The Single Currency's Effects on Eurozone Sectoral Trade: Winners and Losers?

Vicarelli, Claudio; De Santis, Roberta; De Nardis, Sergio Article The Single Currency's Effects on Eurozone Sectoral Trade: Winners and Losers? Economics: The Open-Access, Open-Assessment E-Journal Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Vicarelli, Claudio; De Santis, Roberta; De Nardis, Sergio (2008) : The Single Currency's Effects on Eurozone Sectoral Trade: Winners and Losers?, Economics: The Open-Access, Open-Assessment E-Journal, ISSN 1864-6042, Kiel Institute for the World Economy (IfW), Kiel, Vol. 2, Iss. 2008-17, pp. 1-34, https://doi.org/10.5018/economics-ejournal.ja.2008-17 This Version is available at: https://hdl.handle.net/10419/18030 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc/2.0/de/deed.en Vol. 2, 2008-17 June 10, 2008 The Single Currency’s Effects on Eurozone Sectoral Trade: Winners and Losers? Sergio de Nardis, Roberta De Santis and Claudio Vicarelli Institute for Studies and Economic Analyses, (ISAE) Rome Abstract In this paper we study the effect of the single currency across industries for euro area members. This analysis may help to shed light on the main factors influencing the euro effect on trade flows. We intend to verify whether these factors are specific to individual sectors and/or countries or common to the entire euro area. We use a dynamic specification of an augmented gravity equation. Following the most recent econometric literature, we apply the “System GMM” dynamic panel data estimator of Blundell and Bond to avoid inconsistency and biases in the estimates, and introduce controls for heterogeneity. Aggregate sector results average out country-level behaviours that, on their turn, are affected by different (unobserved) responses of firms, endowed with diverse production costs, to the enhancing and dampening impacts due to the euro. Due to this reason, the cancelling out at aggregate level of heterogenous behaviours induces an aggregation bias. So it is not surprising that when moving from sector to sector/country analysis the picture becomes much more variegated, with the emergence of a whole range of winners and lossers among industries in the different nation. Our empirical results are in line with theoretical framework we assumed as reference that considers the possibility of both stimulative and dampening effects coming from trade integration and points out the fact that sector exports impacts are the aggregation results of firm-level heterogenous behaviours. JEL: F14, F15, F4, F33, C33 Keywords: International trade; currency unions; gravity models; dynamic panel data; Blundell-Bond estimates Correspondence: Roberta De Santis, Institute for Studies and Economic Analyses, Piazza dell' Indipendenza n.4, 00184 Rome, Italy, [email protected] We are very grateful to the associate editor and the referees for all the constructive suggestions and comments. www.economics-ejournal.org/economics/journalarticles © Author(s) 2008. This work is licensed under a Creative Commons License - Attribution-NonCommercial 2.0 Germany Economics: The Open-Access, Open-Assessment E-Journal 1 www.economics-ejournal.org 1 Introduction Empirical analysis on the first few years of existence of the euro has generally reported a modest, although statistically significant, effect. This evidence does not completely fit with the assumption that important reductions in transaction costs would ensue from the replacement of many currencies with one single money. The limited impact may depend, inter alia, on the fact that the euro came at the very end of a long-term path of European integration, adding (maybe) little to a process that has had its main drivers in several former economic policy decisions (e.g. the common market, the EMS, the Single Market). Yet other factors, working below the surface of aggregate behavior and affecting the pervasiveness of the influence of the single currency across products and industries, may have contributed to shape the modest pro-trade impact. Analysis of sectoral variation of the euro effect may hence help shed some light on factors conditioning the single currency influence on trade flows. Despite its relevance, this issue has received scant attention to date. In this paper, we address this rather uninvestigated area, studying the trade-consequences of the single currency across industries of Euro area members. In line with a consolidated tradition in the analysis of the euro’s trade impact, the aim of the study is mainly empirical: we intend to verify whether the euro effect is much differentiated across industries and economies, or whether some common features are detectable for the entire Euro area. Although our work is essentially empirical, nevertheless we need to refer to some theory as a guide for intepretation. We do it assuming as reference a framework that considers the possibility of both stimulative and dampening effects coming from trade integration and points out the fact that sector exports impacts are the aggregation results of firm-level heterogenous behaviours. Given this structure in the background, empirical findings at sector/country level may hint at the mechanisms driving trade put in place by the single currency inception.1 The paper is organized as follows. The second and the third sections conduct a critical survey of the most recent empirical literature and describe the theoretical framework. The fourth and the fifth sections provide a description of the empirical strategy and of the dataset. The sixth and the seventh sections present the estimation results at sector and country level. Conclusions follow. 2 Recent Empirical Literature on the Euro’s Sectoral Trade Effects Analysis on the euro effect on trade has been generally performed at aggregate level.2 Empirical studies that estimate the euro effect at sector level are still very scarce. However, in both approaches (aggregated and sectoral) the main empirical findings highlight a positive and statistically significant effects of euro adoption on bilateral _________________________ 1 It is worth to underline that this paper limits its analysis at the success of the euro adoption in terms of trade volumes only and does not analyze the potentially pro-competitive impact on prices and increased consumer welfare. Furthermore the empirical analysis is run for the manufacturing sectors given the not availability of homogeneous disaggregated data on service sector. 2 See for example de Nardis and Vicarelli (2003) and Micco et al. (2003). 