metadata: authors: 0: author: Fernandes,Ana Margarida
1: author: Freund,Caroline
2: author: Pierola Castro,Martha Denisse
count: World
docna: 0: docna: Exporter behavior, country size and stage of
            development : evidence from the exporter dynamics database
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Access to Finance,Food & Beverage Industry,Common Carriers Industry,Construction Industry,Business Cycles and Stabilization Policies,General Manufacturing,Plastics & Rubber Industry,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Trade and Services,Rural Labor Markets,Labor Markets
teratopic: Macroeconomics and Economic Growth,Industry,Social Protections and Labor,Rural Development,Finance and Financial Sector Development,International Economics and Trade
abstracts: cdata!: This paper presents new data on the
            micro structure of the export sector for 45 countries and
            studies how exporter behavior varies with country size and
            stage of development. Larger countries and more developed
            countries have more exporters, larger exporters, and a
            greater share of exports controlled by the top 5 percent.
            The extensive margin (more firms) plays a greater role than
            the intensive margin (average size) in supporting exports of
            larger countries. In contrast, the intensive margin is
            relatively more important in explaining the exports of
            richer countries. Exporter entry and exit rates are higher
            and entrant survival is lower at an early stage of
            development. The paper discusses the results in light of
            trade theories with heterogeneous firms and the empirical
            literature on resource allocation, firm size, and
            development. An implication from the findings is that
            developing countries export less because the top of the
            firm-size distribution is truncated.
repnme: repnme: Exporter behavior, country size and stage
            of development : evidence from the exporter dynamics database
display_title: Exporter behavior, country size and
            stage of development : evidence from the exporter dynamics database
listing_relative_url: /research/2015/10/25176979/exporter-behavior-country-size-stage-development-evidence-exporter-dynamics-database
topicv3: Labor Markets m1326488 855,Manufacturing Sectors m1326521 1281,Trade m1326924 698,Trade in Services m1326928 780,Access to Finance m1327464 1249
publishtoextweb_dt: 2015-10-21T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,Social Protections and Labor,Rural Development,Finance and Financial Sector Development,International Economics and Trade
no_of_pages: 50
author_keys: 0: author_key: 658876
1: author_key: 641206
2: author_key: 810670
model_extractions: WPS7452 Policy Research Working Paper 7452 Exporter Behavior, Country Size and Stage of Development Evidence from the Exporter Dynamics Database _Ana M. Fernandes Caroline Freund Martha Denisse Pierola_ Development Research Group Trade and International Integration Team October 2015
Policy Research Working Paper 7452

# **Abstract**

This paper presents new data on the micro structure of the export sector for 45 countries and studies how exporter behavior varies with country size and stage of development. Larger countries and more developed countries have more exporters, larger exporters, and a greater share of exports controlled by the top 5 percent. The extensive margin (more firms) plays a greater role than the intensive margin (average size) in supporting exports of larger countries. In contrast, the intensive margin is relatively more important in explaining the exports of richer countries. Exporter entry and exit rates are higher and entrant survival is lower at an early stage of development. The paper discusses the results in light of trade theories with heterogeneous firms and the empirical literature on resource allocation, firm size, and development. An implication from the findings is that developing countries export less because the top of the firm-size distribution is truncated.

This paper is a product of the Trade and International Integration Team, Development Research 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 authors may be contacted at afernandes@worldbank.org, CFreund@PIIE.COM, and mpierola@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
# **Exporter Behavior, Country Size and Stage of Development: Evidence from the Exporter Dynamics Database**<sup>*</sup>

## **Ana M. Fernandes      Caroline Freund      Martha Denisse Pierola**

## **JEL Classification codes** : F14, O1.

## **Keywords:** exporter dynamics, exporter growth, firm-level data, allocative efficiency.

* Ana Fernandes and Martha Denisse Pierola are at the World Bank; Caroline Freund is at the Peterson Institute for International Economics and CEPR. The authors would like to thank two anonymous referees as well as Nathan Nunn. We are grateful to Tolga Cebeci for his collaboration on an earlier version of this paper that focuses on describing the Exporter Dynamics Database in more detail.  Research for this paper has been supported in part by the World Bank’s Multidonor Trust Fund for Trade and Development and through the Strategic Research Partnership on Economic Development. We also acknowledge the generous financial support from the World Bank research support budget and the Knowledge for Change Program (KCP), a trust funded partnership in support of research and data collection on poverty reduction and sustainable development housed in the office of the Chief Economist of the World Bank (www.worldbank.org/kcp).  We thank Erhan Artuc, Richard Baldwin, Peter Klenow, Aaditya Mattoo, Sam Kortum, and Andres Rodriguez-Clare for many useful comments and discussions. We thank workshop participants at the Empirical Investigations in International Trade at Santa Cruz, the Graduate Institute in Geneva, the World Bank, the World Trade Organization, the International Trade Center, and Nova School of Business and Economics for useful suggestions. We thank Mario Gutierrez-Rocha, Jamal Haidar, Alberto Behar and Madina Kukenova for their contribution to this project and Francis Aidoo, Paul Brenton, Maurizio Bussolo, Huot Chea, Julian Clarke, Alain D’Hoore, Pablo Fajnzylber, Elisa Gamberoni, Sidiki Guindo, Javier Illescas, Mehar Khan, Sanjay Kathuria, Josaphat Kweka, Erjon Luci, Mariem Malouche, Eric Manes, Yira Mascaro, Mamadou Ndione, Zeinab Partow, Stefano Paternostro, Jorge Rachid, Richard Record, Jose Guilherme Reis, Jose Daniel Reyes, Nadeem Rizwan, Liviu Stirbat, Dimitri Stoelinga, Daria Taglioni, Maria Nicolas, Erik von Uexkull, Cristian Ugarte, and Ravindra Yatawara for facilitating the access to the data for developing countries. We thank Lina Ahlin, Martin Andersson, Juan de Lucio, Emmanuel Dhyne, Luc Dresse, Cédric Duprez, Richard Fabling, Jaan Masso, Raul Minguez, Asier Minondo, Andreas Moxnes, Francisco Requena, Lynda Sanderson, Joana Silva, Priit Vahter, and Hylke Vandenbussche for providing us with measures for developed countries.
# **1. Introduction**

Exporter characteristics and dynamics vary significantly across countries. The average Chilean exporter is almost three times as large as the average Moroccan exporter, and Chile has one-third more exporters than Morocco. There are twice as many exporters in Bulgaria as in Bangladesh, but a Bulgarian firm exports on average half as much as a Bangladeshi firm. There is also wide variation in exporter dynamics across countries. Firms from Cameroon and Malawi have high entry rates into exporting but only 25 percent of entrants survive after the first year. In contrast, firms from Brazil and South Africa have lower entry rates, but among entrants, about half survive for more than one year. This paper shows that these measures of exporter behavior vary in systematic ways with country size and stage of development and considers potential explanations.

