The lumpiness of German exports and imports of goods

Wagner, Joachim Working Paper The lumpiness of German exports and imports of goods Economics Discussion Papers, No. 2016-19 Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Wagner, Joachim (2016) : The lumpiness of German exports and imports of goods, Economics Discussion Papers, No. 2016-19, Kiel Institute for the World Economy (IfW), Kiel This Version is available at: https://hdl.handle.net/10419/140889 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Licensed under the Creative Commons License - Attribution 4.0 International (CC BY 4.0) Discussion Paper No. 2016-19 | May 11, 2016 | http://www.economics-ejournal.org/economics/discussionpapers/2016-19 The Lumpiness of German Exports and Imports of Goods Joachim Wagner Abstract This paper looks at a hitherto neglected extensive margin of international trade by investigating for the first time the frequency at which German exporters and importers trade a given good with a given country. Imports and exports show a high degree of lumpiness. In a given year about half of all firm-good-country combinations are recorded only once or twice for trade with EU countries, and this is the case for more than 60 percent of all firm-good-country combinations in trade with non-EU countries. The frequency of recorded transactions tends to decline with an increase in the number of transactions per year. This is in accordance with the presence of per-shipment fixed costs that provide an incentive for trading firms to engage in cross-border transactions infrequently. Empirical models show that for Germany the frequency of transactions at the firm-good-country level tends to decrease with an increase in per-shipment costs when unobserved firm and goods characteristics are controlled for. JEL F14 Keywords Lumpiness of trade; imports; exports; Germany Authors Joachim Wagner, Leuphana University Lueneburg and CESIS, KTH Stockholm, Sweden, [email protected] The author thanks Horst Raff for introducing him to the concept of lumpiness of trade. All computations were done at the Research Data Centre of the Federal Statistical Office in Wiesbaden. The author thanks Melanie Scheller for preparing the transaction level data and for checking the output of the do-files for the violation of privacy. The micro data used are strictly confidential but not exclusive; see http://www.forschungsdatenzentrum.de/datenzugang.asp for information on how to access the data. To facilitate replications the Stata do-files used are available from the author on request. Citation Joachim Wagner (2016). The Lumpiness of German Exports and Imports of Goods. Economics Discussion Papers, No 2016-19, Kiel Institute for the World Economy. http://www.economics-ejournal.org/economics/ discussionpapers/2016-19 2 1. Motivation International trade is costly. While tariff-type trade restrictions tend to play a diminishing role only today, other barriers to trade still matter. Hornok and Koren (2015a) argue that some of these trade costs are not proportional to the value of the transaction. Hence, the assumption of iceberg-type trade costs used in most models of international trade is not appropriate here. There are fixed costs that come with every shipment across borders. These costs include paper work (filling in customs declarations and other forms) and the time and monetary costs related to having the cargo inspected. These fixed costs lead to a trade-off between per-shipment trade costs and shipping frequency. On the one hand, firms engaged in international trade would like to economize on these per-shipment costs by sending fewer and larger shipments. On the other hand, this comes at a cost due to time-lags related to waiting to fill a larger shipment and because of the need to keep costly inventories between shipment arrivals. At the firm level, shipping frequency can be considered as an additional margin of trade besides the intensive margin (the volume of trade) and the extensive margins made of the number of goods traded and the number of countries traded with (see Békés et al. 2011). That said, per-shipment costs may make it optimal for traders to engage in cross-border transactions infrequently. If this is the case, trade flows at the microeconomic level – imports by one firm of one good from one country of origin, or exports by one firm of one good to one country of destination – are lumpy. Empirical evidence on the lumpiness of international trade has been reported in a small number of studies. Alessandria et al. (2010) use monthly data on the universe of US exports for goods in narrowly defined categories to six destination countries from January 1990 to April 2005 and find that goods are traded infrequently over the 3 course of a year. Exports are lumpy, trade is highly concentrated in a few months. Békés et al. (2015a) explore transaction level data for exports from France in 2007 at the firm-product-destination level and approximate the number of shipments by the number of months within a year in which a transaction is recorded for a given firmproduct-destination. A large number of firms ship their products only in a few months. The authors report a high degree of lumpiness in exports – almost 45 percent of firms ship a given product to a given destination only once a year to EU markets and more that 60 percent do so to extra-EU markets. Hornok and Koren (2015a) examine disaggregated data on exports of the United States and Spain in 2009 and look at the lumpiness of trade transactions by documenting how frequently the same good is exported to the same destination country within a year. Trade transactions for a given product to a given destination show strong signs of