Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
The Main Factors of the Development of Small Enterprises in
Kazakhstan
Nurseiit Nurlan Aitkaliuly*
The purpose of the study is to determine main factors that affect the
development of small enterprises in Kazakhstan as an oil producing country
at national and regional levels. In this article the factor regression analysis
was conducted and two econometric models were constructed. The results
of the first model time, which was built on time series for 2000-2011,
shows, that small business is positively affected by the growth of country’s
budget deficit to GDP, new loans to small business and current account
deficit to GDP, and negatively by increasing of real exchange rate of
national currency. The second model, which was build on regional panel
data for 2005-2011, determined the main factors influencing share of the
small businesses in GDP of Kazakhstan. They include labor productivity
per employee in small enterprises, an average monthly wage per employee
(as proxy for production cost), and a real effective exchange rate of
national currency, as well as changes in world oil prices.
JEL Codes: O120, M210
1. Introduction
During its transition to a market-based economy, Kazakhstan has experienced economic
setbacks and an increase in both unemployment and poverty. To address these pressing
issues, the government began an intensive exploration of the country's rich natural
resources. This strategy has helped to create new jobs and significantly improve the living
standards of population. However, Kazakhstan's high dependence on oil exports poses a
potential threat to the economy, given the growing economic dependence on raw oil,
volatility in the international oil market, and exposure to the Dutch disease. One of the
most effective solutions to this problem could be the development of small and mediumsized businesses, which could help to diversify the economy and make it more
sustainable. This is evidenced by the experience of many developed and developing
countries. For example, small enterprises, with less than 250 persons, are important
providers of employment in OECD countries, ranging between 45% in Slovak Republic
and above 81% in Portugal (OECD, 2013). In the United States small enterprises (it
accounts firms with fewer than 500 employees) employ just over 50% of the private-sector
workforce and created 65% of the net new jobs since 1995 (Nazar, 2013). In developing
countries, small business is even more important. It is a key engine of job creation and
economic growth in developing countries, particularly during the global financial crisis
(IFC, 2012). The World Bank finds a median employment share of 77% in small firms with
fewer than 250 workers in Africa (Page and Söderbom, 2012, p. 7). In Pakistan small
enterprises (up to 99 persons) employ nearly 78% of the non-agriculture labor force
(Subhan and et. al., 2013). Of course, the definition of „small‟ varies by country and its
income level, which complicates an adequate comparison. In developed countries members of the OECD use cut-off points of fewer than 500 workers to classify small and
medium enterprises (SMEs).
*Dr. Nurlan Nurseiit, Department of Management, Kazakh-British Technical University, 050010, Kazakhstan,
Almaty, Tole bi str., 59. Tel: +7 727 250 46 49, Fax: +7 727 250 46 58, E-mail: askartau@mail.ru
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
In developing countries, where market size and average firm sizes are both much smaller,
cut-off points of fewer than 100 workers or 250 workers are often used (Page and
Söderbom, 2012, p. 7). In Kazakhstan small sized enterprises includes entities with 50
employees, and medium sized enterprises – entities with fewer than 250 workers
(Parliament of Kazakhstan, 2006).
Currently, small enterprises are underrepresented in the economy of Kazakhstan. In 2012
the share of SMEs was about 17.3% of GDP, of which small enterprises accounted for 6%
of GDP. SMEs and small enterprises provide 13.8% and 5.9% of total employment,
respectively. All these facts show the importance of accelerating of the development of
small enterprises in Kazakhstan. The Government of Kazakhstan recognized this
challenge. It targets SMEs development as a way to better diversify its economy and
boost economic development. As a part of this approach, Kazakhstan has established
major state policy strategies for supporting SMEs, created the Damu Entrepreneurship
Development Fund (Damu Fund) in 1997, signed a Law on Entrepreneurship in 2006, and
developed the Business Road Map-2020. As a result of the last program implementation,
over 2,300 contracts have been signed for the subsidizing of interest rates and 135
projects were guaranteed by the government; more than 34,000 businessmen received
the proper trainings; over 130,000 workplaces are created and retained; the tax revenues
to the budget is growing.