Our first goal is to introduce the Exporter Dynamics Database and use it to characterize export behavior at the firm level across countries. We use exporter-level customs information from 38 developing and 7 developed countries as input to build the _“Exporter Dynamics Database”_ (henceforth referred to as the Database). The Database contains the number of exporters, average size of an exporter, exporter concentration, rates of entry and exit and entrant survival among exporters for each country at various levels of disaggregation. Using these measures, we examine how exporter behavior varies with country size and stage of development. Many previous studies use data from individual countries or a small group of countries in a region to study how firms export.<sup>1</sup> However, their focus has been on the size distribution of exporters and the process of resource reallocation in response to changes in trade costs within a country. In the absence of

> 1 See Eaton, Kortum, and Kramarz (2008) for France; Eaton et al. (2008) for Colombia; Amador and Opromolla (2013) for Portugal; Iacovone and Javorcik (2010) for Mexico; Andersson, Lööf, and Johansson (2008) for Sweden, Albornoz et al. (2012) for Argentina; Freund and Pierola (2010) for Peru; Manova and Zhang (2012) for China, Masso and Vahter (20151) for Estonia, De Lucio et al. (2011) for Spain, Ekholm, Moxnes, and Ulltveit-Moe (2012) for Norway, Fabling and Sanderson (2012) for New Zealand, Mayer and Ottaviano (2008) for several EU countries, among others. See also Bernard et al. (2007, 2012) for the U.S. and a review of the empirical literature, respectively.

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cross-country variation, such studies were unable to examine how exporter behavior changes as countries get larger or richer. We seek to fill this gap in the literature.

Our second goal is to examine predictions from trade theory with heterogeneous firms and models with allocative inefficiencies for consistency with the new evidence on exporter behavior across countries. Systematic variation in export-sector characteristics across countries of different sizes or income levels could be a result of differences in resource endowments or the process of resource allocation, among other factors. To explore how differences in resource endowments affect exporter behavior, we stick to the most basic model, the standard heterogeneous firm trade model of Melitz (2003). The model draws a direct link between firm size and inherent productivity, so the biggest firm will always also be the most productive. A stark prediction comes out of this model: larger countries should export more because they have more firms (the extensive margin), as opposed to larger firms (the intensive margin). This theory has little to say about stage of development or firm dynamics.

To explore how variation in allocative efficiency across countries affects exporter behavior, we turn to the growing literature on efficiency gains from within-sector resource reallocation across firms. These studies attribute variation in firm size not only to inherent productivity differences (like Melitz 2003) but also allow a broad set of distortions to resource allocation between firms to affect the size distribution.<sup>2</sup> In support, empirical studies of developing countries find a relatively wide variation in firm performance and weak correlations between size and productivity within narrowly defined sectors in a country, suggesting that misallocation of resources between firms is indeed a major impediment to growth.<sup>3</sup> An implication from this

> 2 The distortions can be associated with several types of institutions and policies, such as labor market regulations, regulations on business entry, access to finance, taxation etc.

> 3 Syverson (2004) finds that a U.S. firm in the 90th percentile in a four-digit SIC industry (443 industries) is on average twice as productive as a firm at the 10<sup>th</sup> percentile in the same industry.  In developing countries, productivity differences tend to be even

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literature is that countries with fewer distortions (more developed countries) should have more exporters and higher survival rates of entrants because the most productive firms have the opportunity to grow, begin exporting and expand into foreign markets. But importantly, the implications for average exporter size and exporter concentration at the top of the distribution depend on which firms are most constrained. If only the most productive firms are able to overcome regulatory hurdles in economies with allocative inefficiencies and mid-productivity firms are held back—the so-called “missing middle” of the size distribution—then exporters should be relatively larger in developing countries and very concentrated at the top of the distribution (Tybout 2000, 2014 and Alfaro, Charlton, and Kanczuk 2009). In contrast, if the highproductivity firms are the most constrained and fail to invest (Hsieh and Klenow 2009, Hsieh and Olken 2014, and Bento and Restuccia 2014), then exporters should be relatively small in more distorted economies with less concentration at the top of the distribution, which we refer to as the “truncated top” of the size distribution.

We examine whether the evidence from the Database is consistent with these predictions. We find that export-sector characteristics are correlated with both country size and stage of development in a systematic way. In particular, larger economies and more developed economies have a greater number of exporters, a larger average exporter size, and a greater concentration of exports in the top 5 percent of exporting firms. The extensive margin (more firms) explains about two-thirds of the increase in exports of larger countries, while the intensive margin (larger firms) explains the remaining third. In contrast, the intensive margin is relatively more important as an explanation for why richer countries export more. These results are robust to different levels of

> larger with Hsieh and Klenow (2009) finding a ratio for the 90:10 percentile of 5:1 for China. Bartelsman et. al (2013) estimate the covariance between productivity and size in narrowly defined industries, in a group European countries and the US.  They find the covariance between size and productivity was near zero (or negative) at transition in Eastern Europe and has since increased, i.e. allocative efficiency has improved sharply.

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aggregation of the data, to controls for various types of fixed effects, to the prevalence of zeros in trade, and to proxies for the different importance of trade intermediaries across sectors.

The results on exporter dynamics reveal a robust and significant relationship with stage of development but not country size. We find that gross rates of entry into and exit from exporting are decreasing in stage of development, but net entry is unrelated to stage of development. There is also evidence that survival rates of entrants into export markets increase as countries develop. These results are robust to different levels of aggregation of the data and to controls for various types of fixed effects.

These stylized facts imply that as countries develop and exports grow, the export expansion happens through both the extensive and the intensive margins, and more resources flow to the largest firms. Consistent with the standard model of trade with heterogeneous firms, we find a relatively large role for the extensive margin in explaining why larger countries export larger volumes. Consistent with resource allocation improving with stage of development, we find that more developed countries have both more exporters and more resilient exporters. Overall, the positive correlations between average exporter size and stage of development and between concentration of exports at the top of the size distribution and stage of development are consistent with models where firm growth is constrained in developing countries, especially among the highproductivity firms, the “truncated top”.

The rest of the paper is structured as follows. Section 2 describes briefly the Database and the variability in outcomes across countries. Section 3 discusses how the theory can help understand this variability across countries. Section 4 presents the new stylized facts on the relationship between export-sector characteristics and dynamics on the one hand, and country size and stage of development on the other hand. Section 5 discusses the results in light of trade theory 5
and allocative efficiency and examines how exporter behavior changes with export growth over time for robustness. Finally, Section 6 concludes.

