it. Using the years of adoption, we create an indicator for each business function equal to 1 from the year the firm adopted a given advanced technology and 0 in the previous years.

We then combine firms’ information on the year of adoption of sophisticated technologies with firms’ export status from the data available in Brazil’s Ministry of Trade. Thus, for each firm in our dataset, we have information on the year it started to export and the year it adopted a sophisticated technology in each business function. We also merge this data with the employer-employee census, including firm-level information on size and average wage. The resulting longitudinal dataset from 1994 to 2020 allows us to use a quasi-experimental design (difference-in-differences estimator) to explore the effect of entering export markets on adopting advanced technologies. In essence, we aim to compare the adoption rates of treated firms over the short and medium run with the adoption that would have occurred if they had not started to export.

In a typical difference-in-differences setting, we are confronted with two time periods: no firm is treated in the first period, and a group is treated in the second. Nevertheless, in our setting, in addition to multiple periods, firms enter exporting markets at different times, thus creating variation in the treatment timing. Traditionally, the response to this challenge is by estimating a model that includes dummies for cross-sectional units ( _αi_ ) and time periods ( _αt_ ) and a treatment dummy ( _Dit_ ). For example, the basic event study model would be: where _yit_ is the outcome of interest. Nevertheless, under the presence of time-varying treatment effects, the difference-in-differences estimator has been found to be biased (Goodman10
Bacon, 2021; Baker et al., 2022). In our case, entering export markets could have heterogeneous effects on technology adoption over time, especially considering variation in costs and technology diffusion. To address this issue, we take advantage of recent developments in the difference-in-differences literature and apply the multiple periods estimator proposed by Callaway and Sant’Anna (2021). The method breaks down several treatment periods into group-time average treatment effects (the average treatment effect in period _t_ for the group of units first treated in period _g_ ) and aggregates them into meaningful measures of the causal effects.<sup>7</sup> The average treatment effect on the treated (ATT) for a treatment-timing group _g_ is thus: where _Gg_ denotes the time when unit _i_ receives treatment and _Gg_ = _g_ for all firms that receive treatment at time period _g_ . For instance, take the case where there are five groups, each of which gets treated in 2010, 2011, 2012, 2013, and 2014, and the panel ends in 2016. – As a result, the model estimates a total of 15 group-time ATTs 5 ATT(g,t) for the first group, 4 for the second, 3 for the third, 2 for the fourth, and 1 for the last.<sup>8</sup> In most of our discussion, we focus on a weighted average of post-treatment average effects from _t_ to _t_ + 5 with weights proportional to the group size. The model assumes parallel trends of the potential outcome in the absence of treatment, which we relax to hold only conditional on the covariates. In addition to a dummy indicating firms in the services sector, we add the logarithm of employment and average wages as control variables so that parallel trends hold only after conditioning on a vector of pre-treatment covariates. Finally, estimates use the doubly robust estimator based on stabilized inverse probability weighting and ordinary least squares proposed by Sant’Anna and Zhao (2020).

# **5 Results**

Table 3 shows the main results of estimating the impact of entering export markets on the probability of adopting, which are based on the average treatment effect on the treated from

> 7Although data from the Ministry of Trade includes information on the first and last years a given firm exported, the method proposed by Callaway and Sant’Anna (2021) assumes that treated units remain treated during all subsequent periods.

> 8Under the no-anticipation and parallel trends assumptions, group-time average treatment effects are identified in periods when _t ≥ g_ (i.e., post-treatment periods for each group). In practice, we also estimate pseudo group-time pre-trend coefficients (when _t < g_ ), which we can use to test the parallel trends assumption.

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_t_ to _t_ + 5. We find a positive and significant impact of entering the international market on adopting more sophisticated technologies for most business functions, with particularly large coefficients for Business Administration, Production Planning, Supply Chain Management, and Quality Control. For instance, after starting to export, establishments tend to have a 13.7% larger propensity of adopting specialized software or ERP for Business Administration, compared to those not exporting. Moreover, in the case of Quality Control, the export status is associated with an 8.9% larger probability of adopting statistical process control with software monitoring and data management or automated systems for inspection. It is also interesting to note that coefficients are positive for all business functions - although not statistically significant in some cases.

