Multi-Product Producers and Exporters: evidence for Italy Sergio De Nardis (Nomisma) sergio.denardis@nomisma.it Carmine Pappalardo (Istat) cpappalardo@istat.it (Preliminary version) Abstract We study multi-product producers and exporters in the Italian case. We get necessary information by merging PRODCOM and Foreign Trade (FT) databases for the year 2006. We obtain proxies for firm-level estimates of labour productivity (total turnover per worker). A smaller dataset, obtained by merging the PRODCOM+FT data with balance-sheet information, is used to allow for more precise measure of labour productivity (value added per worker). On the grounds of these information we are able to show relevance and characteristics of multi-product Italian producers. Furthermore, the link between production and trade data allows us to point out the extreme diffusion of the phenomenon of the goods that are exported, but not produced; such phenomenon is named as Non- Produced Exports (NPE). NPE diffusion (95% among firms, 93% among goods) and distribution by number of products, firms, destination markets is in Italy surprisingly similar to the same phenomenon highlighted in Belgium by Bernard et. al (2010) and termed carry-along-trade. At this stage of our work, we provide empirical evidences for: i) relationships between productivity and margins of trade (much in line with the quoted study for Belgium); ii) relationships of export and import premia with productivity differentials of trading firms; iii) role of self-selection and of learning-by-importing in firms importing activity; iv) multilevel estimates of export performance by products and destination markets.
1. -Introduction In the real world a large share of producers is composed by multi-product firms, especially among exporters. This mere fact raises a number of questions about the way theory and empirical analysis approach the study of firm s behaviour in international trade. It is not sufficient to consider that firms are heterogeneous when investigating the impact of global competition, since there is heterogeneity even within the firm given that production lines are characterised by different productivity levels. And the existence of intra-muros heterogeneity involves further margins of adjustment in response to competition (and even business-cycle) shocks: these are within-firm extensive and intensive margins. Recent contributions (Feenstra and Ma 2008, Eckel and Neary 2008, Eckel Iacovone Javorcik and Neary 2010, Bernard, Redding and Shott 2010a and 2010b, Melitz, Mayer and Ottaviano 2010), even adopting quite different framework to study multi-product firms, share some common features on essential intra-firm working : 1) given product heterogeneity, in each firm there is a product ladder with core competences on top of it and marginal products at the lower layers; 2) firm-level productivity is influenced by product mix, so it cannot be considered as a primitive parameter, quite the contrary it is endogenous; 3) international competition causes within-firm re-allocation of resources, with the elimination of less efficient products and expansion of the best ones and such product selection enhances firm, sector and economy productivity. Consequences obviously do not stop to positive analysis. Overlooking intra-firm margins of adjustment may lead also to misinterpretations of some hot policy issues. If after a shock, neither sector nor producers reshufflings are observed, this does not necessarily mean inertia. Provided firms are multi-product, shifts may take place within the firm and substitute for sector and firm adjustment. As a consequence, what economists call structural change does not necessarily identify exclusively with resource re-location between sectors/firms: part of the churning process takes place in the population of products within the firm. The fact that many firms are multi-product is also relevant for labour market adjustment. To the extent that productivity increase, driven by fiercer competition, is brought about by product selection within the firm, there could be a mitigation of the costs of adjustment for the labour force. As a matter of fact it is relatively easier to re-locate (maybe after a training period) displaced workers from dying to surviving (and expanding) lines of productions within the same firm, than to move workers from dying to surviving firms, especially if these firms are in different sectors and require different workers specializations. Corollary to this is that policy measures aimed at encouraging within-firm product switching and labour re-training are just as important as those targeted to favour labour mobility between firms. These considerations shed light on the importance to have information as much detailed as possible on intra-firm activity. So an essential part of any empirical study of multi-product firms concerns data, that is to have adequate databases providing homogeneous information on products that are produced and traded in foreign markets by each firm. Typically you do not have such an ideal dataset and have to rely on merging different statistical sources. In this paper we study the case of multi-product producers and exporters in Italy. Construction of the dataset constitutes an element of novelty that can be replicated for other European countries that collect information similar to the ones we use in our work. We get necessary information to implement the analysis of multi-product firms by merging PRODCOM and Foreign Trade (FT) databases for the year 2006. The resulting dataset provides firm-product information on: i) what are the goods produced and what are those exported (imported); ii) how
many are the goods produced and how many are those exported (imported); iii) how much is produced of each produced good and how much is exported (imported) of each traded good. We obtain proxies for firm-level estimates of labour productivity (total turnover per worker). A smaller dataset, obtained by merging the PRODCOM+FT data with balance-sheet information, is used to allow for more precise measure of labour productivity (value added per worker). On the grounds of these information we are able to show relevance and characteristics of multiproduct Italian producers. Multi-product firms are more diffused among exporters and they are, on average, larger and more productive than single-product firms. Furthermore, the link between production and trade data allows us to point out the extreme diffusion of the phenomenon of the goods that are exported, but not produced bay manufacturing firms; we name such phenomenon as Non-Produced Exports (NPE). NPE diffusion (95% among firms, 93% among goods) and distribution by number of products, firms, destination markets is in Italy surprisingly similar to the same phenomenon highlighted in Belgium by Bernard et. al (2010) and termed carry-along-trade. At this stage of our work, we provide empirical evidences for: i) relationships between productivity and margins of trade; ii) relationships of export and import premia with productivity differentials of trading firms; iii) role of self-selection and of learning-by-importing in firms importing activity, iv) a multilevel estimates of export performance in order to properly account for the very detailed structure of productions and destination markets available in the dataset. The paper is organized as follows. Next section illustrates the construction of the dataset. Relevance multi-product producers and the diffusion of NPE are described in section 3. Preliminary empirical estimates are reported in section 4. Estimation results from a multilevel model study of export performance by products and countries are discussed in section 5. Section 6 concludes. 