Victoria Golikova, victoria @ hse . ru Ksenia Gonchar, kgonchar @ hse . ru

1. «Learning-by-Exporting» Innovation Effects for Russian Manufacturing Firms: Evidence from Panel Data Victoria Golikova, victoria@hse.ru Ksenia Gonchar, kgonchar@hse.ru Boris Kuznetsov,bkuz@bk.ru Institute for Industrial and Market Studies

2. Structure of the presentation • Motivation • Background facts on Russia’s foreign trade • Research hypotheses • Data description and descriptive statistics • Models and methodology • Results and conclusions 2

3. Motivation – key research questions • Is there a chance for Russian manufacturing firms to take advantage of trade liberalization and learn globalization lessons? If yes, what would be the transmission mechanisms? • What types of firms benefit most from trade incentives? • In what aspects are learning-by-exporting effects most pronounced? • Does export destination matter? • How much different are Russian companies in their ability to learn by exporting from their counterparts in other transition economies, who are more globally integrated and involved? Motivation – key research questions

4. Economic literature on LBE effects • The underlying theoretical model is the Melitz and Bernard model for heterogeneous firms engaged in international trade (Bernard et al, 1999 Melitz, 2003), which predicts that since more productive firms generate higher profit gains, they are able to afford high entry costs. This would lead to inter-firm reallocations toward more productive firms, resulting in aggregate industry productivity growth. Constantini and Melitz(2008)show how the market size may affect a firm’s choice in favor of exports or innovations, and prove that a firm’s productivity growth is endogenous, influenced by its decision to innovate. Theoretical work has proved that the export status and innovations are at least complementary as they provide a potential opportunity for new knowledge (Aw et al., 2005; Castellani and Zanfei, 2007), and also due to possible links between product and process innovations (Damijan et al., 2008). • Empirical testing of interaction between exporting and innovations produces mixed results (Wagner, 2007). Empirical studies utilizing data from emerging and transition economies show that global engagement tends to intensify innovative activities of firms (Bustos, 2011 for Brazil-Argentina bilateral trade, Sutton, 2007 and Gorodnichenko et al, 2010, for emerging market economies). • The question of who has better chances to overcome a technology gap – firms lagging farthest behind or those closer to the leaders – still gets different answers in the literature.

5. Some authors believe that the bigger is the gap the better would be the firm’s chances for LBE and for catching up with the leader (Fagerberg, 1994, Julan Dua et al, 2010). Others argue that the LBE effects are likely to be stronger for firms closer to the technology frontier (Aghion, Bessonova, 2006). • Studying LBE the authors note varying sector-specific firm response. Julan Dua et al, 2010, prove that exporting has virtually no effect on firm behavior in mature low-technology sectors, while LBE effects are more pronounced in medium- and high-technology industries. Moreover, learning effects may not be immediately seen, coming with a lag. • Many studies find that the probability of innovative learning-by-exporting depends on export destinations. Thus, exports directed to high income countries require higher quality workforce and encourage the exporter to develop business models involving fringe distribution, transportation and publicity services (Verhoogen, 2008, Matsuyama, 2007, Brambilla, Lederman, Porto, 2010). The Russian case of LBE effects including the impact of export destinations (CIS and OECD), was explored by Wilhelmsson, Kozlov (2007). They focus more on the learning outcomes, i.e. increased productivity of exporters. The study finds that in this sense of “learning”, exporting to developed countries has a more pronounced effect for export starters. However, later on, the differences between CIS exporters, non-exporters and OECD exporters tends to fade out, which does not allow to make decisive conclusions about the impact of export destination on productivity growth.

6. Background: low share of manufacturing in Russian export Source: WTO. International trade statistics 2009 http://www.wto.org/english/res_e/statis_e/its2009_e/its09_trade_category_e.htm

7. Changes in absolute volumes of exports in selected manufacturing industries, in US$bn, actual prices

8. Research hypotheses • Hypothesis 1 • Exporters tend to be more innovative than non-exporters, as they introduce new technologies and new products, undertake/contract R&D, promote new managerial technologies and retrain and upgrade their managerial staff. • Hypothesis 2 • A long presence in export markets tends to enhance learning effects. In other words, incumbent exporters learn quicker than export starters. • Hypothesis 3 • Destination of trade (to either developed or CIS countries) matters: exporters exclusively to CIS show weaker learning effects than exporters to non-CIS.