lumpiness. Kropf and Sauré (2014) look at transaction level data for Swiss exports from 2007, a subset of which contains a firm identifier so that export data are at the firm-product-destination level. Exports are lumpy; the mean value of shipments per year is 3.5. Hornok and Koren (2015a) investigate how the frequency and the size of shipments vary with the level of per-shipment costs. They estimate a number of gravity-like regressions (that include variables for GDP and GDP per capita of destination countries, and distance to destination countries of exports, among others, as control variables) for exports of the US and Spain at the product-country level and find that the number of shipments decrease ceteris paribus when the time costs or the monetary costs per shipment increase. Up to now, we have no evidence on the degree of lumpiness of international trade in goods by German firms and its relation to per-shipment costs. Given that Germany is one of the leading actors on the world market for goods (according to the 4 WTO’s World Trade Report, it was number three in both exports and imports in 2013; see World Trade Organization (2014), p. 34), empirical evidence here is interesting in itself. This paper contributes to the literature by providing such evidence based on transaction data for complete German exports and imports at the firm-good-country level for the years 2009 to 2012. To anticipate the most import results I document that imports and exports show a high degree of lumpiness. In a given year about half of all firm-good-country combinations are recorded only once or twice for trade with EU-countries, and this is the case for more than 60 percent of all firm-good-country combinations in trade with non-EU countries. Empirical models show that the frequency of transactions at the firm-good-country level tends to decrease with an increase in per-shipment costs when unobserved firm and goods characteristics are controlled for. The rest of the paper is organized as follows. Section 2 introduces the data used and discusses measurement issues. Section 3 reports descriptive results for the lumpiness of German exports and imports of goods. Section 4 presents results from regressions of the number of shipments on per-shipment costs. Section 5 concludes. 2. Data and measurement issues The empirical investigation uses a tailor-made data set that combines high quality transaction level data on Germany’s exports and imports of goods from official statistics with data on per-shipment costs in international trade plus other information for characteristics of the countries traded with. 5 In Germany information on goods 1 traded across borders and on the countries traded with is available from the statistic on foreign trade (Außenhandelsstatistik). This statistic is based on two sources. One source is the reports by German firms on transactions with firms from countries that are members of the European Union (EU); these reports are used to compile the so-called Intrahandelsstatistik on intra-EU trade. The other source is transaction-level data collected by the customs on trade with countries outside the EU (the so-called Extrahandelsstatistik). 2 The raw data that are used to build the statistic on foreign trade are transaction level data, i.e. they relate to one transaction of a German firm with a firm located outside Germany at a time. Published data from this statistic report exports and imports aggregated at the level of goods traded and by country of origin. The data used in this paper are based on the raw data at the transaction level. The unit of observation in these raw data is a single transaction between economic agents located in two countries, e.g. the import of X kilogram of good A with a value of Y Euro from China to Germany. 3 For a given year, the sum over all transactions is identical to the figures published by the Federal Statistical Office for total exports or imports of Germany. 1 Note that in Germany information on international trade in services is compiled by the German Central Bank (Deutsche Bundesbank) to build the balance of services trade (Dienstleistungsbilanz). 2 Note that firms with a value of trade with EU-countries that did not exceed 400,000 Euro in the previous year or in the current year per direction of trade do not have to report to the statistic on intraEU trade. For trade with firms from non-member countries all transactions that exceed 1,000 Euro (or have a weight that exceeds 1,000 kilogram) are registered. For details see Statistisches Bundesamt, Qualitätsbericht Außenhandel, Januar 2011. 3 Transaction level data of this type have been used in numerous empirical studies on international trade for many countries in recent years; see Wagner (2016) for a survey. 6 The record of the transaction usually includes a firm identifier (tax registration number) of the trading German firm. 4 Using this identifier information at the transaction level can be aggregated at the level of the trading firm. These data show which firm trades how much of which good with firms from which country in a given month. Products are distinguished according to very detailed classifications. In the data used for this paper, the Harmonized System at 6-digit level (HS6) is used as the product classification system. For the reporting years 2009 to 2012 the transaction level data at the monthfirm-product-country level were used to compute a proxy-variable for the frequency of export or import transactions by one firm for one HS6-good and one country in a year. This proxy-variable is given by the number of months in a year in which transactions of this firm-good-country combination are recorded. Note that within a month all exports or imports