Agglomeration by export destination: evidence from Spain

Agglomeration by export destination: evidence from Spain Abstract We use a dataset of Spanish exporters with rich spatial information to document the existence of agglomeration economies by export destination. More specifically, we show that, for a large set of export destinations, exporters are geographically too close to be the result of a random outcome. We also analyze the variables that explain the cross-destination heterogeneity in agglomeration. We find that firms selling to countries with worse institutions, a dissimilar language and a different currency are significantly more agglomerated. These results suggest that the value provided by agglomeration is higher concerning destinations where entry is more difficult. 1. Introduction It is now a well-established fact that a large amount of industries are geographically concentrated. This finding together with the observation that firms are on average more productive in denser areas have attracted much attention from economists and policy-makers, who have built a large body of research on the foundations and effects of agglomeration economies. In this article we study a specific form of these agglomeration economies, namely those accruing to export firms. Our main contribution is to uncover the fact that exporters are geographically concentrated by export destination, which is consistent with the existence of agglomeration economies associated with the process of selling abroad. Reaching foreign markets entails additional costs, hence the traditional forces leading to industry agglomeration, namely sharing, matching and learning mechanisms, see Marshall (1920) and Duranton and Puga (2004), apply to this process, and naturally vary by export destination. We are not the first to emphasize the existence of destination-specific export spillovers. In a regression framework, Koenig (2009), Koenig et al. (2010) and Choquette and Meinen (2015) show that the decision to sell to a certain destination is positively affected by the pool of local exporters selling to that destination, while Cassey and Schmeiser (2013) show evidence of clustering by export destination in Russian regions. Our main step forward in this literature is to document the existence of destination-specific agglomeration economies using a firm level dataset with rich spatial information that allows us to apply a nonparametric standard test of agglomeration: Duranton and Overman (2005). This method improved on previous approaches, such as Ellison and Glaeser (1997), Maurel and Sédillot (1999), and Devereux et al. (2004), for two reasons. First, it treats space as continuous, as opposed to using an arbitrary set of spatial units. And second, it allows assessing the statistical significance of departures from randomness.1 The use of the Duranton and Overman (2005) test allows us to refine and expand the results of the extant literature in two fundamental ways. First, we uncover destination-specific exporter agglomeration beyond the overall concentration of exporters with respect to domestic firms and the industry agglomeration of exports to each country. That is, for each destination the counterfactual is built solely from exporters operating in the industries exported to the destination. This addresses a crucial issue: industries are geographically concentrated, and different countries demand goods from different industries. Then, agglomeration by export destination might be the result of countries buying goods intensively from agglomerated industries. We show that exporters are significantly concentrated over and above what would be expected from the fact that exporters to individual countries are concentrated by sector and sectors are concentrated geographically. Furthermore, by restricting the analysis to export firms, we account for different location patterns between domestic and export firms. And second, we compute a continuous index measuring the extent of geographical agglomeration of exporters to each destination. This allows us to investigate the characteristics that explain the cross-destination heterogeneity in agglomeration levels. Our baseline results indicate that for more than half of export destinations exporters are significantly concentrated, i.e. they are too close to be the result of a random outcome.2 We also perform a battery of robustness checks to account for other mechanisms that would result in agglomeration by export destination without relying on export-destination spillovers. There are two mechanisms that are worth emphasizing. First, large firms are able to reach a larger set of destinations, hence agglomeration by destination could be the result of large exporters being concentrated with respect to small exporters. And second, it is documented that firms go hierarchically to more and more destinations. Then agglomeration by popular destinations could induce agglomeration by less popular destinations if exporters to the former disproportionately export to the latter. By restricting the counterfactual, we show that our results are not the mechanical consequence of these and other mechanisms that would result in spurious agglomeration. Our results also show that the level of agglomeration varies meaningfully across destinations. We find that exporters to countries with a dissimilar language, lower institutional quality and a different currency are significantly more agglomerated. We interpret this finding as evidence suggesting that agglomeration provides higher value in countries where entry is more difficult. Overall, our findings are consistent with the existence of externalities in the process of selling to some countries. Although thus far the literature has been to some extent unable to empirically verify the specific driving mechanisms, several possibilities have been rationalized in theoretical models.3 For example, Segura-Cayuela and Vilarrubia (2008) and Fernandes and Tang (2014) emphasize that firms entering foreign markets reveal information, hence reducing the uncertainty faced by potential entrants. Also, Krautheim (2012) and Cassey and Schmeiser (2013) explore the channel of cost reductions brought by a larger number of exporters in the setting of the Melitz (2003) model. On the empirical front, our results are consistent with Lovely et al. (2005), who show, using the Ellison and Glaeser index, that US exporter headquarter activity is more agglomerated when firms sell to countries less integrated in the world economy and with worse credit ratings. Moreover, Koenig (2009), Wagner and Zahler (2015) and Cadot et al. (2013) show that the presence of neighboring export firms and pioneers in foreign markets are significantly associated with a higher probability of foreign entry, suggesting that the flow of information between nearby firms and signals revealed by successful exporters are important in this context. Also, a recent contribution by Paravisini et al. (2015) finds that the distribution of bank lending is skewed toward firms exporting to the same destination, which suggests that agglomeration may be associated with credit markets. We are able to link the observed patterns of concentration across destinations to cultural and institutional differences across importing countries. Beyond this, our article also remains silent on the specific mechanisms driving the clustering by export destination. Yet, our findings as well as those of the literature give new understandings regarding the behavior of export firms and provide useful policy insights on how to help firms access foreign markets. The rest of the article is organized as follows. Section 2 describes the dataset. Section 3 explains the methodology, presents the baseline results and performs a set of robustness checks. Section 4 delves into the determinants of the cross-destination variation in agglomeration levels. Section 5 concludes. 2. Data We use the firm-level data compiled by the Bank of Spain to construct the Balance of Payments statistics for Spain. The dataset contains information on firms making transactions with foreign agents if they are worth more than €12,000 and they are performed through a bank. Therefore, the dataset is likely to exclude only the smallest exporters. In the baseline analysis, we rely on 2007 data and we use previous years to check the stability of the results over time.4 The dataset has several advantages for the study of the geographical location of exporters. First, it is made up from administrative records and it has a large coverage. For example, it accounts for 97% of aggregate exports in 2007 and both transactions within the EU and to third countries are observed. Second, it contains information on total sales of every firm to each export destination. And third, it provides the zip code of every exporter. Hence, we can compute distances between firms and study the agglomeration of export firms by destination. The zip code provided is that of the headquarters, thus our focus in on headquarter agglomeration rather than on establishment agglomeration. We argue that this feature is likely to play a small role in the results, as we estimate that around 91% of exporters in our data have just one plant.5 Moreover, Koenig (2009), Koenig et al. (2010) and Choquette and Meinen (2015) show that export spillovers are not affected by including either single-plant exporters or the headquarter of all exporters. Having said this, focusing on headquarters points to externalities stemming from information flows, rather than cost-sharing mechanisms, which are more likely to be linked to establishments. There is one limitation of the dataset that is worth noting: it has no information on the type of goods being traded. Rather, it only provides the exporter four-digit industry code. In our baseline results, we rely on two-digit industry codes to control for the industry composition of exports. Focusing on two-digit industries provides sufficiently large bins for drawing the counterfactual exporters, which increases the precision of the confidence bands. Yet, it makes us account for the varieties being exported only to a certain extent (see also Section 3.3.5). Besides, the dataset does not include firm characteristics beyond industry, fiscal id and total exports to each country. For this reason, for the robustness check described in Section 3.3.1, we approximate firm size with total firm exports. Table 1 shows some descriptive statistics of the exporters included in our dataset in 2007. Our analysis is restricted to manufacturing firms and export destinations with at least 10 exporters. The data include more than 18,000 exporters located in close to 3200 zip codes, out of a total of around 11,000 zip codes in Spain. The median exporter sells to two destinations and the median zip code hosts two export firms. See also Table A1 in the Appendix for a description of the main variables used in the article and Table A2 for the destinations included in the sample. Table 1 Descriptive statistics: exporters in 2007 (balance of payments) Mean Percentiles (Std. Dev.) 25 50 75 (1) (2) (3) (4) Panel A: exporters (N = 18,715) Total exports (thousand €) 6745 54 255 1435 (100,757) Destinations (N = 166) 5.19 1 2 6 (7.82) Panel B: zip codes (N = 3192) Number exporters 5.89 1 2 6 (10.05) Mean Percentiles (Std. Dev.) 25 50 75 (1) (2) (3) (4) Panel A: exporters (N = 18,715) Total exports (thousand €) 6745 54 255 1435 (100,757) Destinations (N = 166) 5.19 1 2 6 (7.82) Panel B: zip codes (N = 3192) Number exporters 5.89 1 2 6 (10.05) Notes: This table shows descriptive statistics of Spanish manufacturing exporters in 2007 included in the Balance of Payments micro data. The panel A shows statistics of total exports and number of export destinations per exporter. The panel B shows moments of the distribution of the number of export firms located in the zip codes hosting at least one export firm. N corresponds to the number of distinct observations. Table 1 Descriptive statistics: exporters in 2007 (balance of payments) Mean Percentiles (Std. Dev.) 25 50 75 (1) (2) (3) (4) Panel A: exporters (N = 18,715) Total exports (thousand €) 6745 54 255 1435 (100,757) Destinations (N = 166) 5.19 1 2 6 (7.82) Panel B: zip codes (N = 3192) Number exporters 5.89 1 2 6 (10.05) Mean Percentiles (Std. Dev.) 25 50 75 (1) (2) (3) (4) Panel A: exporters (N = 18,715) Total exports (thousand €) 6745 54 255 1435 (100,757) Destinations (N = 166) 5.19 1 2 6 (7.82) Panel B: zip codes (N = 3192) Number exporters 5.89 1 2 6 (10.05) Notes: This table shows descriptive statistics of Spanish manufacturing exporters in 2007 included in the Balance of Payments micro data. The panel A shows statistics of total exports and number of export destinations per exporter. The panel B shows moments of the distribution of the number of export firms located in the zip codes hosting at least one export firm. N corresponds to the number of distinct observations. Table 2 Most localized destinations Rank Country N Localization Rank Country N Localization 1 West Bank and Gaza 36 0.29 6 Montenegro 61 0.10 2 Iraq 23 0.18 7 Andorra 887 0.09 3 Suriname 18 0.18 8 Aruba 19 0.09 4 Chad 18 0.16 9 Tanzania 42 0.08 5 Albania 168 0.11 10 Armenia 53 0.08 Rank Country N Localization Rank Country N Localization 1 West Bank and Gaza 36 0.29 6 Montenegro 61 0.10 2 Iraq 23 0.18 7 Andorra 887 0.09 3 Suriname 18 0.18 8 Aruba 19 0.09 4 Chad 18 0.16 9 Tanzania 42 0.08 5 Albania 168 0.11 10 Armenia 53 0.08 Notes: This table shows the 10 destinations to which exporters exhibit the highest level of agglomeration, according to the country index of localization defined in Section 3.1. Table 2 Most localized destinations Rank Country N Localization Rank Country N Localization 1 West Bank and Gaza 36 0.29 6 Montenegro 61 0.10 2 Iraq 23 0.18 7 Andorra 887 0.09 3 Suriname 18 0.18 8 Aruba 19 0.09 4 Chad 18 0.16 9 Tanzania 42 0.08 5 Albania 168 0.11 10 Armenia 53 0.08 Rank Country N Localization Rank Country N Localization 1 West Bank and Gaza 36 0.29 6 Montenegro 61 0.10 2 Iraq 23 0.18 7 Andorra 887 0.09 3 Suriname 18 0.18 8 Aruba 19 0.09 4 Chad 18 0.16 9 Tanzania 42 0.08 5 Albania 168 0.11 10 Armenia 53 0.08 Notes: This table shows the 10 destinations to which exporters exhibit the highest level of agglomeration, according to the country index of localization defined in Section 3.1. 3. Localization of exporters by export destination In this section, we provide evidence that exporters are significantly agglomerated by export destination by applying the methodology developed by Duranton and Overman (2005), henceforth DO. Among other advantages discussed in the introduction, this methodology allows us to account for the fact that exports to a country are concentrated by sector and sectors are concentrated geographically. Furthermore, it allows controlling for other forces leading to agglomeration by export destination, such as the concentration of large vs small exporters, sequential exporting and the concentration of exporters in large cities, which we discuss below. Before turning to the baseline results, we describe briefly our application of DO. 3.1. Methodology: application of Duranton and Overman (2005) In this section, we give a succinct overview on how we use DO to uncover agglomeration by export destination, see Appendix B for a more comprehensive explanation and technical details. In our baseline analysis, we use data from 2007 and we consider the 166 destinations with at least 10 exporters. For each country, we kernel-estimate the distribution of bilateral distances of exporters to the country by applying to the zip code coordinates the haversine formula, which computes the shortest distance over the Earth’s surface. We then compare this distribution with 1000 counterfactual distributions built as follows. For each two-digit industry, we draw 1000 independent random samples from exporters in the industry; each draw of size the actual number of exporters to the country operating in that industry. Then, we aggregate each draw across the different industries to collect 1000 random samples that replicate the industry composition of exports to the country. Note also that the size of each random draw is the same as the actual number of exporters to the country. We then estimate the distance distribution of each random draw. The resulting counterfactual distributions control for two mechanisms that might result in spurious agglomeration by export destination. First, the fact that exporters have special characteristics relative to nonexporters (see, e.g., Bernard et al. (2003)) and hence they may agglomerate with respect to domestic firms. Indeed, Behrens and Bougna (2015) show that this is the case in 14–16% Canadian industries and the literature on export spillovers has documented that pools of local exporters affects positively the decision to enter foreign markets, see for example Koenig (2009). And second, the fact that the industry composition of exports differs across countries. For example, one country may demand heavily goods from an industry that is highly concentrated. Therefore, exporters to this country can be concentrated either because of industry concentration or because of exporter concentration. By making the random draws replicate the destination-specific industry composition of exports, we are able to disentangle the latter from the former. We then rank, for each kilometer, the 1000 counterfactual distributions in ascending order and define a localization threshold as the percentile that makes 95% of the counterfactual distributions lie below it across all distances. Note that to compare the estimated density with the counterfactual distributions we focus on distances below 100 kilometers, which are more relevant to explain interactions between exporters. This distance horizon has no substantial effects on the results, as shown in Section 3.3.5. We define exporters to a country to be significantly localized if the actual distance distribution is above the localization threshold in at least one kilometer. We also define a dispersion threshold as the percentile that makes 5% of the simulations lie below it across all distances. Given that densities must sum up to one, localization at some distances implies dispersion at others. Therefore, we define exporters to a country to be dispersed if the actual distance distribution is below the dispersion threshold in at least one kilometer and the country does not exhibit localization. These definitions follow DO.6 Finally, we also construct the country version of the industry quantitative index of localization defined by DO. This country index is computed as the sum across distances of the difference between the density of the actual distance distribution and the localization threshold if the former is above the latter and zero otherwise. This index gives a measure of the amount of exporter localization by export destination. 3.2. Baseline results In our baseline results we find significant agglomeration for a large number of export destinations: of the 166 countries in our sample, firms exporting to 107 (64%) are significantly agglomerated, whereas only one destination exhibits dispersion. Figure 1A displays the share of destinations in which exporters exhibit significant localization at each distance. Close to 60% of destinations exhibit significant agglomeration at distances below 40 kilometers, this share decreasing fast at larger distances. This pattern resembles that found by DO regarding industry agglomeration. With respect to the amount of agglomeration at each distance, Panel B plots for each kilometer the sum across destinations of the difference between the distance distribution and the localization threshold when the former is above the latter. As can be seen, the largest amount of agglomeration takes place at very small distances. Again, this result is in line with industry agglomeration patterns. Figure 1 View largeDownload slide Localization of exporters by export destination. Notes: This figure shows the pattern of agglomeration by export destination. (A) Displays the share of destinations to which exporters exhibit significant localization at each level of distance, whereas (B) shows the amount of localization at each distance, i.e. the sum across destinations of the difference between the distance distribution and the localization threshold when the former is above the latter. Figure 1 View largeDownload slide Localization of exporters by export destination. Notes: This figure shows the pattern of agglomeration by export destination. (A) Displays the share of destinations to which exporters exhibit significant localization at each level of distance, whereas (B) shows the amount of localization at each distance, i.e. the sum across destinations of the difference between the distance distribution and the localization threshold when the former is above the latter. Table 2 shows the destinations exhibiting the highest country index of agglomeration and Table A2 in the Appendix reports the value of the index for all destinations. The largest amount of agglomeration is found in rather small destinations, accounting for only 1.4% of all firm-country relationships in 2007. However, larger countries, such as the main EU countries and the USA, also exhibit significant agglomeration. Overall, 85.8% of firm-country relationships correspond to significantly localized destinations. It is also worth highlighting that the extent of agglomeration varies widely across destinations, being the standard deviation of the country index twice as large as the mean. At first sight it is not straightforward to uncover a systematic pattern of cross-destination agglomeration, probably due to the fact that several determinants act in opposing directions. For example, some agglomeration is expected to happen next to the border of neighboring countries. Indeed, one destination exhibiting very large agglomeration is Andorra, with which Spain shares a border. However, nearby countries such as those belonging to the EU have good business practices, which undermines the value of agglomeration in overcoming trade barriers. Hence, we would expect exporters to these countries to be less agglomerated. We acknowledge that given the nature of our data it is very hard to disentangle the specific mechanisms driving agglomeration by export destination. Yet in Section 4 we discuss several possibilities and show some evidence via regression analysis. Before this, we perform several robustness checks in the next section. 3.3. Robustness checks In this section we check the sensitivity of the baseline results to restricting the counterfactual. In doing so, we control for some concentration patterns that would result in agglomeration by export destination without relying on export-destination spillovers. Specifically, we take into account the different concentration patterns of large vs small exporters, the role of sequential exporting, the concentration of firms in large cities, the stability of the results over time and others. 3.3.1. Controlling for destinations served by large vs small exporters The heterogeneous firms literature has documented that there is a substantial amount of heterogeneity within exporters, one important dimension of this heterogeneity being size. This is a relevant issue for the study of agglomeration by destination, since it has been documented that large exporters reach a larger set of export destinations, see Helpman et al. (2008) and Eaton et al. (2011). Therefore, our baseline results raise the concern that agglomeration by destination could be the result of large exporters being agglomerated with respect to small exporters. Being this the case, our findings would not exist among exporters of comparable size. We carry out four tests to address this issue. First, we repeat the baseline exercise with subsamples of the largest exporters. Specifically, we consider exporters above the median and above the 75-th percentile of total firm exports. Second, we explicitly control for the size of exporters when building the counterfactual. To be precise, we restrict further the counterfactual by conditioning on 10 and 20 bins of total firm exports. That is, we construct counterfactual distributions from exporters in the same industry and the same size bin, replicating for each destination the distribution of these variables observed in the data. Hence, this procedure estimates destination-specific agglomeration patterns beyond the concentration of exporters, industries and exporters of comparable size.7 Figure 2 shows the results of these tests. Focusing on the sample of the largest exporters (those above percentile 75) reduces the number of agglomerated destinations to 49%, from 64% in the baseline. A similar result arises when we restrict the counterfactual to 10 or 20 bins of total firm exports, see Figure 2A. Hence, some portion of the localization patterns can be explained by the agglomeration of firms of similar size, although this portion is small and the bulk of the baseline results is preserved. Indeed, the destinations to which exporters are no longer significantly agglomerated are those with the lowest degree of agglomeration in the baseline, see the scatter plot in Figure 2B. This suggests that the lower agglomeration levels can be explained (at least partly) by the smaller populations from which the random samples of the counterfactual simulations are drawn. For example, the median number of exporters per industry is 626, whereas the median number of exporters per industry and size bin is 37. Hence, it is reassuring that a more stringent counterfactual preserves the finding of significant agglomeration to a large set of export destinations, in this case to half of the total. Figure 2 View largeDownload slide Localization controlling for exporter size. Notes: This figure compares the baseline results of agglomeration by export destination with those controlling for the size of exporters. (A) Shows the share of localized destinations at each level of distance. The red dashed and the green short-dashed lines are built from samples of the largest exporters: those above the median and the 75 percentile of total firm exports, respectively. The blue dash-dotted and the orange long-dashed lines are obtained from counterfactuals that control for 10 and 20 bins, respectively, of total firm exports (as well as the industry composition of exports). (B) Displays the correlation between the country index of agglomeration and that from the simulations accounting for 20 bins of exporter size. The value of this correlation is 0.98. Figure 2 View largeDownload slide Localization controlling for exporter size. Notes: This figure compares the baseline results of agglomeration by export destination with those controlling for the size of exporters. (A) Shows the share of localized destinations at each level of distance. The red dashed and the green short-dashed lines are built from samples of the largest exporters: those above the median and the 75 percentile of total firm exports, respectively. The blue dash-dotted and the orange long-dashed lines are obtained from counterfactuals that control for 10 and 20 bins, respectively, of total firm exports (as well as the industry composition of exports). (B) Displays the correlation between the country index of agglomeration and that from the simulations accounting for 20 bins of exporter size. The value of this correlation is 0.98. 