Forecasting the Impact of the Structure of Financing Sources on the
Results of Production Activity in Agriculture
OLESSYA MISNIK1, NURILYA KUCHUKOVA2, ZHANAR LUKPANOVA1,
AISULU KULMAGANBETOVA3
1Department of Finance,
Esil University, Zhubanova 7,
KAZAKHSTAN
2Department of Finance,
L.N. Gumilyov Eurasian National University, Satbaev 2,
KAZAKHSTAN
3Department of Cadastre,
Saken Seifullin Kazakh Agro-Technical Research University, Zhenis avenue 62,
KAZAKHSTAN
Abstract: - The study conducted to assess the impact of the source of financing on the results of production
activities of small and medium-sized businesses in agriculture in Kazakhstan is presented in the article. When
building the model, the authors used the method of correlation and regression analysis, including calculations of
pairwise regression, as well as the method of building statistical equations. Statistical data on the volume of
financing of SMEs in agriculture by way of own funds, government resources (concessional lending, subsidies,
and leasing), and non-state sources of financing such as borrowed funds from banks, microfinance
organizations, credit partnerships, and leasing companies were used as the factors. Firstly, the results of the
study showed that such factors as bank lending and leasing financing by private companies do not have a
significant impact on production volumes. Secondly, of all the factors analyzed, the greatest impact on gross
output was made by financing by microfinance organizations and credit partnerships, which determines and
indicates the need for the development of non-state sources of financing. Thirdly, a comprehensive approach
combining state financial support as well as available non-state financial resources is needed to achieve the
strategic objectives of economic development and ensure the required level of food security.
Key-Words:- Small and medium businesses, Financing, Agriculture, Kazakhstan, Lending, Subsidies, Leasing.
Received: November 15, 2023. Revised: January 11, 2024. Accepted: February 3, 2024. Published: March 1, 2024.
1 Introduction
Against the background of the new economic
reality, one of the most important directions of the
state policy of most countries is to ensure the high-
quality sustainable development of small and
medium-sized businesses in the real sectors of the
economy. Moreover, in the current conditions,
particular importance should be given to supporting
agribusiness entities, as the main category of
producers that ensure the food security of the state.
According to experts, by 2050, demand for food is
predicted to increase by 70% around the world, and
for its satisfaction, it is necessary to invest in
agricultural enterprises not less than 80 billion US
dollars, [1].
SMEs are one of the key drivers in the socio-
economic development of a country and its regions,
as they ensure the formation and emergence of
permanent new jobs. Therefore, they serve as a
guarantee of the population’s income and ability to
pay, [2], [3], [4], [5], [6], [7]. At the same time, the
significance and necessity of developing small and
medium-sized businesses in agriculture and the need
for comprehensive support are noted in the works of
many research scientists. Small and medium-sized
businesses in the agro-industrial complex are an
important sector of the economy, which, unlike
large enterprises, has significantly fewer
opportunities, but makes a significant contribution
to the gross domestic product of the state, [8]. One
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.59
Olessya Misnik, Nurilya Kuchukova,
Zhanar Lukpanova, Aisulu Kulmaganbetova
E-ISSN: 2224-2899
708
Volume 21, 2024
of the decisive conditions for the progressive
development of the agro-industrial complex is the
expansion of small-scale business entities in
agriculture. The agrarian structure of small-scale
production has positive dynamics and significant
growth potential, the implementation of which
currently depends on the economic policy of the
state, [9].
The development of small business forms is the
most important condition for maintaining and
further developing agricultural and non-agricultural
activities in rural regions. Small farms provide jobs
for the bulk of the population employed in
agriculture, [10]. Business structures are
dynamically developing segments of the domestic
agro-industrial complex, which can and should play
an important role in taking into account food
security, and producing environmentally friendly
products. The state, of course, must solve the
problems of agricultural entrepreneurship through
the implementation of a set of measures aimed at
training personnel, improving rural infrastructure,
stimulating young professionals, and developing
agricultural science. But these measures should not
be declarative, they should have a specific financial
and non-financial nature, [11]. All business entities
should have equal access to material, financial,
labor, information, and natural resources and forms
of support, [12].
Thus, having considered the various points of
view of researchers in this field, we concluded that
small and medium-sized businesses in agriculture
are an important part of the economy of any
country. In addition, due to its versatility, it can
solve a number of large-scale tasks and increase the
efficiency of development in several economic and
socially significant areas of the state's activity at
once.