2. Merging PRODCOM and FT databases This section describes the procedures adopted for the construction of a very detailed dataset at the firm level which is the result of the matching of the statistical information contained into two most important sources of information: the Foreign Trade statistics (FT) and the annual survey on industrial production (PRODCOM). The survey on Italian Foreign Trade statistics (FT) tracks the value and quantity of goods traded between Italy and the EU Member States (intra-eu trade) and non-eu countries (extra-eu trade). This is a harmonized source of information about imports, exports and trade balance. This statistical source belongs to census type of statistics. In order to reduce the burden on enterprises, particularly small and medium sized ones, the Intrastat system was introduced in 1993 as a result of completion of the single market on the EU territory after abolishing physical barriers to trade. It exclusively covers the EU internal transactions and is designed in a way that the workload for providers of statistical information varies according to the amount of trade in which they are engaged. Furthermore, FT presents a close link with the VAT system: as a result the surveyed unit is a legal unit and it may not correspond with the firm considered as a reference productive unit. The classification system adopted in the Combined Nomenclature (CN) PRODCOM is a survey to collect statistics on the amount of production of each of the products in the PRODCOM list. It consist in a very detailed classification at 8-digit headings which is fully consistent with NACE and CPA classifications. In order to reduce the burden on small enterprises, the regulation states that only enterprises with more than 20 employees should be surveyed. A
sample survey is then considered for the group of small and medium enterprises sized between 3 and 20 employees. However the regulation stipulates that national survey methods should ensure that 90 % coverage across the whole 4-digit NACE class is achieved. In the following, the procedures used to perform a double-matching procedure are presented. First, the international traders in FT dataset are associated with the corresponding industrial manufacturing companies in the PRODCOM survey; secondly, the headings of the Combined Nomenclature (CN) are matched with those in the PRODCOM list. The exercise is performed with reference to annual 2006 data. Firms In FT dataset, the international trader is uniquely identified by the VAT code.by contrast, in the PRODCOM survey, the enterprise is identified by the ISTAT company-code, an identifier assigned to the firm when the production unit is surveyed in the Italian business register (Statistical Register of Active Enterprises, ASIA). It provides, for each production unit, a unique association between the VAT code and the ISTAT company-code for the same enterprise. The set of production units common to PRODCOM and FT database is obtained as follows. First, ASIA and FT databases are matched using the VAT code in order to obtain pairs of VAT-ISTAT codes for each trader; second, FT and PRODCOM dataset are then matched using the company-code as common information. This procedure allows combining information on the international trading companies with some relevant structural variables of the production unit, such as the number of employees, the legal form, the sector of economic activity. Products The exact matching between each production of the Combined Nomenclature (CN8) and the corresponding one(s) in the PRODCOM list used by ISTAT (REP, hereafter) is one of the methodological issues faced in the paper. The aim of the paragraph is to describe the procedure used to define 1:1 or 1:n associations between (the same) productions in both the PRODCOM and CN classifications. In this view, it should be considered that, in accordance with Article 2(2) of the PRODCOM Regulation, each heading of the PRODCOM list is defined, with a few exceptions, by one or more the 8-digit headings of the external trade nomenclature (CN). This is due to the greater detail of the Combined Nomenclature, which includes more than 9,000 headings compared to the 4,800 products of the PRODCOM list. Therefore, the rules for the interpretation of CN items are also applicable for the interpretation of the PRODCOM list. Specifically, the corresponding CN headings are listed in a column named External trade nomenclature reference for 2008 HS/CN and a correspondence table (TAB hereafter) between PRODCOM and CN headings is than provided. It is provided and updated by Eurostat. The structure of the PRODCOM codes in TAB represents the key for the matching of the two classifications of interest, i.e. CN and REP, where the latter is the ISTAT version of PRODCOM list. The final results strongly depend on the degree of homogeneity between the classification of industrial products available in TAB and the one adopted by ISTAT (REP). In general, TAB and REP show a large degree of correspondence at the level of 8-digit codes. This allows for a 1:1 matching for about 4,000 PRODCOM headings. Difficulties are reported with reference to a small subset of codes, in particular those that are not shared by the various classifications of industrial production considered in this work. For example, the production of textiles, shipbuilding and production of metals present different PRODCOM codes in both the correspondence table and in ISTAT version of PRODCOM list. The latter differs from the official one provided by Eurostat also because includes addition productions which are considered to characterize the Italian manufacturing (i.e., in the food sector) but that do not present any relevance in the EU context. Finally, when miss-matches have been identified, the strategy to increase the
