1
NAŠE GOSPODARSTVO / OUR ECONOMY 69 (3) 2023 Šarlija, N., Šimić, S., Đanković B.
What is the Relationship Between Sales Growth and 
Insolvency Risk?
Nataša Šarlija
a*
, Sanja Šimić
b
, Biljana Đanković
c
a
University of Osijek, Faculty of Economics in Osijek, Trg Lj. Gaja 7, 31000 Osijek, Croatia
b
The Croatian Chamber of Economy, Rooseveltov trg 2, 10000 Zagreb, Croatia
c
Osijek Baranja County, Trg Ante Starčevića 2, 31000 Osijek, Croatia
natasa.sarlija@efos.hr, scoric@hgk.hr, biljana.djankovic@obz.hr
 
ARTICLE INFO
 
Original Scientific Article
Article History:
Received September 2023
Revised September 2023
Accepted September 2023
 
JEL Classification:
G30, G32, M21
 
Keywords:
SMEs growth
Insolvency risk
Sales growth
Financial indicators
Insolvency prevention
Bankruptcy
 
UDK: 005.412:347.736
DOI: 10.2478/ngoe-2023-0013
 
Cite this article as: Šarlija, N., Šimić, 
S., & Đanković, B. (2023). What is the 
relationship between sales growth and 
insolvency risk? Naše gospodarstvo/
Our Economy, 69(3), 1-11. DOI: 10.2478/
ngoe-2023-0013.
 
©2022 The Authors. Published by 
Sciendo on behalf of University of 
Maribor, Faculty of Economics and 
Business, Slovenia. This is an open access 
article under the CC BY-NC-ND license 
(http://creativecommons.org/licenses/
by-nc-nd/4.0/). 
 
Abstract
Sales growth is essential for an enterprise’s survival and financial growth. 
If an enterprise manages to achieve sales growth, its expansion can be 
accomplished. However, does sales growth always have only positive effects? 
If the enterprise is not collecting enough cash, it can miss a payment on its 
debt, triggering a series of events that can lead to its insolvency. The goal 
of the paper is to explore the relationship between an enterprise's sales 
growth and its insolvency. The relationship is tested empirically on the data 
set of 4271 SMEs in Croatia. The results confirmed that there is a relationship 
between sales growth and insolvency. Better indicators exist with solvent and 
growing SMEs than with insolvent and non-growing. Results have also shown 
that high growth can be at the same time a high risk. The paper contributes 
to a deeper understanding of the relationship between sales growth and 
insolvency and empirically demonstrates that sales growth over 200% per 
year induces the highest probability of insolvency compared to other lower 
levels of growth.
Introduction
There are many studies investigating insolvency and sales growth but most 
of them are dealing either with insolvency or sales growth, not with both of 
them together. It is reasonable to expect that there is a relationship between 
sales growth and insolvency. To finance sales growth and become larger, 
enterprises take a lot of debt. If growth slows down and profit decreases, 
it can cause enterprises unable to pay their obligations which can trigger a 
series of events that can lead to insolvency. Sales decline is an undesirable 
scenario for enterprises while sales growth is beneficial. However, even 
though sales growth is important, at the same time it can be dangerous if it 
pushes enterprises into insolvency. Therefore, enterprises need to keep their 
leverage and cash under control. High leverage as well as insufficient cash 
collecting can push enterprises to miss payments on their debt which can 
lead to insolvency. 
* 
Corresponding author

