a5_fisnik Recica Full Paper

8/11/2019 a5_fisnik Recica Full Paper

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*I would like to express my special gratitude to Professor Iraj Hashi and Jean Mangan for helpful comments andsupport with regards to the theoretical and econometric work of this study.

Working Paper

IMPACT OF INNOVATION ON EXPORT PERFORMANCEEVIDENCE FROM TRANSITION ECONOMIES

Fisnik REICA*

PhD Candidate, Staffordshire University Business School, Stoke on Trent, U.K.,

Lecturer at the Faculty of Economics, AABRiinvest University & Researcher at the Riinvest Institute, Prishtine, Kosova

[email protected]

Abstract

Innovation has been widely studied for its impact on firm performance and is considered a

driving force behind international trade. The aim of this paper is to investigate if there is a

significant impact of innovation on export intensity in Central and South Eastern Europe and

possible differences between the regions of Western Balkan, South Eastern candidate countries

for European Union, Central Eastern Europe and Baltic countries controlling for the various

factors which may affect firms export behaviour. We use the datasets from Business

Environment and Enterprise Performance Surveys of 2002, 2005 and 2008 jointly conducted bythe World Bank and European Bank for Reconstruction and Development. One major issue in

the literature is the endogeneity between innovation and export because innovation affects

exporting but improved export performance affects innovation as well. Therefore, export

intensity of firms is analysed using Tobit estimation method with instrumental variable to avoid

for any possible bias of estimates.

The results indicate that innovationis a significant and positive determinant of export intensity.

Other variables indicating positive and significant effect on export intensity are foreign

ownership, affiliation of firms in business association, finance of investments by commercial

banks and proportion of staff with the University education. Firm size indicates that as larger the

firm is the effect is stronger. Western Balkan countries indicate higher degree of export intensity

than SEE candidate countries but lower than others, while innovative firms of Western Balkan

indicate higher degree of export intensity in comparison to other three regions.


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Impact of Innovation on Export Performance: Evidence from Transition Economies

2

1. INTRODUCTION

An important determinant of the success of firms in the modern era is their ability todevelop new products and processes in order to increase their efficiency and gain competitive

advantage. As more innovative they are firms are supposed to be more competitive, have better

performance and have greater chances to approach international markets. Based on the theories

of Vernon (1966) and Krugman (1979) innovation is considered to be a driving force behind the

international trade.

This study aims to estimate the impact of innovation on export intensity of the firms in

transition economies of Central Eastern Europe (CEE) and South Easter Europe (SEE) and thedifferences between Western Balkan countries, European Union candidate countries of SEE,

CEE and Baltic countries. All countries of our sample belong to the transition economies, but our

focus is in the Western Balkan countries which have gone through a similar development phase

in last two decades associated with economical and political problems.

Previous studies which investigated innovation impact on export performance of the

firms have generally found positive and significant effect of innovation (Wakelin 1998,

Sterlachini 1999, etc). Their results may have been biased as they did not control the possibility

of endogeneity since innovation affects performance but improved performance affects

innovation as well. This issue is predicted by the global-economy models of endogenous

innovation and growth (Grossman and Helpman, 1994). To avoid this problem, recent studies

(Damijan and Kostevc 2008, Anh et al 2009, etc.) have additionally controlled for the

endogeneity issue and the results of innovation impact on export performance are similar with

the previous ones.

Using large datasets from the Business Environment and Performance Surveys (BEEPS)

conducted by the World Bank and European Bank for Reconstruction and Development (EBRD)

on 2002, 2005 and 2008 we employ a Tobit estimation method with instrumental variable to

control for the endogeneity problem. Tobit estimation method enables us to incorporate in one

model both exporting and non-exporting firms by censoring the dependent variable. Since panel

estimation is not possible due to the slight changes in the 2008 questionnaire, we estimate our


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export intensity model with pooled data of 2002 and 2005 and separately for 2002, 2005 and

2008 for the robustness of the results. The dependent variable, export intensity,is expressed as

the share of exports on total sales, while innovation activities are indicated by innovation output

captured by the survey questions on new products and processes introduced in previous 36

months. We also control for the age of the firm, size, sector, foreign ownership, business

association membership, finance of investments by credits from commercial banks, quality of

products, changes in organisational structure and human capital factorssuch as proportion of

skilled and trained workers and proportion of workers with university education . Furthermore

we investigate whether there is any difference between the regions of interest in export intensity

in general and for innovative firms in particular.

The paper is structured as follows: the following section critically reviews the literature

on innovation and its impact on export performance performance. Section three presents

empirical analysis starting with the theoretical basis, methodology used for analysis, description

of data and interpretation of our empirical findings. The last section presents concluding

remarks.

2. LITERATURE REVIEW

This section will aim to review the literature on innovation activities and the impact of

innovation on export performance as one of the significant determinants for the success and

growth of companies.

2.1. Innovation activities

The process in which firm decides to innovate, the determinants of that decision and

impacts of the innovation activities on firm performance have been examined by different

authors using different methodologies and different innovation indicators.

Schumpeters work of 1934 defines innovation as an introduction of a new product, a

new process or a new organizational arrangement, opening or identifying of a new market and as


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the conquest of a new source of supply of raw materials or half manufactured goods.1Innovation

is considered an important element of a firms strategy to gain competitive advantage and

maintain its dominant position in the market. The now prevalent view is that firms become

industry leaders by conducting research and development (R&D) leading to innovation in their

production technologies or the products they provide (Pepall, et al., 2008, p.572). This has led to

a more particular focus on the role played by innovation in relation to the productive processes

and the growth of firms. As it is emphasized in the DTI (2003) economics paper 7, innovation

can lead to productivity growth through the development of more valuable products and services

or new processes that increase efficiency. Positive impact of innovative activities on firms

performance gives an advantage to innovative firms compared to those that do not innovate.However, there is no single measure of innovations, and a range of innovation indicators are

required to show innovation activities from input to innovation output.

In the innovative processes, as argued by Vermeulen, et al. (2003) there is a relationship

between innovation inputs, throughputs and outputs, as an indicator that firms which invest more

in first two phases of the process will achieve higher levels of innovative output. The most

emphasized indicators of innovation input is R&D effort (Acs and Audretsch, 1990; Hitt et al.,

1991) and the number of R&D employees (Scherer, 1965; Schmookler, 1966).) These input

indicators, through a transformation process will lead to innovation output such as new or

significantly improved products, processes, services, etc. Vermeulen, et al. (2003) points out that

among various innovation output indicators, three of them have received the most attention:

number of patents, new product announcements and the degree of newness of new products.

Studying the relationship between innovative activities and export performance in non-

R&D intensive industries, Sterlacchini (1999) argues that traditional R&D indicators are not

adequate for studying innovation in non-R&D intensive industries and small firms, giving

priority to other measures of innovation input such as design work and engineering development.

Moreover, Bleaney and Wakelin (2002) suggest that an innovation output measure is likely to be

1Similar to the Schumpeters definition of innovation is given by the Oslo Manual (2005, p.46) which defines

innovation as the implementation of a new or significantly improved product (good or service) or process, a new

marketing method or a new organizational method in business practice, workplace organizations or external

relations.


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preferable to an input measure such as R&D intensity, because as Acs and Audrestch (1987)

argue, R&D measures suffer from indicating only the budgeted resources allocated towards

trying to produce innovative activity, but not the actual amount of resulting innovations.

2.2. The Impact of innovation on export performance

International trade is driven by many factors but innovation is considered to be a driving

force behind it in the process of firm development and its efforts to penetrate into the

international markets based on the models of Vernon (1966) and Krugman (1979). Innovation is

seen as an important segment of firm strategic strengths and given its excepted impact on export

performance it gives firms an incentive to invest more in new product and process developments,

in order to enter new markets worldwide and be more competitive. According to Vernon (1966)

new products are introduced by firms to meet national needs and are first exported to similar

countries2. This is important for companies to reduce risks and costs, increase revenue and

market share through markets outside the national boundaries.