However, in the course of their activities, small
and medium-sized businesses, in comparison with
large businesses, are extremely lacking financial
resources. Agribusiness entities have to constantly
search for available sources of financing that will
ensure not only the creation of their business, but
also allow its further development. At the same
time, agriculture, as an industry characterized by the
presence of a high degree of natural and climatic
risks, and the cyclical nature of production, requires
special approaches to the financing process, both
from the state and various financial organizations,
[13], [14], [15]. Agricultural entrepreneurship is
characterized by so-called systemic risks, which
market mechanisms cannot insure. Furthermore,
agribusiness's riskiness is only increasing due to
climate change, [16].
The experience shows that sharp deterioration in
natural and climatic conditions is immediately
followed by negative consequences in the
development of not only agriculture, but also
producers and providers of means of agricultural
production, and processors of agricultural products,
which usually receive the vast majority of such
products, [17].
The specifics of the agricultural industry
predetermine the need for huge financial
investments to ensure its competitiveness and
sustainable development, which makes the state the
main investor. The state's concern in co-financing
agriculture is because it is significant for the
national economy (contribution to GDP,
employment, natural resources), and even more,
each country seeks to ensure food security, [18].
However, despite this, agricultural production
suffers significant losses every year due to climate,
marketing, and other agricultural risks, and the
degree of loss compensation provided by the state is
very low, [19].
The lack of sources of continuous financing is
because not all resources are converted into
commodities and money. The products of most
agricultural sectors are of a raw material nature and
require transportation, storage, and processing.
These features determine the particular importance
of the effective functioning of a developed financial
and credit infrastructure, which ensures the creation
of conditions for the constant financing of small
agribusiness, and the provision of borrowed funds
for its development, [15].
The study of the world experience in financing
small and medium-sized businesses in agriculture
has led to the conclusion that there is a variety of
forms and tools to support business, most of which
are largely represented in the form of direct state
support for agricultural producers such as
concessional lending, subsidies, rates, insurance
quotas, etc., as well as by the provision of loans by
banks and other non-banking organizations. At the
same time, the mechanisms of state regulation
guarantee agricultural producers a sufficient level of
income and savings for expanded reproduction.
Thus, in the EU countries, 2/3 of the income of
agricultural producers is formed at the expense of
non-state funds and subsidies, and in Japan - up to
80%, [20]. In addition, the state support for
agricultural producers in foreign countries aims not
only to develop agriculture, but also to preserve
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.59
Olessya Misnik, Nurilya Kuchukova,
Zhanar Lukpanova, Aisulu Kulmaganbetova
E-ISSN: 2224-2899
709
Volume 21, 2024
rural areas and ensure their sustainable
development, respect for the natural environment,
support for ecology, and preserve the population in
historical places of residence.
Therefore, the multiplicity of sources of
financing SMEs in agriculture causes an objective
need to assess their impact on the results of
production activities. In this regard, the application
of the most advanced forecasting methods with the
coverage of all sources of SME financing is of
particular relevance. This will enable more effective
and efficient calculation of planned financial needs
of agribusiness entities for the production of a
certain volume of agricultural products, more
rationally and efficiently distribute financial
resources by the state to obtain the final result of
entrepreneurship development in the industry,
region, and country as a whole. At the same time,
forecasting, which precedes planning, allows us to
assess the specific situation in management and
provides a flexible tool for analyzing current
situations.
2 Methods and Data
In modern conditions, the method of economic and
mathematical modeling, which is based on the
method of correlation and regression analysis, is
used to obtain better and more objective information
about the forecast object in the future. In this case,
the most important criterion is to quantify the
closeness of cause-and-effect relationships and to
identify the form of influence on the result.
Determination of quantitative ratios in the form
of regression and comparisons of actual (observed)
values with values obtained as a result of
substitution into the regression equation allows a
better understanding of the nature of the
phenomenon under study, and therefore, intervenes
in the economic process to obtain the planned
results, [21]. The dual and multiple regression
methods, [22], [23], [24], will be used by us in
forecasting the volume of gross output depending on
the sources and forms of financing for small and
medium-sized businesses in agriculture. The applied
technique is based on the construction of statistical
equations of dependencies, which will allow to
forecasting the size of the effective feature for the
medium term, taking into account the influence of
certain factors, [25], [26].
It should also be noted that this method allows
to characterize the quantitative relationship between
the resultant indicator and various factors, hence
determining how much the resultant indicator will
change if a factor is changed by one, as well as
under what change of factors the expected value of
the indicator will be achieved.
The advantages of using this method are the
following:
- the initial term of the statistical equation of
dependencies has a real value (e.g. economic)
because it is always either the minimum or
maximum value of the effective variable in the
sample;
- the parameter values for individual
coefficients and signs for single and multifactor
equations are identical;
- the sum of linear deviations of the theoretical
values of the effective indicator from the actual
values should be minimal (the comparison shows
which type of equation is more suitable for
describing the phenomenon under study), [27].
