=Paper=
{{Paper
|id=Vol-2104/paper_207
|storemode=property
|title=The Determinants of Trade Credit for Firms Listed on the Zagreb Stock Exchange: An
Empirical Analysis
|pdfUrl=https://ceur-ws.org/Vol-2104/paper_207.pdf
|volume=Vol-2104
|authors=Fitim Deari,Nicoleta Barbuta-Misu
|dblpUrl=https://dblp.org/rec/conf/icteri/DeariB18
}}
==The Determinants of Trade Credit for Firms Listed on the Zagreb Stock Exchange: An
Empirical Analysis==
https://ceur-ws.org/Vol-2104/paper_207.pdf
The Determinants of Trade Credit for Firms Listed on
the Zagreb Stock Exchange: An Empirical Analysis
Fitim Deari 1 and Nicoleta Bărbuţă-Mişu 2[0000-0002-4386-4103]
1 South East European University, Macedonia
2 Dunarea de Jos University of Galati, 47 Domneasca Street, Galati, Romania
f.deari@seeu.edu.mk; Nicoleta.Barbuta@ugal.ro
Abstract. The paper examines the firm’s trade credit and its determinants, be-
fore and after financial crisis, for a sample of 26 non-financial firms listed on
Zagreb Stock Exchange. Trade credit provided and trade credit obtained are the
quantitative dependent variables. Profitability, cash to total assets ratio, long-
term financing, short-term financing, converting sales into cash, and inventories
to total assets ratio are the quantitative independent variables. Industry and time
are used as qualitative independent variables. Some of the obtained results are:
firms have hold on average a balance between trade credit provided and ob-
tained; profitability, cash ratio and converting sales into cash were found to be
statistically significant determinants of trade credit provided; cash ratio and
short-term financing were found to be statistically significant determinants of
trade credit obtained; more profitable firms and with higher level of cash have
provided more trade credit then counterparties.
Keywords: trade credit, accounts receivable, accounts payable, crisis, regres-
sion
1 Introduction
View from the historical perspective and nowadays quite often happens that firms
have difficulties in accessing financial markets. This happens for many reasons. But,
one of the possibilities to meet the financial gap is buying on credit. This can be asso-
ciated with selling on credit. Trade credit has intrinsic connections with supply chain
coordination and inventory management, so trade credit enhances supply chain effi-
ciency by allowing the retailer to partially share the demand risk with the supplier [1].
But, when a buyer depends too much on its main supplier, the supplier does not pro-
vide more credit as its relationship with the buyer matures [2]. So both trade credits
can occur simultaneously, thereby generating, on one side, accounts payable and, on
the other side, accounts receivable.
Trade credit management represents an important strategic opportunity for firms to
enhance performance, liquidity and profitability [3]. The level of accounts receivable
and payable can be affected by many factors. There is no a fixed level of accounts
receivable and payable that firm should has. The concept of trade credit explains the
relationship between the firm, its customers and suppliers.
On the other hand, in financial literature the expression cash is king is commonly
used. The firm may be profitable but not liquid. Hence, the level of profitability and
liquidity that a firm should choose is not a simple task. Finding the optimal level of
profitability and liquidity is a complex calculation. Complexity comes from different
factors. Normally, firms do not realize all sales in cash, as they do not pay all invoices
(bills) with cash on transaction date. The crucial problem starts from point of selling
and buying on credit. Moreover, the longer the net trade cycle, the larger is a firm’s
working capital requirement [4].
Doubtless trade credit cannot be analyzed separate from others financial indicators.
Thus, the aim of this paper is to take into account some factors that may explain the
relationship between the credit offered to clients and credit obtained from suppliers.
The rest of paper is organized as follows: Section 2 presents a literature review in
the field, Section 3 develops the methodology, Section 4 presents empirical data and
analysis, followed by Section 5 with results and discussions and conclusions in the
Section 6.
2 Literature Review
Literature related to the firm’s trade credit and its determinants is growing as trade
credit importance is growing as well. A large number of papers are written and evi-
dence is found from micro and macro perspectives. Testing the existence of a trade
credit channel of transmission of monetary policy Guariglia and Mateut [5] found that
both the credit and the trade credit channels operate in the UK, and that the latter
channel tends to weaken the former. Also, during tight monetary periods, trade credit
operates mainly as a substitute for bank borrowing while during loser monetary epi-
sodes even when the economy is weak, trade credit and bank loans are dominated by
a complementary effect [6]. On the other hand, Fisman and Love [7] proved that firms
in countries with less developed financial markets appear to substitute informal credit
provided by their suppliers to finance growth.
There is no a fixed level of accounts receivable and payable that firm should has,
this level being affected by many factors that also determine the contract terms: sup-
pliers' willingness to price discriminate, information asymmetry between suppliers
and customers, market structure, stages of business cycles, and customers' creditwor-
thiness [8]. Delannay and Weill [9] examining the determinants of trade credit of
about 9,300 firms from nine transition countries (Central and Eastern European Coun-
tries) found that both financial and commercial motives explain the credit behaviour
of firms and suppliers act as financial intermediaries in favour of companies with a
limited access to bank credit.