On the other hand, Krugman (1979) developed a model of international trade in which

patterns of trade are determined by a continuing process of innovation and technology transfer.3

In his model, Krugman postulated a world of two countries: innovating North and non-

innovating South. Innovation first takes place in the industrialized North with the introduction of

new products that will lead to a later production of those products in South after a lag in which

the South will adopt new technology and imitate innovative products of the North. In this process

North must continually innovate to maintain its relative position and maintain its real income, by

exporting new products and importing old products imitated by South. This implies that each

good is first exported by North, but eventually it will become an export of South, suggesting a

causation effect between innovation and exports and a rise to product cycle in international trade.

As Grossman and Helpman (1994) predicted, in the economy global- models of innovation and

growth, the increase of exports driven by innovation will in reverse increase firm investments in

innovative activities, thus raising the endogeneity issue between innovation and exports.

2Countries with similar needs, preferences and incomes.

3Process of transformation of new products into old products


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In support of the models which indicated that innovation should have a positive impact in

the exporting activities of the firm, the study by Hirsch and Biaouji (1985) who examined the

impact of R&D intensity on export performance of Israeli firms with a sample of 111 firms,

found positive impact of innovation on export performance. Moreover, the number of R&D

employees is found to have a positive and significant effect on the export growth.

Kumar and Siddharthan (1994) have studied the impact of technology and size on export

performance in developing countries, using the data on 640 Indian firms for the period 1987 to

1990. In their study, technology factor is captured by the R&D intensity as a measure of

innovative activities. They also controlled for the firm size and technology imports, advertising

and capital intensity of operations (automation), affiliation with multinational enterprises, and

government policy factors related to export incentives and concessions. Since a large numbers of

Indian enterprises do not export, the dependent variable has a zero value in many cases, so they

used a Tobit model for empirical analysis as an appropriate one, and the results suggest that

innovation activities captured by R&D of the firms favourably influences export behaviour only

in the low and medium technology industries. Indications that there is no positive impact of

R&D in high technology industries is related to the fact that Indian firms do not have the ability

to engage in export activities if they rely on their own technological activities. Moreover, the

relationship of firm size and export performance suggests an inverted U-shaped form meaning

that firms export more when they start to grow, but after a certain point of growth the effect on

exports starts to decline. This might imply that large enterprises act as oligopolist over the

protected domestic market which generally makes them less prone to export than the other firms.

Wakelin (1998) examined the role of innovation in determining export behaviour for a

sample of UK firms, and treated it in two ways, as a probability of a firm exporting and the

propensity to the export of the exporting firms, including innovating and non-innovating firms.

As a proxy for innovation activities this study used innovation history of the firms. Outlining

characteristics of innovation at the firm level, the author suggests that firms enjoy benefits of the

innovation in terms of cost reductions, new markets and potential monopoly rents. This makes

the firm a suitable unit of analysis in the context of the role of innovation in export behaviour.

Data used for this analysis covers 5 years from 1988 to 1992, however, the number of total


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observation was lowered as data are missing for some firms in some years. The main variables

used in the model included propensity to export measured by the proportion of exports on total

sales, average capital intensity4, average wages, unit labour costs, and the number of produced

innovations. Size variable was set according to the number of employees.

In the first specification, Wakelin estimated a single censored Tobit model, including the

decision of whether or not to export and the level of exports, in one model. In the second

specification, a two stage model was used separating the decision of whether or not to export

from the decision of how much to export. In the first stage, the decision whether or not to export

is considered using a Probit model, while in the second stage only the subset of firms which

export have been considered using a truncated estimation procedure as the dependent variable is

observed only if it is greater than zero. Double specification was tested against the Tobit model

as the restricted model, and Tobit model was rejected, which might indicate model

misspecification.

The findings of the Wakelin study suggest that innovating and non-innovating firms

behave differently both in terms of probability of exporting and the level of exports, implying

that the capacity to innovate changes the behaviour of the firm. In terms of size, small innovating

firms are less likely to enter export markets than non-innovating firms, but large innovative firms

are likely to export, and the more innovations they have had, the higher is the probability to enter

export markets. Indications are that smaller innovative firms are less likely to export, since the

cost of entering export markets is higher for smaller firms, therefore, they would prefer to stay in

domestic markets. Another result of the study is that the production of innovations at the sector

level improves probability of all firms exporting, no matter if they are innovative or non-

innovative, which implies that the innovative environment is important and would encourage

firms to export, even though the same relationship was not supported by the results for the export

4Capital intensity is defined relative to sales and not to labour as the capital to labour ratio was found to be

collinear with the average remuneration variable, making it difficult to separate the effect of the two variables

(Wakelin, 1998).


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propensity.5This might indicate that positive spill-over effects are significant for the increase of

probability for first time exporters, but not for the increase of export propensity.

Taking into account that R&D statistics can be misleading in the case of smaller firms,

which often do not have formal R&D units6, Sterlacchini (1999) takes into account other

innovation inputs such as the innovative content of capital stock7, the ratio of expenditure on

design engineering and trial production to sales, and the shares of costs for acquiring innovative

capital goods on sales. Study was based on the questionnaire administered to firms at the end of

1997 by means of direct interviews, investigating the period between 1994-1996, using a sample

of 143 firms with fewer than 200 employees classified as supplier dominated and specialised

suppliers.

The estimation method used in this analysis follows the method of Wakelin (1998), but

when the whole sample is considered, the likelihood ratio test suggested that the single censored

Tobit model should be preferred to the two-stage model, indicating that the parameters of both

probability to export and export intensity could be measured in one model. This might be a result

of the small sample used for analysis. On the other hand, when only the subset of innovating

firms is considered, the author does not clearly indicate which one is the best model

specification. Tobit estimates are justified as correct estimates for the empirical analysis since the

proportion of export to sales variable has a large number of observations in its lower limit or

better saying when firms do not export at all, and a few observations are in the upper limit when

firms sales derive from exports. Therefore, OLS estimates would be downward biased8, and by

using censored Tobit estimates that bias would be avoided.

Except innovation indicators Sterlacchini used a dummy variable for innovating firms,

taking a value of 1 if the firm introduced at least one innovation during the period 1994-1996.

5Production of innovation at the sector level is measured by the number of total innovations produced in the

sector from 1979-1983, from the survey, excluding each firms individual innovations, scaled by the number of

enterprises in the sector (Wakelin, 1998).6See Santarelli ad Sterlacchini, 1990; and Kleinknecht and Reijnen, 1991).

7To assess the innovative content of a firms capital stock, the main criterion used was the distinction, for each

production phase, between machines and logistical devices based on micro-electronics and those which relied on

electro-mechanical technologies. (Sterlacchini, 1999)8Downward bias of the OLS estimates may predict values of the dependent variable outside its possible range (See

Sterlacchini, 1999 and Wooldridge, 2006, pp. 98-99).


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Furthermore, as control variable for firm characteristics he uses firms size measured by the level

of sales in 1996 and the share of sales in 1996 due to the productive decentralisation of other

firms it has been assumed that if this is greater than 60 percent, the firm is classified as a sub-

contractor. The other firm characteristics variable is a dummy variable identifying the affiliation

of a firm with an industrial business group. The only significant variables were firm size and the

innovation/technological level of capital stock. Results found from the empirical analysis of the

Tobit estimates indicated that export performance has an inverted U-shaped relationship with

firm size. The innovation variable measuring intensity of expenditures on design, engineering

and trial production was positive and particularly significant. Moreover, the innovation dummy

has a less significant but a positive impact on export performance. On the other hand, in the twostage model, apart from the effect of firm size on export performance, the probability of being an

exporter is affected positively but with a low significance level by all three indicators of

innovation. Furthermore, in the second stage, the propensity to export does not seem to be

affected by firm size but is significantly impacted by the innovation dummy and the intensity of

expenditure on design, engineering and trial production.