The calculation of the dependencies of the
effective factor on the factor indicators is made
according to the formula (1).
(1)
A comparison base was chosen to make
calculations, and according to this base the
comparing coefficients are calculated from the floor
if there is an increase in the value of the feature:
(2)
When constructing an economic and
mathematical model, the following indicators will
be used:
- yх - the symbol of the dependency equation
of one-factor relationship;
- ymin, ymax - the minimum and maximum
empirical values of the effective feature;
- xi,…, zi - empirical values of factorial
features;
- xmin,…, zmin - the minimum empirical values of
factorial features;
- d - the symbol of the modules of deviations
from unity of the coefficients of comparison of the
effective and factorial features (dy, dx…,dz);
- b1, b2, …, bn - parameters of the equations of
dependencies for individual factorial features (n –
the number of the factorial features);
B – the cumulative parameter of the multiple
dependency equation;
1 2 10
( , , , )у f x x x
miny
yi
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.59
Olessya Misnik, Nurilya Kuchukova,
Zhanar Lukpanova, Aisulu Kulmaganbetova
E-ISSN: 2224-2899
710
Volume 21, 2024
- ryx- the correlation coefficient of one-factor
relationship;
- dy, dxz - the size of deviations from unity of
the coefficients of comparison of the theoretical
values of the effective feature, [27].
The parameters b for deviations of the
comparing coefficients from unity are determined
by formula (3) and indicate that a change in the size
of deviations of the comparing coefficients of the
factorial feature (хi) per unit leads to a change in the
size of the deviations of the comparing coefficients
of the effective feature by several times.
or
(3)
The forecast values of the considered factors, as
well as the effective feature, will be determined by
formula (4).
yt=ymin(1+bdt) (4)
where, yt is a trend equation;
ymin is the minimum value of the feature
The value of t always starts with 1 and tmin=1.
Based on this, it is considered to be appropriate
to apply this methodology and to forecast the gross
output of small and medium-sized businesses in
agriculture using the example of statistical data for
Kazakhstan and taking into account state and
borrowed sources of financing for 2023-2027.
3 Results and Discussion
A data file for a certain period on the financing of
small and medium-sized businesses in the
agricultural sector of the Republic of Kazakhstan is
going to be used to conduct the regression analysis.
The official statistics of the Bureau of National
Statistics of the Agency for Strategic Planning and
Reforms of the Republic of Kazakhstan and the 10-
year-annual reports of financial development
institutions will be taken as initial data. The main
effective factor (Y) in the model will be the gross
output produced by the small and medium-sized
businesses in the agricultural sector. Various
sources of financing for the activities of small and
medium-sized businesses in agriculture will be
identified as factor indicators that can potentially
affect the value of the performance indicator,
namely:
X1 - own funds;
The group of the public sources of financing:
X2 - the credit supply within the framework of the
government programs (concessional lending); X3 -
the total amount of subsidies to the agricultural
industry; X4 - the amount of financing in the form
of leasing within the framework of the government
sponsored programs;
The group of borrowed sources of financing: X5
- the credit supply allocated by second-tier banks;
X6 - the volume of lending by microfinance
organizations; X7 - the volume of lending by credit
partnerships; Х8 – the volume of financing by
private leasing companies (Table 1, Appendix). The
calculation of the dependencies of the effective
factor on the factor indicators according to the
formula (1) showed that almost all the studied
factors have a close correlation with the effective
feature, except for the X5 and X8 factors, therefore,
when constructing a forecasting model, these factors
will be excluded (Table 2, Appendix).
The correlation coefficient between the gross
output and the source of financing in the form of
own funds was ryx=0,98 and showed a close
relationship, and the theoretical value of Yх
indicates its high level, i.e. sufficient for reliable
calculations: Σŷ=Σуi= 23032736,0.
The use of the equation of dependencies shows
that, in theoretical terms, when the amount of
financing from own funds changes, the effective
feature increases (5).
y=1016202*(1+0,72*dx1) (5)
The calculation of one-factor equations for the
dependencies of gross output on six factors
according to the described methodology made it
possible to obtain the following results (Table 3,
Appendix). The calculations made to create a
multifactorial equation, presented in Table 4
(Appendix), show the coincidence of the sum of
empirical and theoretical values of the effective
feature Σŷi=Σyi=23032736,0 which confirms the
correctness of the calculations.