Firms with better access to credit offer more trade credit [10]. Garcia-Teruel and
Martinez-Solano [11] analyzing 3,589 small and medium sized firms in the UK have
found evidence that larger firms, with better access to alternative internal and external
financing and with a lower cost, use less credit from suppliers. Furthermore, Frank
and Maksimovic [12] found that not all trade can be on credit because the investor
cannot observe the quality of the buyer. When lending becomes less severe, the allo-
cation of lending became more efficient, and the amount of trade credit extended by
private firms declined [13]. Nilsen [14] found that: small firms increase trade credit, a
substitute credit, indicating a strong loan demand; trade credit is widely used by the
small firms suffering the loan decline; the reasons large firms use trade credit are
financial in nature. Also, firms with more inventories, market share and that are fi-
nancially distressed invest less in trade credit, while higher operating cash flow, an-
nual sales growth, export propensity, access to bank credit and larger firms lead to
higher investment in trade credit [15].
In the countries with poorly developed financial institutions, compared to state
owned firms, non-state owned firms use more trade credit, and this higher usage is
primarily for financing their prosperous growth opportunities rather than transactional
purposes [16]. Poorly performing state-owned enterprises are more likely to redistrib-
ute credit to firms with less privileged access to loans via trade credit [13]. Also,
state-controlled listed firms in China receive preferential treatment when borrowing
from commercial banks and, in contrast, private controlled firms rely on informal
finance and on trade credit [17]. They found evidence that private firms located in
higher social trust regions use more trade credit from suppliers, extend more trade
credit to customers, and collect receivables and pay payables more quickly.
Fisman and Love [7] show that, industries with higher dependence on trade credit
financing exhibit higher rates of growth in countries with weaker financial institu-
tions, and that most of the effect reported comes from growth of the size of pre-
existing firms, consistent with barriers to trade credit access among young firms.
Suppliers with smaller market share are associated with more trade credit, confirming
that suppliers with weak market power use trade credit as a competitive tool and buy-
ers with larger market share are associated with more trade credit, whereas suppliers
selling to a concentrated buyer base are associated with less trade credit [18].
Kohler et al. [19] found that firms with direct access to capital markets, quoted on
the UK stock exchange, both extend more and receive less trade credit during a reces-
sion. They therefore unambiguously provide unquoted firms with more net trade cred-
it. Studying the behaviour of trade credit around the time of financial crises, Love et
al. [20] found an increase in trade credit at the peak of financial crises, followed by a
subsequent collapse of trade credit right after crisis events.
The effect of financial deepening on the relationship between trade credit and cash
holdings shows that firms in regions with higher levels of financial deepening hold
less cash for payables while substituting more receivables for cash and a more highly
developed financial sector helps firms to better use trade credit as a short-term financ-
ing instrument [21]. Casey and O'Toole [22] found that credit-rationed firms are more
likely to use, and apply for, trade credit; this increases with firm size and age and
firms that denied credit for working capital tend to turn to trade credit, while informal
and inter-company lending tends to act as a substitute for bank investment loans.
Regarding to the relationship between profitability and trade credit use, the profita-
ble private firms are more likely to extend trade credit than unprofitable ones [13].
When suppliers offer trade credit at their industry-average level, this action facilitates
trade and, thus, is positively associated with both parties’ performance; conversely,
when suppliers are more aggressive in their trade credit strategy than the industry
average, then the excess trade credit is negatively associated with buyer performance
[23].
Using a supplier-client matched sample, Garcia-Appendini and Montoriol-Garriga
[24] studied the effect of the 2007–2008 financial crisis on between-firm liquidity
provision and they found that firms with high pre-crisis liquidity levels increased the
trade credit extended to other corporations and subsequently experienced better per-
formance as compared with ex ante cash-poor firms and also, trade credit taken by
constrained firms increased during this period.
Starting from these findings we proposed to examine the determinants factors that
may explain the conjunction between the credit offered to clients and credit obtained
from suppliers for a sample of 26 non-financial firms listed on the Zagreb Stock Ex-
change, for the period 2007-2013. Thus, trade credit provided and trade credit ob-
tained are the quantitative dependent variables and determinants factors analysed are:
profitability, cash to total assets ratio, long-term financing, short-term financing, con-
verting sales into cash, inventories to total assets ratio and industry and time.
3 Methodology
In this study are used quantitative methods and a deductive approach. Principally, the
case study as a research method is used because the study is limited on two aspects.
Firstly, the sample comprised just non-financial firms which are listed on the Zagreb
Stock Exchange. Secondly, the analyzed period that covers 2007-2013.
Selected firms are non-financial entities that belong to two branches of industry as
agriculture, forestry and fishing; and manufacture of food products, beverages and
tobacco products. Moreover, selected firms are divided for further analyze based on
establishment year. Hence, two groups of firms are examined, established before and
after 1990. Data are derived from firms’ annual reports published on the Zagreb Stock
Exchange web page (http://zse.hr/) and used as accounting data, i.e. kn (Croatian Ku-
na, hereafter kn).
