trade credit in europe
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Trade credit in Europe
Paper prepared for the 31st Meeting of the
EUROPEAN WORKING GROUP ON FINANCIAL MODELING (EWGFM),
November 7-9, 2002, Agia Napa, CYPRUS
By
Nico van der Wijst and Suzan Hol
Norwegian University of Science and Technology
N-7491 Trondheim, Norway
Preliminary version, all comments welcome
ABSTRACT:
The study of trade credit is well justified by its importance as a source of
finance for the corporate sector in most western countries. Trade credit
is special, however, because its use may be driven by different factors
compared to other sources of finance. Trade credit may follow the flow of
goods and services through a company, and thus be sensitive to
operational rather than financial factors. Further, firms that supply trade
credit assume the role of financial intermediaries. This role is commonly
considered to be based on the possibilities to resolve market
imperfections in the normal course of business and not as a separate
activity. This may eliminate, or even reverse, the effect of market
imperfections compared to other forms of financial intermediation such
as banking.
This paper addresses these issues from a theoretical as well as an
empirical point of view. It discusses the theories of trade credit found in
the literature and the market imperfections on which they are based.
Their conclusions are summarized in a number of hypotheses that are
tested empirically. The data used in the study refer to different European
countries. To our knowledge, trade credit has not been studied on this
European wide scale before. The main conclusion of this paper is that
receivables and payables generally are not influenced in opposite
directions by the determinants suggested in the literature, but in the
same direction. Further a strong industry influence is found in the use of
trade credit.
1 IntroductionBy all measures, trade credit is a major source of financing for the
corporate sector. At the end of 1998, US corporations had $2.501 billion
(109) outstanding in accounts payable1, roughly a quarter of their total
debt. This amount also represents roughly a quarter of the total market
value of all shares on NYSE2 in December 1998. Further, vendor
financing is reported to have accounted for approximately 2.5 times the
combined value of all new public debt and primary equity issues in the
USA during a given year in the 1990s (Ng, Smith and Smith, 1999) In
Europe, trade credit is typically 20% to 25% of total liabilities, but
occasionally it can be as high as 80% some industries and low as 5% in
others.
In addition to its obvious importance, trade credit deserves special
attention for at least two other reasons. First, trade credit is generated
by the flow of goods and services through a company rather than by
financial decisions. This could make the use of trade credit dependent on
the determinants of those flows, i.e. operational rather than financial
factors. To the extent that this is the case, trade credit will behave as
noise in models that are exclusively based on financial variables. This
could cause an important omitted variable bias in models of e.g. capital
structure and debt maturity structure. Second, firms that supply trade
credit assume the role of financial intermediaries. This role is commonly
considered to be based on the possibilities to resolve market
imperfections in the normal course of business and not as a separate
activity. This may eliminate, or even reverse, the effect of market
imperfections compared to other forms of financial intermediation such
as banking. This also suggests that trade credit is at least partly
determined by different factors than the other liabilities.
On the other hand, there is probably no area in finance that is so
much pervaded by conventions, rules of thumb and traditions as trade
credit is. Textbooks tell us that, for example, shoe manufacturers
commonly use “5/10, net 30” as terms of sale, while toy manufacturers
generally sell goods on terms of “2/30, net 50” (Brealey and Myers
(2000)). The most usual terms of trade in various industries are published
in handbooks and they are probably widely used as reference by
newcomers in an industry. This suggests that trade credit may also partly
be determined by conventions rather than financial policy.
The purpose of this paper is to analyze the determinants of trade
credit from a theoretical as well as an empirical point of view. The
theoretical determinants are surveyed in a study of the literature. Their
1 Statistical abstract of the United States 2001, http://www.census.gov/2 http://www.nyse.com
1
conclusions are formulated in hypotheses that are tested in the empirical
part of the study. The tests are performed on a large database comprising
industry average data of 10 European countries over a number of years.
To our knowledge, trade credit has not been studied on this European
wide scale before.
The rest of this paper is organised as follows. Section 2 summarizes
the theoretical and empirical literature on trade credit. The data used in
this study are described in section 3, while section 4 presents the models
and the estimation results. Conclusions are formulated in section 5.