correspondences between REP and CN has been to modify, as appropriate (usually by aggregation), the classification of industrial products in REP. 3. Multi-product firms Table 1 reports summary information of the dataset. It shows that, in 2006, out of the total number of considered firms in the dataset (28,000) those producing more than one good are 45.5%. Such a percentage reflects the larger share of multi-product producers among firms selling in foreign markets: exporting firms producing more than one product are 29.7% of total manufacturing firms (15.8 in the case of producers selling only in the domestic market) and constitute almost 50% of all exporters 1. Yet, assessment on the relevance of multi-product activity based exclusively on the number of produced goods yields a very partial vision of the phenomenon. That is because a very large number of exporting firms sell abroad goods that do not produce, so called NPE (nonproduced exports). Allowing for NPE goods, the fraction of multi-product firms is more than 50% of total producers, about 90% of exporters. [Table 1] Table 1 indicates that exporters have on average a larger size (4 times), more productive (1.5 times) and more frequently importers (8 times) than domestic producers 2. It is worth noticing that premia of multi-product firms in terms of size, productivity and importing activity are determined exclusively by the differences observed between multi and single-product exporters. Such premia are even larger when extending the definition of multi-product firms to those that export products they do not produce, namely the NPE firms. Multi-product exporters, including those that sell NPE goods (Table 2), are 87% of total exporters (column 2) and sell 99% of total shipments abroad (column 3). The number of exporters (column 2) decreases with the increase of the number of exported goods (column 1), so that relatively few firms (less than 2,000 that is 12%) export more than 30 products, although their shipments represent 55% of total exports. The number of destination markets (column 6) rises with the number of exported goods (column 1). Average number of per-firm destination markets is 18.5, but with a large dispersion around this figure: firms exporting only one product sell it, on average, in 2.4 destinations, while those that export more than 30 goods sell them, on average, in about 45 destination markets. [Table 2] Average exports per firm-product (column 7), per firm-destination (column 8) and per firm-producdestination (column 9) rise, even though not monotonically, as the number of exported goods increases. In general, the larger is the number of exported goods the larger is the per-firm export value (ratio between figures in column 3 and those in column 2). Coming to NPE phenomenon, as soon as a firm is classified as multi-product exporter (that is from two exported products upward) NPE becomes the rule: the number of goods that are sold abroad (column 1) is systematically larger than the number of production lines realized by the firm (column 1 Former studies on multi-product exporters based on different statistical sources (survey data merged with PRODCOM database) are De Nardis and Pappalardo (2010) and De Nardis and Ventura (2010). 2 Evidence on the behaviour of Italian firms on the import side is reported in Castellani, Serti and Tommasi (2010).
5); on average, the number of exported goods is about 9, while the number of produced goods is just a bit more than 2. Along Bernard et al. (2010), we classify products, firms and flows of exports according to the NPE characteristics. As for exported products two categories are singled out: 1) regular productions, referring to goods that are produced and exported by the firm and whose value of export is not larger than the value of the corresponding production; 2) NPE products, including both mixed NPE goods, whose export value is larger than the corresponding production value, and strictsense NPE, whose exports do not have a counterpart in any production activity within the firm. An analogous classification is made for exporting firms. We define as regular exporters those firms that sell abroad exclusively goods that they produce and whose value of exports is not larger than the value of production. NPE exporters are instead those firms exporting products that they do not produce. On their turn, these latter firms can be partitioned in two mutually exclusive groups: strict-sense NPE exporters, including firms that export at least one non-produced good and no mixed NPE product; mixed NPE exporters including only firms selling abroad at least one good whose export value is larger than the value of production 3. As far as per-firm export values are concerned, they correspond to the sum of regular and NPE export values. The former include also the value of mixed exports up to the value of the corresponding production; the part of the value of mixed exports that exceeds the value of production is attributed to the strict-sense export component. On the grounds of this classification, Table 3 reports descriptive evidence of the NPE phenomenon. NPE is extremely diffused. NPE firms ENP (16,137, column 2) represent 95% del total exporters in the dataset (16,906); more than 88% (14,254, column 6) of NPE exporters are exclusively strictsense MPE firms, while the remaining part (1,829 firms, column 5) consists mostly of producers that export non-produced goods. It follows that almost all exporting firms (99%) export at least one NPE good. Such phenomenon appears to be sufficiently robust such that it persists when considering higher levels of data aggregation: going from 8 to 2 digit, the share of NPE firms on total exporters reduces just 75%, while the portion of strict-sense NPE producers remains at the level of 88%. As for products, the NPE phenomenon concerns 93% of exported goods (4,046 over 4,345 products, columns 9 e 8), while more than 80% of total shipments abroad per product/firm regards at least one NPE flow of export (column 11). The corresponding value of non-produced exports (column 12) is as much as 64% of overall exports; of these two third are constituted by strictsense NPE goods 4. 4. -Preliminary findings [Table 3] The aim of this section is to provide cross-sectional empirical evidence to some relevant stylized facts concerning multi-product firms and exporting. Specifically, we provide empirical evidences 3 Notice that the adopted classification both mixed and strict-sense NPE exporters may sell abroad one or more regular goods. 4 Very similar are the results for Belgium provided by Bernard et al. (2010), who estimate that NPE goods (actually they name such phenomenon as carry-along trade) is 91% on total exported products, while per-firm/product transactions involving NPE exports are 75%. There is instead a difference in the importance of NPE exports in total exports that in Belgium is lower than in Italy (the Belgian NPE share is 40%).