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NAŠE GOSPODARSTVO / OUR ECONOMY 69 (3) 2023 Šarlija, N., Šimić, S., Đanković B.
In contrast to the extensive literature examining the 
correlation between financial indicators and both growth 
and insolvency, there exists a research gap concerning 
the relationship between growth and insolvency itself. 
Bonaccorsi di Patti et al. (2015) showed that the drop in 
sales in combination with the high firm leverage increases 
the probability of default which reduces the resilience 
of the firms. Xuezhou et al. (2022) claimed that growth 
indirectly impacts insolvency risk through leverage 
decisions. The effect of sales growth on insolvency also 
depends on liquidity – if liquidity is higher with the same 
level of sales, the probability of insolvency should be lower. 
Xuezhou et al. (2022) reported a positive effect of liquidity 
on insolvency risk finding that the firms that possess 
more liquid assets experience less financial distress. They 
also argue that the influence of growth on insolvency is 
channeled through the availability of financial resources 
while pursuing growth. If a firm has good liquidity and can 
fulfill its short-term obligations, it will be less exposed to 
insolvency risk (Goel et al., 2015). 
The main goal of the research presented in this paper 
is to investigate both, growth and insolvency together. 
To accomplish the main goal, the following research 
questions have been examined: 
• Does the ratio of solvent and insolvent enterprises 
change according to sales growth levels? What stage 
of growth is risky?
• Do financial indicators in growing and solvent enter-
prises differ from non-growing and insolvent SMEs?
• Which financial indicators are important for predict-
ing insolvency? Is growth an important predictor of 
insolvency?
We utilize a dataset of financial statements from SMEs 
in Croatia to empirically test our research questions. 
The Croatian economy is a transitional economy, whose 
macroeconomic performance started to improve in 2000, 
with GDP growth of 4-6 percent, mostly driven by the 
recovery of tourism and credit-driven consumption. Over 
the same period, inflation remained relatively at a low 
level and currency stable. On 1st July 2013, Croatia joined 
the EU, and 10 years later, on 1st January 2023 joined the 
Eurozone and the Schengen Area. Croatia ended 2021 
with a GDP level of 58.2007 bn. EUR. As for the national 
economic structure, using composition criteria, GDP 
consists of agriculture (4 percent), industry (26 percent), 
and services 70 percent. By the end-use criteria, GDP 
consists of 60 percent of household consumption, 20 
percent of government consumption, and 20 percent of 
investment in fixed capital. At the end of 2022, there were 
1.710 million inhabitants with working capabilities. The 
rate of employment is estimated at 60.2 percent, while 
the unemployment rate is around 7.5 percent (Croatian 
National Bank, 2021). The service sector employs around 
70 percent, industries 28 percent, and the rest 2 percent 
work in agriculture. Public debt as a percentage of GDP, 
varied between 35.4 percent (2000) and 87 percent (2020), 
with a constant growth trend. In 2021, it dropped to 78.4 
percent of GDP. As for the sources of SME financing, the 
most common sources are traditional sources of financing, 
i.e. bank loans and leasing, while other sources of 
financing, such as business angels, venture capital funds, 
and crowdfunding platforms, remain underdeveloped. 
According to the SBA Fact Sheet of the European 
Commission for 2019, Croatia has the lowest-rated equity 
funding component and the lowest ranking for financing 
small and medium enterprises by business angels (SBA 
Fact Sheet, European Commission, 2019; CEPOR, 2020).  
The research questions in this paper should be viewed 
in the context of the stated facts about macroeconomic 
conditions in Croatia. Therefore, the results of the 
research can be beneficial for entrepreneurs, banks, and 
policymakers operating in similar conditions. 
The structure of the paper is the following. After the 
introduction, there is an overview of previous research, 
the description of data, variables, and methodology. 
Results and the interpretation are presented followed by a 
conclusion and discussion.
Literature Review
There is substantial literature investigating enterprise 
insolvency. Most of them are oriented toward finding factors 
that can trigger enterprise insolvency and which can be 
used for developing insolvency prediction models. Many of 
the models are composed of financial ratios. Ever since the 
Altman z-score (Altman, 1968), insolvency prediction models 
have been composed of financial indicators that cover 
liquidity, profitability, leverage, solvency, and activity. Chen 
and Shimerda (1981) reviewed 26 articles that classified 65 
financial ratios incorporated in predictive studies between 
1966 and 1975. They classified useful ratios into seven 
factors: Return on Investment, Capital Turnover, Financial 
Leverage, Short-Term Liquidity, Cash Position, Inventory 
Turnover, and Receivables Turnover. Besides financial 
ratios, equity market indicators can also be important for 
insolvency (Maffett et al., 2017) as well as macroeconomic 
variables – the GDP growth, share prices, exchange rates, and 
lending rates (Nkusu, 2011; Beck et al., 2013). Many studies 
investigate enterprise growth in terms of finding factors 