3.3.2. Controlling for sequential exporting In our empirical specification each destination is treated independently. Given that some firms export to more than one country, this creates a possible dependence of agglomeration patterns across certain markets. Indeed, a large literature has documented that firms go hierarchically to more and more destinations, see for example Albornoz et al. (2012) and Chaney (2014). Therefore, if exporters to one destination are agglomerated and a subset of these exporters disproportionately exports to another destination, this destination will also exhibit agglomeration. We partially addressed this concern when controlling for firm size in the previous subsection, as exporters of the same size are more likely to share some destinations. Here we explore further this issue. First, we inspect if the average number of export destinations of exporters to countries that exhibit significant agglomeration is higher than that of exporters to countries that do not exhibit agglomeration. Finding so would suggest that the large number of agglomerated destinations could be the result of these countries attracting exporters that sell to many (possibly agglomerated) countries. Second, we restrict the counterfactual in the spirit of the previous subsection in order to control for the ability of exporters to sell to more and more difficult destinations. Regarding the first analysis, we find that the distribution of the average number of export destinations of exporters to agglomerated countries lies to the left of the distribution of the average number of export destinations of exporters to countries that do not exhibit agglomeration, see Figure 3. For example, the average number of export destinations of exporters selling to countries that exhibit agglomeration is on average 25.1, whereas this statistic is 27.8 regarding exporters to countries that do not exhibit agglomeration. This provides evidence, at least to a first approximation, against the concern that agglomerated destinations are so because exporters selling there sell also to many other countries. Figure 3 View largeDownload slide Average number of destinations of exporters selling to localized vs nonlocalized destinations. Notes: This figure shows the cross-destination distribution of the average number of destinations reached by exporters. The white bars correspond to countries that exhibit significant agglomeration whereas the gray bars to countries that do not exhibit significant concentration. Figure 3 View largeDownload slide Average number of destinations of exporters selling to localized vs nonlocalized destinations. Notes: This figure shows the cross-destination distribution of the average number of destinations reached by exporters. The white bars correspond to countries that exhibit significant agglomeration whereas the gray bars to countries that do not exhibit significant concentration. Regarding the second exercise, we carry out two additional robustness checks. First, we condition the counterfactual on two bins: exporters selling to only one destination (which represents roughly 40% of exporters) and exporters selling to more than one country. And second, we condition on how ‘difficult’ are the destinations each exporter sells to. We proceed as follows, we rank the destinations in our sample from the highest to the lowest number of exporters, that is, from the most popular to the least popular destinations. We then split the sample in five quintiles, the bottom quintile containing the most popular destinations and the top quintile the least popular. We then assign each exporter to the quintile of the most difficult destination it sells to. Therefore, firms selling to difficult countries are assigned to the top quintile, whereas exporters reaching only the most popular destinations are assigned to the bottom quintile. Hence, we condition the counterfactual to exporters of the same industry and of the same ability to reach difficult destinations. Figure 4 shows that accounting for sequential exporting gives similar results to controlling for firm-size bins. Figure 4A shows that 40% of destinations exhibit significant agglomeration at small distances, a percentage that gradually declines as distance increases. This is a lower share of localized destinations as compared to the baseline. Reassuringly, we find that the relative agglomeration patterns across countries are preserved. Figure 4B displays a high correlation between the baseline country index of agglomeration and that accounting for the difficulty of reaching each destination. Those countries exhibiting the largest amount of agglomeration in the baseline are still those with the highest levels in the more restricted counterfactual. It is worth noting also that the extent of agglomeration is reduced: the average index of significantly agglomerated destinations is around two thirds that of the baseline. This suggests that sequential exporting can be relevant in determining the extent of agglomeration by export destination, yet the smaller samples from which the counterfactual simulations are drawn may partly explain this result. Figure 4 View largeDownload slide Localization controlling for sequential exporting. Notes: This figure compares the baseline results of agglomeration by export destination with those controlling for sequential exporting. (A) Shows the share of localized destinations at each level of distance. The red dashed line is obtained from a counterfactual that controls for two groups of firms according to the number of countries they sell to: one or more than one. The green dotted line is obtained from a counterfactual of 5 bins according to the most difficult destination each exporter is able to sell to. The degree of difficulty of each destination is assessed according to the total number of exporters. Both counterfactuals account also for the industry composition of exports. (B) Displays the correlation between the country index of agglomeration and that from the simulations accounting for the difficulty of reaching each destination. The value of this correlation is 0.97. Figure 4 View largeDownload slide Localization controlling for sequential exporting. Notes: This figure compares the baseline results of agglomeration by export destination with those controlling for sequential exporting. (A) Shows the share of localized destinations at each level of distance. The red dashed line is obtained from a counterfactual that controls for two groups of firms according to the number of countries they sell to: one or more than one. The green dotted line is obtained from a counterfactual of 5 bins according to the most difficult destination each exporter is able to sell to. The degree of difficulty of each destination is assessed according to the total number of exporters. Both counterfactuals account also for the industry composition of exports. (B) Displays the correlation between the country index of agglomeration and that from the simulations accounting for the difficulty of reaching each destination. The value of this correlation is 0.97. 3.3.3. Controlling for destinations served by exporters in large cities Exporters located in large cities might be able to reach a larger set of export destinations, for instance by using better transport facilities, such as airports. Then, if some exporters concentrate in large cities and large cities disproportionately export to difficult destinations, exporters to these destinations will be spatially agglomerated. If this is the case, the destination-specific agglomeration economies would not exist within cities of comparable size. We perform two tests to address this concern. First, we exclude from our sample those firms located in Madrid and Barcelona, which are the largest municipalities in Spain, accounting for around 13% of export firms. Second, we control for the size of each city where each exporter is located when building the counterfactual distributions. We construct city-size bins with cutoffs given by 10 thousand, 100 thousand, 250 thousand and 1 million people.8 We then restrict the counterfactual to exporters in the same industry and the same population bin, replicating for each destination the distribution of these two variables observed in the data. Figure 5A shows that excluding firms located in the largest municipalities has a small effect on the results. At low distances the share of localized destinations does not change, whereas at larger distances agglomeration is higher. Indeed, 60% of destinations exhibit agglomeration until around 70 km, which is a larger scale of agglomeration than that of the baseline. Accounting for city-size bins reduces the extent of agglomeration at small distances, although to a limited extent. The total number of localized destinations goes down from 64% in the baseline to 62%. Figure 5B compares the country index of agglomeration with that of the baseline. We find that they are very highly correlated (0.98) and most of the countries lie very close to the 45° line. Indeed, those destinations for which agglomeration is no longer significant are those that exhibit very low levels in the first place. Hence, it seems that the baseline results cannot be explained by firms located in the largest municipalities or in locations of certain size. Figure 5 View largeDownload slide Localization controlling for city size. Notes: This figure compares the baseline results of agglomeration by export destination with those controlling for the size of cities where exporters are located. (A) Shows the share of localized destinations at each level of distance. The red dashed line corresponds to the sample excluding exporters located in Madrid and Barcelona. The green short-dashed line is built from a counterfactual that controls for 5 bins of city size (as well as the industry composition of exports). (B) Displays the correlation between the baseline country index of agglomeration and that from the simulations controlling for city-size bins. The value of this correlation is 0.98. Figure 5 View largeDownload slide Localization controlling for city size. Notes: This figure compares the baseline results of agglomeration by export destination with those controlling for the size of cities where exporters are located. (A) Shows the share of localized destinations at each level of distance. The red dashed line corresponds to the sample excluding exporters located in Madrid and Barcelona. The green short-dashed line is built from a counterfactual that controls for 5 bins of city size (as well as the industry composition of exports). (B) Displays the correlation between the baseline country index of agglomeration and that from the simulations controlling for city-size bins. The value of this correlation is 0.98. 3.3.4. Stability of the results over time In this subsection we check the stability of the results over time. A recent strand of literature documents that a large portion of exporter-destination relationships are short-lived, see Besedes and Prusa (2006), Nitsch (2009) and Békés and Muraközy (2012). This raises the concern that the agglomeration patterns uncovered so far could vary over time. We carry out two tests to address this issue. First, we repeat the baseline analysis for the years 2003 and 2005. And second, we restrict the baseline sample to continuous exporters from 2005 to 2007. Note that this exercise involves restricting the sample to those firm-country relationships that exist during three consecutive years (2005, 2006 and 2007). This criterion implies dropping around 40% of the exporters in the original sample. Note also that the agglomeration patterns by destination are computed only for continuous exporters to that destination, the counterfactual being random draws of continuous exporters to that and other destinations that satisfy the industry criterion. This implies that the number of destinations is reduced to 121. Figure 6 shows the results. The patterns of agglomeration by destination hold broadly across years, being the share of localized destinations very similar in 2003, 2005 and 2007 (Figure 6A). This is also true for the sample of continuous exporters between 2005 and 2007. The share of localized destinations is 60% vs 64% in the baseline and the scale of agglomeration at short distances is very similar. This finding highlights that agglomeration by a significant amount of export destinations is also a feature of the exporters most able to establish permanent trade relationships. In terms of the country index of agglomeration, the results over time are also relatively stable. The correlation between the 2007 index and that of 2005 and 2003 is 0.66 and 0.75, respectively. The Figure 6B shows the scatterplot between the baseline index and that of the continuous exporters sample. The correlation amounts to 0.71.9 Figure 6 View largeDownload slide Localization across different years. Notes: This figure shows agglomeration by export destination across different years and for the sample of continuous exporters in 2007 (i.e. exporter-destination relationships that exist during 2005 to 2007). (A) Shows the share of localized destinations at each distance. (B) Displays the correlation between the baseline country index of agglomeration and that obtained from the continuous exporter sample. The value of this correlation is 0.71. Note that this panel excludes two destinations with highly localized exporters in both the baseline and the continuous exporter sample in order to keep a meaningful scale. Figure 6 View largeDownload slide Localization across different years. Notes: This figure shows agglomeration by export destination across different years and for the sample of continuous exporters in 2007 (i.e. exporter-destination relationships that exist during 2005 to 2007). (A) Shows the share of localized destinations at each distance. (B) Displays the correlation between the baseline country index of agglomeration and that obtained from the continuous exporter sample. The value of this correlation is 0.71. Note that this panel excludes two destinations with highly localized exporters in both the baseline and the continuous exporter sample in order to keep a meaningful scale. 3.3.5. Additional robustness checks In our counterfactual simulations, we controlled for the industry composition of exports up to two-digit industries. This level of aggregation might be too coarse if countries demand specific varieties of a product that are locally produced. This concern could be partly addressed by using detailed enough product codes such as HS-6, yet our data do not contain such information. As an additional robustness check, we controlled in the counterfactual simulations for the industry composition of exports up to four-digit industries, at the cost of losing degrees of freedom when building the counterfactual. We find that the number of destinations with agglomerated exporters is 57%, slightly less than in the baseline but still high. Moreover, the patterns of agglomeration resemble that of the baseline. The share of localized destinations is close to 50% until about 40 km, decreasing fast from that distance on, see Figure 7A. Figure 7 View largeDownload slide Additional robustness checks. Notes: This figure shows additional robustness checks on the baseline results. (A) Shows agglomeration by export destination at each distance when the counterfactual controls for four-digit industries vs two-digit industries in the baseline. (B) Extends the distance horizon at which the density and the counterfactual distributions are compared to 200 and 400 km vs 100 km in the baseline. Figure 7 View largeDownload slide Additional robustness checks. Notes: This figure shows additional robustness checks on the baseline results. (A) Shows agglomeration by export destination at each distance when the counterfactual controls for four-digit industries vs two-digit industries in the baseline. (B) Extends the distance horizon at which the density and the counterfactual distributions are compared to 200 and 400 km vs 100 km in the baseline. Finally, we also checked if the agglomeration patterns vary when considering an extended distance horizon over which the distance distribution and the counterfactual are compared. Note that DO focus on 180 km, which corresponds approximately to the median distance of manufacturing plants in the UK, and Ellison et al. (2010) provide results for the USA over thresholds ranging from 100 to 1000 miles. Figure 7B shows that extending the distance horizon to 200 and 400 km from the 100-km baseline deliver similar agglomeration patterns. Note that the value of the K-densities is independent of the distance horizon over which they are evaluated. The slightly lower number of countries exhibiting agglomeration is the result of the higher localization threshold needed to fulfill the criterion that 95% of the counterfactual distributions lie below it across all distances, which leads to wider confidence bands. Also, the country indices of agglomeration have a large correlation with respect to the baseline, of 0.99 and 0.93, respectively. 4. Factors behind agglomeration by destination In this section, we aim at identifying the variables that explain the cross-destination variation in agglomeration levels documented above. The process of selling abroad involves both fixed and variable costs, such as learning about the foreign market, establishing a distribution network, tailoring the products to foreign tastes and regulations, clearing the goods through customs, etc. Therefore, the industry agglomeration sources emphasized by the literature, related to sharing, matching and learning mechanisms (see for instance Duranton and Puga (2004)) may also lead, at least to some extent, to agglomeration by export destination. Indeed, there exist potential gains from pooling the costs of selling abroad and from extracting the information revealed by nearby exporters as shown, for instance, by Segura-Cayuela and Vilarrubia (2008) and Fernandes and Tang (2014). Agglomeration may also be the result of geographical proximity to the importing country, which would lead to export firms being concentrated close to the border. To shed some light on the determinants of agglomeration by export destination, we regress the country index of agglomeration on a set of country characteristics borrowed from the gravity literature, which connects trade flows with size and trade barriers, see Helpman et al. (2008). If agglomeration results in lower transport costs, this literature provides a natural guideline to assess the cross-destination heterogeneity in exporter agglomeration. Then, we include proxies of geographical distance, cultural similarity, transaction costs and institutions among other determinants. To take into account the left-censored nature of the dependent variable, we specify the following Tobit model: Localizationc=β0+β1Spanishc+β2Institutional Qualityc+β3Euroc+β4Contiguityc+β5Log Distance to Capitalc+β6Log Per Capita GDPc+β7Log Number of Exportersc+β8Log Populationc+εc. (1) Our variables capturing trade barriers are the following. Cultural similarity is proxied by a dummy variable taking value one if Spanish is spoken in the destination country, and zero otherwise. The institutional environment in the importing country is captured by the principal factor of the six dimensions comprising the Worldwide Governance Indicators, namely rule of law, political stability, control of corruption, government effectiveness, regulatory quality, and voice and accountability (see Appendix C for details). We also include a dummy taking value one if the euro is the currency of the destination. Finally, we add the log average distance between exporters and the destination’s capital and a dummy taking value one if the destination shares a border with Spain. In a different specification, we include the average distance of exporters to the closest port from which shipments are sent to the country.10 Finally, we add a set of controls including per capita GDP, population, and the log number of firms exporting to the country. The latter depends also on export costs. We include it to facilitate the reading of the results: we compare the extent of exporter agglomeration across destinations reached by the same number of firms, and assess how this varies according to characteristics such as language, institutions and distance.11 Note finally that we rescale the dependent variable by its standard deviation to ease the interpretation of the coefficients. Table 3 shows the results. In column (1) we find that, conditional on the rest of the covariates, exporters to countries with a different language, a different currency and a worse business environment are significantly more geographically agglomerated. The most significant variable is language: if Spanish is spoken in the importing country, agglomeration decreases by 0.89 standard deviations.12 In column (2) we restrict the sample to the destinations not belonging to the European Union, where entry is more difficult and hence the value provided by agglomeration is potentially larger. In this specification language and the institutional environment become more important in explaining the concentration of exporters. Speaking Spanish reduces the extent of agglomeration by 1.3 standard deviations and a one standard deviation increase in the business environment is associated with a 0.66 standard deviations decrease in agglomeration, both results pointing to a positive relationship between agglomeration and the difficulty in conducting businesses abroad.13 Our findings in columns (1) and (2) suggest also that distance plays no role in explaining the concentration of exporters. In column (3) we replace the exporter average distance to the country’s capital with the average distance to the closest port shipping to the country. This measure does not have explanatory power in accounting for exporter concentration either. Table 3 Factors behind exporters’ agglomeration by destination Baseline Non-EU Ports Immigrants Region Exporters’ FE Size (1) (2) (3) (4) (5) (6) Dep. variable: country index of exporters’ localization Spanish −0.8901*** −1.2840*** −1.1375*** −0.9416** −0.9035 −1.2434** (0.3358) (0.3750) (0.3200) (0.3305) (0.8289) (0.4834) Institutional Quality −0.4756* −0.6561** −0.5395** −0.2008 −0.3968* −0.7163** (0.2437) (0.3035) (0.2577) (0.1622) (0.2251) (0.3092) Euro −0.6491** −0.2722 0.3424 −0.9010*** −0.7463* (0.3052) (0.2548) (0.2001) (0.3264) (0.4362) Contiguity 0.7024 −0.7600 0.6388*** 0.6165 1.4376 (1.0417) (0.8099) (0.2175) (0.9288) (1.2682) Log Distance to Capital −0.1684 0.0043 0.2156 −0.2861 −0.2592 (0.1914) (0.2008) (0.1614) (0.3398) (0.2521) Log Distance to Port 0.0833 (0.2651) Log Per Capita GDP 0.2849 0.3498 0.3386* 0.7116*** 0.1605 0.3276 (0.1963) (0.2370) (0.1914) (0.2309) (0.1655) (0.2079) Log Number of Exporters 0.1371 0.2096 0.1474 −0.6587** −0.0083 0.1746 (0.1701) (0.2110) (0.1398) (0.2321) (0.2122) (0.1919) Log Population −0.0148 −0.0060 0.0628 0.2162 0.0268 −0.0476 (0.1111) (0.1360) (0.0941) (0.1322) (0.1419) (0.1334) Dispersion of Immigrants −2.3675*** (0.7900) Region Fixed Effects No No No No Yes No Observations 150 123 141 28 150 150 Pseudo R2 0.04 0.04 0.04 0.62 0.10 0.05 Log Likelihood −201.60 −165.90 −184.60 −8.67 −187.60 −181.40 Baseline Non-EU Ports Immigrants Region Exporters’ FE Size (1) (2) (3) (4) (5) (6) Dep. variable: country index of exporters’ localization Spanish −0.8901*** −1.2840*** −1.1375*** −0.9416** −0.9035 −1.2434** (0.3358) (0.3750) (0.3200) (0.3305) (0.8289) (0.4834) Institutional Quality −0.4756* −0.6561** −0.5395** −0.2008 −0.3968* −0.7163** (0.2437) (0.3035) (0.2577) (0.1622) (0.2251) (0.3092) Euro −0.6491** −0.2722 0.3424 −0.9010*** −0.7463* (0.3052) (0.2548) (0.2001) (0.3264) (0.4362) Contiguity 0.7024 −0.7600 0.6388*** 0.6165 1.4376 (1.0417) (0.8099) (0.2175) (0.9288) (1.2682) Log Distance to Capital −0.1684 0.0043 0.2156 −0.2861 −0.2592 (0.1914) (0.2008) (0.1614) (0.3398) (0.2521) Log Distance to Port 0.0833 (0.2651) Log Per Capita GDP 0.2849 0.3498 0.3386* 0.7116*** 0.1605 0.3276 (0.1963) (0.2370) (0.1914) (0.2309) (0.1655) (0.2079) Log Number of Exporters 0.1371 0.2096 0.1474 −0.6587** −0.0083 0.1746 (0.1701) (0.2110) (0.1398) (0.2321) (0.2122) (0.1919) Log Population −0.0148 −0.0060 0.0628 0.2162 0.0268 −0.0476 (0.1111) (0.1360) (0.0941) (0.1322) (0.1419) (0.1334) Dispersion of Immigrants −2.3675*** (0.7900) Region Fixed Effects No No No No Yes No Observations 150 123 141 28 150 150 Pseudo R2 0.04 0.04 0.04 0.62 0.10 0.05 Log Likelihood −201.60 −165.90 −184.60 −8.67 −187.60 −181.40 Notes: This table shows the regression of the country index of exporters’ localization (i.e. a variable capturing to what extent exporters to each export destination are significantly agglomerated) against measures of export costs, comparative advantage and several covariates. The specification is a tobit model described in Equation (1). Column (1) presents the baseline regression. Column (2) restricts the sample to countries not in the European Union. Column (3) replaces the variable distance with the average distance to the closest port shipping to the country. Column (4) introduces a measure of the concentration of immigrants from each country, proxied as the median distance between them. Column (5) introduces seven region fixed effects: Western Europe, Eastern Europe, Western and Central Asia, South-East Asia, Northern Africa, Central and Southern Africa, Central America and Caribbean, North America, South America, and Oceania. Finally, in column (6) the dependent variable is the country index built from a counterfactual that controls for firm-size bins (Section 3.3.1). Robust standard errors are in parenthesis. Significance levels: *10%; **5%; ***1%. Table 3 Factors behind exporters’ agglomeration by destination Baseline Non-EU Ports Immigrants Region Exporters’ FE Size (1) (2) (3) (4) (5) (6) Dep. variable: country index of exporters’ localization Spanish −0.8901*** −1.2840*** −1.1375*** −0.9416** −0.9035 −1.2434** (0.3358) (0.3750) (0.3200) (0.3305) (0.8289) (0.4834) Institutional Quality −0.4756* −0.6561** −0.5395** −0.2008 −0.3968* −0.7163** (0.2437) (0.3035) (0.2577) (0.1622) (0.2251) (0.3092) Euro −0.6491** −0.2722 0.3424 −0.9010*** −0.7463* (0.3052) (0.2548) (0.2001) (0.3264) (0.4362) Contiguity 0.7024 −0.7600 0.6388*** 0.6165 1.4376 (1.0417) (0.8099) (0.2175) (0.9288) (1.2682) Log Distance to Capital −0.1684 0.0043 0.2156 −0.2861 −0.2592 (0.1914) (0.2008) (0.1614) (0.3398) (0.2521) Log Distance to Port 0.0833 (0.2651) Log Per Capita GDP 0.2849 0.3498 0.3386* 0.7116*** 0.1605 0.3276 (0.1963) (0.2370) (0.1914) (0.2309) (0.1655) (0.2079) Log Number of Exporters 0.1371 0.2096 0.1474 −0.6587** −0.0083 0.1746 (0.1701) (0.2110) (0.1398) (0.2321) (0.2122) (0.1919) Log Population −0.0148 −0.0060 0.0628 0.2162 0.0268 −0.0476 (0.1111) (0.1360) (0.0941) (0.1322) (0.1419) (0.1334) Dispersion of Immigrants −2.3675*** (0.7900) Region Fixed Effects No No No No Yes No Observations 150 123 141 28 150 150 Pseudo R2 0.04 0.04 0.04 0.62 0.10 0.05 Log Likelihood −201.60 −165.90 −184.60 −8.67 −187.60 −181.40 Baseline Non-EU Ports Immigrants Region Exporters’ FE Size (1) (2) (3) (4) (5) (6) Dep. variable: country index of exporters’ localization Spanish −0.8901*** −1.2840*** −1.1375*** −0.9416** −0.9035 −1.2434** (0.3358) (0.3750) (0.3200) (0.3305) (0.8289) (0.4834) Institutional Quality −0.4756* −0.6561** −0.5395** −0.2008 −0.3968* −0.7163** (0.2437) (0.3035) (0.2577) (0.1622) (0.2251) (0.3092) Euro −0.6491** −0.2722 0.3424 −0.9010*** −0.7463* (0.3052) (0.2548) (0.2001) (0.3264) (0.4362) Contiguity 0.7024 −0.7600 0.6388*** 0.6165 1.4376 (1.0417) (0.8099) (0.2175) (0.9288) (1.2682) Log Distance to Capital −0.1684 0.0043 0.2156 −0.2861 −0.2592 (0.1914) (0.2008) (0.1614) (0.3398) (0.2521) Log Distance to Port 0.0833 (0.2651) Log Per Capita GDP 0.2849 0.3498 0.3386* 0.7116*** 0.1605 0.3276 (0.1963) (0.2370) (0.1914) (0.2309) (0.1655) (0.2079) Log Number of Exporters 0.1371 0.2096 0.1474 −0.6587** −0.0083 0.1746 (0.1701) (0.2110) (0.1398) (0.2321) (0.2122) (0.1919) Log Population −0.0148 −0.0060 0.0628 0.2162 0.0268 −0.0476 (0.1111) (0.1360) (0.0941) (0.1322) (0.1419) (0.1334) Dispersion of Immigrants −2.3675*** (0.7900) Region Fixed Effects No No No No Yes No Observations 150 123 141 28 150 150 Pseudo R2 0.04 0.04 0.04 0.62 0.10 0.05 Log Likelihood −201.60 −165.90 −184.60 −8.67 −187.60 −181.40 Notes: This table shows the regression of the country index of exporters’ localization (i.e. a variable capturing to what extent exporters to each export destination are significantly agglomerated) against measures of export costs, comparative advantage and several covariates. The specification is a tobit model described in Equation (1). Column (1) presents the baseline regression. Column (2) restricts the sample to countries not in the European Union. Column (3) replaces the variable distance with the average distance to the closest port shipping to the country. Column (4) introduces a measure of the concentration of immigrants from each country, proxied as the median distance between them. Column (5) introduces seven region fixed effects: Western Europe, Eastern Europe, Western and Central Asia, South-East Asia, Northern Africa, Central and Southern Africa, Central America and Caribbean, North America, South America, and Oceania. Finally, in column (6) the dependent variable is the country index built from a counterfactual that controls for firm-size bins (Section 3.3.1). Robust standard errors are in parenthesis. Significance levels: *10%; **5%; ***1%. In column (4) we quantify the role of immigrants in explaining agglomeration across countries. Specifically, we test whether the local concentration of immigrants can explain some of the patterns that we document. A line of research has shown that immigrants help overcome trade barriers, for example by providing specific knowledge about their home countries. For instance, Herander and Saavedra (2005) find an effect of local immigrant groups on export volumes in the USA. To delve into this issue, we construct an origin-specific index of immigrant dispersion, defined as the median distance between immigrants from each country (a higher distance meaning more dispersion). Our analysis is restricted to the 28 countries for which we have information on the population distribution across municipalities, therefore we raise a flag of caution on interpreting the results. With this caveat in mind, column (4) shows that there is a significant relationship between the dispersion of immigrants and the agglomeration of firms selling to their home countries. Conditional on the rest of controls, a 10% increase in the dispersion of immigrants is associated with a 0.53 standard deviation decrease in the degree of agglomeration. Therefore, agglomeration by destinations whose immigrants exhibit some concentration is found to be higher. Note that this result provides some evidence against export spillovers, since the agglomeration of people from the same country may lead to the agglomeration of exporters to their country of origin, even if no information between exporters is shared.14 In column (5) we add region fixed effects. We include 10 region dummies: Western Europe, Eastern Europe, Western and Central Asia, South-East Asia, Northern Africa, Central and Southern Africa, North America, Central America and Caribbean, South America and Oceania. We find that the coefficient associated with language barely changes with respect to the baseline, although it is imprecisely estimated, whereas that of institutional quality is somewhat lower, though it is still statistically significant. Interestingly, controlling for regions increases the estimated effect of the currency: belonging to the euro area reduces the extent of agglomeration by 0.90 standard deviations. These results suggest that the mechanisms connecting agglomeration with trade barriers hold also within broad geographic and economic areas. Finally, column (6) replaces the baseline country index of agglomeration with that obtained from the counterfactual that accounts for 20 bins of exporter size, see Section 3.3.1. This is a pertinent analysis because restricting the counterfactual in some cases reduced the number of significantly agglomerated destinations. However, given the high correlation between the country indices of agglomeration, the results tend to confirm the baseline findings. In fact, the point estimates are even larger in absolute value regarding language, institutional quality and currency. Adding the other country indices constructed in Section 3.3 confirms the baseline estimates. Overall, the previous results suggest that there exists a relationship between trade barriers to enter a country and the degree of spatial agglomeration of exporters selling to it. One limitation of this approach is that the precise mechanism driving these patterns cannot be uncovered, and we cannot rule out that other omitted factors may contaminate this relationship, hence we do not pursue causality. However, they show an insightful correlation between proxies of export costs and the extent of geographical concentration that is systematic and robust. Moreover, the results suggest that agglomeration can be more effective concerning those destinations from which information is more valuable and, in this regard, they inform the theoretical and empirical literature on export spillovers and learning from neighboring firms cited in the introduction. 5. Concluding remarks In this article, we document the existence of agglomeration economies that accrue to firms selling to certain foreign markets. In the pursuit of shedding light on the interpretation of our results, we show that these patterns of geographical concentration are not driven by the spatial location of large vs small exporters, hierarchical exporting or exporters located in large cities. Moreover, we find that these location patterns are quite stable over time. Regarding the determinants of agglomeration, we show that the cross-destination variability in agglomeration levels can be partly explained by language, currency and institutional quality, being agglomeration higher the larger export costs are. These findings are consistent with the existence of externalities in selling to certain foreign countries, having implications for international trade. For example, agglomeration might reduce destination-specific fixed costs, which would rationalize why firms do not follow a strict hierarchy of export destinations, a fact uncovered by Eaton et al. (2011). Also, some policy implications can be derived. The pattern of concentration by export destination suggests that easing the flow of information from exporters to potential entrants can pay off. Moreover, the fact that concentration is higher concerning more difficult destinations suggests that the benefits of these policies can be specially helpful in countries where entry is more difficult. Also, helping companies penetrate new markets can lead nearby firms to follow them. Interestingly, given how we defined the counterfactual, these benefits are not restricted to firms of the same industry, rather they can extend to firms belonging to different industries. The nature of our data prevents us from digging deeper into the specific channels through which agglomeration economies might work. More detailed data would allow to disentangling some sources of export spillovers, such as those related to information (via headquarters) from those linked to costs (via establishments). Also, a larger time span and more categories of goods would allow a geographical analysis of new products exported and new markets accessed. In general, we think that there is room in the literature to test empirically which are the most important channels through which agglomeration economies in international trade operate. Case studies or natural experiments seem a suitable framework to disentangle specific mechanisms playing a role in generating export spillovers. We see this avenue of further research as promising. Footnotes 1 The existing literature on export spillovers studies the agglomeration of exporters in the same administrative unit or economic area. This entails the so-called ‘border effect’ problem, which involves several issues. First, it amounts to treat symmetrically plants not belonging to the same spatial unit, regardless of the distance that separates them. Second, it involves the arbitrary decision of which spatial unit to take. This is relevant, as different levels of aggregation can lead to very different results. Furthermore, it has been showed that bigger units produce more pronounced correlations. This is called the Modifiable Areal Unit Problem (MAUP), see Openshaw and Taylor (1979) and Openshaw (1984). And third, the previous problem and the fact that spatial units are not often defined on the basis of economic significance make the comparison of results across spatial units difficult to interpret. 2 Although localization can be defined as agglomeration controlling for that of general manufacturing, as in Duranton and Overman (2005), in this article we use the words agglomeration, localization and concentration interchangeably, as the indices explicitly control for the overall concentration of exporters and do not lead to confusion. 3 On the mechanisms driving industry agglomeration, see Klepper (2010), who analyzes the historical clustering of firms in Detroit and Silicon Valley, and Ellison et al. (2010), who test the Marshall (1920) theories of industry agglomeration using coagglomeration patterns. 4 The dataset extends to 2013, but the export threshold was increased in 2008 to €50,000. For this reason and to avoid the results being contaminated from the crisis, we use data up to 2007. 5 This estimation is as follows. According to the 2009 Spanish Survey on Business Strategies, 93.1% of firms with less than 200 employees and 62.5% of firms with more than 200 employees have only one plant. In our data 94.5% of exporters have less than 200 employees (vs 99.1% of all firms), then we estimate that the percentage of single-plant exporters is approximately 91.4%. 6 In DO the localization and dispersion thresholds are referred to as global confidence bands. Note that they allow making statements about the overall agglomeration patterns, since they are neutral with respect to distances (at a given distance horizon). DO report local confidence intervals too, constructed as the 5-th and 95-th percentiles of the ranked counterfactual distributions at each kilometer. These intervals only allow local statements to be made, i.e. deviations from randomness at a given distance. Note that the percentiles associated with the localization thresholds are above the 95-th percentile, since the ranking of the counterfactual distributions varies across distances. At the baseline distance horizon of 100 km, they range (across countries) between the 96.4-th and the 99.5-th. 7 Each size bin contains the same number of exporters. The median amount of total exports is given by €0.25 million and the percentile 75 by €1.4 million. 8 The distribution of exporters across the five resulting bins is: 30%, 32%, 15%, 11% and 13%. 9 Note that to keep a meaningful scale this figure excludes two destinations with highly localized exporters in both the baseline and the continuous exporters sample. 10 We do not include it in the baseline because we lack data on exports from Spanish ports to 13 countries and the distance variables are never significant. Also, including the simple distance between the most populated cities yields very similar results. 11 Moreover, Helpman et al. (2008) shows the importance of accounting for the extensive margin of trade in the gravity equation framework. We also checked that excluding this variable does not affect the overall results. 12 Note that our baseline regression is performed on 150 countries because we lack data on 16 small countries. GDP data are missing in 14 countries and institutional quality in 6 observations. 13 We also inspected the role of some specific elements of the institutional environment by replacing the institutional factor in column (1) by each Governance Indicator (one by one). We found that a better rule of law, less corruption and more political stability are significantly associated with lower agglomeration, whereas the rest yielded nonsignificant associations. Moreover, we also found that specific measures of investor protection, such as the number of procedures required to enforce a contract, are also negatively and significantly associated with agglomeration. We also tried another proxies of import costs such as the number of days and the number of documents required to import goods, but found that the estimates were not statistically significant. 14 Note also that the coefficients of some variables change substantially with respect to the baseline (column 1). We found that they can be mainly explained by a composition effect, since repeating the baseline regression with the 28 countries did not yield large changes in the covariate estimates with respect to those obtained in column (4). 15 Given that our aim is to uncover agglomeration of export firms, we treat each firm as one observation and therefore we do not weigh the distances when estimating the distribution. An alternative would be consider the agglomeration of export values and hence weigh distances by exports to the country. There are three reasons that advise us against this strategy. First, the construction of the counterfactual involves firms that do not export to the country, hence they lack a weight. Second, if total firm exports were used as weights, we would disregard the specialization of firms to certain markets, given that weights would not have variation across export destinations. And third, total exports is a highly skewed variable, hence a few firms could distort the results. Acknowledgements This article was previously circulated under the title ‘Agglomeration Matters for Trade’. We are very grateful to Guillermo Caruana, Rosario Crinó and Claudio Michelacci for their constant guidance and help. We are also thankful to César Alonso, Pol Antràs, Manuel Arellano, Stéphane Bonhomme, David Dorn, Gino Gancia, Manuel García-Santana, Horacio Larreguy, Carlos Llano, Marc Melitz, Guy Michaels, Hannes Mueller, Diego Puga, Rafael Repullo, Rubén Segura-Cayuela, Andrei Shleifer, the editor, at least two anonymous referees, and seminar participants at CEMFI, European Winter Meeting of the Econometric Society (Konstanz), SAEe Vigo, IMT Lucca, Universität Mannheim, IAE, CESifo, 2013 Annual Meeting of the Society for Economic Dynamics (Seoul), 67th European Meeting of the Econometric Society (Gothenburg), 2013 Barcelona Workshop on Regional and Urban Economics, Banco de España, Universidad de Murcia, XVII Conference on International Economics (A Coruña), and Universidad Autónoma de Madrid for comments and useful discussions. We are also grateful to Patry Tello for providing us the exporter dataset. 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Control of Corruption Kaufmann et al. (2009) Extent to which public power is exercised for private gain, including corruption, as well as ‘capture’ of the state by elites and private interests. Regulatory Quality Kaufmann et al. (2009) Ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Political Stability Kaufmann et al. (2009) Likelihood that the government will be destabilized or overthrown by unconstitutional or violent means. Government Effectiveness Kaufmann et al. (2009) Quality of public services, the civil service, policy formulation and implementation, and credibility of the government’s commitment to such policies. Voice and Accountability Kaufmann et al. (2009) Extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association and a free media. Euro 1 if the country’s currency is the euro. Contiguity Mayer and Zignago (2011) 1 for contiguity with respect to Spain. Distance to Country’s Capital Average distance of exporters to the country’s capital. Distance to Ports Puertos del Estado Distance between zip code and closest port from which shipments are sent to the country. We assume that from the three main Portuguese ports (Aveiro, Leixoes and Lisbon), all countries are reached. Per capita GDP World Bank Log Real per Capita GDP in constant 2000 US dollars. Population World Bank Country’s population. Location of Immigrants Instituto Nacional de Estadística Number of immigrants in each municipality by country of origin. European Union 1 if the country belongs to the European Union. Doing Business Index World Bank Ranking of economies that assess business regulations and their enforcement. Contract Enforcement World Bank Number of procedures required to enforce a contract. Time to Import World Bank Number of calendar days necessary to comply with all the procedures required to import goods. Variables Source Definition Zip code coordinates Geonames Distance between zip codes Apply haversine formula to the zip code coordinates. Spanish Mayer and Zignago (2011) 1 if a Spanish is spoken by at least 9% of the population. Rule of Law Kaufmann et al. (2009) Quality of contract enforcement, property rights, the police, the courts, and likelihood of crime and violence. Control of Corruption Kaufmann et al. (2009) Extent to which public power is exercised for private gain, including corruption, as well as ‘capture’ of the state by elites and private interests. Regulatory Quality Kaufmann et al. (2009) Ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Political Stability Kaufmann et al. (2009) Likelihood that the government will be destabilized or overthrown by unconstitutional or violent means. Government Effectiveness Kaufmann et al. (2009) Quality of public services, the civil service, policy formulation and implementation, and credibility of the government’s commitment to such policies. Voice and Accountability Kaufmann et al. (2009) Extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association and a free media. Euro 1 if the country’s currency is the euro. Contiguity Mayer and Zignago (2011) 1 for contiguity with respect to Spain. Distance to Country’s Capital Average distance of exporters to the country’s capital. Distance to Ports Puertos del Estado Distance between zip code and closest port from which shipments are sent to the country. We assume that from the three main Portuguese ports (Aveiro, Leixoes and Lisbon), all countries are reached. Per capita GDP World Bank Log Real per Capita GDP in constant 2000 US dollars. Population World Bank Country’s population. Location of Immigrants Instituto Nacional de Estadística Number of immigrants in each municipality by country of origin. European Union 1 if the country belongs to the European Union. Doing Business Index World Bank Ranking of economies that assess business regulations and their enforcement. Contract Enforcement World Bank Number of procedures required to enforce a contract. Time to Import World Bank Number of calendar days necessary to comply with all the procedures required to import goods. Notes: This table shows the definitions and sources of the main variables used throughout the article. Table A1 Data definitions and sources Variables Source Definition Zip code coordinates Geonames Distance between zip codes Apply haversine formula to the zip code coordinates. Spanish Mayer and Zignago (2011) 1 if a Spanish is spoken by at least 9% of the population. Rule of Law Kaufmann et al. (2009) Quality of contract enforcement, property rights, the police, the courts, and likelihood of crime and violence. Control of Corruption Kaufmann et al. (2009) Extent to which public power is exercised for private gain, including corruption, as well as ‘capture’ of the state by elites and private interests. Regulatory Quality Kaufmann et al. (2009) Ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Political Stability Kaufmann et al. (2009) Likelihood that the government will be destabilized or overthrown by unconstitutional or violent means. Government Effectiveness Kaufmann et al. (2009) Quality of public services, the civil service, policy formulation and implementation, and credibility of the government’s commitment to such policies. Voice and Accountability Kaufmann et al. (2009) Extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association and a free media. Euro 1 if the country’s currency is the euro. Contiguity Mayer and Zignago (2011) 1 for contiguity with respect to Spain. Distance to Country’s Capital Average distance of exporters to the country’s capital. Distance to Ports Puertos del Estado Distance between zip code and closest port from which shipments are sent to the country. We assume that from the three main Portuguese ports (Aveiro, Leixoes and Lisbon), all countries are reached. Per capita GDP World Bank Log Real per Capita GDP in constant 2000 US dollars. Population World Bank Country’s population. Location of Immigrants Instituto Nacional de Estadística Number of immigrants in each municipality by country of origin. European Union 1 if the country belongs to the European Union. Doing Business Index World Bank Ranking of economies that assess business regulations and their enforcement. Contract Enforcement World Bank Number of