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

RESEARCH UNIVERSITY - HIGHER SCHOOL OF ECONOMICS

EBES 2011 Conference - Zagreb: October 13-15, 2011 Zagreb, Croatia

«Learning-by-Exporting» Innovation Effects for

Russian Manufacturing Firms: Evidence from Panel Data

Victoria Golikova, [email protected] Ksenia Gonchar, [email protected] Kuznetsov, [email protected]

Institute for Industrial and

Market Studies



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



Motivation – key research questions

Motivation – key research questionsIs 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?



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.

Economic literature on LBE effects



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.



Source: WTO. International trade statistics 2009 http://www.wto.org/english/res_e/statis_e/its2009_e/its09_trade_category_e.htm

Background: low share of manufacturing in Russian export



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

0

10

20

30

40

50

60

1995 2000 2005 2007

Metals, precious stones, fabricated goods

Chemicals

Machines, equipment, transport vehicles

Timber and woodworking

Agriculture and food

Textiles, clothing and footwear



Research hypothesesHypothesis 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.



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; 957 large 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



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

General approach (1)



General approach(2): determinants

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.



Distribution of firms by past and present export activity

"new" exporters; 14%

No exports in 2005 and in

2009; 35%

Ex-exporters; 6%

"old" exporters; 45%



2005 2009

Total exporters, including:

50.7 57.0

-exporters to CIS countries only

23.5 26.5

-exporters to the global market

27.2 30.5

Geography of export flows (% of firms )



0

20

40

60

80

100Formal IT division

ISO certification

R&D spending

Managers holding MBA, etc.

Introduction of a new product

Introduction of a new technology

Domestic benchmarking

International benchmarking

Partial outsourcing

A design division

A marketing division

An after-sales division

«Old» exporters “New” exporters Ex-exporters No exports in both periods

Share of organizational and managerial innovators in export status groups

in 2009, %



0

20

40

60

80

100A formal IT unit

ISO certification

R&D spending

Managers holding MBA, etc.

Introduction of a new product

Introduction of a new technology

Domestic benchmarking

International benchmarking

Partial outsourcing

A formal design unit

A formal marketing unit

A formal after-sales unit

Only CIS exports Non-CIS exports observable No exports

Share of organizational and managerial innovators in groups differing by export direction

in 2009, %



Dependent variables

Model Symbol

of dependent variable

Description of dependent variable

LRN1 IT A formal IT division in the firm (0 or 1 for each period) LRN2 ISO ISO certification (0 or 1 for each period) LRN3 RD_zatr R&D spending (0 or 1 for each period) LRN4 Manadv Managers holding an MBA and or a degree in economics from a Western

university (0 or 1 for each period) LRN5 NewProd Introduction of a new product (0 or 1 for each period) LRN6 NewTech Introduction of a new technology (0 or 1 for each period) LRN7 Bench_otech Domestic benchmarking (comparison with domestic competitors) (0 or 1 for

each period) LRN8 Bench_zarub International benchmarking (comparison with foreign competitors) (0 or 1 for

each period) LRN9 Outsource Outsourcing of selected managerial functions (0 or 1 for each period) LRN10 Dep_design A formal product design unit (0 or 1 for each period) LRN11 Dep_market A formal marketing unit (0 or 1 for each period) LRN12 Dep_service A formal after-sale service unit (0 or 1 for each period)



Exp_status Firm membership in one of the four groups (1 – firms who exported both in 2005 and in 2009; 2 – «new exporters», who had no exports in 2005, but had some in 2009; 3 - «ex-exporters», who exited export markets; 4 – non-exporters, who had no exports in either period of observation);

Size Log number of employees Foreign Foreign ownership (0 or 1) State Government (federal, regional or municipal) among owners (0 or 1) Ch_ownership Change of ownership between 2005-2009 Holding Membership in an integrated group (0 or 1)

Age Membership in one of the three groups of firms (1 – established before 1992, i.e. during the Soviet times, 2 – established early in transition in 1992-1998, 3 – established after 1998)

Ind Dummy variables reflecting type of economic activity (8 manufacturing activities: 1-food production, 2 – textiles and clothing, 3 – timber and woodworking, 4 – metals, 5 – chemicals, 6 – machine-building, 7 – electrical and electronic equipment, 8 - transport vehicles and equipment)

Predictors



LBE effects: Variables and Econometric approach

−LEf i -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

)

_LEf

8

112

3

19

19

18

17

16

4

11

11

Ti

l

l

ll

k

kkk

T

TTTj

jjj

T

indaageaHoldinga

StateaForeignaSizeastatusExpaLEfa

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.