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NAŠE GOSPODARSTVO / OUR ECONOMY 69 (3) 2023 Šarlija, N., Šimić, S., Đanković B.
that influence growth. Reasons for that can be found in the 
fact that growing enterprises make a high contribution to 
economic growth. Factors that influence growth are usually 
arranged in three groups: the entrepreneur, the enterprise, 
and the environment (Wiklund et al., 2009). On the side of 
the entrepreneur, the most important factors that influence 
growth are age (Welter, 2001), education (Kolvereid & 
Bullvag, 1996; Peńa, 2002), experience (Barringer & Jones, 
2005), and personality traits such as motivation (Barringer 
& Jones, 2005; Delmar, 1996), need for achievement (Lau & 
Busenitz, 2001), self-efficacy (Baum, 1994), degree of risk-
taking propensity (Levie & Autio, 2013). Among factors of 
growth on the side of an enterprise, the most important are 
age and size (Yasuda, 2005), human capital (Wang & Chang, 
2005), level of R&D (Helmers, 2011), innovation (Maldonado-
Gonzalo et al., 2018), organizational learning (Hult et al., 
2003) and financial structure. Regarding financial indicators, 
as we have in our research, most of the previous studies 
investigate financial resources and liquidity measures. 
Sampagnaro (2013), Jeger et al. (2016), and Simbaña-Taipe 
et al. (2019) identified internal cash flows as one of the most 
relevant factors of growth. Sampagnaro (2013), Moreno and 
Casillas (2007), Honjo and Harada (2006), Chen and Zhao 
(2006), Becchetti and Trovato (2002) showed that financial 
resources, especially availability of external finance and 
internationalization are related to enterprises’ growth. 
Compared to the literature that investigates the association 
of financial indicators with growth and insolvency, there is 
a lack of research that explores the connection between 
growth and insolvency. Patti et al. (2015) investigated the 
relationship between enterprise insolvency and growth. 
They showed that the negative impact of sales decrease 
on enterprise solvency is larger for the enterprises that are 
highly leveraged compared to the less leveraged enterprises. 
Bjørn and Næs (2010) investigated the relationship between 
bankruptcy risk and expected future sales growth. The results 
showed that financial ratios characterizing enterprises with 
high bankruptcy risk also characterize enterprises with 
high future expected growth. Enterprises with a high risk of 
insolvency are similar to firms with high future sales growth. 
Hussain et al. (2020) proved a negative relationship between 
growth opportunities and insolvency risk. Additionally, 
growth opportunities harm the capital structure while debt 
maturity was influenced positively. Amaral (2008) confirmed 
that fast-growing enterprises are also more exposed to 
failure, which is especially true for smaller enterprises. Some 
studies haven’t shown a significant relationship between 
growth and insolvency. Putri and Arifin (2019) showed that 
liquidity, leverage, and institutional ownership have an 
effect while sales growth does not affect financial distress. 
Nazaruddin and Daulay (2019) explored the effect of activity, 
sales growth, and intellectual capital on financial distress 
in manufacturing enterprises in Indonesia and Malaysia. 
The results showed that sales growth did not affect the 
prediction of financial distress.
Data, Variables, and Methodology 
The data sample for this research consists of financial 
statements and the insolvency status of SMEs in Croatia. 
From the data set of all SMEs in Croatia, SMEs that were 
insolvent in 2019 are selected. Among all solvent SMEs, 
the same number of 3207 was selected randomly. Financial 
indicators for 2018 and variables indicating sales growth/
decline comparing 2019 and 2018 are calculated. After 
data cleansing, the final sample consisted of 4271 SMEs 
for which the data about insolvency and growth were 
available. An SME is defined as insolvent if it failed to 
fulfill its obligations for more than 90 days at least once 
during 2019. Sales growth is calculated in the following 
way (sales2019-sales2018)/sales2018. Since one of our 
research issues is to discover what stage of growth is 
risky, we categorized sales growth in the following way: 
(i) sales growth>2; (ii) 0.4<sales growth≤2; (iii) 0.13<sales 
growth≤0.4; (iv) 0<sales growth≤0.13; (v) -0.3<sales 
growth≤0; (vi) -0.7<sales growth≤-0.3; (vii) -1<sales 
growth≤-0.7; (viii) sales growth≤-1.
16 financial indicators covering profitability, liquidity, 
leverage, activity, productivity, and efficacy are calculated 
for each SME. Their formulas and descriptions are shown 
in Table 1.
To investigate the dependence of enterprises’ solvency/
insolvency with sales growth/decline as well as with 
different sales growth levels, the χ
2
 test is used. Results 
are presented in Tables 2 and 3. To investigate differences 
in distributions of financial indicators considering (non)
growth and (in)solvency – growing and solvent, non-
growing and solvent, growing and insolvent, and non-
growing and insolvent, Kruskal-Wallis ANOVA is used 
(Table 4). Logistic regression is used to develop a model 
for predicting enterprise insolvency. Results are presented 
in Table 6. 
The reasons for using non-parametric tests are dispersed 
distributions of financial indicators.
When the dependent variable is binomial, logistic 
regression is used. In modelling insolvency prediction, 
1 denotes insolvent enterprise and 0 otherwise. Logistic 
regression is used to obtain a function that predicts the 
probability of an enterprise becoming insolvent. The 