# **2. The Exporter Dynamics Database**

The Exporter Dynamics Database contains aggregated measures on export-sector characteristics and dynamics presented at the following levels: a) country-year, b) country-product (HS 2-digit, HS 4-digit, or HS 6-digit)-year and c) country-destination-year, and d) country-HS 2-digit productdestination-year (for a restricted sample of countries). The data are primarily for the period between 2003 and 2010. The measures are constructed using exporter-level customs data as input, covering the universe of annual exporter transactions for 38 developing countries and 7 developed countries.<sup>4</sup> The measures at the country-sector-destination-year level are available for 34 developing countries for which we have access to the raw exporter-level data and for 3 developed countries.<sup>5</sup> The Database includes measures on the basic characteristics of the export sector in each country (e.g., number of exporters, average exporter size and exporter growth rates); concentration/diversification (e.g., Herfindahl index, share of top exporters, number of products and destinations per exporter); firm, product and market dynamics (e.g., entry, exit, first-year and

> 4 The stylized facts presented in this paper use only the aggregated measures contained in the Database. The exporter-level customs datasets used as inputs to construct the Database are described to explain how the measures in the Database were built, but are not utilized in this paper for any other purpose. These exporter-level datasets are available upon request for a selected group of countries, determined by the willingness to share data by the data providers.  Given the confidential nature of the exporter-level (firm-level) data used as input, we structured the Database to provide as much detail as possible on the underlying exporter structure behind an export flow, without revealing any information that could be traceable to an individual firm, as per what we agreed with all of our data providers. While the data underrepresent industrial countries and Asia, the patterns we find are robust and available statistics from other studies, such as for the US and the EU, are consistent with the patterns observed here.  We aim over time to expand coverage of the Database.

> 5 Table 1 marks with an asterisk the developing countries for which we have access to the raw exporter-level data. The measures at the country-year-HS2-digit product-destination level are currently only available upon request to the authors. They will be made publicly available with the second release of the expanded Database. Measures at the country-year-HS2-digit product-destination level for Norway, Portugal, and Spain were made available to the authors by Andreas Moxnes, Joana Silva, Asier Minondo and Francisco Requena, the researchers working with data for those countries.

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second-year survival rates of entrants); and unit prices (e.g., per exporter of a given product). The - - - Database is publicly available at <u>http://data.worldbank.org/data catalog/exporter dynamics database. Details on how the countries’ customs data were cleaned and harmonized and how the</u> Database was constructed are provided in Appendix 1. Further details on all the measures in the Database for each type of aggregation level are provided in Cebeci, Fernandes, Freund, and Pierola (2012).<sup>6</sup> Table 1 presents a summary of the measures used in this paper, reporting averages per country for the period 2006-2008, which is the period most commonly covered across countries and captures trade performance before the global financial crisis that started at the end of 2008.<sup>7</sup> Some interesting cross-country patterns emerge from Table 1. First, there is tremendous variation across countries in the number of exporters and the average exports per firm. Among developing countries, the largest numbers of exporters are found in Turkey and Mexico whereas the smallest pools of exporters are found in Niger and Mali. This pattern seems to mirror the countries’ size and level of development, and these links are studied further in Section 4. The number of exporters per 1000 inhabitants is also the largest in the more developed countries in the sample. Average exports per firm are the highest for Belgium, followed by Brazil, Chile, and Mexico and the lowest in Albania, Lebanon, Macedonia, Yemen, and Kenya. The tremendous difference between mean and median exports per firm reflects the highly skewed exporter size distributions in all countries with some very large exporters driving total exports.<sup>8</sup> For most

> 6 Due to confidentiality concerns, the Database does not provide information on the exporter size measures for cells at countryyear-HS 2-digit, country-year-destination, or country-year-HS 2-digit-destination level that include a single exporter.

> 7 A sharp drop in world trade as a result of the global financial crisis occurs in 2009. For countries for which data is available for only 1 or 2 years within the period 2006-2008 we compute the average based on those years. For Kuwait and Portugal the data coverage does not include that period. Hence we use averages for the 2009-2010 period for Kuwait and averages for the 2003-2005 period for Portugal in Table 1.

> 8 In related work, Freund and Pierola (2015) use the cross-country exporter-level data and show that exports are dominated by a small group of very large exporters (so-called ‘export superstars’) considered as the top exporter or the top 5 exporters, who account for on average 15 and 30 percent of exports, respectively. Their focus is on how this group of large firms defines the sectoral pattern of exports. Focusing on data at the country-industry or product level, Easterly and Reshef (2014) show that both African and non-

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developing countries concentration is smaller than for the developed countries in the sample, such as Norway, Sweden, and New Zealand. Similarly, Bernard, Jensen, and Schott (2009) and Mayer and Ottaviano (2008) show that the United States (U.S.) and other European countries exhibit high concentration in the top firms.

The measures of exporter dynamics also exhibit important variability across countries. Entry rates–defined as the number of exporters in year _t_ not present in year _t-1_ , over the total number of exporters in year _t_ —range from 22 percent in Brazil to more than 50 percent in Malawi, Yemen, the Lao People’s Democratic Republic and Tanzania. Exit rates–defined as the number of exporters in year _t-1_ not present in year _t_ , over the total number of exporters in year _t-1_ —range from 22 percent in Bangladesh to 61 percent in Malawi.<sup>9</sup> First-year survival rates of entrants– defined as the number of exporters in year _t_ and _t+1_ not present in year _t-1_ , over the total number of exporters in year _t_ —vary between 23 percent in Cameroon and 61 percent in Bangladesh. The magnitude of the survival rates in Table 1 suggests an extremely high attrition rate of new entrants after just one year in export markets, particularly in Africa.

Table 2 presents the same set of measures but for groups of HS 2-digit products – henceforth designated as sectors - where the measures for each group of sectors are obtained as averages across all countries that export that particular sector, again focusing on the period 20062008. Table 2 shows a less pronounced variation in the number of exporters across sectors though

> African countries exhibit very fat-tailed distributions of export value across products where export big hits are uncommon but are much more common than would be expected in a normal distribution whereas Hanson, Lind, and Muendler (2014) document a strong empirical regularity across countries in their patterns of comparative advantage which is hyper-specialization in exporting, the fact that in any given year, exports in the typical country tend to be highly concentrated in a small number of industries. 9 These rates of entry into and exit from export markets are substantially larger than the rates of entry into and exit from manufacturing activity reported by Bartelsman, Haltiwanger, and Scarpetta (2013). Focusing on census/registries of manufacturing firms (excluding 1-employee businesses) they document turnover of 10-20 percent in industrial countries and somewhat higher in developing countries.