for the i) relationships between productivity and margins of trade (much in line with the quoted study for Belgium), ii) relations of export and import premia with productivity differentials of trading firms; iii) role of self-selection and of learning-by-importing in firms importing activity. Productivity and margins of trade First of all, the focus is on the role played by firm productivity in aggregate exporting behavior of Italian international traders and its relationships with both country and product extensive margins, as well as the intensive margin of average shipments per product-country. As a result, the dataset described in the previous sections, which includes very detailed information at the firm-productmarket, is aggregated along the extensive and intensive margins of trade. In the analysis below, we follow the same empirical strategy described in Bernard et al. (2010). Total export at the firm level can be decomposed into the extensive margins of trade (number of products exported, number of destination markets), the density of trade (a variable that indicates how many country-product combinations are effectively served by the exporter shipments compared to the theoretical combination of all products by destination markets) and the average value of exports per productcountry, X f = P f C f D f X f. (1) The above four components of total exports at the firm level are the independent variables in a regression model (2) Y f = α + β LP f + δ i + ε f, (2) which includes as main predictor two measures of labour productivity (LP f ): turnover per employee and value added per employee. The former is obtained using the detailed information available in the PRODCOM+FT dataset. Though this indicator may be used as a measure of firm performance, a more reliable measure of firm s efficiency (value added per employee) is also used. This latter information is obtained by matching all the firms in the PRODCOM+FT dataset with the one in surveys collecting balance-sheet information at the firm level. Almost all enterprises whose size is above 20 employees are perfectly matched in the two datasets. By contrast, only a sample of the small sized firms (below the threshold of 20 employees) is surveyed through the balance sheet surveys. The consequence is that the part of the PRODCOM+FT dataset is selected according to data availability. Main results are reported in Table 4-5 which report the estimated parameters for the variable of firm efficiency LP f in equation (2). The analysis is performed with reference to the whole sample of exporting firms and also to the main groups of Italian traders, regular exporters and NEP exporting firms. As expected the estimated parameters for both turnover per employee (Table 4) and value-added per worker (Table 5), both log-transformed, are positive and significant. All regressions include sectoral and regional controls. When turnover per employee is considered, regular traders and the so called pure NPE firms are the most performing ones. Looking at the extensive margins, the number of destinations and products sold rise more rapidly with productivity in the case of pure exporters while the intensive margin response is more important for regular than pure NPE exporters. Findings obtained in terms of value-added per worker partly confirm this evidence: specifically, regular traders seem to benefit more from the extensive margins compared with NPE exporters; the relationships with the intensive margin show an even larger response of regular exporters. Their positive performance in terms of value-added should be considered more carefully. The use of matching procedures has significantly reduced this type of firms (188, about 1/3 of the corresponding sample used for Table 4 regressions) and is likely that the most performing ones have been selected.
[Tables 4-5] Export and import premia The next step in our empirical investigation is to test for the existence of so-called trader premia, defined (ceteris paribus) as the difference of log-labour productivity between exporting enterprises compared to non-trader ones. This study is usually motivated by the fact that firms with different forms of participation in international trade tend to differ in size and might be concentrated in different industries. The question is whether or not this differential exists when controlling for other factors related to productivity. This issue is widely investigated in the empirical literature on the firms involved in the international trade. Recent evidences for Italy are, among others, in Castellani et al. (2010) while findings for other countries are in Muuls and Pisu (2007) for Belgium, Altomonte and Bekes (2008) for Hungary, Vogel and Wagner (2010) for Germany. Focusing on the dataset used in this paper, we consider the kernel density of value-added per employee in order to have same insights from the estimated trading premia (Figure 1). The nonparametric distributions are computed for several groups of firms. In the left panel of Figure 1 the domestic firms, the exporting ones ( regular and NPE ) and the importing enterprises are considered. The figure suggests that domestic traders represent the low productive units, followed by regular trader and NPE. Furthermore, the distribution for importers is the most shifted to the right of that of other traders, partly overlapping with that of NPE units and this proves the superiority of firms buying foreign intermediates. The right hand side panel considers a classification of enterprises in the groups of only exporters, only importers and twoway traders and the presence of a significant import premia is confirmed. [Figure 1] The relationship between internationalization status and firm heterogeneity in performance (the socalled performance premia) is empirically tested by running the following regression: Y f = α + β 1 D f twoway + β 2 D f only imp + β 3 D f only exp + β 4 θ f controls + ε f (3) where y it is labour productivity, the dummy variables D are mutually exclusive denoting whether firm f is, respectively, a two-way trader, a firm engaged in importing or exporting activities only Non-trading firms are the reference group. All regressions also include a control for the size of firm (logarithm of the employees), two-digit sectoral and regional dummies. Cross-sectional results are reported in Table 6. For comparison purposes we also consider two additional groups of firms: exporters and importers. They include units that are splitted across the three groups considered in equation (3). All estimated