4
NAŠE GOSPODARSTVO / OUR ECONOMY 69 (3) 2023 Šarlija, N., Šimić, S., Đanković B.
logistic function, which describes the relationship between 
dependent and independent variables, is:
p
e
e
xx x
xx x
rr
rr




 
 
01 12 2
01 12 2
1


(1)
p is the probability of the dependent variable being 1, 
which means insolvent. Regression coefficients β
i
, i=1,2,….,r, 
are unknown parameters that need to be estimated. Usual 
approach for their estimation is logistic transformation:
log()l nl n it y
p
p
e
xx x
xx
r
x
r
r



  
 
1
01 12 2
01 12 2
 
 


r r
gx  ()
(2)
where 
p
p 1 −
 is called odds and its logarithm is called log 
odds. With this transformation, a linear function, g(x) is 
obtained. It needs to be estimated. Since the assumptions of 
linear regression such as normality and homoscedasticity 
are not met, least square estimation should be avoided. 
By denoting y_i to be the realization of the dependent 
variable, and x
i
'
=(1,x
i,1
,…,x
i,r
) to be observed corresponding 
r explanatory variables, where i=1,…n and n being the 
sample size, where p
e
e
i
gx
gx
i
i


()
()
1
, the entire sample 
likelihood function is conditional on x
i
 is (Jobson,2012): 
Ly pp
i
y
i
y
i
n
ii
(|)( )  



1
1
1
(3)
where β = (β
0
, β
1
 , ..., β
r
). The logarithm is used to obtain a 
more manageable form:
ln (|)l n( )
ln ()
ln
Ly pp
pp
y
i
y
i
y
i
n
i
y
i
y
i
n
i
ii
ii
  








1
1
1
1
1
1
p pyp
i
y
ii
i
n
i
  


 

() ln( 11
1
(4)
To estimate β, (4) is maximized through partial 
differentiation. The solution is obtained using iterative 
processes (Czepiel, 2002).
Results
The main motivation for this research is to deepen 
understanding of the relationship between growth and 
insolvency. In Table 2, solvency/insolvency and sales growth/
decline are crossed for our sample.
From Table 2 it can be noticed that among all SMEs with 
sales growth, there are 70.37 percent solvent and 29.63 
percent insolvent SMEs while among all with sales decline 
there are 38.96 percent solvent and 61.06 percent insolvent. 
Also, of all solvent SMEs, 57.44 percent have sales growth 
and 42.56 percent have a decrease in sales while of all 
insolvent SMEs, the percentage of those with sales growth 
is 26.6 percent and with sales decline 73.4 percent. There 
is a dependence between insolvency and sales growth 
( χ2=414.13; p<0.001) – most SMEs with sales growth are 
solvent but, it can also be noticed that 30 percent of such 
SMEs are at the same time insolvent. These data show that it 
makes sense to investigate this relationship. We will get an 
even better insight into the issue if we look at Table 3, where 
insolvency is crossed with different levels of sales growth.
Table 2
Insolvency/solvency and sales growth/decline for SMEs in the 
sample
Sales growth Sales decline Total
Solvent
% column 70.37% 38.94%
2237
52.38%
% row 57.44% 42.56%
Insolvent
% column 29.63% 61.06%
2034
47.62%
% row 26.60% 73.40%
Total
1826
42.75%
2445
57.25%
4271
100%
Source: The authors
In Table 3, the dependence between insolvency and sales 
growth has been confirmed ( χ2=790.38; p<0.001). It can be 
seen that in all positive stages of sales growth, there are 
more solvent SMEs (between 60 percent and 75 percent) 
than insolvent (between 25 percent and 40 percent), and 
where there is a decrease in sales, it is quite the opposite 
– there are more insolvent (between 65 percent and 80 
percent) than solvent SMEs (between 20 percent and 35 
percent). However, it seems significant that if we compare 
percentages related to only positive stages of sales growth 
(sales growth>0), we can notice that the highest percentage 
of insolvent SMEs is where the sales growth is greater than 
2 which is the highest growth. So, among all SMEs with 
sales growth over 200 percent, 36.56 percent of SMEs were 
insolvent in the next period. It confirms our statement of 
how high growth can be at the same time high risk. 
In the next phase of our research, the aim is to investigate 
financial indicators according to insolvency and growth. Do 
the values of financial indicators in growing and solvent 
SMEs differ from non-growing and insolvent SMEs? A 