Using the formula (3), let's calculate the
parameters b from the deviations of the comparing
n321 dx.............dxdxdx
dy
b
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.59
Olessya Misnik, Nurilya Kuchukova,
Zhanar Lukpanova, Aisulu Kulmaganbetova
E-ISSN: 2224-2899
711
Volume 21, 2024
coefficients from unity and derive a multifactorial
equation (6).
y=1016202[1+0,087 (dx1+ dx2+ dx3+ dx4+ dx6+
dx7)] (6)
The parameter b in this equation indicates that a
change in the size of the deviations of the
comparing coefficients of the factorial feature (хi)
per unit leads to a change in the size of the
deviations of the comparing coefficients of the
effective feature by 0,087 times.
The calculation of the parameter b of the trend
equation for X1 will allow us to determine how
much the effective feature X1 will increase from
2023 to 2027. In this case, the trend equation will be
as follows:
X1=102431,0*(1+0,72*dt) (7)
To establish the forecast indicators of the
volume of own funds, let's calculate the share of the
influence of factors and predict the change in this
indicator for 2023.
  
(8)
Using the formula (6), calculate the forecast
value of the factor X1 for the following years: 2023,
2024, 2025, 2026, 2027:
Х2023 =102431,0 *(1+0,72*10) = 500609,9.
Х2024 =102431,0 *(1+0,72*11) = 540427,8.
Х2025 =102431,0 *(1+0,72*12) = 580245,7.
Х2026 =102431,0 *(1+0,72*13) = 620063,6.
Х2027 =102431,0 *(1+0,72*14) = 659881,4.
Therefore, in 2027, the amount of funds
required to obtain the forecast volume of gross
output should increase by 64381,4 million tenge.
Using the methodology described above, we will
calculate the forecast values of the remaining factors
and the effective feature, i.e. gross output for 2023-
2027.
Based on the calculations, the forecast value of
gross output for 2027 will be 5020424,8 million
tenge, for 2023 – 3876361,1 million tenge, for 2024
– 4162377,0 million tenge, for 2025 – 4448392,9
million tenge, for 2026 – 4734408,8 million tenge
(Figure 1).
Fig. 1: Forecast of gross output by SMEs in
agriculture until 2027
The calculated forecast values of gross output
make it possible to calculate the forecast values of
the factors that influenced the value of the effective
feature. To establish predictive levels of factors, let's
calculate the difference from unity between the
comparing coefficient of the forecast value and the
initial parameter of the equation of multifactor
dependency using the following formula:
min

 
(9)
Using the data, let's calculate the values of the
forecast levels of factors for 2023-2027 according to
the following formula:
(10)
The obtained data on the trend equation and the
equation of multifactor dependency are correct, i.e.
the forecast calculations are reliable. The forecast
levels of the factors and their influence on the
effective feature determine the possibility of its
increase or decrease.
According to Table 5 (Appendix), it can be seen
that, for almost all the indicators, the forecast values
for 2023-2027 tend to increase compared to 2022.
At the same time, in order the gross output in
agriculture to reach 5020424,8 million tenge in
2027, small and medium-sized businesses need to
increase funding from their funds by 10,8%, the
state should increase the volume of concessional
lending by 48,1%, subsidies for entities by 40,3%
and leasing by 2,6%, credit partnerships, and
microfinance organizations to ensure the growth in
4747500,00
3876361,1
4162377,0
4448392,0
4734408,8
5020424,8
2022 2023 2024 2025 2026 2027
Gross output of SMEs in agriculture (forecast), million
tenge
Gross output of SMEs in agriculture (fact), million tenge
;1 min
х
b
d
х
х
у
н
н
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.59
Olessya Misnik, Nurilya Kuchukova,
Zhanar Lukpanova, Aisulu Kulmaganbetova
E-ISSN: 2224-2899
712
Volume 21, 2024
the volume of transactions by 49,2% and 33,2 %,
respectively.
The structural analysis of the impact of funding
sources on gross output showed that non-state
sources of funding have the greatest impact on the
effective feature – 47,89%, public funding takes the
second place – 40,08%, and then they are followed
by own funds – 12,03 % (Table 6, Appendix).
This situation is quite understandable, as these
two factors, despite insignificant volumes of
financing closely interact with SMEs through the
system of direct and indirect lending, as well as
through co-financing with the state. This confirms
the need to develop and improve financing methods
involving these financial institutions.