productivity premia for firms that engage in international trade are highly statistically significant. Results in column 1 and 2 of Table 6 indicate that importers present higher performance premia than exporting firms. Considering the distribution of firms according to their involment in international trade (column 3), we found that two-way traders (that both export and import) have the highest performance premia, followed by firms that only import. In terms of labour productivity differentials, two-way traders are 18.1% more productive than non-trading firms, very close to the premia for only importer (17.4%). It is relevant to consider that the difference between the above performance premia is non significantly different from zero. This result is broadly in line with Castellani et al. (2010). They find that the differential between internationalised and domestic firms is due to a firm specific time invariant effect and they cannot reject the hypothesis of equality of the
trading premia after controlling for fixed effects. Finally, firms that only export have the smallest estimated premia (5.2%). [Table 6] Learning by importing In what follows the focus in on one of the possible sources of efficiency for importing firms. The rationale underlying the hypothesis of learning by importing is twofold. First, imports can act as a way for knowledge flows from international sellers and competitors as well as continuous knowledge and technology transfer (dynamic effect); second, imports can generate a rapid increase in the productivity level of the firm due to inputs of better quality or cheaper inputs that are used in the production process (static effect). We focus on the dynamic effects of import activity. The assumption to empirically test is that importing improves the post-entry productivity growth. In this case, significant differences in the growth rate of labour productivity should be observed for the firms starting to import (during the years after the start) and firms that buy intermediate inputs on the domestic market. We use as outcome variables both value-added (LP) and turnover per employee (T). Due to the short time span over which data are available, the empirical test is performed considering the growth rate of LP over the period 2005-2006 for a cohort of import starters in 2004 compared to the performance of non-importers over the same period. For the same period, the performance of exporters that start to import in 2004 is compared to that of firms that only export between 2004 and 2006. This analysis is carried out estimating the model lnlp f,t+2 lnlp f,t+1 = α + β START f + δ CONTROL f + ε f, (4) where LP is the outcome variable, START is the dummy variable for import starters, CONTROL is a vector of control variables (the number of employees, also included in square terms, and 2-digit industry dummies). Results are in Table 7. Though the estimates of regression coefficients are positive (except for model 2 for turnover per employee) they are not statistically different from zero and, given our time span, we can conclude that there is no evidence for the dynamic effect of learning by importing. But these findings do not provide any information about the underlying causal nexus. [Table 7] A causal analysis is finally performed using the well known method of the propensity score matching (PSM, Rosenbaum and Rubin, 1983). The reason is that the previous findings might suffer from some biases due, for example, to unobserved heterogeneities within the groups of firms. The PSM method allows for the definition of both treated and control groups so that they are homogeneous with respect to a set of covariates (selection on observables). An auxiliary probit model is then estimated using as covariates the characteristics of the firms in the year before the start; a matched sample of non-importers is then selected based on the propensity score, so that the differences over the observables compared with the group of the treated are not statistically different from zero. The same method applies for the selection of the cohort of exporting firms starting to import in 2004. Using the estimated scores, we apply the Nearest Neighbour (NN) matching on the common support; in other terms, we match the starter with the never importing firm having the most similar propensity score and evaluate the differences in the growth rate of
labour productivity over the period 2005 2006. This difference is named average treatment effect on the treated (ATT). The results are reported in Table 8. [Table 8] According to this procedure, the estimated ATT for non-trader starting to import in 2004 is positive and significantly different from zero. This is in some contrast with the previous findings. Finally, the results for exporters starting to import in 2004 confirms the evidence of no learning-byimporting effect of Table 5. Finally, the empirical evidence on this hypothesis is mixed: the results obtained using the PSM method for the cohort of import starters are significant but are obtained using a small sample of treated units (188). At this stage, we cannot conclude in favour of either the existence or absence of the effect of learning by importing. This issue needs additional investigation. A recent paper on the Italian case (Conti et al., 2011) presents more conclusive results. First, starting to purchase intermediates from high income countries does not have any significant impact on firm's productivity. Second, importers may need time to exploit the advantages offered by foreign supply markets. Considering a longer time span, the authors find that imported inputs from developed countries still display no significant impact while a significant effect refers to purchases from low wage countries. The latter effect is essentially driven by a parallel exporting activity. 