In this study are used six independent quantitative variables. Industry and time pe-
riod are used as qualitative variables. Firm’s age is used also as category (not as a part
of regression analysis) and firms are divided into two groups. Profitability, cash to
assets ratio, long-term financing, short-term financing, inventories to assets ratio and
converting sales into cash are independent quantitative variables. Two depended vari-
ables are used, i.e. trade credit provided and trade credit obtained. Both types of quan-
titative variables, dependent and independent are expressed on their book values.
Table 1 describes the methodology of measuring and defining quantitative and
qualitative variables used in this study. By using those independent variables in the
regression model, it is attempted to analyze the dependence of the trade credit on
these proxies.
Table 1. The methodology of quantitative and qualitative variables calculation.
Description Abbreviation Calculation/ Definition
Quantitative variables
Dependent variables:
Trade credit provided tr Accounts receivables / Total assets
Trade credit obtained tp Accounts payables / Total assets
Independent variables:
Profitability prof Net income / Sales
Cash to assets ratio cash Cash / Total assets
Long-term financing longtfin Long-term liabilities / Total assets
Short-term financing shorttfin Short-term liabilities / Total assets
Converting sales into cash inventratio Net cash flows from operating activities / Sales
Inventories to assets ratio convsale Inventories / Total assets
Qualitative variables
Industry: Dummy
Agriculture, forestry and fishing 1
Manufacture of food products, beverages and tobacco products 2
Age:
Established after 1990 0
Established before 1990 1
None-crisis period 0
Crisis period 1
Source: Selected from Grave (2011), Garcia-Teruel and Martinez-Solano (2010), Petersen
and Rajan (1997), and authors’ calculations.
Other analysis performed in this study is to examine whether financial crisis has af-
fected trade credit for selected firms. Different authors have used different measure-
ments or indicators to identify financial crisis. For example, Grave [25] used the GDP
growth rate and the acceptation criteria of the banking sector to identify financial
crisis years. In this study we use percentage change on previous year of the real GDP
growth rate as a measure of the financial crisis. According to the data from Eurostat
(for more see: http://ec.europa.eu/eurostat/data/database) [26] the real Croatian GDP
growth rate (percentage change on previous year) was: 5.2 (2007), 2.1 (2008), -7.4
(2009), -1.4 (2010), -0.3 (2011), -2.2 (2012) and -0.6 (2013). Thus, we consider that
crisis occurred in 2009 and continued in next coming years. Therefore, the period
2007-2013 is divided into two sub-periods, i.e. none and crisis period. For this reason,
a dummy variable is generated that will present financial crisis. Its value is 0 for years
2007 and 2008 (none-before crisis), respectively 1 for years 2009, 2010, 2011, 2012
and 2013 (crisis period).
The initial generalized regression model used in this study is:
6
Yit X
k 1
kit kit it
(1)
where, i = 1, 2, 3, ..., 26, t = 1, 2, 3, …, 7, and k = 1, 2, 3, …, 6.
Since the trade credit is a function of variables of interest then the regression model
(1) can be expanded adding dummy variables as follow (2) and (3).
Trade credit provided:
Accounts receivable Net income Cash Long - term liabilitie s
1 2 3
Total assets it Sales it Total assets it Total assets it (2)
Short - term liabilitie s Net cash flows from operating activities Inventorie s
4 5 6
Total assets it Sales it Total assets it
Industry dummy Year dummy it
Trade credit obtained:
Accounts payable Net income Cash Long - term liabilitie s
1 2 3
Total assets it Sales it Total assets it Total assets it
(3)
Short - term liabilitie s Net cash flows from operating activities Inventorie s
4 5 6
Total assets it Sales it Total assets it
Industry dummy Year dummy it
In this study OLS regressions without and with dummy variables are used. Regres-
sions are performed separately based on two dependent variables using cluster-robust
standards errors (vce). According to Wiggins [27] … “regress ..., vce(cluster) esti-
mates the model by OLS but uses the linearization/Huber/White/sandwich (robust)
estimates of variance (and thus standard errors)”.
Trade credit as dependent variable is differently defined by different authors. Some
authors examined separately trade receivables and payables with others determinants,
while some others authors separately and jointly. For example, Alatalo [28] uses trade
credit provided (trade receivables per sales), trade credit obtained (trade credit paya-
bles per cost of goods sold) and net trade credit (difference between trade receivables
and payables scaled by sales). Grave [25] has examined trade receivables divided by
total assets; trade payables divided by total assets; and trade receivables minus trade
payables divided by total assets. On the other hand, Ge and Qiu [16] as dependent
variable uses accounts payable/total assets, accounts payable/sales, (accounts payable
- accounts receivable)/total assets, (accounts payable - accounts receivable)/sales. In
this study two dependent variables are used for trade credit: trade credit provided and
trade credit obtained calculated according to Table 1. Using these two dependent vari-
ables, we tried to analyze factors that may have determined selling and buying on
credit for selected firms and for selected period: profitability, cash assets ratio, long-
term and short-term financing, converting sales into cash and inventories to assets
ratio.