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some important sectoral differences in other measures remain. The significance of these differences will be accounted for in Section 4.

# **3. Characteristics and Dynamics of the Export Sector: What Theory Predicts on Their Relationship with Country Size and Stage of Development**

In this section, we explore the predictions about how export-sector characteristics and dynamics are likely to vary with country size and stage of development. We draw on the growing theoretical trade literature on heterogeneous firms and recent empirical evidence on how the allocation of resources differs across countries.

# **3.1 Export-Sector Characteristics**

The standard heterogeneous firm model of trade (Melitz 2003) assumes firms are differentiated by the variety they produce and by their productivity, and that there is a fixed entry cost into exporting. A firm will enter the export market if its net export profits cover the fixed exporting cost. Because of the entry costs, there is a cutoff productivity level, such that firms at or above it will export while those below it will produce only for the domestic market. Thus, firms that export are more productive than firms that produce only for the domestic market, and export quantity is increasing in productivity. The basic model assumes a common productivity distribution across countries and a continuum of atomistic firms. This implies that greater exports of larger countries should be driven by a greater number of exporting firms, the extensive margin, and not by larger firms. The standard heterogeneous firm model of trade has little to say about stage of development.

We use recent empirical literature on allocative efficiency between firms to inform us about how stage of development and export-sector characteristics relate. This literature suggests that 9
countries with fewer distortions (richer countries) should have more exporters but the implications for average exporter size and concentration at the top of the size distribution vary, depending on which firms are more constrained. If taxes and regulations (e.g., on the labor market and on business entry) become heavy as firms grow larger, only the most productive can overcome these costs and there would be a “missing middle”: many small firms and a few large firms but few medium-sized firms. Consistent with a “missing middle”, Tybout (2000) shows a U-shaped firmsize histogram for a number of developing countries, when firms are allocated to relatively wide size bins.<sup>10</sup> Tybout (2014) shows that the “missing middle” does not necessarily imply a bimodal firm-size distribution. Assuming that the undistorted firm-size distribution should be an exact Pareto distribution, he compares the actual distribution with an estimated country-specific Pareto distribution. Using data for a number of Asian countries, the U.S., and Mexico, he finds that the actual firm-size distribution is wider than the estimated Pareto distribution, as represented in Panel A of Figure 1, particularly in developing countries. His results, however, are dependent on allowing the Pareto dispersion parameter to vary across countries, and he estimates a lower Pareto index (a more skewed firm-size distribution) in higher-income countries. In related work, Alfaro et al. (2009) use Dun and Bradstreet’s WorldBase data for 79 countries and find that establishment size is decreasing in GDP per capita, and interpret this as being consistent with too few mediumsized firms in lower-income countries.<sup>11</sup> In contrast, Hsieh and Klenow (2009) calculate a hypothetical efficient firm-size distribution, assuming the marginal products of capital and labor are equalized across all firms in

> 10Tybout shows a large spike in the firm-size distribution for the 1-4 workers size bin and a quick drop-off in the 10-49 workers size bin in poor countries.

> 11 An important caveat about their data, as argued by Bento and Restuccia (2014), is that they cover listed firms so small firms are not covered and they have less complete coverage of medium-sized firms in lower-income countries. Therefore, their measure of average firm size is likely to be over-estimated for lower-income countries and this can help explain the negative correlation they find between average firm size and GDP per capita.

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an industry and compare it to the actual firm-size distribution. They find that in China, India, and the U.S. the efficient firm-size distributions were more dispersed than the actual distributions. Panel B of Figure 1 illustrates their general finding. In contrast to the work of Tybout (2014), there are not enough very large or very small firms. They also find that the distortion is larger for the developing countries: the observed U.S. firm-size distribution is closer to the hypothetical efficient firm-size distribution than either the observed Indian or Chinese firm-size distribution. Hsieh and Olken (2014), using data from India, Indonesia and Mexico, find that the fraction of “missing” firms is increasing in firm size. There are more missing large firms than missing medium-sized firms, and this helps explain why less developed countries have lower income. Bento and Restuccia (2014) use comparable firm-census data for 124 countries with size measured by employment and show that the average firm size is increasing in level of development. They develop a model to explain this fact, using distortions correlated with productivity to produce too little investment, especially among firms that should grow large and such distortions can explain productivity differentials between higher- and lower-income countries. This body of research points to a “truncated top” of the firm-size distribution.

A wealth of research shows that small firms do not export because of high fixed entry costs.<sup>12</sup> In Figure 1, it is therefore only the right half of the firm-size distribution that is relevant for exporters. A “missing middle” in less efficient (less developed) economies would suggest that exporters are on average relatively large and their size distribution is more concentrated at the top. A “truncated top”, with firms missing at the top of the size distribution in less efficient economies,

> 12 See Bernard, Jensen, Redding and Schott (2007) for a summary of the literature.

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would imply that exporters are on average smaller and the concentration in the top 5 percent of firms is lower.<sup>13</sup> _Hypothesis on export-sector characteristics: The standard heterogeneous firm trade model implies that larger countries should export more primarily through the extensive margin. Allocative efficiency implies that less developed countries have fewer exporters. If distortions in less developed countries hinder large firms from growing relatively more, a “truncated top” (Hsieh and Klenow 2009, Hsieh and Olken 2014, and Bento and Restuccia 2014), then less developed countries will have smaller exporters on average and lower export concentration at the top. If distortions primarily limit medium-sized firms from growing, a “missing middle”, then the reverse is true (Tybout 2000, 2014 and Alfaro et al. 2009)._

# **3.2 Exporter Dynamics**

Allocative efficiency has important implications for exporter dynamics. In a less distorted environment, where the most productive firms grow most rapidly, the survival rates of new exporters should be relatively high. In contrast, in an economy where regulatory burdens and distortions in access to finance or information uncertainties allow less productive firms to grow, survival rates of entrants in competitive foreign markets will be relatively low. Along these lines, Araujo, Mion, and Ornelas (2012) integrate work on institutions and heterogeneous firm models

> 13 Ideally, to explore the importance of allocative efficiency in explaining the differences in firm-size distributions across countries, we would like to correlate productivity with size as the previous literature has done. Unfortunately, our data do not allow us to measure productivity directly.  But exporting itself is a measure of successful firm performance.  Studies of developing and developed economies show that exporters are relatively more productive than firms that serve only the domestic market and that the most productive firms are the largest exporters (See Wagner 2012 for a recent review of these studies).  This implies that we can glean information about the allocation of resources in an economy from the exporter-size distribution.