The main purpose of this study is to examine factors that affect the development of small
enterprises in Kazakhstan as an oil producing country. In particularly, we wanted to justify
the following scientific hypotheses:
1. Finance is the most important factor determining the survival and growth of small
enterprises (UNCTAD Secretariat, 2005, p. 9).
2. Small enterprises cooperate with large and medium-sized enterprises, so that the
overall economic situation greatly affects the performance of small enterprises. It
enhances or hinders it access to product markets (UNCTAD Secretariat, 2005, p. 10-11).
3. Sustainable development of small enterprises is largely dependent on its productivity,
as it increases their competitiveness in the domestic market.
4. The rise in global oil prices contribute to the development of small enterprises in an oil
producing country, as it leads to an increase in oil exports and thus the inflow of money
into the oil producing country, which in turns encourages domestic consumer demand.
The paper is organized in the following manner. Section II reviews the literature on main
factors of SMEs development. Section III presents the models to be estimated and data to
be used. Section IV describes main findings. Section V discusses econometric results and
concludes.
2. Literature Review
SME development in Kazakhstan is still constrained by the main factors as poor access to
finance, unfavourable economic environment, low productivity, and oil prises changes.
The action of the first factor doesn‟t allow small enterprises to undertake productive
investments, to extend businesses, and to acquire the modern technologies, thus ensuring
their competitiveness (UN, 2001). The banking industry lacks confidence in SMEs, since
their activity is charged with higher risks and lack of collateral. No coincidence that banks
currently lend to SMEs at annualized interest rates of 18 percent or more, which seems to
be high to create favourable conditions for SME‟s development. “Commercial banks are
generally biased toward large corporate borrowers, who provide better business plans,
more reliable financial information, better chances of success and higher profitability for
the banks and have credit rating” (UNCTAD, 2005, p. 14).
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
The problem is strongly exacerbated during the economic crisis, as they have suffered not
only by a drastic drop in demand for goods and services, but also by a tightening in credit
terms. SMEs are generally vulnerable for many reasons among which are: it is difficult for
them to downsize as they are small; they are individually less diversified in their economic
activities; they have a lower capitalisation; they have a lower or no credit rating; they are
heavily dependent on credit and have fewer financing options (OECD, 2009, pp. 2-6).
To examine the impact of economic environment on the development of small enterprises,
the following macro indicators are usually used: GDP, inflation, unemployment, FDI,
imports, changes in businesses, and the general population numbers (Jukna,
Tvaronavičienė, 2004). Unemployment has the biggest impact on profitability of small
businesses, as it helps easier to find skilled labour at a reasonable price. However, such
indicators as inflation, average wages, the number of enterprises, monetary base, paid
taxes were not statistically significant, that could be explained by the larger effect of global
crisis than those indicators (Bekeris, 2012).
The low productivity means that SMEs can‟t compete with larger firms. To survive, they
have to find niches that are not interesting to large business. Small enterprises develop
mainly in such sectors, which are oriented on domestic market and less monopolized, as it
is noticeably inferior to large business. “Firms active in the so-called "hi-tech" and
knowledge intensive industry have often been found to show a particular strong
performance in terms of productivity and employment as well as GVA growth” (Paul
Wymenga and et al., 2012). Rising world oil prices are also favoured the development of
small enterprises in an oil producing country due to the fact that oil revenues lead to an
increase in domestic demand for goods and services, thereby increasing the demand for
the products of small enterprises.
3. The Methodology and Data
Methodology
The study is based on analyses of data on small enterprises in Kazakhstan in recent
periods of time. For testing the first, second, third hypotheses, we applied a factorial
regression analysis with construction of a time series econometric model. With its help, we
tried to assess the impact of different factors on the development of small enterprises and
explain the observed relationships between variables at national level.
For checking the reliability of first two hypotheses, we used time series data, which were
available for 2000-2011. This time series model looks in general form as follows:
Yt = α + β1X1,t + β2 X2,t+ …+ μt,
(1)
where Yt is the dependent variable at time t, α is an interception term, Xk,t represents the
impact of k independent factor at time t, β k is the coefficient by each impact factor, and μt
is a white noise error term.