2 Economics: The Open-Access, Open-Assessment E-Journal www.economics-ejournal.org trade in EMU countries. All the empirical studies use panel data methodology, instead of pooled cross sectional data, to emphasize the time dimension in the estimation of trade flow determinants in gravity models.3 In this section we focus on the studies using sectoral data. Existing studies on sectoral euro effect usually use static models: to best of our knowledge, only one work uses dynamic models4 (Table 1). Table 1: Euro’s Effect on Trade, Sectoral Data Authors Empirical Strategy Main findings-sample period Flam and Nordstrom (2003) Fixed effect panel data estimator, 1 digit ISIC rev.3 sectors. Gravity model Dep variable: bilateral exports, 1 digit ISIC rev.3 sectors Exchange rate as regressor in the gravity equation. 14 EU countries (excluding Greece) Sample period 1995–2002. Intra area euro effect aggregate 15%, increase of trade with non members of 7%; euro effect not widespread across sectors, ranging between 7–50%. Baldwin et al. (2005) Fixed effect panel data. Gravity model Dep variable: bilateral imports, ISIC 2 and 3 digit 18 OECD countries Sample period 1988–2003. Intra area euro effect aggregate 70– 112%, euro effect not widespread across sectors, ranging between 40– 177%. Static models Flam and Nordstrom (2006) Fixed effect panel data estimator Gravity model Dep variable: bilateral exports. 6 digit level HS product categories 20 OECD countries Sample period 1999–2005. euro increased intra area trade by 26% and trade between the eurozone and outsiders by 12% in 2002–2005 compared to 1995–1998. The effects are concentrated in semifinished and finished products, industries with highly processed products Dynamic models Fernandes (2006) A dynamic panel data System GMM estimator , Gravity model. for 25 two digit ISIC rev.3 sectors Dep variable: bilateral exports. 23 OECD countries. Sample period 1988–2003 Intra area euro effect aggregate 2.8%, euro effect not widespread across sectors, ranging between 7– 23%. _________________________ 3 The gravity model has been used extensively in the empirical and theoretical literature to explain bilateral trade (Anderson 1979, Deardorff 1998 and Helpman and Krugman 1985, Evenett and Keller 2002 and Baldwin 2006). 4 Theory and a large body of empirical work support the hypothesis that trade is a dynamic process and that estimating static equations may produce upward biased estimates (see de Nardis at al. 2008). The rationale for considering dynamics in trade is the existence of sunk costs borne by exporters to set up distribution and service networks in the partner country. This sticky behaviour seems all the more important in the EMU case, where trade relationships between countries are affected not only by past investments in export-oriented infrastructure, but also by the accumulation of invisible assets such as political, cultural and geographical factors characterizing the area and influencing the commercial transactions taking place within it. Economics: The Open-Access, Open-Assessment E-Journal 3 www.economics-ejournal.org All such studies (in spite of different time spans, countries samples and empirical strategies) report that the euro effect is not widespread among sectors and among country/sectors. Baldwin et al. (2005) show a correlation between the size of the “Rose Effect”5 (the adoption of a common currency) and the presence of what they call ICIR sectors (Imperfect Competition and Increasing Return Sectors). Ranking the sectors analyzed in a decreasing order, at the bottom of the list (lower “Rose Effect”) they find agriculture, as well mining and quarrying; at the top, (higher “Rose Effect” ) various types of machinery and highly differentiated consumer goods (such as food products, beverages and tobacco). This result suggests that these sector characteristics may be related to the size of the effects on trade due to the adoption of a common currency. The rationale behind this heterogeneous euro effect among sectors is explained by Baldwin (2006) in light of two elements of the “new-new trade theory”: the fixed costs of entering a new market and differences in firms’ marginal production costs. In line with these findings, also Flam and Nordstrom underline that sectors without a “Rose effect” tend to be those marked by fairly homogeneous products. The results set out in their 2003 paper, which are obtained from quite aggregate dataset (1 digit ISIC rev.3 sectors), are confirmed also at a highly disaggregated level (6 digit level HS product categories: Flam and Nordstrom 2006). In this latter work, the authors estimate currency union effects at different stages of processing and for different industries, finding evidence of a positive effect for semi-finished and finished products and for industries characterised by highly processed products, which are those that require relatively high fixed costs for distribution and marketing. 3 Theoretical Framework Sectoral exports are aggregation of foreign sales of heterogenous firms; as such, they reflect the average outcome of a range of different individual behaviours. To gain insights on sector-export responses to the euro introduction it is, hence, useful to refer to a theoretical framework that makes such an aggregation explicit. Among the models of international trade with heterogenous firms, the one by Melitz and Ottaviano (2005) offers ample scope for hypoteses testing. It is similar to the model by Melitz (2003), adopted by Baldwin and Di Nino (2006) to study the euro effect, but with non-CES consumers’ preferences and a linear demand function. In this setting, exports of a firm locatad in county o and selling to a destination market d, indicated by , expod, are given by () () 2 2 ,, exp 4 d od d od Lcc τ γ ⎡⎤ =− ⎣⎦ , (1) where d L is the dimension of the destination market; 0 γ > is a parameter indexing degree of horizontal differentiation between varieties, assumed equal across markets; ,1 od τ ≥ is per unit transport cost incurred by the firm in transferring goods from o to d; c is the firm’s marginal cost drawn from a random distribution () Gc, having _________________________ 5 The “Rose Effect” refers to the large body of empirical literature about the effect of currency unions on trade started with Rose (2000). For a survey see Rose and Stanley (2005). 