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ACCESS TO FINANCE IN SMALL FIRMS’ GROWTH
Dushamova Shirin
Leading specialist
Institute for Fiscal Research under the Ministry of
Economy and Finance of the Republic of Uzbekistan
Small sized enterprises (SSEs) play a huge role in addressing current socio-
economic challenges and expanding employment. A strong entrepreneurial
culture is essential to ensure the competitiveness of all countries economy and
economic growth in the future. SSEs are able to create a significant number of
jobs in a short period of time, expand the tax base, contribute to the growth of
national income and provide import-substituting products. In developed
economies today, SSEs are an important engine of economic growth.
Consequently, creating an enabling environment for entrepreneurship is of
paramount importance for both the small sector and the economy as a whole.
One of the most serious obstacles to small business development is limited
access to finance. The lack of capacity and often the inability to raise external
finance affects all stages of enterprise development, be it the formation,
expansion or capital replacement stages. The problem of limited access to
finance for SSEs has been discussed for quite some time.
This study examines the assumption that borrowing funds from formal
financial institutions is an essential determining factor of firm-level growth.
According to the data analysis, in the case of Chile borrowing funds is not a
significant determinant of small firm growth; other unobservable and
observable firm characteristics are the cause of growth.
The most reliable and used work on access to finance in the growth of small
firms is 'learning model' and its extension by Jovanovic's (1982), Erickson and
Pakes (1998) [1]. They concede that managers can impact their level of
productivity through the formation of human capital. These concepts suggest
that there is an inverse relationship between firm growth and firm size, age,
including the level of human embodied capital in the firm's entrepreneur. The
same conclusions made by Liedholm et al. (1994), Parker (1994), McPherson
(1995), McPherson (1996) and Bilsen et al. (1998) [2].
Regarding the impact of borrowed funds on financing small firm increase
performance, and most datas are descriptive statistics which are available from
national surveys of SSEs. Usually, such surveys analyze the average growth rate
of firms that borrowed funds with the growth rate of firms that did not have
access to borrowing funds. This type of systematic approach does not control
other possible influences; this is its only drawback. This approach’s examp
les are
Daniels and Ngwira (1993), Parker and Torres (1994), Minot (1996), USAID
(1998) and Ebony Consulting International (2000) and All of the above shows
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that debt improves the development prospects of enterprises in many
developing countries [3].
The hypothesis would be:
H
o
:
Borrowing funds (loans, credits) leads to increased growth of small
firms;
H
a
:
Borrowing funds (loans, credits) does not effect on growth of small
firms;
Table 1 indicates the correlation matrix. It can be seen that there is a
correlation coefficient; Internal funds/Retained earnings is negative and
Borrowed from banks (private and state-owned), Borrowed from non-bank
financial institutions, Purchases on credit from suppliers and advances from
customers and Others are positive, very low (-0.006, 0.004, 0.009, 0.001 and
0.010) and statistically significant at 1 and 10% levels of significance
respectively. This means that the calculated positive variables shift each other by
the same percentage and in the same direction.
Table 2 shows the descriptive statistics which presents the mean, standard
deviation, minimum and maximum values of the dependent and independent
variables. It shows that the dependent variable has a negative mean and a
positive standard deviation, -169,374 and 3232,971 respectively. It is important
to note that the mean and standard deviation for each of the variables are
positive.
Table 1
Matrix of correlations
Variables
Employment
Growth
Internal
funds/
Retained
earnings
Borrowed
from banks
(private and
state-
owned)
Borrowed
from non-bank
financial institutions
Purchases on
credit from
suppliers and
advances from
customers
Other
(moneylenders
, friends,
relatives,etc.)
Employment
Growth
1.000
Internal
funds/Retained
earnings
-0.006
1.000
Borrowed from
banks (private and
state-owned)
0.004
-0.525
1.000
Borrowed from
non-bank financial
institutions
0.009
-0.137
-0.030
1.000
Purchases on credit
from suppliers and
advances from
customers
0.001
-0.600
-0.106
0.017
1.000
Other
(moneylenders,
friends, relatives,
etc.)
0.010
-0.160
-0.051
0.031
0.016
1.000
Source: Done by Shirin Dushamova in Stata
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Table 2
Descriptive Statistics
Variable
Obs
Mean
Std. Dev.
Min
Max
Employment Growth
1033
-169.374 3232.971 -100100
1020
Internal funds/Retained earnings
1033
53.189
38.935
-9
100
Borrowed from banks (private and state-
owned)
1033
18.969
27.173
-9
100
Borrowed from non-bank financial institutions
1033
1.384
8.718
-9
100
Purchases on credit from suppliers and
advances from customers
1033
22.018
28.959
-9
100
Other (moneylenders, friends, relatives, etc.)