Profitability – there are many indicators for calculating the profitability of an enti-
ty. For example, profitability often is measured by gross margin, operating margin,
contribution margin, profit margin, return on assets (ROA), return on equity (ROE),
return on total capital (ROTC), etc. In this study is used a measure that takes into
consideration sales. Hence, profitability is measured using profit margin calculated as
net income / sales. This ratio denotes how much kn profit generates every kn sale.
Cash to total assets ratio – this ratio is calculated based on the methodology of
balance sheet vertical analysis. So, cash is divided by total assets. In this study this
ratio measures and denotes the firm’s liquidity, in the sense of share of cash in total
assets.
Long-term financing and short-term financing – those two ratios explain how as-
sets are financed. With other words, these ratios show the percentage of long-term
financing, respectively short-term financing on total assets. Thus, long-term financing
is calculated as long-term liabilities divided by total assets, whereas short-term fi-
nancing is defined as short-term liabilities divided by total assets.
Converting sales into cash – normally, every kn sale is not done on cash. For this
reason cash flow statement is prepared (accruals versus cash accounting base). In this
study this ratio is calculated as net cash flows from operating activities divided by
sales.
Inventories to assets ratio – this is a regular ratio which is calculated in order to
analyze the inventory level. Knowing the inventories level is important to the finan-
cial decision making process. Inventories level may be different to different firms.
Even, this ratio can change for own firm view from different periods. However, many
explanations can be found why a firm has lower or higher inventories level.
4 Empirical Data and Analysis
This section includes descriptive and empirical analysis. Descriptive statistics present
an overall picture of the sample composition. Observations are also divided by indus-
try and age criteria. Summary statistics for examined variables are given. Mean is
presented for examined variables based on year, industry and age category.
Empirical analysis includes Spearman and regression analysis. Regression results
are presented separately for four models: model TR1 where dependent variable is
trade credit provided; model TP1 where dependent variable is trade credit obtained;
models TR2 and TP2 are regression models which take into consideration dummy
variables for year and industry. Regression models are controlled and tested for nec-
essary tests such as VIF for mulitcollinearity, Breusch-Pagan / Cook-Weisberg test
for heteroskedasticity and model specification link test. Results of performed tests are
added to each model. At the end of this part the financial crisis effect is analyzed for
trade credit.
4.1 Descriptive Analysis
Initially 182 observations were examined and 26 firms were selected for the period
2007-2013. Data were checked for outliers, leverage and influential observations.
Hence, after adjustments the number of observations was reduced to 165. But, still for
further analysis 26 firms remained. On agriculture, forestry and fishing industry are 8
firms and 18 firms belong to manufacture of food products, beverages and tobacco
products industry.
Table 2 shows how observations are distributed according to industry and age.
There are 72 percent (119 / 165) observations of firms that belong to food products,
beverages and tobacco products industry; and rests 28 percent (46 / 165) observations
of firms that belong to agriculture, forestry and fishing industry. Hence, majority of
selected firms belong on food products, beverages and tobacco products industry. On
the other hand, 86 percent (142 / 165) are firms established after 1990 and rests 14
percent (23/ 165) are firms established before 1990. Hence, majority of selected firms
are younger. Overall, mainly observations (65 percent) come from younger firms
which belong to manufacture of food products, beverages and tobacco products indus-
try.
Table 2. Frequencies for 26 firms
Age
Industry Total
0 1
1 35 11 46
2 107 12 119
Total 142 23 165
Descriptive statistics presented in this section include the number of observations,
mean, standard deviation, minimum and maximum. As table 3 presents, there are 165
observations per each variable. From observed data there are cases where firms have
negative net cash flow from operating activities, i.e. inflows are less than outflows of
operating activities section. On the other hand, some firms have generated losses for
the analyzed period. This is why on the min column for these two variables there are
negative values. Even to, on average analyzed firms for the period 2007-2013 have
generated losses.
On average term based on obtained results from descriptive statistics, following in-
terpretation can be drawn for selected firms and analyzed period: trade credit provid-
ed and obtained are similar; for each 100 kn sale 2 kn losses is generated; the level of
cash to total assets is 1 percent; firms have financed their assets 14 percent with long-
term liabilities and 34 percent with short-term liabilities; for each 100 kn sale, 4 kn
net cash flow from operating activities is received; inventories to total assets partici-
pate with 15%.
Table 3. Summary statistics
Variable Obs. Mean Std. Dev. Min Max
Tr 165 0.14 0.09 0.00 0.38
Tp 165 0.14 0.07 0.01 0.38
prof 165 -0.02 0.18 -1.53 0.47
cash 165 0.01 0.02 0.00 0.07
longtfin 165 0.14 0.09 0.00 0.42
shorttfin 165 0.34 0.15 0.09 0.92
convsale 165 0.04 0.14 -0.39 0.76
inventratio 165 0.15 0.09 0.01 0.41
Source: own calculations.
Analyzing from the perspective of time horizon on average accounts receivable and
payable we found a decreased trend (table 4). But, on the total they are balanced.
