Trade Complexity and Productivity
Carlo Altomonte
Gábor Békés
CeFiG Working Papers 12
Available online at: http://cefig.eu/view/list/
Trade Complexity and Productivity
Carlo Altomonte Gabor Bekesyz
September 2010
Abstract
We exploit a panel dataset of Hungarian rms merged with country and product-level trade data for the period 1992-2003 to investigate the relation between rms' trading activ-ities (importing, exporting or both) and productivity. From our transaction data a number of proxies are derived, measuring at the rm level some characteristics of the traded bundles associated to various technological and relationship-speci c dimensions of the trade activity, which we generally refer to as `trade complexity'. We nd that our indicators of complexity are jointly correlated to the ex-ante productivity of trading rms, accounting for an addi-tional third of the overall productivity premium. However, the elasticity of productivity to a change in the trade complexity indicators varies with di erent indicators of complexity and with the trade status of the rm. Policy conclusions are drawn from these ndings.
JEL classi cation: D23, F12, F14
Keywords: transaction costs, rms' heterogeneity, productivity
Bocconi University & FEEM, Milan. Corresponding author: IAM - Bocconi University, Via Rontgen 1, 20136 Milano, Italy. Email: [email protected]
yInstitute of Economics, Hungarian Academy of Sciences.
zAcknowledgements: We wish to thank Peter Harasztosi for excellent research assistance, Gabor Korosi
for invaluable help in answering dataset related questions, Balazs Murakozy for helpful insights on earlier drafts. We have received very useful comments from Mark Roberts, Gianmarco Ottaviano, Luigi Benfratello, Marcella Nicolini, Italo Colantone, Rosario Crino as well as seminar participants in IEHAS Budapest, Perugia University, FEEM Milano, ESRI Dublin, IAW Tubingen, KU Leuven, ETSG Warsaw, COST Edinburgh, EEA Barcelona. This work is part of the "Center for Firms in the Global Economy (CEFIG)" network. The paper was produced in the framework of MICRO-DYN (www.micro-dyn.eu ), an international economic research project focusing on the competitiveness of rms, regions and industries in the knowledge-based economy. The project is funded by the EU Sixth Framework Programme (http://cordis.europa.eu), whose nancial assistance is gratefully acknowledged. The authors retain the sole responsibility for their views, errors and omissions.
1
Introduction
Since the seminal work of Bernard and Jensen (1999), a vast body of literature has con rmed the superior performance of exporting rms in terms of size, wages and productivity (e.g. ISGEP [2008] providing cross-country evidence across 14 di erent countries). More recently, it has been found that also importers have a variety of positive attributes, being bigger and more productive than non-importers, with both imports and exports appearing to be highly correlated and concentrated among rms (see evidence provided by Bernard et al. [2007] for US; Muuls and Pisu [2007] for Belgium; Andersson et al. [2007] for Sweden; Kugler and Verhoogen [2009] for Colombia; Castellani, Serti and Tomasi [2010] for Italy).
A number of papers (Amiti and Konings [2007] for Indonesia; Kasahara and Rodrigue [2008] for Chile; Halpern, Koren and Szeidl [2009] for Hungary; Goldberg et al. [2009] for India) have also found that, contrary to the export case, importing intermediate goods tends to improve plant productivity.1 Recent evidence available for Argentina and Chile (Bas 2009) also shows that industries that have lower import tari s or rely more on imported inputs are characterised by a larger number of exporting rms and volumes.
All the above evidence is consistent with the increasing role that global production networks, and thus the importing and exporting of both intermediates and nal goods, are playing in determining trade ows worldwide. And yet, notwithstanding these ndings, national trade policies to date are to a large extent aimed at increasing the number of domestic rms selling their products abroad, while shielding the same rms from international competition at home; accordingly, most policy actions revolve around export promotion programs, with little interest in the relation between importing and exporting activities and the ensuing implications for rms' performance. Moreover, despite the increasing availability of detailed transaction-speci c data at the rm level, much of the analyses insofar have considered trading rms at a binary level, that is whether they are exporters / importers or not, thus not fully controlling for the potentially heterogeneous characteristics of the bundles of products they trade. As a result, national trade policies nowadays do not seem to be equipped to foster a proper integration of domestic rms into global value chains.
In this paper we will argue that ignoring the relationship between the importing and export-ing activities of rms might actually lead to a biased estimation of the productivity premium attributed to exporters, and thus an excessive emphasis given to export promotion policies 1
Evidence of a `learning' e ect going from export to productivity is limited to some developing economies (De Loecker [2007] for Slovenia; Van Biesebroeck [2005] for Sub-Saharan Africa), while self-selection of the most productive rms into the export activity, and no learning e ects, is a standard nding of the literature.
vs. policies aimed at integrating domestic rms into international networks of production. In addition, we will also show how various dimensions of `trade complexity', that is di erent charac-teristics in terms of quantity, quality and technology of the bundles that rms import or export, correlate di erently with productivity across rms' trading activities (importing, exporting or both). Due to this heterogeneity policy actions should thus address the interaction between the diverse facets of trade complexity and the importing/exporting activities of the rm, rather then a rm's generic trade status. The former, more speci c policy focus, is in fact more e ective in stimulating rms' productivity, eventually fostering their integration into global value chains.
This paper is not the rst attempt to relate one or more characteristics of a rm's traded bundle to some of its performance measures: among others, Bernard et al. (2010) use di erent characteristics of the product and countries involved in rms' trade transactions to explain the determinants of US intra- rm trade. Nevertheless, to the best of our knowledge, our study is the rst to de ne and measure di erent dimensions of complexity of the traded bundle and provide a systematic analysis of how and to what extent the latter map into the performance of rms involved into the whole range of trading activities (imports, exports or both), controlling at the same time for the correlations among the latter.
Our identi cation strategy relies on the fact that structural estimations of international trade models with heterogeneous rms have revealed the presence of signi cant sunk and xed costs of trade not only for exporting (Das, Robert and Tybout 2007) but also for importing rms (Kasahara and Lapham 2008), consistently with a self-selection of the most productive rms into trade. We can thus explore the possible sources of these trading costs, how they relate to the productivity of trading rms, and whether they a ect equally the import vs. the export activity.
To perform our analysis we exploit the availability of detailed product-country international transactions data of Hungarian manufacturing rms observed yearly from 1992 to 2003 (for a total of some 192,000 rm-level observations). As our data refer to import and export trans-actions which can be directly matched to the operations of manufacturing rms, we exclude transactions which take place through the activity of an intermediary or a wholesaler, and thus we are able to directly relate the costs born by the rm to its trade transactions.2 Also, the case of Hungary constitutes an interesting quasi-natural experiment, since our data cover the decade in which most Hungarian rms opened up to trade and foreign investment, following the signing of the free trade agreements with the European Union and the other Central and 2A summary of the intermediated dimension of the trade activity can be found in Blum, Claro, and Horstmann
Eastern European countries in the early 1990s.
In particular, we derive from our transaction data a number of proxies measuring at the rm level some characterstics of the traded bundles associated to various technological and relationship-speci c dimensions of the trading activity, which we generally refer to as `trade complexity'. More speci cally, based on the existing literature we distinguish three features of trade complexity: a quantitative dimension, linked to the number of countries and products a rm has to deal with (Eaton, Kortum and Kramarz 2004); a qualitative dimension, related to the costs associated with writing contracts for speci c products which need to be screened for quality and ensured against the risk of a faulty delivery (for the importer) or the monetary risk of not being paid (for the exporter) (Berkowitz, Moenius and Pistor 2006); a technological dimension, which deals with the imperfect substitutability of domestic vs. foreign inputs (Halpern, Koren and Szeidl 2009) and the associated search costs for the `right' input available on international markets. Each indicator we develop is measured symmetrically for both the imported and the exported traded bundles, thus allowing us to control for the possible correlation among the two trading activities within the same rm, when relevant.