of a specific HS6-good to a specific country by a firm are aggregated and reported as one data point only. Therefore, the proxy for trade frequency used here may be biased for high frequency traders which trade the same good with the same country in (nearly) every month several times. For low frequency traders, however, the number of months with recorded transactions is a reliable approximation (see the discussion in Békés et al. 2015). The transaction level data at the firm-good-country level were matched to country-specific information that is taken from two sources. Information on two types of per-shipment trade costs is taken from the World Bank’s Doing Business Data Base (see www.doingbusiness.org). Doing Business 4 Note that this identifier is missing for several transactions for various reasons including traders that do not have a (German) tax identification number; further details were not revealed to me. 7 measures the time and cost (excluding tariffs) necessary to complete every official procedure that is needed for exporting and importing a standardized cargo of goods by ocean transport. Time is recorded in calendar days, costs are in U.S. dollars; for details see appendix. 5 Note that the time and cost of ocean transport are not included in the cost indicators from the Doing Business data base. The time dimension of transport can be considered as another per-shipment cost – it takes X days to ship a container from Germany to country Y, irrespective of the amount of goods in this container. Time for transport is closely linked to distance between countries. Therefore, distance is included as another trade cost variable. Data on distance between Germany and the countries of origin of imports, and the countries of destination of exports, are taken from the CEPII’s GeoDist database (Mayer and Zignago 2011). The “distw” – measure is used that calculates the distance between two countries based on bilateral distances between the biggest cities of those two countries, those inter-city distances being weighted by the share of the city in the overall country’s population (see Mayer and Zignago (2011, p. 11) for details). The empirical models that link the number of international trade transactions at the firm-good-country level to per-shipment costs of trade include two control variables that are standard in gravity models of trade, namely Gross National Income and per capita Gross National Income (see Hornok and Koren (2015a) for a similar approach). Gross National Income per capita (measured in current US-Dollar) is taken from the Doing Business database directly, Gross National Income is 5 Data from the World Bank’s Doing Business database have been used in the literature on the lumpiness of international trade before; see Alessandria et al. (2010) and Hornok and Koren (2015a, 2015b). 8 calculated from the per capita values and the size of the population reported in the data base. 6 In the empirical study two groups of trade partner countries are distinguished, namely countries that are members of the European Union (EU) and Non-EU countries. This controls for the cutoff-point used when imports from and exports to EU-members are recorded. Furthermore, information on per-shipment costs is not relevant for intra-EU trade. 3. The lumpiness of German exports and imports: Descriptive evidence The degree of lumpiness of trade is measured by the number of import or export transactions at the firm-product-country level. In the German trade data used here trade frequency is measured by the number of months in a year in which transactions of this firm-good-country combination are recorded. Note that within a month all exports or imports of a specific HS6-good to or from a specific country by one single firm are aggregated and reported as one data point only. Therefore, the proxy for trade frequency used here may be biased for high frequency traders which trade the same good with the same country in (nearly) every month several times. For low frequency traders, however, the number of months with recorded transactions is a reliable approximation (see the discussion in Békés et al. 2015). 6 Note that information whether a country is landlocked or not (that is available from CEPII’s GeoDist database described in Mayer and Zignago (2011) and that has been used in the literature on the lumpiness of trade) is not used here because this country characteristic is closely related to the time and monetary costs of exports and imports. 15 imports to Germany) or the costs of imports (for exports from Germany). Note that both indicators of per-shipment costs of trade are highly positively correlated with a correlation coefficient of +0.79 for export costs and +0.77 for import costs; therefore, the two indicators are included in the empirical models alternatively. All models include the distance to the country of origin (for imports to Germany) or the distance to the destination country (for exports from Germany). Distance is closely related to the time necessary to transport a good from the country of origin or to the country of destination, and to the costs of transport. For the countries included in the empirical investigation distance is negatively correlated with the time and cost indicators, but the correlation is small (-0.17 for time to export and -0.18 for time to import; -0.24 for cost to export or import). Furthermore, all models include two standard variables from gravity models of trade, Gross National Income and per capital Gross National Income, as control variables. 