1033
1.632
9.904
-9
100
Source:
Done by Shirin Dushamova in Stata
About 22% of firms buy from loans from suppliers and advances from
buyers, with a standard deviation of 28.959, also close to this result can be seen
in Borrowing from banks. The standard deviations of Borrowing from non-bank
financial institutions and Other (loan sharks, friends, relatives, etc.) are quite
close to each other, reflecting the importance of productivity.
Table 3
Linear regression
y
Coef.
St.Err.
t-value
p-value
[95% Conf
Interval]
Sig
k3a
.026
5.321
0.00
.996
-10.416
10.467
k3bc
.631
5.957
0.11
.916
-11.059
12.32
k3e
3.411
12.044
0.28
.777
-20.222
27.044
k3f
.188
5.877
0.03
.975
-11.345
11.721
k3hd
3.173
10.848
0.29
.77
-18.115
24.46
Constant
-196.73
508.547
-0.39
.699
-1194.639
801.179
Mean dependent var
-169.374 SD dependent var
3232.971
R-squared
0.000 Number of obs
1033
F-test
0.042 Prob > F
0.999
Akaike crit. (AIC)
19637.988 Bayesian crit. (BIC)
19667.629
*** p<.01, ** p<.05, * p<.1
Source:
Done by Shirin Dushamova in Stata
Table 3 presents a linear regression model, and the most important thing to
note is that the R-squared, which is the percentage of variance explained by the
model, is 0%, meaning that y is independent of all x data. The following main
indicator is a p-value, all variables exceeded the 10% level of significance.
According to the rule of hypothesis testing:
If
p<.1
the null hypothesis is acceted and alternative hypothesis is rejected;
If
p>.1
the null hypothesis is rejected and alternative hypothesis is acceted.
Thus, in the case of Chile with the above data, we reject the null hypothesis
and accept the alternative hypothesis as the results are not significant.
This paper aimed to establish the causal effect of access to finance on small
firms growth in Chile. The OLS model was applied to test its effect. Employment
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growth was taken as the dependent variablebecause when firms and all types of
access to credit as the independent variable. Findings of results showed that
small firms’ growth, in our case employment growth does not depend on access
to creditin Chile. However, most reviews of the literature suggest a positive
relationship between these two.
In my opinion, this would work in developing
countries rather then developed ones, and Chile is recognised as a developed
country. To have a clear idea of what factors influence firm growth, more data
needs to be checked.
Reference
1.
Chittenden, F., Hall, G. and Hutchinson, P. (1996). Small firm growth, access to capital
markets and financial structure: Review of issues and an empirical investigation. Small Business
Economics, 8(1), pp.59
–
67. doi:10.1007/bf00391976.
2.
Mcpherson, M.A. ed., (2010). Access to Finance and Small Enterprise Growth: Evidence
from
East
Java.
The
Journal
of
Developing
Areas.
Available
from:
https://www.researchgate.net/publication/236828867_Access_to_Finance_and_Small_Enterpri
se_Growth_Evidence_from_East_Java [Accessed 17 Oct. 2022].
3.
Story, R. (n.d.). Small Business Growth, Finance and Innovation Rod Story. Available
from:
https://curve.carleton.ca/system/files/etd/798336d1-d875-4d3e-9705-
1663ffeaad53/etd_pdf/7586639572b7c062cc388161c397c650/story-
smallbusinessgrowthfinanceandinnovation.pdf [Accessed 10 Oct. 2022].
THE IMPACT OF THE DIGITAL ECONOMY ON FINANCIAL
INFRASTRUCTURE
Gulyamova Gulshahnoz Sabirovna
Associate Professor of
University of World Economy and Diplomacy
Studying the experience of advanced countries in introducing innovations
and modernizing the economy, and developing digital technologies is extremely
relevant. In this regard, the concept of financial innovation becomes particularly
relevant. The very nature of financial innovation is inextricably linked to the fact
that the approach to financial products, instruments and mechanisms is
changing. Innovations may be radical, and the market may not respond
adequately to their appearance. Innovations can be incorrectly applied or
implemented, and then they activate crisis situations and increase systemic risks
in all spheres of economic life.
It should be noted that none of the definitions is able to fully cover the entire
diversity of innovations, since, as noted above, they can exist in various aspects
of the financial activities of companies. Innovations can affect a wide range of
operational activities and elements of the corporation, but above all: financial
management, marketing, company strategy, business processes and models,