The study of forecasting the impact of the
structure of financing sources on the results of
production activity in agriculture by small and
medium-sized businesses made it possible to draw
the following conclusions:
1) The total number of models was 8, but only
6 of them received positive results. Comparing the
forecast indicators of gross output, we concluded
that a significant impact on the effective feature is
provided by the government funding under the
existing programs that stimulate business
development, including the agricultural sector. This
indicates a high dependence of Kazakh
entrepreneurship on the state and, at the same time,
limited access to credit resources of financial
organizations. This situation is because in recent
years there has been a trend towards a decrease in
bank financing of agribusiness entities. This is
evidenced by the share of SMEs in the agricultural
sector in the loan portfolio of second-tier banks,
which, as of January 1, 2023, amounted to 5.0%,
[28].
2) Comparing the projected gross output
figures, we conclude that microfinance
organizations and credit partnerships have the
largest impact on the outcome indicator. Forecast
data on gross output showed that by 2027, this
indicator will increase by 272.9 billion tenge. This
indicates that agricultural production output will
only increase by 5,7 percent with the projected
funding levels. Moreover, it confirms the
insufficiency of the existing volumes of financing to
achieve high indicators of industry development.
3) Considering the low rates of financing of
small and medium-sized businesses in the
agricultural sector by second-tier bank leasing
organizations (low percentage of correlation with
the effective feature), the state needs to expand the
SME lending instrument with the involvement of
financial organizations through the funding
mechanism.
4 Conclusion
Currently, Kazakhstan is creating conditions for the
further development of entrepreneurship by
providing financial, proprietary, and information
support. At the same time, small and medium-sized
businesses, including those in the agricultural sector,
need sufficient financing in the form of available
loans and investments to carry out their activities
and fulfill their tasks effectively. The study revealed
that each of the sources presented has a significant
impact on the production performance of the
industry. However, the state financial support is still
quite significant, which reduces the competitiveness
of businesses and requires improvement of
mechanisms for financing the industry from non-
state sources. This is evidenced by the findings of
the study.
Creation of favorable conditions for stable
financial support of agricultural enterprises is
possible only at the appropriate level of
development of financial and credit infrastructure,
which envisages improvement of mechanisms for its
functioning, interaction of subsystems and elements,
distribution and use of financial and credit
resources, [29]. The allocation of funds by the state
should be a reasonable approach and provide a
multiplier effect of the impact of the state's financial
investments on the economy as a whole. In addition,
the mechanism of financial regulation and
incentives should create conditions to develop the
business environment, and not to allow business
entities to receive gratuitous and non-repayable
resources. Lending to small and medium-sized
businesses in the agricultural sector by banks and
other non-banking organizations should be
affordable, flexible, and mutually beneficial, which
will allow entrepreneurs to provide production with
necessary financial resources, and financial
organizations to receive income with minimal credit
risks. In addition, the application the use of modern
methods of forecasting production based on various
factors will allow the state to allocate financial
resources effectively in the face of limited budgetary
funds, and small and medium-sized businesses to
assess how much production can be increased using
various sources of financing.
As limitations of the study conducted, the
following should be highlighted:
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.59
Olessya Misnik, Nurilya Kuchukova,
Zhanar Lukpanova, Aisulu Kulmaganbetova
E-ISSN: 2224-2899
713
Volume 21, 2024
1. the subject of the study is the current system of
financing of small and medium-sized businesses in
Kazakhstan;
2. the quantitative parameters include data on the
volume of financing of SMEs at the expense of the
producer's funds, financial support within the
framework of state programs and at the expense of
non-state sources for the last 10 years, i.e. from
2013 to 2022;
3. the sources of financing of production activities
in agriculture used for forecasting were deliberately
limited by the authors, firstly, due to their annually
growing volume, secondly, due to their stability and
continuity over the period under consideration, and
thirdly, due to the availability of only these data in
official sources of information.
It should be noted that the current course taken
by Kazakhstan to build a diversified and innovative
economy with the functioning of business entities
with high potential, as well as changes in
approaches to the provision of state financial
support and the transition from direct support
measures to indirect ones in the form of guarantees
and funding of financial organizations will certainly
increase the availability and demand for financial
resources by entrepreneurs. However, the
effectiveness of these forms of financing and their
impact on the production performance of
agribusiness entities will have to be assessed in
future research.
References:
[1] Willer, H., Kilcher, L. (Eds.) “The
World of Organic Agriculture. Statistics and
Emerging Trends”. FiBL, Bonn, 2009.
[2] Obi, J., Ibidunni, A.S., Tolulope, A.,
Olokundun, M.A., Amaihian, A.B.,
Borishade, T.T., Fred, P. “Contribution of
small and medium enterprises to economic
development: Evidence from a transiting
economy”. Data Brief 18, 2018, pp. 835–839.