In our case, the share of small enterprises in the gross regional product (EAR_s_GRP)
was chosen as a dependent variable, because this best reflects their role in the economy.
As explanatory variables such variables as unemployment rate (Unemp_LF_s), gross
investments (IOKr), real effective exchange rate of tenge, which is the national currency of
Kazakhstan (REER), government‟s budget deficit to GDP (DEF_RV_S), current account
deficit to GDP (CAB_GD_S), amount of credits to small businesses (T_LOAN_R), real
lending rates (r), oil prices (OIL_PRICE) were used. In addition, we used indicators which
might clarify specific regional features, such as population density (POP_DEN), income
per person (INCr), the amount of subsidies from the state budget per capita
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
(SUBSr_CAP), gross investment per capita (IOKr), the balance of local budget
(SAL_LGOV_S), and etc.
It was expected that the share of active small enterprises in the number of all active
enterprises would be positively related to GDP growth, gross investments, population
density, income per person, the amount of subsidies from the state budget per capita,
gross investment per capita, the level of development of financial institutions, the
government budget deficit to GDP, as all these factors contribute to the development of
production. At the same time, the share of active small enterprises should be negatively
affected by the real effective exchange rate, real lending rates, oil prices, the level of
corruption, the level of taxation, as they limit financial and economic capacity of small
enterprises.
The advantage of the first model was that it allowed us to test the impact of various
macroeconomic factors on the share of active small enterprises in the number of all active
enterprises in Kazakhstan. There were 76 available variables for 2000-2011. So the
sample included 836 observations. However, because of a short time period - only 11
years, this model could not use more than 3-5 variables in order to meet statistical tests.
For an explanation of the third hypothesis a second panel random effect model was built
on the base of regional data for 2005-2001. As a result, the sample was extended until
82,192 observations. This has enabled us to include into the model a set of variables,
since the number of all observation was sufficient to obtain meaningful statistical
evaluations. The disadvantages of second model was a small number of available
explanatory variables – about 40 items, as regional statistics is not collected by all
variables. Due to this fact, the choice of explanatory variables was mainly restricted to the
variables on which statistics were available, such as population density, wage level,
credits of banks to small business, the balance of state budget, labor productivity in small
businesses, the level of region development, and so on. In order to increase the number
of explanatory variables we added addition variables, which were the same for all regions,
such as the real effective exchange rate, or showed similar time dynamics in all regions.
For example, lending interest rate not differs significantly by regions. Therefore, it was not
a big mistake to assume that the dynamics of its change in regions accurately reflects the
dynamics of its change of the whole country.
The panel random effect model assumes that unobservable individual specific variation is
not correlated with the variables in the model, and treats as a component of the error term.
This model can be presented in general as follows:
Yit = α + β1X1,it + β2 X2,it+ …+ ɛit + μit,
(2)
where Yit is the dependent variable where i = entity and t = time, α is the intercept term,
Xk,it represents the impact of k independent factor, β k is the coefficient by this variable, ɛjt is
the individual cross section error and μjt is a white noise error term.
With the construction of the second model, we wanted to check our third hypothesis,
regarding the influence of different factors on the development of small business in
Kazakhstan, which was more detail present in the introduction section of this research.
We may expect a positive impact on the share of small enterprises in the gross regional
product from the following key factors:
 The increase of lending to small businesses (NEW_LOANS), as these type of
business has restricted access to the finance and has an acute shortage of funds for
own expansion.
 The labor productivity per employee in small enterprises (PR_TR), as it increases their
competitiveness.
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
 The share of small business in the number of employed in the region (S_N_EMB). A
higher share of small business facilitates in the employment may accelerate the
growth of small businesses and increases its role in the national economy.
We can also expect a negative impact of the following factors on small business:
 The size of production costs (we use SAL_R as proxy variable), because of the loss of
price competitiveness by ceteris paribus.
 Oil prices (OIL_PRICE) and the real effective exchange rate (REER), because of the
so-called Dutch disease effects. It is very important for Kazakhstan as an oilproducing country. About 60% of its foreign exchange earnings come from oil exports.