4 Economics: The Open-Access, Open-Assessment E-Journal www.economics-ejournal.org positive support on [ ] 0, m c; ( ) dm cc< is the cutoff marginal cost the firm faces when selling in country d; this cost threshold is equal to the maximum price feasible in the destination market, max P, at which product demand sets to zero. The maximum price is an indicator of toughness of competition in market d: since it is rising with the average price of competing varieties, * d P, and decreasing with the number of competitors, * d N, that is6 ** max (; ) ddd cP FPN== , with ** '' 0; 0 dd PN FF>< . (2) Aggregating individual export sales over the set of exporters selling varieties from o to d, with marginal cost d cc≤, one gets the bilateral sectoral export flow from country o to destination marketd () ( ) , ,, 0exp dod a od o od EXP N dG c τ =∫; (3) with o N= number of entrant exporters (fromo to d). Assuming a Pareto parametrization for the marginal cost distribution, () () k m Gc cc=with 0k≥ as shape parameter, former expression is solved as follows () ( ) 2 ,, () k kk od o d d m od EXP N L c c ϕτ − +− =; with 1 2( 2)k ϕγ = + , (4) where sectoral exports from o to d are the outcome of aggregation over a subset of heterogenous firms (those whose marginal cost is lower than the cutoff level); interestingly, expression for bilateral sectoral exports assume a gravity-like form as sales form o to ddepend positively on the size of destination country, d L, and on the number of origin-country exporters, o N, and negatively on the transport costs/trade barriers index, ,od τ .7 Assuming symmetric trade costs ( ,,od do τ ττ = =), in free entry equilibrium (with expected profits driven to zero) the cutoff marginal cost in the destination market is 1 2 1 1 k entry dk d F cK L γ τ + − ⎛⎞ =⎜⎟ + ⎝⎠ , with entry F=fixed entry cost and 232Kk k=++. (5) Former expressions highlight that a fall in trade costs ,od τ , e.g. determined by the adoption of a common currency between the two countries, stimulates sectoral bilateral trade (equation (4)). Yet, more integration means also tougher competition in destination markets ( d c reduces as τ drops in equation (5)), leading to an increase in the number of competing varieties. This exerts dampening effects on bilateral sectoral export volumes. Competition becomes even fiercer when trade integration is accompanied by a decline of entry costs in destination markets ( d c reduces as entry F falls _________________________ 6 ),/()( max dd NPP η γ η α γ ++= where α and η are parameters measuring substitution degree between differentiated varieties and the homogenous good. 7 In terms of gravity variables, the product between the size of the destination country and the number of entrant exporters from the origin country proxies the “mass” affecting bilateral trade. Economics: The Open-Access, Open-Assessment E-Journal 5 www.economics-ejournal.org in (5)). If reduction of cost cutoff d c is large enough, firm selection consequent to lower tansaction and fixed entry costs may even entail the exiting of some less efficient exporters. Toughness of competition may be however mitigated by the degree of horizontal product differentiation, measured by γ , which varies sector by sector. Heterogenous firms, endowed with diverse marginal costs, are differently exposed to these opposite effects; predominance in the population of firms of the supportive or of the dampening influences hence affects the aggregate export outcome at sector level. Given this framework, some general indications can be derived about what one should expect to draw from empirical analysis. They can be described as follows: (i) When considering aggregate sectoral exports, pro-trade effect should emerge only in the sectors where benefiting euro-area firms prevail on unaffected producers. At aggregate sector level, there is no reason to expect significant negative competitive effects, since compensation is at work: if there are losers among the euro-area producers, there are simmetrically winners in the same area. (ii) When considering country-sector exports, pro-trade impacts should emerge only in the country/sectors where benefiting firms prevail on unaffected or negatively affected producers. At a country level, the dampening effects, due to tougher competition, would emerge in the countries/sectors where negatively affected firms prevail on the benefited and unaffected ones; hence at country-sector level winners and losers may be detected. (iii) Given the role of horizontal differentiation in mitigating degree of competition, positive pro-trade effects of the euro should prevail in sectors where there is imperfect competition and goods are more differentiated. 4 Empirical Strategy and Equation In our empirical strategy, we refer to the theoretical underpinnings highlighted in former section in estimating a gravity equation for sectoral bilateral trade volumes. Moreover in accordance with the recent findings in the literature, we introduce dynamics into a panel data model. This raises well known econometric problems: if trade is a static process, the fixed-effect estimator is consistent for a finite time dimension T and a infinite number of country-pairs N; but if trade is a dynamic process, the transformation needed to eliminate the country-pair fixed effects produces a correlation between the lagged dependent variable and the transformed error term that renders the least square estimator biased and not consistent. To avoid the inconsistency problem, Arellano and Bond (1991) suggested transforming the model into first differences and run it using the Hansen two-step GMM estimator.8 However, the first-differenced GMM estimator performs poorly in terms of precision if it is applied to short panels (along the T dimension) including highly persistent time series. Lagged levels of time series with near unit root properties are in _________________________ 8 They show that the two key properties of the first differencing transformation – eliminating the timeinvariant individual effects while not introducing disturbances for periods earlier than period t-1 into the transformed error term – can be obtained using any alternative transformation (i.e. forward orthogonal deviations). 6 Economics: The Open-Access, Open-Assessment E-Journal www.economics-ejournal.org fact weak instruments for subsequent first-differences.9 Since bilateral exports between industrialized countries are expected to be persistent, due to sunk exports costs, one may expect this to affect the estimates.10 Arellano and Bover (1995), describe how, if the original equations in levels are added to the system of first-differenced equations, additional moment conditions may increase efficiency (“System GMM” estimator). This estimator has been refined by Blundell and Bond (1998). The System GMM estimator has several advantages with respect to Arellano and Bond’s estimator. First differencing the equation removes fixed effects but also the time invariant regressors in the specification. If these regressors are of interest, the resulting loss of information may be a serious inconvenience. Owing to the relatively short timespan data available and the relevance of “persistence” effects in bilateral trade relationships, the “System GMM” estimator seemed to be the right choice for our purposes. The application of this methodology in a gravity context is quite new:11 as far as we know , only one study has applied it to investigate the euro effect on trade.12 We introduced into the dynamic gravity equation three sets of variables: i) gravity variables, ii) controls for heterogeneity, iii) controls for other factors affecting bilateral trade. (i) Standard gravity variables. Bilateral distance, as a proxy of transport (and fixedentry) costs, and the sum of importer and exporter’s value added as proxies of the “mass”. (ii) Controls for heterogeneity and bias. Following Baltagi, Egger and Pfaffermayr (2003) we introduce fixed effects for importing and exporting countries and time. Differently from these authors, we did not control for country-pair effects (i.e. the interaction effect between they exporting and importing country picking up unobserved characteristics of country-pairs) because this kind of variable would have included the impact of the euro effect that we wanted to control by a specific dummy. As suggested by Rose and van Wincoop (2001), controlling for exporter and importer effects enabled us to proxy the multilateral “trade resistance index”13 _________________________ 9 More in general, a IV approach is a way to solve the endogeneity problem. See Anderson and Van Wincoop (2003). 10 For an exhaustive survey of GMM estimators, see Roodman(2006). 11 See De Benedictis and Vicarelli (2005); De Benedictis, De Santis and Vicarelli (2005). 12 See Fernandes (2006). 13 Anderson and van Wincoop (2003) developed a theoretical gravity equation by using a CES utility function. Their basic gravity model is subject to: σ − =1 )( ji ij W ji ij PP t y yy x jtPP iij i ij ∀=∑−−− σσσ θ 111 where yW is the world income, θi=yi/yW country i’s world income share, and trade cost tij is a function of border effect bij and distance dij; bij=1 if there are no border barriers between country i and j; otherwise it equals one plus the tariff equivalent of the border barriers between two countries. The model states that trade between country i and j is determined by the share of the multiplier of both countries’ incomes to the world income, as well as trade cost adjusted for the price indexes in both countries. The price index in country j is a function of the price indexes, income shares, and the trade costs of all countries. Price indexes are needed to build a multilateral resistance index. Several methods have been implemented in the empirical literature to proxy these trade resistance terms. The one most widely used seems to be the inclusion of country specific dummies This method has the advantage of capturing unobserved price Economics: The Open-Access, Open-Assessment E-Journal 7 www.economics-ejournal.org (see Anderson and van Wincoop (2003)), obtaining a specification of a gravity equation that can be interpreted as a reduced form of a model of trade with micro foundations. (iii) Controls for other factors affecting bilateral trade in EMU. In the specific case of EMU, there are political, institutional and monetary factors that may have affected bilateral trade flows. After 1992, thanks to the European Monetary System and the convergence process leading to the adoption of the single currency, volatility of the exchange rate among European countries diminished. We controlled for this by introducing a measure of volatility into our equation. It seemed important to distinguish this aspect from a “Currency Union” effect that should capture a structural change (i.e. ERM crisis in 1992–1993) in the markets expectations, due to the fact that a common currency is an irrevocably fixed commitment on exchange rate regime. The introduction of the euro has been the last step of this integration process; we controlled for “EU membership”14 in order to “isolate” this effect on exports by introducing a specific dummy. Indeed, we control for exchange rate movements introducing an index of (bilateral) real exchange rate. The equation was as follows: Ln Expsectijt = b1 ln( Expsectijt-n) + b2 ln( SumVAsectijt ) + b3 lnDistij + b4 volijt + b5 ReRijt+ b6 dueuroijt + b7 duEUijt + b8 α i + b9 β j + b10 τ (6) where: ln = the natural logarithm, i is the exporting country, j is the importing country and t is the year, n is a lag structure for the dependent variable; Expsectijt = exports in volume from country i to country j for 25 sectors ISIC two digit rev. 3; SumVAsect ijt = the sum of value added at constant term for 25 sectors ISIC two digit rev. 3 of the exporting and importing countries, a proxy of the “mass” in gravity models; Distij = bilateral distance between capital cities, expressed in kilometers; dueuroijt = Dummy euro: assumes value 1 for bilateral trade among Eurozone countries from 1999, 0 otherwise, in the case of Greece the dummy assumes value 1 starting from 2001; duEUijt = Dummy European Union membership: assumes value 1 for bilateral trade among European Union countries, taking into account the enlargement process of EU (Austria, Finland and Sweden entered in 1995), 0 otherwise;15 _________________________ effects to produce consistent estimates of parameters. Feenstra (2004) shows that the inclusion of these dummies generates largely the same results as those obtained by Anderson and Van Wincoop (2003). Our empirical strategy took up these suggestions; however, we are aware that this choice excludes the partially time-varying character of the Multilateral Trade Resistance Index and that it can determine some bias (see for example Marques and Spies 2006 and for a survey on this topic see Baldwin 2006). 14 From the late 1950s to the mid-1990s, the European trade integration process were mainly related to the abolition of internal tariffs with a view to the completion and widening of the Single European Market. 15 We consider EU membership instead of other “institutional” variables (i.e. Single Market 1993) because EU membership implies the obligation of a Member State to transpose into national law directives (for example to implement the Single Market) issued by the EU Commission. 