Table 4. Summary statistics: mean by category of year
Year tr tp prof cash longtfin shortt~n convsale invent~o
2007 0.17 0.15 0.03 0.02 0.13 0.31 0.06 0.16
2008 0.17 0.15 -0.05 0.01 0.13 0.34 0.07 0.15
2009 0.13 0.12 -0.07 0.01 0.13 0.35 0.05 0.15
2010 0.14 0.13 -0.03 0.01 0.14 0.34 0.05 0.14
2011 0.13 0.15 0.00 0.01 0.17 0.32 0.04 0.15
2012 0.12 0.13 -0.02 0.01 0.16 0.34 0.00 0.16
2013 0.12 0.13 -0.01 0.01 0.14 0.37 0.02 0.17
Total 0.14 0.14 -0.02 0.01 0.14 0.34 0.04 0.15
Source: own calculations.
The level of profitability is very low, even in most cases and in total loss is generated.
The level of cash that firms hold is same per each year, except for 2007. Firms have
financed assets more with short-term liabilities rather than long-term liabilities, and
this is evident for each year. The ability of firms to convert sales into cash, on general
year after yeas have become lower. The trend of inventories to total assets ratio has a
slight increase.
Relate to firms that belong to agriculture, forestry and fishing industry we found that
(table 5): have provided less trade credit than firms belong to manufacture of food
products, beverages and tobacco products industry; have obtained less trade credit
than counterparties; have obtained more than have provided trade credit; are less prof-
itable than counterparties; have less cash stock than counterparties; have financed
assets slightly more with long-term liabilities and slightly less with short-term liabili-
ties than counterparties; have less ability to convert sales into cash and hold higher
inventory level than counterparties.
On the other hand, firms that belong on manufacture of food products, beverages
and tobacco products industry have provided more than have obtained trade credit.
Hence on term of the net trade credit (accounts payable – accounts receivable), firms
those belong on agriculture, forestry and fishing industry have a positive trade credit
comparing with counterparties which have a negative trade credit.
Table 5. Summary statistics: mean by category of industry
Industry tr tp prof cash longtfin shortt~n convsale invent~o
1 0.08 0.10 -0.08 0.01 0.15 0.33 0.01 0.19
2 0.17 0.15 0.00 0.02 0.14 0.34 0.05 0.14
Total 0.14 0.14 -0.02 0.01 0.14 0.34 0.04 0.15
Younger firms have higher level of trade credit provided and obtained comparing
with older firms (table 6). But, on term of the net trade credit older firms have a posi-
tive net trade credit comparing with younger firms which have a negative trade credit.
Older firms are likely to be slightly more profitable than younger ones (0.06 percent
versus -2.51 percent). Both, younger and older firms have same level of cash stock.
Younger firms have financed assets less with long-term liabilities and more with
short-term liabilities than counterparties. Younger firms are less capable to convert
sales into cash and hold slightly lower inventory levels than counterparties.
Table 6. Summary statistics: mean by category of age
Age tr tp prof cash longtfin shortt~n convsale invent~o
0 0.15 0.14 -0.03 0.01 0.13 0.34 0.04 0.15
1 0.10 0.13 0.00 0.01 0.18 0.31 0.07 0.16
Total 0.14 0.14 -0.02 0.01 0.14 0.34 0.04 0.15
Selected firms for the period 2007-2013 on average have capital-to-asset ratio (de-
fined as total capital and reserves divided by total assets) of 50%. This ratio has a
decreased trend line and this implies that leverage ratio has an increased trend line.
Empirical Analysis
Spearman analysis is performed and results are presented on table 7. Spearman analy-
sis has generated relevant evidence for analyzed trade credit and related variables.
Hence, a summarizing can be as follows.
Significant positive relationship is found between trade credit provided and ob-
tained. Significant positive relationships are found between trade credit provided,
profitability and cash. This means that firms which are more profitable and hold more
cash stock have provided more trade credit then counterparties. Firms with higher
cash stock and which are more able to convert sales into cash are more profitable then
counterparties. Perhaps profitable firms generate internal funds and are more able to
extend the collection period.
Significant positive relationships are found between trade credit obtained, cash,
short-term financing and converting sales into cash. This means that firms which have
higher level of cash stock, that use more short-term financing and are more able to
convert sales into cash obtained more trade credit than counterparties. Significant
negative relationships are found between profitability, long-term financing and short-
term financing. This means that firms with higher liabilities are less profitable than
counterparties. Significant positive relationship is found between inventory ratio and
short-term financing. This means that firms with higher ratio of inventory use more
short-term financing than counterparties.
Table 7. Spearman analysis
tr tp prof cash longtfin shortt~n convsale invent~o
tr 1
tp 0.396* 1
prof 0.176* -0.024 1
cash 0.544* 0.478* 0.293* 1
longtfin 0.051 -0.014 -0.269* -0.076 1
shorttfin 0.124 0.378* -0.230* 0.008 0.012 1
convsale 0.047 0.207* 0.306* 0.206* -0.048 -0.045 1
inventratio -0.056 0.053 -0.109 0.036 0.135 0.393* -0.006 1
* 0.05 Significance level.