5. Export performance by products and destination markets In this section, we present an extension of the empirical results discussed in the previous paragraph. The motivation for this analysis is due to the opportunity to effectively exploit the very detailed information on exports and imports flows of Italian traders at the level of firm-product-market. The empirical starting point of the study is model (1), we have empirically tested in the previous section and that correlates the margins of trade (both extensive and intensive) with performance variables at the firm-level (turnover per employee, value-added per employee). Taking account of the more disaggregated structure of the dataset, the decomposition of total exports in terms of the margins of trade becomes X f = P f C f (5) X fpc c p trans f = D f X f where trans is the number of positive firm-level export transactions at the product-country level. The average flows by firm-product-destination is then obtained in terms of the density measure (D f ) and the average value of exports. By contrast, the extensive margins of trade are not explicitly considered. In what follows, the analysis of exporting behavior of Italian traders is carried out using the very detailed intensive margin of trade (export per firm-product-country) as outcome variable. The information on both the number of products sold and the destination markets may be thought as significant dimensions of the dataset so to represent potential sources of variation of firm s export performance. In this section, model (1) is evaluated in the framework of multilevel models. Multilevel data arise when units are nested in clusters. Traditional examples include students in classes, patients in hospitals, etc. We refer to the elementary units as level-2 units and the clusters (the factors related to the more aggregate dimension of the dataset) as level-1 units. If the clusters are themselves clustered into higher level factors, the data present higher level structure. In terms of the these
definitions, the exporting units in our database may be considered as level-2 units (i), while the classifications corresponding to both the exported products (j) and the destination markets (k) are the upper-level clusters. The units belonging to the same cluster share the same cluster-specific influence. As we cannot expect to include all cluster-specific covariates, cluster-level unobserved heterogeneity is assumed. This entails dependence between responses for units in the same cluster after conditioning on covariates. In multilevel regression, unobserved heterogeneity is modeled by including random effects in a multiple regression model. There are two types of random effects: random intercepts and random coefficients. Whereas random intercepts represent unobserved heterogeneity in the overall response, random coefficients represent unobserved heterogeneity in the effects of explanatory variables on the response variable. If firms are perfectly nested in products and products nested in markets, our dataset presents a fully nested three-level (multilevel) structure. On the contrary, units can also be cross-classified by two or more factors, with each unit potentially belonging to any combination of the different factors. An example is panel data, where the factor individual is crossed with factor time. The structure of PRODCOM+FT dataset show a cross-classified structure: firms are nested within products (the same good j is exported by several firms) and within destination markets (the same country k is served by many enterprises), with factors j and k crossed (the same product is delivered to several markets; the same market is the destination of quite a lot of shipments). Firms (level-2 units) are then nested in a cross-classification of products and destination markets. Following Goldstein (2006), a graphical representation of this crossed classification is in Figure 2. [Figure 2] To reliably use the information on products and markets, we refer to non-hierarchical models as the empirical framework able to properly account for the cross-classified organization of units within the several factors of the dataset. In order to introduce the model adopted for the empirical analysis, we start assuming a simplified version of the data, with firms (level-2 units, i=1,,i) perfectly nested within products ( j=1,,n j ). A (one level) random intercept model is as follows: y ij = β 0j + ij (6) where β 0j are product-specific intercepts and ij are level-2 residual terms. The β 0j are modeled as β 0j = β 00 + 0j, (7) where β 00 is the mean intercept and 0j (random intercept) is the deviation of the product-specific intercept β 0j from the mean. It is typically assumed that clusters j are independent over level-2 units with zero mean and variance φ (between-cluster variance). The component ij is the residual computed as the deviation of y ij from firm i s mean. It is normally distributed with zero mean and variance θ and is assumed to be independent over both level-2 and level-1 units (within-subject variance). The reduced form of the model is obtained by substituting the level-1 model (7) for β 0j into the level-2 model (6) for y ij, yielding
y ij = β 00 + 0j + ij. (8) This is an example of mixed effects model or linear mixed model since it includes both fixed effects (the fixed part of the model, β 00 ) and random effects (the random part, ξ ij = j + ij ). The total residual (unconditional) variance of the model is Var( j + ij ) = φ + θ. As the random intercepts are shared among firms for the same product, they generate dependence between outcomes of firms within the same cluster after conditioning on the covariates. This dependence is often expressed in terms of correlation within the cluster and is named intraclass correlation, ρ = φ φ + θ. It represents the proportion of total residual variance that is due to between-cluster residual variance φ. The model (8) can be extended by including both level-1 and level-2 covariates. Adding level-1 predictors consists in the inclusion of additional fixed effects, while the random part of the model does not change. This specification is potentially useful in explaining the between-product variability. Considering as level-1 covariate the sectoral classification of exported productions (main industrial groupings, MIGS), the model for the product-level (7) becomes β 0j = β 00 + β 01 MIGS j + 0j (9) and the corresponding reduced form is y ij = β 00 + β 01 MIGS + 0j + ij (10) The two-level random intercept model (10) can be extended to a random coefficient model by including at least one firm-specific predictor in the level-2 model and, then, by allowing its slope to vary between clusters in the level-1 model. In this paper, the reference firm-specific covariate is turnover per employee (LP) we consider as a reliable proxy of labour productivity. As a result, level-2 model is specified as y ij = β 0j + β 1j LP ij + ij, (11) and the between-cluster variability is modeled including an additional model for the slope β 1j β 0j = β 00 + β 01 MIGS j + 0j (12) β 1j = β 10 + β 11 MIGS j + 1j. (13) Substituting the level-1 models in (12-13) into (11) we obtain a reduced form model (equation 14), where the corresponding random part is included in brackets.