procedures required to enforce a contract. Time to Import World Bank Number of calendar days necessary to comply with all the procedures required to import goods. Variables Source Definition Zip code coordinates Geonames Distance between zip codes Apply haversine formula to the zip code coordinates. Spanish Mayer and Zignago (2011) 1 if a Spanish is spoken by at least 9% of the population. Rule of Law Kaufmann et al. (2009) Quality of contract enforcement, property rights, the police, the courts, and likelihood of crime and violence. Control of Corruption Kaufmann et al. (2009) Extent to which public power is exercised for private gain, including corruption, as well as ‘capture’ of the state by elites and private interests. Regulatory Quality Kaufmann et al. (2009) Ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Political Stability Kaufmann et al. (2009) Likelihood that the government will be destabilized or overthrown by unconstitutional or violent means. Government Effectiveness Kaufmann et al. (2009) Quality of public services, the civil service, policy formulation and implementation, and credibility of the government’s commitment to such policies. Voice and Accountability Kaufmann et al. (2009) Extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association and a free media. Euro 1 if the country’s currency is the euro. Contiguity Mayer and Zignago (2011) 1 for contiguity with respect to Spain. Distance to Country’s Capital Average distance of exporters to the country’s capital. Distance to Ports Puertos del Estado Distance between zip code and closest port from which shipments are sent to the country. We assume that from the three main Portuguese ports (Aveiro, Leixoes and Lisbon), all countries are reached. Per capita GDP World Bank Log Real per Capita GDP in constant 2000 US dollars. Population World Bank Country’s population. Location of Immigrants Instituto Nacional de Estadística Number of immigrants in each municipality by country of origin. European Union 1 if the country belongs to the European Union. Doing Business Index World Bank Ranking of economies that assess business regulations and their enforcement. Contract Enforcement World Bank Number of procedures required to enforce a contract. Time to Import World Bank Number of calendar days necessary to comply with all the procedures required to import goods. Notes: This table shows the definitions and sources of the main variables used throughout the article. Table A2 List of destinations Country N Localization Country N Localization Country N Localization Afghanistan 13 0.0000 Gambia 20 0.0000 The Netherlands 3010 0.0190 Albania 168 0.1108 Georgia 101 0.0025 New Caledonia 34 0.0000 Algeria 826 0.0027 Germany 6427 0.0174 New Zealand 278 0.0036 Andorra 887 0.0903 Ghana 72 0.0000 Nicaragua 63 0.0000 Angola 89 0.0000 Gibraltar 90 0.0000 Niger 44 0.0000 Antigua and Barbuda 20 0.0000 Greece 1768 0.0178 Nigeria 99 0.0560 Argentina 818 0.0100 Guam 10 0.0000 Norway 733 0.0004 Armenia 53 0.0794 Guatemala 226 0.0000 Oman 103 0.0017 Aruba 19 0.0876 Guinea 19 0.0000 Pakistan 189 0.0407 Australia 795 0.0128 Haiti 13 0.0000 Panama 492 0.0000 Austria 1620 0.0148 Honduras 107 0.0000 Paraguay 67 0.0193 Azerbaijan 28 0.0024 Hong Kong SAR, China 708 0.0315 Peru 435 0.0001 Bahamas, The 48 0.0000 Hungary 778 0.0276 Philippines 191 0.0096 Bahrain 154 0.0329 Iceland 160 0.0001 Poland 1627 0.0182 Bangladesh 67 0.0285 India 643 0.0232 Portugal 6861 0.0000 Barbados 32 0.0000 Indonesia 228 0.0136 Qatar 212 0.0445 Belarus 89 0.0000 Iran, Islamic Rep. 463 0.0109 Romania 902 0.0205 Belgium 3500 0.0217 Iraq 23 0.1835 Russian Federation 915 0.0189 Belize 79 0.0089 Ireland 1308 0.0000 San Marino 19 0.0000 Benin 26 0.0000 Israel 732 0.0307 Saudi Arabia 854 0.0134 Bermuda 16 0.0000 Italy 5166 0.0116 Senegal 86 0.0000 Bolivia 96 0.0000 Jamaica 48 0.0085 Serbia 178 0.0185 Bosnia and Herzegovina 103 0.0422 Japan 829 0.0222 Seychelles 31 0.0000 Brazil 1048 0.0269 Jordan 358 0.0154 Sierra Leone 15 0.0313 British Virgin Islands 124 0.0087 Kazakhstan 102 0.0000 Singapore 429 0.0335 Bulgaria 511 0.0151 Kenya 81 0.0000 Slovak Republic 447 0.0099 Burkina Faso 30 0.0000 Korea, Dem. People’s Rep. 38 0.0000 Slovenia 412 0.0224 Cabo Verde 22 0.0000 Korea, Rep. 561 0.0271 Solomon Islands 11 0.0000 Cameroon 47 0.0000 Kuwait 335 0.0331 South Africa 729 0.0262 Canada 848 0.0057 Kyrgyz Republic 188 0.0114 Sri Lanka 50 0.0000 Cayman Islands 20 0.0000 Latvia 679 0.0255 Sudan 43 0.0163 Chad 18 0.1599 Lebanon 494 0.0139 Suriname 18 0.1829 Chile 872 0.0001 Libya 152 0.0127 Swaziland 21 0.0359 China 1007 0.0102 Liechtenstein 56 0.0159 Sweden 1264 0.0051 Colombia 642 0.0117 Lithuania 642 0.0137 Switzerland 2095 0.0112 Congo, Dem. Rep. 19 0.0000 Luxembourg 282 0.0080 Syrian Arab Republic 166 0.0339 Congo, Rep. 15 0.0000 Macao SAR, China 34 0.0010 Taiwan, China 458 0.0229 Costa Rica 274 0.0000 Macedonia, FYR 66 0.0732 Tanzania 42 0.0844 Croatia 328 0.0147 Madagascar 23 0.0000 Thailand 343 0.0204 Cuba 314 0.0000 Malaysia 281 0.0026 Togo 17 0.0000 Cyprus 708 0.0236 Mali 22 0.0000 Trinidad and Tobago 97 0.0000 Czech Republic 1080 0.0245 Malta 247 0.0121 Tunisia 743 0.0061 Côte d’Ivoire 76 0.0009 Marshall Islands 10 0.0011 Turkey 1385 0.0367 Denmark 1286 0.0184 Mauritania 56 0.0000 Uganda 13 0.0000 Dominica 80 0.0000 Mauritius 65 0.0000 Ukraine 311 0.0052 Dominican Republic 456 0.0000 Mexico 1964 0.0051 United Arab Emirates 962 0.0086 Ecuador 337 0.0008 Micronesia, Fed. Sts. 30 0.0487 United Kingdom 5521 0.0120 Egypt, Arab Rep. 588 0.0254 Moldova 45 0.0000 United States 3882 0.0106 El Salvador 128 0.0000 Monaco 99 0.0348 Uruguay 247 0.0091 Equatorial Guinea 60 0.0000 Montenegro 61 0.0958 Uzbekistan 10 0.0000 Estonia 380 0.0254 Morocco 1703 0.0000 Venezuela, RB 675 0.0056 Ethiopia 28 0.0000 Mozambique 19 0.0000 Vietnam 128 0.0005 Finland 897 0.0029 Namibia 38 0.0154 Virgin Islands (U.S.) 22 0.0068 France 8946 0.0250 Nauru 15 0.0000 West Bank and Gaza 36 0.2886 French Polynesia 20 0.0000 Nepal 12 0.0000 Yemen, Rep. 86 0.0313 Gabon 41 0.0000 Country N Localization Country N Localization Country N Localization Afghanistan 13 0.0000 Gambia 20 0.0000 The Netherlands 3010 0.0190 Albania 168 0.1108 Georgia 101 0.0025 New Caledonia 34 0.0000 Algeria 826 0.0027 Germany 6427 0.0174 New Zealand 278 0.0036 Andorra 887 0.0903 Ghana 72 0.0000 Nicaragua 63 0.0000 Angola 89 0.0000 Gibraltar 90 0.0000 Niger 44 0.0000 Antigua and Barbuda 20 0.0000 Greece 1768 0.0178 Nigeria 99 0.0560 Argentina 818 0.0100 Guam 10 0.0000 Norway 733 0.0004 Armenia 53 0.0794 Guatemala 226 0.0000 Oman 103 0.0017 Aruba 19 0.0876 Guinea 19 0.0000 Pakistan 189 0.0407 Australia 795 0.0128 Haiti 13 0.0000 Panama 492 0.0000 Austria 1620 0.0148 Honduras 107 0.0000 Paraguay 67 0.0193 Azerbaijan 28 0.0024 Hong Kong SAR, China 708 0.0315 Peru 435 0.0001 Bahamas, The 48 0.0000 Hungary 778 0.0276 Philippines 191 0.0096 Bahrain 154 0.0329 Iceland 160 0.0001 Poland 1627 0.0182 Bangladesh 67 0.0285 India 643 0.0232 Portugal 6861 0.0000 Barbados 32 0.0000 Indonesia 228 0.0136 Qatar 212 0.0445 Belarus 89 0.0000 Iran, Islamic Rep. 463 0.0109 Romania 902 0.0205 Belgium 3500 0.0217 Iraq 23 0.1835 Russian Federation 915 0.0189 Belize 79 0.0089 Ireland 1308 0.0000 San Marino 19 0.0000 Benin 26 0.0000 Israel 732 0.0307 Saudi Arabia 854 0.0134 Bermuda 16 0.0000 Italy 5166 0.0116 Senegal 86 0.0000 Bolivia 96 0.0000 Jamaica 48 0.0085 Serbia 178 0.0185 Bosnia and Herzegovina 103 0.0422 Japan 829 0.0222 Seychelles 31 0.0000 Brazil 1048 0.0269 Jordan 358 0.0154 Sierra Leone 15 0.0313 British Virgin Islands 124 0.0087 Kazakhstan 102 0.0000 Singapore 429 0.0335 Bulgaria 511 0.0151 Kenya 81 0.0000 Slovak Republic 447 0.0099 Burkina Faso 30 0.0000 Korea, Dem. People’s Rep. 38 0.0000 Slovenia 412 0.0224 Cabo Verde 22 0.0000 Korea, Rep. 561 0.0271 Solomon Islands 11 0.0000 Cameroon 47 0.0000 Kuwait 335 0.0331 South Africa 729 0.0262 Canada 848 0.0057 Kyrgyz Republic 188 0.0114 Sri Lanka 50 0.0000 Cayman Islands 20 0.0000 Latvia 679 0.0255 Sudan 43 0.0163 Chad 18 0.1599 Lebanon 494 0.0139 Suriname 18 0.1829 Chile 872 0.0001 Libya 152 0.0127 Swaziland 21 0.0359 China 1007 0.0102 Liechtenstein 56 0.0159 Sweden 1264 0.0051 Colombia 642 0.0117 Lithuania 642 0.0137 Switzerland 2095 0.0112 Congo, Dem. Rep. 19 0.0000 Luxembourg 282 0.0080 Syrian Arab Republic 166 0.0339 Congo, Rep. 15 0.0000 Macao SAR, China 34 0.0010 Taiwan, China 458 0.0229 Costa Rica 274 0.0000 Macedonia, FYR 66 0.0732 Tanzania 42 0.0844 Croatia 328 0.0147 Madagascar 23 0.0000 Thailand 343 0.0204 Cuba 314 0.0000 Malaysia 281 0.0026 Togo 17 0.0000 Cyprus 708 0.0236 Mali 22 0.0000 Trinidad and Tobago 97 0.0000 Czech Republic 1080 0.0245 Malta 247 0.0121 Tunisia 743 0.0061 Côte d’Ivoire 76 0.0009 Marshall Islands 10 0.0011 Turkey 1385 0.0367 Denmark 1286 0.0184 Mauritania 56 0.0000 Uganda 13 0.0000 Dominica 80 0.0000 Mauritius 65 0.0000 Ukraine 311 0.0052 Dominican Republic 456 0.0000 Mexico 1964 0.0051 United Arab Emirates 962 0.0086 Ecuador 337 0.0008 Micronesia, Fed. Sts. 30 0.0487 United Kingdom 5521 0.0120 Egypt, Arab Rep. 588 0.0254 Moldova 45 0.0000 United States 3882 0.0106 El Salvador 128 0.0000 Monaco 99 0.0348 Uruguay 247 0.0091 Equatorial Guinea 60 0.0000 Montenegro 61 0.0958 Uzbekistan 10 0.0000 Estonia 380 0.0254 Morocco 1703 0.0000 Venezuela, RB 675 0.0056 Ethiopia 28 0.0000 Mozambique 19 0.0000 Vietnam 128 0.0005 Finland 897 0.0029 Namibia 38 0.0154 Virgin Islands (U.S.) 22 0.0068 France 8946 0.0250 Nauru 15 0.0000 West Bank and Gaza 36 0.2886 French Polynesia 20 0.0000 Nepal 12 0.0000 Yemen, Rep. 86 0.0313 Gabon 41 0.0000 Notes: N denotes the number of exporters and Localization is the country index of localization, which measures the amount of geographical concentration exhibiting exporters to each destination. Table A2 List of destinations Country N Localization Country N Localization Country N Localization Afghanistan 13 0.0000 Gambia 20 0.0000 The Netherlands 3010 0.0190 Albania 168 0.1108 Georgia 101 0.0025 New Caledonia 34 0.0000 Algeria 826 0.0027 Germany 6427 0.0174 New Zealand 278 0.0036 Andorra 887 0.0903 Ghana 72 0.0000 Nicaragua 63 0.0000 Angola 89 0.0000 Gibraltar 90 0.0000 Niger 44 0.0000 Antigua and Barbuda 20 0.0000 Greece 1768 0.0178 Nigeria 99 0.0560 Argentina 818 0.0100 Guam 10 0.0000 Norway 733 0.0004 Armenia 53 0.0794 Guatemala 226 0.0000 Oman 103 0.0017 Aruba 19 0.0876 Guinea 19 0.0000 Pakistan 189 0.0407 Australia 795 0.0128 Haiti 13 0.0000 Panama 492 0.0000 Austria 1620 0.0148 Honduras 107 0.0000 Paraguay 67 0.0193 Azerbaijan 28 0.0024 Hong Kong SAR, China 708 0.0315 Peru 435 0.0001 Bahamas, The 48 0.0000 Hungary 778 0.0276 Philippines 191 0.0096 Bahrain 154 0.0329 Iceland 160 0.0001 Poland 1627 0.0182 Bangladesh 67 0.0285 India 643 0.0232 Portugal 6861 0.0000 Barbados 32 0.0000 Indonesia 228 0.0136 Qatar 212 0.0445 Belarus 89 0.0000 Iran, Islamic Rep. 463 0.0109 Romania 902 0.0205 Belgium 3500 0.0217 Iraq 23 0.1835 Russian Federation 915 0.0189 Belize 79 0.0089 Ireland 1308 0.0000 San Marino 19 0.0000 Benin 26 0.0000 Israel 732 0.0307 Saudi Arabia 854 0.0134 Bermuda 16 0.0000 Italy 5166 0.0116 Senegal 86 0.0000 Bolivia 96 0.0000 Jamaica 48 0.0085 Serbia 178 0.0185 Bosnia and Herzegovina 103 0.0422 Japan 829 0.0222 Seychelles 31 0.0000 Brazil 1048 0.0269 Jordan 358 0.0154 Sierra Leone 15 0.0313 British Virgin Islands 124 0.0087 Kazakhstan 102 0.0000 Singapore 429 0.0335 Bulgaria 511 0.0151 Kenya 81 0.0000 Slovak Republic 447 0.0099 Burkina Faso 30 0.0000 Korea, Dem. People’s Rep. 38 0.0000 Slovenia 412 0.0224 Cabo Verde 22 0.0000 Korea, Rep. 561 0.0271 Solomon Islands 11 0.0000 Cameroon 47 0.0000 Kuwait 335 0.0331 South Africa 729 0.0262 Canada 848 0.0057 Kyrgyz Republic 188 0.0114 Sri Lanka 50 0.0000 Cayman Islands 20 0.0000 Latvia 679 0.0255 Sudan 43 0.0163 Chad 18 0.1599 Lebanon 494 0.0139 Suriname 18 0.1829 Chile 872 0.0001 Libya 152 0.0127 Swaziland 21 0.0359 China 1007 0.0102 Liechtenstein 56 0.0159 Sweden 1264 0.0051 Colombia 642 0.0117 Lithuania 642 0.0137 Switzerland 2095 0.0112 Congo, Dem. Rep. 19 0.0000 Luxembourg 282 0.0080 Syrian Arab Republic 166 0.0339 Congo, Rep. 15 0.0000 Macao SAR, China 34 0.0010 Taiwan, China 458 0.0229 Costa Rica 274 0.0000 Macedonia, FYR 66 0.0732 Tanzania 42 0.0844 Croatia 328 0.0147 Madagascar 23 0.0000 Thailand 343 0.0204 Cuba 314 0.0000 Malaysia 281 0.0026 Togo 17 0.0000 Cyprus 708 0.0236 Mali 22 0.0000 Trinidad and Tobago 97 0.0000 Czech Republic 1080 0.0245 Malta 247 0.0121 Tunisia 743 0.0061 Côte d’Ivoire 76 0.0009 Marshall Islands 10 0.0011 Turkey 1385 0.0367 Denmark 1286 0.0184 Mauritania 56 0.0000 Uganda 13 0.0000 Dominica 80 0.0000 Mauritius 65 0.0000 Ukraine 311 0.0052 Dominican Republic 456 0.0000 Mexico 1964 0.0051 United Arab Emirates 962 0.0086 Ecuador 337 0.0008 Micronesia, Fed. Sts. 30 0.0487 United Kingdom 5521 0.0120 Egypt, Arab Rep. 588 0.0254 Moldova 45 0.0000 United States 3882 0.0106 El Salvador 128 0.0000 Monaco 99 0.0348 Uruguay 247 0.0091 Equatorial Guinea 60 0.0000 Montenegro 61 0.0958 Uzbekistan 10 0.0000 Estonia 380 0.0254 Morocco 1703 0.0000 Venezuela, RB 675 0.0056 Ethiopia 28 0.0000 Mozambique 19 0.0000 Vietnam 128 0.0005 Finland 897 0.0029 Namibia 38 0.0154 Virgin Islands (U.S.) 22 0.0068 France 8946 0.0250 Nauru 15 0.0000 West Bank and Gaza 36 0.2886 French Polynesia 20 0.0000 Nepal 12 0.0000 Yemen, Rep. 86 0.0313 Gabon 41 0.0000 Country N Localization Country N Localization Country N Localization Afghanistan 13 0.0000 Gambia 20 0.0000 The Netherlands 3010 0.0190 Albania 168 0.1108 Georgia 101 0.0025 New Caledonia 34 0.0000 Algeria 826 0.0027 Germany 6427 0.0174 New Zealand 278 0.0036 Andorra 887 0.0903 Ghana 72 0.0000 Nicaragua 63 0.0000 Angola 89 0.0000 Gibraltar 90 0.0000 Niger 44 0.0000 Antigua and Barbuda 20 0.0000 Greece 1768 0.0178 Nigeria 99 0.0560 Argentina 818 0.0100 Guam 10 0.0000 Norway 733 0.0004 Armenia 53 0.0794 Guatemala 226 0.0000 Oman 103 0.0017 Aruba 19 0.0876 Guinea 19 0.0000 Pakistan 189 0.0407 Australia 795 0.0128 Haiti 13 0.0000 Panama 492 0.0000 Austria 1620 0.0148 Honduras 107 0.0000 Paraguay 67 0.0193 Azerbaijan 28 0.0024 Hong Kong SAR, China 708 0.0315 Peru 435 0.0001 Bahamas, The 48 0.0000 Hungary 778 0.0276 Philippines 191 0.0096 Bahrain 154 0.0329 Iceland 160 0.0001 Poland 1627 0.0182 Bangladesh 67 0.0285 India 643 0.0232 Portugal 6861 0.0000 Barbados 32 0.0000 Indonesia 228 0.0136 Qatar 212 0.0445 Belarus 89 0.0000 Iran, Islamic Rep. 463 0.0109 Romania 902 0.0205 Belgium 3500 0.0217 Iraq 23 0.1835 Russian Federation 915 0.0189 Belize 79 0.0089 Ireland 1308 0.0000 San Marino 19 0.0000 Benin 26 0.0000 Israel 732 0.0307 Saudi Arabia 854 0.0134 Bermuda 16 0.0000 Italy 5166 0.0116 Senegal 86 0.0000 Bolivia 96 0.0000 Jamaica 48 0.0085 Serbia 178 0.0185 Bosnia and Herzegovina 103 0.0422 Japan 829 0.0222 Seychelles 31 0.0000 Brazil 1048 0.0269 Jordan 358 0.0154 Sierra Leone 15 0.0313 British Virgin Islands 124 0.0087 Kazakhstan 102 0.0000 Singapore 429 0.0335 Bulgaria 511 0.0151 Kenya 81 0.0000 Slovak Republic 447 0.0099 Burkina Faso 30 0.0000 Korea, Dem. People’s Rep. 38 0.0000 Slovenia 412 0.0224 Cabo Verde 22 0.0000 Korea, Rep. 561 0.0271 Solomon Islands 11 0.0000 Cameroon 47 0.0000 Kuwait 335 0.0331 South Africa 729 0.0262 Canada 848 0.0057 Kyrgyz Republic 188 0.0114 Sri Lanka 50 0.0000 Cayman Islands 20 0.0000 Latvia 679 0.0255 Sudan 43 0.0163 Chad 18 0.1599 Lebanon 494 0.0139 Suriname 18 0.1829 Chile 872 0.0001 Libya 152 0.0127 Swaziland 21 0.0359 China 1007 0.0102 Liechtenstein 56 0.0159 Sweden 1264 0.0051 Colombia 642 0.0117 Lithuania 642 0.0137 Switzerland 2095 0.0112 Congo, Dem. Rep. 19 0.0000 Luxembourg 282 0.0080 Syrian Arab Republic 166 0.0339 Congo, Rep. 15 0.0000 Macao SAR, China 34 0.0010 Taiwan, China 458 0.0229 Costa Rica 274 0.0000 Macedonia, FYR 66 0.0732 Tanzania 42 0.0844 Croatia 328 0.0147 Madagascar 23 0.0000 Thailand 343 0.0204 Cuba 314 0.0000 Malaysia 281 0.0026 Togo 17 0.0000 Cyprus 708 0.0236 Mali 22 0.0000 Trinidad and Tobago 97 0.0000 Czech Republic 1080 0.0245 Malta 247 0.0121 Tunisia 743 0.0061 Côte d’Ivoire 76 0.0009 Marshall Islands 10 0.0011 Turkey 1385 0.0367 Denmark 1286 0.0184 Mauritania 56 0.0000 Uganda 13 0.0000 Dominica 80 0.0000 Mauritius 65 0.0000 Ukraine 311 0.0052 Dominican Republic 456 0.0000 Mexico 1964 0.0051 United Arab Emirates 962 0.0086 Ecuador 337 0.0008 Micronesia, Fed. Sts. 30 0.0487 United Kingdom 5521 0.0120 Egypt, Arab Rep. 588 0.0254 Moldova 45 0.0000 United States 3882 0.0106 El Salvador 128 0.0000 Monaco 99 0.0348 Uruguay 247 0.0091 Equatorial Guinea 60 0.0000 Montenegro 61 0.0958 Uzbekistan 10 0.0000 Estonia 380 0.0254 Morocco 1703 0.0000 Venezuela, RB 675 0.0056 Ethiopia 28 0.0000 Mozambique 19 0.0000 Vietnam 128 0.0005 Finland 897 0.0029 Namibia 38 0.0154 Virgin Islands (U.S.) 22 0.0068 France 8946 0.0250 Nauru 15 0.0000 West Bank and Gaza 36 0.2886 French Polynesia 20 0.0000 Nepal 12 0.0000 Yemen, Rep. 86 0.0313 Gabon 41 0.0000 Notes: N denotes the number of exporters and Localization is the country index of localization, which measures the amount of geographical concentration exhibiting exporters to each destination. Appendix B. Details on the Application of Duranton and Overman (2005) In this section we explain in detail the application of DO to uncover agglomeration by export destination. We proceed as follows. For each export destination we compute the unique bilateral distances between exporters by applying the haversine formula to the zip code coordinates. Next, we estimate the distribution of bilateral distances of each country via kernel estimation. As in DO, we use a Gaussian kernel, choosing the bandwidth so as to minimize the mean integrated squared error. Distances are reflected around zero, using the method proposed by Silverman (1986) to avoid giving positive densities to negative distances. Note also that firms within the same zip code are computed as being separated by 0 kilometers. The kernel density estimation for country c at every kilometer d ( K^c(d)) reads as follows: Kc^(d)=2nc(nc−1)h∑i=1nc−1∑j=i+1ncf(d−di,jh), (2) where nc is the number of export firms to country c, h is the bandwidth and f is the Gaussian probability density function.15Figure 8A shows the spatial distribution of exporters to India in 2007. The existence of several clusters of exporters is apparent. Figure 8B plots the histogram and the kernel estimation of the distance distribution. The high density at very small distances stems from the large number of exporters that are located within very close zip codes. The second peak in the distribution at around 400 kilometers marks the distance that separates the clusters. Figure 8 View largeDownload slide Distribution of distances of firms exporting to India. Notes: This figure plots the spatial distribution of exporters to India in 2007 (A) and the histogram of the unique bilateral distances between them as well as the kernel estimate of the probability density function (B). Figure 8 View largeDownload slide Distribution of distances of firms exporting to India. Notes: This figure plots the spatial distribution of exporters to India in 2007 (A) and the histogram of the unique bilateral distances between them as well as the kernel estimate of the probability density function (B). To test for significant agglomeration, the observed spatial distribution is compared with the counterfactual. As stated in the main text, the counterfactual controls for both the spatial distribution of exporters, which may be agglomerated with respect to domestic firms, as well as the industry composition of exports to each country. We proceed as follows. For each pair of country and two-digit industry, we draw 1000 random samples from exporters in the industry; each draw of size the actual number of exporters to the country operating in that industry. Then, for each country we aggregate each draw across the different industries to collect 1000 random samples of size nc (the actual number of exporters to the country) with an industry composition that replicates the one observed in the data. In our baseline analysis we carry out the test of significant agglomeration at distances below 100 km. As in DO we construct two tests, one of localization and one of dispersion, both with a significance level of 95%. We do the following. For each kilometer, we rank our 1000 counterfactual distributions in ascending order and then pick the percentile that makes 95% of the counterfactual distributions lie below it across all distances. When it is not possible to find a percentile making exactly 95% of the simulations be below it, we use linear interpolation. Note that in our baseline results all the percentiles fulfilling this criterion range between the 96.4-th and the 99.5-th. This percentile is referred to as the localization threshold, whereas a dispersion threshold is defined in a similar way, i.e. the percentile that makes 5% of the counterfactual distributions lie below it across all distances. Note that in DO the localization and dispersion thresholds are referred to as global confidence bands. The Figure 9A plots the density of the distance distribution below 100 km and a small sample of the counterfactual distributions. The Figure 9B displays the localization and dispersion thresholds (the upper and lower dashed lines, respectively). Figure 9 View largeDownload slide Localization and dispersion thresholds of India. Notes: (A) shows the estimated distance density of exporters to India in 2007 as well as a sample of 20 counterfactual distributions. (B) Displays the localization and dispersion thresholds, represented by the upper and lower dashed lines, respectively. The shaded area between the distance distribution and the localization threshold constitutes the country index of agglomeration. Figure 9 View largeDownload slide Localization and dispersion thresholds of India. Notes: (A) shows the estimated distance density of exporters to India in 2007 as well as a sample of 20 counterfactual distributions. (B) Displays the localization and dispersion thresholds, represented by the upper and lower dashed lines, respectively. The shaded area between the distance distribution and the localization threshold constitutes the country index of agglomeration. We define exporters to a destination to be significantly localized if the distance distribution is above the localization threshold in at least one kilometer. Similarly, exporters to a destination are defined to be dispersed if the distance distribution is below the dispersion threshold in at least one kilometer and the country does not exhibit localization. Note that the latter condition stems from the fact that densities sum up to one, hence localization at some distances implies dispersion at others. Following these criteria, Figure 9B shows that exporters to India are significantly localized. Note finally that localization and dispersion can be assessed at each distance, by comparing the distance distribution and the thresholds at each kilometer. In the example, localization takes place at distances below 70 km. Finally, we define a country index of agglomeration as the sum across distances of the difference between the distance distribution and the localization threshold if the former is above the latter and zero otherwise. This index accounts for the amount of exporter agglomeration by destination and it is the counterpart of the industry index of agglomeration defined by DO. In Figure 9B it is depicted as the shaded area between the distance density and the upper dashed line. Appendix C. Details on the Principal Component Analysis In Section 4, we create an index of institutional quality by applying a principal component analysis (PCA) on the World Bank Worldwide Governance Indicators (WGI). These measures account for six dimensions of governance, namely voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption, see Kaufmann et al. (2010). Given the high correlation between these measures, a PCA is a useful tool to summarize all the information and construct a synthetic index that accounts for the overall institutional quality in each country. The PCA finds a set of uncorrelated linear combinations of the measures that accounts for most of the variance. In our case, the first of such combinations accounts for 88% of the variance and constitutes the index of institutional quality that we include in Table 3. Figure 10 plots the correlation of the institutional quality index with the rule of law (WGI), the Doing Business Index, investor protection and time to import goods. The high correlations suggest that our index of institutional quality provides a good approximation of the business environment that firms face when exporting to every foreign country. Figure 10 View largeDownload slide Correlation of the synthetic index of institutional quality (PCA) with other measures. This figure shows the correlation of our synthetic index of institutional quality, computed from a PCA on the Worldwide Governance Indicators, with other measures proxying the institutional environment of every country. Figure 10 View largeDownload slide Correlation of the synthetic index of institutional quality (PCA) with other measures. This figure shows the correlation of our synthetic index of institutional quality, computed from a PCA on the Worldwide Governance Indicators, with other measures proxying the institutional environment of every country. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Economic Geography Oxford University Press

Agglomeration by export destination: evidence from Spain

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© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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1468-2702