On the other hand, four regression models are performed. Regression and performed
tests results are presented on table 8. In model TR1 profitability, cash, short-term
financing and inventory ratio are found statistically significant determinants which
have affected trade credit provided. In model TP1, cash and short-term financing are
found statistically significant determinants which have affected trade credit obtained.
In model TR2 profitability, cash and converting sales into cash are found statistically
significant determinants which have affected trade credit provided. In model TP2,
cash and short-term financing are found statistically significant determinants which
have affected trade credit obtained.
Industry 2 as dummy is found significant determinant for trade credit provided and
not for trade credit obtained. Time as dummy is found significant determinant for
trade credit provided just for 2008, respectively at 2009 for trade credit obtained.
Constant is found significant at all four models.
Table 8. Regressions results and tests
Variable TR1 TP1 TR2 TP2
0.09 0.03 0.07 0.01
prof
3.82* 1.17 3.96* 0.28
2.64 1.21 2.01 0.79
cash
3.83* 3.23* 2.51* 1.99*
0.04 -0.02 0.06 -0.03
longtfin
0.5 -0.31 0.72 -0.42
0.08 0.16 0.05 0.14
shorttfin
2.1* 2.88* 0.99 2.37*
-0.05 0.08 -0.08 0.06
convsale
-0.99 1.68 -2.28* 1.46
-0.21 -0.09 -0.08 -0.01
inventratio
-2.15 -0.91 -0.7 -0.12
0.06 0.04
_Iindustry_2
3.05* 1.27
0.02 0
_Iyear_2008
2.18* -0.22
-0.01 -0.04
_Iyear_2009
-0.75 -3.01*
-0.02 -0.02
_Iyear_2010
-0.83 -1.81
-0.02 0
_Iyear_2011
-0.98 -0.15
-0.02 -0.01
_Iyear_2012
-0.91 -1
-0.03 -0.03
_Iyear_2013
-1.19 -1.46
_cons 0.11 0.08 0.08 0.07
3.95* 4.24* 2.98* 2.87*
N 165 165 165 165
r2 0.28 0.22 0.38 0.31
legend: b/t; * 0.05 Significance level
Mean VIF 1.15 1.15 1.56 1.56
Lower VIF 1.04 1.04 1.08 1.08
Upper VIF 1.34 1.34 1.92 1.92
hettest (Prob > chi2) 0.51 0.00 0.09 0.11
linktest (_hatsq, t) -0.13 -1.82 -0.61 0.41
Controlling for mulitcollinearity, is used Variance Inflation Factor (VIF) as measure,
results of which are presented on table 8. Results indicate that for all variables mean
of VIF is lower than 10 per each model. Therefore, it means that multicollinearity is
not a problem for used models. Controlling for heteroskedasticity, is used Breusch-
Pagan / Cook-Weisberg test. Breusch-Pagan / Cook-Weisberg test for heteroskedas-
ticity (hettest) is done after performing regression without cluster. At this test the null
hypothesis says that the variance is homogeneous (constant variance), and alternative
hypothesis says that the variance is not homogenous. Results of tests are given on
table 8 according to models. In all cases, except for TP1, results of the test show that
p-values are higher than 0.05. Therefore, indicating that the variance of the residuals
is homogenous.
Controlling whether regression models are correctly specified is used a model
specification link test for single-equation models (linktest). Results of this test pre-
sented on table 8 denote that _hatsq is not significant in any model (t = -0.13 < 1.96, t
= -1.82 < 1.96, t = -0.61 < 1.96, t = 0.41 < 1.96). In case of TP2 specification is slight-
ly concerning even t = -1.82 is still lower than 1.96, i.e. not significant. Hence, gener-
ally it looks like that there is no specification error for models used in this study.
Moreover, comparing mean of trade credit provided and obtained before and after
crisis has generated following results as presented on table 9 and table 10. Two-tailed
p-value is 0.01. This value is lower than 0.05. Hence, coming to conclusion that the
mean difference of trade credit provided before and after crisis is different from zero.
With other words, on average trade credit provided by analyzed firms statistically
significantly is different view from the perspective of before and after crisis (see table
9).
Table 9. Mean of trade credit provided, before and after crisis
[95%
Group Obs. Mean Std. Err. Std. Dev. Interval]
Conf.
0 47 0.17 0.01 0.09 0.14 0.19
1 118 0.13 0.01 0.09 0.11 0.15
combined 165 0.14 0.01 0.09 0.13 0.15
diff 0.04 0.01 0.01 0.07
diff = mean(0) - mean(1) t = 2.56
Ho: diff = 0 degrees of freedom = 163
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.99 Pr(|T| > |t|) = 0.01 Pr(T > t) = 0.01
Two-tailed p-value is 0.06. This value is higher than 0.05. Hence, coming to conclu-
sion that the mean difference of trade credit obtained before and after crisis is not
different from zero. With other words, on average trade credit obtained by analyzed
firms is not significantly different view from the perspective of before and after crisis
(see table 10).