As the implication of the analysis by Sterlacchini (1999), it might be suggested that given

the positive and significant impact of the innovation input indicators used in the study, the

product and process innovations development which are shown to be important for the

development of the small firms, do not necessarily require R&D efforts.

Using the data on Slovenian firms for the period 1996-2002, and combining firm level

information of foreign trade flows and innovation activity for the same period from Community

Innovation Surveys (CIS1, CIS2, CIS3), Damijan and Kostevc (2008) examined the causal

relationship of exports and innovation. Slovenian domestic market is small, therefore, 85 percent

of the Slovenian manufacturing firms are exporters, and most of their exports are oriented

towards European Union markets. Employing bivariate Probit estimation, and including firm

innovation outcome as the variable of interest measured by actual product or process innovations

undertaken by firms, they test the prediction that positive effects of exporting will manifest

themselves in improved firm ability to innovate, while improved ability to innovate would

increase the probability of becoming an exporter. Based on their findings, they could not clearly


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establish the direction of the causal relationship between exporting and innovation. They found

that innovating status increases the probability of exporting, but it does not increase the

probability of becoming first time exporter, and exporting status increases probability of

innovating, but it does not increase the probability of becoming a first time innovator. Even

though their findings do not clearly establish the causal relationship, they emphasize the

importance of the endogeneity problem when measuring the impact of innovation on exports

which might lead to biased estimates if not considered.

Another study which has considered the endogenour relationship between innovation and

exports is done by Anh, et al. (2009) for the developing country of Vietnam, based on the

knowledge that the export sector is a major driving force in Vietnams economic growth. They

based their initiative for the study on the assumption that innovation can affect a firms

competitiveness and hence export status by increasing productivity and developing new goods

for the international market. They captured innovation activities as a new product innovation, a

new production process and a modification of existing products. The result suggests a significant

impact of all three innovation measures on export performance, amongst which product

innovation showed the strongest effect.

Generally, the literature reviewed suggests a positive and significant effect of innovation

indicators on export performance, no matter if input or output indicators have been used as

innovation measure. As noted in the literature review, innovation affects export performance

positively, but increased performance affects innovative activities as well, thus suggesting the

causal relationship. Most of the studies have not taken into account the endogeneity issue, so

their results might have been biased and therefore questionable, but some studies have indeed

dealt with the issue by controlling for endogeneity.

3. EMPIRICAL ANALYSIS

Empirical analysis in this paper aims to assess the impact of innovation on export

intensity, controlling also for other variables which are important in determining the firms

export behaviour. The first part covers theoretical basis of the model while the second one


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describes methodology used. Then, we describe data sources and its limitations, following with

the variable specification and empirical findings.

3.1. Theoretical basis

As discussed in the previous section, trade models first developed by Vernon (1966) and

Krugman (1979) highlighted the importance of the role of innovation as the main determinant of

the international trade and an important tool to achieve competitive advantage. According to

Lefebvre and Lefebvre (2001) competition is increasingly technology based, therefore, it is

expected that the role of technological capabilities from which new product and processes

emerge would be very important in determining the firms ability to export. However, this raises

the endogeneity issue as predicted by global-economy models of endogenous innovation and

growth (Grossman and Helpman, 1994) because with the increase of exports driven by

innovative activities firms are expected to invest more in future innovation, thus creating the

reverse relationship.

From the literature review it is known that a variety of factors affect export performance,

both the firms probability of exporting and the export intensity (Wakelin, 1998; Sterlacchini,

1999; Kumar and Siddharthan, 1994; Anh et al., 2009; etc.). Following studies which estimated

both the probability of exporting and the export intensity, we may assume that generally the

same variables affect both outcomes even though they may differ in the direction and magnitude

of the impact on the two separate decisions.

Considering the availability of data and the control variables used by other authors we

control for innovative activities as the main variable of interest and also other variables which

are expected to affect export behaviour of the firms. Therefore, Export intensity is expressed as a

function of innovative activities, human capital factors, and firm characteristics (Higon and

Driffield, 2007; Gashi, 2008).

Export intensity = f (Innovation activities, human capital factors, firm characteristics).


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3.2. Methodology

Most of the firms observed included in the Business Environment and EnterprisePerformance Survey (BEEPS) dataset which we use in our analysis are not exporters, having a

zero value for the share of exports in total sales. Therefore, to examine export intensity of firms,

only the subset of exporting firms might be used, but this may produce some degree of sample

selection bias since most of the firms in the whole sample are not exporters, so the Ordinary

Least Square (OLS) estimation method is not suitable. OLS can give estimates which imply

predictions of the propensity to export outside its possible range, i.e., higher than one and lower

than zero. OLS estimates of a sample considering only one subset of observations while omitting

others creates sample selection bias, thus OLS estimates will be biased and inconsistent.

(Wakelin, 1998; and Gujarati, 2003).

Following Wakelin (1998) and Sterlacchini (1999), a single censored Tobit model9will

be employed, because it includes all the available information from the explanatory variables

where the decision on whether the firm exports at all and the decision on the level of exports are

incorporated into one model. The Tobit model is referred to as the corner solution response

where the dependent variable is zero for a nontrivial fraction of the population but is roughly

continuously distributed over positive values (Wooldridge, 2006). Information on the regressand

is available only for some observations, and the sample is known as a censored sample which

makes a Tobit model a censored regression model (Gujarati, 2003).

Following Wooldridge (2006) and Greene (2003) we investigate the appropriateness of

the Tobit model as it incorporates in one model both the probability to export and export

intensity of the firms. Wooldridge (2006) refers to it as an informal way to evaluate the

appropriateness of the Tobit model. This test is carried out by dividing the Tobit estimates with

the overall standard error sigma from the Tobit regression and comparing them with Probit

coefficients. If they are not significantly different from Probit coefficients, then the Tobit model

can be considered as appropriate. As Wooldridge notes, sign changes and differences in

magnitude for explanatory variables that are insignificant in both can be ignored.

9Tobit model is originally developed by James Tobin, the Nobel laureate economist. (Gujarati, 2003).


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To control for the endogeneity issue of innovation and export intensity we employ Tobit

model with instrumental variable.

The Tobit model of export intensity function can be expressed as:

)0(

otherwiseexporters-nonfor0

0ifexportersfor

2, ~N

xxy

i

iiii

i

Independent variables in the model are expressed asix , represents the coefficients of the

variables and the intercept and i is the error term. The latent variable iy satisfies the classical

linear model assumptions; in particular, it has a normal, homoskedastic distribution with a linear

conditional mean (Wooldridge, 2006).

3.3. Data

This study uses the BEEPS data which has been conducted by the World Bank and

EBRD in 28 transition economies as well as Turkey in years 1999, 2002, 2005 and 2008. In these

Surveys the sectoral composition is determined relative to the contribution to GDP. The sample

was designed to be as representative as possible to the population of firms which are selected

randomly from manufacturing and services (including trade) and to be distributed between at

least two major industrial regions within each country. Moreover, at least ten percent of the

sample in each country was designed to be in small, large, foreign owned and exporting firms.10

Because the 2008 questionnaire has slight changes it was not possible to combine all of

them in the pooled or panel estimation, therefore only 2002 and 2005 surveys are used in pooled

regression for this analysis. Data from 15 SEE11

and CEE12

countries in 2002 and 2005 surveys

are pooled together to give a larger number of observations. This pooling technique is supported

by Wooldridge (2006, p. 449) who argues that by pooling random samples drawn from the same

population but at different points in time, we can get more precise estimators and test statistics

with more power. Our pooled dataset has approximately 7715 observations (firms) in 15

10See BEEPS dataset available at http://www.ebrd.com/country/sector/econo/surveys

11SEE countries included are Albania, BiH, Bulgaria, Croatia, Macedonia, Rumania and FRY

12CEE countries included are Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia.