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1468-2710
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10.1093/jeg/lbx038
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Abstract

Abstract We use a dataset of Spanish exporters with rich spatial information to document the existence of agglomeration economies by export destination. More specifically, we show that, for a large set of export destinations, exporters are geographically too close to be the result of a random outcome. We also analyze the variables that explain the cross-destination heterogeneity in agglomeration. We find that firms selling to countries with worse institutions, a dissimilar language and a different currency are significantly more agglomerated. These results suggest that the value provided by agglomeration is higher concerning destinations where entry is more difficult. 1. Introduction It is now a well-established fact that a large amount of industries are geographically concentrated. This finding together with the observation that firms are on average more productive in denser areas have attracted much attention from economists and policy-makers, who have built a large body of research on the foundations and effects of agglomeration economies. In this article we study a specific form of these agglomeration economies, namely those accruing to export firms. Our main contribution is to uncover the fact that exporters are geographically concentrated by export destination, which is consistent with the existence of agglomeration economies associated with the process of selling abroad. Reaching foreign markets entails additional costs, hence the traditional forces leading to industry agglomeration, namely sharing, matching and learning mechanisms, see Marshall (1920) and Duranton and Puga (2004), apply to this process, and naturally vary by export destination. We are not the first to emphasize the existence of destination-specific export spillovers. In a regression framework, Koenig (2009), Koenig et al. (2010) and Choquette and Meinen (2015) show that the decision to sell to a certain destination is positively affected by the pool of local exporters selling to that destination, while Cassey and Schmeiser (2013) show evidence of clustering by export destination in Russian regions. Our main step forward in this literature is to document the existence of destination-specific agglomeration economies using a firm level dataset with rich spatial information that allows us to apply a nonparametric standard test of agglomeration: Duranton and Overman (2005). This method improved on previous approaches, such as Ellison and Glaeser (1997), Maurel and Sédillot (1999), and Devereux et al. (2004), for two reasons. First, it treats space as continuous, as opposed to using an arbitrary set of spatial units. And second, it allows assessing the statistical significance of departures from randomness.1 The use of the Duranton and Overman (2005) test allows us to refine and expand the results of the extant literature in two fundamental ways. First, we uncover destination-specific exporter agglomeration beyond the overall concentration of exporters with respect to domestic firms and the industry agglomeration of exports to each country. That is, for each destination the counterfactual is built solely from exporters operating in the industries exported to the destination. This addresses a crucial issue: industries are geographically concentrated, and different countries demand goods from different industries. Then, agglomeration by export destination might be the result of countries buying goods intensively from agglomerated industries. We show that exporters are significantly concentrated over and above what would be expected from the fact that exporters to individual countries are concentrated by sector and sectors are concentrated geographically. Furthermore, by restricting the analysis to export firms, we account for different location patterns between domestic and export firms. And second, we compute a continuous index measuring the extent of geographical agglomeration of exporters to each destination. This allows us to investigate the characteristics that explain the cross-destination heterogeneity in agglomeration levels. Our baseline results indicate that for more than half of export destinations exporters are significantly concentrated, i.e. they are too close to be the result of a random outcome.2 We also perform a battery of robustness checks to account for other mechanisms that would result in agglomeration by export destination without relying on export-destination spillovers. There are two mechanisms that are worth emphasizing. First, large firms are able to reach a larger set of destinations, hence agglomeration by destination could be the result of large exporters being concentrated with respect to small exporters. And second, it is documented that firms go hierarchically to more and more destinations. Then agglomeration by popular destinations could induce agglomeration by less popular destinations if exporters to the former disproportionately export to the latter. By restricting the counterfactual, we show that our results are not the mechanical consequence of these and other mechanisms that would result in spurious agglomeration. Our results also show that the level of agglomeration varies meaningfully across destinations. We find that exporters to countries with a dissimilar language, lower institutional quality and a different currency are significantly more agglomerated. We interpret this finding as evidence suggesting that agglomeration provides higher value in countries where entry is more difficult. Overall, our findings are consistent with the existence of externalities in the process of selling to some countries. Although thus far the literature has been to some extent unable to empirically verify the specific driving mechanisms, several possibilities have been rationalized in theoretical models.3 For example, Segura-Cayuela and Vilarrubia (2008) and Fernandes and Tang (2014) emphasize that firms entering foreign markets reveal information, hence reducing the uncertainty faced by potential entrants. Also, Krautheim (2012) and Cassey and Schmeiser (2013) explore the channel of cost reductions brought by a larger number of exporters in the setting of the Melitz (2003) model. On the empirical front, our results are consistent with Lovely et al. (2005), who show, using the Ellison and Glaeser index, that US exporter headquarter activity is more agglomerated when firms sell to countries less integrated in the world economy and with worse credit ratings. Moreover, Koenig (2009), Wagner and Zahler (2015) and Cadot et al. (2013) show that the presence of neighboring export firms and pioneers in foreign markets are significantly associated with a higher probability of foreign entry, suggesting that the flow of information between nearby firms and signals revealed by successful exporters are important in this context. Also, a recent contribution by Paravisini et al. (2015) finds that the distribution of bank lending is skewed toward firms exporting to the same destination, which suggests that agglomeration may be associated with credit markets. We are able to link the observed patterns of concentration across destinations to cultural and institutional differences across importing countries. Beyond this, our article also remains silent on the specific mechanisms driving the clustering by export destination. Yet, our findings as well as those of the literature give new understandings regarding the behavior of export firms and provide useful policy insights on how to help firms access foreign markets. The rest of the article is organized as follows. Section 2 describes the dataset. Section 3 explains the methodology, presents the baseline results and performs a set of robustness checks. Section 4 delves into the determinants of the cross-destination variation in agglomeration levels. Section 5 concludes. 2. Data We use the firm-level data compiled by the Bank of Spain to construct the Balance of Payments statistics for Spain. The dataset contains information on firms making transactions with foreign agents if they are worth more than €12,000 and they are performed through a bank. Therefore, the dataset is likely to exclude only the smallest exporters. In the baseline analysis, we rely on 2007 data and we use previous years to check the stability of the results over time.4 The dataset has several advantages for the study of the geographical location of exporters. First, it is made up from administrative records and it has a large coverage. For example, it accounts for 97% of aggregate exports in 2007 and both transactions within the EU and to third countries are observed. Second, it contains information on total sales of every firm to each export destination. And third, it provides the zip code of every exporter. Hence, we can compute distances between firms and study the agglomeration of export firms by destination. The zip code provided is that of the headquarters, thus our focus in on headquarter agglomeration rather than on establishment agglomeration. We argue that this feature is likely to play a small role in the results, as we estimate that around 91% of exporters in our data have just one plant.5 Moreover, Koenig (2009), Koenig et al. (2010) and Choquette and Meinen (2015) show that export spillovers are not affected by including either single-plant exporters or the headquarter of all exporters. Having said this, focusing on headquarters points to externalities stemming from information flows, rather than cost-sharing mechanisms, which are more likely to be linked to establishments. There is one limitation of the dataset that is worth noting: it has no information on the type of goods being traded. Rather, it only provides the exporter four-digit industry code. In our baseline results, we rely on two-digit industry codes to control for the industry composition of exports. Focusing on two-digit industries provides sufficiently large bins for drawing the counterfactual exporters, which increases the precision of the confidence bands. Yet, it makes us account for the varieties being exported only to a certain extent (see also Section 3.3.5). Besides, the dataset does not include firm characteristics beyond industry, fiscal id and total exports to each country. For this reason, for the robustness check described in Section 3.3.1, we approximate firm size with total firm exports. Table 1 shows some descriptive statistics of the exporters included in our dataset in 2007. Our analysis is restricted to manufacturing firms and export destinations with at least 10 exporters. The data include more than 18,000 exporters located in close to 3200 zip codes, out of a total of around 11,000 zip codes in Spain. The median exporter sells to two destinations and the median zip code hosts two export firms. See also Table A1 in the Appendix for a description of the main variables used in the article and Table A2 for the destinations included in the sample. Table 1 Descriptive statistics: exporters in 2007 (balance of payments) Mean Percentiles (Std. Dev.) 25 50 75 (1) (2) (3) (4) Panel A: exporters (N = 18,715) Total exports (thousand €) 6745 54 255 1435 (100,757) Destinations (N = 166) 5.19 1 2 6 (7.82) Panel B: zip codes (N = 3192) Number exporters 5.89 1 2 6 (10.05) Mean Percentiles (Std. Dev.) 25 50 75 (1) (2) (3) (4) Panel A: exporters (N = 18,715) Total exports (thousand €) 6745 54 255 1435 (100,757) Destinations (N = 166) 5.19 1 2 6 (7.82) Panel B: zip codes (N = 3192) Number exporters 5.89 1 2 6 (10.05) Notes: This table shows descriptive statistics of Spanish manufacturing exporters in 2007 included in the Balance of Payments micro data. The panel A shows statistics of total exports and number of export destinations per exporter. The panel B shows moments of the distribution of the number of export firms located in the zip codes hosting at least one export firm. N corresponds to the number of distinct observations. Table 1 Descriptive statistics: exporters in 2007 (balance of payments) Mean Percentiles (Std. Dev.) 25 50 75 (1) (2) (3) (4) Panel A: exporters (N = 18,715) Total exports (thousand €) 6745 54 255 1435 (100,757) Destinations (N = 166) 5.19 1 2 6 (7.82) Panel B: zip codes (N = 3192) Number exporters 5.89 1 2 6 (10.05) Mean Percentiles (Std. Dev.) 25 50 75 (1) (2) (3) (4) Panel A: exporters (N = 18,715) Total exports (thousand €) 6745 54 255 1435 (100,757) Destinations (N = 166) 5.19 1 2 6 (7.82) Panel B: zip codes (N = 3192) Number exporters 5.89 1 2 6 (10.05) Notes: This table shows descriptive statistics of Spanish manufacturing exporters in 2007 included in the Balance of Payments micro data. The panel A shows statistics of total exports and number of export destinations per exporter. The panel B shows moments of the distribution of the number of export firms located in the zip codes hosting at least one export firm. N corresponds to the number of distinct observations. Table 2 Most localized destinations Rank Country N Localization Rank Country N Localization 1 West Bank and Gaza 36 0.29 6 Montenegro 61 0.10 2 Iraq 23 0.18 7 Andorra 887 0.09 3 Suriname 18 0.18 8 Aruba 19 0.09 4 Chad 18 0.16 9 Tanzania 42 0.08 5 Albania 168 0.11 10 Armenia 53 0.08 Rank Country N Localization Rank Country N Localization 1 West Bank and Gaza 36 0.29 6 Montenegro 61 0.10 2 Iraq 23 0.18 7 Andorra 887 0.09 3 Suriname 18 0.18 8 Aruba 19 0.09 4 Chad 18 0.16 9 Tanzania 42 0.08 5 Albania 168 0.11 10 Armenia 53 0.08 Notes: This table shows the 10 destinations to which exporters exhibit the highest level of agglomeration, according to the country index of localization defined in Section 3.1. Table 2 Most localized destinations Rank Country N Localization Rank Country N Localization 1 West Bank and Gaza 36 0.29 6 Montenegro 61 0.10 2 Iraq 23 0.18 7 Andorra 887 0.09 3 Suriname 18 0.18 8 Aruba 19 0.09 4 Chad 18 0.16 9 Tanzania 42 0.08 5 Albania 168 0.11 10 Armenia 53 0.08 Rank Country N Localization Rank Country N Localization 1 West Bank and Gaza 36 0.29 6 Montenegro 61 0.10 2 Iraq 23 0.18 7 Andorra 887 0.09 3 Suriname 18 0.18 8 Aruba 19 0.09 4 Chad 18 0.16 9 Tanzania 42 0.08 5 Albania 168 0.11 10 Armenia 53 0.08 Notes: This table shows the 10 destinations to which exporters exhibit the highest level of agglomeration, according to the country index of localization defined in Section 3.1. 3. Localization of exporters by export destination In this section, we provide evidence that exporters are significantly agglomerated by export destination by applying the methodology developed by Duranton and Overman (2005), henceforth DO. Among other advantages discussed in the introduction, this methodology allows us to account for the fact that exports to a country are concentrated by sector and sectors are concentrated geographically. Furthermore, it allows controlling for other forces leading to agglomeration by export destination, such as the concentration of large vs small exporters, sequential exporting and the concentration of exporters in large cities, which we discuss below. Before turning to the baseline results, we describe briefly our application of DO. 3.1. Methodology: application of Duranton and Overman (2005) In this section, we give a succinct overview on how we use DO to uncover agglomeration by export destination, see Appendix B for a more comprehensive explanation and technical details. In our baseline analysis, we use data from 2007 and we consider the 166 destinations with at least 10 exporters. For each country, we kernel-estimate the distribution of bilateral distances of exporters to the country by applying to the zip code coordinates the haversine formula, which computes the shortest distance over the Earth’s surface. We then compare this distribution with 1000 counterfactual distributions built as follows. For each two-digit industry, we draw 1000 independent random samples from exporters in the industry; each draw of size the actual number of exporters to the country operating in that industry. Then, we aggregate each draw across the different industries to collect 1000 random samples that replicate the industry composition of exports to the country. Note also that the size of each random draw is the same as the actual number of exporters to the country. We then estimate the distance distribution of each random draw. The resulting counterfactual distributions control for two mechanisms that might result in spurious agglomeration by export destination. First, the fact that exporters have special characteristics relative to nonexporters (see, e.g., Bernard et al. (2003)) and hence they may agglomerate with respect to domestic firms. Indeed, Behrens and Bougna (2015) show that this is the case in 14–16% Canadian industries and the literature on export spillovers has documented that pools of local exporters affects positively the decision to enter foreign markets, see for example Koenig (2009). And second, the fact that the industry composition of exports differs across countries. For example, one country may demand heavily goods from an industry that is highly concentrated. Therefore, exporters to this country can be concentrated either because of industry concentration or because of exporter concentration. By making the random draws replicate the destination-specific industry composition of exports, we are able to disentangle the latter from the former. We then rank, for each kilometer, the 1000 counterfactual distributions in ascending order and define a localization threshold as the percentile that makes 95% of the counterfactual distributions lie below it across all distances. Note that to compare the estimated density with the counterfactual distributions we focus on distances below 100 kilometers, which are more relevant to explain interactions between exporters. This distance horizon has no substantial effects on the results, as shown in Section 3.3.5. We define exporters to a country to be significantly localized if the actual distance distribution is above the localization threshold in at least one kilometer. We also define a dispersion threshold as the percentile that makes 5% of the simulations lie below it across all distances. Given that densities must sum up to one, localization at some distances implies dispersion at others. Therefore, we define exporters to a country to be dispersed if the actual distance distribution is below the dispersion threshold in at least one kilometer and the country does not exhibit localization. These definitions follow DO.6 Finally, we also construct the country version of the industry quantitative index of localization defined by DO. This country index is computed as the sum across distances of the difference between the density of the actual distance distribution and the localization threshold if the former is above the latter and zero otherwise. This index gives a measure of the amount of exporter localization by export destination. 3.2. Baseline results In our baseline results we find significant agglomeration for a large number of export destinations: of the 166 countries in our sample, firms exporting to 107 (64%) are significantly agglomerated, whereas only one destination exhibits dispersion. Figure 1A displays the share of destinations in which exporters exhibit significant localization at each distance. Close to 60% of destinations exhibit significant agglomeration at distances below 40 kilometers, this share decreasing fast at larger distances. This pattern resembles that found by DO regarding industry agglomeration. With respect to the amount of agglomeration at each distance, Panel B plots for each kilometer the sum across destinations of the difference between the distance distribution and the localization threshold when the former is above the latter. As can be seen, the largest amount of agglomeration takes place at very small distances. Again, this result is in line with industry agglomeration patterns. Figure 1 View largeDownload slide Localization of exporters by export destination. Notes: This figure shows the pattern of agglomeration by export destination. (A) Displays the share of destinations to which exporters exhibit significant localization at each level of distance, whereas (B) shows the amount of localization at each distance, i.e. the sum across destinations of the difference between the distance distribution and the localization threshold when the former is above the latter. Figure 1 View largeDownload slide Localization of exporters by export destination. Notes: This figure shows the pattern of agglomeration by export destination. (A) Displays the share of destinations to which exporters exhibit significant localization at each level of distance, whereas (B) shows the amount of localization at each distance, i.e. the sum across destinations of the difference between the distance distribution and the localization threshold when the former is above the latter. Table 2 shows the destinations exhibiting the highest country index of agglomeration and Table A2 in the Appendix reports the value of the index for all destinations. The largest amount of agglomeration is found in rather small destinations, accounting for only 1.4% of all firm-country relationships in 2007. However, larger countries, such as the main EU countries and the USA, also exhibit significant agglomeration. Overall, 85.8% of firm-country relationships correspond to significantly localized destinations. It is also worth highlighting that the extent of agglomeration varies widely across destinations, being the standard deviation of the country index twice as large as the mean. At first sight it is not straightforward to uncover a systematic pattern of cross-destination agglomeration, probably due to the fact that several determinants act in opposing directions. For example, some agglomeration is expected to happen next to the border of neighboring countries. Indeed, one destination exhibiting very large agglomeration is Andorra, with which Spain shares a border. However, nearby countries such as those belonging to the EU have good business practices, which undermines the value of agglomeration in overcoming trade barriers. Hence, we would expect exporters to these countries to be less agglomerated. We acknowledge that given the nature of our data it is very hard to disentangle the specific mechanisms driving agglomeration by export destination. Yet in Section 4 we discuss several possibilities and show some evidence via regression analysis. Before this, we perform several robustness checks in the next section. 3.3. Robustness checks In this section we check the sensitivity of the baseline results to restricting the counterfactual. In doing so, we control for some concentration patterns that would result in agglomeration by export destination without relying on export-destination spillovers. Specifically, we take into account the different concentration patterns of large vs small exporters, the role of sequential exporting, the concentration of firms in large cities, the stability of the results over time and others. 3.3.1. Controlling for destinations served by large vs small exporters The heterogeneous firms literature has documented that there is a substantial amount of heterogeneity within exporters, one important dimension of this heterogeneity being size. This is a relevant issue for the study of agglomeration by destination, since it has been documented that large exporters reach a larger set of export destinations, see Helpman et al. (2008) and Eaton et al. (2011). Therefore, our baseline results raise the concern that agglomeration by destination could be the result of large exporters being agglomerated with respect to small exporters. Being this the case, our findings would not exist among exporters of comparable size. We carry out four tests to address this issue. First, we repeat the baseline exercise with subsamples of the largest exporters. Specifically, we consider exporters above the median and above the 75-th percentile of total firm exports. Second, we explicitly control for the size of exporters when building the counterfactual. To be precise, we restrict further the counterfactual by conditioning on 10 and 20 bins of total firm exports. That is, we construct counterfactual distributions from exporters in the same industry and the same size bin, replicating for each destination the distribution of these variables observed in the data. Hence, this procedure estimates destination-specific agglomeration patterns beyond the concentration of exporters, industries and exporters of comparable size.7 Figure 2 shows the results of these tests. Focusing on the sample of the largest exporters (those above percentile 75) reduces the number of agglomerated destinations to 49%, from 64% in the baseline. A similar result arises when we restrict the counterfactual to 10 or 20 bins of total firm exports, see Figure 2A. Hence, some portion of the localization patterns can be explained by the agglomeration of firms of similar size, although this portion is small and the bulk of the baseline results is preserved. Indeed, the destinations to which exporters are no longer significantly agglomerated are those with the lowest degree of agglomeration in the baseline, see the scatter plot in Figure 2B. This suggests that the lower agglomeration levels can be explained (at least partly) by the smaller populations from which the random samples of the counterfactual simulations are drawn. For example, the median number of exporters per industry is 626, whereas the median number of exporters per industry and size bin is 37. Hence, it is reassuring that a more stringent counterfactual preserves the finding of significant agglomeration to a large set of export destinations, in this case to half of the total. Figure 2 View largeDownload slide Localization controlling for exporter size. Notes: This figure compares the baseline results of agglomeration by export destination with those controlling for the size of exporters. (A) Shows the share of localized destinations at each level of distance. The red dashed and the green short-dashed lines are built from samples of the largest exporters: those above the median and the 75 percentile of total firm exports, respectively. The blue dash-dotted and the orange long-dashed lines are obtained from counterfactuals that control for 10 and 20 bins, respectively, of total firm exports (as well as the industry composition of exports). (B) Displays the correlation between the country index of agglomeration and that from the simulations accounting for 20 bins of exporter size. The value of this correlation is 0.98. Figure 2 View largeDownload slide Localization controlling for exporter size. Notes: This figure compares the baseline results of agglomeration by export destination with those controlling for the size of exporters. (A) Shows the share of localized destinations at each level of distance. The red dashed and the green short-dashed lines are built from samples of the largest exporters: those above the median and the 75 percentile of total firm exports, respectively. The blue dash-dotted and the orange long-dashed lines are obtained from counterfactuals that control for 10 and 20 bins, respectively, of total firm exports (as well as the industry composition of exports). (B) Displays the correlation between the country index of agglomeration and that from the simulations accounting for 20 bins of exporter size. The value of this correlation is 0.98. 