SMEs and Export Performance in South East Europe
Staffordshire University Business School
Centre for Applied Business Research (CABR)
Working Paper No. 002/2012
The Small and Medium Enterprise Sector and Export
Performance: Empirical Evidence from South Eastern
Europe
Petrit Gashi,
Faculty of Economics, University of Prishtina, St. Ramiz Sadiku, nn.10000 Prishtina, Kosova
Iraj Hashi
Staffordshire University, Stoke-on-Trent, United Kingdom
Geoff Pugh
Staffordshire University Business School, Leek Road, Stoke-on-Trent, ST4 2DF, UK.
March 2012
Final_1: September 10, 2008
1
SMEs and Export Performance in South East Europe
The Small and Medium Enterprise Sector and Export
Performance: Empirical Evidence from South Eastern
Europe
Abstract
The export performance of small and medium-sized enterprises (SMEs) is analysed using an
eclectic theoretical framework. Using the not-much-explored large data base of the World
Bank/EBRD Business Environment and Enterprise Performance Surveys (BEEPS), we
investigate the influences of various internal and external factors on SME export performance
in South Eastern Europe (SEE). Our econometric estimates highlight firm size, ownership,
sector of activity, the availability of external finance, affiliation with business organisations,
the education of the workforce and, to a lesser extent, technology-related factors as major
influences. The BEEPS data base contains a large number of missing observations for many
variables – a shortcoming not highlighted by previous authors using this data base. We use
recently developed multiple imputation techniques (MI) to avoid sample bias arising from
missing values, an otherwise endemic problem associated with survey data.
JEL classifications: F23, M16, O16
Key words: export behaviour, SMEs, human and technology related factors, multiple
imputation.
Final_1: September 10, 2008
2
SMEs and Export Performance in South East Europe
1. INTRODUCTION
It is now well established that small and medium sized enterprises (SMEs) play a vital role in
the process of transition to a market economy. As the large firm sector, the prevalent form of
organisation under central planning, underwent restructuring and decline, thousands of new
SMEs took advantage of liberalised entry conditions and entered the market. They responded
rapidly to systemic shocks, produced goods and services demanded by the population and, in
the process, contributed to the generation of new jobs and incomes.1 While the contribution of
SMEs to domestic output and employment has been studied by a variety of authors for many
transition economies, their role in cross-border trade and their contribution to exports has not
been studied widely. The aim of this paper is to develop the research in this area by
investigating the factors influencing the export propensity of SMEs and by providing empirical
evidence for countries of South Eastern Europe (SEE).
We have chosen to focus on these countries because of the relative similarities in their
development trajectories and also because of the paucity of the literature in this area. The bulk
of previous studies have relied on small sample surveys conducted by governments, SMEsupport institutions or by the authors themselves. This paper employs large datasets drawn
from the Business Environment and Enterprise Performance Surveys (BEEPS) conducted
jointly by the World Bank and EBRD, which have remained relatively underutilised (Carlin et
al., 2001b; Vagliasindi, 2001; Vagliasindi, 2006; Svejnar and Commander, 2007;
Gorodnichenko et al., 2008; and Transition Report 2005 are some of the studies based on
BEEPS).
An important constraint on our analysis is the absence of well-developed theory on the
behaviour of SMEs (Brock and Evans, 1989) and, in particular, on SMEs and international
trade. Moreover, papers linking the small firm sector with international trade in the transition
context are especially scarce. For this reason, in Section 2 we adopt an eclectic approach
drawing not only on a largely empirical SME literature but also on a range of economic
theories. Section 3 presents our empirical strategy, the datasets, and applies Multiple
Imputation (MI) to deal with missing data. Section 4 reports and discusses the econometric
results, and Section 5 reports the robustness checks. The final section concludes.
1
See, for example, Bartlett and Prasnikar (1995); Futo, et al. (1997); Scase (1998); McMillan and Woodruff
(2002); Hoshi, et al. (2003); Iakovleva (2005); and Estrin, et al. (2006) among many other contributions.
Final_1: September 10, 2008
3
SMEs and Export Performance in South East Europe
2. DETERMINANTS OF EXPORT PERFORMANCE: AN ECLECTIC
THEORETICAL FRAMEWORK
In line with previous research, we use export intensity (foreign sales as a percentage of total
sales) to measure the degree of firms’ involvement in foreign markets (Bonaccorsi, 1992;
Calof, 1993; Wakelin, 1998; Becchetti and Rossi, 2000; Wagner, 2001; Gorodnichenko et al.,
2008; and others). Similarly, we assess the firm’s export behaviour in two ways: namely, by
measuring the likelihood of exporting as well as the level of firm’s involvement in export
markets (Kumar and Siddharthan, 1994; Wakelin, 1998; Sterlacchini, 1999; and others). To
guide the specification of an empirical model to explain the export performance of firms, we
draw upon the SME literature and a range of economic theories to argue for three types of
independent variables: human-related factors; technology-related factors; and other firm
characteristics (such as firm size, ownership structure, type of sector, etc.). We discuss each of
these in turn.
Human capital related factors
Human capital is at the core of the New Growth Theory, which argues that the creation and
diffusion of knowledge is the primary engine of economic growth (Grossman and Helpman,
1994). At the micro level, human capital is considered to contribute to sustainable competitive
advantage of firms (Bryan, 2006). Accordingly, we hypothesise that human capital factors
affect firms’ export performance indirectly through increases in productivity. Chevalier et al.
(2004) argue that greater levels of education or skill acquisition signal or enhance
productivity. In addition, according to Bryan (2006), training helps to sustain higher levels of
productivity. In our model of export behaviour we measure the impact of human capital
accumulation through several proxies: [i] the education of the workforce; [ii] the presence of
highly skilled workers within the firm, which includes also the managerial staff and other
professionals; [iii] on-the-job-training; [iv] the general manager’s tenure; and [v] the general
manager’s education.
A number of studies (Keeble et al., 1991; Wood, 1991; Dex and Scheibl, 2001, 2002; Power
and Reid, 2005; etc.) argue that SMEs are more inclined to have flexible organisational
arrangements than are larger firms, because of their limited scope of operations, wellunderstood relationships within the firm, relatively simple organisational structures, ease of
accessing networks of firms, etc. Conversely, Meijaard et al. (2005) argue that organisational
structures within SMEs are much more complex than is argued by transaction costs and
Final_1: September 10, 2008
4
SMEs and Export Performance in South East Europe
agency costs theories. We investigate whether or not organisational flexibility translates into
better export performance by a dummy variable indicating if a firm underwent any
organisational transformation (from minor reallocations to adoption of completely new
organisational arrangements) in the previous three-year period.
Technology-related factors
The New Growth Theory also highlights the role of innovation and technological change in
improving the competitiveness of firms and economies. There are several ways of measuring
or accounting for innovation and technical change. Firstly, following Carlin et al. (2001a),
and relying on the Endogenous Growth Theory, we use investment in capital goods as a proxy
for embodied technological change, and expect it to have a positive impact on the export
performance of the firms under consideration.2 The investment-export relationship may also
be explained by the accelerator model. According to this approach, investment responds
positively to changes in demand conditions, including new export opportunities. This, of
course, raises the empirical complication of potential endogeneity.
Secondly, R&D expenditure can be used as an indicator of the innovation process (an input
measure of innovation) to investigate its effect on the export performance of firms.3 Thirdly,
the introduction of new or upgraded technology and new or upgraded products can be used as
another indicator of the innovation process (another input measure), expected to have
positively affected the firm’s export performance. Finally, a firm’s level of technology
relative to its main rivals may also be used as an indication of technological progress, with
positive impact on export performance.4 The last two indicators reflect process or product
innovation and the level of technological sophistication and are expected to translate into
greater export capabilities. Likewise, firms with higher observed levels of technology are
2
The 2002 data base provides information on the average investment-sales ratio in the previous three years
(measuring investment expenditure on new buildings, machinery and equipment in the previous three years as a
percentage of annual sales over the same period). For the 2005 data base, however, BEEPS does not provide the
investment-sales ratio but only the expenditure on new buildings, machinery and equipment (i.e. investment) in
the previous year (2004). Given that the total sales figure is not published in the 2005 BEEPS, it is not possible
to calculate a comparable investment-sales ratio for the 2005 dataset, and also for the pooled and panel datasets.