Data Collection
As a source of information for small enterprises statistics collected by the National
Statistical Agency of Kazakhstan for the period from 2000 to 2011 (AKS, 2004, 2005a and
2005b, 2006, 2007a and 2007b, 2008, 2009a, 2009b, 2010, 2012a, 2012b, 2012c), data
from the National Bank (NBK, 2013a and 2013b), the country database of the World Bank
(WB, 2013) has been used.
All nominal variables were transformed into real variables, in constant prices of 2000. This
was done in order to avoid or reduce possible problems with multicollinearity and
autoregressions, that make the evaluations biased and not reliable. For this purpose, we
used the GDP deflator as the most accurate indicator for measuring the level of inflation
than the consumer price index (CPI) and the wholesale price index (WPI). In Kazakhstan,
the last indexes are not accurately reflect actual inflation, as the government can
manipulate the composition of consumer‟s basket, beforehand excluding the most
inflationary items from it. With respect to the GDP deflator, it is not likely due to the
complexity of its calculation.
The methodology of calculation of some individual variables is given below:
 Real GDP (GDPr) in real terms shows Gross Domestic Product in nominal terms
deflated by the deflator of GDP (mln tenge in constant prices of 2000).
 Gross investments per capita (IOKr) in real terms it amount of gross investment
divided by the size of population (tenge in constant prices of 2000/person).
 Unemployment rate (UNEMP_LF_S) is the number of unemployed people in total
labor force (%).
 Real effective exchange rate (REER) of the national currency of Kazakhstan was
taken from statistics of the National Bank of Kazakhstan. The metodology of its
calculation is given in Appendix 1.
 Earnings from sales of small enterprises (S_GDP) show the proportion of income that
receives small enterprises in relation to GDP (% of GDP).
 The labor productivity growth of small enterprises (PR_TR) was found by dividing the
income from sales of small enterprises in current prices by the number of employees
in small enterprises in regions (thousand tenge per employee).
 The share of small enterprises in the production of GDP (EAR_S_GDP) shows the
production share of small business in the Gross Domestic Product (% of GDP).
 The share of the budget deficit to GDP (DEF_RV_S) indicates the ratio of the
republican budget deficit relative to GDP (% of GDP).
 Current account deficit to GDP (CAB_GD_S) calculated as the proportion of the
current account balance of a country to its GDP (% of GDP).
 New loans to small business (NEW_LOANS) in real terms are new loans given by
country's banks to small businesses deflated by the deflator of GDP (million tenge in
constant prices of 2000).
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
 The share of active small and medium-sized enterprises in the total number of active
enterprises in the region (S_NA_FIRM) is calculated as proportion of the number of
small active enterprises to the number of all active enterprises in the country (%);
 Credits of banks to small business in real terms (T_LOAN_R) are calculated as the
amount of bank loans given to small business at the end of the period in real terms
(mln tenge in constant prices of 2000). We used data on bank loans to small
businesses, as the data on bank loans to small businesses were not collected.
 Deficit (-), surplus (+) of the local budget in real terms (SAL_LGOV_R) is calculated as
a percentage of balance of local budget to budget expenditure at the end of the period
in constant prices of 2000 (mln tenge).
 The share of small enterprises in the number of employed in the region (S_N_EMB)
shows the number of employees in small enterprises in the regions of Kazakhstan at
the beginning of the year (thousand persons).
 World oil prices (OIL_PRICE) is calculated by the Europe Brent Spot Price in FOB
(Dollars per Barrel).
 Average monthly wage (SAL_R) in real terms is used as a proxy variable for
production costs in small enterprises. Is calculated as the proportion of the average
monthly wage per employee in Kazakhstan (tenge per worker in constant 2000
prices).
 The real interest rate (r) was found by the formula:
r=(i – π )/(1+ π ),
where
i – is nominal interest rate and π is an annual rate of inflation.
Descriptive statistics and corresponding correlation matrix between all variables, included
in the models, are given in the Tables 1-4. It should be noted that for the best subsequent
interpretation of received models, all their variables except equity indices and indexes
have been transformed in logarithmic form.