3.3.2. Controlling for sequential exporting In our empirical specification each destination is treated independently. Given that some firms export to more than one country, this creates a possible dependence of agglomeration patterns across certain markets. Indeed, a large literature has documented that firms go hierarchically to more and more destinations, see for example Albornoz et al. (2012) and Chaney (2014). Therefore, if exporters to one destination are agglomerated and a subset of these exporters disproportionately exports to another destination, this destination will also exhibit agglomeration. We partially addressed this concern when controlling for firm size in the previous subsection, as exporters of the same size are more likely to share some destinations. Here we explore further this issue. First, we inspect if the average number of export destinations of exporters to countries that exhibit significant agglomeration is higher than that of exporters to countries that do not exhibit agglomeration. Finding so would suggest that the large number of agglomerated destinations could be the result of these countries attracting exporters that sell to many (possibly agglomerated) countries. Second, we restrict the counterfactual in the spirit of the previous subsection in order to control for the ability of exporters to sell to more and more difficult destinations. Regarding the first analysis, we find that the distribution of the average number of export destinations of exporters to agglomerated countries lies to the left of the distribution of the average number of export destinations of exporters to countries that do not exhibit agglomeration, see Figure 3. For example, the average number of export destinations of exporters selling to countries that exhibit agglomeration is on average 25.1, whereas this statistic is 27.8 regarding exporters to countries that do not exhibit agglomeration. This provides evidence, at least to a first approximation, against the concern that agglomerated destinations are so because exporters selling there sell also to many other countries. Figure 3 View largeDownload slide Average number of destinations of exporters selling to localized vs nonlocalized destinations. Notes: This figure shows the cross-destination distribution of the average number of destinations reached by exporters. The white bars correspond to countries that exhibit significant agglomeration whereas the gray bars to countries that do not exhibit significant concentration. Figure 3 View largeDownload slide Average number of destinations of exporters selling to localized vs nonlocalized destinations. Notes: This figure shows the cross-destination distribution of the average number of destinations reached by exporters. The white bars correspond to countries that exhibit significant agglomeration whereas the gray bars to countries that do not exhibit significant concentration. Regarding the second exercise, we carry out two additional robustness checks. First, we condition the counterfactual on two bins: exporters selling to only one destination (which represents roughly 40% of exporters) and exporters selling to more than one country. And second, we condition on how ‘difficult’ are the destinations each exporter sells to. We proceed as follows, we rank the destinations in our sample from the highest to the lowest number of exporters, that is, from the most popular to the least popular destinations. We then split the sample in five quintiles, the bottom quintile containing the most popular destinations and the top quintile the least popular. We then assign each exporter to the quintile of the most difficult destination it sells to. Therefore, firms selling to difficult countries are assigned to the top quintile, whereas exporters reaching only the most popular destinations are assigned to the bottom quintile. Hence, we condition the counterfactual to exporters of the same industry and of the same ability to reach difficult destinations. Figure 4 shows that accounting for sequential exporting gives similar results to controlling for firm-size bins. Figure 4A shows that 40% of destinations exhibit significant agglomeration at small distances, a percentage that gradually declines as distance increases. This is a lower share of localized destinations as compared to the baseline. Reassuringly, we find that the relative agglomeration patterns across countries are preserved. Figure 4B displays a high correlation between the baseline country index of agglomeration and that accounting for the difficulty of reaching each destination. Those countries exhibiting the largest amount of agglomeration in the baseline are still those with the highest levels in the more restricted counterfactual. It is worth noting also that the extent of agglomeration is reduced: the average index of significantly agglomerated destinations is around two thirds that of the baseline. This suggests that sequential exporting can be relevant in determining the extent of agglomeration by export destination, yet the smaller samples from which the counterfactual simulations are drawn may partly explain this result. Figure 4 View largeDownload slide Localization controlling for sequential exporting. Notes: This figure compares the baseline results of agglomeration by export destination with those controlling for sequential exporting. (A) Shows the share of localized destinations at each level of distance. The red dashed line is obtained from a counterfactual that controls for two groups of firms according to the number of countries they sell to: one or more than one. The green dotted line is obtained from a counterfactual of 5 bins according to the most difficult destination each exporter is able to sell to. The degree of difficulty of each destination is assessed according to the total number of exporters. Both counterfactuals account also for the industry composition of exports. (B) Displays the correlation between the country index of agglomeration and that from the simulations accounting for the difficulty of reaching each destination. The value of this correlation is 0.97. Figure 4 View largeDownload slide Localization controlling for sequential exporting. Notes: This figure compares the baseline results of agglomeration by export destination with those controlling for sequential exporting. (A) Shows the share of localized destinations at each level of distance. The red dashed line is obtained from a counterfactual that controls for two groups of firms according to the number of countries they sell to: one or more than one. The green dotted line is obtained from a counterfactual of 5 bins according to the most difficult destination each exporter is able to sell to. The degree of difficulty of each destination is assessed according to the total number of exporters. Both counterfactuals account also for the industry composition of exports. (B) Displays the correlation between the country index of agglomeration and that from the simulations accounting for the difficulty of reaching each destination. The value of this correlation is 0.97. 3.3.3. Controlling for destinations served by exporters in large cities Exporters located in large cities might be able to reach a larger set of export destinations, for instance by using better transport facilities, such as airports. Then, if some exporters concentrate in large cities and large cities disproportionately export to difficult destinations, exporters to these destinations will be spatially agglomerated. If this is the case, the destination-specific agglomeration economies would not exist within cities of comparable size. We perform two tests to address this concern. First, we exclude from our sample those firms located in Madrid and Barcelona, which are the largest municipalities in Spain, accounting for around 13% of export firms. Second, we control for the size of each city where each exporter is located when building the counterfactual distributions. We construct city-size bins with cutoffs given by 10 thousand, 100 thousand, 250 thousand and 1 million people.8 We then restrict the counterfactual to exporters in the same industry and the same population bin, replicating for each destination the distribution of these two variables observed in the data. Figure 5A shows that excluding firms located in the largest municipalities has a small effect on the results. At low distances the share of localized destinations does not change, whereas at larger distances agglomeration is higher. Indeed, 60% of destinations exhibit agglomeration until around 70 km, which is a larger scale of agglomeration than that of the baseline. Accounting for city-size bins reduces the extent of agglomeration at small distances, although to a limited extent. The total number of localized destinations goes down from 64% in the baseline to 62%. Figure 5B compares the country index of agglomeration with that of the baseline. We find that they are very highly correlated (0.98) and most of the countries lie very close to the 45° line. Indeed, those destinations for which agglomeration is no longer significant are those that exhibit very low levels in the first place. Hence, it seems that the baseline results cannot be explained by firms located in the largest municipalities or in locations of certain size. Figure 5 View largeDownload slide Localization controlling for city size. Notes: This figure compares the baseline results of agglomeration by export destination with those controlling for the size of cities where exporters are located. (A) Shows the share of localized destinations at each level of distance. The red dashed line corresponds to the sample excluding exporters located in Madrid and Barcelona. The green short-dashed line is built from a counterfactual that controls for 5 bins of city size (as well as the industry composition of exports). (B) Displays the correlation between the baseline country index of agglomeration and that from the simulations controlling for city-size bins. The value of this correlation is 0.98. Figure 5 View largeDownload slide Localization controlling for city size. Notes: This figure compares the baseline results of agglomeration by export destination with those controlling for the size of cities where exporters are located. (A) Shows the share of localized destinations at each level of distance. The red dashed line corresponds to the sample excluding exporters located in Madrid and Barcelona. The green short-dashed line is built from a counterfactual that controls for 5 bins of city size (as well as the industry composition of exports). (B) Displays the correlation between the baseline country index of agglomeration and that from the simulations controlling for city-size bins. The value of this correlation is 0.98. 3.3.4. Stability of the results over time In this subsection we check the stability of the results over time. A recent strand of literature documents that a large portion of exporter-destination relationships are short-lived, see Besedes and Prusa (2006), Nitsch (2009) and Békés and Muraközy (2012). This raises the concern that the agglomeration patterns uncovered so far could vary over time. We carry out two tests to address this issue. First, we repeat the baseline analysis for the years 2003 and 2005. And second, we restrict the baseline sample to continuous exporters from 2005 to 2007. Note that this exercise involves restricting the sample to those firm-country relationships that exist during three consecutive years (2005, 2006 and 2007). This criterion implies dropping around 40% of the exporters in the original sample. Note also that the agglomeration patterns by destination are computed only for continuous exporters to that destination, the counterfactual being random draws of continuous exporters to that and other destinations that satisfy the industry criterion. This implies that the number of destinations is reduced to 121. Figure 6 shows the results. The patterns of agglomeration by destination hold broadly across years, being the share of localized destinations very similar in 2003, 2005 and 2007 (Figure 6A). This is also true for the sample of continuous exporters between 2005 and 2007. The share of localized destinations is 60% vs 64% in the baseline and the scale of agglomeration at short distances is very similar. This finding highlights that agglomeration by a significant amount of export destinations is also a feature of the exporters most able to establish permanent trade relationships. In terms of the country index of agglomeration, the results over time are also relatively stable. The correlation between the 2007 index and that of 2005 and 2003 is 0.66 and 0.75, respectively. The Figure 6B shows the scatterplot between the baseline index and that of the continuous exporters sample. The correlation amounts to 0.71.9 Figure 6 View largeDownload slide Localization across different years. Notes: This figure shows agglomeration by export destination across different years and for the sample of continuous exporters in 2007 (i.e. exporter-destination relationships that exist during 2005 to 2007). (A) Shows the share of localized destinations at each distance. (B) Displays the correlation between the baseline country index of agglomeration and that obtained from the continuous exporter sample. The value of this correlation is 0.71. Note that this panel excludes two destinations with highly localized exporters in both the baseline and the continuous exporter sample in order to keep a meaningful scale. Figure 6 View largeDownload slide Localization across different years. Notes: This figure shows agglomeration by export destination across different years and for the sample of continuous exporters in 2007 (i.e. exporter-destination relationships that exist during 2005 to 2007). (A) Shows the share of localized destinations at each distance. (B) Displays the correlation between the baseline country index of agglomeration and that obtained from the continuous exporter sample. The value of this correlation is 0.71. Note that this panel excludes two destinations with highly localized exporters in both the baseline and the continuous exporter sample in order to keep a meaningful scale. 3.3.5. Additional robustness checks In our counterfactual simulations, we controlled for the industry composition of exports up to two-digit industries. This level of aggregation might be too coarse if countries demand specific varieties of a product that are locally produced. This concern could be partly addressed by using detailed enough product codes such as HS-6, yet our data do not contain such information. As an additional robustness check, we controlled in the counterfactual simulations for the industry composition of exports up to four-digit industries, at the cost of losing degrees of freedom when building the counterfactual. We find that the number of destinations with agglomerated exporters is 57%, slightly less than in the baseline but still high. Moreover, the patterns of agglomeration resemble that of the baseline. The share of localized destinations is close to 50% until about 40 km, decreasing fast from that distance on, see Figure 7A. Figure 7 View largeDownload slide Additional robustness checks. Notes: This figure shows additional robustness checks on the baseline results. (A) Shows agglomeration by export destination at each distance when the counterfactual controls for four-digit industries vs two-digit industries in the baseline. (B) Extends the distance horizon at which the density and the counterfactual distributions are compared to 200 and 400 km vs 100 km in the baseline. Figure 7 View largeDownload slide Additional robustness checks. Notes: This figure shows additional robustness checks on the baseline results. (A) Shows agglomeration by export destination at each distance when the counterfactual controls for four-digit industries vs two-digit industries in the baseline. (B) Extends the distance horizon at which the density and the counterfactual distributions are compared to 200 and 400 km vs 100 km in the baseline. Finally, we also checked if the agglomeration patterns vary when considering an extended distance horizon over which the distance distribution and the counterfactual are compared. Note that DO focus on 180 km, which corresponds approximately to the median distance of manufacturing plants in the UK, and Ellison et al. (2010) provide results for the USA over thresholds ranging from 100 to 1000 miles. Figure 7B shows that extending the distance horizon to 200 and 400 km from the 100-km baseline deliver similar agglomeration patterns. Note that the value of the K-densities is independent of the distance horizon over which they are evaluated. The slightly lower number of countries exhibiting agglomeration is the result of the higher localization threshold needed to fulfill the criterion that 95% of the counterfactual distributions lie below it across all distances, which leads to wider confidence bands. Also, the country indices of agglomeration have a large correlation with respect to the baseline, of 0.99 and 0.93, respectively. 4. Factors behind agglomeration by destination In this section, we aim at identifying the variables that explain the cross-destination variation in agglomeration levels documented above. The process of selling abroad involves both fixed and variable costs, such as learning about the foreign market, establishing a distribution network, tailoring the products to foreign tastes and regulations, clearing the goods through customs, etc. Therefore, the industry agglomeration sources emphasized by the literature, related to sharing, matching and learning mechanisms (see for instance Duranton and Puga (2004)) may also lead, at least to some extent, to agglomeration by export destination. Indeed, there exist potential gains from pooling the costs of selling abroad and from extracting the information revealed by nearby exporters as shown, for instance, by Segura-Cayuela and Vilarrubia (2008) and Fernandes and Tang (2014). Agglomeration may also be the result of geographical proximity to the importing country, which would lead to export firms being concentrated close to the border. To shed some light on the determinants of agglomeration by export destination, we regress the country index of agglomeration on a set of country characteristics borrowed from the gravity literature, which connects trade flows with size and trade barriers, see Helpman et al. (2008). If agglomeration results in lower transport costs, this literature provides a natural guideline to assess the cross-destination heterogeneity in exporter agglomeration. Then, we include proxies of geographical distance, cultural similarity, transaction costs and institutions among other determinants. To take into account the left-censored nature of the dependent variable, we specify the following Tobit model: Localizationc=β0+β1Spanishc+β2Institutional Qualityc+β3Euroc+β4Contiguityc+β5Log Distance to Capitalc+β6Log Per Capita GDPc+β7Log Number of Exportersc+β8Log Populationc+εc. (1) Our variables capturing trade barriers are the following. Cultural similarity is proxied by a dummy variable taking value one if Spanish is spoken in the destination country, and zero otherwise. The institutional environment in the importing country is captured by the principal factor of the six dimensions comprising the Worldwide Governance Indicators, namely rule of law, political stability, control of corruption, government effectiveness, regulatory quality, and voice and accountability (see Appendix C for details). We also include a dummy taking value one if the euro is the currency of the destination. Finally, we add the log average distance between exporters and the destination’s capital and a dummy taking value one if the destination shares a border with Spain. In a different specification, we include the average distance of exporters to the closest port from which shipments are sent to the country.10 Finally, we add a set of controls including per capita GDP, population, and the log number of firms exporting to the country. The latter depends also on export costs. We include it to facilitate the reading of the results: we compare the extent of exporter agglomeration across destinations reached by the same number of firms, and assess how this varies according to characteristics such as language, institutions and distance.11 Note finally that we rescale the dependent variable by its standard deviation to ease the interpretation of the coefficients. Table 3 shows the results. In column (1) we find that, conditional on the rest of the covariates, exporters to countries with a different language, a different currency and a worse business environment are significantly more geographically agglomerated. The most significant variable is language: if Spanish is spoken in the importing country, agglomeration decreases by 0.89 standard deviations.12 In column (2) we restrict the sample to the destinations not belonging to the European Union, where entry is more difficult and hence the value provided by agglomeration is potentially larger. In this specification language and the institutional environment become more important in explaining the concentration of exporters. Speaking Spanish reduces the extent of agglomeration by 1.3 standard deviations and a one standard deviation increase in the business environment is associated with a 0.66 standard deviations decrease in agglomeration, both results pointing to a positive relationship between agglomeration and the difficulty in conducting businesses abroad.13 Our findings in columns (1) and (2) suggest also that distance plays no role in explaining the concentration of exporters. In column (3) we replace the exporter average distance to the country’s capital with the average distance to the closest port shipping to the country. This measure does not have explanatory power in accounting for exporter concentration either. Table 3 Factors behind exporters’ agglomeration by destination Baseline Non-EU Ports Immigrants Region Exporters’ FE Size (1) (2) (3) (4) (5) (6) Dep. variable: country index of exporters’ localization Spanish −0.8901*** −1.2840*** −1.1375*** −0.9416** −0.9035 −1.2434** (0.3358) (0.3750) (0.3200) (0.3305) (0.8289) (0.4834) Institutional Quality −0.4756* −0.6561** −0.5395** −0.2008 −0.3968* −0.7163** (0.2437) (0.3035) (0.2577) (0.1622) (0.2251) (0.3092) Euro −0.6491** −0.2722 0.3424 −0.9010*** −0.7463* (0.3052) (0.2548) (0.2001) (0.3264) (0.4362) Contiguity 0.7024 −0.7600 0.6388*** 0.6165 1.4376 (1.0417) (0.8099) (0.2175) (0.9288) (1.2682) Log Distance to Capital −0.1684 0.0043 0.2156 −0.2861 −0.2592 (0.1914) (0.2008) (0.1614) (0.3398) (0.2521) Log Distance to Port 0.0833 (0.2651) Log Per Capita GDP 0.2849 0.3498 0.3386* 0.7116*** 0.1605 0.3276 (0.1963) (0.2370) (0.1914) (0.2309) (0.1655) (0.2079) Log Number of Exporters 0.1371 0.2096 0.1474 −0.6587** −0.0083 0.1746 (0.1701) (0.2110) (0.1398) (0.2321) (0.2122) (0.1919) Log Population −0.0148 −0.0060 0.0628 0.2162 0.0268 −0.0476 (0.1111) (0.1360) (0.0941) (0.1322) (0.1419) (0.1334) Dispersion of Immigrants −2.3675*** (0.7900) Region Fixed Effects No No No No Yes No Observations 150 123 141 28 150 150 Pseudo R2 0.04 0.04 0.04 0.62 0.10 0.05 Log Likelihood −201.60 −165.90 −184.60 −8.67 −187.60 −181.40 Baseline Non-EU Ports Immigrants Region Exporters’ FE Size (1) (2) (3) (4) (5) (6) Dep. variable: country index of exporters’ localization Spanish −0.8901*** −1.2840*** −1.1375*** −0.9416** −0.9035 −1.2434** (0.3358) (0.3750) (0.3200) (0.3305) (0.8289) (0.4834) Institutional Quality −0.4756* −0.6561** −0.5395** −0.2008 −0.3968* −0.7163** (0.2437) (0.3035) (0.2577) (0.1622) (0.2251) (0.3092) Euro −0.6491** −0.2722 0.3424 −0.9010*** −0.7463* (0.3052) (0.2548) (0.2001) (0.3264) (0.4362) Contiguity 0.7024 −0.7600 0.6388*** 0.6165 1.4376 (1.0417) (0.8099) (0.2175) (0.9288) (1.2682) Log Distance to Capital −0.1684 0.0043 0.2156 −0.2861 −0.2592 (0.1914) (0.2008) (0.1614) (0.3398) (0.2521) Log Distance to Port 0.0833 (0.2651) Log Per Capita GDP 0.2849 0.3498 0.3386* 0.7116*** 0.1605 0.3276 (0.1963) (0.2370) (0.1914) (0.2309) (0.1655) (0.2079) Log Number of Exporters 0.1371 0.2096 0.1474 −0.6587** −0.0083 0.1746 (0.1701) (0.2110) (0.1398) (0.2321) (0.2122) (0.1919) Log Population −0.0148 −0.0060 0.0628 0.2162 0.0268 −0.0476 (0.1111) (0.1360) (0.0941) (0.1322) (0.1419) (0.1334) Dispersion of Immigrants −2.3675*** (0.7900) Region Fixed Effects No No No No Yes No Observations 150 123 141 28 150 150 Pseudo R2 0.04 0.04 0.04 0.62 0.10 0.05 Log Likelihood −201.60 −165.90 −184.60 −8.67 −187.60 −181.40 Notes: This table shows the regression of the country index of exporters’ localization (i.e. a variable capturing to what extent exporters to each export destination are significantly agglomerated) against measures of export costs, comparative advantage and several covariates. The specification is a tobit model described in Equation (1). Column (1) presents the baseline regression. Column (2) restricts the sample to countries not in the European Union. Column (3) replaces the variable distance with the average distance to the closest port shipping to the country. Column (4) introduces a measure of the concentration of immigrants from each country, proxied as the median distance between them. Column (5) introduces seven region fixed effects: Western Europe, Eastern Europe, Western and Central Asia, South-East Asia, Northern Africa, Central and Southern Africa, Central America and Caribbean, North America, South America, and Oceania. Finally, in column (6) the dependent variable is the country index built from a counterfactual that controls for firm-size bins (Section 3.3.1). Robust standard errors are in parenthesis. Significance levels: *10%; **5%; ***1%. Table 3 Factors behind exporters’ agglomeration by destination Baseline Non-EU Ports Immigrants Region Exporters’ FE Size (1) (2) (3) (4) (5) (6) Dep. variable: country index of exporters’ localization Spanish −0.8901*** −1.2840*** −1.1375*** −0.9416** −0.9035 −1.2434** (0.3358) (0.3750) (0.3200) (0.3305) (0.8289) (0.4834) Institutional Quality −0.4756* −0.6561** −0.5395** −0.2008 −0.3968* −0.7163** (0.2437) (0.3035) (0.2577) (0.1622) (0.2251) (0.3092) Euro −0.6491** −0.2722 0.3424 −0.9010*** −0.7463* (0.3052) (0.2548) (0.2001) (0.3264) (0.4362) Contiguity 0.7024 −0.7600 0.6388*** 0.6165 1.4376 (1.0417) (0.8099) (0.2175) (0.9288) (1.2682) Log Distance to Capital −0.1684 0.0043 0.2156 −0.2861 −0.2592 (0.1914) (0.2008) (0.1614) (0.3398) (0.2521) Log Distance to Port 0.0833 (0.2651) Log Per Capita GDP 0.2849 0.3498 0.3386* 0.7116*** 0.1605 0.3276 (0.1963) (0.2370) (0.1914) (0.2309) (0.1655) (0.2079) Log Number of Exporters 0.1371 0.2096 0.1474 −0.6587** −0.0083 0.1746 (0.1701) (0.2110) (0.1398) (0.2321) (0.2122) (0.1919) Log Population −0.0148 −0.0060 0.0628 0.2162 0.0268 −0.0476 (0.1111) (0.1360) (0.0941) (0.1322) (0.1419) (0.1334) Dispersion of Immigrants −2.3675*** (0.7900) Region Fixed Effects No No No No Yes No Observations 150 123 141 28 150 150 Pseudo R2 0.04 0.04 0.04 0.62 0.10 0.05 Log Likelihood −201.60 −165.90 −184.60 −8.67 −187.60 −181.40 Baseline Non-EU Ports Immigrants Region Exporters’ FE Size (1) (2) (3) (4) (5) (6) Dep. variable: country index of exporters’ localization Spanish −0.8901*** −1.2840*** −1.1375*** −0.9416** −0.9035 −1.2434** (0.3358) (0.3750) (0.3200) (0.3305) (0.8289) (0.4834) Institutional Quality −0.4756* −0.6561** −0.5395** −0.2008 −0.3968* −0.7163** (0.2437) (0.3035) (0.2577) (0.1622) (0.2251) (0.3092) Euro −0.6491** −0.2722 0.3424 −0.9010*** −0.7463* (0.3052) (0.2548) (0.2001) (0.3264) (0.4362) Contiguity 0.7024 −0.7600 0.6388*** 0.6165 1.4376 (1.0417) (0.8099) (0.2175) (0.9288) (1.2682) Log Distance to Capital −0.1684 0.0043 0.2156 −0.2861 −0.2592 (0.1914) (0.2008) (0.1614) (0.3398) (0.2521) Log Distance to Port 0.0833 (0.2651) Log Per Capita GDP 0.2849 0.3498 0.3386* 0.7116*** 0.1605 0.3276 (0.1963) (0.2370) (0.1914) (0.2309) (0.1655) (0.2079) Log Number of Exporters 0.1371 0.2096 0.1474 −0.6587** −0.0083 0.1746 (0.1701) (0.2110) (0.1398) (0.2321) (0.2122) (0.1919) Log Population −0.0148 −0.0060 0.0628 0.2162 0.0268 −0.0476 (0.1111) (0.1360) (0.0941) (0.1322) (0.1419) (0.1334) Dispersion of Immigrants −2.3675*** (0.7900) Region Fixed Effects No No No No Yes No Observations 150 123 141 28 150 150 Pseudo R2 0.04 0.04 0.04 0.62 0.10 0.05 Log Likelihood −201.60 −165.90 −184.60 −8.67 −187.60 −181.40 Notes: This table shows the regression of the country index of exporters’ localization (i.e. a variable capturing to what extent exporters to each export destination are significantly agglomerated) against measures of export costs, comparative advantage and several covariates. The specification is a tobit model described in Equation (1). Column (1) presents the baseline regression. Column (2) restricts the sample to countries not in the European Union. Column (3) replaces the variable distance with the average distance to the closest port shipping to the country. Column (4) introduces a measure of the concentration of immigrants from each country, proxied as the median distance between them. Column (5) introduces seven region fixed effects: Western Europe, Eastern Europe, Western and Central Asia, South-East Asia, Northern Africa, Central and Southern Africa, Central America and Caribbean, North America, South America, and Oceania. Finally, in column (6) the dependent variable is the country index built from a counterfactual that controls for firm-size bins (Section 3.3.1). Robust standard errors are in parenthesis. Significance levels: *10%; **5%; ***1%. In column (4) we quantify the role of immigrants in explaining agglomeration across countries. Specifically, we test whether the local concentration of immigrants can explain some of the patterns that we document. A line of research has shown that immigrants help overcome trade barriers, for example by providing specific knowledge about their home countries. For instance, Herander and Saavedra (2005) find an effect of local immigrant groups on export volumes in the USA. To delve into this issue, we construct an origin-specific index of immigrant dispersion, defined as the median distance between immigrants from each country (a higher distance meaning more dispersion). Our analysis is restricted to the 28 countries for which we have information on the population distribution across municipalities, therefore we raise a flag of caution on interpreting the results. With this caveat in mind, column (4) shows that there is a significant relationship between the dispersion of immigrants and the agglomeration of firms selling to their home countries. Conditional on the rest of controls, a 10% increase in the dispersion of immigrants is associated with a 0.53 standard deviation decrease in the degree of agglomeration. Therefore, agglomeration by destinations whose immigrants exhibit some concentration is found to be higher. Note that this result provides some evidence against export spillovers, since the agglomeration of people from the same country may lead to the agglomeration of exporters to their country of origin, even if no information between exporters is shared.14 In column (5) we add region fixed effects. We include 10 region dummies: Western Europe, Eastern Europe, Western and Central Asia, South-East Asia, Northern Africa, Central and Southern Africa, North America, Central America and Caribbean, South America and Oceania. We find that the coefficient associated with language barely changes with respect to the baseline, although it is imprecisely estimated, whereas that of institutional quality is somewhat lower, though it is still statistically significant. Interestingly, controlling for regions increases the estimated effect of the currency: belonging to the euro area reduces the extent of agglomeration by 0.90 standard deviations. These results suggest that the mechanisms connecting agglomeration with trade barriers hold also within broad geographic and economic areas. Finally, column (6) replaces the baseline country index of agglomeration with that obtained from the counterfactual that accounts for 20 bins of exporter size, see Section 3.3.1. This is a pertinent analysis because restricting the counterfactual in some cases reduced the number of significantly agglomerated destinations. However, given the high correlation between the country indices of agglomeration, the results tend to confirm the baseline findings. In fact, the point estimates are even larger in absolute value regarding language, institutional quality and currency. Adding the other country indices constructed in Section 3.3 confirms the baseline estimates. Overall, the previous results suggest that there exists a relationship between trade barriers to enter a country and the degree of spatial agglomeration of exporters selling to it. One limitation of this approach is that the precise mechanism driving these patterns cannot be uncovered, and we cannot rule out that other omitted factors may contaminate this relationship, hence we do not pursue causality. However, they show an insightful correlation between proxies of export costs and the extent of geographical concentration that is systematic and robust. Moreover, the results suggest that agglomeration can be more effective concerning those destinations from which information is more valuable and, in this regard, they inform the theoretical and empirical literature on export spillovers and learning from neighboring firms cited in the introduction. 5. Concluding remarks In this article, we document the existence of agglomeration economies that accrue to firms selling to certain foreign markets. In the pursuit of shedding light on the interpretation of our results, we show that these patterns of geographical concentration are not driven by the spatial location of large vs small exporters, hierarchical exporting or exporters located in large cities. Moreover, we find that these location patterns are quite stable over time. Regarding the determinants of agglomeration, we show that the cross-destination variability in agglomeration levels can be partly explained by language, currency and institutional quality, being agglomeration higher the larger export costs are. These findings are consistent with the existence of externalities in selling to certain foreign countries, having implications for international trade. For example, agglomeration might reduce destination-specific fixed costs, which would rationalize why firms do not follow a strict hierarchy of export destinations, a fact uncovered by Eaton et al. (2011). Also, some policy implications can be derived. The pattern of concentration by export destination suggests that easing the flow of information from exporters to potential entrants can pay off. Moreover, the fact that concentration is higher concerning more difficult destinations suggests that the benefits of these policies can be specially helpful in countries where entry is more difficult. Also, helping companies penetrate new markets can lead nearby firms to follow them. Interestingly, given how we defined the counterfactual, these benefits are not restricted to firms of the same industry, rather they can extend to firms belonging to different industries. The nature of our data prevents us from digging deeper into the specific channels through which agglomeration economies might work. More detailed data would allow to disentangling some sources of export spillovers, such as those related to information (via headquarters) from those linked to costs (via establishments). Also, a larger time span and more categories of goods would allow a geographical analysis of new products exported and new markets accessed. In general, we think that there is room in the literature to test empirically which are the most important channels through which agglomeration economies in international trade operate. Case studies or natural experiments seem a suitable framework to disentangle specific mechanisms playing a role in generating export spillovers. We see this avenue of further research as promising. Footnotes 1 The existing literature on export spillovers studies the agglomeration of exporters in the same administrative unit or economic area. This entails the so-called ‘border effect’ problem, which involves several issues. First, it amounts to treat symmetrically plants not belonging to the same spatial unit, regardless of the distance that separates them. Second, it involves the arbitrary decision of which spatial unit to take. This is relevant, as different levels of aggregation can lead to very different results. Furthermore, it has been showed that bigger units produce more pronounced correlations. This is called the Modifiable Areal Unit Problem (MAUP), see Openshaw and Taylor (1979) and Openshaw (1984). And third, the previous problem and the fact that spatial units are not often defined on the basis of economic significance make the comparison of results across spatial units difficult to interpret. 2 Although localization can be defined as agglomeration controlling for that of general manufacturing, as in Duranton and Overman (2005), in this article we use the words agglomeration, localization and concentration interchangeably, as the indices explicitly control for the overall concentration of exporters and do not lead to confusion. 3 On the mechanisms driving industry agglomeration, see Klepper (2010), who analyzes the historical clustering of firms in Detroit and Silicon Valley, and Ellison et al. (2010), who test the Marshall (1920) theories of industry agglomeration using coagglomeration patterns. 4 The dataset extends to 2013, but the export threshold was increased in 2008 to €50,000. For this reason and to avoid the results being contaminated from the crisis, we use data up to 2007. 5 This estimation is as follows. According to the 2009 Spanish Survey on Business Strategies, 93.1% of firms with less than 200 employees and 62.5% of firms with more than 200 employees have only one plant. In our data 94.5% of exporters have less than 200 employees (vs 99.1% of all firms), then we estimate that the percentage of single-plant exporters is approximately 91.4%. 6 In DO the localization and dispersion thresholds are referred to as global confidence bands. Note that they allow making statements about the overall agglomeration patterns, since they are neutral with respect to distances (at a given distance horizon). DO report local confidence intervals too, constructed as the 5-th and 95-th percentiles of the ranked counterfactual distributions at each kilometer. These intervals only allow local statements to be made, i.e. deviations from randomness at a given distance. Note that the percentiles associated with the localization thresholds are above the 95-th percentile, since the ranking of the counterfactual distributions varies across distances. At the baseline distance horizon of 100 km, they range (across countries) between the 96.4-th and the 99.5-th. 7 Each size bin contains the same number of exporters. The median amount of total exports is given by €0.25 million and the percentile 75 by €1.4 million. 8 The distribution of exporters across the five resulting bins is: 30%, 32%, 15%, 11% and 13%. 9 Note that to keep a meaningful scale this figure excludes two destinations with highly localized exporters in both the baseline and the continuous exporters sample. 10 We do not include it in the baseline because we lack data on exports from Spanish ports to 13 countries and the distance variables are never significant. Also, including the simple distance between the most populated cities yields very similar results. 11 Moreover, Helpman et al. (2008) shows the importance of accounting for the extensive margin of trade in the gravity equation framework. We also checked that excluding this variable does not affect the overall results. 12 Note that our baseline regression is performed on 150 countries because we lack data on 16 small countries. GDP data are missing in 14 countries and institutional quality in 6 observations. 13 We also inspected the role of some specific elements of the institutional environment by replacing the institutional factor in column (1) by each Governance Indicator (one by one). We found that a better rule of law, less corruption and more political stability are significantly associated with lower agglomeration, whereas the rest yielded nonsignificant associations. Moreover, we also found that specific measures of investor protection, such as the number of procedures required to enforce a contract, are also negatively and significantly associated with agglomeration. We also tried another proxies of import costs such as the number of days and the number of documents required to import goods, but found that the estimates were not statistically significant. 14 Note also that the coefficients of some variables change substantially with respect to the baseline (column 1). We found that they can be mainly explained by a composition effect, since repeating the baseline regression with the 28 countries did not yield large changes in the covariate estimates with respect to those obtained in column (4). 15 Given that our aim is to uncover agglomeration of export firms, we treat each firm as one observation and therefore we do not weigh the distances when estimating the distribution. An alternative would be consider the agglomeration of export values and hence weigh distances by exports to the country. There are three reasons that advise us against this strategy. First, the construction of the counterfactual involves firms that do not export to the country, hence they lack a weight. Second, if total firm exports were used as weights, we would disregard the specialization of firms to certain markets, given that weights would not have variation across export destinations. And third, total exports is a highly skewed variable, hence a few firms could distort the results. Acknowledgements This article was previously circulated under the title ‘Agglomeration Matters for Trade’. We are very grateful to Guillermo Caruana, Rosario Crinó and Claudio Michelacci for their constant guidance and help. We are also thankful to César Alonso, Pol Antràs, Manuel Arellano, Stéphane Bonhomme, David Dorn, Gino Gancia, Manuel García-Santana, Horacio Larreguy, Carlos Llano, Marc Melitz, Guy Michaels, Hannes Mueller, Diego Puga, Rafael Repullo, Rubén Segura-Cayuela, Andrei Shleifer, the editor, at least two anonymous referees, and seminar participants at CEMFI, European Winter Meeting of the Econometric Society (Konstanz), SAEe Vigo, IMT Lucca, Universität Mannheim, IAE, CESifo, 2013 Annual Meeting of the Society for Economic Dynamics (Seoul), 67th European Meeting of the Econometric Society (Gothenburg), 2013 Barcelona Workshop on Regional and Urban Economics, Banco de España, Universidad de Murcia, XVII Conference on International Economics (A Coruña), and Universidad Autónoma de Madrid for comments and useful discussions. We are also grateful to Patry Tello for providing us the exporter dataset. 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Control of Corruption Kaufmann et al. (2009) Extent to which public power is exercised for private gain, including corruption, as well as ‘capture’ of the state by elites and private interests. Regulatory Quality Kaufmann et al. (2009) Ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Political Stability Kaufmann et al. (2009) Likelihood that the government will be destabilized or overthrown by unconstitutional or violent means. Government Effectiveness Kaufmann et al. (2009) Quality of public services, the civil service, policy formulation and implementation, and credibility of the government’s commitment to such policies. Voice and Accountability Kaufmann et al. (2009) Extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association and a free media. Euro 1 if the country’s currency is the euro. Contiguity Mayer and Zignago (2011) 1 for contiguity with respect to Spain. Distance to Country’s Capital Average distance of exporters to the country’s capital. Distance to Ports Puertos del Estado Distance between zip code and closest port from which shipments are sent to the country. We assume that from the three main Portuguese ports (Aveiro, Leixoes and Lisbon), all countries are reached. Per capita GDP World Bank Log Real per Capita GDP in constant 2000 US dollars. Population World Bank Country’s population. Location of Immigrants Instituto Nacional de Estadística Number of immigrants in each municipality by country of origin. European Union 1 if the country belongs to the European Union. Doing Business Index World Bank Ranking of economies that assess business regulations and their enforcement. Contract Enforcement World Bank Number of procedures required to enforce a contract. Time to Import World Bank Number of calendar days necessary to comply with all the procedures required to import goods. Variables Source Definition Zip code coordinates Geonames Distance between zip codes Apply haversine formula to the zip code coordinates. Spanish Mayer and Zignago (2011) 1 if a Spanish is spoken by at least 9% of the population. Rule of Law Kaufmann et al. (2009) Quality of contract enforcement, property rights, the police, the courts, and likelihood of crime and violence. Control of Corruption Kaufmann et al. (2009) Extent to which public power is exercised for private gain, including corruption, as well as ‘capture’ of the state by elites and private interests. Regulatory Quality Kaufmann et al. (2009) Ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Political Stability Kaufmann et al. (2009) Likelihood that the government will be destabilized or overthrown by unconstitutional or violent means. Government Effectiveness Kaufmann et al. (2009) Quality of public services, the civil service, policy formulation and implementation, and credibility of the government’s commitment to such policies. Voice and Accountability Kaufmann et al. (2009) Extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association and a free media. Euro 1 if the country’s currency is the euro. Contiguity Mayer and Zignago (2011) 1 for contiguity with respect to Spain. Distance to Country’s Capital Average distance of exporters to the country’s capital. Distance to Ports Puertos del Estado Distance between zip code and closest port from which shipments are sent to the country. We assume that from the three main Portuguese ports (Aveiro, Leixoes and Lisbon), all countries are reached. Per capita GDP World Bank Log Real per Capita GDP in constant 2000 US dollars. Population World Bank Country’s population. Location of Immigrants Instituto Nacional de Estadística Number of immigrants in each municipality by country of origin. European Union 1 if the country belongs to the European Union. Doing Business Index World Bank Ranking of economies that assess business regulations and their enforcement. Contract Enforcement World Bank Number of procedures required to enforce a contract. Time to Import World Bank Number of calendar days necessary to comply with all the procedures required to import goods. Notes: This table shows the definitions and sources of the main variables used throughout the article. Table A1 Data definitions and sources Variables Source Definition Zip code coordinates Geonames Distance between zip codes Apply haversine formula to the zip code coordinates. Spanish Mayer and Zignago (2011) 1 if a Spanish is spoken by at least 9% of the population. Rule of Law Kaufmann et al. (2009) Quality of contract enforcement, property rights, the police, the courts, and likelihood of crime and violence. Control of Corruption Kaufmann et al. (2009) Extent to which public power is exercised for private gain, including corruption, as well as ‘capture’ of the state by elites and private interests. Regulatory Quality Kaufmann et al. (2009) Ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Political Stability Kaufmann et al. (2009) Likelihood that the government will be destabilized or overthrown by unconstitutional or violent means. Government Effectiveness Kaufmann et al. (2009) Quality of public services, the civil service, policy formulation and implementation, and credibility of the government’s commitment to such policies. Voice and Accountability Kaufmann et al. (2009) Extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association and a free media. Euro 1 if the country’s currency is the euro. Contiguity Mayer and Zignago (2011) 1 for contiguity with respect to Spain. Distance to Country’s Capital Average distance of exporters to the country’s capital. Distance to Ports Puertos del Estado Distance between zip code and closest port from which shipments are sent to the country. We assume that from the three main Portuguese ports (Aveiro, Leixoes and Lisbon), all countries are reached. Per capita GDP World Bank Log Real per Capita GDP in constant 2000 US dollars. Population World Bank Country’s population. Location of Immigrants Instituto Nacional de Estadística Number of immigrants in each municipality by country of origin. European Union 1 if the country belongs to the European Union. Doing Business Index World Bank Ranking of