Therefore these three specifications are estimated without the investment-sales ratio variable.
3
The 2002 data base includes information on R&D intensity - R&D expenditure in the previous three years as a
percentage of total sales in the previous three year. However, as with the investment intensity, the 2005 data base
provides information on R&D expenditure in the previous year (2004) and not R&D intensity. The 2005
estimation is subject to the same problem as that discussed for investment expenditure.
4
This information, too, is contained in the 2002 survey only.
Final_1: September 10, 2008
5
SMEs and Export Performance in South East Europe
expected to be more export oriented. As in the case of the investment-sales ratio variable, one
may raise concerns about potential endogeneity affecting the latter variable.
Other firm characteristics
The literature largely supports the export proficiency of larger firms relative to smaller firms
on the grounds of resource availability and lower transaction costs (Brock and Evans, 1989;
Kim et al. 1997; Acs et al. 1997; Wakelin, 1998; Bleaney and Wakelin, 2002; etc.). However,
a number of studies support the idea that smaller firms perform better in export markets due to
their inherent flexibility (Mills, 1984; Mills and Schumann, 1985; etc). Accordingly,
ambiguous results may be obtained for firm size variables, which are expressed as dummies
for small and medium sized firms.
The Industrial Economics literature has established the impact of ownership structure on firm
performance. Demsetz (1997, p. 429), for example, argues that wealth and its distribution
among different stakeholders matters to society’s productivity. Furthermore, the transition
economics literature has established the superior performance of foreign owned firms in many
countries. Here, focusing on export activities, we investigate the impact of ownership
structure on the export performance of firms – ownership structure identifying (i) whether the
firm is state owned or privately owned, and (ii) whether a firm is foreign owned or domestic
owned. The percentage share of private capital and the percentage share of foreign capital in a
company are used to measure these two aspects of ownership. There is some evidence
showing that the performance gap between foreign and domestic companies is not solely a
result of foreign ownership, but also of firm-specific assets and firm characteristics such as
size, industry, parent country, etc. (Moosa, 2002; Bellak, 2004).
We draw also upon the so-called Base-Multiplier Hypothesis (Fujita et al. 1999), according to
which manufacturing export is the ‘economic base’ of an economy. In turn, ‘non-base
activities’ are derived from the base and grow and shrink depending on the base’s
performance. From this theory, we hypothesise that production activities (which consist of
manufacturing together with mining and quarrying) should show a greater tendency towards
exporting. To capture these effects we use the percent of company sales generated by
production activities.
Final_1: September 10, 2008
6
SMEs and Export Performance in South East Europe
Institutional Economics has highlighted the impact of formal and informal institutions on
economic performance at both firm and country levels. Two variables reflecting the
development of the institutional framework (both formal institutions and the supporting legal
framework) are used in this study. On the one hand, we investigate the impact of membership in
business associations on SME export performance. For successful export activities, systematic
collection of information is required, since it can act as a catalyst to reduce the uncertainties of
the international environment (Leonidou and Adams-Florou, 1999). In addition, due to their
resource constraints, SMEs appear to be more dependent than large firms on services,
information and contacts generated through associations (Bennett, 1998). On the other hand, we
assess the impact of the level of financial development on SMEs exporting activity. Many
country-level studies have demonstrated that financial development positively affects the
export performance of firms reliant on external funding (Beck, 2001 and 2003; Manova, 2006;
and Becker and Greenberg, 2005) especially in industries where scale factors and sunk costs
play an important role. SMEs have even greater need for credit relative to large firms due to
their limited capital resources. Moreover, SMEs face greater difficulties in obtaining external
finance (due to information asymmetries and/or institutional factors) which may be reflected
in their overall performance including international activities (Beck et al. 2004 and 2006;
Hutchinson and Xavier, 2006). Here, we use the percentage of a firm’s working capital and
fixed investment financed by credit from commercial banks as a measure of the availability of
external finance, and test for the link between external financing and the export behaviour of
SMEs in the SEE region.
Two indicators of the firm’s experience are also included in our models, expected to be
conducive to export activity: the number of years since the firm was established; and the
number of years since the company started exporting. We rely on Learning Theory – rooted in
the behavioural theory of the firm – which argues that development of knowledge may have
an impact on perceptions of opportunities offered by greater internationalisation (Clercq et al.,
2005). Regarding the effect of exporting experience, “learning-by-exporting” suggests a
positive association between current export performance and the firm’s past export
experience. In addition, we allow for a non-linear relationship between business experience
and export growth, hence increasing or decreasing returns to experience. We anticipate a Ushaped relationship between firm’s age and exports due to the pattern of firm survivorship
(Everett and Watson, 1998); namely, that the rate of failure among younger firms is higher
than among experienced ones, due to the greater variability in their cost functions while
Final_1: September 10, 2008
7
SMEs and Export Performance in South East Europe
learning. Everett and Watson concentrate on firms’ experience in the domestic market.
However, this effect may be more pronounced in foreign markets, where cost variability is
likely to be higher to the extent that foreign markets are unfamiliar and entrepreneurs face
lack of information and different systems as well as different languages and cultures. On the
other hand, New Trade Theory has focused on the effect of productivity and sunk costs on the
firm’s decision to enter/exit export markets. Export experience has been used in several
studies to explain patterns of firms’ entry and exit strategies in the presence of sunk costs. In
this literature, an inverse U-shaped relationship has been identified between export experience
and export performance (Roberts and Tybout, 1997; Bernard and Jensen, 2004; and Bernard
and Wagner, 1998).
Two more firm-related aspects that we investigate are the level of capacity utilisation
(facilities and manpower) and market share. First, a greater utilisation of resources is expected
to influence export performance positively as it is an indication of improved efficiency and
competitiveness. On the other hand, greater exports should increase capacity utilisation,
thereby raising the issue of endogeneity. Second, we assume that firms with a greater share of
the domestic market (more than 5 percent) would have the incentive to try to expand their
activity across borders to take advantage of greater demand in foreign markets. Accordingly,
we anticipate that the likelihood of exporting would be higher for firms that have a 5 percent
or larger share of the domestic market.
Finally, we control for potential differences in the exporting behaviour of firms in different
sub-regions of SEE. We identify two sub-regions based on their level of economic and
institutional development: the countries of the Western Balkans (i.e. Albania, Bosnia,
Macedonia, and Serbia and Montenegro); and the countries that are already EU members or
candidate countries (Bulgaria, Croatia, and Romania). Although countries within these two
sub-regions may have had many similarities in terms of social and political environment, core
economic activities and their socialist past, their initial economic conditions and transition
paths have been different and the experience of joining the EU or becoming a candidate
country has tended to increase the development gaps between them.5 In addition, these
countries have had different success with policy and institutional reforms. The development
Although Macedonia has now achieved the status of a ‘candidate country’, this was not the case in the period
under consideration. Were it not for political reasons, Croatia would have joined the EU some time ago.
5
Final_1: September 10, 2008
8
SMEs and Export Performance in South East Europe
path may have led to companies in Bulgaria, Croatia and Romania having different
approaches to exporting compared to their counterparts in the Western Balkans region.