y ij = β 00 + β 01 MIGS j + β 10 LP ij + β 11 (MIGS j LP j ) + [ 0j + 0j LP ij + ij ] (14) Under the assumption of a fully nested structure, we can extend the model specification to consider the additional cluster in our data, the destination countries. In this case, we obtain a three-level model where firms are the level-3 units (i), products the level-2 units (j) and countries the level-1 units (k). The model can be written in reduced form (using matrix notation) as y ijk = x ijk β + z (2) ijk (2) jk + z (1) ijk (1) k + ijk (15) The right-hand side terms represent, respectively, the fixed part of the model, the level-2 random part (products), the level-1 random part (destination countries) and the unit-level residuals; x ijk is a vector of explanatory variables with fixed regression coefficients β. At this point of the analysis, the assumption of a fully hierarchical structure of the data is removed and cross-classification may be taken into account. In the PRODCOM+FT dataset, the cross classification is at level-1 (products j cross-classified with countries k) with firms at level-2. In spite of the two separate factors (j, k), the empirical investigation is carried out using a two-level model, with level-2 units within a combination of products and countries. 5 In this framework, the random intercept model may be written as y i(jk ) = β 0(jk ) + i(jk ) (16) and the firm-level response variable, within the cross classification of products and destination markets, is modeled in terms of the overall mean (intercept) and a residual error term. The subscripts (jk) are in parenthesis to indicate that those factors are conceptually at the same level. The intercept can be modeled as β 0(jk ) = β 00 + 0j + 0k + i(jk ), (17) where 0j is a residual error term for products, 0k for countries and i(jk) is the individual residual error term for firm i in the cross-classification of factors j and k. We start estimating a two-level random intercept models with cross-classified units (equation 17). Furthermore, individual level explanatory variables are added to this specification and regression slopes may be allowed to vary across products and/or destinations. In this exercise, level-2 variables include the ratio of turnover per employee (LP) and a set of dummy variables that identify multiproduct firms (MULTI), those directly serving the final market (DOM), the enterprises producing by third parties (CT) and those who carry out both activities (BOTH). These covariates may be used to explain variations in the slopes of firm performance across clusters. Level-1 variables include the sectoral classification of exported productions by main industrial groupings (MIGS) and the ratio of turnover per employee averaged over products. 6 5 Goldstein (1987) described a trick for expressing a model with crossed random effects as a hierarchical model with a larger number of random effects. It consists in an extra level (third level) within which both products and markets are nested. If it is not possible to find a virtual third level, this could be defined as a single unit encompassing the whole dataset. 6 In order to ease the computational burden, the structure of level-1 products classification is simplified. We consider as product the corresponding heading of the NACE classification at the 3-digit level.
Furthermore, the results discussed in this section refer to the sub-sample of regular exporters, defined as the firms that sell abroad goods that they produce and whose value of exports is not larger than the value of the production. We motivate the choice of this group of exporting firms as it allows to estimate a proxy for underlying firm productivity (turnover per employee). In fact, the measurement of true firm productivity is a difficult task both for single-product firms (that can choose between products with different production technologies or demand characteristics) and for multi-product firms where inputs are measured at the firm-level rather than per product. The main findings for the cross-classified models are reported in Table 9. It consists of two panels. The estimated regression coefficients and corresponding standard errors for the several estimated models (considered by columns) are reported in the Fixed Part of the table. The estimated variances (and corresponding standard errors) for the random intercepts, for the two crossed level-1 factors, Var( oj ) Var( ok ), and for the so-called interaction variance, Var( i(jk) ), are presented in the Random part. For each model, intraclass correlation, AIC and BIC statistics are reported. [Table 9] The first column of Table 9 presents results for the intercept-only model assuming a cross-classified structure of the data within both products and markets. The total estimated variance amounts to 4.8. If one look at the intraclass correlations, 5.7% of the total variance is accounted for by the products level and, a broadly similar value refers to the other level-1 factor (5.4%). This first finding suggest that for the sample of exporting firms considered in this paper, differences in value of export are equally attributable to both the composition of goods produced and exported (product mix) and the structure of destination markets. Taken together, the level-1 factors account for 11% of the total variance. The model in column 2 includes as level-2 variable a proxy for labour productivity (turnover per employee). All the parameters in the fixed effects part are significantly estimated and with the expected signs. This variable contributes to explain roughly 40% of the total variation of the outcome variable. In addition, this inclusion significantly reduces level-2 variance whereas increases the level-1 variances. This has an impact on the conditional intraclass correlation, which is larger than the unconditional one. The total unexplained variance is 2.9, significantly lower compared to 4.8 in the random-intercept model. Of this statistics, 16.3% is attributable to the products classification while a substantial part (20.6%) is associated to the structure of destination markets. Overall, the level-1 factors contribute for 37% of the total unexplained variance. The model in column 3 of Table 9 presents the inclusion of additional firm-specific dummy variables concerning the multi-product exporters, the international traders whether they serve the final demand, produce goods for a third party or carry out both activities. Aggregated sectoral dummies (MIGS) are included as product-specific predictors. The inclusion of these additional covariates, though estimated statistically significant, has not significantly changed the previous empirical findings. First of all, the additional contribution of such level-2 variables to the unexplained variance is rather modest (around 1%). Secondly, the total unexplained variance is in line with the previous specification and a similar fact is also observed for the variances associated with both level-1 factors. All this suggests that that there is additional variation in export performance which is not explained by the full list of covariates (but interactions are not considered). Furthermore, the estimated variance for both level-1 factors may be considered to be relatively low while it remains a still large amount of residual interaction variance. As level-1 factors account for about 38% of this measure, an improvement could be obtained by including