economies that assess business regulations and their enforcement. Contract Enforcement World Bank Number of procedures required to enforce a contract. Time to Import World Bank Number of calendar days necessary to comply with all the procedures required to import goods. Variables Source Definition Zip code coordinates Geonames Distance between zip codes Apply haversine formula to the zip code coordinates. Spanish Mayer and Zignago (2011) 1 if a Spanish is spoken by at least 9% of the population. Rule of Law Kaufmann et al. (2009) Quality of contract enforcement, property rights, the police, the courts, and likelihood of crime and violence. Control of Corruption Kaufmann et al. (2009) Extent to which public power is exercised for private gain, including corruption, as well as ‘capture’ of the state by elites and private interests. Regulatory Quality Kaufmann et al. (2009) Ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Political Stability Kaufmann et al. (2009) Likelihood that the government will be destabilized or overthrown by unconstitutional or violent means. Government Effectiveness Kaufmann et al. (2009) Quality of public services, the civil service, policy formulation and implementation, and credibility of the government’s commitment to such policies. Voice and Accountability Kaufmann et al. (2009) Extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association and a free media. Euro 1 if the country’s currency is the euro. Contiguity Mayer and Zignago (2011) 1 for contiguity with respect to Spain. Distance to Country’s Capital Average distance of exporters to the country’s capital. Distance to Ports Puertos del Estado Distance between zip code and closest port from which shipments are sent to the country. We assume that from the three main Portuguese ports (Aveiro, Leixoes and Lisbon), all countries are reached. Per capita GDP World Bank Log Real per Capita GDP in constant 2000 US dollars. Population World Bank Country’s population. Location of Immigrants Instituto Nacional de Estadística Number of immigrants in each municipality by country of origin. European Union 1 if the country belongs to the European Union. Doing Business Index World Bank Ranking of economies that assess business regulations and their enforcement. Contract Enforcement World Bank Number of procedures required to enforce a contract. Time to Import World Bank Number of calendar days necessary to comply with all the procedures required to import goods. Notes: This table shows the definitions and sources of the main variables used throughout the article. Table A2 List of destinations Country N Localization Country N Localization Country N Localization Afghanistan 13 0.0000 Gambia 20 0.0000 The Netherlands 3010 0.0190 Albania 168 0.1108 Georgia 101 0.0025 New Caledonia 34 0.0000 Algeria 826 0.0027 Germany 6427 0.0174 New Zealand 278 0.0036 Andorra 887 0.0903 Ghana 72 0.0000 Nicaragua 63 0.0000 Angola 89 0.0000 Gibraltar 90 0.0000 Niger 44 0.0000 Antigua and Barbuda 20 0.0000 Greece 1768 0.0178 Nigeria 99 0.0560 Argentina 818 0.0100 Guam 10 0.0000 Norway 733 0.0004 Armenia 53 0.0794 Guatemala 226 0.0000 Oman 103 0.0017 Aruba 19 0.0876 Guinea 19 0.0000 Pakistan 189 0.0407 Australia 795 0.0128 Haiti 13 0.0000 Panama 492 0.0000 Austria 1620 0.0148 Honduras 107 0.0000 Paraguay 67 0.0193 Azerbaijan 28 0.0024 Hong Kong SAR, China 708 0.0315 Peru 435 0.0001 Bahamas, The 48 0.0000 Hungary 778 0.0276 Philippines 191 0.0096 Bahrain 154 0.0329 Iceland 160 0.0001 Poland 1627 0.0182 Bangladesh 67 0.0285 India 643 0.0232 Portugal 6861 0.0000 Barbados 32 0.0000 Indonesia 228 0.0136 Qatar 212 0.0445 Belarus 89 0.0000 Iran, Islamic Rep. 463 0.0109 Romania 902 0.0205 Belgium 3500 0.0217 Iraq 23 0.1835 Russian Federation 915 0.0189 Belize 79 0.0089 Ireland 1308 0.0000 San Marino 19 0.0000 Benin 26 0.0000 Israel 732 0.0307 Saudi Arabia 854 0.0134 Bermuda 16 0.0000 Italy 5166 0.0116 Senegal 86 0.0000 Bolivia 96 0.0000 Jamaica 48 0.0085 Serbia 178 0.0185 Bosnia and Herzegovina 103 0.0422 Japan 829 0.0222 Seychelles 31 0.0000 Brazil 1048 0.0269 Jordan 358 0.0154 Sierra Leone 15 0.0313 British Virgin Islands 124 0.0087 Kazakhstan 102 0.0000 Singapore 429 0.0335 Bulgaria 511 0.0151 Kenya 81 0.0000 Slovak Republic 447 0.0099 Burkina Faso 30 0.0000 Korea, Dem. People’s Rep. 38 0.0000 Slovenia 412 0.0224 Cabo Verde 22 0.0000 Korea, Rep. 561 0.0271 Solomon Islands 11 0.0000 Cameroon 47 0.0000 Kuwait 335 0.0331 South Africa 729 0.0262 Canada 848 0.0057 Kyrgyz Republic 188 0.0114 Sri Lanka 50 0.0000 Cayman Islands 20 0.0000 Latvia 679 0.0255 Sudan 43 0.0163 Chad 18 0.1599 Lebanon 494 0.0139 Suriname 18 0.1829 Chile 872 0.0001 Libya 152 0.0127 Swaziland 21 0.0359 China 1007 0.0102 Liechtenstein 56 0.0159 Sweden 1264 0.0051 Colombia 642 0.0117 Lithuania 642 0.0137 Switzerland 2095 0.0112 Congo, Dem. Rep. 19 0.0000 Luxembourg 282 0.0080 Syrian Arab Republic 166 0.0339 Congo, Rep. 15 0.0000 Macao SAR, China 34 0.0010 Taiwan, China 458 0.0229 Costa Rica 274 0.0000 Macedonia, FYR 66 0.0732 Tanzania 42 0.0844 Croatia 328 0.0147 Madagascar 23 0.0000 Thailand 343 0.0204 Cuba 314 0.0000 Malaysia 281 0.0026 Togo 17 0.0000 Cyprus 708 0.0236 Mali 22 0.0000 Trinidad and Tobago 97 0.0000 Czech Republic 1080 0.0245 Malta 247 0.0121 Tunisia 743 0.0061 Côte d’Ivoire 76 0.0009 Marshall Islands 10 0.0011 Turkey 1385 0.0367 Denmark 1286 0.0184 Mauritania 56 0.0000 Uganda 13 0.0000 Dominica 80 0.0000 Mauritius 65 0.0000 Ukraine 311 0.0052 Dominican Republic 456 0.0000 Mexico 1964 0.0051 United Arab Emirates 962 0.0086 Ecuador 337 0.0008 Micronesia, Fed. Sts. 30 0.0487 United Kingdom 5521 0.0120 Egypt, Arab Rep. 588 0.0254 Moldova 45 0.0000 United States 3882 0.0106 El Salvador 128 0.0000 Monaco 99 0.0348 Uruguay 247 0.0091 Equatorial Guinea 60 0.0000 Montenegro 61 0.0958 Uzbekistan 10 0.0000 Estonia 380 0.0254 Morocco 1703 0.0000 Venezuela, RB 675 0.0056 Ethiopia 28 0.0000 Mozambique 19 0.0000 Vietnam 128 0.0005 Finland 897 0.0029 Namibia 38 0.0154 Virgin Islands (U.S.) 22 0.0068 France 8946 0.0250 Nauru 15 0.0000 West Bank and Gaza 36 0.2886 French Polynesia 20 0.0000 Nepal 12 0.0000 Yemen, Rep. 86 0.0313 Gabon 41 0.0000 Country N Localization Country N Localization Country N Localization Afghanistan 13 0.0000 Gambia 20 0.0000 The Netherlands 3010 0.0190 Albania 168 0.1108 Georgia 101 0.0025 New Caledonia 34 0.0000 Algeria 826 0.0027 Germany 6427 0.0174 New Zealand 278 0.0036 Andorra 887 0.0903 Ghana 72 0.0000 Nicaragua 63 0.0000 Angola 89 0.0000 Gibraltar 90 0.0000 Niger 44 0.0000 Antigua and Barbuda 20 0.0000 Greece 1768 0.0178 Nigeria 99 0.0560 Argentina 818 0.0100 Guam 10 0.0000 Norway 733 0.0004 Armenia 53 0.0794 Guatemala 226 0.0000 Oman 103 0.0017 Aruba 19 0.0876 Guinea 19 0.0000 Pakistan 189 0.0407 Australia 795 0.0128 Haiti 13 0.0000 Panama 492 0.0000 Austria 1620 0.0148 Honduras 107 0.0000 Paraguay 67 0.0193 Azerbaijan 28 0.0024 Hong Kong SAR, China 708 0.0315 Peru 435 0.0001 Bahamas, The 48 0.0000 Hungary 778 0.0276 Philippines 191 0.0096 Bahrain 154 0.0329 Iceland 160 0.0001 Poland 1627 0.0182 Bangladesh 67 0.0285 India 643 0.0232 Portugal 6861 0.0000 Barbados 32 0.0000 Indonesia 228 0.0136 Qatar 212 0.0445 Belarus 89 0.0000 Iran, Islamic Rep. 463 0.0109 Romania 902 0.0205 Belgium 3500 0.0217 Iraq 23 0.1835 Russian Federation 915 0.0189 Belize 79 0.0089 Ireland 1308 0.0000 San Marino 19 0.0000 Benin 26 0.0000 Israel 732 0.0307 Saudi Arabia 854 0.0134 Bermuda 16 0.0000 Italy 5166 0.0116 Senegal 86 0.0000 Bolivia 96 0.0000 Jamaica 48 0.0085 Serbia 178 0.0185 Bosnia and Herzegovina 103 0.0422 Japan 829 0.0222 Seychelles 31 0.0000 Brazil 1048 0.0269 Jordan 358 0.0154 Sierra Leone 15 0.0313 British Virgin Islands 124 0.0087 Kazakhstan 102 0.0000 Singapore 429 0.0335 Bulgaria 511 0.0151 Kenya 81 0.0000 Slovak Republic 447 0.0099 Burkina Faso 30 0.0000 Korea, Dem. People’s Rep. 38 0.0000 Slovenia 412 0.0224 Cabo Verde 22 0.0000 Korea, Rep. 561 0.0271 Solomon Islands 11 0.0000 Cameroon 47 0.0000 Kuwait 335 0.0331 South Africa 729 0.0262 Canada 848 0.0057 Kyrgyz Republic 188 0.0114 Sri Lanka 50 0.0000 Cayman Islands 20 0.0000 Latvia 679 0.0255 Sudan 43 0.0163 Chad 18 0.1599 Lebanon 494 0.0139 Suriname 18 0.1829 Chile 872 0.0001 Libya 152 0.0127 Swaziland 21 0.0359 China 1007 0.0102 Liechtenstein 56 0.0159 Sweden 1264 0.0051 Colombia 642 0.0117 Lithuania 642 0.0137 Switzerland 2095 0.0112 Congo, Dem. Rep. 19 0.0000 Luxembourg 282 0.0080 Syrian Arab Republic 166 0.0339 Congo, Rep. 15 0.0000 Macao SAR, China 34 0.0010 Taiwan, China 458 0.0229 Costa Rica 274 0.0000 Macedonia, FYR 66 0.0732 Tanzania 42 0.0844 Croatia 328 0.0147 Madagascar 23 0.0000 Thailand 343 0.0204 Cuba 314 0.0000 Malaysia 281 0.0026 Togo 17 0.0000 Cyprus 708 0.0236 Mali 22 0.0000 Trinidad and Tobago 97 0.0000 Czech Republic 1080 0.0245 Malta 247 0.0121 Tunisia 743 0.0061 Côte d’Ivoire 76 0.0009 Marshall Islands 10 0.0011 Turkey 1385 0.0367 Denmark 1286 0.0184 Mauritania 56 0.0000 Uganda 13 0.0000 Dominica 80 0.0000 Mauritius 65 0.0000 Ukraine 311 0.0052 Dominican Republic 456 0.0000 Mexico 1964 0.0051 United Arab Emirates 962 0.0086 Ecuador 337 0.0008 Micronesia, Fed. Sts. 30 0.0487 United Kingdom 5521 0.0120 Egypt, Arab Rep. 588 0.0254 Moldova 45 0.0000 United States 3882 0.0106 El Salvador 128 0.0000 Monaco 99 0.0348 Uruguay 247 0.0091 Equatorial Guinea 60 0.0000 Montenegro 61 0.0958 Uzbekistan 10 0.0000 Estonia 380 0.0254 Morocco 1703 0.0000 Venezuela, RB 675 0.0056 Ethiopia 28 0.0000 Mozambique 19 0.0000 Vietnam 128 0.0005 Finland 897 0.0029 Namibia 38 0.0154 Virgin Islands (U.S.) 22 0.0068 France 8946 0.0250 Nauru 15 0.0000 West Bank and Gaza 36 0.2886 French Polynesia 20 0.0000 Nepal 12 0.0000 Yemen, Rep. 86 0.0313 Gabon 41 0.0000 Notes: N denotes the number of exporters and Localization is the country index of localization, which measures the amount of geographical concentration exhibiting exporters to each destination. Table A2 List of destinations Country N Localization Country N Localization Country N Localization Afghanistan 13 0.0000 Gambia 20 0.0000 The Netherlands 3010 0.0190 Albania 168 0.1108 Georgia 101 0.0025 New Caledonia 34 0.0000 Algeria 826 0.0027 Germany 6427 0.0174 New Zealand 278 0.0036 Andorra 887 0.0903 Ghana 72 0.0000 Nicaragua 63 0.0000 Angola 89 0.0000 Gibraltar 90 0.0000 Niger 44 0.0000 Antigua and Barbuda 20 0.0000 Greece 1768 0.0178 Nigeria 99 0.0560 Argentina 818 0.0100 Guam 10 0.0000 Norway 733 0.0004 Armenia 53 0.0794 Guatemala 226 0.0000 Oman 103 0.0017 Aruba 19 0.0876 Guinea 19 0.0000 Pakistan 189 0.0407 Australia 795 0.0128 Haiti 13 0.0000 Panama 492 0.0000 Austria 1620 0.0148 Honduras 107 0.0000 Paraguay 67 0.0193 Azerbaijan 28 0.0024 Hong Kong SAR, China 708 0.0315 Peru 435 0.0001 Bahamas, The 48 0.0000 Hungary 778 0.0276 Philippines 191 0.0096 Bahrain 154 0.0329 Iceland 160 0.0001 Poland 1627 0.0182 Bangladesh 67 0.0285 India 643 0.0232 Portugal 6861 0.0000 Barbados 32 0.0000 Indonesia 228 0.0136 Qatar 212 0.0445 Belarus 89 0.0000 Iran, Islamic Rep. 463 0.0109 Romania 902 0.0205 Belgium 3500 0.0217 Iraq 23 0.1835 Russian Federation 915 0.0189 Belize 79 0.0089 Ireland 1308 0.0000 San Marino 19 0.0000 Benin 26 0.0000 Israel 732 0.0307 Saudi Arabia 854 0.0134 Bermuda 16 0.0000 Italy 5166 0.0116 Senegal 86 0.0000 Bolivia 96 0.0000 Jamaica 48 0.0085 Serbia 178 0.0185 Bosnia and Herzegovina 103 0.0422 Japan 829 0.0222 Seychelles 31 0.0000 Brazil 1048 0.0269 Jordan 358 0.0154 Sierra Leone 15 0.0313 British Virgin Islands 124 0.0087 Kazakhstan 102 0.0000 Singapore 429 0.0335 Bulgaria 511 0.0151 Kenya 81 0.0000 Slovak Republic 447 0.0099 Burkina Faso 30 0.0000 Korea, Dem. People’s Rep. 38 0.0000 Slovenia 412 0.0224 Cabo Verde 22 0.0000 Korea, Rep. 561 0.0271 Solomon Islands 11 0.0000 Cameroon 47 0.0000 Kuwait 335 0.0331 South Africa 729 0.0262 Canada 848 0.0057 Kyrgyz Republic 188 0.0114 Sri Lanka 50 0.0000 Cayman Islands 20 0.0000 Latvia 679 0.0255 Sudan 43 0.0163 Chad 18 0.1599 Lebanon 494 0.0139 Suriname 18 0.1829 Chile 872 0.0001 Libya 152 0.0127 Swaziland 21 0.0359 China 1007 0.0102 Liechtenstein 56 0.0159 Sweden 1264 0.0051 Colombia 642 0.0117 Lithuania 642 0.0137 Switzerland 2095 0.0112 Congo, Dem. Rep. 19 0.0000 Luxembourg 282 0.0080 Syrian Arab Republic 166 0.0339 Congo, Rep. 15 0.0000 Macao SAR, China 34 0.0010 Taiwan, China 458 0.0229 Costa Rica 274 0.0000 Macedonia, FYR 66 0.0732 Tanzania 42 0.0844 Croatia 328 0.0147 Madagascar 23 0.0000 Thailand 343 0.0204 Cuba 314 0.0000 Malaysia 281 0.0026 Togo 17 0.0000 Cyprus 708 0.0236 Mali 22 0.0000 Trinidad and Tobago 97 0.0000 Czech Republic 1080 0.0245 Malta 247 0.0121 Tunisia 743 0.0061 Côte d’Ivoire 76 0.0009 Marshall Islands 10 0.0011 Turkey 1385 0.0367 Denmark 1286 0.0184 Mauritania 56 0.0000 Uganda 13 0.0000 Dominica 80 0.0000 Mauritius 65 0.0000 Ukraine 311 0.0052 Dominican Republic 456 0.0000 Mexico 1964 0.0051 United Arab Emirates 962 0.0086 Ecuador 337 0.0008 Micronesia, Fed. Sts. 30 0.0487 United Kingdom 5521 0.0120 Egypt, Arab Rep. 588 0.0254 Moldova 45 0.0000 United States 3882 0.0106 El Salvador 128 0.0000 Monaco 99 0.0348 Uruguay 247 0.0091 Equatorial Guinea 60 0.0000 Montenegro 61 0.0958 Uzbekistan 10 0.0000 Estonia 380 0.0254 Morocco 1703 0.0000 Venezuela, RB 675 0.0056 Ethiopia 28 0.0000 Mozambique 19 0.0000 Vietnam 128 0.0005 Finland 897 0.0029 Namibia 38 0.0154 Virgin Islands (U.S.) 22 0.0068 France 8946 0.0250 Nauru 15 0.0000 West Bank and Gaza 36 0.2886 French Polynesia 20 0.0000 Nepal 12 0.0000 Yemen, Rep. 86 0.0313 Gabon 41 0.0000 Country N Localization Country N Localization Country N Localization Afghanistan 13 0.0000 Gambia 20 0.0000 The Netherlands 3010 0.0190 Albania 168 0.1108 Georgia 101 0.0025 New Caledonia 34 0.0000 Algeria 826 0.0027 Germany 6427 0.0174 New Zealand 278 0.0036 Andorra 887 0.0903 Ghana 72 0.0000 Nicaragua 63 0.0000 Angola 89 0.0000 Gibraltar 90 0.0000 Niger 44 0.0000 Antigua and Barbuda 20 0.0000 Greece 1768 0.0178 Nigeria 99 0.0560 Argentina 818 0.0100 Guam 10 0.0000 Norway 733 0.0004 Armenia 53 0.0794 Guatemala 226 0.0000 Oman 103 0.0017 Aruba 19 0.0876 Guinea 19 0.0000 Pakistan 189 0.0407 Australia 795 0.0128 Haiti 13 0.0000 Panama 492 0.0000 Austria 1620 0.0148 Honduras 107 0.0000 Paraguay 67 0.0193 Azerbaijan 28 0.0024 Hong Kong SAR, China 708 0.0315 Peru 435 0.0001 Bahamas, The 48 0.0000 Hungary 778 0.0276 Philippines 191 0.0096 Bahrain 154 0.0329 Iceland 160 0.0001 Poland 1627 0.0182 Bangladesh 67 0.0285 India 643 0.0232 Portugal 6861 0.0000 Barbados 32 0.0000 Indonesia 228 0.0136 Qatar 212 0.0445 Belarus 89 0.0000 Iran, Islamic Rep. 463 0.0109 Romania 902 0.0205 Belgium 3500 0.0217 Iraq 23 0.1835 Russian Federation 915 0.0189 Belize 79 0.0089 Ireland 1308 0.0000 San Marino 19 0.0000 Benin 26 0.0000 Israel 732 0.0307 Saudi Arabia 854 0.0134 Bermuda 16 0.0000 Italy 5166 0.0116 Senegal 86 0.0000 Bolivia 96 0.0000 Jamaica 48 0.0085 Serbia 178 0.0185 Bosnia and Herzegovina 103 0.0422 Japan 829 0.0222 Seychelles 31 0.0000 Brazil 1048 0.0269 Jordan 358 0.0154 Sierra Leone 15 0.0313 British Virgin Islands 124 0.0087 Kazakhstan 102 0.0000 Singapore 429 0.0335 Bulgaria 511 0.0151 Kenya 81 0.0000 Slovak Republic 447 0.0099 Burkina Faso 30 0.0000 Korea, Dem. People’s Rep. 38 0.0000 Slovenia 412 0.0224 Cabo Verde 22 0.0000 Korea, Rep. 561 0.0271 Solomon Islands 11 0.0000 Cameroon 47 0.0000 Kuwait 335 0.0331 South Africa 729 0.0262 Canada 848 0.0057 Kyrgyz Republic 188 0.0114 Sri Lanka 50 0.0000 Cayman Islands 20 0.0000 Latvia 679 0.0255 Sudan 43 0.0163 Chad 18 0.1599 Lebanon 494 0.0139 Suriname 18 0.1829 Chile 872 0.0001 Libya 152 0.0127 Swaziland 21 0.0359 China 1007 0.0102 Liechtenstein 56 0.0159 Sweden 1264 0.0051 Colombia 642 0.0117 Lithuania 642 0.0137 Switzerland 2095 0.0112 Congo, Dem. Rep. 19 0.0000 Luxembourg 282 0.0080 Syrian Arab Republic 166 0.0339 Congo, Rep. 15 0.0000 Macao SAR, China 34 0.0010 Taiwan, China 458 0.0229 Costa Rica 274 0.0000 Macedonia, FYR 66 0.0732 Tanzania 42 0.0844 Croatia 328 0.0147 Madagascar 23 0.0000 Thailand 343 0.0204 Cuba 314 0.0000 Malaysia 281 0.0026 Togo 17 0.0000 Cyprus 708 0.0236 Mali 22 0.0000 Trinidad and Tobago 97 0.0000 Czech Republic 1080 0.0245 Malta 247 0.0121 Tunisia 743 0.0061 Côte d’Ivoire 76 0.0009 Marshall Islands 10 0.0011 Turkey 1385 0.0367 Denmark 1286 0.0184 Mauritania 56 0.0000 Uganda 13 0.0000 Dominica 80 0.0000 Mauritius 65 0.0000 Ukraine 311 0.0052 Dominican Republic 456 0.0000 Mexico 1964 0.0051 United Arab Emirates 962 0.0086 Ecuador 337 0.0008 Micronesia, Fed. Sts. 30 0.0487 United Kingdom 5521 0.0120 Egypt, Arab Rep. 588 0.0254 Moldova 45 0.0000 United States 3882 0.0106 El Salvador 128 0.0000 Monaco 99 0.0348 Uruguay 247 0.0091 Equatorial Guinea 60 0.0000 Montenegro 61 0.0958 Uzbekistan 10 0.0000 Estonia 380 0.0254 Morocco 1703 0.0000 Venezuela, RB 675 0.0056 Ethiopia 28 0.0000 Mozambique 19 0.0000 Vietnam 128 0.0005 Finland 897 0.0029 Namibia 38 0.0154 Virgin Islands (U.S.) 22 0.0068 France 8946 0.0250 Nauru 15 0.0000 West Bank and Gaza 36 0.2886 French Polynesia 20 0.0000 Nepal 12 0.0000 Yemen, Rep. 86 0.0313 Gabon 41 0.0000 Notes: N denotes the number of exporters and Localization is the country index of localization, which measures the amount of geographical concentration exhibiting exporters to each destination. Appendix B. Details on the Application of Duranton and Overman (2005) In this section we explain in detail the application of DO to uncover agglomeration by export destination. We proceed as follows. For each export destination we compute the unique bilateral distances between exporters by applying the haversine formula to the zip code coordinates. Next, we estimate the distribution of bilateral distances of each country via kernel estimation. As in DO, we use a Gaussian kernel, choosing the bandwidth so as to minimize the mean integrated squared error. Distances are reflected around zero, using the method proposed by Silverman (1986) to avoid giving positive densities to negative distances. Note also that firms within the same zip code are computed as being separated by 0 kilometers. The kernel density estimation for country c at every kilometer d ( K^c(d)) reads as follows: Kc^(d)=2nc(nc−1)h∑i=1nc−1∑j=i+1ncf(d−di,jh), (2) where nc is the number of export firms to country c, h is the bandwidth and f is the Gaussian probability density function.15Figure 8A shows the spatial distribution of exporters to India in 2007. The existence of several clusters of exporters is apparent. Figure 8B plots the histogram and the kernel estimation of the distance distribution. The high density at very small distances stems from the large number of exporters that are located within very close zip codes. The second peak in the distribution at around 400 kilometers marks the distance that separates the clusters. Figure 8 View largeDownload slide Distribution of distances of firms exporting to India. Notes: This figure plots the spatial distribution of exporters to India in 2007 (A) and the histogram of the unique bilateral distances between them as well as the kernel estimate of the probability density function (B). Figure 8 View largeDownload slide Distribution of distances of firms exporting to India. Notes: This figure plots the spatial distribution of exporters to India in 2007 (A) and the histogram of the unique bilateral distances between them as well as the kernel estimate of the probability density function (B). To test for significant agglomeration, the observed spatial distribution is compared with the counterfactual. As stated in the main text, the counterfactual controls for both the spatial distribution of exporters, which may be agglomerated with respect to domestic firms, as well as the industry composition of exports to each country. We proceed as follows. For each pair of country and two-digit industry, we draw 1000 random samples from exporters in the industry; each draw of size the actual number of exporters to the country operating in that industry. Then, for each country we aggregate each draw across the different industries to collect 1000 random samples of size nc (the actual number of exporters to the country) with an industry composition that replicates the one observed in the data. In our baseline analysis we carry out the test of significant agglomeration at distances below 100 km. As in DO we construct two tests, one of localization and one of dispersion, both with a significance level of 95%. We do the following. For each kilometer, we rank our 1000 counterfactual distributions in ascending order and then pick the percentile that makes 95% of the counterfactual distributions lie below it across all distances. When it is not possible to find a percentile making exactly 95% of the simulations be below it, we use linear interpolation. Note that in our baseline results all the percentiles fulfilling this criterion range between the 96.4-th and the 99.5-th. This percentile is referred to as the localization threshold, whereas a dispersion threshold is defined in a similar way, i.e. the percentile that makes 5% of the counterfactual distributions lie below it across all distances. Note that in DO the localization and dispersion thresholds are referred to as global confidence bands. The Figure 9A plots the density of the distance distribution below 100 km and a small sample of the counterfactual distributions. The Figure 9B displays the localization and dispersion thresholds (the upper and lower dashed lines, respectively). Figure 9 View largeDownload slide Localization and dispersion thresholds of India. Notes: (A) shows the estimated distance density of exporters to India in 2007 as well as a sample of 20 counterfactual distributions. (B) Displays the localization and dispersion thresholds, represented by the upper and lower dashed lines, respectively. The shaded area between the distance distribution and the localization threshold constitutes the country index of agglomeration. Figure 9 View largeDownload slide Localization and dispersion thresholds of India. Notes: (A) shows the estimated distance density of exporters to India in 2007 as well as a sample of 20 counterfactual distributions. (B) Displays the localization and dispersion thresholds, represented by the upper and lower dashed lines, respectively. The shaded area between the distance distribution and the localization threshold constitutes the country index of agglomeration. We define exporters to a destination to be significantly localized if the distance distribution is above the localization threshold in at least one kilometer. Similarly, exporters to a destination are defined to be dispersed if the distance distribution is below the dispersion threshold in at least one kilometer and the country does not exhibit localization. Note that the latter condition stems from the fact that densities sum up to one, hence localization at some distances implies dispersion at others. Following these criteria, Figure 9B shows that exporters to India are significantly localized. Note finally that localization and dispersion can be assessed at each distance, by comparing the distance distribution and the thresholds at each kilometer. In the example, localization takes place at distances below 70 km. Finally, we define a country index of agglomeration as the sum across distances of the difference between the distance distribution and the localization threshold if the former is above the latter and zero otherwise. This index accounts for the amount of exporter agglomeration by destination and it is the counterpart of the industry index of agglomeration defined by DO. In Figure 9B it is depicted as the shaded area between the distance density and the upper dashed line. Appendix C. Details on the Principal Component Analysis In Section 4, we create an index of institutional quality by applying a principal component analysis (PCA) on the World Bank Worldwide Governance Indicators (WGI). These measures account for six dimensions of governance, namely voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption, see Kaufmann et al. (2010). Given the high correlation between these measures, a PCA is a useful tool to summarize all the information and construct a synthetic index that accounts for the overall institutional quality in each country. The PCA finds a set of uncorrelated linear combinations of the measures that accounts for most of the variance. In our case, the first of such combinations accounts for 88% of the variance and constitutes the index of institutional quality that we include in Table 3. Figure 10 plots the correlation of the institutional quality index with the rule of law (WGI), the Doing Business Index, investor protection and time to import goods. The high correlations suggest that our index of institutional quality provides a good approximation of the business environment that firms face when exporting to every foreign country. Figure 10 View largeDownload slide Correlation of the synthetic index of institutional quality (PCA) with other measures. This figure shows the correlation of our synthetic index of institutional quality, computed from a PCA on the Worldwide Governance Indicators, with other measures proxying the institutional environment of every country. Figure 10 View largeDownload slide Correlation of the synthetic index of institutional quality (PCA) with other measures. This figure shows the correlation of our synthetic index of institutional quality, computed from a PCA on the Worldwide Governance Indicators, with other measures proxying the institutional environment of every country. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

Journal of Economic GeographyOxford University Press

Published: Nov 13, 2017

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