Table 1: Descriptive statistics for 1st model
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
Jarque-Bera
Probability
Observations
S_GDP
9.533434
9.601823
13.79229
7.176707
1.902861
0.766057
3.034122
1.076414
0.583794
11
DEF_RV_S
-1.167950
-0.799120
-0.140736
-2.896891
0.942540
-0.640698
1.819831
1.390938
0.498840
11
SUBS_GD_S
8.400490
7.953550
11.70166
6.312830
1.726587
0.605824
2.092878
1.050024
0.591548
11
NEW_LOANS
739975.5
689848.0
1869852.
148531.0
512618.4
0.916648
2.835549
1.552841
0.460050
11
REER
100.5513
100.2917
111.7592
89.23692
6.847406
-0.043696
1.845284
0.614628
0.735420
11
SUBS_GD_S
-0.477536
-0.941934
1.000000
0.461576
0.697646
NEW_LOANS
-0.063205
-0.495286
0.461576
1.000000
0.608780
REER
-0.596875
-0.659614
0.697646
0.608780
1.000000
Table 2: Correlation matrix for 1st model
S_GDP
DEF_RV_S
SUBS_GD_S
NEW_LOANS
REER
S_GDP
1.000000
0.586719
-0.477536
-0.063205
-0.596875
DEF_RV_S
0.586719
1.000000
-0.941934
-0.495286
-0.659614
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
Table 3: Descriptive statistics for 2nd model
Mean
Median
Maximum
Minimum
Std. Dev.
EAR_S_GRP
9.662826
8.426425
26.56193
1.659819
4.864862
PR_TR_R
1020.281
938.5000
3434.000
224.0000
528.1874
S_N_EMB
7.330067
6.111608
22.92067
2.384931
4.053060
SALR
25860.21
23652.62
69881.60
8560.000
12064.94
OIL_PRICE
57.24500
58.15500
111.2600
24.46000
28.08134
1.143927
3.867477
47.89435
0.000000
192
1.372485
5.649298
116.4291
0.000000
192
2.013122
6.903225
251.5665
0.000000
192
1.168084
4.468227
60.90699
0.000000
192
0.429422
2.019183
13.59693
0.001115
192
SALR
0.286485
0.666287
0.435434
1.000000
0.543086
0.422084
OIL_PRICE
Skewness
Kurtosis
Jarque-Bera
Probability
Observations
REER
101.1004
101.9462
111.7592
89.23692
6.527725
0.209043
1.986384
9.617711
0.008157
192
Table 4: Correlation matrix for 2nd model
EAR_S_GRP
PR_TR_R
S_N_EMB
1.000000
0.234281
0.393643
-0.286485
-0.352271
-0.342669
0.234281
1.000000
0.336103
0.666287
0.317141
0.214523
0.393643
0.336103
1.000000
0.435434
0.032270
0.038749
EAR_S_GRP
PR_TR_R
S_N_EMB
SALR
OIL_PRICE
REER
-0.352271
0.317141
0.032270
0.543086
1.000000
0.820470
REER
0.342669
0.214523
0.038749
0.422084
0.820470
1.000000
4. The Findings
To test the first and second hypotheses, we have used the factorial regression analysis.
Our calculations yielded the following econometric models that determine the behavior of
small enterprises in Kazakhstan in 2000-2011(table 5).
Table 5. Earnings from sales of small enterprises in the GDP
LS // Dependent Variable is EAR_s_GDP
Sample (adjusted): 2000 2010
Included observations: 11 after adjusting endpoints
Variable
DEF_RV_S
SUBS_GD_S
LOG(NEW_LOANS)
REER
C
Coefficient
3.281578
1.309082
1.565184
-0.188790
0.562309
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.753065
0.588441
1.220740
8.941238
-14.46861
2.770880
Std. Error
1.272797
0.703511
0.667411
0.081271
10.31251
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
t-Statistic
2.578242
1.860784
2.345158
-2.322960
0.054527
Prob.
0.0419
0.1121
0.0574
0.0592
0.9583
9.533434
1.902861
0.701870
0.882731
4.574462
0.049075
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
First we obtained model that explains the change in the share of small enterprises in the
production of GDP (EAR_s_GDP) at 59%.