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NAŠE GOSPODARSTVO / OUR ECONOMY 69 (3) 2023 Šarlija, N., Šimić, S., Đanković B.
comparison between financial indicators for growing and 
non-growing SMEs shows that differences exist in all 
financial indicators. SMEs with sales growth showed better 
financial performance compared to SMEs that had no sales 
growth. The same pattern exists when differentiating 
between solvent and insolvent SMEs. SMEs with better 
financial indicators have higher odds of being solvent in 
the next period. To additionally investigate the relationship 
between insolvency and growth, we divided our sample into 
4 groups of SMEs: (i) SMEs that are solvent and growing 
(30.1 percent); (ii) SMEs that are solvent and non-growing 
(22.2 percent); (iii) SMEs that are insolvent and growing 
(12.7 percent); (iv) SMEs that are insolvent and non-growing 
(35 percent). Financial indicators are compared according to 
these groups. Results are presented in Table 4. 
From Table 4 it can be noticed that differences exist in all 
financial indicators according to the (non)growth and (in)
solvency. Overall, it can be noticed that better indicators 
exist with solvent and growing SMEs than with insolvent 
and non-growing. SMEs with better indicators have higher 
chances to be solvent and grow in the future as opposed 
to SMEs with poor financial indicators. Additionally, results 
show that somewhat poorer financial indicators are present 
in SMEs that are insolvent and growing compared to those 
that are solvent and non-growing. Regarding profitability, 
there is a noticeable difference in profitability between 
growing-solvent and non-growing-insolvent. SMEs with 
high profitability indicators have much higher chances of 
being solvent and growing in the future period than those 
with very low or even negative profitability. It is interesting 
to notice a negative return on equity in growing insolvent 
SMEs. Despite negative ROE, SMEs can accomplish growth, 
probably due to high leverage but it ends in insolvency. 
Higher liquidity measures can be observed in growing-
solvent and non-growing-solvent compared to growing-
insolvent and non-growing insolvent SMEs. It suggests that 
liquidity is more important for ensuring solvency than for 
accomplishing growth. More favorable leverage indicators 
can be observed in growing-solvent and non-growing-
solvent compared to growing-insolvent and non-growing-
insolvent. We can notice that growing-solvent SMEs have 
higher values of debt equity and debt to EBITDA ratios 
compared to non-growing solvent SMEs, which means that 
they are more leveraged. It seems they used more debt to 
finance their growth and at the same time managed to stay 
solvent. However, it is important to emphasize that debts are 
not allowed to be too high nor EBIT negative because it leads 
to insolvency. The worst activity ratios are present in non-
growing-solvent SMEs. It is worth noticing that total assets 
Table 3
Insolvency and different stages of sales growth for SMEs in the sample
Solvent Insolvent Total
sales growth>2
% solvent 7.91 5.01
6.53
% sales growth 63.44 36.56
0.4<sales growth≤2
% solvent 15.15 7.33
11.43
69.47 30.53
0.13<sales growth≤0.4
% solvent 16.97 6.19
11.82
% sales growth 75.05 24.95
0<sales growth≤0.13
% solvent 16.32 6.24
11.52
% sales growth 74.19 25.81
-0.3<sales growth≤0
% solvent 25.70 17.40
21.75
% sales growth 61.89 38.11
-0.7<sales growth≤-0.3
% solvent 8.85 18.73
13.56
% sales growth 34.20 65.80
-1<sales growth≤-0.7
% solvent 5.14 19.22
11.85
% sales growth 22.73 77.27
sales growth≤-1
% solvent 3.98 19.86
11.54
% sales growth 18.05 81.95
52.38 47.62 100
Source: The authors

