metadata: authors: 0: author: Vargas Da Cruz,Marcio Jose
count: Brazil
docna: 0: docna: Do export promotion agencies promote new
            exporters ?
docty: Policy Research Working Paper
subtopic: Governance Diagnostic Capacity Building,Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Export Competitiveness,Educational Sciences,International Trade and Trade Rules,Economic Forecasting,Macroeconomic Management,Business Cycles and Stabilization Policies
teratopic: International Economics and Trade
abstracts: cdata!: Do export promotion agencies impact the
            probability of non-exporting firms to export? In the last
            decade many countries have introduced export promotion
            agencies to support their firms to deal with asymmetric
            information problems and make feasible additional gains from
            trade. Some recent studies have found that the support of
            these agencies has been effective with respect to the
            intensive and extensive margins of trade. Nevertheless,
            because of the lack of information on non-exporting firms,
            few of them analyze their impact on the probability of
            promoting new exporters. This paper evaluates the impact of
            the Brazilian Trade and Investment Promotion Agency
            (Apex-Brasil) on firms' export status using a unique
            firm-level dataset that covers the full manufacturing sector
            in Brazil. To identify the impact of Apex's assistance
            on firms' export propensity, the paper relies on a
            procedure of matching difference-in-difference estimators.
            The empirical results show evidence of the program's
            positive impact on the probability of promoting new
            exporters. The effect is heterogeneous according to
            firms' size categories and sectors. Furthermore, the
            findings suggest that the program has spillover effects.
            Although the evidence of positive effect is robust, the low
            propensity to export for both the treated and the control
            groups reinforces the importance of other firms'
            determinants (for example, productivity), which is widely
            emphasized by the trade literature.
geo_reg_key: 119228,517191
repnme: repnme: Do export promotion agencies promote new
            exporters ?
display_title: Do export promotion agencies promote
            new exporters
listing_relative_url: /research/2014/08/20119993/export-promotion-agencies-promote-new-exporters
topicv3: Manufacturing Sectors m1326521 1281,Trade m1326924 698,Education m1327048 470,Market Failures m1327149 1068,Export Promotion m1504271 2566
publishtoextweb_dt: 2015-09-02T00:00:00Z
historic_topic: International Economics and Trade
no_of_pages: 43
author_keys: 0: author_key: 1161834
model_extractions: WPS7004 Policy Research Working Paper 7004 Do Export Promotion Agencies Promote New Exporters?

_Marcio Cruz_ Development Prospects Group Global Modeling and Analytics Team August 2014
Policy Research Working Paper 7004

# **Abstract**

Do export promotion agencies impact the probability of non-exporting firms to export? In the last decade many countries have introduced export promotion agencies to support their firms to deal with asymmetric information problems and make feasible additional gains from trade. Some recent studies have found that the support of these agencies has been effective with respect to the intensive and extensive margins of trade. Nevertheless, because of the lack of information on non-exporting firms, few of them analyze their impact on the probability of promoting new exporters. This paper evaluates the impact of the Brazilian Trade and Investment Promotion Agency (ApexBrasil) on firms’ export status using a unique firm-level dataset that covers the full manufacturing sector in Brazil. To identify the impact of Apex’s assistance on firms’ export propensity, the paper relies on a procedure of matching difference-in-difference estimators. The empirical results show evidence of the program’s positive impact on the probability of promoting new exporters. The effect is heterogeneous according to firms’ size categories and sectors. Furthermore, the findings suggest that the program has spillover effects. Although the evidence of positive effect is robust, the low propensity to export for both the treated and the control groups reinforces the importance of other firms’ determinants (for example, productivity), which is widely emphasized by the trade literature.

This paper is a product of the Global Modeling and Analytics Team, Development Prospects Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mvargasdacruz@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
Do Export Promotion Agencies Promote New Exporters?<sup>_∗_</sup>

# Marcio Cruz<sup>_†_</sup>

**JEL Classification:** D04, D22 F13, F14

**Keywords:** Trade, Export Promotion Agencies; New Exporters; Impact Evaluation; Brazil.

> _∗_ I thank Jean-Louis Arcand, Nicolas Berman, Marcelo Olarreaga, Leonardo Iacovone, Maurizio Bussolo, Christian Volpe Martincus, Jer ́onimo Carballo, Richard Baldwin, Jonathan Eaton, Gordon Hanson, Johannes Biesebroeck, Mark Dutz, Leila Rafei, Aleksandra Zaronina, Ieva Zumbyte, participants of the Development Therapy in Geneva (November, 2011) and participants of the IDB Workshop on Internationalization of SMEs in WashingtonDC (October, 2013) for their comments. I also thank the team of DISET-IPEA in Bras ́ılia, particularly Fernanda De Negri, Jo ̃ao De Negri, Bruno Oliveira and Lucas Rocha, for granting access to data from the Brazilian Ministry of Labor and for comments received in a seminar (February, 2013). I also thank Marcos L ́elis, Rˆomulo Clezar and all the team of Intelligence Unit of Apex-Brasil for granting access to Apex’s data and for their help in understanding the program. The data was used under confidentiality agreement.

> _†_ Graduate Institute of International and Development Studies, Federal University of Paran ́a and the World Bank. Email: marcio.cruz@graduateinstitute.ch.
# **1 Introduction**

Recent literature on international trade has focused on understanding the determinants of exporting at the firm level, in an environment characterized by heterogeneous firms. Since the seminal paper of Melitz (2003) and following a sharp increase in the availability of access to firm- and plant-level data, many empirical studies have supported an approach that emphasizes two main components to explain why only some firms become exporters: their heterogeneous productivity and sunk costs of entry into foreign markets.

An important feature surrounding this literature is the fact that exporters are typically larger, more productive and pay higher wages, besides other positive characteristics. Therefore, identifying a possible causal relationship between exporting status and productivity has become a topic that has received significant attention in the trade literature. The most prevalent view is that most efficient firms self-selected themselves into the export market (see Roberts and Tybout (1997); Clerides et al. (1998); Bernard and Jensen (2004)). However, De Loecker (2007) and Lileeva and Trefler (2010) find that improving foreign market access may increase the productivity of some exporting plants.

A usual justification for the services of export promotion agencies (EPAs) is the existence of market failures which prevent producers from reaching foreign markets despite the fact that they might have enough productivity to compete abroad. For example, Waugh (2010) demonstrates the existence of “asymmetric frictions” in which developing countries face systematically higher costs for exporting than industrialized ones. Also, Cadot et al. (2011) show evidence of network effects (positive externality) for exporters of similar products to those firms exporting for the same destinations. Furthermore, Eaton et al. (2010) show that the behavior of exporting firms reflects a process of searching and learning in the foreign market, where these firms spend resources on identifying buyers and learn about the potential of their products for that market. A high entry cost for learning about foreign markets and uncertainty due to information frictions can deter potential exporters from doing so (see Allen (2011)).

Therefore, the presence of market failures such as externalities and asymmetric information might support some mechanisms of government-sponsored export promotion as a way to make some additional gains from trade feasible.<sup>1</sup> Although export promotion policies should be taken cautiously<sup>2</sup> , they have been applied in many different countries and a significant amount of

> 1Copeland (2007) provides a critical review on the literature and emphasizes two sources of market failure that could justify this kind of interventions: information spillovers and asymmetries of information.

> 2There are important aspects regarding political economy and efficiency to be taken into consideration.

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resources have been allocated to that, which justifies the necessity of a more rigorous analysis to understand the results of these agencies.<sup>3</sup> During the last two decades, a wide variety of literature has covered the impact of export promotion agencies around the world looking into questions related to different margins of trade (see Espinoza and Crespi (2000), Bernard and Jensen (2004), Volpe Martincus (2010), Lederman et al. (2010), Gil-Pareja et al. (2014), Hayakawa et al. (2011), Cadot et al. (2012), Schminke and Van Biesebroeck (2013)). However, few of them (e.g. Bernard and Jensen (2004) and Schminke and Van Biesebroeck (2013)) are analyzing the impact of EPAs on the pure extensive margin.<sup>4</sup> In this paper I evaluate the impact of services provided by the Brazilian Trade and Investment Promotion Agency (Apex-Brasil) on the export propensity of non-exporting firms. The paper uses a unique firm-level database covering the full manufacturing sector in Brazil with information on approximately 300,000 firms from 2005 to 2010. In addition to providing detailed information on firms’ characteristics, the data allows the identification of firms that received support from Apex. To identify the causal effect of the program on the probability to export, I rely on a procedure of matching difference-in-difference estimators.

This essay contributes to the prevailing literature in several ways. First, it provides policyrelevant evidence on a largely unexamined issue: the impact of EPAs on promoting new exporters, using a uniquely rich dataset for a large economy. Second, it evaluates potential heterogeneous effects with respect to the different size of the firms. Third, it evaluates the impact of the program on promoting new exporters among untreated firms by testing the spillover hypothesis. To my knowledge, the impact of EPAs’ assistance on the export propensity of non-exporting firms in developing or emerging countries has not been addressed before.

The results show that Apex’s treatment increases the probability of a non-exporting firm to start exporting by 2.3 percentage points (pp) one year after the program is implemented and by about 2.2 pp in the year of program implementation. This means that firms that received the program increased their propensity to export by almost 130% (from 1.75% to 4.05%) when compared with other similar non-exporting firms one year after the treatment. Also, the program seems to be more effective when focusing on micro, small and medium firms, particularly the

> 3It is worth noting that even with few recent findings about the effectiveness of export promotion, the World Association of Investment and Promotion Agencies (WAIPA) has 236 affiliated members from 162 different countries. Most of theses agencies are focusing on attracting FDI and promoting export. Further information is available on www.waipa.org.

> 4The definition of margins of trade refers to the different dimensions (e.g. market, product) of transactions that can be accounted as intensive or extensive. The pure extensive margin refers to the variation on export induced by a non-exporting firm that becomes an exporter. The extensive margin can also account for previously exporting firms that start to export a new product or to a new market, or both. The intensive margin refers to the export performance from a previously exporting firm to a previously exported market or previously exported product (see Chaney (2008)).

2
former which is defined as firms between 50 and 150 employees.<sup>5</sup> Moreover, I find evidence of a positive, although small effect of the program on untreated firms that belong to the same sector and region of Apex’s treated exporting firms.

The remainder of this paper is organized as follows: Section 2 provides a literature review on the impact of EPAs on exports. Section 3 describes the data and the program and provides some descriptive statistics. Section 4 discusses the identification strategy. Section 5 shows the empirical results followed by robustness checks and section 6 concludes the paper.