As already anticipated, our results provide two main contributions to the existing literature. First of all, after having detected evidence of a self-selection e ect of the most productive rms into both the importing and exporting activities, we nd that, when taking into account the importing status of exporting rms, the productivity premium of exporters is still present but it is greatly reduced, with the premium more than halved (from around 36 to 15% in our baseline speci cation). In other words, failing to control for the correlation between importing and exporting activities within a rm, as a large part of the debate has done insofar, might lead to a signi cant upward bias in the estimated productivity premium of traders.3 These results are robust when limiting our analysis to the case of some 6,500 switching rms recorded in our dataset, that is rms which in our sample start to either import and/or export. They are also robust to di erent measures of total factor productivity, among which a modi ed Olley and Pakes (1995) semi-parametric algorithm which controls for both the import and export status of the rm (as in Amiti and Konings, 2007) as well as for the origin/destination of the traded bundle, a re nement aimed at tackling the potential omitted price variable bias accruing from imported inputs.
Second, we have found that di erent characteristics of the product bundles (as proxied by our indicators of complexity) are jointly correlated to the ex-ante productivity of trading rms, 3
Both Kasahara and Rodrigue (2008) and Kugler and Verhoogen (2009) nd evidence of an upward bias in the productivity premium of importing rms when the export status is not taken into account, although they do not speci cally discuss the nature or the implications of this bias.
accounting for an additional third of the overall productivity premium. In particular, when testing our baseline TFP speci cation augmented with our main set of complexity measures, the latter are signi cant while the productivity premia are reduced from 15 to 10% for exporters (already controlling for the upward bias induced by the import status) and from 45 to 31% for importers. Importantly, we also nd the elasticity of productivity to a change in our trading complexity indicators to be larger for importers vs. exporters, and to be di erent for di erent in-dicators of complexity. The latter calls for policy actions able to address the interaction between speci c features of the traded bundle and the rm's di erent trading activities, rather then their generic trade status. Since we identify these e ects within rms who are both importing and exporting, we can exclude that our results are driven by some unobserved rm heterogeneity a ecting exporters di erently than importers in terms of complexity. Our results also holds when controlling for switching rms or for di erent proxies of complexity.
Finally, it is important to notice that in this exercise we analyze proxies related to the possible
sources of costs a ecting the importing and exporting activity, and the associated productivity
premia, while we abstain from discussing the speci cnature of these costs, i.e. whether the costs associated to the complexity of the trading activity can be considered as sunk or xed. The preliminary evidence we are able to gather when analysing switching rms seems to be more in line with the presence of sunk, rather than xed, costs, but we leave to further lines of research a more structured answer to this issue.
The paper is structured as follows. Section 2 reviews the relevant literature on international rms and derives our main proxies of trade complexity, together with some preliminary evidence on their evolution over time and across industries. Section 3 presents our rm-level data and derives our measure of TFP for trading rms. Section 4 discusses our baseline TFP speci cation for importers and exporters, and relates it to our complexity measures, while Section 5 applies our analysis to the case of some 6,500 switching rms recorded in our dataset. Section 6 discusses the main policy implications of our ndings and concludes.
2
Related literature and complexity measures
Our aim is to provide a systematic analysis of how di erent features of the traded bundle, each a ecting various possible sources of trading costs, map into the performance of importing vs. exporting rms. To that extent, we have identi ed from the literature three di erent sets of measurable characteristics of the traded bundle that are worth exploring: quantitative indicators related to the number of countries and products involved in the trading activity, the quality of
the traded goods and the degree of substitutability of the technology they embed. We generally refer to these sets of characteristics as `trade complexity'.
First, starting from the work of Eaton, Kortum and Kramarz (2004) on French exporters, a number of papers have shown how the number of countries and products a rm deals with relate to various characteristics of the same rm, such as productivity and size. In order to capture this quantitative feature of trade complexity, we thus calculate the (simple) average number of HS6 products4 imported or exported by a rm, as well as the (simple) average number of countries the rm trades with.
Second, looking at the contractual side of the trading relationship, one can assume that both the exporter and the importer face a hold-up problem and the associated costs of solving it. On the one hand, the exporter has a perfect monitoring of the quality of the good being shipped, but it faces the monetary risk of not being paid once a purchaser has been identi ed; thus, it has to use legal instruments (e.g. letters of credit, counter-trade agreements or pre-payments), and their associated costs, in order to partly o set its asymmetric side of the bargain. On the other hand, the importer has to underwrite the purchase contract but can verify only ex-post the quality of the received good. Alhough legal instruments have been developed to counter this problem, such as letters of acceptance which allow the importer to withold payment until the state of the goods received is veri ed, typically these instruments impose short deadlines for the payment, often not compatible with the time required for an appropriate quality check of the purchased good (Head 2007; Berkowitz, Moenius and Pistor 2006).
As a result, both exporters and importers face some uncertainty within their trading rela-tionship, with the uncertainty being higher the higher the complexity of the goods being traded. In this case, in fact, the `true' quality of the product is more di cult to verify ex-post by the importer (or by an external court in case of controversies), while the risk of not getting paid for the exporter is likely to be more costly, to the extent that the production of complex goods involves a higher sunk investment.5 As a result, complex goods are likely to require ex-ante the setup of a contract with a larger number of speci cations and provisions, or the establishment of a relationship of `trust' between exporter and importer, all factors likely associated to higher costs of trade.6
In order to summarize these di erent, qualitative features of complexity, we exploit the 4Goods are divided into product categories according to Harmonised Standards. Hungarian data records trans-action at the HS10 level, but we have aggregated them up to the 6-digit level. HS6 categories typically de ne a product by type, size and quality (e.g. size of an engine in a motor vehicle).
5
Think for example to the case of a complex, custom-made piece of machinery tting part of a production line. Clearly, if the transaction can be carried out through organized markets or relates to homogeneous products, these costs might be lower or non-existing.
fact that we have information, for every rm/year, on both the values and the quantities of the traded products; thus we can calculate the (weighted) average unit value of the traded bundle (UVT) imported and/or exported by every rm in our sample in a given year t.7 In the next sections we will discuss the extent to which we can use unit values as an overall proxy for the qualitative dimension of complexity, showing how UVTs are strongly correlated in our sample to a number of alternative product and country-speci c indicators used in the literature, among which the Rauch index of product di erentiation, the Bernard et al. (2010) index of intermediation intensity or the quality of a country's legal framework. We expect higher UVTs to be associated, among others, to a higher complexity of the traded bundle.
Finally, another facet of trade complexity might derive from the particular technology of production, in particular from the imperfect substitutability between foreign and domestic in-puts. As shown in Halpern, Koren and Szeidl (2009), a relatively large part of the productivity gains from importing are due to the complementarity channel, that is the fact that inputs have to be combined in `just the right proportions' in oder to generate productivity gains. To the extent that production of a given nished product requires the use of a speci c number of specialized parts and components, eventually sourced from speci c suppliers in particular coun-tries, it then follows that importing rms might face higher input-speci c costs linked to the imperfect substitutability of their intermediates. Mutatis mutandis, the same argument applies to export activities, to the extent that rms exporting very speci c, low-substitutability goods might require particular production processes or specialised channels for their sale.