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NAŠE GOSPODARSTVO / OUR ECONOMY 69 (3) 2023 Šarlija, N., Šimić, S., Đanković B.
turnover and days sales outstanding are better in growing 
insolvent than in non-growing insolvent. This could mean 
that better turnover and faster collection of receivables are 
more important for growth than for solvency. Regarding 
productivity measures – higher productivity means higher 
odds for growth and solvency. 
Finally, if we want to predict whether an enterprise will be 
insolvent in the next period, we would like to investigate 
if sales growth is a significant predictor of insolvency. To 
realize that, we developed a model for insolvency prediction. 
Our data set is divided into development and validation 
samples. The structure is presented in Table 5.
Table 5
Development and validation sample for insolvency prediction model
Enterprise
Development 
sample
Validation 
sample
Total
Solvent 1442 592 2034
Insolvent 1549 688 2237
Total 2991 1280 4271
Source: The authors
Table 4
Differences in financial indicators according to growth/non-growth and solvent/insolvent
Me (IQR) H (p)**
growth/solvency* GR-SOL NONGR-SOL GR-INSOL NONGR-INSOL
Efficiency indicators
EF_TB 1.05 (0.15) 1.01 (0.26) 1 (0.31) 0.74 (0.81) 757.9 (<.001)
Profitability ratios
P_ROA 6.75 (20.44) 0.9 (15.2) 0.4 (32.67) -7.43 (78.93) 779.7 (<.001)
P_ROE 17.9 (46.57) 1.06 (39.16) -3.87 (62.52) -10.77 (65.76) 660.8 (<.001)
P_PM 4.22 (11.35) 1.21 (21.87) 0.76 (32.34) -21.63 (142.9) 568.8 (<.001)
Liquidity ratios
L_CR 1.49 (2.37) 1.41 (3.14) 0.76 (1.39) 0.75 (1.75) 270.9 (<.001)
L_QR 1.18 (2.31) 1.1 (2.75) 0.5 (1.23) 0.51 (1.56) 217.2 (<.001)
L_CDR 0.15 (0.65) 0.08 (0.47) 0.009 (0.11) 0.004 (0.09) 584.4 (<.001)
Leverage ratios
D_TDR 0.62 (0.61) 0.64 (0.77) 1 (1.55) 1.01 (2.91) 310.3 (<.001)
D_DER 0.65 (2.4) 0.31 (2.67) -1 (3.44) -1 (2.21) 248.7 (<.001)
D_LA 0.13 (0.46) 0.14 (0.57) 0.16 (0.91) 0.15 (1.05) 20.2 (<.001)
D_DEB 2.22 (6.69) 0.89 (8.22) 1.39 (14.69) -1.13 (11.54) 461.6 (<.001)
Activity ratios
A_TAT 1.73 (2.23) 0.97 (1.76) 1.33 (3.91) 0.43 (1.74) 400.8 (<.001)
A_DSO 43 (71) 62 (139) 56 (203) 139 (456) 222.6 (<.001)
A_DPO 41 (75) 60 (158) 122 (368) 200 (838) 404.7 (<.001)
A_WC 1.96 (7.25) 0.42 (3.52) -0.11 (3.97) 0 (0.67) 196.8 (<.001)
Productivity ratios
PR_S
299794
(443212)
243806
(343966)
184850
(280620)
104785
(210566)
385.5 (<.001)
Source: The authors
* GR-SOL – growing and solvent; NONG-SOL – non-growing and solvent; GR-INS – growing and insolvent; NONG-INS – non-growing 
and insolvent ** Kruskal-Wallis test