# **2 Related Literature and Motivation**

The discussions in trade literature about the effectiveness of export promotion policies in the 1980s were marked by criticism of their distance from the private sector.<sup>6</sup> This topic has gained attention again, particularly in the context of developing and emerging economies facing a more open-to-trade environment. As highlighted by Lederman et al. (2010), the number of EPAs has tripled over the past two decades and most of them have focused on assisting exporters in understanding and finding markets for their products.

There are two main approaches that have been used to analyze EPAs’ effectiveness. The first one is based on a more aggregated and bilateral trade flow.<sup>7</sup> Overall, these studies find positive impacts of export promotion efforts in different regions. Rose (2007) finds a positive effect of opening a country’s embassy on export performance. Ferguson and Forslid (2014) also find a positive effect of opening Swedish embassies abroad on boosting the number of Swedish exporting firms, particularly for medium-sized firms.<sup>8</sup> Gil-Pareja et al. (2014) also provide evidence of a positive impact of regional export promotion offices for Spain. These offices seem to be more effective on promoting export, the more distant they are from their country.<sup>9</sup> Hayakawa et al. (2011) also find a positive effect of promotion agencies on the export increases in Japan and Korea. Their results suggest that establishing an EPA office abroad is equivalent to signing a free trade agreement (FTA) with the same country. Lederman et al. (2010) show the results of an international survey of 88 EPAs and find positive impact of EPA budgets on export, particularly in Eastern Europe, Asia, Latin America and Sub-Saharan Africa.

Yet, the previous approach does not identify the impact at the level of benefited firms.

> 5Firms’ sizes are defined according to the number of employees. See section 5.2 for further details.

> 6See Krueger (1980), Hogan et al. (1991) and Lederman et al. (2010)

> 7Hogan et al. (1991) is also an example of this approach.

> 8Those firms may be more sensitive to fixed entry cost related to informational barriers.

> 9They attribute this effect to the incomplete information barriers’ problem.

3
Since the 2000s, there have been numerous papers evaluating impacts of EPAs using firm-level data from different countries and regions.<sup>10</sup> Most of these studies take advantage of a greater availability of data and new econometric techniques used in impact evaluation literature to deal with selection issues in non-experimental programs. All in all, most findings converge with the positive results found by the more aggregated approach.

In the emerging and developing countries context, Espinoza and Crespi (2000) show a positive impact of Prochile (Trade Comission of Chile, responsible for implementing and enhancing Chile’s trade policy) on firms’ export performance in Chile, particularly on extensive margin due to a scheme called “export committee.” Volpe Martincus (2010) shows some evidence of the effectiveness of EPAs in many Latin American countries on the intensive and the extensive margins of trade. His conclusion is reinforced by different studies using firm-level data to analyze the impact of these agencies and export programs in Peru (Promperu), Costa Rica (Procomer), Uruguay (Uruguay XXI), Chile (Prochile), Argentina (Fundancion ExportAR) and Colombia (Proexport).<sup>11</sup> In addition, these studies find that the impact is larger for smaller firms (taking into account the cases of Argentina and Chile) and that bundled services work better.

In addition to these findings for Latin American, Girma et al. (2009a) show evidence of a positive but heterogeneous impact of production-related subsidies on exports in China. According to them, the program was more effective among more profitable firms and capital intensive industries. Cadot et al. (2012) also find a positive impact of Famex, a program that provided firms in Tunisia with matching grants for promoting exporters through co-financing half of the cost of export business plans. However, they find a positive impact on export value only in the short run (two periods after the treatment). After this period treated and control groups of firms seem to have similar performance. Nonetheless, the impact seems to be persistent, even after five years for new products and destinations.

In high-income economies, Chen et al. (2011) analyze the case of Canadian Trade Promotion Services using firm-level data from 1999 to 2006 and find a robust positive effect at the intensive margin. Hiller (2012) analyzes the impact of export association membership on export sales and the number of products in Denmark and finds a more significant effect on the intensive margin (export sales).<sup>12</sup> Most of these studies use standard procedures to deal with selection bias in a non-experimental

> 10Seringhaus and Botschen (1991) also used a small sample from a survey carried with 312 firms in Austria and 271 firms in Canada (271) to compare the difference between the services provided by EPAs in both countries taking into account the fact that they were public based in Canada and private in Austria.

> 11See Volpe Martincus and Carballo (2008), Jordana et al. (2010), Volpe Martincus and Carballo (2010a), Volpe Martincus and Carballo (2010b), Volpe Martincus et al. (2010) and Volpe Martincus and Carballo (2010c).

> 12Hiller (2012) also finds a positive impact on number of products exported.

4
fashion. They find some positive, though heterogeneous impact of EPAs on firms’ export performance, particularly on export value, new destination markets and new products. However, one question that still demands further attention at the firm level is whether EPAs’ services impact the probability of non-exporting firms to become exporters. Although some studies are quoted as showing evidence that EPA activities are not effective in promoting new exporters, this is not clear for two reasons: a) they do not identify benefiting firms (e.g. Bernard and Jensen (2004)); b) the support they are analyzing is production-related subsidies that are not necessarily targeting export promotion (e.g. G ̈org et al. (2008); Girma et al. (2009a,b)).

Bernard and Jensen (2004) find no impact of export promotion on the probability of exporting for firms from the United States. However, they use the log of export promotion expenditures per plant for each state as covariate, which does not allow them to identify the groups of treated and untreated firms. G ̈org et al. (2008) find little evidence on Irish firms that grants encourage non-exporters to start exporting. Yet, they find that if grants are large enough they can be effective to make already exporting firms more competitive on the international market. Girma et al. (2009b) find a positive correlation between firms that export and those which receive subsidies in Germany, but they find no impact of subsidies on the probability of starting export and weak evidence that subsidies affect the share of exports in total sales. Girma et al. (2009b), Girma et al. (2009a) and G ̈org et al. (2008) focus on the impact of production-related subsidies, which are not necessarily a typical service provided by EPAs for improving the matching between domestic producers and foreign buyers.

This paper is closely related to Schminke and Van Biesebroeck (2013), who identify firms that receive support from EPAs in Belgium and find positive effect on firms’ propensity score to export. However, Belgium is a relatively small country in the European Union (an important common market), where exports account for approximately 80% of its GDP. Thus, there is a lack of evidence on the effectiveness of EPAs on promoting new exporting firms in a large middle income country. Furthermore, when compared to other countries with a similar level of GDP per capita, Brazil shows a relatively low number of exporting firms per capita (see figure 1). It also has one of the lowest average new exporters entry rate when compared to other countries available in the World Bank firm-level export dynamic database (see Canuto et al. (2013) for further discussion on this topic). Therefore, further understanding of policy impacts aimed to promote new exporters in this context is much needed.

5
<!-- Start of picture text --> Exporters per capita − GDP per capita Entry Rate − Total Number of Exporters<br>EST NZL SWE NOR<br>MKDLBNBGRMUS PRTESPBEL KWT YEMMWI KWT<br>MWI MLIUGABFATZABGDKENSENKHMLAOCMRYEMPAK NICMARGTMJOREGYSLVECUALBDOMPERCOLZAFBRACRIIRNTURBWAMEXCHL NER MLIBFALAOKHMSENCMRUGANICBWATZAALBJORMUSSLVDOMCRIMKDECUGTMESTLBNKENMARBGDPERCHLCOLIRNNZLBGRPAKPRTNORZAFBELSWEMEXTUR ESP<br>EGY<br>NER R2=0.83 BRA R2=0.35<br>6 7 8 9 10 11 4 6 8 10 12<br>Ln GDP per capita Ln Total Exporters<br>.6<br>−6<br>.5<br>−8<br>.4<br>Entry Rate<br>−10<br>.3<br>Ln Exporting firms per capita<br>−12 .2<br><!-- End of picture text --> Figure 1: Exporters per capita and export entry rate in Brazil Source: World Bank Export Dynamics Database (Cebeci et al., 2012) In this regard, an important question that comes from analyzing a specific policy in a single country is related to external validity. Does this experience in Brazil reflect the expected outcome from similar policies in other countries? Even though policy outcomes can vary over different institutional environments among different countries, it is important to highlight that the export behavior in Brazil is very similar to that in leading export countries such as France and the United States, as described by Arkolakis (2010). Also, I find many similarities with the patterns of Colombian exporting firms (Eaton et al., 2010). However, the comparison of EPAs’ impact should incorporate the heterogeneity among the agencies and countries’ institutions. Hence, accumulating further knowledge on the effect of these programs in different contexts is critical to fully understand how and in which circumstances they work.

# **3 Data and Descriptive Statistics**

This paper is based on a unique firm-level dataset that resulted from merging three different sources of information in Brazil: 1) the _Rela ̧c ̃oes Anual de Informa ̧c ̃oes Sociais_ (RAIS), a matched employer-employee dataset from the Ministry of Labor;<sup>13</sup> 2) the SECEX/MDIC dataset, which provides firms’ exporting status; and 3) the APEX data, identifying the firms which received the treatment from Apex-Brasil.<sup>14</sup> The data was merged using a unique identifier at the firm level.<sup>15</sup> The data from Apex cover the period between 2007 and 2010. I used RAIS and SECEX/MDIC from 2005 to 2010 in order to capture the previous characteristics of exporting firms and the

> 13RAIS is a register of all formal firms in Brazil. It provides information about the size of the establishment, geographic distribution and workers, such as wage, education, age and gender.

> 14 _Secretaria de Com ́ercio Exterior_ (SECEX) from the Ministry of Development, Industry and Trade (MIDC). A list of exporting firms by year is publicly available on www.mdic.gov.br.

> 15RAIS provides information at the plant level with a 14-digit identifier that can be converted to the firm level using 8 digits, which was used in this paper. The location of the firm is defined as the location of the larger plant.

6
impact of the treatment.

In addition to the counterfactual that is missing,<sup>16</sup> another issue is that export and import activities can be carried by traders. Indeed, promoting the relationship between producers and traders might be one of the targets of EPAs,<sup>17</sup> but traders are accounted as services that are not directly related to the firms that produce the goods that are exported, so results could be misleading. I concentrate on the manufacturing sector where I assume that trade (export and import) is more likely to be accounted for producers, though manufacturing firms also may trade goods they do not produce (see Bernard et al. (2012) and Crozet et al. (2013)). I use the sectoral definition of CNAE 2.0<sup>18</sup> (Portuguese acronym for National Economic Activity Classification) that is equivalent to the International Standard Industrial Classification of All Economic Activities (ISIC, Rev.4.).