To proxy for this technological feature of complexity, we have constructed an index of sub-stitutability of the technology embedded in each traded bundle (Subst); the index is measured as the weighted average number of countries each HS6 product is traded with, and thus 1 is the index lower bound.8 The idea is that if a rms trades one product with only one country, it signals a certain `uniqueness' of the product-country transaction, and thus a lower substitutabil-ity with respect to the case of a product which can be imported from / exported to di erent countries. As such, we expect a lower value of this index to be associated to a higher trade 6
For example, Brembo, an Italian multinational company market leader in the production of
brakes for sport cars, before importing screens prospective suppliers worldwide on the basis
of a 10-pages long questionnaire, involving detailed information on some 200 di erent items.
The details of Brembo purchasing policy, together with the questionnaire, are available at:
http://www.brembo.com/ENG/AboutBrembo/Suppliers/CriteriaBrakes/SuppliersCriteria.htm 7
When referring to weighted averages in the construction of our indicators, we have always used as weight the value of each traded product in the overall value of the bundle imported or exported by the rm. Robustness checks performed with simple averages have not yielded signi cant di erences in our results. All price measures used throughout the paper are FOB and expressed in US dollars.
8For example, if a rm trades two products (1,2) with two countries (A,B), the index will depend on the structure of trade ows: if the rms trades product 1 with country A and product 2 with country B, then Subst=1; if instead the rms trades product 1 with country A (SubstA= 1), but product 2 with both countries (SubstB= 2),
complexity. As a robustness check, we will also control for the number of BEC cathegories to which the traded bundle belongs:9 the idea is that if a rm is able / has to deal with products
pertaining to di erent BEC cathegories (e.g. intermediates vs. consumer and/or capital goods), its technological complexity is higher.
In addition to these three di erent dimensions of complexity, we also control for other possible drivers of trade costs, such as the (weighted) average distance from which the bundle of products is traded as a proxy for transport costs (Gorg, Halpern and Murak•ozy 2010), and the (weighted) average size of the partner countries, as measured by a country's population, as a proxy for marketing/search costs (Arkolakis 2008).
The next Figure (1) depicts our main indicators of complexity calculated as weighted averages across our transaction-speci c measures for each HS2 industry and year, for both the imported and the exported traded bundles. The precise de nition of all our indicators of complexity is reported in Annex. We also report the average trend of these values across all industries.10 Although we nd a substantial level of heterogeneity across industries and trade activities, imported bundles seem in general to be characterised by a higher level of complexity.
In the next sections we try to link this evidence stemming from transaction-level data to individual rms' characteristics, in particular total factor productivity.
3
Data and productivity measures
3.1 Data
We exploit a large and comprehensive panel of Hungarian rms obtained by merging tax and customs data for the period 1992-2003. The rst dataset contains accounting and nancial data of Hungarian rms. The source of data is the Hungarian Tax Authority (APEH). This database represents more than 90% of Hungarian employment, value added and exports and is almost exhaustive of the universe of rms outside the scope of non-trading micro enterprises. To avoid a number of potential problems in the calculation of rms' performance, in this paper we have restricted the analysis to manufacturing data only, with the Annex reporting the number of
rms per year and by NACE2 industry.11
The APEH dataset has then been merged at the rm-level with a trade dataset, containing 9Broad Economic Categories (BEC) are de ned by the United Nations and include ve categories: raw materials, intermediaries, parts, capital goods and consumption goods. Our BEC variables thus ranges between 1 and 5. 10
All indicators are measured in logs with the exception of the substitutability index; the latter is also reported on an inverted scale for ease of comparability (lower values of the index are associated to higher complexity). 11
Figure 1: Complexity indicators across HS2 industries and time .5 1 1.5 2 2.5 3 1990 1995 2000 2005 year Imp Exp avg_Imp avg_Exp N. of Products 0 .5 1 1.5 2 1990 1995 2000 2005 year Imp Exp avg_Imp avg_Exp N. of Countries -2 0 2 4 6 8 1990 1995 2000 2005 year Imp Exp avg_Imp avg_Exp Unit Values 0 2 4 6 8 1990 1995 2000 2005 year Imp Exp avg_Imp avg_Exp Substitutability 6 6.5 7 7.5 8 1990 1995 2000 2005 year Imp Exp avg_Imp avg_Exp Distance 16 16 .5 17 17 .5 18 1990 1995 2000 2005 year Imp Exp avg_Imp avg_Exp Population
transaction-level data as registered by the customs o ce. The unit of observation is thus rm-product-destination. The dataset includes information on both the (FOB) US dollar value of shipments and their physical quantity, while exported and imported products are measured at the HS6 category.12 We also have information on the countries of origin and destination rms export to or import from. Finally, the dataset also contains information on a rm's ownership, and thus allows us to control for the presence of multinational rms,13 a critical dimension of our analysis since foreign-owned rms might be trading within their international network, and thus could di er along several dimension from other rms. The following Table 1 reports some descriptive statistics of the sample over time, together with the relevance of foreign-owned rms in our sample.14
12
`Motor cars and vehicles for transporting persons' is an example for a 4-digit category, while `Other vehicles, spark-ignition engine of a cylinder capacity not exceeding 1,500 cc' is an example 6-digit category. The number of varieties ranges in case of import from 1 to 797 and in case of export from 1 to 355.
13
Throughout the paper, a rm is considered as foreign-owned if at least 10% of its capital is controlled by a foreign owner. We carried out robustness checks on the threshold. Given that most foreign acquistion leads to majority ownership within a few years, results are not sensitive to raising the 10% threshold to, say, 25%. 14
Note that our sample of rms shrinks after 1999 as we lose information on most non-trading rms with less than 5 employees. In the next sections we will provide a robustness check of this feature of the data.
Table 1: Descriptive statistics, selected years
All firms N. of firms Sales Employment VA Avg.wage Capital Foreign
1993 12832 123.8 46.3 46.4 0.44 75.0 38.0%
1999 21063 393.0 31.7 117.8 0.70 117.9 27.4%
2003 14486 930.2 42.0 210.3 1.23 248.4 23.5%
Note: Sales, Value Added, Wage, Capital in HUF mn; Average employment refers to full time employees;
Foreign is the share of firms with at least 10% equity hold by foreigners. 3.2 Trade status
In our framework a rm can be in one of the following four trading status F Z in a given year: rms that both import and export (two-way traders, F M X); rms that either import (F M) or export (F X); rms not engaged in any trade activity (no traders, F Z = 0). Moreover, a rm can remain permanently into that trade status, or switch from one trade status to another. In order to attribute each trade status to each rm, for the time being the export/import status is measured as a year-speci c dummy equal to one if the dollar value of imported and/or exported shipments is positive in a given year. A relatively large share of rms are active in trade activities in our dataset: 32.4% of rms have been at least once involved in some export activities within our period of analysis, and 41% of them in import activities, thus showing the important role international trade plays for rms operating in a small and open economy, like Hungary. However, there is also a great deal of overlap in the trading activities, as 26.1% of
rms have been both exporting and importing.
To properly assess the di erent possible trade statuses, we can part rms who only export from those who both import and export, that is two-way traders; similarly we can distinguish importers-only within the importing rms. This yields four non-overlapping categories of rms, whose most relevant characteristics are reported in Table 2 for a given year (1999).
Table 2: Trading activity and rms' characteristics
Trading status Obs. Sales Employment VA Avg.wage Capital
Non-traders 101485 42.3 9.6 12.7 0.554 12.7
Exporters-only 12074 83.4 17.3 23.6 0.671 25.2
Importers-only 28627 155.4 17.3 42.3 0.843 44.4
Two-way traders 50162 1409.6 117.4 375.5 0.946 434.0
Note: Sales, Value Added, Wage, Capital in HUF mn; Average employment refers to full time employees;
Foreign is the share of firms with at least 10% equity hold by foreigners.
more capital and pay higher wages (per worker). Two-way traders are the largest followed by importers-only and exporters-only. The di erences tend to be large and signi cant.