Table 10. Mean of trade credit obtained, before and after crisis
[95%
Group Obs Mean Std. Err. Std. Dev. Interval]
Conf.
0 47 0.15 0.01 0.08 0.13 0.17
1 118 0.13 0.01 0.06 0.12 0.14
combined 165 0.14 0.01 0.07 0.13 0.15
diff 0.02 0.01 -0.00 0.04
diff = mean(0) - mean(1) t = 1.92
Ho: diff = 0 degrees of freedom = 163
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.97 Pr(|T| > |t|) = 0.06 Pr(T > t) = 0.03
Table 11 presents mean of independent variables before and after financial crisis. As
this table present, a decrease is evidenced for profitability, cash and conversation
sales into cash. Long-term and short-term financing have increased. Inventories level
remains unchanged.
Table 11. Mean of independent variables, before and after crisis
Crisis prof cash longtfin shortt~n convsale invent~o
0 -0.01 0.02 0.13 0.32 0.06 0.15
1 -0.03 0.01 0.14 0.34 0.03 0.15
Total -0.02 0.01 0.14 0.34 0.04 0.15
5 Results and Discussions
Results obtained in this paper are related explicitly with selected firms and period.
The paper shows that divergences exist on trade credit according to industry and age.
Profitability, cash and converting sales into cash have determined the trade credit
provided. Cash and short-term financing has determined trade credit obtained. Profit-
ability and cash as statistically significant determinants have affected positively,
whereas converting sales into cash negatively to trade credit provided. Cash ratio is a
liquidity measure. Hence, firms that are more liquid and profitable have provided
more trade credit to their customers than counterparties firms. On the other hand, as
firms get less able to convert their sales into cash, trade credit provided to customers
gets higher.
Cash and short-term financing as statistically significant determinants have affect-
ed positively trade credit obtained. Accounts payable belong to short-term liabilities
section. Thus, as accounts payable are increased, trade credit obtained is increased
too. Liquidity again is an important determinant. Perhaps, creditors allow these firms
to buy on credit because of liquidity. Thus, as liquidity is increased, trade credit ob-
tained is increased too.
The correlation analysis among other results denotes also that a positive significant
relationship exists between trade credit provided and obtained. Hence, firms have
bought and sell on credit their goods and services.
Mean of trade credit provided and obtained after financial crisis has started be-
comes lower. Even to, in case of trade credit obtained the relationship is not signifi-
cant, still a decrease is evidenced. The difference is more pronounced at trade credit
provided than obtained. The expression mentioned earlier that cash is king seems that
better explains this result. Under financial crisis conditions firms may have tried to
decrease accounts receivable because cash was needed. Profitability and cash level
are decreased after crisis. But, the decrease in more pronounced at converting sales
into cash (from 6.5% to 3.4%). This implies that firms’ ability to convert sales into
cash is almost halved due to crisis. Thus, one way to compensate this drop maybe was
shorting the collection period and lowering accounts receivable. Love and Zaidi [29]
analysing a sample of SME-s in four East Asian countries before and after the finan-
cial crisis between November 1998 and February 1999 offered evidence which proves
that on average the use of trade credit declines and the cost of trade credit increases
following the crisis. On the other hand, Carbó-Valverde et al. [30] analyzing a sample
of Spanish SMEs during the recent crisis found evidence that trade creditors play a
role in the SME sector as lenders of last resort and this role becomes more important
during a credit crisis. Moreover, after crisis an increase is evidenced on debt financ-
ing. Accounts payable after crisis are decreased on average by 2%, whereas short-
term liabilities are increased by 2%. This clarifies that another type of short-term
liabilities is increased, probably borrowing, to finance worsen profitability and liquid-
ity.
6 Conclusions
The purpose of this paper was to analyze trade credit and its determinants for 26 non-
financial firms listed on the Zagreb Stock Exchange. The paper among other findings
revealed that: trade credit provided is positively significantly associated with profita-
bility and cash ratio, whereas negatively significantly associated with converting sales
into cash; trade credit obtained is positively significantly associated with cash ratio
and short-term financing; significant positive relationship is found between trade
credit provided and obtained; firms which are more profitable and hold more cash
stock have provided more trade credit then counterparties; firms which have higher
level of cash stock, that use more short-term financing and are more able to convert
sales into cash obtained more trade credit than counterparties.
The paper has own limitations as number of firms and the analyzed period. For fu-
ture studies it might be interesting to focus on the increase number of firms on sam-
ple, and adding new independent and dependent variables.
References
1. Yang, S. A., Birge, J. R.: Trade Credit, Risk Sharing, and Inventory Financing Portfolios.
Forthcoming, Management Science, (2017), Available at SSRN:
https://ssrn.com/abstract=2746645 or http://dx.doi.org/10.2139/ssrn.2746645.
2. Uchida, H., Udell, G.F., Watanabe, W.: Are Trade Creditors Relationship Lenders?. Japan
and the World Economy 25–26, 24–38 (2013).
3. Paul, S., Boden, R.: The Secret Life of UK Trade Credit Supply: Setting a New Research
Agenda. British Accounting Review 40, 272-281 (2008).