RAIS provides yearly information for approximately 250,000 manufacturing firms per year, which can work as an annual census for the formal manufacturing sector in Brazil. This is critical because we can observe all formal firms over time, disregarding their export status. Since my identification uses lagged variables in the first stage, I first impose a restriction in the dataset according to which firms should appear in RAIS at periods _t −_ 1 and _t_ .<sup>19</sup> For example, there are 257,473 manufacturing firms in RAIS in 2007. Among these firms 224,232 also appear in 2006 and so on.<sup>20</sup> The basic services provided by Apex-Brasil are related to matching domestic sellers and foreign buyers (e.g. through participation in trade fairs) and providing standard information on foreign markets, including standard qualification for exporting. There are two alternative ways to have access to these services: a) through an industry association (this scheme, called “Sectoral Project,” is more common if a firm is going to participate in trade fairs) or b) directly with Apex (this is more common for special projects related to participation in sports and cultural events, such as Formula Indy, Carnaval and the Peiex<sup>21</sup> project). Further details about how Apex’s assistance works are available in section 7.1 of the appendix.

There are spatial and sectoral heterogeneities among treated firms. Table 1 shows the regional distribution of firms according to their treatment and export status from 2007 to 2010. The

> 16We can observe only one status of the firm as being treated or untreated.

> 17According to Apex, this is among their goals.

> 18I used CNAE 2.0, 2 digits, from 10 to 33, which defines manufacturing.

> 19I am assuming that this is an existence condition for the firm.

> 20This number for the other years is 267,303 firms in 2008, 273,582 firms in 2009 and 292,680 firms in 2010.

> 21The Peiex (Portuguese acronym for Industrial Extension Project for Exporting) is an export capacity-building program program focusing on enhancing Brazilian SMEs competitiveness by providing coaching and consultancy on best management practices.

7
Southeast<sup>22</sup> concentrates most of the economic activity in Brazil. It has approximately 42% of the population, 50% of manufacturing firms and contributes to almost 58% of GDP. The treatment distribution follows the distribution of exporting firms relatively close, more concentrated in the states of S ̃ao Paulo, Minas Gerais and Rio Grande do Sul.<sup>23</sup>

|State|Num|ber of frms-y|ear||Share (%)||
|---|---|---|---|---|---|---|
||Total|Exporters|Apex|Total|Exporters|Apex|
|Rondˆonia|5,948|218|11|0.63|0.46|0.06|
|Acre|1,243|23|-|0.13|0.05|-|
|Amazonas|4,497|515|21|0.48|1.09|0.11|
|Roraima|533|56|-|0.06|0.12|-|
|Par ́a|9,880|710|47|1.04|1.51|0.25|
|Amap ́a|668|16|9|0.07|0.03|0.05|
|Tocantins|2,634|11|6|0.28|0.02|0.03|
|Maranh ̃ao|4,961|68|-|0.52|0.14|-|
|Piau ́ı|5,300|69|10|0.56|0.15|0.05|
|Cear ́a|25,511|577|594|2.70|1.22|3.18|
|Rio Grande do Norte|8,053|110|87|0.85|0.23|0.47|
|Para ́ıba|7,967|158|80|0.84|0.34|0.43|
|Pernambuco|23,026|372|268|2.43|0.79|1.43|
|Alagoas|3,544|73|77|0.37|0.15|0.41|
|Sergipe|4,371|60|109|0.46|0.13|0.58|
|Bahia|25,954|629|752|2.74|1.33|4.02|
|Minas Gerais|119,941|3,249|2,731|12.67|6.89|14.61|
|Esp ́ırito Santo|19,949|710|211|2.11|1.51|1.13|
|Rio de Janeiro|46,857|1,816|563|4.95|3.85|3.01|
|So Paulo|281,284|22,306|5,254|29.72|47.31|28.12|
|Paran ́a|87,092|3,945|1,717|9.20|8.37|9.19|
|Santa Catarina|92,977|3,904|1,130|9.82|8.28|6.05|
|Rio Grande do Sul|107,333|6,536|4,456|11.34|13.86|23.85|
|Mato Grosso do Sul|7,424|164|36|0.78|0.35|0.19|
|Mato Grosso|14,020|357|38|1.48|0.76|0.20|
|Goi ́as|29,935|477|344|3.16|1.01|1.84|
|Distrito Federal|5,679|24|135|0.60|0.05|0.72|
|Total|946,581|47,153|18,687|-|-|-|
|Average (year)|236,645|11,788|4,672|-|-|-|

Table 1: Descriptive statistics (Export- Treated and Untreated firms (2007-2010) Note: If the number of treated firms by Peiex is excluded, there are 12,759 treated firms over this period, 3,190 by year on average and the share of treated firms goes down from about 2% to 1.32%. Among these firms, 57.4% are in the Southeast, 33% in the South and 7% in the Northeast.

Table 2 shows the sectoral distribution of manufacturing firms according to their export and treatment status. It is noticeable that three sectors (food, wearing apparel and metal) represent about 40% of the total manufacturing firms. On the other hand, only machinery and equipment have a share larger than 10% among exporters, followed by rubber and plastic, chemicals and food. Among the firms which received Apex’s treatment, wearing apparel takes the largest share, followed by leather and food. When taking into account the total number of firms, none of the sectors has more than 5% of their firms receiving Apex’s support. Graph 4 (in section 7.4 of the

> 22Composed by the states of S ̃ao Paulo, Rio de Janeiro, Minas Gerais and Esp ́ırito Santo.

> 23In the case of Minas Gerais and Rio Grande do Sul the difference is driven by the support of Peiex, a program focusing on improving competitiveness of smaller firms (see Cruz et al. (2013)).

8
appendix) shows the yearly number of (treated and untreated) exporting firms.

||Sector (CNAE 2.0)|Numbe|r of frms-|year||Share (%)||
|---|---|---|---|---|---|---|---|
|||Total|Export|Apex|Total|Export|Apex|
|10|Food|111,209|3,499|1,588|11.75|7.42|8.50|
|11|Beverages|6,946|398|254|0.73|0.84|1.36|
|12|Tobacco|514|105|0|0.05|0.22|0.00|
|13|Textiles|32,163|1,743|890|3.40|3.70|4.76|
|14|Wearing apparel|163,472|2,687|3,835|17.27|5.70|20.52|
|15|Leather|44,489|2,616|1,814|4.70|5.55|9.71|
|16|Wood and cork|54,416|2,457|343|5.75|5.21|1.84|
|17|Paper|14,158|732|182|1.50|1.55|0.97|
|18|Printing|42,627|339|188|4.50|0.72|1.01|
|19|Refned petroleum|1,566|245|61|0.17|0.52|0.33|
|20|Chemicals|26,652|3,497|816|2.82|7.42|4.37|
|21|Pharmaceutical|3,054|698|95|0.32|1.48|0.51|
|22|Rubber and plastics|48,569|3,686|1,007|5.13|7.82|5.39|
|23|Non-metallic|75,631|1,965|850|7.99|4.17|4.55|
|24|Basic metals|15,146|1,383|337|1.60|2.93|1.80|
|25|Metal products|108,889|3,079|1,043|11.50|6.53|5.58|
|26|Computer, electronic|10,952|2,133|533|1.16|4.52|2.85|
|27|Electrical equipment|14,060|1,893|278|1.49|4.01|1.49|
|28|Machinery and equip.|42,500|6,488|1,441|4.49|13.76|7.71|
|29|Motor vehicles|17,167|2,423|450|1.81|5.14|2.41|
|30|Other transport|3,185|338|56|0.34|0.72|0.30|
|31|Furniture|56,999|1,670|1,344|6.02|3.54|7.19|
|32|Other manufact.|25,667|2,651|1,181|2.71|5.62|6.32|
|33|Repair machinery|26,550|428|101|2.80|0.91|0.54|

Table 2: Sectoral distribution - Manufacturing, Exporting, Treated firms (2007-2010) Tables 1 and 2 show that the distribution of manufacturing firms according to their exporting and Apex’s treatment status are very heterogeneous among sectors and regions. This heterogeneity should be taken into account, since there are common shocks (e.g. exchange rate, labor market, interest rate, infrastructure, etc.) that might have a heterogeneous impact across firms in different sectors and location. Next, I compare firms’ characteristics that vary over time and provide some descriptive statistics for those variables that have been highlighted as important in explaining export performance in the trade literature.

Table 3 shows the number of firms’ observations (N), from 2007 to 2010, their mean, the standard deviation (sd), the median (p50) and the percentiles (p10) and (p90), for a set of variables used for the empirical analysis. The number of employees is available on RAIS as the number of contracts the firm has in a year _t_ . This variable considers the period a worker was hired by the firm over the year. In this case a worker is weighted as 1 only if she was hired over 12 months in a year. Otherwise she is weighted proportionally to the time she was hired. Firm’s age is a proxy for the age of the firm that takes into account the maximum period of employees’ experience working at firm _i_ available in RAIS over time. Employee’s age is the average age 9
of employees. Schooling is the employees’ average years of schooling.<sup>24</sup> “Wage” is the average monthly wage by firm. This information is available in current domestic currency and it was converted to Reais (R$) of 2010.<sup>25</sup> As it is known from trade literature (see Roberts and Tybout (1997), Bernard and Jensen (2004), Melitz and Trefler (2012)), exporting firms are larger, older, employ more educated workers and pay higher wages.<sup>26</sup> If we compare them with Apex’s treated firms we can see that their characteristics concerning these variables are much closer to exporters than to the average manufacturing firm.<sup>27</sup> This finding reinforces some potential selection issues to identify the impact of the program.

Another variable that tries to capture time-varying information on management quality is a dummy identifying firms that hired workers from other exporting firms in Management positions. In order to create this variable I used a unique worker identifier (RAIS at employee level) that allows for the tracking of a worker over time.<sup>28</sup> Firstly, I identify employees working in an exporting firm _i_ at period _t-1_ that were not working in the same firm at period _t_ . Then I check the firm they were working at period _t_ and based on their CBO classification (Portuguese acronym for Brazilian Classification of Occupation) I classified them as managers according to different layers.<sup>29</sup> Based on this information, I generated three dummy variables (at firm level) that take the value 1 when a firm _k_ at period _t_ hired an employee who was working at an exporting firm _i_ at period _t-1_ . This procedure is in line with the findings of Mion and Opromolla (2011), who show that hiring an employee with previous experience in exporting firms impacts the probability of export, but only if she has a management position. This also can be considered as a proxy for an effort from the firm towards having access to the foreign market. Again for these variables the Apex-treated firms are more similar to the average exporting firms.