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of trade, and predict greater exporter survival rates in countries with better institutions (which tend to be at a higher stage of development), where uncertainty is lower.

Allocative efficiency’s implications for exporter entry depend on what type of distortions tend to block efficient firms from expanding and/or allow inefficient firms to remain in the export market. Entry should be lower in developing countries if the sunk costs associated with entering foreign markets (such as bureaucratic hurdles, wait times at customs, and corruption) are exceptionally high. However, uncertainty about export success at the firm level will enhance entry and exit, all else equal. With idiosyncratic uncertainty, several models of exporter dynamics show that some firms will enter the export market only to immediately exit (Albornoz et al. 2012, Segura-Cayuela and Vilarrubio 2008 and Freund and Pierola 2010). The intuition is that if export profitability is uncertain at the firm level then firms may enter foreign markets to learn their potential. Since a firm can exit, there is a one-period loss if the firm gets a poor draw, but there is a stream of future profits if the firm gets a good draw. For a given entry cost, the greater the uncertainty, the higher the “option value of entry” will be—implying more exporter entry and exit.<sup>14</sup> _Hypothesis on exporter dynamics: Allocative efficiency implies that entrant survival rates should be higher in more developed countries. If uncertainty is the critical distortion affecting export decisions in less developed countries, then there should be relatively higher exporter entry and exit. If sunk entry costs are higher in less developed countries and matter more than uncertainty_

> 14 Another type of distortion that affects allocative efficiency in a similar way is uncertainty about match quality in a search model of trade. For example, Rauch and Watson (2003) show that with imperfect information about the ability of a developing country exporter to supply a large order, developed country buyers may have more small trials before investing in an exporter for a longterm trade relationship.  This would lead to more entry and exit by developing country exporters, as importers test them out.  Again, there is a lot of trial and error with less information (more uncertainty) in developing countries, and a less efficient process of resource allocation.

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_for export decisions, then there should be relatively less exporter entry (and exit consequently). The models make no predictions about exporter dynamics and country size._ One remark in order at this point is that, although we focus on differences in resource endowments and resource allocation across countries, we are aware that other factors may be at work. For example, differences in the underlying productivity distribution of firms or in market access across countries may also help to explain our stylized facts. In our empirical work, we control for potential variation in productivity distributions across sectors, or any other differences that may affect the size distribution of firms across sectors, by focusing on exports within a sector; and, similarly, for market access by focusing on exports within a destination or sector-destination, as well as controlling for distance and other bilateral trade costs. Still, to the extent the underlying productivity distribution varies across countries within a sector-destination, this would influence our results. We discuss this possibility in more detail in Section 5.

# **4. Characteristics and Dynamics of the Export Sector: What the Database Shows on Their Relationship with Country Size and Stage of Development**

In this section, we evaluate the empirical evidence from the Database on how export-sector characteristics and dynamics vary with country size and stage of development. We first show the importance of both country and sector characteristics in explaining exporter behavior. We then present two stylized facts on how export-sector characteristics and dynamics change as countries get larger or richer, controlling for sectoral variation in exports and variation across destinations, as well as sector-destination variation.

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We begin by evaluating the relative importance of country fixed effects and either HS2digit sector fixed effects or destination fixed effects in explaining export-sector characteristics and dynamics. Specifically, we perform an ANOVA with country and sector fixed effects, and separately with country and destination fixed effects.<sup>15</sup> Panel A of Table 3 shows the results of the ANOVA with country and sector fixed effects. On average, country and sector effects explain about 60 percent of the variation in export-sector characteristics, though somewhat less of the variation in exporter dynamics.<sup>16</sup> With the exception of average exporter size and entrant survival, country effects explain a greater share of the variation than sector effects. Still, the significance of sector effects implies that it will be important to control for them as we examine the relationships between country characteristics and export-sector characteristics.

We next repeat the ANOVA exercise using country-destination level data. The results, reported in Panel B of Table 3, show that country and destination fixed effects together explain somewhat less of the variation in export-sector characteristics than country and sector fixed effects. However, with the exception of number of exporters, destination effects prevail over country effects. In that sense, it will be important to control for destinations to ensure the results are not being driven by variation in trade barriers or other destination-specific market access costs.

We now examine the hypotheses discussed in the previous section and present stylized facts, first focusing on basic characteristics of the export sector and then on exporter dynamics. In the analysis below, we use data from the World Development Indicators (WDI) of the World Bank

> 15 The ANOVA analysis focuses on the measures’ averages in 2006-2008 for simplicity. The sample used for concentration (fourth column) is relatively smaller because the share of exports accounted for by the top 5 percent of exporting firms can be calculated only if a country-sector-year cell includes more than 20 exporters. This remark applies naturally to all specifications in the rest of the section whose dependent variable is exporter concentration.

> 16 The percentages accounted for by country, sector, and residual effects in Panel A or country, destination, and residual effects in Panel B do not add up to 100 percent due to the unbalanced nature of each of the cross-sectional datasets. Specifically, in Panel A not all sectors are exported by all countries while in Panel B not all destinations are served by all countries.

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on GDP and on GDP per capita in constant 2005 Purchasing Power Parity (PPP) USD.<sup>17</sup> We also use data on determinants of bilateral trade – distance, contiguity, common language and common colonizer - from CEPII described in Mayer and Zignago (2011).

# **4.1 Export-Sector Characteristics and Country Characteristics**

Figure 2 presents a scatter plot of the number of exporters, average exporter size, and concentration against both income and income per capita, using country-year level data averaged for the period 2006-2008.<sup>18</sup> The figure shows that larger as well as more developed economies have a larger number of exporters and larger average size of exporters, and richer countries have greater concentration at the top of the firm-size distribution.

Using the yearly data from 2004 to 2008, we next explore these relationships in more detail, controlling for sectoral variation in exports, variation within destinations, and sector-destination variation, in addition to controlling for year fixed effects in all specifications. The standard errors reported in all tables are clustered by country (the most conservative type of clustering possible).<sup>19</sup> Table 4 reports the results from regressing log total exports, log number of exporters, log average exporter size, and share of exports accounted for by the top 5 percent of firms on log GDP per capita and log GDP. Columns (1)-(4) report the results using the country-sector-year level data for years 2004 to 2008 and controlling for year and sector fixed effects. Accounting for sector fixed effects is key as some sectors exhibit much larger average exporter sizes than others, for example due to their higher capital intensity or degree of returns to scale, as shown by the

> 17  Results presented in Tables 4-7 and Appendix Tables A2.1-A2.2 are robust to the use of GDP and GDP per capita in current USD or in constant USD.

