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 address critical endogeneity concerns, the analysis applies international trade in the diffusion of technologies. This difference-in-differences with multiple periods to examine paper analyzes the impact of exporting on firms’ adoption of the effects of entering export markets on technology adop- more sophisticated technologies, using a novel dataset, the tion. The findings show that exporting has a positive effect Firm-level Adoption of Technology survey, which includes on firms’ likelihood of adopting advanced technologies in more than 1,500 firms in Brazil. The survey provides business functions related to business administration, pro- detailed information on the use of more than 300 tech- duction planning, supply chain management, and quality nologies, combined with data from Brazil’s census of formal control, which are important for managing tasks associated workers and export data from the Ministry of Trade. To 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 Cirera1 , Diego Comin2 , Marcio Cruz3 , Kyung Min Lee1 , and Antonio Martins-Neto1 1 The World Bank 2 Dartmouth College 3 IFC, World Bank Group 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,1 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.2 A smaller second set of studies, the focus of this paper, analyzes the impact of exports 1 Regarding imports, Shu and Steinwender (2019) summarize the empirical evidence. The authors dif- ferentiate 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 syn- thesis 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. 2 Two sources of heterogeneity are important when looking at the evidence. First, the type of sector com- petition 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. 1 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. 2 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.3 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.4 Module D focuses on barriers and drivers of technology adoption, while module E gathers information about the firm’s balance sheet and employment. 3 The survey is designed, implemented, and weighted at the establishment level. For multi-establishment firms, the survey targets the establishment randomly selected in the sample. 4 The 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 pharmaceuti- cals); and services (wholesale and retail, financial services, land transport services, accommodation, and health services). 3 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 .5 We define the relative rank of a r −1 technology as rˆf = Rff −1 . Note that rˆf ∈ [0, 1]. The technology sophistication of business 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 (ˆrf,j ). For example, our baseline sophistication measure is sf,j = 1 + 4 ∗ r ˆf,j . (1) 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.6 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 establish- ments are in wholesale and retail. Data were collected face-to-face in 2019 for the state of Cear´ ao Paulo and Paran´ a. For the states of S˜ 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- 5 In 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. 6 The non-uniqueness of latent cardinal variables associated with an ordinal rank such as r ˆf 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. 6 Table 2: Differences between exporters and non-exporters Non-exporter Exporter Mean Std. Dev. Mean Std. Dev. Difference 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 tech- nologies, we perform some correlations between trade status and the technology index. Fig- ure 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. 7 Figure 1: Technology adoption and participation in international trade Note: Figure provides the coefficients of exporter status with 95% confidence intervals from the regres- sions for technology sophistication measures. Each technology measure is regressed on a dummy equal to one if the firm exports. Linear regressions control for sector, size, age, multinational and innovation status, use of formal incentives, financial constraint, and manager’s education and experience abroad. Looking at different business functions and pooling all the data in Figure 2, the average technology index for each business function shows the same ranking in terms of sophistica- tion by exporting groups, with larger sophistication scores for business administration and sales; and much narrower gaps for the intensive measures. Exporters use more sophisticated technologies than non-exporters, particularly at the intensive margin. Figure 2: Technology Sophistication by Business Function by Exporting Status (a) Extensive margin (b) Intensive margin Note: Figure presents simple averages for each group in each business function. 8 Figure 3: Technology Sophistication of Exporter and Non-exporter by Sector Note: Each technology measure is regressed on a dummy equals to one if the firm exports. Linear regressions control for sector, size, age, multinational and innovation status, use of formal incentives, financial constraint, and manager’s education and experience abroad. We define the frontier as a tech- nology sophistication index higher than 3.5, representing around 5% of firms in our sample. Finally, Figure 3 shows the average technology sophistication measures by sector in the sample, excluding services. Differences between the two exporting status groups are larger in food processing and agriculture. These correlation results are consistent and complement other empirical work in developed economies showing that firms that participate in interna- tional trade concentrate a significant number of patents (see Aghion et al. (2018) for French firms) and R&D (see Foster et al. (2020) for US firms). While the results so far just show correlations controlling for key firm characteristics, in the next section we try to disentangle the causal effect of trade exporting on technology sophistication. 