Regression results for the model estimating dependence of firm innovative

behavior on its export statusLRN1

IT division

LRN2ISO certi

fication

LRN3R&D

spending

LRN4managers with MBA

LRN5New

product

LRN6New

technology

LRN7Domestic

benchmarking

LRN8Inter-

nationa bench

marking

LRN9Out

sourcing

LRN10Design unit

LRN11Marketing

unit

LRN12After-

sale service unit

LRN_05_i ***1.01 ***0.96 *0.27 ***0.50 ***0.43 *0.10 0.18 ***0.30 ***1.21 ***0.88 ***0.84 ***1.11

DE_1 ***0.57 **0.43 ***0.57 *0.32 **0.36 ***0.38 **0.53 ***1.04 0.33 **0.34 0.09 -0.29

DE_2 ***0.47 0.12 *0.38 0.33 0.16 0.18 0.20 ***0.71 **0.51 0.18 -0.11 -0.09

DE_3 -0.34 -0.23 0.12 -0.25 -0.36 -0.32 0.22 0.25 0.00 *-0.56 -0.18 -0.30

Size05 ***0.33 ***0.31 ***0.28 ***0.20 **0.15 ***0.22 0.15 0.09 ***0.41 0.11 **0.16 ***0.28

F05 -0.20 -0.43 0.10 0.24 -0.25 0.09 **-0.73 0.03 -0.18 -0.11 0.07 0.22

S05 -0.06 -0.03 -0.06 -0.06 0.14 -0.23 0.44 -0.02 0.25 0.42 0.10 0.18

Holding05 0.02 -0.05 0.05 0.08 -0.06 -0.06 0.07 0.001 0.23 **-0.30 -0.15 -0.20

age1 0.31 0.02 -0.20 ***-0.53 0.25 *0.38 **0.51 0.14 *-0.40 -0.11 -0.05 0.12

age2 0.45 0.25 dropped dropped 0.33 **0.58 0.43 0.24 -0.20 0.15 -0.11 0.13

age3 dropped dropped -0.87 -0.33 dropped dropped dropped dropped dropped dropped dropped dropped

N obs 487 493 456 472 499 499 499 499 499 499 499 499

R2 0.27 0.25 0.19 0.13 0.08 0.08 0.08 0.14 0.2 0.19 0.11 0.35

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



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.

)

05_05_LEf8

110

3

17

17

16

15

1432

11

Ti

l

l

ll

k

kkk

T

TTTT

indaageaHoldinga

StateaForeignaSizeaNCISaCISaLEfa

LBE effects and destination of exports



LRN1IT division

LRN2ISO certi-fication

LRN3R&D

spending

LRN4managers with MBA

LRN5New

product

LRN6New

technology

LRN7Domestic

bench-marking

LRN8Internationa

bench-marking

LRN9Out-

sourcing

LRN10Design

unit

LRN11Marketing

unit

LRN12After-sale

service unit

LRN_05 ***1.01 ***0.99 **0.27 ***0.50 ***0.46 0.098 0.20 ***0.34 ***1.19 ***0.88 ***0.84 ***1.11

Non CIS05 0.27 ***0.65 ***0.50 0.22 0.09 0.23 **0.58 ***0.76 -0.09 **0.34 0.22 -0.13

CIS05 0.21 -0.02 0.21 0.03 *0.26 0.18 0.28 ***0.50 0.18 -0.05 -0.03 *-0.35

Size05 ***0.34 ***0.29 ***0.28 ***0.20 **0.18 ***0.24 0.15 0.12 ***0.46 0.11 **0.14 ***0.25

F05 -0.13 -0.48 0.15 0.27 -0.16 0.15 **-0.72 0.13 -0.10 -0.08 0.05 0.18

S05 -0.06 -0.02 -0.05 -0.09 0.16 -0.22 0.45 -0.01 0.25 *0.41 0.09 0.17

Holding05 0.07 -0.02 0.06 0.12 -0.02 -0.03 0.08 0.03 0.23 *-0.26 -0.13 -0.20

age1 -0.15 -0.00 0.64 ***-0.54 0.24 0.35 **0.51 0.09 *-0.45 -0.12 -0.05 0.00

age2 dropped 0.20 0.84 dropped 0.31 **0.55 0.42 0.22 -0.19 0.10 -0.14 dropped

age3 -0.42 dropped dropped -0.32 dropped dropped dropped dropped dropped dropped dropped -0.12

ind8 0.12 ***0.88 ***0.74 -0.21 0.28 0.20 -0.05 0.36 -0.30 -0.29 -0.06 ***1.36

_cons ***-3.27 ***-2.97 *-3.39 ***-1.42 ***-1.83 ***-2.48 ***-0.29 **-1.12 -***3.55 -0.68 ***-1.48 ***-3.00

N obs 487 493 456 472 499 499 499 499 499 499 499 499

R2 0.25 0.26 0.18 0.13 0.07 0.07 0.08 0.11 0.19 0.18 0.11 0.36

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



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.

Key conclusions



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.



Thank youfor your attention

Documents

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