Statistical testing was carried out to test the model for the presence of autocorrelation,
multicollinearity and heteroscedasticity. By all statistics were obtained acceptable results.
This model showed that the share of small enterprises in the production of GDP of
Kazakhstan (EAR_s_GDP) depends positively on the share of the country‟s budget deficit
to GDP (DEF_RV_S), current account deficit to GDP (SUBS_GD_S), new loans to small
business (NEW_LOANS), as all these factors create more favorable conditions for its
development, and negatively affected by the appreciation of real effective exchange rate
of national currency (REER), which usually associated with a loss of competitiveness of
domestic production (table 4).
The most significant factor was the country‟s budget deficit to GDP (DEF_RV_S, t statistic
= 2.58), the second one was new loans to small business (NEW_LOANS, t statistic = 2.34), followed by real effective exchange rate (REER, t statistic = -2.32) and current
account deficit to GDP (SUBS_GD_S, t statistic = 1.86). Thus, data on Kazakhstan
confirmed the faithfulness of the first and second hypotheses that finance is one of the
most important factor determining the survival and growth of small enterprises, and the
importance of overall economic situation, which were reflected through the REER and
current account deficit to GDP.
By influence the most powerful factor turned to be the size of country‟s budget deficit to
GDP. Its growth by 1% led to an increase of share of small enterprises in the GDP by
3.3%. It followed new loans to small business. Their growth by 1% caused an increase of
the share of small enterprises in the GDP by 1.5%. The third powerful factor was current
account deficit to GDP (SUBS_GD_S). Its growth by 1% raised the share of small
enterprises in the GDP by 1.3%. At the same time the influence of the real exchange rate
on small enterprises was not significant.
Appreciation of the real effective exchange rate by 1% was accompanied a decrease of
small enterprises share in GDP by 0.2%. Thus, we can say that small enterprises in
general is positively affected by the growth of country‟s budget deficit to GDP, new loans
to small business and current account deficit to GDP, and negatively by increasing of real
exchange rate of national currency. These findings are fully consistent with our
hypothesis.
To test the third hypothesis that the sustainable development of small enterprises is highly
dependent on its labour productivity, we have built on the regional database of
Kazakhstan an econometric model that describes the influence of various factors on the
share of small enterprises in the gross regional product (table 6).
The most significant factor was average monthly wage per employee (SAL_R, t = 20.9),
followed by the labour productivity per employee in small enterprises (PR_TR_R, t=18.3),
the share of small business in the number of employed persons in the region (S_N_EMB, t
= 16.8), real effective exchange rate of national currency (REER, t = 3.6) and world oil
price (OIL_PRICE, t = 2.8).
All variables showed the expected signs. The increase of the labour productivity per
employee (PR_TR_R) means greater competitiveness of small enterprises, which leads to
an increase of its share in the regional economy. So an increased share of small
enterprises in the number of employees of the region (S_N_EMB) exerts upward pressure
on the share of small enterprises in GRP. In the oil-producing country the rising oil prices
means the flow of money into the country and the increase in domestic demand, so oil
prices growth should be accompanied by an increase of small enterprises share in GRP.
Indeed, the model shows a positive relationship between these variables. At the same
time, it should be expected a negative impact of oil prices and real effective exchange rate
on the development of small enterprises, because they increase the costs of imported
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
products, while ease their access to foreign markets in case of export. Average monthly
wage per employee (SAL_R) as proxy variable for production costs also shows right sign,
because increase of production cost adversely affects the share of small enterprises in
GRP.
Table 6: Change in the share of production of small enterprises in the GRP
LS // Dependent Variable is EAR_S_GRP
Sample: 1 192
Included observations: 192
Variable
LOG(PR_TR_R)
S_N_EMB
LOG(SALR)
LOG(OIL_PRICE)
LOG(REER)
C
Coefficient
8.464797
0.766513
-13.42837
1.639447
-14.49228
141.9899
Std. Error
0.462747
0.045547
0.643748
0.594958
4.042498
17.95944
t-Statistic
18.29251
16.82909
-20.85965
2.755567
-3.584982
7.906144
Prob.