Clearly, these data fail to capture the evolution of rms over time, both in terms of charac-teristics and trading status, a dynamic potentially very relevant for a transition economy like Hungary. To that extent, looking at the data over time we actually nd that on average 32% of our rms have altered their trade status within a period of ve years. In order to evaluate the persistency of the trading activities, we have constructed a transition matrix of the various trade statuses in which rms are engaged for two periods: from 1993 to 1997 (reported in Annex) and from 1998 to 2003.15 The matrix shows very similar dynamics in the two periods, with the only relatively signi cant di erence being the fact that 79% of rms not engaged in any trade activity in 1993 remain such in 1997, compared to 68% for the period 1998-2003. Also, one can notice some turbulence in the o -diagonal part of the transition matrix, signalling the fact that some rms have a transitory experience of exporting or importing, then either reverting back to a non trading status, or becoming two-way traders.16
The latter ndings are consistent with the increasing international openness of the Hungarian economy in the course of market liberalization and European integration. They also signal how transition did create some churning in the trading activities of rms, nevertheless without generating structural breaks over the considered period.17 More importantly, this preliminary evidence clearly shows not only that trading rms seem to di er in a number of characteristics from non-traders, but also that the same trading rms are a relatively heterogeneous group across di erent trade statuses. The next section tries to link these ndings to rms' productivity premia.
3.3 Measuring productivity in trading rms
The measurement of rm-level total factor productivity (TFP) is subject to a well-known prob-lem of endogeneity known as simultaneity bias (see the Appendix for details). The latter is tackled by the literature through several methods, each with pros and cons. In particular, in this exercise we use a modi ed version of the standard semiparametric TFP measure of Ol-ley and Pakes (1996), henceforth OP. The main reason for this choice is that the original OP 15
The two period re ect economic history: the rst one is when Hungary liberalized its market and the second is when the process of European integration was initiated.
16
We will explicitly deal with these switching rms in Section 5 of the paper.
17Additional evidence, available on request, indeed shows that the presence of switching rms is balanced across sectors. Also, we have redone Table (2) for any particular year, nding no signi cant di erences in the ranking of rms along trade statuses. We can thus exclude that compositional e ect in terms of sectors or years drive our preliminary results.
algorithm already allows to explicitly treat attrition (another potential source of endogeneity arising from sample selection), and thus is particularly suited for the analysis of a dataset as ours characterised by a relative high degree of churning.
Since we are dealing with rms active in di erent trading statuses, we also need a number of re nements to the original OP methodology, as a given trade status (importer, exporter or both) might a ect the rm's input choices, and thus drive part of the simultaneity bias in productivity estimation (De Loecker 2007). To that extent, we start by following Amiti and Konings (2007), henceforth AK, and measure rms' TFP through an industry-speci c OP estimation, in which, as they suggest, we control for the simultaneity bias induced by the productivity shock, the selection equation of rms' survival and the impact of the rm's trade status (importer, exporter or both) on input choices.
We then proceed to modify the latter methodology to explicitly control for the fact that output and factor prices might be di erent and/or evolve di erently over time for trading rms, which might induce an omitted price variable bias in our estimates. The problem is particularly relevant for importing rms, since di erentials in TFP across rms might accrue from di erences in domestic and (imperfectly measured) import prices, rather than actual changes in the quality of imported inputs. To account for such a possible price e ect in TFP estimation, we have augmented the original AK algorithm by including two separate dummies for rms importing the largest part of their inputs from low vs. high-wage countries, thus being able to better discriminate between labor-intensive, low-priced imports vs. high-quality, capital intensive ones. The latter re nement, whose technical details are discussed in the Appendix, should help us in minimizing potential price distorsions in the estimation of productivity.
Table 3 reports the average TFP for the di erent groups of trading rms in our sample, as obtained with di erent estimation techniques: OLS; the semi-parametric estimation suggested by Levinshon and Petrin (2003), denoted as Lev-Pet; the modi ed OP procedure proposed by Amiti and Konings (2007), denoted OP-AK ; our TFP measurement algorithm which controls for the origin of the trade ows, denoted as OP-TR. As it can be seen, in the (biased) OLS speci cation non-traders are the least productive rms, the di erence between the productivity of importers vs. exporters-only is not statistically signi cant, while the productivity premium of two-way traders is magni ed. All the other semi-parametric speci cations yield instead a robust result showing that, consistently with the analysis of rms' characteristics discussed in the previous section, two-way traders are the most productive group of rms. The di erence in productivity between non-traders and exporters-only is signi cant in favor of the latter, though relatively small, while the TFP of importers-only is closer to that of two-way traders. These
results are comparable to those of Muuls and Pisu (2007) for Belgium and Castellani, Serti and Tomasi (2010) for Italy, who have also found two-way traders to be the most productive rms, followed, in descending order, by importers-only, exporters-only and non-traders.
Table 3: Trading activity and productivity premia
Trading status OLS Lev-Pet OP-AK OP-TR
Non-traders 2.441 0.692 0.644 0.680 Exporters-only 2.855 0.701 0.831 0.870 Importers-only 2.787 1.317 1.257 1.290 Two-way traders 4.146 1.479 1.539 1.607
The ranking is con rmed not only for the mean rm, but also in terms of dominance of the cumulative distribution of (log) TFP, using both our OP-TR algorithm and the Lev-Pet measure of TFP as robustness check. Moreover, the same ranking remains constant within each industry of our dataset.18 In terms of variation over time, we also nd that the previously detected stochastic dominances are constant across the years.
The ranking of productivity by trade status is also con rmed when partitioning our sample according to ownership, as shown in Table 4. For both domestic and foreign-owned rms, two-way traders are the most productive group, followed by importers-only, exporters-only and non-traders. Simple TFP averages reveal that foreign-owned rms are more productive than domestic ones, consistent with the theory. When disentangling this information across the trade status, domestic and foreign-owned rms do not di er much in terms of productivity when they are either non-traders or exporters-only,19 while the productivity premia accruing to foreign-owned rms vs. domestic ones become larger for importers-only or two-way traders.
All these results consistently show that the importing activity seems to be more strongly associated with higher productivity levels than exporting, in line with the preliminary evidence indicating that trade complexity is higher for the imported bundle. Although suggestive, the detected correlation might however derive from some unobserved rms' characteristics associated to both TFP and the trade status, an issue to which we now turn.
18
Details are available on request. The only slight deviations from the reported ranking have been detected in industry NACE-19 (leather) with exporting rms slightly more productive than importing ones, and NACE-26 (metals), where exporters and non traders showed a very similar TFP. These are however sectors accounting for less than 7% of total rms in our sample.
19We nd an export premium with respect to non-trading rms, but very similar across domestic and foreign-owned rms. A closer look at the productivity distribution of these rms for those two trade statuses reveals that the least productive rms are domestic non-traders, and the most productive ones are foreign-owned exporters, consistently with the theory. Some slight deviations from the log-normal distribution of TFP then lead to simple means becoming quite similar across the two groups of rms.
