Multi-product producers and exporters Emma De Angelis (ISTAT) Sergio de Nardis (NOMISMA) Carmine Pappalardo (ISTAT) ISTAT, Rome, November 22nd, 2011
Aim of the paper • Analysis of firm-heterogeneity and of multi-product exporters for the Italian case. • Empirical analysis of the relationship between the margins of trade (extensive, intensive) and firm productivity (VA per employee at current prices). • Complement evidence on Italian exporting firms with a focus on importing activities.
1. – Main issues: inter-firm adjustment • High-productivity firms self-select into export • Bernard-Jensen (1995, 1999, 2004): exporters are more productive, larger, more skill and capital intensive than non exporters (empirics) • Bernard et al. (2003), Ricardian model of heterogeneous firms, imperfect competition and endogenous markup • Melitz (2003), Dixit-Stiglitz model with constant markup • Melitz-Ottaviano (2008), linear demand, markup is endogenous and affected by market-specific toughness of competition • Features shared by these models • With fixed entry and trade costs, productivity discriminates between exporters and the others • Productivity is a primitive parameter for the firm, randomly drawn from a distribution • Opening to trade: selection and resource reallocation between firms: exit of the least efficient and expansion of the fittest (exporting firms) lead to rise in aggregate productivity
1. – Main issues: intra-firm adjustment • High-productivity goods self-select within the firm • Bernard et al. (2006b), Broda-Weinstein (2007), Mayer-Ottaviano (2007), Arkolakis-Muendler (2008), Iacovone-Javorcik (2008): single-product firms are the exception; most firms are multi-product and change number of products along the cycle and in response to technology/competition shocks. • Bernard et al. (2006b): productivity of MPFs depends on firm-level “ability” common to products of the firm and firm/product “expertise” • Eckel and Neary (2008): competition affects scale and scope of MPFs’ (intensive and intra-firm extensive margins) • Meyer et al. (2009), endogenous markup affected by toughness of competition (market size and geography); productivity/quality ladder • Features shared by these models • Within-firm productivity is different across products: at the top “core competences” • Firm-level productivity is affected by product scope and product mix • Opening to trade improves firm productivity through within-firm reallocations of resources. Focus on core competences, shed marginal products
2. - Dataset (1) • Firm-level matched dataset involving information gathered, respectively, by PRODCOM and COE ISTAT surveys. • Data cover the year 2006. • PRODCOM • EU harmonized survey to collect statistics on the amount of production of each of the products in the PRODCOM List. Each reporting country should achieve 90% coverage across the whole 4-digit NACE class. • surveyed enterprises: all firms with at least 20 employees; sample of the businesses below this threshold (3-19 employees). • The survey allows the construction of firm-level indicators (i.e., turnover per employee); provides details on both total output and the number of within-firm product lines (8 digit Nace Rev. 2). • the regulation states that reporting countries should achieve at least 90% coverage across the whole 4-digit NACE class.
2. - Dataset (2) • COE • Trade statistics track the value and quantity of goods traded by the universe of Italian trade operators (operatore economico) with EU partners (intra-EU trade) and non-EU countries (extra EU trade). • Statistics compiled on the basis of Community regulations. Customs records are the main source of statistical data on international trade. • Full detail on the external trade transactions of the universe of Italian (manufacturing) firms. • Provides information extremely useful for the computation of extensive margins: number of products exported, number of destination countries. • Next step: construction of the COE-PRODCOM integrated dataset. • Two-stage matching procedure for linking production and export/import information at firm-product level.
2. 1 - Matching firms • PRODCOM and COE databases do not share any common element for the identification of the corresponding production units. • In the COE dataset, the trader is uniquely identified by the VAT code (IVA). In the PRODCOM dataset, firms are identified by the "ISTAT code", an identifier that is assigned when the production unit is surveyed in the Statistical Register (ASIA). • ASIA provides, for each production unit, an unique association between the VAT and the corresponding ISTAT company code. • First, the integration of ASIA-COE databases is obtained using VAT as the unique key variable. We obtain unique pairs of VAT-ISTAT code for each trader in the COE dataset. • Second, the matching “traders-producers” is performed using the ISTAT company code as a common key variable in COE and PRODCOM datasets. • We are able to identify the set of trading enterprises; to combine information with structural variables of the firm; to carry out data consistency checks.
2. 2 - Matching products (1) • PRODCOM survey follows the PRODCOM list (PRD). 8 digit code (XX.XX.YY.ZZ) list defined by Eurostat. • The headings of the PRODCOM list “shall in principle consist of article or groups of articles from the combined nomenclature ...” • The PRODCOM list includes almost 4.800 products. The COE classification about 9.000 product codes. • PRODCOM and COE surveys do not cover the same field of activities. • COE includes transactions for non-manufacturing goods (A); • PRODCOM covers industrial services (I) and intermediate goods (INT) • Some PRODCOM headings breaks down its into greater detail than CN list (textiles, shipbuilding). Relationship CN8-PRD is n: m (n <m, n, m≠0). • Z aggregates (Z in the 7th position of a 8-digit code) are considered to preserve comparison PRD:CN data (1:n). It follows that m codes of PRD list (Zdis) are associated with a single Z code (m:1).