[3] Ndiaye, N., Razak, L.A., Nagayev, R., Ng, A.
“Demystifying small and medium enterprises’
(SMEs) performance in emerging and
developing economies”. Borsa Istanb. 18,
2018, pp. 269–281.
[4] Prasetyo, P.E.; Kistanti, N.R. “Human capital,
institutional economics and entrepreneurship
as a driver for quality & sustainable economic
growth”. Entrepreneurship and Sustainability
Issues 7, 2020, pp. 2575–2589.
[5] Gamidullaeva, L.A., Vasin, S.M., Wise, N.
“Increasing small- and medium-enterprise
contribution to local and regional economic
growth by assessing the institutional
environment”. Journal Small Enterprises and
Development 27, 2020, pp. 259–280.
[6] Ronda, L., Valor, C., Abril, C. “How small
traditional businesses can become attractive
employers: A means-end analysis.”. Journal
of Small Business Management 58, 2020, pp.
362–389.
[7] Razumovskaia, E., Yuzvovich, L., Kniazeva,
E., Klimenko, M., Shelyakin, V. “The
effectiveness of Russian government policy to
support SMEs in the COVID-19 pandemic”.
Journal of Open Innovation: Technology,
Market, and Complexity 6 (4), 2020, pp. 1–
20.
[8] Savkin, V.I. „Small forms of farming:
paradigm shift of agrarian economy“. (Малые
формы хозяйствования: смена парадигмы
аграрной экономики). Bulletin of
Agricultural Science 5, 2017, pp. 95-100.
[9] Dozorova, T.A. “State support for the
development of small-scale farming in
agriculture”. (Государственная поддержка
развития малых форм хозяйствования в
сельском хозяйстве). Bulletin of the
Ulyanovsk State Agricultural Academy:
scintific-theoretical Journal 4, 2014, pp. 181-
186.
[10] Gumarova, F.Z. “State support for the
development of small farming forms of AIC
as a key factor in the development of
agribusiness”. (Государственная поддержка
развития малых форм хозяйствования
АПК как ключевой фактор развития
агробизнеса). Bulletin of Mari State
University 1, 2015, 53-56.
[11] Dovbii, I.P., Mitrofanova, I.V. “Program-
targeted instruments of support of excellent
agrarian entrepreneurship“. (Программно-
целевые инструменты поддержки
отечественного аграрного
предпринимательства). Theory and practice
of social development 10, 2016, pp. 61-67.
[12] Vlasov, M.M. “The role of small and
medium-sized businesses in the modern
economy”. (Роль малого и среднего
бизнеса в отечественной экономике).
Economy and society 4, 2014, pp. 211-218.
[13] Nurumov, A.A., Aymurzina, B.T. “State
financial support for the development of
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.59
Olessya Misnik, Nurilya Kuchukova,
Zhanar Lukpanova, Aisulu Kulmaganbetova
E-ISSN: 2224-2899
714
Volume 21, 2024
agriculture of the Republic of Kazakhstan”.
Actual Problems of Economics 152 (2), 2014,
pp. 87–93.
[14] Ramazanova, S., Kuchukova, N., Abdulova,
G., Bulakbay, Z., Zhumanova, D. “Financial
incentives to increase efficiency of activity of
agro-industrial complex”. Entrepreneurship
and Sustainability Issues 7(2), 2019, pp.
1525–1541.
[15] Shkarupa, E.A. „Improvement of financial
support instruments for small business in
agriculture“. (Совершенствование
инструментов финансовой поддержки
малых форм бизнеса в сельском
хозяйстве). Bulletin of Volgograd State
University 1, 2011, pp. 124-130.
[16] Kusaiynov, T., Bulasheva, A., Tokenova, S.,
Mussina, G. „Quantifying systemic and
idiosyncratic risks in agriculture: A study of
Kazakhstan's grain production volatility“.
Journal of Eastern and Central Asian
Research (JEECAR) 10 (7), 2023. pp.1112-
1120.
[17] Deren, V.I. “World agriculture: peculiarities
of development and problems of fulfillment”.
(Мировое сельское хозяйство: особенности
развития и проблемы использования).
Economic Journal 4, 2020, pp. 63-80.
[18] Kuzman, B., Djurić, K., Mitrović, L.,
Prodanović, R. “Agricultural budget and
agriculture development in Republic of
Serbia”. Economics of Agriculture 64 (2),
2017, pp. 515-531.
[19] Baimagambetova, Z., Ismailova, R.,
Kassimbekova, M., Misnik, O., Mataibaeva,
G. “Strategic methods for managing risk
insurance in crop production”. Journal of
Applied Economic Sciences XIII, 2018, pp.