Also, Caliendo and Rossi-Hansberg (2012) and Caliendo et al. (2012) show that the organization of firms under a form of hierarchies based on knowledge matters on their performance and export status. In order to capture this information I build a similar variable using the CBO classification. Firms are divided in five layers that represent different levels of management hi-

> 24In RAIS, education attainment is a categorical information that is converted to years of schooling based on a methodology available on the PNAD (National Household Sample Survey) carried by the IBGE (Portuguese acronym for Brazilian Institute of Geography and Statistics).

> 25In order to convert the annual value to 2010 I used the IPCA-IBGE (Consumer Price Index produced by IBGE used by the Brazilian Central Bank as official reference to inflation target). The exchange rate in December 2010 was approximately R$ 1.00 for US$ 1.67 (source: Central Bank of Brazil).

> 26These findings in Brazil are highlighted by Kannebley Jr et al. (2009).

> 27It is even more similar when we compare with treated firms excluding Peiex-treated firms, which are smaller

> 28The identifier I used to track workers is available from 2006.

> 29These variables are respectively called Manager exp L1, Manager exp L2 and Manager exp L3, which corresponds to the following occupations: CEOs and directors (L1), senior staff with higher level of experience (L2) and supervisors (L3).

10
erarchy.<sup>30</sup> We can see that the mean and the median regarding number of layers is larger for exporting firms. Apex’s treated firms also have more layers.

|**Total Firms**|**N**|**mean**|**sd**|**p50**|**p10**|**p90**|
|---|---|---|---|---|---|---|
|Size (Employees)|946,455|30.21|291.67|6.00|1.00|41.25|
|Firms’ age|946,581|12.06|9.10|9.91|3.16|23.08|
|Employee’s age|946,455|34.43|7.26|33.65|26.00|43.74|
|Schooling|946,422|8.79|2.06|9.04|5.79|11.04|
|Wage|946,455|885.39|635.49|742.33|503.50|1,386.43|
|Layers|917,765|2.23|1.18|2.00|1.00|4.00|
|Manager exp L1|946,581|0.002|0.04|0.00|0.00|0.00|
|Manager exp L2|946,581|0.002|0.04|0.00|0.00|0.00|
|Manager exp L3|946,581|0.005|0.07|0.00|0.00|0.00|
|Export-spillovers|946,581|26.10|53.91|4.00|0.00|77.00|
|Multi|946,581|0.01|0.08|0.00|0.00|0.00|
|Import Dummy|946,581|0.06|0.23|0.00|0.00|0.00|
|Export Dummy|946,581|0.05|0.22|0.00|0.00|0.00|
|Apex|946,581|0.02|0.14|0.00|0.00|0.00|
|**Exporters**|||||||
|Size (Employees)|47,151|296.67|1,230.53|61.17|6.83|550.08|
|Firms’ age|47,153|21.73|13.93|18.63|6.24|41.91|
|Employee’s age|47,151|34.34|4.55|34.00|29.13|39.89|
|Schooling|47,151|9.61|1.85|9.78|7.14|11.68|
|Wage|47,151|1,698.85|1,324.47|1,326.14|706.51|3,027.30|
|Layers|46,654|4.03|1.20|5.00|2.00|5.00|
|Manager exp L1|47,153|0.02|0.12|0.00|0.00|0.00|
|Manager exp L2|47,153|0.02|0.13|0.00|0.00|0.00|
|Manager exp L3|47,153|0.04|0.19|0.00|0.00|0.00|
|Export-spillovers|47,153|49.19|72.94|19.00|2.00|154.00|
|Multi|47,153|0.11|0.32|0.00|0.00|1.00|
|Import Dummy|47,153|0.60|0.49|1.00|0.00|1.00|
|Apex|47,153|0.17|0.38|0.00|0.00|1.00|
|**Apex’s treated frms**|||||||
|Size (Employees)|18,685|257.62|1,451.88|30.08|3.42|413.58|
|Firms’ age|18,687|18.91|13.29|15.71|4.99|38.08|
|Employee’s age|18,685|33.96|5.06|33.50|28.18|40.12|
|Schooling|18,683|9.28|1.71|9.43|7.06|11.11|
|Wage|18,685|1,163.50|761.54|955.94|568.73|1,975.07|
|Layers|18,531|3.52|1.36|4.00|2.00|5.00|
|Manager exp L1|18,687|0.01|0.10|0.00|0.00|0.00|
|Manager exp L2|18,687|0.01|0.10|0.00|0.00|0.00|
|Manager exp L3|18,687|0.02|0.14|0.00|0.00|0.00|
|Export-spillovers|18,687|32.10|52.80|10.00|0.00|103.00|
|Multi|18,687|0.02|0.15|0.00|0.00|0.00|
|Import Dummy|18,687|0.34|0.48|0.00|0.00|1.00|
|Export Dummy|18,687|0.44|0.50|0.00|0.00|1.00|

Table 3: Characteristics of manufacturing, exporting and Apex’s treated firms (2007-2010) In addition, as emphasized by Koenig (2009) there might be geographically driven shocks that affect export performance, such as neighbourhood-effect, spillovers and other externalities (think

> 30In order to build this index and the management’s dummies, I used RAIS at employee level (which provides yearly information for about 8 millions of employees on manufacturing sector - 7,756 millions in 2010) and first, counted the number of employees according to their CBOs. Then I generated a dummy variable by firm-layer and summed them up for building a proxy for the level of hierarchy of each firm, which is presented in the variable “layer.” For further details see Cruz et al. (2013).

11
of the case of Silicon Valey as a good example, but it can be negative such as a big player that transfers their production to another region). I am trying to capture these dynamics through the variable “Export-spillovers.”<sup>31</sup> This variable accounts for the number of other exporting firms in the same micro-region (a geographic definition from IBGE that divides Brazil in 555 regions) and sector (CNAE 2 digits) excluding the own firm.<sup>32</sup> It is noticeable that among exporting and treated firms the number of other exporters in the same micro-region is larger than for non-exporters.

The variable multi comes from CEB-BCB (Brazilian acronym for National Census of Foreigner capital in Brazil elaborated by the Brazilian Central Bank) and it is a register of foreigners’ share in Brazilian firms from 1995 to 2000. It is a dummy variable that takes the value of 1 if firms have more than 50% of their assets held by foreigners. Since this information is not updated it should be interpreted cautiously. Nonetheless, it is useful to identify well established multinational firms, those which are more likely to have a wider network, be more productive and export.<sup>33</sup> Finally, trade literature emphasizes that importing is very common among exporting firms. Table 3 shows that almost 60% of exporting firms in Brazil also import. Apex’s treated firms have a lower share (17%) than exporters,<sup>34</sup> but this share is much larger than the average among manufacturing firms (6%).

# **4 Identification Strategy**

A critical question in any program evaluation is how to identify its effect on the variable of interest. In the case of firms that received support from Apex-Brasil, since we can only observe their actual outcome we have no counterfactual. Therefore, we do not know what the performance of these firms would be if they had not received this support, which is a typical problem of missing information.<sup>35</sup> An alternative solution is to create a control group with firms that have not participated in the program and use them as counterfactual. However, this procedure also leads to another missing

> 31Bernard and Jensen (2004) also use a similar variable as a control and call it Spillovers: Exporter(stateindustry), which accounts for the share of exporting plants in the state and industry.

> 32It means that if a firm _i_ is an exporting firm in time _t_ and it is located in the micro-region _k_ with _n_ number of exporters, this variable considers _(n-1)_ exporters _i_ in region _k_ .

> 33There are approximately 1,700 firms identified as multinational according to this criteria. Among them, 80% export and less than 6% received Apex support (almost all of them under non-Peiex program), which would be expected.

> 34This share becomes very similar if Peiex’s treated firms are excluded.

> 35Indeed, if we were able to answer this question we could identify the difference between both outcomes (with and without treatment) as the impact of the program. For a review about recent development in program evaluation literature see Imbens and Wooldridge (2009).

12
information problem due to the fact that there might be important firms’ characteristics that simultaneously impact treatment status and the outcome, but cannot be observed in the data<sup>36</sup> (e.g. firms can improve the quality of their management and this may increase their likelihood of receiving the assistance and becoming an exporter at the same time). Apex’s assistance and other programs evaluated in EPA literature I am aware of are mostly non-experimental.<sup>37</sup> Therefore, a critical issue to evaluate EPAs’ programs is potential bias due to self-selection driven by unobservable time-varying characteristics of the firm that can confound the estimator.

I rely on a combination of techniques of propensity score matching (PSM) and differencein-difference (DID) to deal with potential observable and unobservable selection bias, following Heckman et al. (1997).<sup>38</sup> The matching literature suggests we can rely on a set of potential comparison units for those where we observe a similar set of pretreatment covariates, _Xi_ (Rubin, 1977).<sup>39</sup> However, as the number of covariates increases, it gets more difficult to find similar pairs among the untreated units to implement exact matching. An option to deal with this dimensionality issue is by matching units on their vector of pretreatment covariates, _Xi_ , through their propensity score (PS) for receiving the program (Rosenbaum and Rubin, 1983)<sup>40</sup> . To implement the PSM, first I ran a probit to identify the PS of a firm _i_ to receive Apex’s treatment at period _t_ based on _Xi_ ( _t−_ 1 _, ..., t−n_ ) covariates.

I used the PS from equation (1) for matching treated and untreated firms that existed over _t−_ 2, _t−_ 1 and _t_ 0.<sup>41</sup> I analyzed the probability of these firms to receive Apex assistance in _t_ 0 based on their characteristics on _t−_ 2 and _t−_ 1. The option for having firms’ characteristics with two periods before the treatment for matching firms aims to capture available information on the dynamics of both groups of firms, treated and control, which also imposes a parallel trend in the covariates using for matching. The matching procedure adopted was the nearest neighbor following the

> 36A widely accepted solution is to carry a randomized control trial (RCT) in which firms are randomly chosen from the population to be part of treated and control groups (see Duflo and Kremer (2005)). However, this procedure demands ex-ante design that is not always feasible.