14 Economics: The Open-Access, Open-Assessment E-Journal www.economics-ejournal.org Table 5: The Country/Sector Euro Effect in a Classification “à la Pavitt”a SITC 2 digits Industry description Dummy euro positive and significant Dummy euro negative and significant Traditional sectors 01_05 Agriculture, hunting, forestry and fishing France, Spain Finland, Germany, The Netherlands 10_14 Mining and quarrying Spain 15_16 Food products beverages and tobacco Germany, The Netherlands 17_19 Textiles, textile products, leather and footwear Finland, Italy 25 Rubber and plastic products Belgium, France 27 Basic metals Austria, the Netherlands, Spain France, Finland 36_37 Manufacturing nec Italy Scale intensive sectors 20 Wood and wood and cork products The Netherlands 21_22 Pulp, paper, paper products, printing and publishing The Netherlands 23 Coke, refined petroleum products and nuclear fuel Austria 26 Other non metallic mineral products 27_28 Basic metals and fabricated metals products Greece and Portugal, 31 Electrical machinery and apparatus nec Greeece Finland 32 Radio tv and comunnication equipment Austria, Germany, Spain France 34_35 Transport equipment Spain 34 Motor vehicles Italy, France, Greece, Spain Finland Specialised suppliers 29_33 Machinery and equipment Finland 29 Machinery and equipment nec Belgium 35 Other transport equipment Italy Science Based 23_25 Chemical, rubber, plastics and fuel products Spain, Portugal France, Germany 24 Chemicals and chemical products Belgium, Spain 30_33 Electrical and optical equipment Germany, Belgium, the Netherlands, Spain France, Finland 30 Office accounting and computing machinery Austria, Germany France 33 Medical precision and optical instruments Greece, Spain * Sectors in bold are those with a euro effect positive and significant for the entire set of EU countries. countries, though diffused across industries, are mainly concentrated in sectors where horizontal product differentiation matters (scale intensive, specialized suppliers and science based sectors). Finally, leaving aside sector specificity, these results may provide a general view on the country distribution of winners and losers (Table 6). It comes out that small and medium-sized economies were those that benefited the most from the euro introduction: in Spain, Netherlands, Austria, Greece, Belgium and Portugal the number of sectors where the single currency impacted positively exceeds the number of sectors where negative effects were identified. Quite as an exception, Germany fits into this group. On the opposite side, this evidence indicates that Italy and, particularly, France and Finland were the less benefited, in terms of balance between winning and losing sectors. A common feature shared by all countries is constituted by the large majority of sectors where the euro had no statistically significant influence. Economics: The Open-Access, Open-Assessment E-Journal 15 www.economics-ejournal.org Table 6: Country/Sector Euro Effect: Number of Sectors with Positive, Negative and No Impact Positive effects Negative effects No effect Spain 8 1 16 The Netherlands 5 1 19 Austria 4 0 21 Greece 4 0 21 Germany 4 2 19 Belgium 3 1 21 Portugal 2 0 23 Italy 2 2 21 France 2 6 17 Finland 0 7 18 8 Conclusions Empirical literature has reported a modest pro trade effect deriving from the euro introduction in 1999. Analysis has been usually conducted at the aggregate level, with respect to both trade flows (total exports and/or imports flows) and country aggregates (Eurozone as a whole). To gain better understanding of the main factors influencing the single currency effect on trade flows, it is of some help to use a sectoral analysis. We did it adopting as reference a theoretical framework that, on one side, makes explicit the fact that sector exports are the aggregation outcome of foreign sales of heterogenous firms and, on the other side, highlight the possibility that the single currency may have both enhancing (thanks to lower trade costs) and dampening (due to tighter competition) influences on the exports of a nation. We performed the empirical analysis in a dynamic setting to take account of the persistence phenomena that characterize bilateral trade relations between industrialized countries. Aggregate-sector estimates show that the euro effect was not uniformly distributed among sectors: only in 11 industrial sectors out of 25 there was a positive and significant impact of the euro on export flows. Particularly, most of these sectors are those characterized by imperfect competition, increasing returns to scale and horizontal product differentiation. These results seem consistent with other findings in the empirical literature and with the prediction of the theory, pointing to a mitigation effect of variety differentiation on toughness of competition. What differs with respect to earlier sectoral studies is the magnitude of the positive euro effect, which is lower and less widespread among industries. We believe that our dynamic specification fitted this phenomenon better. Yet, aggregate sector results average out country-level behaviours that, on their turn, are affected by different (unobserved) responses of firms, endowed with diverse production costs, to the enhancing and dampening impacts of the euro. Due to this reason, the cancelling out at aggregate level of heterogenous behaviours induces an aggregation bias. So it is not surprising that when moving from sector to sector/country analysis the picture becomes much more variegated, with the emergence of a whole range of winners and lossers among industries in the different nations. However 16 Economics: The Open-Access, Open-Assessment E-Journal www.economics-ejournal.org identification of benefited/hampered sectors is mainly an empirical matter, depending on the predominance in each industry and in each nation of either expanding or contracting exporting firms. Despite the increase of heterogeneity, some salient points could nonetheless be singled out from country-level analysis: 1) the majority of sectors displayed no significant effect of the euro introduction at country level (confirming the aggregate result); 2) although the single-currency pro-trade impact was pretty much diffused across all sectors of member countries, it resulted mainly concentrated in industries where product differentiation matters (in line with the aggregate outcome); 3) small and medium-sized countries plus the European core-economy, Germany, were those where the number of sectors whose exports benefited from the euro was larger than the number of sectors that registered a dampening effect; 4) in couple of large economies and in a smaller one (Italy, France and Finland) the benefits of the trade impact of the euro on sector exports, at this level of analysis, were quite limited or even absent. Economics: The Open-Access, Open-Assessment E-Journal 17 www.economics-ejournal.org Appendix 1 Table A1: Estimates Sector/Country Austria Belgio Finlandia Francia Germania Grecia Industry description Coeff. t Coeff. t Coeff. t Coeff. t Coeff. t Coeff. t 01_05 Agriculture, hunting, forestry and fishing 0.13 0.99 –0.08 0.66 –0.38 –2.91 0.16 1.79 –0.17 –1.87 0.09 0.68 10_14 Mining and quarrying –0.09 0.48 –0.05 0.21 0.01 0.07 –0.13 0.72 –0.09 0.80 15_16 Food products beverages and tobacco 0.07 0.72 0.06 0.94 –0.17 1.91 –0.04 0.96 0.12 2.25 –0.04 0.49 17_19 Textiles, textile products, leather and footwear –0.04 0.78 0.08 0.25 –0.14 2.14 –0.10 1.56 –0.00 0.04 0.13 1.61 20 Wood and wood and cork products 0.02 0.10 0.04 0.06 0.02 0.12 –0.16 1.07 0.02 0.12 –0.27 0.71 21_22 Pulp, paper, paper products, printing and publishing 0.11 1.28 0.00 0.37 –0.000 0.05 –0.07 1.14 0.08 1.51 0.08 0.70 23_25 Chemicals, rubber, plastics and fuel products –0.04 0.79 0.20 1.39 –0.08 0.86 –0.11 –1.80 –0.15 –1.90 –0.11 1.02 23 Coke, refined petroleum products and nuclear fuel 0.95 3.79 0.22 1.27 –0.2 0.39 –0.23 1.12 –0.23 0.87 –1.25 1.46 24 Chemical and chemical products –0.01 0.13 0.11 2.06 –0.07 1.22 –0.05 –1.75 –0.22 0.68 –0.08 0.64 25 Rubber and plastic products –0.02 0.56 –0.10 –2.18 0.07 1.19 –0.12 –2.75 –0.07 1.47 0.15 1.16 26 Other non metallic mineral products –0.02 0.23 0.05 1.00 0.12 1.33 –0.05 0.82 –0.05 0.87 –0.24 1.69 27_28 Basic metals and fabricated metals products 0.02 0.31 –0.04 0.74 –0.03 0.33 0.00 0.05 –0.05 0.88 0.27 2.48 27 Basic metals 0.01 1.54 0.07 0.79 0.04 0.78 –0.04 –0.76 0.21 1.99 28 Fabricated metal products except machinery and equipment 0.02 0.38 –0.03 –0.41 –0.02 –0.36 –0.01 0.13 0.09 0.69 29_33 Machinery and equipment 0.08 1.98 –0.07 –1.75 –0.09 2.61 0.03 0.59 0.10 0.96 29 Machinery and equipment nec 0.06 1.21 0.25 3.34 –0.11 –1.86 –0.04 1.05 –0.07 –1.52 0.15 0.96 30_33 Electrical and optical equipment 0.06 1.32 0.18 3.30 –0.14 –3.46 –0.13 –3.16 0.06 1.77 –0.12 –1.28 30 Office accounting and computing machinery 0.50 4.02 –0.19 –1.05 –0.33 –3.00 0.33 2.96 –0.13 –0.44 31 Electrical machinery and apparatus nec 0.05 0.96 –0.31 –4.56 –0.02 0.34 0.00 0.06 0.27 1.85 32 Radio tv and comunnication equipment 0.26 2.23 0.22 1.51 –0.33 3.0 0.22 2.28 –0.37 –1.21 33 Medical precision and optical instruments 0.02 0.32 –0.03 –0.78 0.03 0.64 0.00 0.01 0.37 2.56 34_35 Transport equipment 0.06 0.69 –0.16 –1.47 0.14 1.50 0.04 0.37 0.30 1.43 34 Motor vehicles –0.02 –0.22 –0.25 –2.69 0.18 2.43 –0.01 –0.14 0.49 3.16 35 Other transport equipment 0.14 0.80 –0.41 –1.11 0.09 0.43 0.01 0.05 –0.43 –1.00 36_37 Manufacturing nec 0.06 0.78 –0.04 –0.40 –0.11 –1.31 0.07 0.99 –0.09 –0.74 18 Economics: The Open-Access, Open-Assessment E-Journal www.economics-ejournal.org Table A1 continued Italia Olanda Portogallo Spagna Industry description Coeff. t Coeff. t Coeff. t Coeff. t 01_05 Agriculture, hunting, forestry and fishing 0.06 0.77 –0.21 2.63 0.05 0.39 0.30 2.71 10_14 Mining and quarrying –0.23 1.37 0.10 0.50 –0.33 2.29 15_16 Food products beverages and tobacco 0.00 0.02 0.10 2.41 0.10 1.26 0.05 1.51 17_19 Textiles, textile products, leather and footwear –0.15 2.72 0.07 1.59 0.04 0.4 –0.02 0.32 20 Wood and wood and cork products 0.27 2.27 –0.05 0.32 21_22 Pulp, paper, paper products, printing and publishing 0.09 1.68 0.06 0.46 23_25 Chemicals, rubber, plastics and fuel products 0.06 1.01 0.01 0.20 0.17 2.01 0.17 2.68 23 Coke, refined petroleum products and nuclear fuel –0.12 0.34 0.27 1.33 0.27 1.15 24 Chemical and chemical products 0.08 1.57 0.03 1.10 0.10 1.78 25 Rubber and plastic products 0.02 1.03 0.03 0.66 0.05 1.00 26 Other non metallic mineral products –0.06 1.10 0.04 0.78 –0.00 0.04 0.02 0.23 27_28 Basic metals and fabricated metals products 0.08 1.61 0.03 0.67 0.28 2.68 0.08 1.51 27 Basic metals 0.10 1.75 0.08 1.75 0.08 1.16 28 Fabricated metal products except machinery and equipment 0.05 1.0 –0.09 –1.63 0.60 0.71 29_33 Machinery and equipment 0.02 0.40 0.13 3.3 0.03 0.38 0.14 3.02 29 Machinery and equipment nec 0.00 0.03 –0.00 0.19 0.06 0.84 30_33 Electrical and optical equipment –0.02 0.43 0.13 3.90 –0.02 –0.25 0.14 2.72 30 Office accounting and computing machinery –0.11 –0.67 – – – – 0.03 1.35 31 Electrical machinery and apparatus nec – – – – – – 0.11 1.37 32 Radio tv and comunnication equipment – – – – – – 0.43 3.4 33 Medical precision and optical instruments 0.01 0.15 – – – – 0.17 1.80 34_35 Transport equipment 0.22 2.32 –0.03 –0.38 – – 0.25 1.65 34 Motor vehicles 0.08 1.94 – – – – 0.15 1.89 35 Other transport equipment 0.21 1.93 – – – – 0.28 1.40 36_37 Manufacturing nec –0.12 2.06 – – – – 0.05 0.67 Economics: The Open-Access, Open-Assessment E-Journal 19 www.economics-ejournal.org Appendix 2 Sectoral estimates Sector 01_05 ------------------------------------------------------------------------------ Group variable: cod Number of obs = 3659 Time variable : time Number of groups = 285 Number of instruments = 261 Obs per group: min = 1 F(56, 285) = 5501.53 avg = 12.84 Prob > F = 0.000 max = 15 ------------------------------------------------------------------------------ | Robust lexpK01_05 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lexpK01_05 | L1. | .5563564 .0445842 12.48 0.000 .4686004 .6441124 dueuro1 | -.0186244 .0502452 -0.37 0.711 -.1175233 .0802744 lReR | -.0427621 .0223715 -1.91 0.057 -.0867965 .0012722 dueu | .253976 .0900563 2.82 0.005 .0767162 .4312359 vol1 | -.2533348 .1885855 -1.34 0.180 -.624532 .1178623 ldist | -.7025445 .0890976 -7.89 0.000 -.8779173 -.5271717 SumVA01_05 | -.1293157 .2049538 -0.63 0.529 -.5327309 .2740996 Alphai yes Betaj yes Tau yes ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -7.33 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = -1.86 Pr > z = 0.063 Hansen test of overid. restrictions: chi2(205) = 228.21 Prob > chi2 = 0.128 Sector 10_14 ------------------------------------------------------------------------------ Group variable: cod Number of obs = 2780 Time variable : time Number of groups = 219 Number of instruments = 194 Obs per group: min = 1 F(52, 219) = 1914.61 avg = 12.69 Prob > F = 0.000 max = 15 ------------------------------------------------------------------------------ | Robust lexpK10_14 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lexpK10_14 | L1. | .5052496 .0554133 9.12 0.000 .396038 .6144612 dueuro1 | -.1537626 .0896263 -1.72 0.088 -.3304031 .0228779 lReR | .0083641 .0396591 0.21 0.833 -.0697981 .0865264 dueu | -.2270209 .1008088 -2.25 0.025 -.4257005 -.0283414 vol1 | -.0222257 .1767489 -0.13 0.900 -.3705722 .3261208 ldist | -1.074637 .1632935 -6.58 0.000 -1.396465 -.7528095 SumVA10_14 | .0253639 .1048421 0.24 0.809 -.1812648 .2319925 Alphai yes Betaj yes Tau yes ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -5.48 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = 1.72 Pr > z = 0.086 ------------------------------------------------------------------------------ Hansen test of overid. restrictions: chi2(142) = 171.44 Prob > chi2 = 0.047 Long term coefficient test HP0 _b[dueuro1]/(1-_b[l.lexpK10_14]) = 0 F(1, 219) = 3.08 Prob > F = 0.0805 20 Economics: The Open-Access, Open-Assessment E-Journal www.economics-ejournal.org Sector 15_16 ------------------------------------------------------------------------------ Group variable: cod Number of obs = 3424 Time variable : time Number of groups = 286 Number of instruments = 261 Obs per group: min = 5 F(56, 286) = 33531.28 avg = 11.97 Prob > F = 0.000 max = 15 ------------------------------------------------------------------------------ | Robust lexpK15_16 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lexpK15_16 | L1. | .6628005 .0466757 14.20 0.000 .5709291 .7546718 dueuro1 | .041318 .0256148 1.61 0.108 -.0090993 .0917354 lReR | -.0184017 .0100685 -1.83 0.069 -.0382194 .0014161 dueu | .1107465 .0436397 2.54 0.012 .0248508 .1966422 vol1 | -.043243 .0903665 -0.48 0.633 -.2211107 .1346247 ldist | -.4479891 .0694051 -6.45 0.000 -.5845987 -.3113795 SumVA15_16 | .0592027 .0832093 0.71 0.477 -.1045775 .222983 Alphai yes Betaj yes Tau yes ---------------------------------------------------------------------------- Arellano-Bond test for AR(1) in first differences: z = -6.11 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = -1.31 Pr > z = 0.190 ------------------------------------------------------------------------------ Hansen test of overid. restrictions: chi2(205) = 249.99 Prob > chi2 = 0.017 Long term coefficient test Hp0 _b[dueuro1]/(1-_b[l.lexpK15_16]) = 0 F(1, 286) = 2.95 Prob > F = 0.0867 Sector 17_19 ------------------------------------------------------------------------------ Group variable: cod Number of obs = 3008 Time variable : time Number of groups = 261 Number of instruments = 260 Obs per group: min = 1 F(55, 261) = 39528.35 avg = 11.52 Prob > F = 0.000 max = 15 ------------------------------------------------------------------------------ | Robust lexpK17_19 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lexpK17_19 | L1. | .7274711 .045656 15.93 0.000 .63757 .8173722 dueuro1 | -.0466712 .0286994 -1.63 0.105 -.1031831 .0098407 lReR | -.0162103 .0144563 -1.12 0.263 -.0446761 .0122555 dueu | .0010154 .0301975 0.03 0.973 -.0584464 .0604772 vol1 | -.0843574 .0744554 -1.13 0.258 -.2309672 .0622524 ldist | -.3555947 .0665332 -5.34 0.000 -.4866049 -.2245846 SumVA17_19 | .229142 .0774292 2.96 0.003 .0766765 .3816075 Alphai yes Betaj yes Tau yes ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -3.71 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = -0.39 Pr > z = 0.696 ------------------------------------------------------------------------------ Hansen test of overid. restrictions: chi2(205) = 239.46 Prob > chi2 = 0.050 Long term coefficient test HP0 _b[dueuro1]/(1-_b[l.lexpK17_19]) = 0 F(1, 261) = 2.39 Prob > F = 0.1234 Economics: The Open-Access, Open-Assessment E-Journal 21 www.economics-ejournal.org Sector 20 ------------------------------------------------------------------------------ Group variable: cod Number of obs = 1377 Time variable : time Number of groups = 142 Number of instruments = 144 Obs per group: min = 1 F(47, 142) = 8.46 avg = 9.70 Prob > F = 0.000 max = 15 ------------------------------------------------------------------------------ | Robust lexpK20 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lexpK20 | L1. | .452 .0809919 5.58 0.000 .2918943 .6121056 dueuro1 | .0205199 .0758299 0.27 0.787 -.1293816 .1704213 lReR | -.0869558 .0345622 -2.52 0.013 -.1552787 -.0186329 dueu | -.1948627 .1321902 -1.47 0.143 -.4561777 .0664523 vol1 | -.4357962 .3300796 -1.32 0.189 -1.088301 .2167088 ldist | -.6908465 .1652221 -4.18 0.000 -1.017459 -.3642337 SumVA20 | .6419977 .3097951 2.07 0.040 .0295913 1.254404 Alphai yes Betaj yes Tau yes ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -4.25 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = -1.62 Pr > z = 0.105 ------------------------------------------------------------------------------ Hansen test of overid. restrictions: chi2(97) = 111.92 Prob > chi2 = 0.143 Sector 21_22 ------------------------------------------------------------------------------ Group variable: cod Number of obs = 1432 Time variable : time Number of groups = 144 Number of instruments = 145 Obs per group: min = 1 F(48, 144) = 26.61 avg = 9.94 Prob > F = 0.000 max = 15 ------------------------------------------------------------------------------ | Robust lexpK21_22 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lexpK21_22 | L1. | .6486452 .0503153 12.89 0.000 .5491933 .7480972 dueuro1 | .0723922 .0346969 2.09 0.039 .0038113 .1409731 lReR | -.0208856 .0145414 -1.44 0.153 -.0496278 .0078565 dueu | -.0509999 .0537639 -0.95 0.344 -.1572682 .0552684 vol1 | -.2225252 .1296355 -1.72 0.088 -.4787595 .033709 ldist | -.3425284 .0682136 -5.02 0.000 -.4773577 -.2076992 SumVA21_22 | .1400326 .0563249 2.49 0.014 .0287022 .251363 Alphai yes Betaj yes Tau yes ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -4.27 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = -1.18 Pr > z = 0.237 ------------------------------------------------------------------------------ Hansen test of overid. restrictions: chi2(97) = 116.11 Prob > chi2 = 0.090 Long term coefficient test HP0 _b[dueuro1]/(1-_b[l.lexpK21_22]) = 0 F(1, 144) = 4.40 Prob > F = 0.0376 22 Economics: The Open-Access, Open-Assessment E-Journal www.economics-ejournal.org Sector 23_25 ------------------------------------------------------------------------------ Group variable: cod Number of obs = 3009 Time variable : time Number of groups = 263 Number of instruments = 261 Obs per group: min = 1 F(56, 263) = 24292.51 avg = 11.44 Prob > F = 0.000 max = 15 ------------------------------------------------------------------------------ | Robust lexpK23_25 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lexpK23_25 | L1. | .5550064 .0483166 11.49 0.000 .4598698 .6501429 dueuro1 | .0314894 .0333814 0.94 0.346 -.0342394 .0972182 lReR | -.0038479 .0114522 -0.34 0.737 -.0263976 .0187018 dueu | -.0564633 .0439188 -1.29 0.200 -.1429404 .0300138 vol1 | -.0522849 .1016521 -0.51 0.607 -.2524403 .1478706 ldist | -.522988 .074686 -7.00 0.000 -.6700467 -.3759294 SumVA23_25 | .0003604 .0563928 0.01 0.995 -.1106784 .1113992 Alphai yes Betaj yes Tau yes ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -3.91 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = 1.87 Pr > z = 0.062 ------------------------------------------------------------------------------ Hansen test of overid. restrictions: chi2(205) = 231.76 Prob > chi2 = 0.097 Sector 23 ------------------------------------------------------------------------------ Group variable: cod Number of obs = 1879 Time variable : time Number of groups = 185 Number of instruments = 147 Obs per group: min = 1 F(50, 185) = 2365.21 avg = 10.16 Prob > F = 0.000 max = 15 ------------------------------------------------------------------------------ | Robust lexpK23 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lexpK23 | L1. | .4167569 .1179232 3.53 0.001 .1841097 .6494041 dueuro1 | .0447957 .1459045 0.31 0.759 -.2430549 .3326463 lReR | .0295246 .0482062 0.61 0.541 -.0655799 .1246292 dueu | -.1950667 .2041213 -0.96 0.341 -.5977715 .2076381 vol1 | -.1548557 .4058599 -0.38 0.703 -.9555646 .6458532 ldist | -.9898666 .2761841 -3.58 0.000 -1.534742 -.4449913 SumVA23 | -.4122085 .18891 -2.18 0.030 -.7849032 -.0395137 Alphai yes Betaj yes Tau yes ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -3.86 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = 0.28 Pr > z = 0.781 ------------------------------------------------------------------------------ Hansen test of overid. restrictions: chi2(97) = 119.88 Prob > chi2 = 0.058 (Robust, but can be weakened by many instruments.) Economics: The Open-Access, Open-Assessment E-Journal 23 www.economics-ejournal.org Sector 24 ------------------------------------------------------------------------------ Group variable: cod Number of obs = 1814 Time variable : time Number of groups = 179 Number of instruments = 176 Obs per group: min = 1 F(50, 179) = 282161.96 avg = 10.13 Prob > F = 0.000 max = 15 ------------------------------------------------------------------------------ | Robust lexpK24 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lexpK24 | L1. | .8222065 .0580066 14.17 0.000 .7077417 .9366712 dueuro1 | .0136017 .0245932 0.55 0.581 -.0349282 .0621317 lReR | -.0096682 .0079704 -1.21 0.227 -.0253962 .0060598 dueu | -.0430795 .034977 -1.23 0.220 -.1120999 .0259408 vol1 | -.0829933 .0714679 -1.16 0.247 -.2240213 .0580347 ldist | -.1585253 .0747124 -2.12 0.035 -.3059556 -.011095 SumVA24 | .0371159 .029261 1.27 0.206 -.0206249 .0948568 Alphai yes Betaj yes Tau yes ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -4.91 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = 0.94 Pr > z = 0.349 ------------------------------------------------------------------------------ Hansen test of overid. restrictions: chi2(126) = 152.62 Prob > chi2 = 0.053 Sector 25 ------------------------------------------------------------------------------ Group variable: cod Number of obs = 2348 Time variable : time Number of groups = 231 Number of instruments = 214 Obs per group: min = 1 F(54, 231) = 61738.23 avg = 10.16 Prob > F = 0.000 max = 15 ------------------------------------------------------------------------------ | Robust lexpK25 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lexpK25 | L1. | .7422958 .0595396 12.47 0.000 .6249858 .8596058 dueuro1 | -.0111434 .0235161 -0.47 0.636 -.0574768 .03519 lReR | -.0245976 .012225 -2.01 0.045 -.0486843 -.0005109 dueu | .0279768 .0339889 0.82 0.411 -.038991 .0949446 vol1 | -.1280158 .0899431 -1.42 0.156 -.3052294 .0491978 ldist | -.3263448 .0765496 -4.26 0.000 -.4771695 -.1755202 SumVA25 | .142192 .0580754 2.45 0.015 .0277668 .2566173 Alphai yes Betaj yes Tau yes ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -4.12 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = -0.53 Pr > z = 0.599 ------------------------------------------------------------------------------ Hansen test of overid. restrictions: chi2(160) = 191.30 Prob > chi2 = 0.046 30 Economics: The Open-Access, Open-Assessment E-Journal www.economics-ejournal.org Sector 34 ------------------------------------------------------------------------------ Group variable: cod Number of obs = 1943 Time variable : time Number of groups = 192 Number of instruments = 149 Obs per group: min = 1 F(52, 192) = 111346.37 avg = 10.12 Prob > F = 0.000 max = 15 ------------------------------------------------------------------------------ | Robust lexpK34 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lexpK34 | L1. | .7130738 .0668753 10.66 0.000 .5811692 .8449784 dueuro1 | .0911124 .0448035 2.03 0.043 .002742 .1794828 lReR | -.0500458 .0208456 -2.40 0.017 -.0911615 -.0089301 dueu | -.0722063 .0572658 -1.26 0.209 -.1851571 .0407446 vol1 | -.5440304 .2225059 -2.45 0.015 -.9829003 -.1051605 ldist | -.3752151 .0909736 -4.12 0.000 -.5546512 -.1957791 SumVA34 | .0011346 .1126137 0.01 0.992 -.2209842 .2232534 Alphai yes Betaj yes Tau yes ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -3.13 Pr > z = 0.002 Arellano-Bond test for AR(2) in first differences: z = 1.17 Pr > z = 0.242 ------------------------------------------------------------------------------ Hansen test of overid. restrictions: chi2(97) = 116.86 Prob > chi2 = 0.083 Long term coefficent tesr H0=0 _b[dueuro1]/(1-_b[l.lexpK34]) = 0 F(1, 192) = 4.85 Prob > F = 0.0288 Sector 35 ------------------------------------------------------------------------------ Group variable: cod Number of obs = 1782 Time variable : time Number of groups = 180 Number of instruments = 148 Obs per group: min = 1 F(51, 180) = 8.19 avg = 9.90 Prob > F = 0.000 max = 15 ------------------------------------------------------------------------------ | Robust lexpK35 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lexpK35 | L1. | .3655726 .0764875 4.78 0.000 .2146452 .5165001 dueuro1 | -.0132823 .1242853 -0.11 0.915 -.2585258 .2319613 lReR | -.1139 .0622594 -1.83 0.069 -.236752 .0089521 dueu | -.371509 .1922578 -1.93 0.055 -.750878 .00786 vol1 | -.5353148 .4483392 -1.19 0.234 -1.419992 .3493619 ldist | -.7669301 .141905 -5.40 0.000 -1.046941 -.4869188 SumVA35 | .3906799 .1561207 2.50 0.013 .0826178 .6987421 Alphai yes Betaj yes Tau yes ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -5.40 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = 0.33 Pr > z = 0.742 ------------------------------------------------------------------------------ Hansen test of overid. restrictions: chi2(97) = 88.67 Prob > chi2 = 0.715 Economics: The Open-Access, Open-Assessment E-Journal 31 www.economics-ejournal.org Sector 36_37 ------------------------------------------------------------------------------ Group variable: cod Number of obs = 1930 Time variable : time Number of groups = 200 Number of instruments = 149 Obs per group: min = 1 F(52, 200) = 20497.90 avg = 9.65 Prob > F = 0.000 max = 15 ------------------------------------------------------------------------------ | Robust lexpK36_37 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lexpK36_37 | L1. | .6208955 .0642725 9.66 0.000 .4941568 .7476342 dueuro1 | -.0423854 .0384104 -1.10 0.271 -.1181266 .0333559 lReR | -.0535295 .0206432 -2.59 0.010 -.0942357 -.0128232 dueu | -.0958173 .0577432 -1.66 0.099 -.2096809 .0180464 vol1 | -.1819876 .1640435 -1.11 0.269 -.5054644 .1414891 ldist | -.5636127 .1043495 -5.40 0.000 -.7693792 -.3578462 SumVA36_37 | .0038612 .0134958 0.29 0.775 -.0227512 .0304735 Alphai yes Betaj yes Tau yes ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -5.83 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = -1.93 Pr > z = 0.054 ------------------------------------------------------------------------------ Hansen test of overid. restrictions: chi2(97) = 111.98 Prob > chi2 = 0.142 32 Economics: The Open-Access, Open-Assessment E-Journal References Anderson, J. 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