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transition economies of CEE and SEE. To control for any changes taking place between 2002

and 2005 a year dummy is included. Observations in 2005 belonging to the same firms in 2002

have been excluded from the dataset as they would be correlated. Panel estimation would be

desired as it controls for dynamic effects but because of only two years and insufficient number

of observations it might not give appropriate results, therefore it is not part of this analysis.

One limitation of the BEEPS dataset is the quality of responses since the answers are

provided by the managers of the firms and may be subjective. Another is the large number of

missing observations for some variables such as Research and development as an innovation

input indicator andInternational cooperation for product development as a knowledge spillover

effect. As it was not possible to establish if the data is missing randomly, these variables could

not be included in the model as this might have produced biased estimates. The dataset of 2008

survey has slight changes as it is missing some important variables that we use in our model such

as process innovation, membership in business associations and change in organisational

structureand it has large number of missing observations forfinance from commercial banksand

reinvested profits.Therefore, being unable to incorporate it in a pooled regression or run panel

estimation, 2008 regression is estimated separately for the robustness of results.

3.4. Variable specification13

The dependent variable, Export intensity, is expressed as proportion of exports on total

sales and the variable is constructed by adding together the proportion of the sales exported

directly and indirectly.

Innovation is an important determinant of the export performance which affects firms

productivity and ability to compete on international markets. From the BEEPS survey,

innovation activities can be indicated by the innovation output captured by three survey

questions: if a firm introduced a new product or service, if it upgraded an existing product or

service or if it has introduced a new technology as a process innovation in the previous 36

months. Following Sterlacchini (1999), a dummy variable taking a value of 1 if the firm

introduced at least one innovation is constructed, which will measure the impact of innovation on

13See Appendix A.1. for the detailed description of the variables


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export intensity. The use of R&D investment as the innovation input indicator is limited due to

the large number of missing observations in the dataset. Using this variable would have resulted

in the loss of many observations, which cannot be treated as random, and therefore it had to be

excluded from the model.14On the basis of the theory and the empirical findings of previous

studies innovation is expected to have a positive impact on the export intensity.

Although the possible endogeneity of exports and innovation may have caused an

estimation problem, the wording of the question on innovation avoids this problem. The

questions on innovation indicators refer to the innovation activities which took place in previous

36 months while the export intensity refers to the current period. As argued by Gashi (2008), the

effect of current export performance could not have influenced the past innovation activities, and

this can imply that the causality effect between the two variables is minimized in this case.

However, considering the limitations of the dataset, the potential endogeneity issue might be

investigated using instrumental variables which are assumed to be exogenous to exports. 15In our

case only the variable reinvested profitsin the previous yearis available as an instrument that is

expected to be correlated with the innovative activities but not with export intensity.16

Quality accreditation is expected to have positive impact on export behaviour because

higher quality products might be better accepted in foreign markets. As Kesidou and Szirmai

(2007) argue, quality accreditation is a necessary step for many firms in developing countries

that try to enter foreign markets and gain the trust of demanding consumers. So, the quality

variable controls if the accreditation with ISO 9000, 9002 or 14000 and AGCCP has an impact

on export intensity. The relevant question in the 2002 survey asks only for ISO 9000

accreditation, while in the 2005 survey the question refers to ISO 9000 as well as a number of

1434 percent of observations for R&D investment in 2002 and 83.5 percent in 2005 are missing.

15Lachenmaier and Woessmann (2006) treated innovation impulses and obstacles such as suggestions from the

firms production and resource management and on innovation obstacles such as lack of equity capital as the proxy

variable for innovation arguing that these variables are credibly exogenous to firms export performance.16

Gashi (2008) used percentage of profits reinvested in the previous year of the survey as the proxy variable for

innovation. In 2002 the answers are given in interval values of the percentage, while in 2005 are given as

percentage. In pooled dataset the 2002 interval values are taken as mean value of percentage intervals.

Atanassov, et al. (2007) argue that banks being unable to evaluate novel technologies will tend to discourage

investing in innovative projects and be more prone to shut down ones that are ongoing, which suggests that

reinvested profit is an important financial source for innovation.


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other kite marks. Therefore, it will be used in the pooled regression and is expected to have

positive impact.

We investigate the impact of the size of the firm to see whether there is a positive effect

from bigger size firms as they have more resources to enter foreign markets. The relationship

between export propensity and firm size in general has been found positive but non-linear (Roper

and Love, 2002). However, given that the size of the firms is reported as a dummy variable in

this data set, the non-linearity and expected inverse U-shaped relationship cannot be investigated.

An inverse U-shaped relationship is also found by Wakelin (1998) and Sterlacchini (1999).

The age of the firm is expected to play an important role in exporting behaviour sincemore experienced firms may be expected to have more advantages to penetrate in foreign

markets though there is no consensus on this in the literature. As argued by Higgon and Driffield

(2008), years of accumulated experience may capture learning by doing effects, but the

opposite is expected if younger firms may behave more proactive, flexible and aggressively.

Therefore, the expected sign of the age coefficient is ambiguous and a U-shaped relationship

may also be possible.

Another useful control variable is Foreign Ownership as an indicator that firm might

have better export performance. If the business group is international, firm could take advantage

of the access to resources such as finance, physical or human capital, branding, marketing, and

distribution (Roper, et al. 2006) which may prove useful to an exporting firm, therefore we

expect positive impact on export intensity.

Dummies for sectors are used to check for the differences in exporting activity in three

distinct sectors of production, services and trade because some of the sectors are more inclined to

exports than others and generally the sector of production is expected to be more prominent in

exporting.

To investigate differences in export behaviour of the SEE and CEE countries, four

dummy variables are included. Two regions are divided into four sub-regions, so we control for

differences between west Balkan countries (Albania, BiH, Macedonia, Serbia and Montenegro),

countries which at the time of the 2002 and 2005 surveys were EU candidate members in SEE


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(Croatia, Bulgaria and Romania), Central Eastern Europe (Poland, Czech Republic, Slovakia,

Slovenia and Hungary) and the Baltic countries (Estonia, Latvia, Lithuania). Western Balkan

countries of the SEE region are expected to have lower exports intensity compared to more

developed CEE countries due to the economic and political problems they faced during the

1990s.

Higgon and Driffield (2008) argue that information constraints limit the exporting

behaviour of firms, thus highlighting the importance of networking activities. Therefore, we

control for the membership in business associations as an indicator of networking connections of

the firm potentially enabling it to create new cooperation and go beyond the local market. The

impact of this indicator on export intensity is expected to be positive17.

Organizational structure might also influence a firms export performance; therefore we

investigate if the change in organisational arrangements in the previous three years has a positive

effect. Dosoglu-Guner (2001) points out that since organizational culture affects corporate

behavior and strategy it can be argued that firms that have an entrepreneur, adaptable, risk-

taking, and future-oriented organizational culture might perceive international expansion as part

of their corporate culture.

According to Grossman and Helpman (1994), human capital factors indicated by the

knowledge capacity are the engine of economic growth. At the micro level these factors

positively affect productivity because the improved knowledge will increase working efficiency

and utilisation of the capital, so we can assume that these factors positively affect export

performance of the firms. Following Higon and Driffield (2007) and Gashi (2008), we control

for human capital factors using proxy variables such as education level of the workers,

proportion of skilled workers and training of the skilled workers which are expected to have a

positive relationship with exporting activities.