> 37In these cases, a standard procedure is relying on econometric techniques geared to deal with this kind of data generated process, such as propensity score matching (PSM), difference-in-difference estimator (DID), Instrumental Variables (IV), Regression Discontinuity Design and possible combinations among them (Blundell and Dias, 2009).

> 38For more examples of the application of this method in applied literature, see Volpe Martincus and Carballo (2008), Volpe Martincus and Carballo (2010a), Chen et al. (2011) and Cadot et al. (2012).

> 39According to Rubin (1977) if for each unit we observe a vector of covariates _Xi_ and _Yi ⊥ Ti|Xi, ∀i_ , then the population treatment effect for the treated, Φ _|T_ =1, is identified: it is equal to the treatment effect conditional on covariates and on assignment to treatment, Φ _|T_ =1 _,X_ , averaged over the distribution _X|Ti_ = 1.

> 40For further details on matching procedure, see Dehejia and Wahba (2002).

> 41The exception are the variables ManagerexpL1, ManagerexpL2 and ManagerexpL3. For those variables I used information from _t−_ 2 in order to identify workers that were in an exporting firm and moved to another firm in _t−_ 1 in an occupation classified as manager.

13
Greedmtch matching algorithm (see Parsons (2001)), discarding control observations with PS smaller (or larger) than minimum (or maximum) PS value of treatment group.

Matching requires a clear definition of population of interest and outcome (Caliendo and Kopeinig, 2008). In the case of APEX, firms join the program in different years and this decision, as well as its outcome might be strongly affected by macro variables, such as exchange rate and economic growth, among others. In addition, as we see in the descriptive statistics there is sectoral heterogeneity, which results in different economic environments. Heckman et al. (1997) suggest that two important features to reduce bias in nonexperimental programs are to use the same questionnaire for both groups and having participants and controls placed in a common economic environment.<sup>42</sup> In order to deal with this issue I adopted two different procedures. First I used a propensity score as a partial balancing score complemented by exact matching on year and sector (see Lechner (2002) and Todo (2011)).<sup>43</sup> Second, I followed Heckman et al. (1997) and matched the sample, year by year. Both procedures generated similar results. I kept the first one due to the fact that I have a unique covariate’s balance analysis. Under this procedure, the baseline model<sup>44</sup> was a pooled probit from 2007 to 2009 using firms’ characteristics from _t-1_ and _t-2_ as covariates. I used exact matching for year and sector (CNAE 2 digits). For example, a firm belonging to sector _j_ at period _t_ was matched with another firm (control group) at sector _j_ with the nearest PS in period _t_ . Furthermore, I used firms that appear for the first time in the sample as receiving Apex treatment.<sup>45</sup> The results of the probit and the descriptive statistics of the matched sample is available in section 7.2 of the Appendix.<sup>46</sup> A critical assumption in the PSM procedure is the fact that it controls for potential bias relying on observable covariates _Xi_ . However, the performance of exporters as well the probability of firms’ self selection into the program might be lead by unobservable firms’ characteristics. Assuming they are time-fixed characteristics, we can identify the average treatment effect by DID. I used a panel fixed effects model<sup>47</sup> (using log for continuum covariates) to obtain a DID

> 42The dataset used in the paper was obtained through a similar questionnaire between firms (e.g. RAIS).

> 43I also tested exact matching for year, sector and region which provides similar results.

> 44I tested different matching approaches. The mainly difference is regarding the ratio (control firms/treated firms), variables for exact matching was implemented (i.e. year, region, sector) and time lag for the covariates. The results are consistent for different matching procedures and are available under request.

> 45Results using all apex treated firm are available under request.It means that if a firm was receiving apex for 2 years without becoming an exporter it was considered as treated both period.

> 46I also provide further assessment of the matching quality in section 5 and in section 7.2 of the Appendix.

> 47Lechner (2011) suggests the use of a linear specification as an alternative of identification for the non-linear outcome such as exporting status.

14
estimator (see Imbens and Wooldridge (2009)): Where _Yit_ + _n_ is the outcome of interest (export status in _t+n_ ), _Dit_ stands for the treatment status in period t, _ρi_ controls for unobserved time-invariant heterogeneity, _βt_ controls for sectoral (j) calendar year (t) fixed effects. _Xit_ is a vector of firms covariates described in section 3, including the following variables at the firm level: number of employees, age of the firm and its square, average wages of employees, years of schooling of employees, number of exporters in the same sector and same region at period t, three dummy variables identifying if the firm had hired managers in different levels of hierarchy with previous experience in exporting firms (Manager exp L1, L2 and L3), a proxy for firms organization (number of layers), sector and location of the firm. Different specifications including these variables were used as a robustness check.

Assuming there is a time gap for the program to impact the propensity to export, I kept the baseline of the outcome as _Yit_ +1. Due to the fact that my population of interest is nonexporting firms<sup>48</sup> and the outcome variable is binary, the first difference with respect to the pre-treatment year is the value of the own dependent variable in period _t-n_ . In addition to the non-linearity<sup>49</sup> of the endogenous variable (which is binary), the longitudinal structure of the data and the fact that firms join the program in different years bring additional issues regarding the definition of the counterfactual group, particularly to evaluate its effect in t+n, in the presence of contemporaneous effect.

For example, let us assume that the impact of _Dit_ on _Yit_ is positive and significant. If a firm receives the program in 2008, it may affect its probability of exporting in 2008. If this same firm was non-exporting in 2006 and 2007, but became an exporter in 2008, it could take part of the untreated group of firms in 2007. When estimating the effect on _Yit_ +1, the contemporaneous effect of _Dit_ on _Yit_ would be captured by _εit_ + _n_ , which would lead to a downward bias. While there is a clear timing definition of pre- and post-treatment for the treated units, the same does not happen with untreated firms. For this reason, the matching procedure allows for exploiting the panel data structure by having a conterfactual group exclusively composed of firms that did not receive the program and defining a common pre- and post-treatment period using equivalent firm-year observation.

> 48Defined here as those firms that did not export in either period _t-1_ or _t-2_ .

> 49For further details about the use of LPM to estimate causal effect in non-linear models, see Lechner (2011). See also Puhani (2012) and Ai and Norton (2003).

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# **5 Results**

This section presents the empirical results as follows: First I show the estimation for the average effect of Apex’s programs on promoting new exporters. The population of interest is manufacturing firms that were non-exporters neither in _t−_ 2 nor in _t−_ 1. Second, I test for heterogeneous effect according to firms’ size. Finally, I test the spillover effect hypothesis of Apex treatment on untreated

|Numb|er of frms t|hat switc|hed export s|tatus||
|---|---|---|---|---|---|
|Year|Total<br>No|n Apex|share (%)|Apex|share (%)|
|Expor|ting status:|t-1 (non|exporter) to|t(expo|rter)|
|2007|2050|1927|0.94|123|0.06|
|2008|1882|1736|0.92|146|0.08|
|2009|1910|1718|0.90|192|0.10|
|2010|1823|1621|0.89|202|0.11|
|Expor|ting status:|(t-2 and|t-1 (non exp|orter)|to t(exporter)|
|2007|1603|1512|0.94|91|0.06|
|2008|1471|1365|0.93|106|0.07|
|2009|1484|1346|0.91|138|0.09|
|2010|1369|1227|0.90|142|0.10|
|Expor|ting status:|(t-2 and|t-1 (non exp|orter)|to t+1(exporter)|
|2007|2025|1892|0.93|133|0.07|
|2008|1930|1805|0.94|125|0.06|
|2009|1866|1713|0.92|153|0.08|
|2010|881|779|0.88|102|0.12|

Table 4: Number of firms that switched export status by year Note: Manufacturing firms that appear in RAIS in periods t and t-1.

To begin with, table 4 shows the number of firms that switched condition from non-exporting to exporting between 2007 and 2010, by Apex treatment status. The share of firms that received Apex treatment is relatively small in terms of the total number of non-exporting firms. However, when we compare the number of non-exporting firms that switched export status we notice that the share of new exporters that received Apex’s support is larger. This reinforces the necessity of a careful procedure to deal with selection due to the fact that some of these firms with higher probability of becoming an exporter may get self-selected into the program.

16
# **5.1 Does Apex assistance promote new exporters?**

Due to the fact that Apex’s data starts in 2007, I have no information on firms’ treatment status for the previous years. Hence, in the baseline specification I focus on firms that received their first assistance after 2007.<sup>50</sup> Let us start with a pooled OLS (ordinary least squares) estimation (see table 5). Columns (1) and (2) show the marginal effect of Apex’s treatment for the propensity to export in _t+1_ on a sample of non-exporting firms.<sup>51</sup> Due to the nonlinearity in the outcome, I compared the predictive margins between control and treated groups at the mean. Under pooled OLS the effect of Apex without additional covariates (column 1) is 3.4 percentage points (pp). The propensity to export in the full sample of non-exporting firms is very low (0.78%) and the estimation suggests that Apex’s assistance increases by 4.37 times the chance of a non-exporting firm to become an exporter. If additional covariates are included (column 2) the treatment effect accounts for 2.36 times the chance of becoming an exporter. Columns (3) and (4) show that when controlled for firms’ fixed effects (FE) the coefficient of Apex is remarkably smaller than in the pooled OLS. In this case, the treatment Apex increases the propensity to export in 0.9 pp, which is approximately 54% (column 3). When additional covariates are included, it increases the export propensity by 105%.

Columns (5) and (6) show the pooled OLS estimation for the contemporaneous effect (propensity to export in the year that firms received the treatment). Apex’s treatment increases the export propensity in period _t_ in 3.4 pp and 2.4 pp respectively for the specifications without and with additional covariates. This accounts for 4.3 and 2.6 times the chance of export, if compared with the control group. For the contemporaneous effect, the FE estimator is slightly smaller when there are no additional covariates at the firm level - columns (5) and (7) - but it is larger when all covariates are included - columns (6) and (8). Moreover, the magnitude of the coefficients for the pooled OLS or FE estimators are relatively similar under contemporaneous effect, which may be expected due to the exclusion restriction.<sup>52</sup> The specifications in columns (7) and(8) account for an increase of about 2.6 and 2.9 times the chance of exporting. Most of the coefficients are positive and statistically significant at 1%.

> 50This procedure allows me to exclude firms that received the program in period _t-1_ from the control group. In section 7.3 of the Appendix, I present results keeping the treated firms in 2007 under the assumption that those firms did not receive the program in the previous year.

> 51Firms that had not exported in the previous two periods.