3. EMPIRICAL STRATEGY
3.1 Methodology
As the BEEPS database contains information on exporters and non-exporters, the dependent
variable (y) – percentage share of export sales in total sales – is zero in a significant number
of cases (i.e. for non-exporters), and the observations for exporters are roughly continuous
over the positive range of values. This type of data is addressed by the generalised tobit model
(Wooldridge, 2003, p.565). In our specific case, there is an additional complication as the
dependent variable is a proportion, therefore bounded by zero and one. However, diagnostic
checks endorse the tobit model as a valid estimator for our data.6 The model for cross-section
data has the following form:
y   x i   ε i
yi   i
0
ε i ~ N(0, σ 2 )
for exporters
for non - exporters
And, for panel data:
x   α i  ε it
y *it   it
0
α i ~ N(0, σ ε2 )
for exporters
wher e i  1, 2, ..., N and, t  2002 and 2005
for non - exporters
ε it ~ N(0, σ ε,2 t )
x is a 1k vector containing the k variables of interest discussed in Section 2; and β is the
6
Maddala (1977, 162-63) and Wooldridge (2002, pp.518-19) discuss the use of tobit models to estimate models where
the dependent variable is generated by, in effect, a dual decision making process: in our case, firms’ decisions as to
whether or not to export and, if so, how much to export. The advantage of tobit estimation is that zero observations are
incorporated into the model as the outcome of a decision-making process. Accordingly, zero observations potentially
yield useful information. Moreover, truncation at one is unlikely to affect our estimates in a substantial manner: in our
pooled sample, for example, only 2.45 percent of firms generate 100 percent of their sales from exports (four percent
when the upper limit is set at 95 percent). Nonetheless, we implemented two robustness checks to address residual
concerns on this issue. We replicated our preferred model using our pooled sample: firstly, we implemented tobit
estimation with censoring at both zero and one; secondly, we implemented the generalized linear model recommended
by Baum (2008, p.301) for modelling ‘proportions data in which zeros and ones may appear as well as intermediate
values’. In neither case were the estimates substantially different from those reported below. Finally, we note that in
Tobin’s (1956) original presentation of what came to be known as the tobit model, his dependent variable is a
proportion.
Final_1: September 10, 2008
9
SMEs and Export Performance in South East Europe
corresponding k1 vector of coefficients to be estimated.
Although a 2-year panel sample is not sufficient to identify any dynamics in the data, it is
sufficient to estimate a random effects tobit model, which accounts for unobserved effects that
are constant over time but vary between firms by means of the firm-specific error term i.
We follow Wooldridge (2002, pp. 521-524; and 2003, pp.567-569) who distinguishes
between two types of marginal effects: the ‘conditional’ marginal effects, which account for
changes in the expected (or predicted) value of exports (y) for the subpopulation of firms for
which exporting activity is observed (y>0); and the ’unconditional’ marginal effects that
account, in addition, for the effect of changing values of the independent variables on the
probability that exporting will take place at all (i.e., will change from zero to positive and,
hence, be observed). For dummy variables, both conditional and unconditional marginal
effects are calculated as the discrete change in the expected value of the dependent variable as
the dummy variable changes from zero to one.
3.2 The data 7
The data used in this investigation are a sub-sample of BEEPS, an extensive survey targeting
the business environment and the performance of enterprises in Central and Eastern Europe
(CEE) and the Commonwealth of the Independent States (CIS). The survey has been conducted
three times: 1999 - 4,104 firms (excluding farms) in 25 post-communist countries (Serbia and
Montenegro was not covered in this year); 2002 - 6,667 firms in 27 countries; and 2005 - 9,655
enterprises in CEE, CIS, and Turkey.8 We derive four sets of data from the 2002 and 2005
BEEP surveys to estimate the econometric models presented earlier; namely, two for the
individual years: a pooled dataset; and a panel component of SEE companies surveyed in both
years. The use of these different datasets provides an internal check on the robustness of the
results. Our SME datasets consist respectively of 1,246 observations for 2002; 1,563 for 2005;
2,498 for the pooled data; and, 588 for the panel sample. It encompasses seven SEE countries:
Albania; Bosnia; Bulgaria; Croatia; Macedonia; Romania; and Serbia and Montenegro. The
definition of our variables and their summary statistics are provided in Tables 1 and 2.
7
Only a short description of the content of the BEEPS dataset is provided here as the details can be found on:
http://info.worldbank.org/governance/beeps and http://www.ebrd.com/pubs/econo/beeps.htm (both accessed on
February, 2008). See also the EBRD 2005 Transition Report.
8
We do not present results estimated from the 1999 survey partly for reasons of space and partly because many
variables covered in the 2002 and 2005 surveys were not included in the 1999 survey. The 1999 estimates are
generally consistent with those presented here.
Final_1: September 10, 2008
10
SMEs and Export Performance in South East Europe
Table 1. Description of variables used in the econometric specifications
Dependent variable
expint
Export intensity – the percentage of total sales generated by exportsa
Independent variables
ftwor_edu
Education of the workforce – the share of the workforce with some university or
higher education
training
Dummy for firms who have conducted on-the-job-training
skilled
The percentage of a firm’s current skilled full-time workers (including also the
managerial staff and other professionals) in the total workforce
org_str
Dummy for firms which underwent changes in organisational structures
gross_inv
Spending on new buildings, machinery and equipment since 1998 as a percentage of
the firm’s sales over the same period (for 2002 only)
inv_rd
Spending on research and development (including wages and salaries of R&D
personnel, materials, R&D related education and training costs) since 1998 as a
percentage of the firm’s sales over the same period (for 2002 only)
prli_tech
Dummy for firms who established/upgraded products or introduced new technology
between surveys
med
Dummy for medium sized firms
private
Ownership structure – the percentage share of private capital in the company
foreign
Ownership structure – the percentage share of foreign capital in the company
entact
Type of activity – the share of sales generated by production activities
credit
Access to external finance – the proportion of the firm’s working capital and new
fixed investments financed by external sources
bus_assoc
Dummy for membership in business associations
age
Business experience – years since establishment
exp_age
Export experience – years since starting with exports
cap_util
Current capacity utilisation of facilities/manpower
see4
Dummy for the Western Balkans sub-region (Albania, Bosnia, Macedonia, and
Serbia and Montenegro - distinguished from Bulgaria, Romania and Croatia, which
are already members or candidate countries of the EU)
reinvest_prof For 2002: the percentage (expressed in levels/intervals) of profits reinvested in
2001; for 2005: the percentage of profits in 2003 reinvested in the firm in 2004
The variable is expressed in percentage terms (from 0-100 percent). The question in BEEPS asks: ‘What percentage
of your firm’s sales is exported directly?’
a
The summary statistics in Table 2 show that the average share of export sales in total sales is
almost identical for all years in which the survey was conducted; namely, approximately 10
percent. In all sub-samples small firms comprise almost three quarters of the total, and are
mainly private, domestically owned. They engaged in trade and services, with the share of
production activities in 2002 and 2005 being just over 25 percent. We omit further discussion
of the descriptive statistics for reasons of space.
Final_1: September 10, 2008
11
SMEs and Export Performance in South East Europe
Table 2. Summary statistics for the variables used in the parsimonious specification
Variable
Datasetsa
expint
ftwor_edu
training
skilled
org_str
gross_inv
inv_rd
prli_tech
med
private
foreign
entact
credit
bus_assoc
age
exp_age
cap_util
Final_1: September 10, 2008
Panel
2002
2005
Panel
2002
2005
Panel
2002
2005
Panel
2002
2005
Panel
2002
2005
2002
2002
Panel
2002
2005
Panel
2002
2005
Panel
2002
2005
Panel
2002
2005
Panel
2002
2005
Panel
2002
2005
Panel
2002
2005
Panel
2002
2005
Panel
Panel
2002
2005
Fractions
1
42.95
38.95
40.39
45.08
51.16
35.32
32.07
28.44
31.18
22.88
22.95
23.93
60.13
53.21
54.83
-
0
57.05
61.05
59.61
54.92
48.84
64.68
67.93
71.56
68.82
77.12
77.05
76.07
39.87
46.79
45.17
-
Mean
10.33
9.91
9.89
25.85
26.67
24.60
78.15
78.25
78.06
6.13
1.73
83.57
80.68
91.40
9.23
13.10
8.37
25.56
25.25
27.16
22.70
16.30
24.00
16.24
12.73
15.32
2.44
80.19
81.63
81.26
Std. dev.