9 The indicators for trade costs and the control variables are included in Model 1 and Model 2 (where Model 1 includes the time to trade, and Model 2 includes the costs of trade from the Doing Business Database detailed above). All these variables are constant for a given country of destination (for exports) or origin (for imports). Descriptive evidence reported in Table 3 and Table 7 (for import and export transactions with the United States and China) demonstrates that the number of transactions per year by firm-good-country is not constant. For a given country of 9 Gross National Income per capita (measured in current US-Dollar) is taken from the Doing Business database directly, Gross National Income is calculated from the per capita values and the size of the population reported in the data base. Information for 2012 used here is taken from the 2014 edition. 16 destination or origin with given values for trade costs (and control variables) the number of transactions varies widely between one and twelve. This illustrates that for some firms trading some goods with a specific country the same measured trade costs lead to a high degree of lumpiness in trade, and for others they lead to a low degree of lumpiness. This might be caused by differences between firms with respect to productivity, size, or other characteristics. Unfortunately, the data available have no information on the trading German firm (besides the firm identifier). To control for unobserved firm characteristics in the link between trade costs and lumpiness of trade Model 3 and Model 4 include firm fixed effects. Identification of the role of trade costs for the number of transactions per year by firm-good-country here comes from the within-firm variation over goods and countries. Descriptive evidence reported in Table 4 (for imports) and Table 8 (for exports) shows that the average number of transactions per year by firm-good-country differs between different groups of goods. This variation is expected to be related to the differences in the fixed costs of trade with the different countries of destination or origin of these goods, but it might as well be related to the characteristics of the goods itself (irrespective of the countries traded with). To control for these unobserved characteristics of goods in the link between trade costs and lumpiness of trade, and to take care of the role of unobserved firm characteristics discussed above, Model 5 and Model 6 include fixed effects at the firm-good level. Identification of the role of trade costs for the number of transactions per year by firm-good-country here comes from the within-firm within-good variation over countries. Following the literature on the lumpiness of trade all variables are included in logs. The regression coefficients, therefore, are estimates for the elasticity of the 17 number of trade transactions per year by firm-good-country with respect to an indicator of trade costs (or a control variable). 10 If higher per-shipment costs make it optimal for traders to engage in crossborder transactions more infrequently and if the degree of lumpiness is positively related to fixed costs of trade this means that the number of transactions per year at the firm-good-country level decreases with an increase in trade costs. In the empirical models this implies a negative sign of the estimated elasticity of the number of transactions with respect to a variable that measures trade costs. 4.2 Imports Results for the empirical models for the lumpiness of imports are reported in Table 9. 11 From Model 5 and 6, which are the preferred models because here the unobserved characteristics of both firms and goods are controlled for, we see that the costs of exports in the country of origin and the distance between Germany and the country of origin, are negatively related to the number of transaction per year at the firm-good-country level. These results are in line with the expectations regarding the link between per-shipment costs and the degree of the lumpiness of trade, and this holds in the other models (without fixed effects, and with firm fixed-effects only), too. The exception is the time to export in the country of origin. The estimated regression coefficient of cost indicator is statistically insignificant at a conventional level in Model 5 (and positive and significant in Model 1 and Model 3). 10 The big picture is identical when all variables enter the empirical models in levels; details are available on request. 11 Note that all p-values are based on estimated standard errors that are clustered at the level of the firm. 18 [Table 9 near here] Regarding the estimated size of the elasticities of the number of transactions with respect to trade costs, from Model 6 we see that a one hundred percent increase in the cost of export in the country of origin leads to a reduction in the number of import transactions by 15.3 percent. Doubling the distance between Germany and the country of origin reduces the number of transactions by 11 percent according to Model 5 and by 14.5 percent according to Model 6. As is documented in Appendix Table 3 trade costs vary considerably between the countries of origin; therefore, the estimated elasticities can be considered to be significant from an economic point of view (and not only from a statistical point of view), too. It was pointed out in section 3 that within a month all imports of a specific HS6good from a specific country by one single firm are aggregated and reported as one data point only. Therefore, the proxy for trade frequency used here may be biased for high frequency traders which trade the same good with the same country in (nearly) every month several times. The large increase in the frequency of the number of import transactions per year from 11 to 12 reported in Table 1 to Table 3 illustrates this. As a robustness check, therefore, all empirical models were estimated using a restricted sample that excludes cases with a calculated number of 12 transactions (see the discussion in Békés et al. 2015). The big picture from this robustness check is identical to the one reported in Table 9; details are available on request. 