> 18 For simplicity, all figures present relationships based on data averaged for the period 2006-2008 instead of being based on all years of data but the patterns are the same whether averages or all years of data are used.

> 19 Our results are, however, robust to clustering standard errors by sector for the country-sector-year data, by destination for the country-destination-year data, and by sector-destination for the country-sector-destination-year data.

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importance of sector effects in the ANOVA (Table 3).<sup>20</sup> The results indicate that, within sectors, larger countries and richer countries have more exporters, larger-sized exporters (on average), as well as higher concentration in the top 5 percent of firms.<sup>21</sup> The economic magnitude of these effects is such that if GDP per capita increased from the value for the country in the 25<sup>th</sup> percentile (Cameroon) to the value for the country in the 75<sup>th</sup> percentile (Lebanon) the number of exporters would increase by 80 percent, average exports per firm would increase by 71 percent, and the share of the top 5% exporters would increase by 8 percentage points.<sup>22</sup> Columns (5)-(8) in Table 4 use the country-destination-year level data for years 2004 to 2008 and results are qualitatively similar. In addition to destination fixed effects these specifications control for traditional determinants of bilateral trade in gravity equations: distance, contiguity, common language, and common colonizer. The estimates show that controlling for year and market-specific characteristics, such as trade costs, more developed countries and larger countries have more exporters that on average export larger volumes and have higher export concentration at the top of the size distribution.<sup>23</sup>

> 20 The results remain similar if we use country-HS 4-digit-year level data and control for 4-digit sector fixed effects.

> 21 Total exports are equal to the product between the number of exporters and average exporter size. Since our regressions use the log of each of those variables as dependent variable and are estimated linearly, the coefficient on GDP (or GDP per capita) in the total exports regression is exactly equal to the sum of the coefficients on GDP (or GDP per capita) in the number of exporters regression and in the average exporter size regression, provided that the samples used in the three regressions are similar. Therefore in Tables 4, 5, 6 and Appendix Table A2.2 we restrict the sample in the number of exporters’ regression to be the same as that used in the total exports or average exporter size regressions, that is, it excludes observations with the number of exporters equal to 1. However, note that in unreported results where we estimate number of exporter regressions including those observations the effects of GDP per capita and GDP are stronger in magnitude and significance that those shown.

> 22 These magnitudes are obtained as the product of the difference in logs GDP per capita, 1.704, and the corresponding coefficient in columns (2)-(4) of Table 4: 0.471, 0.419, and 0.047.

> 23 As robustness check to the result on average exporter size, in Appendix Table A2.1, we re-estimate the specifications for average exporter size using data on incumbent exporters only, in order to exclude possible biases due to the use of “partial year” data as discussed in Bernard et al. (2013). In their analysis, they show that the entry and exit of firms into and from export markets at different points in a calendar year means that the use of calendar annual data on export values to measure the average size of exporter entrants or exiters may introduce a bias - mainly their size being under-estimated during the first year - which they refer to as “partial year” effects. Our consideration of the average size of incumbent exporters addresses this concern to the extent that bias should not permeate the calculation of average size for exporters that have not interrupted their exporting activity. We find that the results from Table 4 remain qualitatively similar under this alternative sample.

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Note that the coefficients in columns (1)-(3) as well as columns (5)-(7) imply that the number of exporters, the extensive margin, explains about two-thirds of the variation in total exports across countries of different size, and about 60 percent of the variation in total exports as a result of stage of development.

Columns (9)-(12) in Table 4 present results using the country-sector-destination-year level data for years 2004 to 2008 for the sample of 34 developing countries and 3 developed countries.<sup>24</sup> This allows us to control for interacted sector-destination fixed effects in the same regression (as well as for bilateral gravity determinants), but has the disadvantage of less variation in terms of stage of development, with only 3 developed countries included. The results confirm that within a given sector-destination, larger countries and richer countries have more exporters, larger exporters, and a greater share of exports controlled by top 5 percent of exporters. Moreover, the findings are maintained in specifications that include more stringent sector-destination-year fixed effects to account for the possibility of temporal shifts affecting sectors and destination markets and influencing exporter behavior due to trade networks and relationships, as seen in Appendix Table A2.2 (Panel A). Of interest, in the country-sector-destination regressions, the contribution of the intensive margin rises when we control for sector and destination, especially with respect to GDP per capita: the intensive margin now accounts for more than half of the variation in exports with GDP per capita.

It is worth noting that the coefficients on trade costs shown in columns (5)-(12) of Table 4 indicate that they have opposite effects on export-sector characteristics, relative to country size and stage of development. Higher trade costs (proxied by higher distance, absence of either

> 24 As noted above, we do not have the raw exporter-level customs data for all the rich countries to be able to calculate the corresponding measures to estimate these regressions for all countries. We obtained the measures at the country-sector-year level for three of the rich countries, Norway, Portugal, and Spain which are used in the regressions.

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common language or border) reduce exports through both the extensive and intensive margins and lead to lower exporter concentration. These results are similar to results obtained using data on U.S. exports (Lawless 2010).

One potential concern about the average exporter size results is that they may pick up the activity of intermediaries, individual firms that intermediate trade for many producers. An intermediary will be observed in our data as a single firm, but it actually consolidates exports for many firms, and there is no way to distinguish these firms from direct exporters. Ahn, Khandelwal, and Wei (2011) offer evidence on the importance of these firms in trade. They find that a significant share of exports in China went through intermediaries in the early 2000s. However, they also find that as trade expands, the importance of intermediaries declines—their share fell from 35 to 22 percent between 2000 and 2005 as China’s exports surged. In part, this is because as firms expand their exports they switch from exporting through intermediaries to direct exporting. This suggests that the average exporter size variable is more likely to overstate actual exporter size and the number of exporters is more likely to understate exporter count in the less developed or small countries as compared with richer or larger countries. To examine explicitly the importance of intermediaries we estimate specifications where GDP per capita and GDP enter by themselves and interacted with a dummy variable identifying sectors with a larger presence of export intermediaries.<sup>25</sup> The corresponding estimates shown in Table 5 reveal significantly larger numbers of exporters, larger average exporter size and higher concentration in richer countries and in larger countries as in Table 4.<sup>26</sup> In sectors with more intermediaries, the overall effect of income

> 25 We follow Chan (2014)’s choice of sectors with a larger proportion of firms exporting indirectly according to World Bank Enterprise Surveys data for 118 countries as those with a higher prevalence of export intermediaries.