Access to finance is an important factor affecting the development of new products and

processes and also the level of investments, both of which are essential for maintaining

competitiveness in international markets. Beck (2002) found that economies with more

developed financial sectors export more. The percentage of a firms working capital and fixed

17Positive impact of the affiliation of a firm with an business is group is found by Gashi 2008.


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investment financed by credit from banks is used to measure the extent of external financing of

firms and is expected to have a positive impact on export intensity.

We have also included a dummy variable to control for the year difference from 2002 to

2005.

3.5. Descriptive statistics

Descriptive statistics of all variables used in the pooled data model of export behaviour

are given in table 3.1.

Table 3.1. Descriptive Statistics of the pooled dataset of Export Behaviour Variables inCEE and SEE Region

Variable Mean Std. Dev. Min Max

Expint 12.25 26.09 0 100

Expprob 0.32 0.46 0 1

Age 16.16 18.80 3 202

Agesq 614.80 1965.45 9 40804

W_balk 0.20 0.40 0 1

SEE_EU 0.21 0.41 0 1

CEE 0.45 0.50 0 1Baltic 0.14 0.34 0 1

y05 0.56 0.50 0 1

Prodsect 42.81 46.98 0 100

Sersect 24.43 41.60 0 100

Trsect 32.76 43.97 0 100

Forown 0.08 0.27 0 1

Buss_Ass 0.48 0.50 0 1

Finbank 10.23 22.66 0 100

Innov 0.64 0.48 0 1

Quality 0.15 0.36 0 1

Org_Str 0.19 0.40 0 1Skilled 51.58 32.00 0 100

Uni_Edu 23.23 27.44 0 100

Trskill 0.44 0.50 0 1

L_size 0.11 0.32 0 1

M_size 0.18 0.39 0 1

S_size 0.70 0.46 0 1

Reinvpr 38.58 39.73 0 100


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Most of the variables in the model are dummies or are given as proportions ranging from

zero to one hundred. Only age is a continuous variable, used together with age square to

investigate the U-shaped relationship between the firms experience and exports. Descriptive

statistics for the 2008 sample are presented in the Appendix A.9.

Statistical figures from table 3.2. indicate that CEE countries are proportionally more

engaged in exporting activities (though only marginally above the Western Balkan countries) but

they have the lowest rate of innovation. On the other hand, SEE EU candidate countries have the

largest number of innovation activities but the lowest proportion of exports compared to other

regions, while the SEE countries of Western Balkans and Baltic countries have a similar level of

engagement in innovation and exports which is in between of other two regions.

The findings of the empirical analysis and the appropriateness of the model used will be explored

in the next section.

Table 3.2. Proportion of innovation and exports.

Region W_balk SEE_EU CEE Baltic

Introduced Product orProcess Innovation 66% 73.03% 57.75% 63.58%

Proportion of firms

which have exported 32.29% 27.33% 32.47% 30.84%

Proportion of

Exporting firms which

have Introduced a

product or a processInnovation 74.14% 83.48% 71.78% 74.92%

3.6. Empirical findings

This section of empirical findings consists of section on diagnostics, interpretation of

results and a short presentation of 2008 sample estimation results.


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3.6.1. Diagnostics

The available econometric software does not provide straight forward information onestimation diagnostics, therefore we have undertaken couple of diagnostic checks.

Table 3.3. Tobit model estimation with endogenous regressor using pooled dataset

Tobit model with endogenous regressors Number of obs = 5737

Wald chi2(19) = 814.05

Log likelihood = -15111.267 Prob > chi2 = 0.0000

Coef. Std. Err. Z P>z

Innov 65.8673*** 23.6916 2.7800 0.0050

y05 1.3590 2.1165 0.6400 0.5210SEE_EU -7.5232** 3.3786 -2.2300 0.0260

CEE 10.2489*** 3.8802 2.6400 0.0080

Baltic 6.6944* 3.7662 1.7800 0.0750

Age 0.1199 0.1271 0.9400 0.3460

Agesq -0.0001 0.0011 -0.0900 0.9250

Prodsect 0.2390*** 0.0436 5.4800 0.0000

Sersect 0.1717*** 0.0309 5.5600 0.0000

Forown 31.0335*** 3.5136 8.8300 0.0000

Buss_Ass 14.3562*** 2.1493 6.6800 0.0000

Finbank 0.1541*** 0.0509 3.0300 0.0020

Quality 3.3477 3.7756 0.8900 0.3750

Org_Str 0.0293 3.7851 0.0100 0.9940

Skilled 0.0121 0.0393 0.3100 0.7590Uni_Edu 0.1710*** 0.0520 3.2900 0.0010

Trskill -6.2520 4.1084 -1.5200 0.1280

M_size -6.1045* 3.3837 -1.8000 0.0710

S_size -28.4019*** 3.4975 -8.1200 0.0000

_cons -87.1532*** 13.9524 -6.2500 0.0000

Sigma 55.1014 1.0321

Instrumented: Innov

Instrument:Reinvpr

Wald test of exogeneity (/alpha = 0): chi2(1) = 5.83 Prob > chi2 = 0.0157

Obs. summary: 3912 left-censored observations at expint


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the estimation with endogenous regressor is preferred, suggesting that the point estimates from

Ivtobit are consistent18

. Since our endogenous variable is given as a dummy variable indicating if

the firm carried out any product or process innovation, it was not possible to test for the possible

weakness of the instrument used because there is no statistical test or a rule given for Wald test

values in order to define an instrument as weak or strong.

Additionally, a Tobit regression without considering the endogeneity issue is run for the

purpose of comparison. The statistically significant estimates of Ivtobit regression are more

significant and two of them which were previously insignificant (quality and orgstr) are now

significant19. The slight change in estimates of the two regressions might support the relaxation

of the endogeneity assumption.

Table. 3.4. Comparison of IVTobit estimates divided by sigma with IVProbit estimates20

Var Tobit Coef. IVTobit Estimate/sigma IVProbit estimates

Innov 65.8673 1.195383*** 1.177720***

y05 1.3590 0.024663 -0.014073

SEE_EU -7.5232 -0.136534** -0.255211***

CEE 10.2489 0.186001*** 0.202572***

Baltic 6.6944 0.121492* 0.061602

Age 0.1199 0.002175 0.004944**Agesq -0.0001 -0.000002 -0.000002

Prodsect 0.2390 0.004337*** 0.003101***

Sersect 0.1717 0.003116*** 0.001596***

Forown 31.0335 0.563207*** 0.531962***

Buss_Ass 14.3562 0.260541*** 0.253954***

Finbank 0.1541 0.002796*** 0.003201***

Quality 3.3477 0.060755 0.126054

Org_Str 0.0293 0.000531 0.019026

Skilled 0.0121 0.000219 -0.000189

Uni_Edu 0.1710 0.003103*** 0.004455***

Trskill -6.2520 -0.113464 -0.062404

M_size -6.1045 -0.110787* -0.105297

S_size -28.4019 -0.515447*** -0.464204***

_cons -87.1532 -1.581686*** -1.487750***

Sigma 55.1014*** Significant at 1 percent level of significance.

** Significant at 5 percent level of significance.*Significant at 10 percent level of significance.

18Information for this test is provided by Stata Base Reference Manual Volume 2 I-P release 10

19See Appendix A.2. for results of tobit model estimation

20See Appendix A.3. for more details on IVProbit regression of pooled data.


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However, because the Wald test of exogeneity suggests that estimation should control for

endogeneity, the results of Ivtobit will be reported even though that the standard errors of Tobit

estimates are likely to be smaller.