> 52Because my population of interest is composed of non-exporting firms, I imposed a parallel trend on previous exporting status. If we assume that the main issue regarding selection bias is due to firms’ characteristics that are unobservable to an econometrician and can affect both the outcome and the treatment status, this exclusion restriction itself already aims to deal with the selection issue.

17
Under the DID assumption the within FE estimator identifies Apex’s effect on the propensity of non-exporting firms to become exporters in period _t+1_ - columns (3) and (4) - and period _t_ - columns (7) and (8). It is noticeable that under this condition, Apex’s effect in _t+1_ is substantially smaller than its contemporaneous effect. In section 4 I explained the reason why the within estimator for the period _t+1_ may be downward biased due to the contemporaneous effect of firms that enter into the program in different years.

|Dependent variable:||Export du|mmy (t+1)|||Export d|ummy (t)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Apex|0.034***<br>(0.004)|0.020***<br>(0.004)|0.009***<br>(0.003)|0.009***<br>(0.003)|0.028***<br>(0.003)|0.017***<br>(0.003)|0.023***<br>(0.003)|0.022***<br>(0.003)|
|lfrmssize||0.001***||0.003***||0.000**||0.004***|
|||(0.000)||(0.000)||(0.000)||(0.000)|
|frmsage||-0.000***||-0.000||-0.000||0.001|
|||(0.000)||(0.001)||(0.000)||(0.001)|
|frmsage2||0.000||0.000***||-0.000||0.000***|
|||(0.000)||(0.000)||(0.000)||(0.000)|
|lwage||0.001***||-0.000||0.001***||0.000|
|||(0.000)||(0.000)||(0.000)||(0.000)|
|lschool||0.002***||-0.001||0.002***||-0.001|
|||(0.000)||(0.001)||(0.000)||(0.001)|
|shexpsrnet||0.064***||-0.110***||0.073***||0.283***|
|||(0.005)||(0.018)||(0.004)||(0.022)|
|ManagerexpL1||0.222***||0.032||0.116***||-0.030|
|||(0.019)||(0.032)||(0.017)||(0.029)|
|ManagerexpL2||0.172***||0.039||0.140***||0.076**|
|||(0.020)||(0.035)||(0.019)||(0.032)|
|ManagerexpL3||0.366***||0.063***||0.276***||0.072***|
|||(0.012)||(0.024)||(0.011)||(0.023)|
|Import||0.073***||0.020***||0.063***||0.033***|
|||(0.003)||(0.004)||(0.002)||(0.004)|
|layer||0.003***||0.001**||0.002***||0.000|
|||(0.000)||(0.000)||(0.000)||(0.000)|
|Fixed Efects|.|.|.|.|.|.|.|.|
|Firms|-|-|YES|YES|-|-|YES|YES|
|Year*Sector|YES|YES|YES|YES|YES|YES|YES|YES|
|State|YES|YES|-|-|YES|YES|-|-|
|Number of id|270,969|270,354|270,969|270,354|270,969|270,354|270,969|270,354|
|R-squared|0.010|0.159|0.004|0.007|0.007|0.114|0.011|0.022|
|Observations|655,021|653,267|655,021|653,267|655,021|653,267|655,021|653,267|

Note: Linear Probability Model (LPM) using pooled OLS and panel fixed effects. Treated firms in 2007 are excluded from the sample. Standard errors clustered at firm level are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table 5: Impact of Apex on the probability to export (full sample without matching) Now I show the results using the matched sample based on the PSM method and a linear probability model (LPM) with firm and time-sector fixed effects to identify the impact of Apex’s assistance on the probability of becoming an exporter (see table 6). Apex’s treatment increases by approximately 2.3 percentage points (pp) the probability of a non-exporting firm to become an exporter (see column (1) in table 6). What does this effect mean? The propensity to export in period _t+1_ for an average matched firm in the control group is 1.75%. Apex’s treatment increases this probability to 4.05%. Therefore it more than doubles the chances of a non-exporting firm to become an exporter (it increases its propensity by 130%). I also added other covariates for the 18
period _t-1_ and _t_ for each firm in the matched sample, as a robustness check. Results seem robust for different specifications (the propensity increases from 1.8% to 4.0%). Another robustness check is based on a sample that excludes firms that received assistance through a specific and relatively new program (Peiex) that is targeting smaller firms to improve competitiveness to export.<sup>53</sup> When excluding Peiex’s treated firms and their matched pairs from the sample, Apex’s treatment increases by approximately 1.07 times the export propensity for non-exporters at _t+1_ (column 4), from about 5% to 10%. The effect is larger for the specification without additional covariates at firm level (see column 3). In this case, the propensity to export in _t+1_ goes from 3.5% to 11.3%.

|Dependent variable:||Export du|mmy (t+1)|||Export d|ummy (t)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Apex|0.023***<br>(0.005)|0.020***<br>(0.005)|0.078***<br>(0.015)|0.067***<br>(0.015)|0.022***<br>(0.004)|0.020***<br>(0.004)|0.062***<br>(0.013)|0.056***<br>(0.013)|
|lfrmssize||0.009*||0.042***||0.002||0.000|
|||(0.005)||(0.015)||(0.008)||(0.020)|
|frmsage||0.033||0.061||0.094||-0.012|
|||(0.038)||(0.099)||(0.060)||(0.037)|
|frmsage2||0.000**||0.000||0.000*||-0.000|
|||(0.000)||(0.000)||(0.000)||(0.000)|
|lwage||-0.001||0.002||0.003||0.011|
|||(0.006)||(0.018)||(0.005)||(0.013)|
|lschool||0.021||0.120**||0.024||0.098|
|||(0.014)||(0.059)||(0.016)||(0.063)|
|shexpsrnet||0.002||0.003||0.010***||0.031***|
|||(0.003)||(0.007)||(0.003)||(0.007)|
|ManagerexpL1||0.207**||0.188||0.125||0.065|
|||(0.089)||(0.119)||(0.086)||(0.100)|
|ManagerexpL2||-0.036||-0.117||0.104||-0.023|
|||(0.100)||(0.135)||(0.095)||(0.115)|
|ManagerexpL3||0.010||0.051||-0.015||0.044|
|||(0.100)||(0.133)||(0.067)||(0.106)|
|Import||0.001||-0.014||0.025||0.039|
|||(0.025)||(0.056)||(0.023)||(0.054)|
|layer||0.003||0.013||-0.001||-0.009|
|||(0.004)||(0.012)||(0.003)||(0.011)|
|Fixed efects|.|.|.|.|.|.|.||
|Firms|YES|YES|YES|YES|YES|YES|YES|YES|
|Year*Sector|YES|YES|YES|YES|YES|YES|YES|YES|
|Number of id|4,888|4,888|1,128|1,128|4,888|4,888|1,128|1,128|
|Observations|9,776|9,776|2,256|2,256|9,776|9,776|2,256|2,256|

Note: Linear Probability Model (LPM) using panel fixed effects. Standard errors clustered at firm level are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table 6: Impact of Apex on the probability to export (matched sample) Due to the exclusion restriction used to define the population of non-exporting firms in periods _t-1_ and _t-2_ , the dependent variable does not vary in period _t-1_ . Hence, Apex’s treatment coefficients for the specifications in columns (1), (3), (5) and (7) in table 6 are equivalent to the marginal effect at the mean from a probit using a cross-section for the period of treatment

> _(t)_ .<sup>54</sup> Based on a probit estimation with sector-year fixed effects, we can compare the expected

> 53Peiex started at the end of 2008 and has a large number of firms that began to receive the program in 2009. 54Results for Apex’s treatment are also relatively similar for the specification with covariates used as robustness

19
probability for becoming an exporter according to different treatment status conditioned on sectors.<sup>55</sup> Is there a contemporaneous effect of Apex’s treatment on promoting new exporters? Table 6 shows that the impact in the year these firms received the program is also positive and statistically significant. These results converge with others that find a positive effect of the program in the year of implementation (e.g. Volpe Martincus and Carballo (2008)). The contemporaneous effect of Apex’s treatment increases the export propensity by 2.2 pp, from 1.28% to 3.61%. It means that it raises by almost 1.8 times the propensity of a non-exporting firm to become an exporter in period t.

In section 4 I explained the method I used for matching firms based on the propensity of receiving Apex treatment according to their characteristics in periods _t-1_ and _t-2_ . The identification relies on the assumption that the distribution of the relevant variables is balanced. I analyzed the quality of matching by following common testing procedures used in the program evaluation literature (see Dehejia and Wahba (2002), Caliendo and Kopeinig (2008), Volpe and Carballo (2008) and Diamond and Sekhon (2013)). Graph 2 (see section 7.2 of the Appendix) shows the histogram of the density of propensity score. It is noticeable that the probability to receive Apex’s assistance is low, but the distribution of the control group after the matching procedure is similar to the treatment group.

In order to check the similarity between the distribution of the propensity scores I used the Kolmogorov-Smirnov test for equality of distribution functions, following Delgado et al. (2002). The results in table 7 suggest that after the matching procedure the difference in the distribution of the propensity score to access Apex’s treatment is not statistically different from zero, which is not the case for the raw data.<sup>56</sup> Caliendo and Kopeinig (2008) propose another procedure to test the balance of covariates by running the probit again on the same covariates used to estimate the propensity score and

> check (see columns 2, 4, 6 and 8 in table 6) if a probit specification is used.

> 55When analyzing the marginal effect for different manufacturing sectors in both specifications, with and without firm level covariates (columns 1 and 2), Apex’s treatment is positive and significant to: food (3.1 pp), beverage (4.5 pp), wearing apparel (1.2 pp), leather (3.0 pp), chemicals (3.7 pp), rubber and plastic (4.5 pp), basic metals (3.5 pp), metal products (2.6 pp), computer (4.2 pp), electrical (4.1 pp), machinery (5.0 pp), motor vehicles (4.9 pp) and furniture (1.1 pp). The value in parentheses refer to the marginal effect at the mean for each sector in the specification in column 1. The result is relatively similar for the specification with additional covariates at firm level (column 2).

> 56The first line (Untreated) tests the hypothesis that the propensity score to the untreated group contains smaller values than for Apex’s treated group. It shows that the largest difference between the distribution function is 0.0021, which it is not statistically significant. The second line (Treated) tests the hypothesis that PS for the untreated group contains larger values than for the treated group. It shows that the largest difference in the distribution function in this direction is 0.0012, which is also not statistically significant. The p-value for the combined test approximates 1, which suggests that the PS distribution between both groups is not statistically significant.