> 26 The coefficient on GDP per capita in the share of the top 5% of exporters regression in column (8) of Table 5 that is the most stringent specification including sector-destination-year fixed effects with only 37 degrees of freedom (of which only 3 correspond to developed countries) is significant at a slightly lower level than the 10 percent confidence level and the magnitude of the coefficient remains similar to previous specifications.

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per capita on the number of exporters and on average exporter size is smaller but remains positive and significant overall.

Finally, a potential concern about the results in Table 4 is that our measures are defined only for cells with a positive export flow but trade zeros can be important. By expanding our original data to have zeros in cells with no export flows, we find that the prevalence of trade zeros is negligible in the country-sector-year data (5.9% on average across countries) but is very large in the country-destination-year data (47% on average across countries) and the country-sectordestination-year data (91% on average across countries). The trade zeros in our data are not random but rather are more prevalent both in smaller countries and in less developed countries. Hence our findings could be influenced by the relationship between zeros, country size and stage of development. To investigate this possibility we use the non-linear Poisson pseudo-maximumlikelihood (PPML) estimator introduced by Silva and Tenreyro (2006) to estimate our specifications in Table 4 using the expanded data.<sup>27</sup> The corresponding estimates, shown in Table 6, reveal that our main results on stage of development are robust to the inclusion of zeros.<sup>28</sup> With the exception of average exporter size, the results on country size are robust in the country-sector and country-destination regressions, and all results remain strong in the country-sector-destination regressions.

_Stylized Fact on export-sector characteristics: Larger economies and more developed economies_ _have more exporters, a larger average exporter size, and more concentrated export sectors among_

> 27 Egger and Staub (2015) and Fally (2015) provide extensive discussions of the theoretical and econometric properties of the PPML estimator.

> 28 We do not report results on the share of the top 5% using the expanded data in Table 6 because concentration is undefined, but not necessarily small, when trade is missing.  Interpreting missing values as low levels biases the results towards finding higher concentration in larger and more developed countries, which have fewer cells with missing exports. Indeed, when we estimate using PPML, the results are qualitatively similar to previous results but with larger coefficients.

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_firms, after controlling for the sectoral distribution of exports and for export destinations. The extensive margin explains about two thirds of the variation in exports across countries of different size. The extensive margin is relatively less important in explaining why richer countries export more._

# **4.2 Exporter Dynamics and Country Characteristics**

We next turn to exporter entry, exit and survival. Figure 3 shows how exporter entry, exit, and survival vary with country size and stage of development, using country-year level data averaged for the period 2006-2008. With respect to entry and exit, there is a negative relationship indicating that richer countries and larger countries have lower entry and exit rates. The relationship with entrant survival is less transparent.

Columns (1)-(5) of Table 7 show the results from regressions of exporter entry, exit, firstyear survival of new exporters, net entry, and turnover on log GDP per capita and log GDP based on the country-sector-year level data for years 2004 to 2008, controlling for sector and year fixed effects.<sup>29</sup> It is crucial to account for sector fixed effects as some sectors have much lower turnover than others, as highlighted by the relative importance of sector effects in the ANOVA (Table 3). The results show that controlling for the sectoral composition of trade, stage of development has important effects on exporter dynamics. In particular, richer countries have lower turnover and higher rates of entrant survival, leaving net entry roughly unchanged. The economic magnitudes of the results are sizable—increasing GDP per capita from the value for the country in the 25<sup>th</sup> percentile (Cameroon) to the value for the country in 75<sup>th</sup> percentile (Lebanon) would lead to a

> 29 Turnover is calculated as the sum of the entry rate and exit rate in year _t_ , while net entry is the difference between entry and exit rates in year _t_ .

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decline in entry and exit rates by 11 percentage points, and to an increase in the first-year survival rate of entrants by 6 percentage points.<sup>30</sup> In columns (6)-(10) of Table 7 we report results using country-destination-year level data for years 2004 to 2008 and controlling for destination and year fixed effects, along with bilateral gravity variables. The results on the effect of stage of development are qualitatively similar to the results using country-sector-year level data, though the magnitudes are smaller. In this case, there is evidence that country size also matters, with larger countries having lower entry and exit and higher entrant survival rates.

Columns (11)-(15) of Table 7 show results based on the country-sector-destination-year level data for years 2004 to 2008, considering interacted sector-destination fixed effects, along with year fixed effects and bilateral gravity variables. The evidence indicates a strong negative effect of income per capita on exporter turnover and a strong positive effect of income per capita on entrant survival within sector-destination cells for this sample including 34 developing countries and 3 developed countries. Moreover, the findings are maintained in specifications that include more stringent sector-year, destination-year and sector-destination-year fixed effects, as seen in Appendix Table A2.2 (Panel B).

These results show that exporter dynamics change as countries get richer. In less developed countries, turnover is largely a process of entry and exit where many firms enter into export markets and exit almost immediately. In richer countries, fewer but more resilient exporters enter in any given year.

> 30 These magnitudes are obtained as the product of the difference in logs GDP per capita, 1.704, and the corresponding coefficient in columns (1)-(3) of Table 7: -0.063, -0.063, and 0.038.

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_Stylized Fact on exporter dynamics: Exporter turnover is significantly lower in more developed countries, exporter entrant survival is higher, while net entry is invariant with respect to income per capita, controlling for the sectoral distribution of exports and for export destinations._

# **5. Discussion of the Results**

The standard heterogeneous firm trade model predicts that holding the distribution of firm productivity constant across countries, the extensive margin should explain why larger countries export more. As theory predicts, we do find that the extensive margin dominates the intensive margin in explaining cross-sectional variation in exports, though there is still a significant role for the intensive margin. Specifically, the extensive margin explains about two thirds of the effect of country size on total exports. The importance of the intensive margin in explaining why larger countries export more suggests that the firm productivity distribution may vary in a systematic way with country size and warrants further study.<sup>31</sup> The intensive margin is relatively more important in explaining the variation in exports owing to stage of development. Specifically, within a sector-destination, average exporter size explains more than half of the effect of GDP per capita on total exports. We argue that the relationship between stage of development and both the intensive margin and concentration at the top of the distribution is consistent with better resource allocation across firms in more developed countries along the lines suggested by Bento and Restuccia (2014), where the most productive

> 31 Consistent with our finding, Spearot (2013) adapts a heterogeneous firm model, allowing the Pareto shape parameter of the firm productivity distribution to vary across countries.  He shows that such an accommodation can explain variation in the average size of exporters across countries.  He further shows that the response of export flows across countries of varying sizes to a trade shock can be used to estimate the Pareto shape parameters across countries.  Using this methodology, he finds that larger and richer countries have more skewed firm productivity distributions (hence exporter size distributions), a result that could explain our findings on the larger average size in larger and richer countries.  His model, however, does not explain why the Pareto shape parameter is more skewed in larger or richer countries. Alternatively, Head, Mayer, and Thoenig (2014) show that a lognormal distribution fits the observed exporter size distribution of French and Chinese firms better than a Pareto.  Such a distribution could allow for a link between the intensive margin and country size, within a heterogeneous firm framework.