Min
Max
%
missing
24.60
23.80
24.13
27.64
29.32
27.40
26.17
26.07
26.62
7.03
5.02
35.99
38.13
26.81
26.58
31.30
25.84
40.75
40.40
42.05
45.17
38.82
45.40
16.83
14.80
16.15
7.90
20.53
19.38
21.01
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
3
4
0
10
15
10
100
100
100
100
100
100
100
100
100
58
70
100
100
100
100
100
100
100
100
100
200
200
200
138
138
180
104
100
100
100
0.33
0.96
0.19
0.49
1.60
1.80
3.76
2.57
9.34
0.51
2.32
1.02
0.33
1.20
0.45
11.80
11.80
0.65
1.12
0.96
0
0
0
0
0
0
1.30
0
0
0.43
0
0
0.98
2.09
2.11
0.14
0
0
0
0
0
22.79
1.36
4.57
1.28
12
SMEs and Export Performance in South East Europe
see4
reinvest_prof
a
Panel
2002
2005
2002
2005
38.56
52.81
51.70
77.12
-
61.44
47.19
48.30
22.88
-
48.18
42.32
0
100
0
0
0
5.54
5.25
Not all information are available for all variables; hence specifications differ (see Table 3).
3.3 HANDLING MISSING DATA
The rate of missing values in the 2002 and 2005 samples is fairly low for most variables (see
Table 2). The highest rate of missing values is for export experience in the panel data (23
percent). Furthermore, in the 2002 dataset the investment to sales ratio and R&D intensity
variables have about 12 percent of values missing. Given the prominence of these variables in
our discussion (Section 2 above), to drop these variables would be to risk omitted variables bias
in our estimates. Neither is it a satisfactory option to drop observations with missing values of
these variables. For this could be justified only on the implausible assumption of values missing
completely at random (MCAR); otherwise, the consequence is again inefficient and biased
coefficient estimates (Schafer and Graham, 2002), arising from differences between the
distribution of the missing observations and the distribution of the observed items. Because it is
not satisfactory to drop either variables or observations, we imputed the missing values (the
percentage of imputed values for each variable corresponds to the percentage of missing values
detailed in Table 2). To this end, we applied multiple imputation (MI) as the technique most
favoured in the statistical literature on analysing survey data with missing values, because it
yields valid estimates from imputed datasets of the standard errors in addition to approximately
unbiased estimates of all parameters.9
Accordingly, in all four datasets used for estimation – i.e., 2002, 2005, pooled, and panel - all
the missing values are imputed, regardless of the number of missing values for individual
variables (see Table 2 above). Consequently, the sample sizes have increased substantially in
relation to the non-imputed samples: the 2002 dataset by 28 percent; the 2005 dataset by 17
percent; the pooled 2002 and 2005 dataset by 16 percent; and the two-year panel
9
We omit further discussion of MI for reasons of space. The theoretical underpinnings of MI were provided by
Rubin (1978 and 1987) and later developed by Van Buuren et al. (1999), Allison (2000), Schafer and Olsen
(1998), Schafer (1999), Schafer and Graham (2002), Kenward and Carpenter (2007), and Harel and Zhou
(2007). For practical implementation of MI, we use the programmes written for STATA (see Royston, 2005a,
2005b, 2007; and, Carlin et al., 2008). The syntax written to implement MI for this paper is available on request.
Final_1: September 10, 2008
13
SMEs and Export Performance in South East Europe
(longitudinal) dataset by 41 percent.10 This large increase of the size of the dataset is reflected
in the results. Although none of the coefficients change direction, in some cases they differ in
terms of statistical significance and even in their magnitude. The differences in the results
point to bias caused by a relatively large proportion of missing observations in the dataset;
hence, to the importance of imputation techniques to address this problem.
4. RESULTS AND DISCUSSION
The outcomes are in the majority of cases significant and consistent across specifications and
samples, and the magnitude and direction of coefficients are mostly as anticipated. The
marginal effects and their respective p-values are presented in Table 3.11 We comment in
detail only on the unconditional marginal effects, because these refer to the whole population
of firms (i.e. both those that may begin exporting and those that may export more), and are
therefore the effects most relevant for policy discussion. For reasons of space, we discuss
mainly those estimates that appear to be robust; namely, those variables whose importance is
supported by coefficients with a consistent sign and at least two of which are statistically
significant, except in the case of investment-sales ratio and R&D intensity for the 2002
dataset. The latter exceptions reflect the importance of these variables in our theoretical
discussion.
Not all the variables discussed in Section 2 have been included in the model. For 2002 we have
reduced the full model to a more parsimonious one by dropping two variables related to the CEO
impact on firm’s export performance (i.e. level of education and tenure) as well as the market
share variable. The primary reasons for dropping these variables are that they are highly
insignificant and that they do not appear in the 2005 survey. The joint statistical insignificance of
the deleted variables is confirmed by a Wald test. In addition, the estimated coefficients on the
other variables are robust to testing down; dropping these variables did not make any difference to
the results estimated from the 2002 sample, either in terms of size or direction of effect. The latter
fact is an indication of the robustness of the results, an issue to be discussed in Section 5.
10
Rubin (1987, p. 2) suggests m repeated imputations in a range of 2 to 10. However, recent research shows that
in some cases a larger m is required for reliable estimation and inference (Kenward and Carpenter, 2007, p. 208),
especially in cases when the proportion of missing data is high. Because the percent of missing data for some of
our variables is relatively large, we apply m=100.
11
Different specifications are reported, because the three rounds of the BEEP Surveys did not contain the same
set of questions.
Final_1: September 10, 2008
14
SMEs and Export Performance in South East Europe
The number of specification tests one can perform after multiple imputation is currently rather
limited (indeed, imputation techniques in general, and multiple imputation specifically, is a
developing field). Fortunately, to assess the validity of tobit estimation there is the diagnostic
check suggested by Greene (2003, p. 768) and Wooldridge (2002, p. 534). As this check
requires, we find that the probit and tobit coefficient estimates are consistent after appropriate
transformations.12 This diagnostic check suggests that the tobit model provides consistent and
unbiased estimates for the sample of seven SEE countries. The discussion of these estimates
follows.
Human capital related factors
The human capital measures affect positively firms’ propensity to export. The higher
education indicator (ftwor_edu) shows that the greater the percentage of employees with
higher education the higher the expected percentage share of exports in firm’s sales. The
unconditional marginal effect for the pooled sample indicates, ceteris paribus, that a one
percentage increase in the pool of employees with higher education will increase the
percentage share of exports in a firm’s turnover, on average, by 0.058 percent. The literature
provides abundant evidence on the relationship between education (measured by mean years
of schooling) and export performance (for instance Carlin et al., 2001a). Generally, the
relationship is explained through the contribution of education to quality-adjusted
productivity. This finding for human capital is consistent with the results suggesting that
export competitiveness is associated with continuous training (training). Keeping other
variables constant, the unconditional marginal effects suggest that SMEs providing on-the-job
training generate, on average, up to three percent more of their total sales from exports than do
SMEs that do not provide formal training.
Changes in the organisational structure (org_str) do not have a consistently significant effect
on export performance. Yet the consistent sign and two estimated coefficients displaying
significance at, respectively, just below and just above the 10 percent level are consistent with
emphasis on the effective organisation of labour as the key to firm performance (Meijaard et
al., 2005).
12
These results are not reported, but are available on request. Henceforth, the same form of words may be
applied to all empirical results referred to but not reported in detail.