4.2 Exports Results for the empirical models for the lumpiness of exports are reported in Table 10. From Model 5 and 6, which are again the preferred models because the 19 unobserved characteristics of both firms and goods are controlled for, we see that all three indicators of trade costs are negatively related to the number of transaction per year at the firm-good-country level. These results are in line with the expectations regarding the link between per-shipment costs and the degree of the lumpiness of trade, and this holds in the other models (without fixed effects, and with firm fixedeffects only), too. [Table 10 near here] Regarding the estimated size of the elasticities of the number of transactions with respect to trade costs, from Model 5 we see that a one hundred percent increase in the time to import in the country of destination leads to a reduction in the number of import transactions by 6.7 percent. According to Model 6, doubling the costs of imports in the destination country reduces the number of export transactions by 2.4 percent. This estimated elasticity is considerable smaller than the value for import transactions. Doubling the distance between Germany and the destination country reduces the number of transactions by ca. 18 percent according to Model 5 and Model 6. As is documented in Appendix Table 3 trade costs vary considerably between the countries of destination; therefore, the estimated elasticities can be considered to be significant from an economic point of view (and not only from a statistical point of view), too. Like in the case of import transactions, as a robustness check all empirical models were estimated using a restricted sample that excludes cases with a calculated number of 12 transactions. Again, the big picture from this robustness check is identical to the one reported in Table 10; details are available on request. 20 5. Concluding remarks This paper looks at a hitherto neglected extensive margin of international trade by investigating for the first time the frequency at which German exporters and importers trade a given good with a given country over a year. Imports and exports show a high degree of lumpiness. In a given year about half of all firm-good-country combinations are recorded only once or twice for trade with EU-countries, and this is the case for more than 60 percent of all firm-good-country combinations in trade with non-EU countries. These findings for Germany are in line with the big picture from empirical studies for firms from the US, France, Spain and Switzerland. The frequency of recorded transactions tends to decline with an increase in the number of transactions per year. This is in accordance with the presence of pershipment fixed costs that provide an incentive for trading firms to engage in crossborder transactions infrequently. Empirical models show that for Germany the frequency of transactions at the firm-good-country level tends to decrease with an increase in per-shipment costs when unobserved firm and goods characteristics are controlled for. This is in line with results reported by Hornok and Koren (2015a) for exports of the US and Spain at the product-country level (without control for the exporting firms). That said, a reduction of per-shipment costs can be expected to lead to a decrease in the degree of lumpiness of trade and to a reduction of costly inventories. This will foster international trade by pushing a hitherto neglected extensive margin of international trade of firms – the number of transactions at the firm-good-country level. 21 References Alessandria, George, Joseph P. Kaborski, and Virgiliu Midrigan (2010): Inventories, Lumpy Trade, and Large Devaluations. American Economic Review 100 (December), 2304-2339. Békés, Gábor, Lionel Fontagné, Balázs Murakösy, and Vincent Vicard (2011): Frequency of export: an additional margin of trade. Extended abstract, December 9. Békés, Gábor, Lionel Fontagné, Balázs Murakösy, and Vincent Vicard (2015): Shipment Frequency of Exporters and Demand Uncertainty: An Inventory Management Approach. Centre for Economic Policy Research CEPR Discussion Paper No. 11013, December. Hornok, Cecília and Miklós Koren (2015a): Per-shipment Costs and the Lumpiness of International Trade. Review of Economics and Statistics 97 (2), 525-530. Hornok, Cecília and Miklós Koren (2015b): Administrative barriers to trade. Journal of International Economics 96 , Supplement 1, S110-S122. Kropf, Andreas and Philip Sauré (2014): Fixed Costs per Shipment. Journal of International Economics 92 (1), 166-184. Mayer, Thierry and Soledad Zignago (2011): Notes on CEPII’s distance measures: The GeoDist database. CEPII Document de Travail No 2011-25, December. Wagner, Joachim (2016): A survey of empirical studies using transaction level data on exports and imports. Review of World Economics 152 (1), 215-225. World Trade Organization (2014): World Trade Report 2014. Geneva: WTO. 22 Table 1: Number of import transactions per year by firm-good-country of origin in 2012 _________________________________________________________________________________ EU countries Non-EU countries Frequency Share (Percent) Frequency Share (Percent) Number of transactions per year _________________________________________________________________________________ 1 475,589 35.46 1,135,184 55.95 2 190,471 14.20 286,341 14.11 3 117,854 8.79 144,043 7.10 4 86,268 6.43 91,788 4.52 5 69,206 5.16 66,050 3.26 6 58,412 4.36 51,017 2.51 7 53,006 3.95 41,885 2.06 8 52,214 3.89 36,207 1.78 9 51,163 3.81 33,181 1.64 10 50,252 3.75 31,752 1.56 11 54,671 4.08 33,863 1.67 12 82,096 6.12 77,578 3.82 ---------------------------------------------------------------------------------------------------------------------------------------- Average number of transactions 4.168 2.783 _________________________________________________________________________________ Note: Number of transactions refers to months with recorded import transactions at the firm-productcountry of origin level; goods refer to categories at the HS6 level. 