9. Data description • First round of the Survey: • Conducted in Autumn 2005 for Russian Ministry for Economic development in cooperation with the World Bank; • 1002 large and medium size firms surveyed in 8 2-digit manufacturing sectors and in 49 regions of Russia • Second round of the Survey: • Conducted in Spring 2009 for Russian Ministry for Economic Development; • 957large and medium size firms surveyed in 8 2-digit manufacturing sectors and in 48 regions of Russia • Panel - 499 firms (surveyed twice) NB: Small (less than 100 employees) and very large companies (over 10 000 employees) were not included in the sample

10. General approach (1) • To verify H1 and H2 we divide the sample by 4 groups of firms: • “Old” exporters – firms which reported export both in 2005 and 2009 (NB: we presume those forms to export continuously) • “New”exporters – firms which reported no export in 2005 but some in 2009 • Ex-exporters – firms which reported export in 2005 but reported no export in 2009 • Non-exporting firms – no export reported in both rounds • To verify H3 we divide the sample in three groups by destination of export in 2009: • Firms with some export outside of CIS • Firms exporting exclusively to CIS • Non-exporting firms • Those groups are used as dependent variables in multinominal regressions

11. General approach(2): determinants • To estimate dependent variables, which take discrete values of 0-1, we use standard probit regression to estimate the dependance of an indicator in 2009 on the previous value of the same indicator in 2005 (lagged values of dependent variables). • To avoid endogeneity issues, related to firm size-ownership causality direction, we use lagged values of these predictors. • We use log of number of employees in 2005 to catch the size effects • We control for foreign owners and for the state as an owner as well as to be a part of a large holding company. • One additional factor that we presume may be important is the age of a firm by dividing them into three groups: those which existed (were established) before 1992, created between 1992 and 1999 and the rest of the sample. • For the second model where the geographical destination of exports is a dependent variable we use the same list of independent variables. • In both models industries are controlled for. General approach(2): determinants

12. Distribution of firms by past and present export activity

13. Geography of export flows (% of firms )

14. Share of organizational and managerial innovators in export status groups in 2009, %

15. Share of organizational and managerial innovators in groups differing by export direction in 2009, %

16. Dependent variables

17. Predictors

18. LBE effects: Variables and Econometric approach To estimate dependent variables , which take discrete values of 0-1, we use standard probit regression to estimate the dependence of an indicator in 2009 on the previous value of the same indicator, firm export status and other firm characteristics. • LEfi -various mesures describing firm activities in innovations, managerial and organizational improvements • Exp_status – reflects export activity in both rounds of the survey, • Size – the size of firms as measured by the number of employees • Foreign – indicates a foreign shareholder • State – indicates a government share in the ownership structure • Holding – indicates that a firm is part of larger integrated group of companies • Age – period of establishment of a firm • Ind – dummy variable for 8 two-digit manufacturing industry codes • T-1 indexes show the lagged variables that we measure for the previous period of observation. • We use standard probit regression with non-exporting firms as a reference group

19. Regression results for the model estimating dependence of firm innovative behavior on its export status Note: *** - significance at 1 percent, ** - 5 percent, * - 10 percent. In groups by export status, non-exporters (those who did not report exporting in either round of the survey) are a reference group. LRN_05_i – values of respective dependent variables in the previous period. Industrial dummies are included in the model but not reported in the table

20. LBE effects and destination of exports To test third hypothesis assuming LBE effects depending on CIS or non-CIS export direction, we modify the model by replacing the export status variables with variables indicating if the firm exports to non-CIS, only to CIS or is not engaged in exporting at all.

21. Impacts of export destination on innovative behavior of firms Note: *** - significance at the 1 percent level, ** - at 5 percent, * - at 10 percent. In export destination groups, the reference group is provided by non-exporters, i.e. those who did not report any exporting in either the first, or the second round of the survey. LRN_05 denotes a lagged value of the dependent variable. Industrial dummies are included in the model but not reported in the table

22. Key conclusions The results obtained suggest some tentative conclusions about a positive effect of exporting on embracement of new technologies, primarily those in organization and management. Exporters, and, first and foremost, long-time and continuous exporters, are more active in monitoring their competitors, both domestically and internationally, and more frequently engage highly qualified managers. Exporters are more active in IT implementation. Some evidence has been obtained in support of their higher quality concerns, as they establish special-purpose product design units. The most encouraging result may be seen in the evidence on exporters’ higher R&D financing. Our analysis indicate that positive changes in firm innovative behavior seem to occur subsequently to their export entry rather than prior to it. Moreover, this response to changes in the competitive environment does not seem to come instantly. In other words, firms tend to gradually learn new process and management approaches and practices.

23. This conclusion may be supported by the evidence that comparatively recent export starters tend to outperform non-exporters on much fewer parameters than the group of continuous incumbent exporters. Moreover, “learning” starts from borrowing and embracement of managerial decisions and behavior tactics of quicker returns, including regular benchmarking, IT implementation, ISO certification, etc. There is another conclusion that we can suggest with some caution: non-CIS exporters are more prone to learning. Meanwhile, firms exporting only to CIS, differ from non-exporters mostly by their closer watching of foreign competitors. This finding is quite consistent with other studies, specifically, with the paper by Wilhelmsson, Kozlov (2007), which shows that productivity gains are more likely for exporters to industrially advanced economies. We have hardly discovered any dependence of firm behavior on owner characteristics. This evidence is also in line with other studies, showing that firm competitive environment has a more significant effect on firm behavior patterns than its ownership.

24. Thank youfor your attention