Final_1: September 10, 2008
15
SMEs and Export Performance in South East Europe
Table 3. Unconditional marginal effects after tobit regression for the determinants of SME
export performance in SEE countries
Dependent variable: Export intensity – the
percentage of total sales generated by exports
Panel
samplea
Pooled
sample
2002
sample
2005
sample
% of workforce with higher education
(ftwor_edu)
0.023
(0.190)
0.058***
(0.000)
0.045**
(0.013)
0.071***
(0.000)
Dummy: 1-if formal training provided
(training)
0.714
(0.459)
2.546***
(0.002)
3.350***
(0.003)
1.673*
(0.102)
Highly skilled workers as a % of total
(skilled)
-0.007
(0.683)
-0.012
(0.377)
-0.008
(0.686)
-0.011
(0.536)
Dummy: 1-changes in the organisational
structure in previous 3 years (org_str)
0.251
(0.763)
1.233*
(0.098)
1.617
(0.130)
0.934
(0.338)
Investment-sales ratio (gross_inv)
-
-
-0.040
(0.614)
-
R&D intensity (inv_rd)
-
-
0.082
(0.416)
-
0.140
1.615**
2.111*
0.807
(0.879)
(0.052)
(0.083)
(0.435)
4.315**
(0.022)
5.756***
(0.000)
7.560***
(0.000)
4.859***
(0.001)
% of private capital in the firm (private)
-0.030**
(0.039)
-0.00004
(0.997)
-0.006
(0.651)
0.010
(0.585)
% of foreign capital in the firm (foreign)
0.059***
(0.000)
0.068***
(0.000)
0.080***
(0.000)
0.060***
(0.000)
% of sales generated in production (entact)
0.002
(0.863)
0.070***
(0.000)
0.056***
(0.000)
0.078***
(0.000)
% of firm’s working capital and new fixed
inv. financed by external sources (credit)
0.011
(0.224)
0.011
(0.165)
0.012
(0.345)
0.017*
(0.075)
1.860**
(0.043)
5.502***
(0.000)
4.628***
(0.000)
5.811***
(0.000)
-0.353***
(0.001)
0.037
(0.499)
-0.093
(0.252)
0.121*
(0.095)
0.003**
(0.013)
-0.0003
(0.629)
0.001
(0.139)
-0.001
(0.179)
HUMAN RELATED FACTORS
TECHNOLOGY RELATED FACTORS
Dummy: 1-firm established or upgraded
prod. line or intro. new tech. in previous 3
years (prli_tech)
CONTROL VARIABLES
Dummy: 1-medium firms (med)
Dummy: 1- if member of a business
association (bus_assoc)
Year since establishment (age)
age squared (agesq)
Final_1: September 10, 2008
16
SMEs and Export Performance in South East Europe
Years since it started exporting (exp_age)b
1.248***
(0.000)
-
-
-
-0.011***
(0.000)
-
-
-
Capacity utilisation of facilities/man
power expressed in % (cap_util)
-0.011
(0.614)
-0.012
(0.516)
-0.013
(0.621)
0.014
(0.503)
Western Balkan region (see4)
-0.433
(0.694)
1.426**
(0.045)
0.678
(0.507)
1.895**
(0.038)
588
436
152
0
2498
1855
643
0
1246
922
324
0
1563
1156
407
0
exp_age squared (exp_agesq)
Number of observations
Left censored observations
Uncensored observations
Right censored observations
Note: p-values in brackets. Levels of statistical significance are denoted as follows: *** – 1 percent; ** – 5 percent;
and * – 10 percent
a
There are no observations for Bosnia in the panel estimates.
b
Although data on export experience were available for 2005, we had to drop them from the regressions, as they were
predicting perfectly the zero outcomes (i.e. non-exporters) in estimations. A 2-year panel enabled greater variation in
the data, hence we have been able to include the export experience variable in the panel estimations.
Firm technology
The 2002 results show that the effects of the investment-sales ratio (gross_inv) and R&D
intensity (inv_rd) on export performance are insignificant. Yet, the influence of technical
progress on export performance is suggested by the dummy variable for whether or not the
firm has “established new, upgraded a product line or introduced a new technology” in the
recent past (prli_tech). The estimated coefficients are consistently positive and they are
significant in both the 2002 and pooled samples. The size of the estimated coefficients
suggests that firms reporting recent technical progress export around two percent more of
their output than firms that do not. As we discussed above, the estimated relationship between
investment and export performance is potentially flawed by endogeneity, caused by reverse
causation. However, in the case of the present variable of interest, the way in which it is
defined makes endogeneity less likely. From new growth theory, we hypothesise that past
technical progress may influence current export intensity. However, we have no such reasons
for hypothesising that current export intensity could affect past technical progress. In the 2002
survey, the question put to firms was: “Has your company undertaken any of the following
initiatives since 1998?”. And in 2005: “…over the last 36 months?” In both cases, the
activities captured by these questions substantially lag current export performance, our
dependent variable.
Final_1: September 10, 2008
17
SMEs and Export Performance in South East Europe
Although implausible, given the way in which our variable of interest is defined, we
investigate the matter further to clear up any doubts with respect to the exogeneity of our
technology variable of interest (prli_tech). This investigation is reported in Appendix 1 and
confirms our contention that the particular definition of our technology variable is such as to
minimise the impact of potential endogeneity.
Control variables
Firm size (med) is positively related to performance in export markets. Compared to small
firms, medium firms generate a larger share of their sales in export markets. Across the subsamples the unconditional marginal effects range from just over four percent additional sales
generated from export activities by medium companies in 2005 to over seven percent in 2002.
This is an indication that when small firms reach a certain size they may commit more fully to
exporting.
The share of private capital (private) seems to have a statistically insignificant effect on
export intensity (except in the panel estimates where the impact is negative and significant).
Although it was expected that the private capital share would have a positive effect on firm
performance, the empirical evidence is relatively inconclusive on this matter. Indeed, this is
consistent with some of the earlier studies, which also failed to observe the positive
relationship of private ownership on firms’ behaviour.13 On the other hand, the unconditional
marginal effects for the panel data indicate that a one percent increase in the foreign
ownership (foreign) of the SMEs increases the percentage of export sales in total sales by
0.059 percent. This means that, for instance, a 10 percent increase in the foreign stake of the
SMEs increases the percentage of sales generated by a little more half a percent. The effect is
quite consistent across the various datasets.
Sector of activity (entact) reveals that companies involved in production export a greater share
of their turnover relative to trade and service companies. The pooled unconditional marginal
effects indicate, ceteris paribus, that a one percent increase in sales generated by production
activities will increase, on average, the percentage share of sales generated by export activities
by 0.070 percent.
13
See for example Bevan, et al. (1999) who argue that the absence of a significant positive relationship between
private ownership and performance in the early transition period (except for de novo firms) is due to significant
inside ownership, a poor corporate governance framework, and the absence of an effective and functioning
capital market.
Final_1: September 10, 2008
18
SMEs and Export Performance in South East Europe
Being a member of a business association (bus_assoc) in the SEE region appears to be of
great importance in SME export performance. The panel unconditional marginal effects
suggest that members of business associations typically generate two percent more of their
total sales from exports than do non-members. In other samples the effect is even greater,
suggesting an advantage of five percent or more.
In general, the results for experience indicators are in line with predictions from theory. The
firm’s general experience (age and agesq) has a significant effect on export performance only in
the panel estimates and, partially, in the 2005 specification. In the latter case, only the outcome
for age is significant, and that at the 10 percent level, and the direction of the effect is reversed
relative to that in the panel estimates. The panel estimates on business experience are in line
with the firm survivability argument: namely, that there is a lower probability of firm’s survival
at the early stages of their life cycle; yet at some later stage business experience becomes
conducive to the companies’ activities, including exporting. Regarding export experience, the
association with firms’ export intensity reveals an inverted U-shaped relationship. Each
additional year of experience in foreign markets (expage) increases the export intensity of
exporting firms. Once an export market has been entered, then the sunk costs of entry have been
acquired (e.g. costs of identifying potential target markets, establishing distribution networks,
adapting products and services to foreign requirements and tastes, etc.). However, the longer the
firm is in an export market (i.e., the more ‘experience’ is gained) the easier it becomes to
increase exports to that existing market. This is a ‘learning-by-exporting’ effect, whereby the
longer the experience in an export market, the greater the knowledge acquired and the better the
firm becomes at exporting to that market. Moreover, experience and the resulting export ‘knowhow’ might even reduce the sunk costs of entering new markets. In sum, the greater the firm’s
export experience the greater its export ‘know-how’ and the lower the sunk costs of increasing
export activity in both existing and new markets. However, the quadratic effect (expagesq)
between export experience and export performance means that after a certain point this
advantage declines steadily.
5. ROBUSNESS OF THE RESULTS
There are a number of ways in which we check the robustness of our econometric results.
First, we assess the robustness of results as we move from the full to a more parsimonious
Final_1: September 10, 2008
19
SMEs and Export Performance in South East Europe
model; secondly, we apply a number of different specifications to different datasets; and,
thirdly, we apply model diagnostic tests and other specification checks.
In the first case – applying both the full and a more parsimonious model to the 2002 dataset –
we find that the results are thoroughly consistent in terms of direction of the effect. Indeed,
there are no noteworthy changes in magnitude. Moreover, no variable moves from statistical
significance to insignificance, or vice versa, at conventionally accepted levels, although there
are some slight gains in the significance of coefficients (especially for the variables ftwor_edu
and prli_tech).