Table 4: Trading activity, productivity premia and rms' ownership
Trading status Avg. TFP Domestic-owned Foreign-owned Non-traders 0.636 0.618 0.744 Exporters-only 0.888 0.909 0.793 Importers-only 1.337 1.258 1.557 Two-way traders 1.631 1.422 1.847 All firms 1.006 0.864 1.424
Note: OP-TR measure of TFP, described in details in Annex
4
Trade complexity and productivity
4.1 Importers' vs. Exporters' productivity
We rst validate the relation between trade status and productivity via a multi-variate regression which controls for other rm-level characteristics that might a ect TFP. To this extent, we have estimated the following Equation:
!it= 0+ 1F Zit+ 2Xit+az+ar+at+"it (1)
where the log level of TFP of each rm !it is regressed against a dummy indicating the trade
status of the rmF Z at timet. We control for a number of rms' characteristicsX widely used in the literature, such as rm's size (log annual employment),20foreign ownership (dummy = 1 if foreign equity above 10%), the rm's age (here proxied by the time spent in the sample), a dummy (SOE) indicating whether the rm existed before 1992, and thus was likely owned by the State. We also include a full set of industry (z), regional (r, to control for border e ects and market access) and time (t) xed-e ects. See the Annex for further details on the control variables.
The results, presented in Table 5, show that, when considering a pure export dummy, as it is standard in the literature (Column 1), we retrieve the usual export premium in terms of productivity (36.2% in this speci cation), even when controlling for rms' characteristics typically associated with productivity. However, recalling our results on the importing activity of rms, such a model speci cation overlooks the fact that some of those exporters might also be involved into importing activities, and thus, to the extent that import and exports activities take place jointly, the productivity premium of exporters might actually derive from their import, 20
Note that we use rm size (employment) as a control, even after having included it in the algorithm of TFP estimation, due to the fact that semi-parametric methods for the calculation of productivity, as opposed to a translog speci cation of the production function, embed in the residual also the presence of economies of scale, and thus tend to overestimate the e ect of rm size on TFP. Also note that we cannot use rm- xed e ects, since in our speci cation we need to explicitely incorporate the rm trade status.
Table 5: Trading activity, productivity premia and rms' ownership
Sample All sample All sample All sample Exportingfirms Importingfirms
Dep. Var: TFP (1) (2) (3) (4) (5) Exporter dummy 0.362*** 0.157*** 0.149*** (0.006) (0.007) (0.009) Importer dummy 0.463*** 0.449*** (0.007) (0.011) Exporter-only 0.461*** (0.008) Importer-only 0.153*** (0.010) Two-way trader 0.621*** (0.007) Firm size 0.166*** 0.143*** 0.143*** 0.133*** 0.141*** (0.002) (0.002) (0.002) (0.003) (0.003) Foreign firm 0.210*** 0.143*** 0.142*** 0.240*** 0.258*** (0.007) (0.007) (0.007) (0.008) (0.008) Firm age 0.0482*** 0.0466*** 0.0466*** 0.0464*** 0.0469*** (0.001) (0.001) (0.001) (0.002) (0.002) Former SOE -0.0376*** -0.0218*** -0.0217*** -0.0252** -0.0268*** (0.007) (0.007) (0.007) (0.010) (0.009) Constant -0.722*** -0.819*** -0.818*** -0.768*** -0.265*** (0.037) (0.036) (0.036) (0.053) (0.043) Observations 149,797 149,797 149,797 56,695 69,822 R-squared 0.25 0.274 0.274 0.294 0.236
Robust standard errors. Industry, time and regional dummies included. *** p<0.01, ** p<0.05, * p<0.1
rather than export, status. In fact, if we add the import status in our regression (Column 2), we nd that also the importing activity is positively and signi cantly correlated with productivity, yielding an import premium of some 46.3%; most importantly, the inclusion of the import dummy lowers the magnitude of the productivity premium for exporters by more than half, from 36.2 to 15.7%.
A possible explanation of this nding is that the import and export dummies might be correlated, to the extent that some of our rms are two-way traders. The latter correlation might then be driving both results. To clarify things, in Column 3 we have thus partitioned our sample in mutually exclusive categories (importers-only, exporters-only, two-way traders), always controlling for rms' characteristics, and thus keeping non-traders as the control group. As it can be seen, every trade activity is positively and signi cantly associated to productivity,
with our ranking of productivity premia by trade status con rmed.
As a further check, in Columns (4) and (5) we have changed our control group, by running the import and export dummies on the restricted sample of exporting rms and importing rms, respectively (thus excluding non traders): again, we nd that the premium in terms of TFP accruing to two-way traders is larger when it comes from exporters adding the importing activity, rather than importers which add export and become two-way traders, always controlling for rms' characteristics.
These preliminary results may indeed depend on the de nition of a trading rm, which insofar follows the standard in the literature: a year-speci c dummy equal to one if the value of export and/or import is positive in a given year. However, rms might be heterogeneous in terms of the timing of the trade exposure, with some rms trading more regularly over time then others. As a rst robustness check, we have therefore checked our results against a more restrictive de nition of trading rms, considering as exporters (importers) those rms who have exported (imported) at timetat least 0.5% of their sales for more than 50% of the time between
t and exit/end of sample (stable traders).21 Second, to control for the potential impact of the real exchange rate on exports, we have calculated value added using sales = domestic sales + export sales*REER de ator (VA1), instead of simply adding domestic and export sales. Finally, we have also experimented excluding micro rms from our sample, i.e. rms with less than 5 employees (No micro rms).
Table 6 reports the coe cients obtained for our trade status dummies in regressions where we have implemented our robustness checks, always controlling for the same rms' characteristics and industry, regional and time xed e ects as in Equation (1).22 The rst column of Table 6 reports our baseline result (Table 5 - Column 3): as it can be seen, our ranking in terms of productivity perfectly holds.
As a further con rmation of these nding, we have followed Bernard and Jensen (1999) and estimated a linear probability model of starting a trading activity. The results, available on re-quest, show that the trade activity is highly persistent and associated to rm's characteristics as foreign ownership and size. Consistently with our rankings, we have also found that productivity is a stronger predictor of the probability of becoming an importer rather than an exporter. 21
This is the de nition employed by Mayer and Ottaviano (2007) in their comparative study of the trade perfor-mances of European rms.
22
Technically, based on the results of Table 3, which show a non-constant e ect of foreign ownership on the trade status, we could have interacted the trade dummies with foreign ownership. However, that would have further boosted our premia for importers and two-way traders vs. exporters and non-trading rms, without altering their relative rankings. As a result, in the remaining of the paper we will only include a foreign-dummy as control, without interaction terms.
Table 6: Robustness checks
Baseline Stable traders VA1 No micro firms Importers only 0.461*** 0.508*** 0.496*** 0.394*** (0.008) (0.008) (0.008) (0.009) Exporters only 0.153*** 0.147*** 0.141*** 0.0528*** (0.010) (0.011) (0.010) (0.011) Two-way traders 0.620*** 0.631*** 0.6154*** 0.503*** (0.007) (0.007) (0.007) (0.007) Observations 149,797 149,797 149,268 101,173
All the above evidence is compatible with a possible self-selection e ect of trading rms and the associated productivity premium, an e ect which in our data is larger for importers than exporters. In the remaining of the paper we will thus explore whether, at the rm level, these productivity premia can be driven by the trading complexity associated to the importing vs. exporting activity.
4.2 Measuring trade complexity at the rm level
Table 7 displays the descriptive statistics for our main indicators of complexity measured at the rm level, across di erent categories of trading rms. As it can be seen, in line with our previous aggregate results the importing side of trade seems to be associated on average to more complex bundles, along all the three measured dimensions of complexity. More speci cally, considering all trading rms the average imported bundle contains three times more products, traded with 20% more countries, than the average exported one. Also the average unit values of the imported bundles are signi cantly higher than those measured for the exported products. Finally, the substitutability on individual products is lower for the imported bundle vs. the exported one, as the average number of countries from which each product is imported is smaller than the number of countries it is on average exported to. Imports also come, on average, from more distant (by 31%) and larger (35%) countries with respect to exports.