relevant information concerning some additional relevant characteristics of the exporting firms, not identified at this stage of the analysis. To partially face this drawback, a random coefficient model is finally considered in which we allow for the variation in the slope of labour productivity across markets, i.e. across one of the level-1 factors for which we do not use any specific predictor (such as GDP, composition of demand, etc. ). The aim of this exercise is to evaluate the contribution of a random coefficient specification to lower the amount of level-1 unexplained variance. Results are reported in column 4. All fixed effects are statistically significant and different from zero (with the exception of the firms that produce by third parties). Concerning random effects, the aim is to evaluate the contribution of the random slope for labour productivity. In terms of the benchmark random-intercept model with covariates (not reported in the table), it emerges a significant reduction in the variance of the market structure. This accounts for about 5.1% of total residual variance (compared to 6.5 of the benchmark model). Overall, there is a small but significant slope variance for labour productivity at the market level. The estimated variance for the other level-1 factor remains unchanged. These findings may suggest the opportunity to consider a simpler model, involving only random intercepts and not slopes, similar to the one considered in column 3. 6. - Conclusions This paper provides empirical evidences on the relevance of multi-product manufacturing firms, their larger presence between exporters and the phenomenon of so-called non produced exports (NPE). Using a specific dataset that combines firm-level information on the production activity with foreign trade data at the enterprise-product-destination level, it is shown that in 2006 multi-product enterprises represent more than 45% of our sample. Their incidence is relatively higher within exporters: of the latter, almost 50% has more than one production line. Furthermore, if we consider that exporting firms usually sell more goods than the number actually produced, the market share of exporting multi-product firms rises to 87% (99% in terms of the value of exports recorded in dataset). However identified, in terms of the number of lines produced or exported goods, multiproduct traders are on average larger, more productive and with hjgher import propensity than single-product exporters. The matching of production with foreign trade data at the enterprise-product level also allows highlighting the importance of the phenomenon, defined here, of not manufactured exports (NPE). The vast majority of multi-product exporters sell abroad goods for a value that exceeds firm s actual production for the same product; in addition, some traders export goods that are not produced at all. Considering the highest level of disaggregation (8-digit level), in 2006, the ENP phenomenon occurs for about 95% of exporting firms (84% in the case of companies that sell strict-sense not manufactured goods), for 93% of products exported and 80% of firm-product shipments abroad. Such phenomenon appears to be sufficiently robust such that it persists when considering higher levels of data aggregation: going from 8 to 2 digit, the share of NPE firms on total exporters reduces just 75%, while the portion of strict-sense NPE producers remains at the level of 88%. Concerning the relationships between productivity and margins of trade, we find that both extensive and intensive margins rise with productivity, and that regular and so called strict-sense NPE firms are the most performing ones. For these group, number of destinations and number of firms rise more rapidly with productivity compared with the other traders. By contrast, the intensive margin response is more important for regular than NPE exporters.
The empirical estimates of export and import premia confirm the main results in the literature. Two-way traders (firms that both export and import) show the highest performance premia, followed by firms that only import, while firms that only export have the smallest estimated premia. In terms of labour productivity differentials, two-way traders are 18.1% more productive than nontrading firms and very close to the premia for only importer (17.4%). The difference between the above performance is non significantly different from zero. This result is broadly in line with Castellani et al. (2010). We do not find any conclusive evidence on the hypothesis of learning-by-importing. Unobserved heterogeneities that could have biased the above estimates are taken into account and a causal analysis is performed using the propensity score matching (PSM, Rosenbaum and Rubin, 1983). A matched sample of non-importers is then selected based on the score, so that the differences over the observables compared with the group of the import starters are not statistically different from zero. The same method applies for the selection of the cohort of exporting firms starting to import in 2004. The estimated impact of starting to import (in terms of differentials of productivity growth) is positive and significantly different from zero. This is in some contrast with the previous findings whereas the results for exporters starting to import confirms the evidence of no learning-byimporting effect. Finally, the relationships between export performance at the firm-product-country and a (restricted) set of firm level covariates is evaluated using the empirical framework of two-level random intercept multilevel models. First, turnover per employees, as main firm-level predictor, is estimated positive and strongly significant. Second, when the cross-classified structure of enterprises within products and markets is accounted for, firm-level covariates explain roughly 40% of the total variation of the outcome variable. Third, those estimates allow to pointing out the contribution of the structure of both product mix and geographical distribution of production (at the firm-level) to explain the variation of export performance. Considered together, the above two factors account for a large share (about 38%) of the residual interaction variance. The estimated variance remains unchanged once random slopes for labour productivity are considered at the country level.
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0 0 kdensity lp.2.4.6.8 density.2.4.6.8 1 1 Figure 1 Kernel distributions of labour productivity by traders -2 0 2 4 6 lp -2 0 2 4 6 labour productivity domestic ENP regular importer only exporters twoway traders only importers Figure 2. Cross-classification of firms by products and destination countries Product1 Product1 Product j Country 1 XXXXX X XX XXX Country 2 XX XXX XXXXX XX X XXXXX XXX XXX Country k XXXX XXX XX X
Table 1 - dataset Coe-Prodcom Firms Turnover Size Turnover per employee Importing firms Import % % level level % % Non exporting 39.9 9.6 24.7 123.1 11.0 1.9 Mono-product 24.1 5.1 23.6 124.2 6.2 0.8 Multi-product 15.8 4.4 26.5 121.6 4.7 1.1 Exporting 60.1 90.4 104.0 191.7 89.0 98.1 # produced goods # exported goods Mono-product 30.4 29.5 78.1 184.7 43.6 28.4 Multi-product 29.7 60.9 128.5 199.0 45.4 69.7 Mono-product 7.7 3.5 38.1 156.5 6.5 1.6 Multi-product 52.5 86.9 113.6 196.9 82.5 96.5 Total 100 100 72.4 164.4 100 100