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NAŠE GOSPODARSTVO / OUR ECONOMY 69 (3) 2023 Šarlija, N., Šimić, S., Đanković B.
insolvency by 1.4. The model shows that a decline in sales 
is riskier than an increase in sales. However, too much sales 
growth is also risky. An enterprise with a sales growth of 
over 2 has a greater chance of insolvency compared with an 
enterprise with a sales growth of less than 2. The least risky 
are enterprises with sales growth of up to 0.13. It should be 
noticed that of all growth stages, the one with the highest 
growth, over 2, is the riskiest.
The results of the model testing performed on the validation 
sample are as follows: the area under the curve is 0.736, 
the GINI coefficient is 0.47, the insolvency-hit rate is 62.3 
percent and the solvency hit rate is 76.5 percent.
Discussion and Conclusion
Sales are the driving force behind any business. Every aspect 
of enterprise operations, such as employees, equity, assets, 
inventory, and way of financing are determined by future 
sales. Sales decline as well as excessive sales growth can 
push enterprises toward insolvency. When enterprises take a 
lot of debt to accomplish desired growth, sometimes it can 
cause a situation where earnings are not enough to cover 
the debt. 
In this paper, we investigated the relationship between sales 
growth and insolvency by analyzing three issues. The first 
issue analyzed in our research is the ratio of solvent and 
insolvent SMEs according to the sales growth levels. Our 
research showed that 70 percent of SMEs with sales growth 
are solvent and 30 percent are insolvent while 39 percent 
In our model, it can be noticed that the probability of 
insolvency is increased with the increase of the debt-equity 
ratio. On the other side, an increase in total asset turnover 
as well as return on assets will decrease the probability of 
insolvency. Increasing the equity ratio by 1, the odds ratio for 
insolvency will increase by 1.001 (e0.00128). Also, the odds 
ratio for insolvency will be decreased by 0.59 (e-0.5257) by 
increasing total asset turnover by 1. Unit increase of return 
on assets decreases the odds ratio of insolvency by 1 (e-
0.000015). By reviewing the estimated logistic regression 
coefficient of sales growth, it can be noticed that the worst 
stage regarding insolvency is ‘sales growth≤-1’ and the best 
‘0<sales growth≤0.13’.  The odds ratio for insolvency of an 
enterprise having a sales decline of 1 or more compared 
with an enterprise with sales growth of more than 2 
will increase by 7.6 (e2.03). If we compare sales decline 
‘ -0.7<sales growth≤-0.3’ with the reference category ‘sales 
growth>2’, we also see an increase of 5.75 (e1.7491). The 
same increasing trend of odds ratio for insolvency related 
to reference category of growth ‘sales growth>2’ applies 
for categories with sales decline ‘ -0.7<sales growth≤-0.3’ 
and ‘ -0.3<sales growth≤0’. But, for the rest, the trend is 
changing – the odds ratio for insolvency is decreasing. The 
odds ratio for insolvency of an enterprise having sales up to 
0.13 compared with an enterprise with sales growth of more 
than 2 will decrease by 0.55 (e-0.5968). In other words, the 
odds ratio for solvency will increase by 1.81 (1/0.55). If we 
compare sales growth in the range ‘0.13<sales growth≤0.4’ 
with reference category ‘sales growth>2’, we also see a 
decrease of 0.57 (e-0.5671) or an increase of odds ratio for 
solvency by 1.75 (1/0.57). Sales growth between 0.4 and 2 
compared to ‘sales growth>2’ will increase the odds ratio for 
Table 6
Results of logistic regression model for insolvency prediction 
Variable Estimate Lower CL 95% Upper CL 95% p
Total asset turnover -0.5257 -0.0033 -0.0002 0.0205
ROA -0.000015 -0.00002 -0.000006 0.0015
Debt equity ratio 0.00128 0.0004 0.0021 0.0037
sales growth≤-1 2.0300 1.6185 2.4415 0.0000
-1<sales growth≤-0.7 1.7491 1.3562 2.1418 0.0000
-0.7<sales growth≤-0.3 1.1882 0.8246 1.5517 0.0000
-0.3<sales growth≤0 0.0272 -0.3132 0.3675 0.8756
0<sales growth≤0.13 -0.5969 -0.9868 -0.2068 0.0027
0.13<sales growth≤0.4 -0.5671 -0.9522 -0.1819 0.0039
0.4<sales growth≤2 -0.3494 -0.7300 0.0313 0.0720
sales growth>2 0.0000
Source: The authors