Table 7: Complexity indicators: descriptive statistics
Export Import Export Import
Mean SD Mean SD Mean Mean Mean Mean
N. of products (HS6) 8.8 16.3 24.1 43.4 2.6 5.6 10.4 33.8
N. of countries 3.7 6.3 4.5 5.5 1.4 1.8 4.2 5.7
Average unit values (UVT) 1846.3 50532.9 3116.2 24958.8 599.9 4036.1 2059.8 3285.6
Population (mil.) 46.4 53.8 63.0 105.0 42.5 77.4 46.2 56.5
Distance (km) 955.7 1494.9 1255.1 1669.7 1015.9 1553.3 922.3 1087.7
Substitutability Index 2.5 4.0 1.7 1.4 1.2 1.2 2.8 1.9
Two-way traders Export bundle Import bundle
Even more interestingly, as shown in Table 7 these di erences hold also across rms in di er-ent trade statuses, as the same relative gures are obtained for complexity indicators measured for the imported vs. exported products across all our rms, within two-way traders, or for rms switching into importing or exporting.
An important issue which we have not properly discussed yet is however related to the extent to which we can use unit values as a proxy for our qualitative feature of complexity. The following Table 8 shows the results of an estimation in which the average unit value of the traded bundle, imported and/or exported by every rm in our sample in a given year t, is regressed against various indexes of contractual complexity of the same bundle, plus the controls of distance and population.
Our explanatory variables include: (i) the Rauch index of product di erentiation, constructed as a weighted average of the degree of di erentiation of the products traded by the rm (based on the classi cation of industries in Rauch [1999]), the idea being that the more di erentiated the traded goods are, the more relationship-speci c (thus involving higher trade complexity) the trade transactions are likely to be (Nunn 2007).23 The index ranges from 0 (homogeneous products traded on organised markets) to 1 (di erentiated products) with higher value thus associated to higher complexity; (ii) the Intermediation Intensity index developed by Bernard et al. (2010), which captures the extent to which a certain product is traded directly or via intermediaries: the index is constructed as a weighted average of the degree of intermediation of the products traded by the rm, where the latter is estimated using data for di erent HS2 industries in the US. The original index is a continuous value bounded between zero, if within the industry only rms without commercial arms trade a given product, and 1, if products within a given HS2 industry are solely traded by wholesalers/retailers.24 For expositional convenience, our index is the inverse of the original index used in Bernard et al. (2010), with higher values thus suggesting less intermediation intensity, i.e. more contractual complexity at the rm level; (iii) a composite index of countries' governance quality from the World Bank, namely the `Rule of law' indicator, as the quality of national institutions in uences various aspects of international trade (Berkowitz, Moenius and Pistor 2006; Nunn 2007; Bernard et al. 2010); (iv) an index measuring the amount of time and number of documents necessary, in each country/year, to process trade, always calculated by the World Bank. See the Annex for further details on all 23Mandel (2010) also provides evidence that a higher scope for product di erentiation tends to be associated in US data to higher unit values of the traded bundle, although the channel he identi es is related to quality sorting.
24
According to Bernard et al. (2010), intermediation is de ned to be a weighted average of the retail and wholesale employment shares of each rm importing the product, using the rms' importance in a product market in value terms as weights. Products are de ned at an HS10 level, before being aggregated into the HS2 level.
these indicators.
The following Table 8 presents the result of these indicators regressed against the UVT of the imported and exported product bundles, including industry, time and regional xed e ects, as well as distance and population.25
Table 8: Complexity indicators: determinants of unit values of traded bundles (UVT)
Dep Var: UVT Imported Exported
Rauch index 1.424*** 0.592*** (0.026) (0.034) Intermediation Intensity 7.142*** 3.679*** (0.114) (0.105) Rule of law 0.305*** -0.172*** (0.027) (0.03) Time/docs to process trade index -0.850*** -0.372**
(0.113) (0.159) Distance 0.222*** 0.368*** (0.015) (0.017) Population 0.0569*** -0.0590*** (0.012) (0.012) Constant 1.155*** 4.550*** (0.41) (0.533) Observations 71,854 55,570 R-squared 0.289 0.298
Robust standard errors. Industry, time and regional dummies included.
*** p<0.01, ** p<0.05, * p<0.1
We nd our variables all signi cantly associated to unit values, with plausible signs. Both for exports and imports, unit values are higher when the product is di erentiated or when products are characterised by low contractibility. As shown in Gorg, Halpern and Murak•ozy (2010), distance is positively related to unit prices. Bureaucracy, as proxied by the amount of time and number of documents necessary in each country to process trade, has a dampening e ect on unit values. Export prices are higher when the destination market has weaker institutions, while the opposite is true for import unit values, suggesting quality sorting of inputs. A similar story is present when looking at market size, suggesting a competition e ect (lower prices) in larger markets for exports, but sorting on the import side in terms of quality.
Although only suggestive, we believe the latter results justify the choice of average unit values as a synthetic measure of the `quality-driven' dimension of complexity we want to capture. In any case, in what follows we will also present a robustness check in which the qualitative dimension 25
of complexity is proxied by the Rauch index, extensively used by the literature as a measure of contractual intensity (Nunn 2007).
4.3 Identi cation
In order to identify whether the reported productivity premia can be driven by our measures of trading complexity, one could in principle regress lagged productivity against the complexity indicators at the rm level, controlling for rmlevel characteristics. However, such an identi -cation strategy might still su er from a potential endogeneity driven by unobserved rm-speci c e ects. Using rm xed-e ect would not solve the problem, as we deal with an unbalanced panel in which the same rm over time might start (stop) either importing or exporting or both; or the rm, maintaining the same trade status, might in any case change the composition (the complexity) of the bundle of products it trades. Thus, identifying the coe cients exploiting only the within variation of the data is likely to generate e ciency problems with our estimators.
To deal with this issue, we have constructed a model design that exploits the symmetry of our complexity indicators for importers and exporters and uses our sample of two-way traders for identi cation.26 The idea is that if any measure of complexity drives self-selection we should observe the ex-ante more productive rms operating the most complex activities. Moreover, if complexity matters more on the importing vs. the exporting side, as we posit from the previous analysis, we should observe within two-way traders that the ratio of imported / exported complexity is increasing with lagged productivity: in other words, based on our preliminary evidence, we expect that the more productive a rm is ex-ante, the more complex will be the traded bundle it imports vs. the one it exports. Since identi cation takes place within the same rm (two-way trader), we can exclude that our results are driven by some unobserved rm heterogeneity a ecting exporters di erently than importers. We can therefore use a between estimator in order to avoid potential problems of serial correlation of the error terms, thus identifying our coe cients of interest only through the (pooled) cross variation of rms, always controlling for time, industry and region-speci c xed e ects, as well as the usual bunch of rms' characteristics.
The above speci cation is implemented by de ning a set of relative complexity measures,
RCit = log(CM=CX) calculated as the ratio of our complexity measures between the imported
26
Our dataset records around 50,000 rms which at a certain moment in time are two-way traders, thus consti-tuting the largest majority of trading rms in the data. Around 5,500 of them enter (exit) over time from this status, as they start (stop) either importing or exporting or both. Those rms persisting in the two-way trader status however change the number of products or countries in their traded bundle, thus altering some of the trade complexity features they face.
(CM) and the exported (CX) bundles, respectively. We then run the following regression:
!it 1= 0+ 1RCit+ 2Xit+az+ar+at+"it (2)
where we have regressed the level of lagged TFP of each two-way trader !i against the set of
relative complexity measures and the usual controls.