20
||M|atching sa|mple||Raw Dat|a|
|---|---|---|---|---|---|---|
|Smaller group|D|P-value|Corrected|D|P-value|Corrected|
|Untreated|0.0021|0.985||0.4904|0.000||
|Treated|-0.0012|0.995||0.000|1.000||
|Combined K-S:|0.0021|1|1|0.4904|0.000|0.000|

Table 7: Kolmogorov-Smirnov test for equality of distribution functions compare the pseudo _R_<sup>2</sup> , which should be much lower in the matched data. Table 11 (in section 7.2 of the appendix) shows that both conditions are satisfied. The pseudo _R_<sup>2</sup> decreases from approximately 0.15 to about 0.008. Regarding the covariates used in the matching specification, most of them are not statistically significant at 5%.<sup>57</sup> Also, Caliendo and Kopeinig (2008) suggest checking the t-test for treated and control group means.<sup>58</sup> Table 12 (in section 7.2 of the Appendix) shows that for most of the covariates I do not reject the null hypothesis that the means of the treated and control groups (with respect to the characteristics of the firms in _t-1_ and _t-2_ ) are similar at 5% of significance.<sup>59</sup> In addition to the mean, it is important that the distribution of the covariates is balanced. Figure 3 (in section 7.2 of the appendix) shows the QQ plots to the covariates used in the matching as procedures to assess balance suggested by Ho et al. (2011). It is noticeable that the matching algorithm worked well, not only on the mean values but also on their distribution.

A critical assumption used for identification was the fact that the treated and the control groups would have a similar trend over time if the program had not been available, the socalled parallel trend assumption.<sup>60</sup> Regarding this assumption, an important feature of the identification strategy was the fact that the matching was carried out on firms that had not exported at periods _t-1_ and _t-2_ . It means that the sample had similar trends concerning the outcome (export status) and firms’ covariates for two periods prior to the treatment.

# **5.2 Is Apex’s impact heterogeneous according to firms’ size?**

When confronted with public policy alternatives, policy makers need to decide not only which policy to be implemented, but also who should be the beneficiaries of these policies. Therefore, in addition to understanding an average effect of a program, it is also important to identify

> 57The few exceptions are lag of manager L3 and Import at period t-2. The variables (Manager L3) and Import are used as covariates in the robustness specifications, where I include the time-varying covariates as control. 58Imai et al. (2008) argues that although it is widely used, t-test of the difference in the mean may be misleading.

> 59I tested various specifications for the PSM procedure, including changes in the variables for which exact matching was done and covariates included in the model. Overall the positive effect of Apex’s treatment on the propensity to export for non-exporting firms remains.

> 60See Arcand and Bassole (2007).

21
the mechanism at work and the characteristics of those firms for which an intervention is more effective, in case there are heterogeneous effects. My previous estimations suggest that an increase in the number of employees is positively correlated to the propensity of a firm to export. This section aims to evaluate if there is heterogeneity in the effect of Apex according to different firms’ size cohorts.

In the case of EPAs, Volpe Martincus and Carballo (2010a) find evidence of heterogeneity with respect to export performance based on different groups defined according to their export value distribution.<sup>61</sup> Also, Volpe Martincus et al. (2012) show that EPAs’ impact differs by firm’s size categories.<sup>62</sup> However, the heterogeneous effect of EPAs according to different size categories of firms on promoting new exporters is still an open question.

In order to evaluate the heterogeneous effect of Apex I ran the baseline model using different sub-samples, according to different categories of firms’ size, based on the number of employees. Firms were classified as follows: Micro ( _size <_ 20), Small (20 _< size <_ = 50), Medium (50 _< size <_ = 150) and Large ( _size >_ 150), where size refers to the number of employees. Due to the fact that I matched firms using their characteristics ex-ante treatment, I classified the firms according to their number of employees in period _t-1_ . Although size of the firm was among the covariates in the matching procedure and their average and distribution was very similar between the treated and control groups (see table 12, in appendix), the number of treated and control group firms in each size cohort can differ. I used as a baseline sample a definition based on treated firms (number of employees in period _t-1_ ). Then I added their respective matched-pairs in the same (size cohort) group.<sup>63</sup> I also used as an alternative sample a classification based on the number of employees for all firms in period `t-1` . Results are relatively similar among these two procedures.

Table 8 shows the results regarding the impact of Apex’s treatment on export status in period _t+1_ . The effect is positive and statistically significant for micro, small and medium firms. However, the effect is not significant for large firms. Similar results are obtained if large firms are classified as being above 250 employees.<sup>64</sup> As mentioned in the previous section, the results for columns (1), (3), (5) and (7) are equivalent to the marginal effect at the means under a

> 61Using Chilean firm level data.

> 62Using Argentinian firm level data.

> 63Those are firms in the control group that were matched with the respective firm in the treatment group.

> 64For micro firms the effect is positive and significant for wearing apparel (1.1 pp), leather (4.3 pp), machinery (6.2 pp). For small firms the effect is positive for rubber and plastic (17.0 pp) and machinery (7.3 pp). For medium firms the effect is positive for food (9.0 pp), beverage (18.3 pp), rubber and plastic (18.3), basic metals (11.5 pp), metal products (10.8), computer (23.8 pp), electrical (12.2 pp), machinery (12.8 pp) and motor vehicles (17.9 pp). If the alternative definition of firms’ size is used, also leather, printing, chemicals, pharmaceutical and furniture become significant, while electrical becomes insignificant. I have not found heterogeneous effect between industries for large firms.

22
probit specification. If the marginal effects at the means for different industries are compared, it is noticeable that the impact is heterogeneous and seems to be larger and for more industries among medium firms. Also the effect seems positive for some industries that are highlighted as their main target by Apex.<sup>65</sup>

|Dependent Vari|able: Expor|t status (t+|1)||||||
|---|---|---|---|---|---|---|---|---|
|Variable|M|icro|S|mall|Med|ium||Large|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|apex|0.014***|0.013***|0.024**|0.024***|0.067***|0.048**|0.038|0.035|
||(0.004)|(0.004)|(0.010)|(0.009)|(0.022)|(0.019)|(0.052)|(0.050)|
|lfrmssize||0.007||0.024**||-0.002||0.083*|
|||(0.006)||(0.012)||(0.026)||(0.048)|
|frmsage||-0.007||-0.013||0.166||0.109|
|||(0.005)||(0.018)||(0.164)||(0.085)|
|frmsage2||0.000||-0.000||0.000||-0.000|
|||(0.000)||(0.000)||(0.001)||(0.001)|
|lwage||-0.003||-0.017||-0.049*||0.172***|
|||(0.004)||(0.028)||(0.028)||(0.040)|
|lschool||0.012||0.033||0.103||-0.121|
|||(0.014)||(0.039)||(0.096)||(0.334)|
|shexpsrnet||0.003||-0.002||-0.008||0.027*|
|||(0.002)||(0.007)||(0.013)||(0.015)|
|ManagerexpL1||0.036||0.069||0.422***||0.256**|
|||(0.277)||(0.203)||(0.139)||(0.121)|
|ManagerexpL2||-0.445***||0.087||0.220||-0.258*|
|||(0.169)||(0.250)||(0.175)||(0.136)|
|ManagerexpL3||0.330***||-0.005||-0.152||-0.050|
|||(0.119)||(0.189)||(0.164)||(0.208)|
|Import||0.010||-0.027||0.129**||-0.192**|
|||(0.038)||(0.031)||(0.054)||(0.092)|
|layer||-0.001||0.003||0.028**||-0.143*|
|||(0.004)||(0.007)||(0.013)||(0.075)|
|Fixed efects|||||||||
|Firms FE|YES|YES|YES|YES|YES|YES|YES|YES|
|SectorYear FE|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.053|0.101|0.106|0.114|0.197|0.284|0.264|0.417|
|Number of id|3,104|3,104|1,062|1,062|566|566|156|156|
|Observations|6,208|6,208|2,124|2,124|1,132|1,132|312|312|

Notes: Linear Probability Model (LPM) using panel fixed effects and the baseline definition of firms’ size categories. Standard errors clustered at firm level are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The number of id refers to the total number of firms (treated and untreated in each size cohort). In the alternative specification of large firms that includes all firms in the matched sample (treated and untreated firms) above 150 employees, instead of using the definition based on treated firms and using their respective matched pairs as a counterfactual, the coefficient for “Import” is not significant. In addition, many large firms are importers in both periods and many in this sample changed their importing status from _t_ to _t+1_ in this sample. A similar issue happens with ManagerexpL2 in the sample of micro Table 8: Heterogeneous effect: Impact of Apex treatment on the probability to export in _t+1_ according to firms’ size (baseline) I used the same procedure to estimate the contemporaneous effect. Again, the effect of Apex is positive and statistically significant at 5% for micro, small and medium firms (see table 9), but not for large firms. Furthermore, the contemporaneous effect is significant only at 10% for small<sup>66</sup>

> 65See information on sector projects available on www.apexbrasil.com.br. These sectors include: food, beverage, agribusiness, bulding, furniture, culture (e.g. musical instrument, art), design, machinery and equipment, fashion industry (e.g. wearing apparel, leather, shoes), health devices, among others.