2 Literature review
2.2 Theoretical studies
Although trade credit is a comparatively ‘small’ topic in finance, many
theories explaining its use have been presented over the years. The
number of empirical studies is correspondingly large. A common
denominator in many theories of trade credit is that it offers the
possibility to resolve market imperfections in the normal course of
business and not as a separate activity. This can make trade credit a
more efficient way to resolve market imperfections than other forms of
financial intermediation such as banking. For instance, the normal cycle
of ordering, delivery and payment may generate information that
otherwise would remain hidden or would be costly to obtain by parties
not involved in the transaction. The market imperfections incorporated in
theories of trade credit are summarised in Table 1.
- Information asymmetries:
-default risk (Smith, 1987)
-monitoring costs (Jain, 2001)
-product quality (Long, Malitz and Ravid, 1993) , (Lee and Stowe,
1993)
- Arbitrage:
-tax differences (Brick and Fung, 1984)
-interest rate differences (Emery, 1984), (Schwartz, 1974)
- Bankruptcy costs (Mian and Smith, 1992)
- Transaction costs (Ferris, 1981)
Table 1: Market imperfections modelled in theories of trade credit
Smith (1987) views trade credit as a contractual device for dealing with
informational asymmetry. The terms are set such that they function as a
screening contract that elicits information about buyer default risk: the
2
seller learns the state of the buyer from the fact that it takes the
expensive trade credit or not. The value of this information can be higher
to a seller who made a nonsalvageable investment in the buyer than it is
to an outside financier. This theory predicts that trade credit terms will
be relatively uniform within industries but differ between industries
depending on the size of nonsalvageable investments in buyers. Cash or
net terms are expected when these investments are not significant, while
deep cash discounts are expected in risky industries where goods are
subject to more volatility in value.
A similar line of reasoning is followed in Jain (2001) and Mian and
Smith (1992) where it is argued that sellers can inspect the buyers’
financial position at lower costs than outside parties as banks. These
lower monitoring costs justify a role for the sellers as financial
intermediaries between banks and the buyers. This theory predicts that
trade credit is more important in industries that are more concentrated
on the supply side and less concentrated on the demand side (such as the
wholesale and retail trade) and in industries where monitoring costs are
more severe (e.g. manufacturing).
Trade credit can also play a role in signalling information about
product quality. This idea is elaborated in, among others, Long et al.
(1993) and Lee and Stowe (1993). When there is informational
asymmetry about product quality, trade credit can serve to distinguish
high and low quality goods and producers. Thus, trade credit can be
interpreted as an implicit warranty guaranteeing product quality by
giving the buyer a net period over which to test the product. This relates
trade credit to the length of the production cycle, the difficulty of
ascertaining product quality and possibly size (as proxy for established
reputation).
Another financial approach to trade credit, and one of the earliest, is
the suggestion that it reflects arbitrage. Emery (1984) argues that when
different firms are confronted with different borrowing and lending rates,
trade credit may be used to arbitrage the difference. Similarly, Schwartz
(1974) develops a model that suggests that trade credit will flow
predominantly from firms that have relatively easy access to capital
markets to firms that have productive uses for funds but relatively poor
access to capital markets. Thus, better-established firms that have
already enjoyed a substantial rate of growth and profitability are led to
participate in the financing of smaller, newer firms.
Alternatively, Brick and Fung (1984) bases its model on tax
differences. Firms in high tax brackets can gain by offering trade credit
to firms in lower tax brackets. An empirical implication is that, within an
industry, buyers prefer trade credit if their tax bracket is lower than that
of the seller.
3
Ferris (1981) presents a transactions theory of trade credit, in which
trade credit is viewed as an instrument facilitating the exchange of
goods. This theory ties trade credit use to the variability and uncertainty
in the firms trading flows. Trade credit can substantially reduce
transaction costs by separating the exchange of goods from the exchange
of money, e.g. by paying bills only monthly rather than every time goods
are delivered. This enables the planning of payments and by letting
payments accumulate the patterns of receipts and disbursements can be
matched. It greatly simplifies cash management. The transactions motive
is widely presumed to underly a substantial part of the aggregate stock of
trade credit, even in studies that analyse financing motives (e.g. Schwartz
(1974), and Nilsen (2002)).