Table (9) provides a synthesis of these regressions for the complexity variables
Table 9: Two-way traders and complexity
Dep Var: Lagged TFP (1) (2) (3) (4) (5) (6)
N. of HS6 Import/exported 0.119*** 0.0655*** 0.0709*** (0.004) (0.005) (0.006) N. of Import/export countries 0.115*** 0.149*** 0.147*** (0.005) (0.007) (0.007) Import/export UVT 0.0282*** 0.0163*** 0.0142*** (0.002) (0.002) (0.002) Import/export substitutability -0.0220*** -0.0361*** -0.0361*** (0.001) (0.001) (0.001)
Rauch index of Import/export products 0.0929***
(0.013)
N. of good categories (BEC) import/export 0.00109
(0.005)
Product Information index import/export -0.0137***
(0.005) Import/export population, in logs 0.00531 0.0153*** 0.00514 -8.19E-05 0.00711 0.00742
(0.005) (0.005) (0.005) (0.005) (0.005) (0.005) Import/export distance, in logs 0.0479*** 0.0311*** 0.0573*** 0.0736*** 0.0425*** 0.0422***
(0.007) (0.007) (0.007) (0.007) (0.007) (0.007) Firm size 0.128*** 0.135*** 0.130*** 0.116*** 0.0999*** 0.101*** (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) Foreign firm 0.226*** 0.265*** 0.265*** 0.269*** 0.217*** 0.214*** (0.01) (0.009) (0.01) (0.009) (0.009) (0.01) Firm age 0.0651*** 0.0632*** 0.0639*** 0.0640*** 0.0638*** 0.0641*** (0.003) (0.003) (0.003) (0.003) (0.002) (0.003) Former SOE -0.0123 -0.0069 -0.00706 -0.0049 -0.00515 -0.0067 (0.012) (0.012) (0.012) (0.012) (0.012) (0.012) Constant -0.820*** -0.782*** -0.577 -0.676*** -0.639 -0.641 (0.067) (0.068) (12,820) (0.068) (12,571) (15,081) Observations 40,729 40,729 40,086 40,729 40,086 38,787 R-squared 0.281 0.274 0.274 0.273 0.301 0.304
Robust standard errors. Industry, time and regional dummies included. *** p<0.01, ** p<0.05, * p<0.1
In Columns 1 and 2 we can see how the indicators of the number of imported products and countries tend to increase with productivity proportionally more than for exporting prod-ucts/countries, since the ratio between the measures is always positive and signi cant, in line with our priors. When testing the quality-related dimension of complexity, in Column 3 the unit value ratio is also positive and signi cant.27 This nding is once again in line with our previous results, pointing to the higher complexity of the importing activity also in terms of
relationship-speci c aspects. Column 4 shows the (relative) substitutability index to be nega-tively associated to ex-ante productivity, signalling that the more the importing activity depends on single country/product pairs with respect to the exporting activity (thus a lower value of the ratio), the higher the ex-ante TFP of rms, in line with the preliminary evidence on the higher technological complexity of importers. All these results hold controlling for the (relative) distance and partner country's size, as well as the usual set of rm level characteristics.
In Column 5 we put all these complexity indicators together, to exclude potential spurious correlations among them: signi cance remains unchanged. We repeat this exercise in Column 6 where, as a robustness check, we have added the ratio of the Rauch index of product di eren-tiation as an additional control for the qualitative side of complexity, while our substitutability index has been complemented with the number of BEC product categories imported vs. those exported. Moreover, to avoid having our results being driven by particular country/product choices undertaken by some very productive rms, we also include for every traded bundle an additional robustness control (the product information index) measuring the average number of rms who import (export) the very same traded bundle (see the Annex for further details); the idea once again is that the more `unique' the product is, also in terms of number of rms importing vs. exporting it (thus a lower ratio), the higher its complexity, and the associated ex-ante productivity. Even including these alternative proxies for the qualitative and techno-logical complexity of the traded bundle, the sign and signi cance of our main results remains unchanged.
4.4 Productivity premia and trade complexity
In the previous sections we have shown that a number of variables correlated with the idea of a (costly) complexity of the traded bundle might drive the self-selection of rms into the trading activity, especially as far as importers are concerned.
We are now interested to assess the extent to which, controlling for all our indicators of complexity, we are able to explain the di erence between importing and exporting productivity premia which we have reported at the beginning of our analysis (see Table 5). In order to design a proper test for the latter, we have to take into account the fact that our indicators of complexity have been developed separately for importers and exporters within two-way trading rms. As a 27
One could wonder whether the correlation between TFP and the average unit value of the traded bundle (UVT) is spuriously driven by an omitted price variable bias deriving from an improper de ation of our production function. But a potential bias, if existing, would work against our main hypothesis: suppose TFP is imperfectly measured because of an improper de ationing; the latter would be negatively correlated to rising UVTs for importing rms, and positively for exporting ones, i.e. contrary to our hypothesis and the results we have obtained.
result, in order to estimate a regression similar to our Eq. 1, we need to treat two-way traders as separate trading rms, regressing rm-level productivity against the complexity measures of the import side (plus a dummy for being an exporter as well), and then against the complexity measures of the export side (plus a dummy for being an importer).28 Thus, with respect to the
original results reported in Table 5, our number of rm-level observations is increased due to the di erentiated treatment of two-way traders, but the model design remains the same, ensuring comparability.
On this extended sample, we run the following simple regression:
!it= 0+ 1F Zit+ 2GCit+ 3Xit+az+ar+at+"it (3)
where as usual we have regressed the level of TFP of rms !i against the various complexity
measures of the traded bundle GCit = CM if the rm is as an importer, and GCit = CX if
the rm is an exporter. As usual we include rm-level controls and industry, region and time xed-e ects.
Results are reported in Table 10. In Column 1 results replicate the productivity premia obtained in our baseline estimates of Table 5, with exporters reported some 14.9% more pro-ductive than the average rm vs. the 45.1% premium of importers. In Column 2 we add our main proxies for complexity, controlling for the number of products, countries, the UVT and the substitutability of inputs: in line with our hypothesis, the premia decrease to around 10% and 31% for exporters and importers, respectively. Finally, in Column 3 we complement our set of complexity measures with all the robustness checks already incorporated in Table 9: results, as expected, do not change, thus indicating that our main set of complexity measures are able to explain explain between 30 to 40% of the overall productivity premia of trading rms.
5
Switching
rms
5.1 Switching rms and productivity
Focusing on switching rms allows to inspect how adding a trade activity alters the performance of the rm with respect to its pre-switch productivity, thus deriving better insights on the potential self-selection of rms into the same trade activity. To that extent, data discussed in Section 2 and reported in the transition matrix (see Annex) show that about one-third of rms 28
Inserting the dummies for exporter and importer when regressing productivity of two-way traders against the complexity of the imported and exported trade bundle, respectively, controls for the possibile complementarity in the productivity premium of two-way traders between the importing and the exporting activity.