Table 3: Effect of exporting on the adoption of advanced technologies for business functions

||(1)<br>Business<br>Administration|(2)<br>Production<br>Planning|(3)<br>Supply<br>Chain|(4)<br>Marketing|(5)<br>Sales|(6)<br>Payment|(7)<br>Quality<br>Control|(8)<br>Fabrication|
|---|---|---|---|---|---|---|---|---|
|ATT|0.137***|0.065**|0.063**|0.035|0.043*|0.025|0.089***|0.008|
||(0.043)|(0.033)|(0.029)|(0.023)|(0.026)|(0.033)|(0.028)|(0.043)|
|N|19,916|19,916|19,916|19,916|19,916|19,916|19,916|2,183|

Note: For each business function, the dependent variable is a dummy equal to 1 from the year the firm *** ** * adopted an advanced technology and 0 otherwise. p _<_ 0.01, p _<_ 0.05, p _<_ 0.1.

Figure 4 panel (a) shows the disaggregated coefficient estimates for Business Administration from _t−_ 5 to _t_ +5 from the event study. The results indicate that during the years before treatment, coefficients are not statistically different from zero, which we interpret as an indication that the parallel trends assumption holds and that there is no anticipation effect. In contrast, following the treatment, we observe a clear positive effect, which increases over time. The results are consistent with a model in which export increases firms’ managerial layers (Caliendo and Rossi-Hansberg, 2012; Garicano and Rossi-Hansberg, 2014). To cope with more complex tasks induced by trade participation, firms raise the number of managers and adopt more sophisticated technologies for business administration. Results are also consistent with the scale effect channel, through which larger demand induces the adoption of new technologies (Bustos, 2011).

We also find similar results for Production Planning, Supply Chain Management, and Quality Control. Coefficients are positive from _t_ to _t_ + 5, without signs of preparation to export. The findings align with the literature showing that firms raise product quality as they enter international markets (Alvarez<sup> ́</sup> and Fuentes, 2011). Export markets carry higher quality requirements, and exporting firms produce higher-quality products by increasing the quality of their inputs and varying the quality of their products across destinations (Kugler 12
and Verhoogen, 2008; Manova and Zhang, 2012).<sup>9</sup> Our results show that as firms adapt to more restrictive quality standards, they adopt more advanced technologies for quality control.

Finally, the positive effect on the adoption of advanced technologies in Production Planning and Supply Chain Management is likely to be associated with the need to manage more efficiently and timely the production process and the increasing number of buyers and suppliers. For instance, availability of high-quality intermediate goods is often limited in developing countries’ local markets. As firms enter export markets, they not only engage with additional buyers but are also likely to expand the range of suppliers to acquire better intermediate goods and better manage risks associated to disruptions in the supply chain, since the costs of not fulfilling export orders are higher.

> 9In fact, Iacovone and Smarzynska Javorcik (2012) show that firms raise output prices two years before entering exporting markets, which suggests that the quality-upgrading process takes place in preparation to export.

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export is associated with a 13.7% larger probability of adopting specialized software or ERP for Business Administration; and an 8.9% larger probability of adopting statistical process control with software monitoring and data management for inspection in quality control. We also find positive and significant effects on the probability of adoption in Production Planning or Supply Chain Management. The evidence presented is consistent with models that suggest that exporting increases the complexity of tasks and processes within the firm, and these requires better technologies to aid managing these tasks and processes.

While the evidence presented here is also aligned with other empirical work showing a positive impact of exporting on innovation, more evidence is needed to identify the key channels that explain this positive relationship. For example, what is the role that international buyers play in transferring these more advanced technologies, or what role does competing in more contested international markets play in incentivizing technology upgrading.

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