0.0000
0.0000
0.0000
0.0064
0.0004
0.0000
R-squared
Adjusted Rsquared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson
stat
0.804874
Mean dependent var
9.662826
0.799629
2.177650
882.0416
-418.8116
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
4.864862
1.587244
1.689041
153.4463
1.963304
Prob(F-statistic)
0.000000
Examination of the coefficients of the variables allows us to estimate the impact power of
various explanatory variables on explanatory variables. The most powerful among these
factors was the labour productivity per employee in small enterprises. Its growth by 1% led
to an increase of share of small enterprises in the GRP on 8.5 percentage points. It
followed by an average monthly wage per employee, which growth by 1% caused a
decrease of the share of small enterprises in the GRP by 13.4%. Almost the same effect
had strong appreciation of the real exchange rate. Its growth by 1% leads to a decrease in
the share of small business in GRP by 14.5%.
Rising world oil prices favour the development of small enterprises, as Kazakhstan is an
oil country. Rising oil prices of 1% induced an increase in the share of small enterprises in
the region's economy by 1.6%. The share of small enterprises in the number of
employees of the region has a small effect. Its growth by 1% caused increase in the share
of small business in the region by only 0.8%.
According to the model, labour productivity per employee (R_TR_R) has a significant
impact on the share of small enterprises in GRP (EAR_S_GRP). This confirms the validity
of the third hypothesis, that there is a significant relationship between the sustainable
development of small enterprises and their productivity.
5. Summary and Conclusions
Based on the development of the first econometric models, the first and second
hypotheses have been validated that finance is the most important factor determining the
survival and growth of small enterprises, and the overall economic situation (throw current
Proceedings of 28th International Business Research Conference
8 - 9 September 2014, Novotel Barcelona City Hotel, Barcelona, Spain, ISBN: 978-1-922069-60-3
account, REER) greatly affects the performance of small enterprises. Among the financial
factors the budget deficit to GDP, new loans to small business, were key factors that has
influenced the development of small enterprises in Kazakhstan in 2000-2011.
To test the third hypothesis, about a substantial connection between the sustainable
development of small enterprises and its productivity on the data of Kazakhstan in 20002011, an econometric model was construct and that describes the impact of regional
factors on the share of small enterprises in a gross regional product. According to the
second model, the most significant factor is an average monthly wage per employee,
followed by the labor productivity per employee in small enterprises, the share of small
enterprises in the number of employed persons in the region, real effective exchange rate
of national currency and world oil price. Thus, the hypothesis that small enterprises with a
higher productivity has better prospects for its development in the future was confirmed.
In addition, it was found that in oil-producing countries the growth of world oil prices
support the development of small enterprises, but it also causes the increase of real
effective exchange rate, which adversely affects its development. As for wages per
employee as proxy variable for production costs, then their rise adversely affects to the
development of small enterprises. This is due to the fact that one of the main advantages
of small enterprises compared to large and medium enterprises are low salaries.
Therefore, when they grow, small enterprises lose their main competitive advantage,
resulting in a relative slowdown of growth compared to other types of enterprises.
Thus, the Kazakh authorities should be advised with the following measures to ensure the
sustainable growth of small enterprises: a) the growth of subsidies and tax breaks for
small businesses; b) new lending to such enterprises; c) maintaining a stable exchange
rate of the national currency.
These findings, which directly follow from the first economic model, can substantially
improve the government‟s economic policy in Kazakhstan. However, the most important
measures to enhance the competitiveness of small enterprises are government‟s
measures to increase labour and capital productivity in such firms by supporting them in
conducting marketing research, acquisition of new technologies, obtaining advices from
professionals on running business. This finding directly follows from the second model.
Moreover, in the conditions of oil-producing country in order to support the development of
small enterprises, government should not allow the revaluation of national currency,
coupled with an increase in oil and gas revenues, allowing so-called "Dutch disease", as it
has strongly negative impact on the development of small enterprises.
The increase in world oil prices is beneficial to the development of small enterprises in
Kazakhstan as an oil-producing country, because it helps to improve its economic and
investment climate, but only, if their positive effect is not undermined by the revaluation of
the national currency, because second factor had a more stronger impact on the
development of small enterprises than world oil prices.
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