Table 10: Self-selection and trade complexity
Dep. Var: TFP Baseline
Importer dummy 0.451*** 0.311*** 0.303*** (0.006) (0.01) (0.011) Exporter dummy 0.149*** 0.0986*** 0.102*** (0.006) (0.007) (0.007) N. of products at HS6, in log 0.0678*** 0.0319*** (0.003) (0.004) N. of countries, in log 0.162*** 0.152*** (0.005) (0.005) UVT 0.0142*** 0.0140*** (0.001) (0.001) Substitutability 0.0248*** 0.0262*** (0.001) (0.001) Rauch index -0.0935*** (0.008)
Product Information index 0.00550**
(0.003) Number of good categories (BEC), in log 0.0520***
(0.004) Population -0.00625*** -0.00586*** (0.001) (0.002) Distance 0.000169 6.29E-05 (0.004) (0.004) Firm size 0.142*** 0.0773*** 0.0767*** (0.002) (0.002) (0.002) Foreign firm 0.181*** 0.124*** 0.120*** (0.005) (0.005) (0.005) Firm age 0.0474*** 0.0428*** 0.0427*** (0.001) (0.001) (0.001) Former SOE -0.0246*** -0.00115 -8.55E-05
(0.006) (0.006) (0.006)
Constant -0.88 -0.818 -0.811
(3,402) (3,337) (3,335)
Observations 196,288 196,288 196,288
R-squared 0.299 0.325 0.326
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Baseline & complexity
alter their trade status within four years, with many rms switching from being a non-trader to importing or exporting. A small but not negligible group of rms switches from no trade to two-way trade.
In order to prevent the already discussed phenomenon of `occasional' traders to bias upwards our count of rms changing trade status, we de ne as `permanent switcher' a rm which in a given year starts to either import or export (or both) at least 0.5% of its output, and then does not revert back to the previous status in the remaining of the time in which it is present in the data. We will then show how results change when controlling for the latter de nition of switching rms vs. the standard one.
(1999) methodology, we look at how a switch of a rm iin time tfrom a di erent trade status in time t 1, denoted F Zit relates to the ex-ante TFP of each rm !it 1, controlling for the
usual key characteristics. A positive coe cient would suggest that switching rms are ex-ante more productive. We run:
!it 1 = 0+ 1 F Zit+ 2Xit+az+ar+at+"it (4)
where we have regressed the level of TFP of each rm!i at time t 1 against the switch in the
trade status of the rm at timet, measured through a dummy variable F Z taking value 1 if a rm switches to a new status (export or import) at timetand 0 otherwise. In the regression we then control for the same set of rms' characteristics already employed throughout the paper plus the usual xed e ects.
As stated, we control for the phenomenon of `occasional' traders running Eq. (4) rst on a sample of switching rms measured as it is standard in the literature, that is any rms changing trade status during the time they are observed in the sample. We then re-run the same estimates on our sample of `permanent switchers', that is rms which in a given year start to either import or export (or both) at least 0.5% of their output and then do not revert back to the previous status in the remaining of the time in which they are present in the data.
Columns (1) and (2) report the results of the standard measure of switching rms, regressing TFP at (t-1) over a standard switch variable for exporters and then for importers, controlling for the usual set of rms' characteristics. Results con rm the traditional nding of self-selection of trading rms, according to which rms switching into the trading activity (both imports and exports) are ex-ante signi cantly more productive than the average rm. Once again, importer-switchers appear to be more productive than export-importer-switchers.
In Columns (3) and (4) of Table 11 we have instead checked whether rms permanently switching at timetfrom a no-trade status to the exporting-only vs. the importing-only activity, respectively, are characterized by a productivity level at timet 1 signi cantly higher than non-trading rms. To broaden the scope of our analysis, in Columns (5) and (6) we have included rms switching, respectively, into exporting vs. importing activity from any other trade status, comparing their productivity levels at time t to other non-switching rms.
Table 11: Switching rms and ex-ante TFP
Sample
Dep. Var: lagged TFP No-trade to EXP No-trade to IMP No-trade to EXP No-trade to IMP Any EXP switch Any IMP switch
EXP Switch dummy 0.0686*** -0.0271 -0.0334
(0.017) (0.033) (0.026)
IMP Switch dummy 0.0924*** 0.126*** 0.0590**
(0.015) (0.029) (0.024) Firm size 0.187*** 0.187*** 0.191*** 0.191*** 0.198*** 0.180*** (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) Foreign firm -0.0255** -0.0240* -0.0292** -0.0274** 0.144*** -0.0404*** (0.013) (0.013) (0.013) (0.013) (0.011) (0.012) Firm age 0.0554*** 0.0559*** 0.0559*** 0.0557*** 0.0563*** 0.0553*** (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) Former SOE -0.0171* -0.0166* -0.0190* -0.0183* -0.0513*** -0.0218** (0.01) (0.01) (0.01) (0.01) (0.009) (0.009) Constant -0.591 -0.58 -0.861*** -1.014*** -1.101*** -0.642*** (0.053) (0.063) (0.06) (0.07) (0.053) (0.051) Observations 66,249 66,249 63,741 64,068 82,360 71,804 R-squared 0.169 0.17 0.172 0.171 0.184 0.167
Robust standard errors. Industry, time and regional dummies included. *** p<0.01, ** p<0.05, * p<0.1
Any switcher Permanent switcher
Results show that rms permanently switching into exports-only do not di er ex-ante from non-trading rms, while rms permanently switching into imports-only are ex-ante 12.5% more productive than the average non-trading rm. The results hold controlling for or a number of rms' characteristics, as well as industry, time and regional xed e ects. The same is true when we consider a more general case of switching into export (import) from any other trade status: in this case, rms switching into imports are ex-ante 6% more productive than the control group. Thus, we con rm our nding that self-selection of rms into trade seems to be more associated with the importing, rather than the exporting, activity.
5.2 Switching rms and complexity
The above analysis is consistent with the idea that the imported bundle of goods seems to be associated to a higher complexity than the exported bundle also for switching rms. In order to test for the latter hypothesis also for this category of rms, we report in Table (12) the result of a multivariate regression of the form
!it 1j F Z = 0+ 1C(M; X)it+ 2Xit+az+ar+at+"it (5)
in which (lagged) TFP of a given category of switching rms !it 1j F Z is regressed against the set of complexity indicators C(M;X)it measured during the rst year of trading activity for
Table 12: Switching rms and complexity
Sample
Dep Var: Lagged TFP No-trade to IMP No-trade to EXP N. of products at HS6, in log 0.0711** 0.06 (0.033) (0.051) N. of countries, in log 0.220*** 0.00112 (0.059) (0.097) UVT 0.0253** 0.0114 (0.011) (0.017) Number of good categories (BEC), in log 0.0711** -0.00941
(0.035) (0.057)
Substitutability 0.0928* -0.035
(0.055) (0.066)
Coefficients from a set of OLS regressions including one complexity indicator at the time Population, distance and industry, time and region dummies included as controls *** p<0.01, ** p<0.05, * p<0.1
Permanent switcher
both rms switching into imports, C(M), and export C(X), respectively; we also include the usual set of individual rms' controls Xit and xed e ects.
Results show in general that the ex-ante productivity of rms switching into imports tends to be positively and signi cantly associated to our di erent proxies of complexity, individually considered, while the latter is not necessarily true for rms switching into exports, consistently with the evidence on the self-selection of importers (but not of exporters) we have gathered for switching rms.
Interestingly, repeating the same estimation one year after the switch, the signi cance of some of our variables tends to disappear, thus pointing prima facie at the sunk, rather than
xed, nature of the costs a rm has to incur when dressing up for importing.
In particular, ex-ante more productive rms end up importing a signi cantly higher number of products from more countries, while the same relation does not hold for rms switching into exports. In line with the descriptive statistics, also the `qualitative' complexity of the traded bundle, as proxied by the average unit values, is positively and signi cantly associated to lagged TFP in the year of the switch for importers, but not for exporters. Finally, the proxy for substitutability of the traded bundle is weakly or not signi cant in our regression for both cathegories of rms, as well as the other control variables associated to trade costs, that is the average distance and the average size of markets.
The latter results thus seem to con rm also for switching rms the relation between a higher complexity of the importing activity and self-selection. Note also that the latter analysis is more