In our analysis we utilise a range of datasets and apply different specifications (reflecting
nonconformities between the 2002 and 2005 BEEP surveys). Nonetheless, the results are
overwhelmingly consistent in terms of the direction of the estimated effects. Moreover, most
of the coefficients are consistent across different specifications in terms of statistical
significance. There are slight differences in the magnitude of the coefficients, albeit not worth
dwelling upon as they do not imply any change in our policy conclusions. Regarding the
diagnostics and other statistical checks, the diagnostic check suggested by Greene (2003,
p.768) and Wooldridge (2002, p. 534) suggests that the tobit estimates are consistent and
unbiased for the whole range of specifications that we apply here. In addition, the joint
significance of the coefficients is established by the model Wald tests.
6. CONCLUSIONS
This paper investigates the determinants of export performance of SMEs in seven SEE
countries, using the World Bank/EBRD Business Environment and Enterprise Performance
Survey (BEEPS) carried out in 2002 and 2005. We were concerned, in particular, with the
impact on export performance of human capital and technology related factors. In addition, we
investigated the effects on export performance of firm-size, type of sector, ownership structure,
financial constraints, membership in business associations, experience-related factors, and
location in SEE sub-regions. Tobit models were estimated to analyse the relationship between
firms’ export performance (measured by the share of total sales generated by exports) and
these potential influences. This econometric approach enabled us to differentiate between
exporters and non-exporters, while including both in our investigation. Hence, we have
analysed firms’ export behaviour by taking into account not only the level of export activity
but also the likelihood that firms will export at all.
Final_1: September 10, 2008
20
SMEs and Export Performance in South East Europe
Well-established theories explaining the behaviour of SMEs in the economy are scarce,
especially the literature linking SMEs with international trade. As a result, we adopted an
eclectic approach to this investigation. Table 4 shows the strands of economic theory from
which our research questions were derived together with the outcomes for each econometric
model estimated for SEE. In addition, the final column summarises the effects of MI, by
providing information on the imputed results as well as highlighting differences from the nonimputed estimates.
A number of checks indicate that the results are overwhelmingly robust. In addition, we also
highlight the importance of using MI in SME and other firm-level based research. Missing data
are endemic to any survey analysis, hence the implementation of this technique for imputing
missing values is important for obtaining valid estimates and inference. In particular, the 25.5
percent average increase in the size of our datasets made a substantial difference to the precision
of the estimates (Table 4). Where non-imputed estimates were more precise, this is likely to be
due to sample bias induced by non-random “missingness”. In general, non-imputed survey
datasets are likely to have missing values that are not random. In this case, econometric
estimates will be biased, hence invalid. This suggests that applied economic research will
benefit from a more routine application of new data imputation techniques.
The independent variables examined in the model for the SEE countries were, in the majority of
cases, significant and consistent across specifications and samples, and the signs of the
estimated coefficients are overwhelmingly as anticipated. Although the analysis is unable to
explore any dynamic relationships, these results take the empirical analysis as far as is permitted
by the available data, which is restricted to cross-section samples and a two-year panel.
Nonetheless, our results do indicate the importance of various factors influencing firms export
performance. Under the ceteris paribus condition, the tobit estimates show that human capital
more than technology-related factors seems to be an important source of international
competitiveness for SEE companies. Nonetheless, although companies with a larger share of
educated and skilled workers export more, investments in introducing or upgrading products
and technologies also promote exports. The size variable indicates that the bigger the size of the
firm the larger the share of sales generated in export markets. In this context, the results indicate
Final_1: September 10, 2008
21
SMEs and Export Performance in South East Europe
Table 4. A summary of outcomes and motivations for SME export performance in SEE
Dependent variable: Export intensity – percentage of total sales generated by export sales
Motivation/Theory
Expected
sign
Human capital resources
(education; on-the-jobtraining; skilled employees;
etc.)
New Growth Theory
+
Coefficient on the education and on-the- Similar outcomes as after MI, however
job-training variables positive and
workforce education is significant also in
highly statistically significant in the
the panel estimates.
2002, 2005 and pooled dataset. Changes
in organisation structure positive and
significant in the pooled dataset.
Technology-related factors
(invest.-sales ratio; R&D
intensity; introduced new or
upgraded products or new
technology etc.)
New Growth Theory
+
Investment-sales ratio and R&D
intensity highly insignificant.
Introduction or replacement of products
and old technologies positive and
significant in the 2002 and pooled
datasets.
Similar as after MI; however the dummy
for companies that introduced new or
established products or new technology
is insignificant for the pooled dataset.
Firm size
Factor endowment
and transaction-cost
approach
+/-
Positive and highly significant
throughout.
Similar to the results after MI.
Private or state ownership
Industrial economics
+/-
Overwhelmingly statistically
insignificant, except for the negative
coefficient in the panel estimates.
In all cases highly statistically
insignificant.
Foreign capital share
International
economics
+
Highly significant and positive
throughout.
Insignificant in the panel estimates.
Otherwise similar to those after MI.
Type of activity
Base-multiplier
approach
+
Highly significant and positive, except
in the panel estimates.
Highly significant and positive for all
datasets.
Variables
Final_1: September 10, 2008
Outcomes (imputed)
22
Outcomes (non-imputed)
SMEs and Export Performance in South East Europe
Access to external finance
Financial economics
+
Positive but insignificant except for
2005, which is moderately significant.
Insignificant just for 2002. In other cases
positive and significant.
Membership in bus.
associations
Institutional
economics
+
Highly significant and positive
throughout.
Insignificant in the panel estimates. The
rest are similar to those after MI.
Business experience
Industrial economics
Quadratic
relationship
Mainly insignificant. U-shape
relationship identified for panel data.
Similar to the results after MI.
Export experience
International
economics
Quadratic
relationship
Inverse U-shape relationship identified
for panel data.
Similar to the results after MI.
Capital utilisation
Industrial economics
Highly statistically insignificant
throughout.
Similar to the results after MI.
Final_1: September 10, 2008
+
23
SMEs and Export Performance in South East Europe
that within the group of SMEs the medium sized firms are those who are more intensive in
exporting activities relative to small firms, which, as hypothesised in theory, mainly operate in
the domestic market. Companies with a foreign capital share have better prospects for exports;
the same applies to those companies involved in production activities, which are more active
than non-production firms in foreign markets. Moreover, experience in foreign markets plays an
important role in successful export engagement, albeit with diminishing returns. Regarding
export experience, the results are consistent with theories suggesting that patterns of firms’
entry and exit strategies in foreign markets are affected by the presence of sunk costs. Namely,
the greater the export experience the greater the export ‘know how’ and, consequently, the
lower the sunk costs of additional export activity, hence the lower the barrier to entry. The
results show that the availability of external finance has no statistically significant effect on
exporting by SMEs in SEE. Finally, membership in a business association is positively
associated with export activity.
The results have demonstrated that most of the hypothesised effects are significant and have
non-negligible effects. However, taken individually, the magnitude of most of the effects is
rather small. This suggests that there is no single policy that will, on its own, transform the
export activity of SMEs, or indeed the entire enterprise sector in SEE. Rather, a whole range
of well designed and consistently implemented policies will be required to boost the growth
of SME exporting and, indeed, to promote the sector more generally.
Final_1: September 10, 2008
24
SMEs and Export Performance in South East Europe
Appendix 1: Investigation of the potential endogeneity of the technology variable of interest
Within BEEPS only one variable satisfied the testable requirements of a valid instrument for
the potentially endogenous technology variable; namely, reinvested profit (reinvest_prof).
The major problem with testing for endogeneity is the choice of instrument. First, this
variable does not prove significant at any conventionally acceptable level of significance
when added to the models either for the 2002 data (t-statistic=0.92) or for the 2005 data (tstatistic=0.64). Second, Staiger and Stock (1997) demonstrated that instrumental variables
estimation and inference may be invalid if conducted with ‘weak instruments’. Accordingly,
for both the 2002 and the 2005 specifications we regress the potentially endogenous variable
(i.e. prli_tech) on the exogenous variables plus the instrument. In this regression, the tstatistic (robust to heteroskedasticity) measures the level of significance of the partial
correlation of the instrument with the potentially endogenous variable (Wooldridge, 2002, pp.