23 Table 2: Number of import transactions per year by firm-good-country of origin in 2012 for imports from the Netherlands and France _________________________________________________________________________________ Netherlands France Frequency Share (Percent) Frequency Share (Percent) Number of transactions per year _________________________________________________________________________________ 1 71,647 34.07 49,291 34.14 2 29,803 14.17 20,223 14.01 3 18,324 8.71 13,018 9.02 4 13,515 6,43 9,408 6.52 5 10,996 5.23 7,475 5.18 6 9,555 4.54 6,474 4.48 7 8,642 4.11 5,776 4.00 8 8,765 4.17 5,700 3.95 9 8,811 4.19 5,591 3,87 10 8,753 4.16 5,735 3.97 11 9,073 4.32 6,031 4.18 12 12,482 5.89 9,659 6.69 ---------------------------------------------------------------------------------------------------------------------------------------- Average number of transactions 4.273 4.284 _________________________________________________________________________________ Note: Number of transactions refers to months with recorded import transactions at the firm-productcountry of origin level; goods refer to categories at the HS6 level. 24 Table 3: Number of import transactions per year by firm-good-country of origin in 2012 for imports from the United States and China _________________________________________________________________________________ United States China Frequency Share (Percent) Frequency Share (Percent) Number of transactions per year _________________________________________________________________________________ 1 203,598 57.38 261,148 51.69 2 49,297 13.89 74,316 14.71 3 24,133 6.80 39,135 7.75 4 15,456 4.36 25,794 5.11 5 10,866 3.06 18,754 3.71 6 8,463 2.38 14,459 2.86 7 6,914 1.95 12,008 2.38 8 5,981 1.69 10,362 2.05 9 5,495 1.55 9,770 1.93 10 5,333 1.50 9,086 1.80 11 5,890 1.66 9,578 1.90 12 13,427 3.78 20,765 4.11 ---------------------------------------------------------------------------------------------------------------------------------------- Average number of transactions 2.724 2.991 _________________________________________________________________________________ Note: Number of transactions refers to months with recorded import transactions at the firm-productcountry of origin level; goods refer to categories at the HS6 level. 31 Table 10: Determinants of lumpiness of German exports of goods to non-EU countries 2012 ________________________________________________________________________________________________________________________________ Endogenous variable: Log of (number of transactions for firm-HS6good-country of destination combination) Model 1 2 3 4 5 6 Exogenous variables ________________________________________________________________________________________________________________________________ Log (time to import) ß -0.051 -0.039 -0.067 (days) p 0.000 0.000 0.000 Log (costs of import) ß -0.027 -0.011 -0.024 (US-Dollar) p 0.000 0.002 0.001 Log (distance to country of ß -0.059 -0.056 -0.107 -0.103 -0.181 -0.176 destination (kilometer) p 0.000 0.000 0.000 0.000 0.000 0.000 Log (Gross National Income ß 0.054 0.053 0.084 0.083 0.155 0.154 of country of destination) p 0.000 0.000 0.000 0.000 0.000 0.000 Log (per capita Gross National ß 0.010 0.028 0.018 0.032 0.028 0.051 Income of country of destin.) p 0.000 0.000 0.000 0.000 0.000 0.000 Constant ß 0.567 0.441 0.458 0.284 0.144 -0.134 P 0.000 0.000 0.000 0.000 0.053 0.025 Firm fixed effects (N = 106,550) no no yes yes no no Firm-HS6 fixed effects (N = 1,168,442) no no no no yes yes R-squared 0.024 0.023 0.218 0.218 0.564 0.563 Number of observations 3,388,205 3,388,205 3,388,205 3,388,205 3,388,205 3,388,205 ________________________________________________________________________________________________________________________________ Note: For a definition of exogenous variables see text. ß is the estimated regression coefficient, p is the prob-value of this estimate (based on estimated standard errors that are clustered at the level of the firm). 32 Appendix Table 1: Average number of import transactions per year by firm-goodcountry of origin for selected countries of origin in 2012 _________________________________________________________________________________ Average number of Number of total import transactions import transactions Country _________________________________________________________________________________ Argentina 2.42 5,110 Australia 2.04 14,614 Austria 4.00 154,996 Bangladesh 3.79 8,281 Belgium 4.26 93,742 Bosnia Herzegovina 4.15 5,106 Brazil 2.82 17,969 Bulgaria 3.71 8,416 Canada 2.30 31,891 China 2.99 505,175 Croatia 2.68 9,105 Czech Republic 4.63 55,445 Denmark 4.03 55,234 Egypt Arab Republic 2.43 6,176 Finland 3.75 14,193 France 4.28 144,381 Greece 3.59 8,388 Hong Kong 1.98 47,839 Hungary 4.43 26,325 India 2.84 69,065 Indonesia 2.85 20,388 Ireland 4.09 11,447 Israel 2.40 21,616 Italy 4.21 207,884 Japan 3.20 99,257 Korea Republic 2.67 43,730 Lithuania 3.58 5,036 Luxembourg 3.31 12,017 Malaysia 2.88 24,893 Mexico 2.79 23,214 Morocco 3.14 5,920 Netherlands 4.27 210,266 33 Norway 2.16 20,595 Pakistan 2.80 14,106 Philippines 2.93 10,177 Poland 4.53 63,449 Portugal 3.88 16,446 Romania 4.15 15,077 Russian Federation 2.38 14,031 Singapore 2.38 18,496 Slovak Republic 4.42 15,956 Slovenia 4.25 13,567 South Africa 2.52 13,950 Spain 3.94 62,551 Sweden 3.98 36,236 Switzerland 2.67 279,411 Taiwan China 2.83 90,017 Thailand 2.95 32,147 Tunisia 3.45 5,691 Turkey 2.70 97,903 Ukraine 2.96 7,141 Unit Arab Emirates 1.82 8,790 United Kingdom 3.94 102,918 United States 2.72 354,853 Vietnam 3.27 18,344 _________________________________________________________________________________ Note: Number of transactions refers to months with recorded import transactions at the firm-productcountry of origin level. Countries of origin with more than 5,000 recorded import transactions are included in the table. 