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NAŠE GOSPODARSTVO / OUR ECONOMY 69 (3) 2023 Šarlija, N., Šimić, S., Đanković B.
of SMEs with sales decline are solvent and 61 percent are 
insolvent. If we analyze SMEs with different levels of sales 
growth, the highest percentage of insolvent SMEs is where 
the sales growth is over 200 percent. Among all SMEs with 
sales growth over 200 percent, 36.56 percent of SMEs were 
insolvent in the next period. It can be concluded that high 
growth can be at the same time high risk. The second issue 
analyzed in our research refers to the investigation of the 
values of financial indicators according to 4 groups of SMEs: (i) 
SMEs that are solvent and growing; (ii) SMEs that are solvent 
and non-growing; (iii) SMEs that are insolvent and growing; 
(iv) SMEs that are insolvent and non-growing. Better indicators 
exist with solvent and growing SMEs than with insolvent 
and non-growing. SMEs with better indicators have higher 
chances to be solvent and grow in the future as opposed to 
SMEs with poor financial indicators. Results have also shown 
that somewhat better financial indicators exist among solvent 
and non-growing than in insolvent and growing. SMEs with 
high profitability indicators have much higher chances of 
being solvent and growing in the future period than those 
with very low or even negative profitability. More favorable 
leverage indicators can be observed in growing-solvent 
and non-growing-solvent compared to growing-insolvent 
and non-growing-insolvent. Lower liquidity measures are 
observed in growing insolvent and non-growing insolvent 
SMEs while better measures of liquidity are more related 
to solvent SMEs both growing and non-growing. It suggests 
that liquidity is more important for ensuring solvency than 
for accomplishing growth. Activity ratios showed somewhat 
different patterns. The worst activity ratios are present in non-
growing insolvent and non-growing solvent SMEs. It suggests 
that activity ratios are more important for ensuring growth 
than for solvency. The third issue analyzed in our research is 
related to finding important predictors of insolvency. Because 
financial indicators were calculated a year before insolvency is 
measured, it can be concluded that a certain level of financial 
indicators can have an impact on whether the SME would be 
solvent or insolvent in the next period. The logistic regression 
model revealed that important predictors of insolvency for 
Croatian SMEs are total asset turnover, return on assets, debt-
equity ratio, and sales growth. The probability of insolvency is 
increased with the increase in debt-equity ratio and decrease 
in return on assets and total asset turnover. The model shows 
that a decline in sales is riskier than an increase in sales. 
However, too much sales growth is also risky. An enterprise 
with a sales growth of over 200 percent has a greater chance 
of insolvency compared with an enterprise with a sales growth 
of less than 200 percent. The least risky are enterprises with 
sales growth of up to 13 percent. It should be noticed that of 
all growth stages, the one with the highest growth, over 200 
percent, is the riskiest.
The results of our research are in line with some previous 
research that showed the positive impact of sales growth on 
enterprise solvency (Patti et al., 2015; Hussain et al., 2020) 
but also those that emphasized how high growth can be at 
the same time high risk (Bjørn & Næs, 2010; Amaral, 2008). 
This research has implications for entrepreneurs, banks, and 
policymakers. Entrepreneurs must strive to ensure sales 
growth, but they must keep that growth under control so that 
it does not lead them into insolvency. Banks and investors 
should pay attention to sales growth when assessing risk, 
in addition to all other elements they take into analysis. 
Enterprises that manage to ensure sales growth are less risky. 
Policymakers should create programs by which they support 
growing SMEs because they are less risky and can contribute 
to the economic development of the country. 
The limitation of the research is the analyzed variables that 
include only financial indicators, given that previous research 
has shown that growth and insolvency are also affected by 
non-financial characteristics.
As a guideline for future research, we plan to include 
additional characteristics in the analysis because previous 
research has shown that growth and insolvency are also 
affected by non-financial characteristics.
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Kakšna je povezava med rastjo prodaje in tveganjem plačilne 
nesposobnosti?
Izvleček
Rast prodaje je bistvenega pomena za preživetje in finančno rast podjetij. Če podjetjem uspe doseči rast prodaje, lahko dosežejo 
širitev celotnega podjetja. Vendar ali ima vedno le pozitivne učinke? Če podjetje ne zbere dovolj gotovine, lahko to povzroči, 
da zamudi plačilo dolga, kar sproži vrsto dogodkov, ki lahko pripeljejo do njegove plačilne nesposobnosti. Cilj tega prispevka 
je raziskati razmerje med rastjo prodaje in plačilno nesposobnostjo. Povezava je empirično preverjena na podatkovnem nizu 
4271 malih in srednje velikih podjetij na Hrvaškem. Rezultati so potrdili, da obstaja povezava med rastjo prodaje in plačilno 
nesposobnostjo. Boljši kazalniki obstajajo pri solventnih in rastočih MSP kot pri insolventnih in nerastočih. Rezultati so pokazali 
tudi, da je lahko visoka rast hkrati tudi visoko tveganje. Članek prispeva k globljemu razumevanju razmerja med rastjo in plačilno 
nesposobnostjo ter empirično dokazuje, da rast prodaje nad 200 % na leto povzroča največjo verjetnost plačilne nesposobnosti v 
primerjavi z drugimi nižjimi stopnjami rasti.
Ključne besede: rast MSP, tveganje plačilne nesposobnosti, rast prodaje, finančni kazalniki, preprečevanje plačilne nesposobnosti, 
stečaj