> 66Regarding micro firms, the contemporaneous (export in period _t_ ) marginal effect at the means (MEM) are statistically significant for wearing apparel (1.3 pp), leather (7.0 pp), chemicals (4.4 pp), machinery (5.0 pp) and other manufacturing products (5.3 pp) - this group of products is equivalent to division 32 of the ISIC Rev.4, which includes jewellery, bijouterie, musical instruments, sports goods, games, toys and medical instruments. With respect to small firms, there is no statistically significant difference between industries. Among medium

23
|Dependent Vari|able: Expor|t status (t)|||||||
|---|---|---|---|---|---|---|---|---|
|Variable|Mi|cro|Sm|all|Me|dium|La|rge|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|apex|0.019***|0.016***|0.015**|0.013*|0.046**|0.035**|0.051|0.056|
||(0.004)|(0.004)|(0.007)|(0.007)|(0.019)|(0.017)|(0.051)|(0.052)|
|lfrmssize||0.008*||0.005||0.010||-0.076|
|||(0.005)||(0.008)||(0.025)||(0.067)|
|frmsage||-0.006||-0.002||0.177||0.107|
|||(0.004)||(0.013)||(0.147)||(0.087)|
|frmsage2||-0.000||0.000||-0.000||-0.000|
|||(0.000)||(0.000)||(0.000)||(0.001)|
|lwage||0.000||-0.026||-0.018||0.060|
|||(0.004)||(0.020)||(0.023)||(0.045)|
|lschool||0.008||0.051*||0.097||0.141|
|||(0.011)||(0.027)||(0.075)||(0.356)|
|shexpsrnet||0.008***||0.006||0.024*||0.036*|
|||(0.003)||(0.007)||(0.014)||(0.021)|
|ManagerexpL1||0.097||-0.015||0.208||0.179|
|||(0.258)||(0.191)||(0.156)||(0.141)|
|ManagerexpL2||0.163||-0.063||0.186||0.084|
|||(0.203)||(0.187)||(0.160)||(0.151)|
|ManagerexpL3||0.166||0.011||-0.036||-0.121|
|||(0.175)||(0.044)||(0.116)||(0.192)|
|Import||0.004||0.002||0.156***||-0.106|
|||(0.033)||(0.009)||(0.059)||(0.097)|
|layer||-0.007*||0.004||0.020*||0.083|
|||(0.004)||(0.006)||(0.011)||(0.068)|
|Fixed efects|||||||||
|Firms FE|YES|YES|YES|YES|YES|YES|YES|YES|
|SectorYear FE|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.057|0.077|0.078|0.086|0.178|0.246|0.185|0.276|
|Number of id|3,104|3,104|1,062|1,062|566|566|156|156|
|Observations|6,208|6,208|2,124|2,124|1,132|1,132|312|312|

Notes: Linear Probability Model (LPM) using panel fixed effects and the baseline definition of firms’ size categories. Standard errors clustered at firm level are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The number of id refers to the total number of firms (treated and untreated in each size cohort).

Table 9: Heterogeneous effect: Impact of Apex on the probability to export in _t_ according to firms’ size (baseline) Tables 8 and 9 show the LPM for the subsamples defined according to the size of the firm in t-1 (micro, small, medium and large). Overall, it is noticeable that the effect is positive and statistically significant for firms smaller than 150 employees. The effect seems to be larger for medium firms (between 50 and 100 employees), but it is not statistically different than zero for large firms.<sup>67</sup> These results are in line with the findings of Volpe Martincus et al. (2012) that show evidence of more effectiveness of EPAs’ activity for smaller firms.

firms, the marginal effect at the means (MEM) are significant for wearing apparel (4.8 pp), pharmaceuticals (20 pp), rubber and plastic (16 pp), computer (20 pp) and machinery (15 pp). If the definition of size does not take the matched pairs into account, then non-metalic and other manufacturing products become significant. Although the MEM seems very large the propensity to export in these industries is larger. For example, the treatment raises the propensity to export from 17% to 33% in the rubber and plastics industry. With respect to the large firms I have not found differences between industries that are statistically significant. Also, Apex’s effect is significant at 5% in the alternative definition of firms’ size.

> 67The effect is not statistically significant when considering other thresholds for large firms (e.g. 250 employees or more).

24
# **5.3 Are there spillovers of Apex’s treatment on promoting new exporters?**

This subsection analyzes the effect of being exposed to Apex treatment on firms that did not receive the program. In order to generate a proxy for exposure measurement I generated a variable with the total amount of Apex’s treated firms in the same sector (CNAE 2 digits) and micro-region (official IBGE definition for 549 sub-regions in Brazil) of the firms that did not receive Apex.<sup>68</sup> The assumption is that the potential mechanism of transmission is sectorlocation related. The population of interest is composed by non-exporting untreated firms<sup>69</sup> belonging to the same sector (CNAE 2 digits) and located in the same micro-region of those that received Apex’s assistance. On the other hand, the control group consists of firms that were not in the same sector and micro-region of an assisted firm.<sup>70</sup> I assume that the intensity and the quality of the exposure matter. Also, I assume a possibility of heterogeneity regarding quality of exposure. Therefore, I tested two alternatives: i) Total number of Apex’s treated firms in the same sector and micro-region; ii) Total number of Apex’s treated firms in period t that exported in _t+1_ in the same sector and micro-region.

The identification strategy used to test the spillover effect is based on a panel fixed effects without matching, under DID assumption. Therefore, I am assuming that the number of firms that belong to the same sector and micro-region and were assisted by Apex are exogenous to the probability of a non-exporting untreated firm to become an exporter, once controlled for firms’ fixed effects, sectoral-year fixed effects and share of exporters in the same sector and region. Table 10 shows the impact of being exposed to Apex’s treated firms on the export propensity for non-exporting firms, taking into account number of Apex’s treated firms by micro-region and CNAE (2 digits). The two variables measuring exposure are the following: a) Exposure to Apex’s treated exporting firms; b) Exposure to Apex, which takes into account the total number of Apex’s treated firms.

The effect of being exposed to Apex-treated exporting firms is positive (see column 1 in table 10). The effect seems robust for different specifications with additional covariates (columns 2 and 3), including the share of other exporting firms. I controlled for the share of other exporting firms in the same sector-region at period _t_ (shexpsrnet) in column (2) and the share of other

> 68It was generated in four different versions including CNAE 2 and CNAE 4 digits interacting with States (27) and Micro-region (549).

> 69This means that those firms did not receive Apex treatment between 2007 and 2010. I am keeping the definition of non-exporting firms as being those firms that did not export in either of the two years before the treatment was considered.

> 70I also tested the effect for the share of treated firms in a given sector-region.

25
|Dependent variable:|Export sta|tus in(t+1)|||||
|---|---|---|---|---|---|---|
||Apex|’s Treated (Exp|orters)|A|pex’s Treated|(total)|
|||Treated*Exporte|rs||Treated||
||(1)|(2)|(3)|(4)|(5)|(6)|
|Exposure to Apex|0.00030***<br>(0.0001)|0.00025***<br>(0.0001)|0.000201**<br>(0.00010)|-0.00005<br>(0.0000)|-0.00001<br>(0.00004)|0.000043***<br>(0.00001)|
|lfrmssize||0.0025***|0.00244***||0.0025***|0.0025***|
|||(0.0003)|(0.00034)||(0.0003)|(0.00034)|
|frmsage||-0.00079|-0.00080||-0.00082|-0.00083|
|||(0.0009)|(0.00088)||(0.0009)|(0.00088)|
|frmsage2||0.000045***|0.00005||0.00005***|0.000046***|
|||(0.00001)|(0.00001)||(0.00001)|(0.00001)|
|lwage||0.00007|0.00003||0.00007|0.00003|
|||(0.0001)|(0.00013)||(0.0001)|(0.00013)|
|lschooling||-0.00057|-0.00066||-0.00057|-0.00067|
|||(0.0006)|(0.00059)||(0.0006)|(0.00059)|
|shexpsrnet (t)||-0.00105***<br>(0.0002)|-<br>-||-0.00106***<br>(0.0002)|-<br>-|
|shexpsrnet (t+1)||-|0.00325***||-|0.00326***|
|||-|(0.00024)||-|(0.00023)|
|ManagerexpL1||0.01905|0.01775||0.01908|0.01777|
|||(0.0339)|(0.03375)||(0.0339)|(0.03375)|
|ManagerexpL2||0.04358|0.04202||0.04361|0.04204|
|||(0.0366)|(0.03653)||(0.0366)|(0.03653)|
|ManagerexpL3||0.0681***<br>(0.0251)|0.06752***<br>(0.02500)||0.0681***<br>(0.0251)|0.06752***<br>(0.02500)|
|Import||0.0203***|0.0201***||0.02031***|0.0201***|
|||(0.0035)|(0.00353)||(0.0035)|(0.00353)|
|layer||0.0005**|0.00052**||0.00052**|0.00052**|
|||(0.0003)|(0.00026)||(0.0003)|(0.00026)|
|Fixed efects|.|.|.|.|.|.|
|Firms|YES|YES|YES|YES|YES|YES|
|Year*Sector|YES|YES|YES|YES|YES|YES|
|R-squared|0.0037|0.0073|0.0104|0.0037|0.0072|0.0104|
|Number of id|266,769|266,155|266,155|266,769|266,155|266,155|
|Observations|644,674|642,927|642,927|644,674|642,927|642,927|

Note: Linear Probability Model (LPM) using panel fixed effects using the alternative definition of firms’ size categories. Treatment variable: Number of treated firms in the same region and same sector in period (t).

Table 10: Impact of Apex treatment on untreated firms in period (t+1) - spillover effect exporting firms in the same sector-region at period _t+1_ (shexpsrnet _t+1_ ). The magnitude of the effect is low, which is consistent with the low dynamic of new exporters in Brazil. Also, the effect is significant only at 10% with additional covariates (equivalent to specification in column 2) when binary treatment status is considered. Therefore, the interpretation of the empirical results on spillover effect should be undertaken cautiously.

# **6 Conclusion**

The number of EPAs has increased sharply in the last decades, particularly in developing and emerging economies. Usually, these agencies focus on providing support to domestic firms in order to deal with incomplete information to access foreign markets and realize gains from trade.

This paper shows evidence of positive impact of EPA services on the export propensity of 26
non-exporting firms (the pure extensive margin of trade). Using a procedure of matching DID estimator for controlling potential selection bias I find that: 1) Apex’s treatment has a positive effect on promoting new exporters. An average non-exporting treated firm is about 1.3 times more likely to become a new exporter one year after the treatment. 2) There is a contemporaneous effect. This means that the average effect of the program on treated firms is positive in the year of treatment. 3) The impact is positive and statistically significant for micro, small and medium firms, but not for large firms. 4) I find evidence of spillovers on untreated firms that are in the same region and sector of Apex’s treated exporting firms.

As a robustness check for the empirical results, I carried out some standard procedures for assessing the quality of the covariates’ balance in the matched sample. Overall they performed well. In addition, I ran a linear probability model (LPM) using panel fixed effects (also keeping those firms that have not exported in the two previous years) for the full sample and the results seem consistent. Moreover, I checked the robustness for different specifications by controlling for a large number of covariates and the effect is robust. Also, I ran the model using firms that received Apex’s treatment in 2007; for these firms there is no information of treatment status in 2006 and the results are also qualitatively similar.

These findings bring additional evidence to a rich literature on the impact of EPAs on export. When properly performed, export promotion may not only boost export value, product and market diversification as already suggested by the previous literature, but also can act in the pure extensive margin. In addition, there is evidence that these programs are more effective among micro, small and medium firms and the existence of spillover effect is not rejected.

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