34 Appendix Table 2: Average number of export transactions per year by firm-gooddestination country for selected destination countries in 2012 _________________________________________________________________________________ Average number of Number of total Country export transactions export transactions _________________________________________________________________________________ Afghanistan 2.10 6,068 Albania 2.27 9,557 Algeria 2.07 13,204 Angola 1.84 5,571 Argentina 3.14 23,330 Armenia 2.05 9,766 Australia 3.44 75,705 Austria 5.29 408,875 Azerbaijan 2.09 20,000 Bahrein 2.19 12,200 Bangladesh 2.30 6,645 Belarus 2.77 41,084 Belgium 4.73 234,168 Bolivia 1.97 5,852 Bosnia Herzegovina 3.04 30,790 Brazil 3.55 65,121 Bulgaria 3.58 68,289 Cameroon 2.17 5,640 Canada 3.18 58,022 Chile 2.92 34,272 China 3.52 150,409 Colombia 3.00 21,893 Costa Rica 2.53 8,034 Cote D’Ivoire 2.06 5,415 Croatia 3.47 71,531 Cyprus 2.76 27,312 Czech Republic 4.73 217,071 Denmark 4.47 154,696 Dominican Republic 2.51 6,438 Ecuador 2.42 11,495 Egypt Arab Republic 2.44 41,162 Estonia 3.63 55,689 Ethiopia 1.71 5,463 Finland 4.15 113,999 35 France 5.17 317,737 Georgia 2.28 18,147 Ghana 2.06 12,132 Greece 3.70 87,598 Guatemala 2.54 7,170 Hong Kong 3.06 55,894 Hungary 4.41 154,500 Iceland 2.88 24,740 India 3.25 80,580 Indonesia 2.87 28,163 Iran Islamic Republic 1.95 26,741 Iraq 1.86 13,057 Ireland 3.72 61,386 Israel 3.09 54,268 Italy 4.77 250,195 Japan 3.51 81,998 Jordan 2.17 19,624 Kazakhstan 2.49 43,487 Kenya 2.23 10,250 Korea Republic 3.23 64,477 Kuwait 2.33 20,621 Kyrgyz Republic 1.81 5,453 Latvia 3.52 57,823 Lebanon 2.27 25,643 Libya 1.78 12,123 Liechtenstein 2.59 10,514 Lithuania 3.65 66,230 Luxembourg 4.22 125,158 Macedonia FYR 3.15 21,626 Malaysia 2.95 39,291 Malta 2.85 22,221 Mauritius 2.28 7,024 Mexico 3.42 51,643 Moldova 2.42 18,757 Mongolia 1.89 8,355 Montenegro 2.48 6,265 Morocco 2.63 25,425 Netherlands 4.98 299,294 New Zealand 2.99 25,005 Nigeria 2.31 22,909 36 Norway 3.53 105,112 Oman 2.37 13,822 Pakistan 2.48 15,487 Panama 2.36 7,971 Paraguay 2.25 6,509 Peru 2.79 18,350 Philippines 2.53 18,910 Poland 4.58 247,609 Portugal 3.90 90,222 Qatar 2.37 19,826 Romania 4.00 120,169 Russian Federation 3.41 218,922 Saudi Arabia 2.77 52,843 Singapore 3.29 64,283 Slovak Republic 4.19 105,368 Slovenia 4.08 101,438 South Africa 3.41 68,383 Spain 4.66 198,416 Sri Lanka 2.31 8,914 Sweden 4.60 151,848 Switzerland 3.90 463,713 Syrian Arab Republic 1.67 6,632 Taiwan China 2.97 44,556 Tanzania 2.04 5,397 Thailand 3.09 44,929 Tunisia 2.99 28,350 Turkey 3.33 118,634 Turkmenistan 1.89 5,131 Ukraine 3.17 84,334 Unit Arab Emirates 2.81 73,974 United Kingdom 4.80 211,467 United States 3.84 204,330 Uruguay 2.48 9,756 Uzbekistan 1.83 6,994 Venezuela 2.28 13,380 Vietnam 2.52 20,899 _________________________________________________________________________________ Note: Number of transactions refers to months with recorded export transactions at the firm-productcountry of origin level. Destination countries with more than 5,000 recorded export transactions are included in the table. 37 Appendix Table 3: Trade cost data for 2012 _____________________________________________________________________________ Country Time to Cost of Time to Cost of Dist.to export export import import Germany (Days) (US-$) (Days) (US-$) (km) _______________________________________________________________________ Afghanistan 74 3545 77 3830 4946 Albania 19 745 18 730 1384 Angola 48 1850 45 2690 6826 Antigua Barbados 16 1440 23 1870 7278 Argentina 13 1650 30 2260 11646 Armenia 13 1815 18 2195 2934 Australia 9 1100 8 1120 15935 Azerbaijan 38 3430 38 3490 3218 Bahamas 19 930 13 1405 7666 Bahrein 11 955 15 995 4423 Bangladesh 25 1025 34 1430 7348 Belarus 15 1510 30 2315 1262 Belize 19 1355 20 1600 9065 Benin 29 1079 30 1549 4912 Bhutan 38 2230 38 2330 7014 Bolivia 19 1425 23 1747 10576 Bosnia Herzegovina 15 1240 13 1200 1020 Botswana 27 2945 37 3445 8473 Brazil 13 2215 17 2275 9396 Brunei Daressalam 19 680 15 745 10614 Burkina Faso 41 2412 47 4030 4503 Burundi 32 2965 46 5005 6374 Cambodia 22 755 26 900 9311 Cameroon 23 1379 25 2167 5072 Canada 7 1610 11 1660 6542 Capa Verde 19 1200 18 1000 4979 Central Africa 54 5491 62 5554 5231 Chad 75 5902 101 8525 4511 Chile 15 980 12 965 12267 China 21 580 24 615 8032 Colombia 14 2255 13 2830 9137 Comoros 31 1295 26 1295 7765 Congo Dem Rep 44 3155 63 3435 6393 Congo Republic 50 3818 62 7709 6192 Costa Rica 13 1030 14 1020 9425 Cote D‘Ivoire 25 1999 34 2710 5223 Croatia 20 1300 16 1180 853 Djibouti 18 836 18 911 5357 Dominica 13 1340 14 1350 7388 Dominican Rep 8 1040 10 1150 7710 Ecuador 20 1535 25 1530 10096 Egypt Arab Repub 12 625 13 755 2957 El Salvador 14 980 10 980 9440 Equatorial Guinea 29 1390 44 1600 5422 Eritrea 50 1460 59 1600 4826 Ethiopia 42 2160 44 2660 5379 Fiji 22 655 23 635 16158 Gabon 20 1945 22 1955 5731 Gambia 23 1180 21 885 4839 38 Georgia 9 1355 10 1595 2771 Ghana 19 815 34 1315 5105 Grenada 9 1300 9 