4. Bernstein, L. A., Wild, J. J.: Financial Statements Analysis: Theory, Application, and Inter-
pretation. Boston: Irwin/McGraw-Hill (1998).
5. Guariglia, A., Mateut, S.: Credit Channel, Trade Credit Channel, and Inventory Investment:
Evidence from a Panel of UK Firms. Journal of Banking and Finance 30(10), 2835–2856
(2006).
6. Yang, X.: Trade Credit versus Bank Credit: Evidence from Corporate Inventory Financing.
Quarterly Review of Economics and Finance 51(4), 419–43 (2011).
7. Fisman, R., Love, I.: Trade Credit, Financial Intermediary Development, and Industry
Growth. Journal of Finance 58(1), 353-374 (2003).
8. Altunok, F.: Determinants of Trade Credit Contract Terms. North Carolina State University
(2011).
9. Delannay, A.F., Weill, L.: The Determinants of Trade Credit in Transition Countries. Eco-
nomics of Planning 37(3-4), 173-193 (2004).
10.Petersen, M.A., Rajan, R.G.: Trade Credit: Theories and Evidence. Review of Financial
Studies 10(3), 661-669 (1997).
11.Garcia-Teruel, P. J., Martinez-Solano, P.: A Dynamic Perspective on the Determinants of
Accounts Payable. Review of Quantitative Finance and Accounting 34(4), 439-457 (2010).
12.Frank, Z. M., Maksimovic, V.: Trade Credit, Collateral, and Adverse Selection (2005),
available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=87868.
13.Cull, R., Colin Xu, L., Zhu, T.: Formal Finance and Trade Credit during China's Transition.
Journal of Financial Intermediation 18(2), 173–192 (2009).
14.Nilsen, J. H.: Trade Credit and the Bank Lending Channel. Journal of Money, Credit, and
Banking 34, 226–253 (2002).
15.Afrifa, G.A., Gyapong, E.: Net Trade Credit: what are the Determinants?. International
Journal of Managerial Finance 13(3), 246-266 (2017). doi:https://doi.org/10.1108/IJMF-12-
2015-0222
16.Ge, Y, Qiu, J.: Financial Development, Bank Discrimination and Trade Credit. Journal of
Banking and Finance 31(2), 513–530 (2007).
17.Wu, W., Firth, M., Rui, O.M.: Trust and the Provision of Trade Credit. Journal of Banking
and Finance 39, 146–159 (2014).
18.Lee, H.-H., Zhou, J., Wang, J.: Trade Credit Financing Under Competition and Its Impact on
Firm Performance in Supply Chain. Manufacturing & Service Operations Management
20(1) (2017a), DOI:10.1287/msom.2017.0640.
19.Kohler, M., Britton, E., Yates, T.: Trade Credit and the Monetary Transmission Mechanism.
Bank of England, London (2000).
20.Love, I., Preve, L.A., Sarria-Allende, V.: Trade Credit and Bank Credit: Evidence from
Recent Financial Crises. Journal of Financial Economics 83(2), 453–469 (2007).
21.Wu, W., Rui, O.M., Wu, C.: Trade Credit, Cash Holdings, and Financial Deepening: Evi-
dence from a transitional economy. Journal of Banking and Finance 36(11), 2868–2883
(2012).
22.Casey, E., O'Toole, C.M.: Bank Lending Constraints, Trade Credit and Alternative Financ-
ing during the Financial Crisis: Evidence from European SMEs. Journal of Corporate Fi-
nance 27, 173–193 (2014).
23.Lee, H.-H., Zhou, J., Wang, J.: Impact of Trade Credit Financing on Firm Performance in
Supply Chains. Foundations and Trends® in Technology, Information and Operations
Management 10(3-4), 253-269 (2017b). http://dx.doi.org/10.1561/0200000062
24.Garcia-Appendini, E., Montoriol-Garriga, J.: Firms as Liquidity Providers: Evidence from
the 2007–2008 Financial Crisis. Journal of Financial Economics 109, 272–291 (2013).
25.Grave, Th.: Trade Credit as a Shock Absorber?: The Effects of the Financial Crisis on the
use of Trade Credit by Dutch Manufacturing Firms. University of Twente (2011).
26.Eurostat, http://ec.europa.eu/eurostat/data/database [online].
27.Wiggins, V.: StataCorp, Comparing xtgls with Regress, vce(cluster). STATA.
http://www.stata.com/support/faqs/statistics/xtgls-versus-regress/ (1999; 2011).
28.Alatalo, R.: Trade Credit Use During a Financial Crisis: Evidence from the U.K. of a Non-
Existing Trade Credit Channel. Aalto University, School of Economics (2010).
29.Love, I., Zaidi, R.: Trade Credit, Bank Credit and Financial Crisis. International Review of
Finance 10(1), 125-147 (2010) https://doi.org/10.1111/j.1468-2443.2009.01100.x
30.Carbó-Valverde, S., Rodríguez-Fernández, F., Udell, F. G.: Trade Credit, the Financial
Crisis, and SME Access to Finance, 26th Australasian Finance and Banking Conference
(2013).