2484 – 2492.
[20] Kibirov, A.Ya., Rassuhanov U.A-A.,
Hairbekov, A.U. “Stimulation of investment
activity in agro-industrial complex: current
and foreign approach”. (Стимулирование
инвестиционной деятельности в АПК:
отечественный и зарубежный опыт).
Agroindustrial Policy of Russia 3 (63), 2017,
pp. 23-27.
[21] Sakaeva, E.Z., Iremadze, E.O., Grigorieva
T.V. “Forecasting and analysis of indicators
of financial stability of the enterprise on the
basis of mathematical modeling”.
(Прогнозирование и анализ показателей
финансовой устойчивости предприятия на
основе математического моделирования).
Bulletin of the Moscow State University for
the Humanities 3, 2010, pp. 78-88.
[22] Nabendu, P., Wooi, K.L. “On the coefficient
of multiple determination in a linear
regression model”. Journal of the Italian
Statistical Society 7 (2), 1998, pp. 129–157.
[23] Revan, O.M. “Predictive performance of
linear regression models”. Statistical Papers
56 (2), 2015, pp. 531–567.
[24] Zhanfeng, W., Yuan-Chin, I.C. “Sequential
estimate for linear regression models with
uncertain number of effective variables”.
International Journal for Theoretical and
Applied Statistics 76 (7), 2013, pp. 949–978.
[25] Aivazyan, S.A., Yenyukov, I.S., Meshalkin,
L.D. “ Exemplary statistics. Investigation of
addiction ”. (Прикладная статистика.
Исследование зависимостей). Finance and
Statistics, Moscow, 1985.
[26] Kulinich, E.I. “Ekonometriia“.
(Эконометрика). Finansy i statistika,
Moscow, 2001.
[27] Ishkova, L.V. “ Statistical equations of
dependence in scientific research".
(Статистические уравнения зависимостей
в научных исследованиях). Textbook for
undergraduate and graduate students, 2007.
[28] National Bank of Kazakhstan. Official portal.
[Online]. https://www.nationalbank.kz/en
(Access Date: November 27, 2023).
[29] Andriushchenko, K., Ishchenko, M.,
Sahaidak, M., Tepliuk, M., Domina, O.
“Prerequisites for the creation of financial and
credit infrastructure of support for agricultural
enterprises in Ukraine”. Banks and Bank
Systems 14(2), 2019, pp. 63-75.
[30] Bureau of National statistics Agency for
Strategic planning and reforms of the
Republic of Kazakhstan. [Online].
https://stat.gov.kz (Accessed Date: September
12, 2023).
[31] “Baiterek” National management holding”
JSC. [Online]. https://baiterek.gov.kz
(Accessed Date: October 25, 2023).
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.59
Olessya Misnik, Nurilya Kuchukova,
Zhanar Lukpanova, Aisulu Kulmaganbetova
E-ISSN: 2224-2899
715
Volume 21, 2024
APPENDIX
Table 1. Indicators for constructing a regression model, million tenge
Period
Y
Х1
Х2
Х3
Х4
Х5
Х6
Х7
Х8
2013
1016202
102431
72200
88400
41527
42817
198
8515
32222
2014
1156746
116091
53514
148900
57770
80735
188
9002
16392
2015
1298194
120900
70599
152000
57530
84620
311
8732
18541
2016
1540413
171300
122202
220200
55760
205872
426
12946
23014
2017
1822652
236100
153490
260400
40176
363294
1489
22808
23100
2018
2057209
273800
258700
225900
62971
198213
573
35554
13311
2019
2510170
363400
289800
323200
98513
97295
675
35701
17077
2020
3118669
364300
340400
365100
110000
91742
2826
42889
18048
2021
3764981
472293
322500
372600
141600
151800
2182
45700
37032
2022
4747500
595500
351200
443500
171100
193600
2000
50000
37050
Source: [28], [30], [31]
Table 2. Matrix of paired correlation coefficients
Indicators
Y
X1
X2
X3
X4
X5
X6
X7
X8
Y
1
X 1
0,98834
1
Х 2
0,902690
0,92918
1
X 3
0,952059
0,9606
0,9258
1
X 4
0,964556
0,94121
0,8363
0,8877
1
X 5
0,169571
0,2123
0,1439
0,2907
-0,0582
1
X 6
0,804209
0,7654
0,7921
0,8384
0,711
0,2390
1
X 7
0,929909
0,9533
0,9890
0,9317
0,8565
0,1992
0,8037
1
X 8
0,535237
0,4896
0,2499
0,3573
0,5408
0,0738
0,3742
0,3048
1
Note: Complied by authors
Table 3. One-factor dependency equations
Factor
Equation
Own funds
y=1016202*(1+0,72*dx1)
Concessional lending
y=1016202*(1+0,45*dx2)
Subsidies
y=1016202*(1+0,65*dx3)
Leasing
y=1016202*(1+1,17*dx4)
Financing by credit societies
y=1016202*(1+0,26*dx6)
Financing by leasing companies
y=1016202*(1+0,58*dx7)
Note: Compiled by the authors using the method of statistical equations of dependencies
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.59
Olessya Misnik, Nurilya Kuchukova,
Zhanar Lukpanova, Aisulu Kulmaganbetova
E-ISSN: 2224-2899
716
Volume 21, 2024
Table 4. Parameters of the multifactorial equation of dependencies of the effective feature
Year
Y (gross
output)
Deviation of the comparing coefficients of the factorial
features
Σdx1-7
Theoretical
value of y, bln.