To test the appropriateness of the model, a Probit estimation with endogenous regressors

is undertaken in order to compare the estimates of Ivtobit model divided by the estimated

standard error of the regression sigmawith the Ivprobit estimates. Comparison of the estimates is

presented in table 3.4. indicating that significant coefficients of both estimations have no large

differences between them indicating that the Ivtobit model is appropriate for estimating export

intensity21. This suggests that truncation of the sample to only exporting firms does not give

significantly different results, so, it can be estimated as a one model with all firms included.

Table 3.5. Estimates for separate years 2002, 2005, and pooled data22

.

2002 2005 Pooled

y05 1.3590

Innov 70.0463* 60.4201** 65.8672***

SEE_EU -5.5830 -8.7256* -7.5232**

CEE 12.5671** 9.3136 10.2489***

Baltic 7.2461 8.5653 6.6943*

Age 0.1491 0.1148 0.1198

Agesq -0.0004 -0.0001 -0.0001Prodsect 0.1705** 0.3104*** 0.2389***

Sersect 0.1133** 0.2366*** 0.1717***

Forown 25.0515*** 38.4733*** 31.0334***

Buss_Ass 17.8232*** 11.0206*** 14.3561***

Finbank 0.0657 0.2193*** 0.1540***

Quality 7.0022 0.9104 3.3476

Org_Str -3.5758 4.2323 0.0292

Skilled -0.0212 0.0468 0.01205

Uni_Edu 0.1242* 0.2292*** 0.1709***

Trskill -6.1983 -6.1695 -6.2520

M_size -3.8367 -8.7088* -6.1045*

S_size -24.1051*** -33.2918*** -28.4018***

_cons -85.6715*** -88.0523*** -87.1531****** Significant at 1 percent level of significance

** Significant at 5 percent level of significance*Significant at 10 percent level of significance

As another robustness check of the pooled estimation, we have carried out individual regressions

for 2002 and 2005 and the comparative estimates are given in table 3.5. The estimates in all three

21Wooldridge (2006) suggests that only significant variables is important to have close estimates.

22See appendices A.4. and A.5. for IVTobit regression results of 2002 and 2005.


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regressions are consistent as regards to the sign of coefficients, while the significance of the

estimates is generally similar.

When we look at the statistical significance of the coefficients, the impact of innovation

on export intensity seems to be stronger in 2005 relative to 2002. This is the case also with

sectors, finances from commercial banks and university education. However, relying on the

consistency of statistically significant estimates and because pooled data estimates are stronger

and more precise we will interpret only pooled data estimates. The 2008 estimation results are

presented in a separate sub-section.

3.6.2. Interpretation of empirical results

Innovation, foreign ownership, business association, financing from commercial banks,

university education of staff and size of the firms indicate a significant impact on export

intensity. To measure the actual effects of each variable, the marginal effects are presented in

table 3.6.

Here, both conditional marginal effects (when the sub-sample of exporting are

considered) and the unconditional marginal effects (when the whole sample of the firms is

considered) are presented. Even though unconditional marginal effects are slightly larger, we

will report only them to avoid possible biasness of the estimates as the result of sample selection

bias when only the subset of exporting firms is considered.

Innovationas expected indicates to be a positive determinant of export intensity and is

highly significant (significant at 1, 5 and 10 percent level of significance). Unconditional

marginal effects suggest that ceteris paribus, on average, holding other variables at their mean

values, if a firm introduces an innovation will have 17.4 percent higher export intensity than

firms which did not. This supports the theory that innovation is a driving force behind

international trade.


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Table 3.6. Unconditional and Conditional Marginal effects for pooled estimation23

variable

Unconditional Marginal Effects

for Pooled estimation dy/dx

Conditional Marginal Effects

for Pooled estimation dy/dx

Innov* 17.38177*** 16.34038***

y05* 0.41900 0.36472

SEE_EU* -2.22865** -1.97727**

CEE* 3.18111*** 2.76065***

Baltic* 2.16635* 1.84414*

Age 0.03701 0.03219

Agesq -0.00003 -0.00003

Prodsect 0.07378*** 0.06418***

Sersect 0.05302*** 0.04612***

Forown* 12.10510*** 9.54626***

Buss_Ass* 4.45498*** 3.86691***

Finbank 0.04757*** 0.04138***

Quality* 1.05584 0.90941

Org_Str* 0.00904 0.00787

Skilled 0.00372 0.00324

Uni_Edu 0.05279*** 0.04592***

Trskill* -1.91695 -1.67304

M_size* -1.81941* -1.60946*

S_size* -9.72543*** -8.08266***

(*) on variable name - dy/dx is for discrete change of dummy variable from 0 to 1*** Significant at 1 percent level of significance

** Significant at 5 percent level of significance*Significant at 10 percent level of significance

When adding control interaction dummies of innovations and regions in the model, the

innovation estimate is still significant while the interaction dummies suggest that innovative

firms in western Balkans are more export intensive than innovative firms from the other three

regions24

. This may indicate that firms with export intentions in this region are oriented to

innovative products more than the ones in more advanced economies where they may be in astable state of development or their innovative products may be targeting more significantly local

markets. However, caution is required when interpreting the results because as Hashi and

Krasniqi (2008) argue the measures used for innovation activities in the BEEPS surveys are

23See Appendices A.7. and A.8. for estimation results of unconditional and conditional marginal effects for the

pooled regression.24

See Appendix A.6. for the IVTobit regression including dummy interactions of innovation and regions.


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rather crude (whether certain changes have taken place or implemented over the three years prior

of the survey) and lack any information on the quality of those changes.

Estimates for control variables of regions are statistically significant for all three regions,

with CEE countries being highly significant; SEE candidate countries are significant at 5 percent,

while Baltic countries are significant only at 10 percent level of statistical significance. Ceteris

Paribus, at the sample mean, comparing to western Balkan countries, CEE region export

intensity is 3.18 percent higher while in the Baltic countries around 2.16 percent higher. An

interesting result is indicated for SEE candidate countries which suggest that they have lower

export intensity than the western Balkan countries for about 2.3 percent which is contrary to

what we expected. An explanation for this might be that the proportion of exporting firms in the

sample for this group of countries is lower compared to the western Balkan countries.

Year dummy indicates that export performance was better in 2005 but it is insignificant.

Age estimates are statistically insignificant, but given that the age coefficient has a

positive sign while age squarecoefficient has a negative sign, it indicates an inverse U-shaped

relationship with export intensity. This indication is consistent with the theory and suggests that

after a certain point of experience firms export intensity starts to decline but the indicated effect

seems to be insignificant.

Productionand Servicesector are both positive and highly significant on export intensity

as it was expected. Unconditional marginal effects suggest that in comparison with the Trade

sector, being part of the Production sector, ceteris paribus, at the sample mean, firm has 0.07

percent higher export intensity, while being part of Service sector firm has 0.05 percent higher

export intensity.

Foreign ownershipis statistically highly significant and positive, and consistent with the

expectations. Ceteris paribus, at the sample mean, the unconditional marginal effects suggests

that if a firm belongs to a foreign company its export intensity is 12 percent higher in comparison

to other forms of ownerships. The magnitude of the coefficient indicates a vital importance of

foreign companies in transition economies as the generators of exports, which would be an


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indicator for companies in these regions to look forward for joint ventures with foreign

companies.

Membership of business associations is also highly significant and positive, raising the

importance of networking as a determinant of entrance and expansion in foreign markets. Ceteris

paribus, at the sample mean, unconditional marginal effects suggest that being a member of

business association on average it increases the export sales as share of total sales by 4.5 percent

relative to non members.