83-86, 90-92 and 104-105). Buono and Coviello (2004, p.4) suggest that to reject the null of a
weak instrument this t-statistic ‘as a rule of thumb must be greater than 3.5’. In the 2002 data,
the robust t-statistic on the coefficient for the instrument is 3.94 and in the 2005 data it is
3.25. Although the t-statistic in the letter case does not exceed the benchmark one can safely
argue that the null of a weak instrument can be rejected even for 2005 as it approaches very
closely to 3.5. Although discussion of weak instruments is still developing and, to our
knowledge, the focus of attention has been on linear models rather than on the models
employed here, there is no reason to think that our investigation of the potential endogeneity
of the prli_tech has been unduly impaired by weakness of the instrument.
In addition, we gain confidence in our instrument not only from applying current statistical
practice but also from economic interpretation. We find strong correlation between reinvested
profit and the variable that identifies companies which have established new, upgraded a
product line or introduced a new technology. In the presence of a perfect capital market, one
would not expect such a strong correlation, because companies would be able to borrow (or
issue equity) to finance their investment needs. Yet, in the presence of imperfect capital
markets – indeed, of highly imperfect capital markets as expected in SEE – plagued by
incomplete and asymmetric information (which lead to problems of adverse selection and
moral hazard), one would expect investment to be constrained by ‘own finance’ (including, in
particular, retained profit/earnings). This means that firms’ investment decisions in SEE
depend heavily on their own financial sources. Theoretical underpinnings of this discussion
Final_1: September 10, 2008
25
SMEs and Export Performance in South East Europe
(i.e. profitability as the determinant of investment) rely on the premise that investment
decisions of firms constrained with respect to external finance are dependent on their
generated profit. Pugh (1998, p.98) explains that ‘to the extent that finance is a binding
constraint relaxed by increased profits, we should expect a strong positive relationship
between changes in profitability and changes in investment’. A relevant finding for our
discussion can be found in Budina et al. (2000) who find that the investments of relatively
large firms was not liquidity constrained, but that the opposite holds for smaller firms.
We used our instrument in two related ways: to implement a standard test for the null of no
endogeneity; and to implement instrumental variables estimation of our model. First, the
testing procedure could not confirm that an endogenous relationship exists within either the
2002 or the 2005 models; both the Smith-Blundell test of exogeneity and the Wald test of
exogeneity show that the models are appropriately specified with the investment variable as
exogenous. Second, instrumental variables estimation confirmed this result and was thus able
to add no further information.14 Of course, this investigation of potential endogenity does not
establish in general that technical progress is exogeneous with respect to export performance.
However, these findings are consistent with the contention that the particular definition of our
technology variable is such as to minimise the impact of potential endogeneity.
The results - obtained from Stata’s ivtobit estimator - are not valid because, in the absence of demonstrable
endogeneity, instrumental variables estimation is not appropriate. With this caveat, the coefficients from both the
2002 and the 2005 samples were positive, much larger and, as expected, less precisely estimated than those
reported in Table 3 above.
14
Final_1: September 10, 2008
26
SMEs and Export Performance in South East Europe
References
Acs, Z. J., Morck, R., Shaver, J. M. and Yeung, B. (1997), The Internationalisation of Small and Medium –
Sized Enterprises: A Policy Perspective, Small Business Economics 9: 7 – 20.
Allison, P. (2000), Multiple Imputation for Missing Data: A Cautionary Tale, Sociological Methods and
Research 28: 301-309.
Bartlett, W. and Prasinkar, J. (1995). Small Firms and Economic Transformation in Slovenia, Communist
Economies and Economic Transformation 7 (1): 81-101.
Baum, C. (2008). Stata tip 63: Modelling proportions, The Stata Journal, 8 (2): 299–303.
Becchetti, L. and Rossi, P. S. R (2000), The Positive Effects of Industrial District on the Export Performance of
Italian Firms, Review of Industrial Organisation 16: 53 – 68.
Beck, T. (2001) Financial Development and International Trade: Is There a Link? World Bank Policy Research
Working Paper No. 2608.
Beck, T. (2003), Financial Dependence and International Trade, Review of International Economics 11 (2): 296-316.
Beck, T., Demirgüç-Kunt, A. and Maksimovic, V. (2004), Financing Patterns around the World: Are Small
Firms Different?, World Bank Working Paper. Available at: http://www.worldbank.org/research/projects/sme/
Financing%20Patterns091604.pdf
Beck, T., Demirgüç-Kunt, A., Laeven, L. and Maksimovic, V. (2006), The Determinants of Financing Obstacles,
Journal of International Money & Finance 25 (6): 932-952.
Becker, B. and Greenberg, D. (2005), Financial Development and International Trade, University of Illinois at
Urbana-Champaign, mimeo.
Bellak, C. (2004), How Domestic and Foreign Firms Differ and why does it Matter?, Journal of Economic
Surveys 18 (4): 483-514.
Bennett, R. J. (1998), Business Associations and their Potential Contribution to the Competitiveness of SMEs,
Entrepreneurship & Regional Development 10: 243-260.
Bernard, B. A. and Wagner, J. (1998), Export Entry and Exit by German Firms, NBER Working Paper 6538
Bernard, B. A. and Jensen, J. B. (2004), Why Some Firms Export, The Review of Economics and Statistics 86
(2): 561-569
Bevan, A.A., Estrin, S. and Schaffer, M.E. (1999), Determinants of Enterprise Performance during Transition,
Centre for Economic Reform and Transformation (CERT) Discussion Paper No. 99/03 (January).
Bleaney, M. and Wakelin, K. (2002), Efficiency, Innovation, and Exports, Oxford Bulletin of Economics and
Statistics 64 (3): 3-15.
Bonaccorsi, A. (1992), On the relationship between firm size and export intensity, Journal of International
Business Studies, 23 (4): 605-635.
Brock, W. A. and Evans, D. E. (1989), Small Business Economics, Small Business Economics 1: 7 – 20.
Bryan, J. (2006), Training and Performance in Small Firms, International Small Business Journal 24(6): 635660.
Budina, N., Garretsen, H, and de Jong, E. (2000), Liquidity Constraints and Investment in Transition Economies:
The Case of Bulgaria, Economics of Transition 8(2): 453-475
Buono I. and Coviello D. (2004) Instrumental Variables Regressions in Estimating the Effects of Institutions on
Economic Development, mimeo. Available on request from ines.buono@upf.edu; or decio.coviello@upf.edu.
Calof, J. L (1993), The Impact of Size on Internationalization, Journal of Small Business Management, 31 (4): 6069.
Final_1: September 10, 2008
27
SMEs and Export Performance in South East Europe
Carlin, J. B., Galati, J. C. and Royston, P. (2008), A new framework for managing and analyzing multiply
imputed data in Stata, The Stata Journal, 8 (1): 49–67.
Carlin, W., Glyn, A. and Van Reenen, J. (2001a), Export Market Performance of OECD Countries: An
Empirical Examination of the Role of Cost Competitiveness, The Economic Journal 111: 128-162.
Carlin, W., Fries, S., Schaffer, M. And Seabright, P. (2001b), Competition and enterprise performance in
transition economies: evidence from a cross-country surey, EBRD Working Paper No. 63, June.
Chevalier, A., Harmon, C., Walker, I. and Zhu, Y. (2004), Does Education Raise Productivity, or Just Reflect it?,
The Economic Journal 114: 499-517.
Clercq, D. D., Sapienza, H. J. and Crijns, H. (2005), The Internationalisation of Small and Medium-Sized Firms,
Small Business Economics 24: 409-419.
Demsetz, H. (1997), The Firm in Economic Theory: A Quiet Revolution, American Economic Review 87 (2): 426 –
429.
Dex, S. and Scheibl, F. (2001), Flexible and Family-Friendly Working Arrangements in UK-Based SMEs:
Business Cases, British Journal of Industrial Relations 39 (3): 411 – 431.