tenge
у
dy
dx1
dx2
dx3
dx4
dx6
dx7
2013
1016202,0
0,00
0,00
0,35
0,00
0,03
0,05
0,00
0,43
1016202,00
2014
1156746,0
0,14
0,13
0,00
0,68
0,44
0,00
0,06
1,31
1114323,16
2015
1298194,0
0,28
0,18
0,32
0,72
0,43
0,65
0,03
2,33
1148866,69
2016
1540413,0
0,52
0,67
1,28
1,49
0,39
1,26
0,52
5,62
1510894,97
2017
1822652,0
0,79
1,30
1,87
1,95
0,00
6,91
1,68
13,70
1976359,91
Year
Y (gross
output)
Deviation of the comparing coefficients of the factorial
features
Σdx1-7
Theoretical
value of year,
million tenge
у
dy
dx1
dx2
dx3
dx4
dx6
dx7
2018
2057209,0
1,02
1,67
3,83
1,56
0,57
2,04
3,18
12,85
2247162,8
2019
2510170,0
1,47
2,55
4,42
2,66
1,45
2,59
3,19
16,85
2890768,6
2020
3118669,0
2,07
2,56
5,36
3,13
1,74
14,00
4,04
30,82
2897233,4
2021
3764981,0
2,70
3,61
5,03
3,21
2,52
10,58
4,37
29,33
3672958,0
2022
4747500,0
3,67
4,81
5,56
4,02
3,26
9,62
4,87
32,14
4557966,4
Total
23032736,0
12,67
17,49
28,02
19,41
10,83
47,70
21,93
145,38
23032736,0
Note: Compiled by the authors using the method of statistical equations of dependencies
Table 5.The forecast values of financial indicators for 2023-2027, million tenge
Indicators
2022
Forecasts, years
2023
2024
2025
2026
2027
Y
4747500,0
3876361,1
4162377,0
4448392,9
4734408,8
5020424,8
Х 1
595500,0
500609,9
540427,8
580245,7
620063,6
659881,4
Х 2
351200,0
386728,4
420049,9
453371,3
486692,8
520014,2
Х 3
443500,0
469777,8
507915,6
546053,3
584191,1
622328,9
Х 4
171100,0
136884,2
146555,0
156225,9
165896,7
175567,5
Х 6
2000,0
2185,4
2385,1
2584,8
2784,5
2984,2
Х 7
50000,0
50003,4
54152,3
58301,1
62450,0
66598,8
Note: Compiled by the authors
Table 6.The share of the influence of each factor on the gross output, %
Name of the factor
The sum of deviations of the factor
comparison coefficients
The share of the influence of the factor on the
effective feature, %
Own sources of financing
Х 1
17,49
12,03
Public sources of financing
Х 2
28,02
19,27
Х 3
19,41
13,35
Х 4
10,83
7,45
Borrowed sources
Х 6
47,70
32,81
Х 7
21,93
15,08
Total
145,38
100,0
Note: Compiled by the authors
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.59
Olessya Misnik, Nurilya Kuchukova,
Zhanar Lukpanova, Aisulu Kulmaganbetova
E-ISSN: 2224-2899
717
Volume 21, 2024
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
- Olessya Misnik, Nurilya Kuchukova collection
and manuscript writing
- Zhanar Lukpanova, and Aisulu Kulmaganbetova
were responsible for the Statistics.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflict of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.e
n_US
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.59
Olessya Misnik, Nurilya Kuchukova,
Zhanar Lukpanova, Aisulu Kulmaganbetova
E-ISSN: 2224-2899
718
Volume 21, 2024