4 Methodology To better identify causality on the effect of entering international markets on the adoption of advanced technologies, we use an event study and apply the difference-in-differences with multiple periods described in Callaway and Sant’Anna (2021). As a dependent variable, we focus on adopting advanced technologies for eight general business functions: business admin- istration, production planning, supply chain management, marketing, sales, payment, quality 9 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 Admin- istration; (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: yit = αi + αt + β DD Dit + ϵit (2) where yit is the outcome of interest. Nevertheless, under the presence of time-varying treat- ment effects, the difference-in-differences estimator has been found to be biased (Goodman- 10 Bacon, 2021; Baker et al., 2022). In our case, entering export markets could have hetero- geneous 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.7 The average treatment effect on the treated (ATT) for a treatment-timing group g is thus: AT T (g, t) = E [Yt (g ) − Yt (0)|Gg = 1], for t ≥ g (3) 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.8 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 7 Although 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. 8 Under 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 esti- mate pseudo group-time pre-trend coefficients (when t < g ), which we can use to test the parallel trends assumption. 11 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) (2) (3) (4) (5) (6) (7) (8) Business Production Supply Marketing Sales Payment Quality Fabrication Administration Planning Chain Control 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 Administra- tion 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 in- dication 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 con- sistent 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 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).9 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 Plan- ning 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. 9 In 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. 13 Figure 4: Effect of exporting on the adoption of advanced technologies (a) Business Administration (b) Production Planning (c) Supply Chain Management (d) Quality Control Note: The figure shows the estimates of the interaction between time-to-event dummies and a treatment indicator from a regression including firm fixed effects, time-to-event dummies, and year fixed effects. Estimates also include a dummy for the services sector, the logarithm of wages, and the logarithm of total employment as controls. The dependent variable is a dummy equal to 1 if the establishment adopted a advanced technology in each business function. Vertical bars show estimated 95% confidence intervals. 6 Conclusions Understanding the role of participating in international trade in the diffusion of advanced technologies is critical for developing countries. But while a large literature has focused on the import channels, much less is known on the role of entering export markets in facilitating the diffusion and adoption of new technologies. This paper contributes to this literature by identifying the impact of exporting on the adoption of more sophisticated technologies in Brazil. Using a novel dataset with longitudinal information on exporting and technology use and implementing a difference-in-differences estimator to a sub-sample of establishments in Brazil, we find a positive and statistically significant effect on the likelihood of adopting sophisticated technologies in key business functions for exporting. For example, starting to 14 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. 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Zhang (2012, January). Export Prices Across Firms and Destinations*. The Quarterly Journal of Economics 127 (1), 379–436. eprint: https://academic.oup.com/qje/article-pdf/127/1/379/5187459/qjr051.pdf. Sant’Anna, P. H. and J. Zhao (2020). Doubly robust difference-in-differences estimators. Journal of Econometrics 219 (1), 101–122. Shu, P. and C. Steinwender (2019). The impact of trade liberalization on firm productivity and innovation. Innovation Policy and the Economy 19, 39–68. ´ Alvarez, R. and J. R. Fuentes (2011). Entry into export markets and product quality. The World Economy 34 (8), 1237–1262. 17 Appendix A Examples of the Technology Grid Figure A1 shows the grid for general business functions that all firms, regardless of the sector, respond. Figure A2 shows an example of sector-specific business functions for the food processing sector. Figure A1: General Business Functions and Their Technologies Source: Cirera, Comin, Cruz, and Lee (2020) 18 Figure A2: Sector Specific Business Functions and Technologies in Food Processing Source: Cirera, Comin, Cruz, and Lee (2020) 19