2. 2 - Matching products (2) • Manufacture of basic metals. Additional requirement to gather total production by volume: T headings • 1:1 correspondence with the sold production by value and volume (Tdis). • 1:n association with CN8 products. Some of the T codes cannot be associated with CN8 codes (\CN8). • The national PRODCOM list (REP) may partially differ from the Eurostat one (PRD). • REP may include "new" headings to account for the specific national productions (PN). A subset of the REP products (NPN) is not fully comparable with PRD classification (codes are the same up to the sixth digit). • Our aim is to define correspondences between REP and CN8 product classification. The starting point is the correspondence table for 2006 provided by Eurostat.
2. 2 - Matching products (3) • We have three PRODCOM classifications: Eurostat (PRD), national (REP), correspondence table (TAB). No differences across the several CN8 classifications. • Compared with PRD: • TAB does not include Zdis, I and \CN8 headings; • REP includes NP and NPN codes; it does not include Z codes. • Linking production and export data at the firm-product level, we discover new and unknown facts about multi-product manufacturing exporters. The large majority of Italian manufacturing firms export products that they do not produce (Not-Produced-Exports: NPE).
3. – Non-produced exports (NPE) • Products • “non-exported” products, reported as produced but not recorded as exports; • “regular“ production includes goods actually produced and exported by the firms and such that the value of overseas sales is not higher than the corresponding production; • NPE productions, which include both NPE “mixed” goods, whose export value is higher than the actual production, and NPE “pure", for which any productive activity is gathered. • Firms • “regular” exporters export only regular products, i.e. products that are produced by the firm • NPE exporters export at least one NPE product. • NPE exporters include two mutually exclusive groups: • mixed-NPE exporters export at least one mixed-NPE product (but could also export pure-NPE products and/or regular products) • pure-NPE exporters export at least one pure-NPE product but no mixed-NPE products. Pure-NPE exporters might also export one or more regular products.
3. - Some descriptive insights (1) • In 2006, firms that achieve more than one product represent 45.5% of total manufacturing firms (Table 1). • High incidence of multi-product producers among the exporting companies: they represent more about 30% of total manufacturing and more than 40% of all exporters (15.8% of domestic producers). • Compared to domestic ones, exporting firms show, on average, larger size (4 times), higher productivity (1.5 times) and import incidence (8 times). • Overall, multi-product firms constitute more than 50% of total manufacturing and almost 90% of exporting firms. • Partial picture. A large number of firms export goods which do not produce (NPE) (Table 2). • Multi-product exporters represent 87% of total manufacturing firms exporting and cover 99% of the value of exports (Table 2). • The number of exported goods increases with destination markets. • The number of goods sold in foreign markets is systematically higher than that of production, so that the fact of being a MPF is greater on the sales side than the production side.
3. - Some descriptive insights (2) • Extreme diffusion of the phenomenon of non-manufactured export. NPE firms represent more than 95% of the number of exporting firms (Table 3). • It follows that companies that export at least one NPE product represent almost 99% of these types of business. • NPE products applies to approximately 93% of exported goods; more than 80% of transactions for firm/product show an NPE export. • The value of NPE exports is roughly 64% of total and, of this amount, about ⅔ consist of export sales of NPE “pure” goods. • NPE firms show, on average, larger size than “regular” exporters. This is not reflected in productivity levels. “Regular” firms show higher productivity levels than NPE ones (Table 4). • Export propensity amounts to 19.6% (9.6% for the “regular”, 23% for NPE “mixed”).The latter show high import penetration (44%). • NPE firms provide significant support to the increase of products exported. “Regular” products account for about 35% of destination markets. The share attributable to NPE “pure” products is larger (57%).
4. – Empirical analysis • Labour productivity and margins of trade (extensive/intensive) • Export and Import premia and productivity differentials among internationalized firms • Focus on importing activity: • Self-selection into importing • Learning by importing
4.1. – Productivity and margins of trade • Empirical strategy (BRS, 2010): • relate margins of international trade to proxies for firm productivity (defined as turnover per employee, as in Wagner and Vogel, 2010) • Total firm exports can be decomposed into the following margins: • number of destination countries served by the firm (extensive margin, Cf) • number of distinct products exported (extensive margin, Pf) • share of the firms exported products sent to the average destination (measure of density, Df) • average exports per product-country (intensive margin, Xf)
4.1. – Productivity and margins of trade Note: Reported are the estimatedregressioncoefficient of log labourproductivity (turnover per employee). Allregressions include sectoral and regionalcontrols. Robust standard errors in parenthesis.