Financing by commercial banksas expected has a highly significant and positive impact

on export intensity. Ceteris paribus, at the sample mean, if a firm increases its investments inworking capital and new fixed investments financed from commercial banks by 100 percent, on

average it will increase export sales as share of total sales by 4.7 percent.

Qualityaccreditationof products as predicted indicates a positive impact on export sales

but it is insignificant. We may say that because the foreign products are expected to have higher

quality even in the absence of accreditations, the effect of quality accreditations is marginally

insignificant.

Human capital related variables such as changes in organizational structure of the firm,

training of employees and proportion of skilled workers in total number of workers have

insignificant effect on export intensity. On the other hand, the proportion of the employees with

some university degree and higher is highly significant and positive. Ceteris paribus, at the

sample mean, unconditional marginal effects suggest that on average if the proportion of workers

with some university degree or higher increases by 100 percent it will impact an increase of

exports sales as the share on total sales by 5 percent, which is not so strong.

In terms of the size, the dummy for small firms is highly significant while for medium

firms it is significant only at 10 percent level of statistical significance and both are negative.

Ceteris paribus, at the sample mean, unconditional marginal effects suggest that on average a

firm of medium size has 1.81 percent lower export intensity than a large firm, while a small firm

has 9.72 percent lower export intensity in comparison to large firms. As predicted by the theory

the larger firms are expected to have higher export intensity. This again might lead to the


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conclusion that large firms have more resources to enter foreign markets, and they may also be

more efficient and therefore more competitive on foreign markets. One limitation was that we

could not investigate for U-shaped relationship of the size due to missing data on actual number

of employees in the pooled dataset.

3.6.3. Interpretation of the 2008 sample estimation results

Due to the important missing variables in the 2008 dataset such as process innovation,

membership of business associations, etc., and large number of missing observations on

reinvested profitas our instrumental variable for innovation, it was not possible to include this

data in a pooled estimation or undertake panel estimation. Therefore, we estimated it separatelyin order to get another robustness specification for our results and conclusions. Descriptive

statistics and results of 2008 estimation are presented in Appendices A.9. and A.10. Differently

from previous estimation of 2002 and 2005 samples, Bulgaria and Romania were EU members at

the time of the 2008 survey, while Kosova is added in the Western Balkan group of countries in

the 2008 regression since data were available.

In terms of sign and significance 2008 regression gives different results compared to

pooled and separate regressions for 2002 and 2005 only for Quality accreditationwhich is now

significant at 1 percent level of significance and University education is now insignificant. In

terms of size, as expected, the variable sizehas a positive value and size squarea negative value

indicating an inverse U-shaped relationship. Even though Bulgaria and Romania became EU

members, their group with Croatia as an EU candidate still indicates lower export intensity

compared to Western Balkan group of countries. Innovationas our main variable of interest is

captured by two questions of the survey: if the firms introduced a new product or service, or if it

has upgraded a new product or service. The innovationcoefficient is positive and significant at 1

percent level of significance. In general, other factors indicate similar significance level and sign

as in the pooled regression which may be considered as another fact for the robustness of results.

However, since we could not control for endogeneity, these results should be considered with

caution.


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To sum up, as suggested by the literature, innovation seems to have a strong impact on

export intensity both in terms of magnitude and significance. This indication further supports the

importance of innovation as a key segment for the firm growth and expansion in foreign markets.

Foreign companies also indicate significant impact of a large magnitude. Other factors have the

expected impact on export intensity but most of the effects are small, hence, policy makers who

deal with the international trade and firm exports should consider all significant determinants of

export performance in general, but innovation and foreign ownership in particular.

4. CONCLUSION

Innovation has always been important for the firm performance but in the era of

globalization and international competition it becomes more important through its effects on

export performance. Innovation is by many authors referred to as a driving force behind the

international trade based on theoretical models of Vernon (1966) and Krugman (1979). Using the

BEEPS datasets of 2002, 2005 and 2008 which are jointly conducted by the World Bank and

EBRD this study examined impact of innovation on export intensity in transition countries of

CEE and SEE. Part of the SEE is the Western Balkan which countries have gone through a

difficult phase of political and economical problems during the 1990s.

Theoretical work review identified variables that affect export performance of the firm

with the special focus on innovation as main variable of interest examining their direction and

magnitude of impact. Based on the global economy models of endogenous innovation and

growth (Grossman and Helpman, 1994) the reverse relationship of innovation and exports is

expected since innovation affects export performance but export performance affects innovation

as well. This causes the possibility of endogeneity problem which is controlled for by the recent

studies (Damijan and Kostevc 2008, Anh et al 2009, etc.), an issue we control as well.

The methodology used is a Tobit estimation with instrumental variable. Tobit model is

employed following Wakelin (1998) and Sterlacchini (1999) as it includes all the information

from the explanatory variables where the decision on whether the firm exports at all and the

decision on the level of exports are incorporated into one model. We estimated the model of


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2002 and 2005 pooled data as it gives more powerful and precise estimates but also separate

regressions for the robustness of results which are consistent in terms of sign and significance.

The appropriateness of the Tobit model is supported by comparing the Tobit estimates divided

by overall standard error sigmawith the probit estimates which are similar.

Empirical findings of this study indicate that innovation as it is suggested by the literature

has positive and statistically significant effect on export intensity. Under the ceteris paribus

conditions, on average, the unconditional marginal effects of the pooled data estimation suggest

that if a firm introduces an innovation it will have 17.4 percent higher export intensity than firms

which did not. This result supports the argument that innovation is a driving force behind the

international trade, therefore it must be considered seriously by companies which target

international markets. Age as expected is shown to have an inverse U-shaped relationship

suggesting marginal diminishing returns as firms grow older but the indicated effect is

insignificant to export intensity. The size of the firm indicates that as bigger the firm is it will

have higher rate of export intensity. Companies with foreign ownership indicate positive and

significant impact on export intensity which supports foreign investments in transition

economies, while similar result is suggested if the firm is a member of business associations

giving importance to networking activities as a way towards foreign markets. Credit from

commercial banks is as expected indicates significant and positive effect on export intensity.

From human capital factors, the proportion of employees with some university degree or higher

is indicated to be significant and positive but the magnitude of the effect is not so high which is

the case also with some other variables such as business association membership and finance of

investments from commercial banks. Moreover, quality certification of products, changes in

organisation structure and the proportion of skilled workers and their training do not indicate

significant effects.

Furthermore, firms in production sector and service sector indicate positive and

significant impact on export intensity relative to the trade sector, with production sector

indicating the strongest effect. By controlling for regional differences we found that Balkan

countries have higher degree of export intensity than SEE candidate countries but lower than


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CEE and Baltic countries. When controlling for innovative firms of the regions, Western Balkan

countries indicate higher level of export intensity compared to the other three regions.

A number of limitations in our study should be mentioned. One limitation is the quality

of data since the answers are provided by the managers of the firms and may be subjective.

Another is the data availability because variables which we wanted to use such as R&D as an

innovation input indicator and the knowledge spillover indicators could not be used due to large

number of missing observations. Furthermore, the instrumental variable used in the model could

not be investigated for its strength. Moreover, the 2008 dataset which would enable us to

undertake panel estimation could be used only as a separate regression because of the slight

changes in the 2008 questionnaire.

To conclude, innovation indicates to be a very important determinant of export behaviour

and policies in Western Balkan countries as our region of interest should be designed to help

firms innovate and invest more in relevant innovative activities in order to help them grow and

improve the international trade balance. Being part of the same region and because of the similar

trend of challenges and developments, indications from the conclusions drawn for Western

Balkan countries can be referred to Kosova as well, which had a period of stagnation and war

between 1990 and 1999. All in all, as most of the estimated effects are relatively small, policies

that would give rise to exports should give more support to factors such as innovation and

foreign ownership which are indicated to have stronger impact on export intensity.