2235 7687 Guatemala 17 1307 17 1425 9459 Guinea 35 855 32 1391 5072 Guinea Bissau 23 1448 22 2006 4960 Guyana 19 730 22 745 7928 Haiti 33 1185 31 1545 7873 Honduras 12 1342 16 1510 9221 Hong Kong 5 575 5 565 9026 Iceland 19 1465 14 1620 2317 India 16 1120 20 1200 6566 Indonesia 17 644 23 660 11030 Iran Islamic Rep 25 1470 32 2100 3811 Iraq 80 3550 82 3650 3449 Israel 10 620 10 565 2972 Jamaica 20 1500 17 1560 8244 Japan 10 880 11 970 9086 Jordan 13 825 15 1335 3037 Kazakhstan 81 4685 69 4665 4333 Kenya 26 2255 26 2350 6410 Kiribati 21 1120 21 1120 13979 Korea Republic 7 665 7 695 8505 Kuwait 15 1775 15 1810 3999 Kyrgyz Republic 63 4160 75 4700 4849 Lao PDR 26 2140 26 2120 8725 Lebanon 22 1080 30 1365 2849 Liberia 15 1220 28 1320 5355 Macedonia FYR 12 1376 11 1380 1404 Madagascar 21 1197 24 1555 8666 Malawi 34 2175 43 2870 7701 Malaysia 11 435 8 420 9987 Maldives 21 1550 22 1526 7886 Mali 26 2202 31 3067 4526 Marshall Islands 21 945 25 970 13191 Mauretania 34 1520 38 1523 4293 Mauritius 10 660 10 695 9224 Mexico 12 1450 12 1780 9476 Micrones Fed 30 1295 30 1295 12591 Moldova 32 1545 35 1870 1463 Mongolia 49 2555 50 2710 6409 Morocco 11 577 16 950 2405 Mozambique 23 1100 28 1545 8426 Namibia 25 1800 20 1905 8196 Nepal 41 1975 38 2095 6636 New Zealand 10 870 9 825 18220 Nicaragua 21 1140 20 1245 9364 Niger 59 3676 64 3711 4182 Nigeria 24 1380 39 1540 4847 Norway 7 1125 7 1100 1039 Oman 10 745 9 680 5139 Pakistan 21 660 18 705 5551 Palau 29 970 33 930 11639 Panama 9 615 9 965 9247 Papua New Guinea 23 949 32 1130 13779 Paraguay 33 1440 33 1750 10734 Peru 12 890 17 880 10747 39 Philippines 15 585 14 660 10309 Puerto Rico 15 1300 15 1350 7477 Qatar 17 885 17 1033 4554 Russian Federation 21 2820 36 2920 2655 Rwanda 29 3245 31 4990 6238 Samoa 25 690 28 775 15845 Sao Tome Principe 26 690 28 577 5689 Saudi Arabia 13 935 17 1054 4211 Senegal 11 1098 14 1740 4746 Seychelles 16 876 17 876 7589 Sierra Leone 24 1385 27 1780 5205 Singapore 5 456 4 439 10181 Solomon Islands 24 1070 20 1037 14596 South Africa 16 1620 23 1940 9111 Sri Lanka 20 720 19 775 8004 St Kitts Nevis 11 805 12 2635 7274 St Lucia 14 1375 17 2675 7480 St Vinct Grenadines 12 935 13 1575 7566 Sudan 32 2050 46 2900 4552 Suriname 23 1000 21 1165 7793 Swaziland 18 1880 27 2085 8916 Switzerland 8 1435 9 1440 543 Syrian Arab Rep 15 1190 21 1625 2843 Taiwan China 10 655 10 720 9275 Tajikistan 71 8450 72 9800 4724 Tanzania 18 1040 31 1565 6900 Thailand 14 585 13 750 8878 Timor-Leste 25 750 26 755 12548 Togo 24 940 28 1109 4983 Tonga 22 755 26 740 16597 Trinidad Tobago 11 843 14 1260 7813 Tunisia 13 773 17 858 1729 Turkey 13 990 14 1235 2168 Uganda 33 3050 33 3215 6039 Ukraine 30 1865 33 2155 1696 Unit Arab Emirates 7 630 7 590 4824 United States 6 1090 5 1315 7595 Uruguay 16 1125 18 1440 11496 Uzbekistan 80 4585 99 4750 4539 Vanuatu 21 1690 20 1690 15745 Venezuela 49 2590 71 2868 8290 Vietnam 21 610 21 600 9259 Yemen 29 995 25 1490 5136 Zambia 44 2765 56 3560 7517 Zimbabwe 53 3280 73 5200 8044 _______________________________________________________________________ N 151 151 151 151 151 mean 23.93 1565 27.04 1876 7072 sd 15.79 1134 18.49 1449 3530 min 5 435 4 420 543 max 81 8450 101 9800 18220 p1 5 456 5 439 853 p50 20 1240 22 1510 7014 p99 80 5902 99 8525 16597 _______________________________________________________________________ Note: For description and source of data, see text. 40 Appendix: The measurement of trade costs in the Doing Business reports [from Doing Business 2012 available at http://www.doingbusiness.org/reports/global-reports/doingbusiness-2012, p. 55-56] Doing Business measures the time and cost (excluding tariff s) associated with exporting and importing a standardized cargo of goods by ocean transport. The time and cost necessary to complete every official procedure for exporting and importing the goods—from the contractual agreement between the 2 parties to the delivery of goods—are recorded. All documents needed by the trader to export or import the goods across the border are also recorded. For exporting goods, procedures range from packing the goods into the container at the warehouse to their departure from the port of exit. For importing goods, procedures range from the vessel’s arrival at the port of entry to the cargo’s delivery at the warehouse. The time and cost for ocean transport are not included. Payment is made by letter of credit, and the time, cost and documents required for the issuance or advising of a letter of credit are taken into account. Local freight forwarders, shipping lines, customs brokers, port officials and banks provide information on required documents and cost as well as the time to complete each procedure. To make the data comparable across economies, several assumptions about the business and the traded goods are used. Assumptions about the business The business: • Has at least 60 employees. • Is located in the economy’s largest business city. • Is a private, limited liability company. It does not operate in an export processing zone or an industrial estate with special export or import privileges. • Is domestically owned with no foreign ownership. • Exports more than 10% of its sales. Assumptions about the traded goods The traded product travels in a dry-cargo, 20-foot, full container load. It weighs 10 tons and is valued at $20,000. The product: • Is not hazardous nor does it include military items. • Does not require refrigeration or any other special environment. • Does not require any special phytosanitary or environmental safety standards other than accepted international standards. • Is one of the economy’s leading export or import products. Documents All documents required per shipment to export and import the goods are recorded.