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firms fail to invest enough.<sup>32</sup> Our finding in Tables 4 and 6 that as trade costs (as measured by the gravity variables) fall, average size and concentration at the top of the exporter size distribution rise, is also consistent with the allocative efficiency story. To the extent that higher trade costs impede competition, allowing a less efficient allocation of resources to persist, we would expect the largest and most productive firms to perform especially well when the distortions are reduced. In Section 5.1, we turn to time-series evidence as a further robustness test of the results.

The results on exporter dynamics also speak to a more efficient allocation of resources in more developed countries. Better firms grow and these high-productivity firms are less likely to exit immediately following entry into the export market. The relatively higher entry and exit in developing countries suggests that the distortions relating to uncertainty about export profitability are greater than entry cost distortions. Effectively, there is a lot of trial and error in exporting, especially in less developed countries where exit goes up precisely because there are more entrants. Indeed, this is consistent with high correlations between entry and exit at the country level and country-sector level.<sup>33</sup>

# **5.1 Average Exporter Size and Concentration and Allocative Efficiency**

While the results are consistent with high-productivity firms failing to invest enough, resulting in too few large firms and lower exports at an earlier stage of development, they could alternatively reflect systematic differences in the underlying productivity distributions and not be a result of resource allocation. To explore how changes in the allocation of resources affects the number of

> 32 Consistent with better resource allocation in richer countries, we also find in unreported results evidence of more diversification at the firm level as countries get richer.  This is consistent with models of multiproduct firms, such as Bernard, Redding and Schott (2011), where only higher-ability firms are able to generate variable profits to cover fixed costs of entry for more products and thus supply a wider range of products to each market.

> 33 For example, using the data for years 2004 to 2008, the simple within-country correlation between entry and exit is 0.77 and the within-country-sector correlation between entry and exit is 0.76.

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exporters, average exporter size, and concentration, we consider times-series evidence. We use country-sector-destination-year level data for years 2004 to 2008 for the sample of 34 developing countries and 3 developed countries. Assuming the productivity distribution is constant within country-sectors over time, focusing on within-country-sector variation allows us to rule out differences in the productivity distribution as the cause. The disadvantage of panel data is that the effect of independent variables that have little within-country-sector variation, like country size and stage of development, are harder to isolate with precision. For this reason, we focus on export growth, which varies across countries, sectors, and destinations.

One problem with using export growth is that its underlying cause is important. For example, exports could grow because of demand or supply shocks, which may affect exporter behavior but be unrelated to the resource allocation across firms in the country. To examine export growth that is likely to be a result of changes in resource allocation, we control for country-sectoryear fixed effects, which capture productivity shocks (as well as other country-sector time-varying factors in the exporting country-sector). We also include destination-sector-year fixed effects, which capture demand shocks (or other destination-sector-year time-varying factors) in the importing country-sector in our most stringent specification. This allows us to decompose export growth in a sector that is specific to the bilateral relationship, and hence very likely related to changes in trade costs, which we know affect resource allocation. For example, we are decomposing Peru’s export growth in apparel to the US that did not result from an increase in overall US demand for apparel or from Peruvian export growth in apparel.

The null hypothesis that we test is that export growth in a country-destination-sector is uncorrelated with average exporter size and exporter concentration, controlling for country-sectoryear shocks and destination-sector-year shocks. A positive correlation between export growth and 25
both variables shows that exporters become on average larger and the firm-size distribution becomes heavier at the top as exports grow.

To the extent that better resource allocation promotes export growth, we would expect average exporter size to increase and exporter concentration to intensify as exports grow. If differences in average exporter size and concentration are more closely related to the underlying firm productivity distribution than to allocative efficiency then there need not be any correlation between export growth and average exporter size and concentration, especially after controlling for country-sector-year effects and destination-sector-year fixed effects.

Table 8 reports results using the country-sector-destination-year level data with countrysector-destination (panel) fixed effects and country-sector-year and destination-sector-year fixed effects. Nearly 90 percent of the time-series variation in exports in comes from larger exporters. The estimated within-effects show that, as exports grow, the average size of exporters and the share of exports by the top 5 percent increase significantly. To the extent that export growth reflects improved resource allocation over time, these results suggest that export growth is associated with more resources being allocated to the largest (and presumably most productive) exporters.<sup>34</sup>

# **6. Conclusion**

While the literature on exporter dynamics is rapidly growing, there has not been a comparison across a large number of countries of different sizes and at differing stages of development. The

> 34 In a robustness check we estimated similar regressions using the country-sector-destination-year level data but focusing on a sample of countries for which 8 years of data are available, which allows to observe differences over a relatively longer period of time. The results are very similar to those in Table 8.

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Exporter Dynamics Database fills this gap and establishes two novel stylized facts on how exportsector characteristics and dynamics vary with country characteristics.

The data point to systematic ways in which the micro structure of exports changes as countries develop. Larger countries and richer countries have both more and larger exporters on average, with greater concentration in the top 5 percent of firms. Together with results from previous studies showing that exporters are more productive than non-exporters and larger exporters are more productive than smaller exporters, our results are consistent with allocative efficiency in export markets improving as countries develop, with more resources flowing to the largest and most productive firms.

Of interest is how reallocation happens. In particular, exporter dynamics are also closely linked with stage of development, with richer countries experiencing higher entrant survival rates and lower entry and exit rates. This is consistent with allocative efficiency improving as countries develop: only the more productive firms grow and enter the export market in more developed countries, and these relatively good firms are less likely to exit. The results suggest that the distortion that affects exporter dynamics across countries is more closely related to variation in the idiosyncratic uncertainty about the profitability of exporting than to the costs of entry into exporting.

Overall, we hope that the measures in the Database will allow the examination of several interesting cross-country questions, cross-country cross-sector questions, and within-country questions. The measures can also be used as controls in estimations that require exporter characteristics at the country-sector level. In particular, as the measures in the Database offer the first opportunity to study exporter behavior on a global basis, some of the facts described above open the door to questions such as: Does trade promote growth via firm size or firm count? How 27
can countries attract more large multi-product firms? What determines exporter survival? How does comparative advantage relate to the typical exporter characteristics in an industry? These and many other questions will hopefully be addressed in future research to be conducted using the Database.

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