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APPENDICES

A.1. Description of the variables used in the model

Dependent Variable

Expint

Sum of direct and indirect export sales of the firm as the share of total sales expressed

in terms of percentage

ExpprobDummy for exporting firms (Firms with positive value of export sales as the share oftotal sales)

Independent Variables

Innov

Dummy for innovation - if the firm has developed a major product line/service,upgraded an existing product line/service or has acquired new production technology

over the last 36 months

y05 Dummy for 2005

SEE_EU Dummy for SEE EU candidate countries (Croatia, Bulgaria, Romania)

CEE Dummy for CEE countries (Czech Republic, Hungary, Poland, Slovakia and Slovenia)

Baltic Dummy for Baltic countries (Estonia, Latvia and Lithuania)

Age Business Experince of the firm - since establishmentProdsect Production sector - share of sales generated by production sector

Sersect Service sector - share of sales generated by service sector

Forown Dummy for firms with foreign ownership

Buss_Ass Dummy for firms which are member of business associations

Finbank

Percentage of firms working capital and new fixed investments financed by

commercial banks

Quality Dummy for firms which have obtained any quality accreditation

Org_StrDummy for firms which have completely new organizational structure or a major

reallocations of responsibility and resources between departments

Skilled Percentage of skilled workers as the share of total workforceUni_Edu Percentage of the workforce with some University Education or higher

Trskill Dummy for firms which have offered formal training to skilled workers

M_size Dummy for medium size firms

S_size Dummy for small size firms


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A.2. Tobit Regression

Tobi t r egress i onNumber of obs = 5950

LR chi 2(19) = 1119. 29

Prob > chi 2 = 0. 0000

Log l i kel i hood = - 12072. 898

Pseudo R2 = 0. 0443

expint Coef. Std. Err. T P>t [95% Conf. Interval]

y05 0. 815178 1. 909856 0. 43 0. 67 - 2. 928834 4. 559191

SEE_EU - 5. 01712 2. 901701 - 1. 73 0. 084 - 10. 70551 0. 671268

CEE 2. 857965 2. 515893 1. 14 0. 256 - 2. 074102 7. 790032

Baltic 3. 95874 3. 342365 1. 18 0. 236 - 2. 593513 10. 51099

Age 0. 003271 0. 110393 0. 03 0. 976 - 0. 2131401 0. 219682

Agesq 0. 000652 0. 000968 0. 67 0. 501 - 0. 0012465 0. 00255

Prodsect 0. 323114 0. 025646 12. 6 0 0. 2728385 0. 37339

Sersect 0. 183697 0. 02831 6. 49 0 0. 1281998 0. 239194

Forown 31. 6923 3. 088718 10. 26 0 25. 63729 37. 74732

Buss_Ass 14. 97247 1. 965162 7. 62 0 11. 12004 18. 8249

Finbank 0. 216587 0. 037677 5. 75 0 0. 142726 0. 290448

Innov 8. 625281 2. 086327 4. 13 0 4. 535321 12. 71524

Quality 9. 86398 2. 397043 4. 12 0 5. 164903 14. 56306

Org_Str 6. 810254 2. 243677 3. 04 0. 002 2. 41183 11. 20868

Skilled 0. 017505 0. 036253 0. 48 0. 629 - 0. 0535637 0. 088573

Uni_Edu 0. 238216 0. 042124 5. 66 0 0. 1556373 0. 320795

Trskill 2. 241131 1. 952803 1. 15 0. 251 - 1. 587073 6. 069335

M_size - 5. 17759 3. 039413 - 1. 7 0. 089 - 11. 13595 0. 780767

S_size - 29. 8148 3. 093376 - 9. 64 0 - 35. 87893 - 23. 7507

_cons - 56. 7658 5. 402839 - 10. 51 0 - 67. 3573 - 46. 1742

/ si gma 55. 02033 1. 008739 53. 04283 56. 99782

Obs. summary: 4046 l ef t - censored obser vat i ons at expi nt


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1904 uncensored obser vat i ons0 r i ght - censored

observati ons

A.3. IVProbit Regression

Probi t model wi t h endogenous r egr essor s Number of obs = 5753

Wal d chi 2(19) = 1321. 04Log l i kel i hood = - 6589. 619 Pr ob> chi 2 = 0. 0000

Coef. Std. Err. z P>z [95% Conf. Interval]

Innov 1. 17772 0. 326737 3. 6 0 0. 5373275 1. 818112

y05 - 0. 01407 0. 038151 - 0. 37 0. 712 - 0. 088848 0. 0607021

SEE_EU - 0. 25521 0. 056785 - 4. 49 0 - 0. 3665084 - 0. 143914

CEE 0. 202572 0. 058884 3. 44 0. 001 0. 0871624 0. 3179816

Baltic 0. 061602 0. 066116 0. 93 0. 351 - 0. 0679837 0. 1911868

Age 0. 004944 0. 002355 2. 1 0. 036 0. 0003287 0. 0095583

Agesq - 1. 91E- 06 2. 23E- 05 - 0. 09 0. 932 - 0. 0000457 0. 0000419

Prodsect 0. 003101 0. 000989 3. 14 0. 002 0. 0011632 0. 0050389

Sersect 0. 001596 0. 000568 2. 81 0. 005 0. 0004815 0. 0027098

Forown 0. 531962 0. 088102 6. 04 0 0. 3592858 0. 7046374

Buss_Ass 0. 253954 0. 04522 5. 62 0 0. 1653241 0. 3425838

Finbank 0. 003201 0. 001121 2. 86 0. 004 0. 0010051 0. 0053976

Quality 0. 126054 0. 076812 1. 64 0. 101 - 0. 024494 0. 2766016

Org_Str 0. 019026 0. 069341 0. 27 0. 784 - 0. 1168797 0. 1549308

Skilled - 0. 00019 0. 000698 - 0. 27 0. 786 - 0. 0015567 0. 0011782

Uni_Edu 0. 004455 0. 001144 3. 9 0 0. 0022135 0. 0066961

Trskill - 0. 0624 0. 069362 - 0. 9 0. 368 - 0. 1983508 0. 073542

M_size - 0. 1053 0. 064602 - 1. 63 0. 103 - 0. 2319158 0. 0213211

S_size - 0. 4642 0. 081761 - 5. 68 0 - 0. 6244521 - 0. 303956

_cons - 1. 48775 0. 143976 - 10. 33 0 - 1. 769938 - 1. 205561

/ at hrho - 0. 47232 0. 191136 - 2. 47 0. 013 - 0. 8469402 - 0. 097702

/ l nsi gma - 0. 80291 0. 009323 - 86. 13 0 - 0. 8211832 - 0. 784639

Rho - 0. 44007 0. 15412 - 0. 6894676 - 0. 097392

si gma 0. 448023 0. 004177 0. 4399108 0. 4562843


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I nstr umente :i nnov

I nst r ument s: y05 see_eu cee bal t i c age agesq prodsect ser sect f orown

bussass f i nbank qual i t y ski l l ed uni edu tr ski l l m_si ze s_si ze

orgstr rei nvpr

Wal d t est of exogenei t y ( / athr ho = 0) : chi 2( 1) = 6. 11 Prob > chi 2 = 0. 0135

A.4. IVTobit regression of 2005

Tobit model with endogenous regressors Number ofobs = 3183

Wal d chi 2(18) = 463. 25Log l i kel i hood = - 8240. 2123

Prob > chi 2 = 0. 0000

Coef. Std. Err. z P>z [95% Conf. Interval]

Innov 60. 4202 27. 2669 2. 2200 0. 0270 6. 9781 113. 8622

SEE_EU - 8. 7256 4. 5970 - 1. 9000 0. 0580 - 17. 7356 0. 2844

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