4.1. – Productivity and margins of trade • Main findings: • both the extensive margins – (number of destinations and products) - are increasing in firm productivity • Regular and NPE “pure” firms are the most performing ones • The source of the differential response to firm productivity is found in the extensive margins of trade • Number of destinations and number of products increase more rapidly with firm productivity for NPE “pure” exporters • By contrast, the intensive margin response (still positive) is greater for regular than NPE pure exporters • Exports are greater for more productive firms likely because they export greater numbers on NPE products to more countries
4.2. – Export and import premia • The next step in our empirical investigation is to test for the existence or not of so-called “trader premia”, defined ceteris paribus as the difference of log labour productivity between exporting enterprises compared to non-traders (benchmark) • This is 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 if other factors related to productivity are controlled for. • Main empirical approaches: • OLS • Quantile regression (differences in premia focusing on several moments of the productivity distribution).
4.2. – Export and import premia Note: Reported are the estimatedregressioncoefficients from the estimations of log labourproductivity (turnover per employee). Allmodels include the number of employees and itssquaredvalue, sectoral and regionalcontrols. Robust standard errors in parenthesis.
4.2. – Export and import premia Note: Reported are the estimatedregressioncoefficients of from the estimation of quantile regressions of log labourproductivity (turnover per employee). Allmodels include the number of employees and itssquaredvalue, sectoral and regionalcontrols. Robust standard errors in parenthesis. The secondpanelreportspvaluesfor the null of equalcoefficients in different quantile regressions.
4.2. – Export and import premia • Two-way traders do have the highest performance premia, followed by firms that only import, while firms that only export have the smallest estimated premia (Table 6). • Consistent with findings in CST (2009) and Wagner-Vogel (2010). • The premium is roughly constant between q25 and q75 and, for only importers, along the whole productivity distribution. • It is significantly decreases for only exporters and two-way traders as productivity increases (Table 7). • A comparison of the premia for only exporter and two-way trader provides indications that productivity might be a more stringent pre-condition (self-selection) for being an only importer than for being an only exporter (CST, 2009). • Important to consider import activities, even if one is mainly interested in the relationship between exports and productivity.
4.3. – Self-selection into importing • Labour productivity premia of future importers compared to future non-importers are estimated controlling for plant size and industry affiliation by estimating an empirical model. • Propensity score estimates are provided for sensitivity analysis • Models show a significant average productivity difference at t - 3 between import starters at t and enterprises with no international activities. • The pre-entry productivity premia of import starters are statistically different from zero, large from an economic point of view when non-traders that start to import in t are compared to firms that do not trade at all over the whole period. • Estimates show the average percentage productivity difference at t - 3 between exporters that start to import at t and exporters that do not start to import.
4.3. – Self-selection into importing Note: Reported are the estimatedregressioncoefficients and standard errorsform OLS estimation of log labourproductivity at t-3 (2003) and t-2 (2004). Allfirms start to import in 2006. Averagetreatmenteeffects (ATT) aswellasbootstrapped standard errors are alsoreported. Matchingisperformedbynearestneighbourspropensity score matching (Ichino and Becker, 2002).
4.4. – Learningbyimporting • If importing improves the post-entry productivity growth, we should expect to find significant differences in the rate of growth of labour productivity between import starters and firms that continue to buy intermediate inputs on the national market only during the years after the start. • Hypothesis tested by looking at the growth rate of LP over the period 2005–2006 and 2004-2005 for a cohort of import starters in 2003 compared to the growth performance of non-importers over the same period. • The same applies for the performance of exporters that start to import in 2003. • Results: • point estimates of two out of four coefficients are negative but not statistically significant. We have no evidence for learning by importing. • Also from the matching approach we have no evidence in favour of the learning by importing hypothesis.
4.4. – Learningbyimporting Note: Reported are the estimatedregressioncoefficients and standard errorsform OLS estimation of (lnLP(2006) - lnLP(2005)) and (lnLP(2005) - lnLP(2004)). Allfirms start to import in 2003. Averagetreatmenteeffects (ATT) aswellasbootstrapped standard errors are alsoreported. Matchingisperformedbynearestneighbourspropensity score matching (Ichino and Becker, 2002).
5 – Some Conclusions • Relevance of the matching of firm level data. It allows to: • perform empirical analysis of the theories of firm heterogeneity and MPFs; provide quantitative estimates on the dimension of firms that export goods which do not produce (NPE). • Trade margins: • both the extensive margins (number of products and number of destinations) are increasing in firm productivity. Source of differential response of firm productivity for NPE firms. • Regular and “pure” NPE are the most performing firms • Productivity premia: • Two-way traders do have the highest premia, then firms that only import; only exporting and domestic firms have the smallest premia. • Focus on imports: • significant findings on self-selection into importing • No findings on post-entry effect of importing on productivity.
6 – Future research… • Rich and insightful dataset: the right one to test theories of firm heterogeneity and trade. Improve it • by adding time: merge Prodcom-Coe for more years; have a Panel • by adding balance sheet information (and work with TFP) • Understand the nature of NPE products and exporters: • do they come from imports or domestic production? • What firm behaviour does this imply (pure intermediation, complementarity, brand,...) • Investigate further the import side: focus on intermediate imported goods rather than total imports • Exploit information to study firm-level pricing to market (export unit values per destination vis à vis domestic production unit values) and quality-to-market