Dex, S. and Scheibl, F. (2002), SME and Flexible Working Arrangements, Bristol: The Policy Press.
EBRD (2005), Transition Report: Business in Transition, European Bank for Reconstruction and Development:
London.
Estrin, S., Meyer, K, E., and Bytchkova, M. (2006), Entrepreneurship in Transition Economies, in: Mark Casson
et al., eds.: Oxford Handbook of Entrepreneurship, Oxford: Oxford University Press.
Everett, J. and Watson, J. (1998) Small Business Failure and External Risk Factors, Small Business Economics,
11 (4): 371-390.
Fujita, M., Krugman, P. R. and Venables, A. J. (1999), The Spatial Economy: Cities, Regions, and International
Trade, Cambridge: The MIT Press.
Futo, P., Hoggett, P., and Kallay, L. (1997), “Small Firms and Economic Transformation in Hungary”, The
European Journal of Social Sciences, Vol. 10, No. 2, pp. 171-184.
Gorodnichenko, Y., Svejnar, J. and Terrell, K. (2008), Globalization and Innovation in Emerging Markets, IZA
Discussion Paper No. 3299 (January)
Greene, W. (2003), Econometric Analysis. 5th edition. New Jersey: Prentice Hall.
Grossman, G. M. and Helpman, E. (1994) Endogenous Innovation in the Theory of Growth, Journal of
Economic Perspectives 8 (1): 23 – 44.
Harel, O. and Zhou, X. H. (2005), Multiple Imputation – Review of Theory, Implementation and Software,
Statistics in Medicine 00: 1-22.
Hoshi, I., Balcerowicz, E. and Balcerowicz, L. (2003), Barriers to Entry in Early Transition, New York: Kluwer
Publishers.
Hutchinson, J. and Xavier, A. (2006), Comparing the Impact of Credit Constraints on the Growth of SMEs in a
Transition Country with an Established Economy, Small Business Economics 27: 169 – 179.
Iakovleva, T. (2005), “Entrepreneurial Orientation of Russian SMEs”, in Vinig, T. and Van Der Voort, R. (Ed.)
The Emergency of Entrepreneurial Economics, Amsterdam: Elsevier.
Keeble, D., Bryson, J. and Wood, P.(1991), Entrepreneurship and Flexibility in Business Services: The Rise of
Small Management Consultancy and Market Research Firms in the UK, Small Business Research Centre,
University of Cambridge Working Paper No. 13.
Kenward, M. G. and Carpenter, J. (2007), Multiple Imputation: Current Perspectives, Statistical Methods in
Medical Research 16: 199 - 218
Kim, L., Nugent, J. B. and Yhee, S. (1997), Transaction Costs and Export Channels of Small and Medium –
Sized Enterprises: The Case of Korea, Contemporary Economic Policy 15: 104 – 120.
Final_1: September 10, 2008
28
SMEs and Export Performance in South East Europe
Kumar, N. and Siddharthan, N. S. (1994), Technology, Firm Size and Export Behaviour in Developing
Countries: The Case of Indian Enterprises, The Journal of Development Studies 31 (2): 289-309.
Leonidou, L. C. and Adams-Florou, A. S. (1999), Types and Sources of Export Information: Insights from Small
Businesses, International Small Business Journal 17(3): 30-48.
Maddala, G. (1977). Econometrics. London: McGraw-Hill.
Manova, K. B. (2006), Credit Constraints in Trade: Financial Development and Export Composition. Available
at SSRN: http://ssrn.com/abstract=805027.
McMillan, J. and Woodruff, C. (2002), “The Central Role of Entrepreneurs in Transition Economies”, Journal of
Economic Perspectives, Vol. 16, No. 3, pp. 153-170.
Meijaard, J., Brand, M. J. and Mosselman, M. (2005), Organisational Structure and Performance in Dutch Small
Firms, Small Business Economics 25: 83-96.
Mills D. E. (1984), Demand Fluctuations and Endogenous Firm Flexibility, The Journal of Industrial Economics
XXXIII: 55 – 71.
Mills, D. E. and Schumann, L. (1985), Industry Structure with Fluctuating Demand, The American Economic
Review 75 (4): 758 – 767.
Moosa, I. (2002), Foreign Direct Investment: Theory, Evidence and Practice, New York: Palgrave
Power, B. and Reid, G. C. (2005), Flexibility, Firm-Specific Turbulence and the Performance of the Long-lived
Small Firm, Review of Industrial Organisation 26(4): 415-443.
Pugh, G. (1998), The Profit Elasticity of Investment in West Germany and the Investment Diversion Effects of
Unification, Applied Economics Letters 5: 97-99.
Roberts, M. J. and Tybout, J. R. (1997), The Decision to Export to Columbia: An Empirical Model of Entry with
Sunk Costs, American Economic Review 87 (4): 545-564.
Royston, P. (2005a), Multiple Imputation of Missing Values: Update, The Stata Journal 5(2): 1-14.
Royston, P. (2005b), Multiple Imputation of Missing Values: Update of ice, The Stata Journal 5(4): 527-536.
Royston, P. (2007), Multiple Imputation of Missing Values: Further Update of ice, with an Emphasis on Interval
Censoring, The Stata Journal, 7 (4): 445–464
Rubin, D. B. (1978), Multiple Imputations in Sample Surveys – A Phenomenological Bayesian Approach to
Nonresponse. Proceedings of the Survey Research Methods Section, American Statistical Association 20-28.
Available at: http://www.amstat.org/Sections/Srms/Proceedings.
Rubin, D. B. (1987), Multiple Imputation for Nonresponse in Surveys, New York: Willey.
Scase, R. (1998), The Role of Small Businesses in the Economic Transformation of Eastern Europe: Real But
Relatively Unimportant?, International Small Business Journal, Vol. 16, No 1, pp 13-21.
Schafer J. L. (1999), Multiple Imputation: A Primer, Statistical Methods in Medical Research 8: 3-15.
Schafer J. L. and Graham J. W. (2002), Missing data: Our View of the State of the Art, Psychological Methods
7(2): 147-177.
Schafer J. L. and Olsen, M. K. (1998), Multiple Imputation for Multivariate Missing-Data Problems: A Data
Analyst's Perspective, Multivariate Behavioral Research 33: 545-571.
Staiger, D. and Stock, J. H. (1997), Instrumental Variables Regression with Weak Instruments, Econometrica,
65: 557-586.
Sterlacchini, A. (1999), Do Innovative Activities Matter to Small Firms in Non-R&D-Intensive Industries? An
Application to Export Performance?, Research Policy 28: 819-832.
Svejnar, J. and Commander, S. J. (2007), Do Institutions, Ownership and Competition Explain Firm
Performance? Evidence from 26 Transition Countries, IZA Discussion Paper No. 2637 (February)
Final_1: September 10, 2008
29
SMEs and Export Performance in South East Europe
Tobin, J (1956). Estimation of Relationships for Limited Dependent Variables. Cowles Foundation Discussion
Paper 3R (July).
Vagliasindi, M. (2001), Competition across transition economies: an enteprise-level analysis of the main policy
and structural determinants, EBRD Working Paper No. 68, December.
Vagliasindi, M. (2006), Does Competition Policy Implementation Affect the Intensity of Competition?,
September; http://www.ebrd.com/country/sector/econo/jrp27.pdf (accessed on 16 August 2009).
van Buuren, S., Boshuizen, H. C. and Knook, D. L. (1999), Multiple Imputation of Missing Blood Pressure
Covariates in Survival Analysis, Statistics in Medicine 18: 681-694.
Wagner, J. (2001), A Note on the Firm Size – Export Relationship, Small Business Economics 17: 229-237.
Wakelin, K. (1998), Innovation and Export Behaviour at the Firm Level. Research Policy 26: 829-841.
Wood, P. (1991), Small Firms, Business Services and Flexibility, Small Business Research Centre, University of
Cambridge Working Paper No. 3.
Wooldridge, J. M. (2002), Econometric Analysis of Cross Section and Panel Data, Cambridge: MIT Press.
Wooldridge, J. M. (2003), Introductory Econometrics: A Modern Approach, 2nd edition, Mason: South-Western
Final_1: September 10, 2008
30