Finally, if a buyer defaults, the seller is likely to be in a better
position to reclaim value from repossessed goods than a financial
institution. This is argued by Mian and Smith (1992) in an extensive study
of receivables management policies. Since the seller already has the
expertise and the network to sell the goods, the costs of repossessing and
resale will be lower. The more durable the goods are, and the less they
are transformed by the buyer, the greater the advantage of the seller
over financial institutions will be.
Some other theories stress the importance of trade credit as a
marketing instrument e.g. Emery (1987). When firms face a highly
seasonal demand, varying price or production or using customer- or
product-queues (inventories) may be comparatively costly. Varying the
terms of trade credit, i.e. providing more lenient terms in slack periods,
can be an efficient alternative. This reduces the effective price that
buyers pay, while leaving the nominal price unaltered.
Brennan et al. (1988) and Mian and Smith (1992) take this argument
a step further and suggest that trade credit may be used to effectuate
price discrimination in situations where ‘normal’ price discrimination is
either too costly or illegal.
As a final remark it is noted that many of the above theories argue
for a different role of trade credit compared with other forms of finance.
This can be illustrated with the tax effect. In Brick and Fung (1984, p.
1174) it is argued that, all other things being equal, buyers with low
effective tax rates would prefer trade credit and therefore are more likely
to have higher levels of accounts payable relative to similar buyers with a
higher effective tax rate. In the neo-classical trade-off theory of capital
structure, on the other hand, debt levels are chosen as a trade-off
between the expected tax advantage of debt and the expected costs of
financial distress. In this theory, low effective tax rates are associated
with low levels of debt. Similar arguments apply to the advantage that
trade partners have in collecting information or recovering value from a
4
bankrupt estate. This clearly illustrates the hazards of analysing capital
structure without regard for debt maturity and the composition of short-
term debt in trade credit and other short-term debt.
2.2 Empirical studies
In view of the variety of different theories of trade credit, it is hardly
surprising that the empirical evidence, both inside and outside the
context of these theories, is rather mixed. Table 2 summarises the
support found for the various theories in different empirical studies.
The arbitrage-based theories seem to get the least support in the
empirical literature, while informational asymmetries get the widest
support. Among the marketing instrument theories, demand variation
smoothing gets no support while price discrimination is supported by
more than one study. The widest support by far is found for the
transactions theory. Early studies as Herbst (1974) as well as more
recent papers like Ferris (1981), Long et al. (1993) and Nilsen (2002)
underline the importance of the transactions theory.
The evidence regarding the effect of monetary policy variables on
trade credit is also mixed. Ng et al. (1999) report that trade credit
policies are stable over time and do not vary with interest rates.
However, Nilsen (2002) concludes that non-rated firms increase their use
of trade credit in period of contractionary monetary policy. A similar
conclusion was reached much earlier by Nadiri (1969).
Finally, descriptive statistics of trade credit policies in different
periods, industries and countries can be found in several studies, see e.g.
Petersen and Rajan (1997), Ng et al. (1999) and Wilson and Summers
(2002).
- Information asymmetries
-default risk/monitoring costs: supported Petersen & Rajan ‘97
-product quality: supported Long et al ’93, Ng et al, ’99
- Arbitrage
-tax differences: not supported Long et al ’93
-interest rate differences/liquidity: not supported Long et al ’93, Ng et
al, ’99
- Bankruptcy costs: supported Petersen & Rajan ‘97
- Transaction costs minimization: supported Herbst, ’74, Ferris, ’81,
Long et al ’93,
Nilsen, 2002
- Price discrimination: supported Petersen & Rajan ’97, Ng et al,
’99
5
- Demand variation smoothing: not supported Ng et al, ’99
Table 2: Empirical evidence regarding theories of trade credit
3 Data and descriptive analysis
3.1 Data
The data used in this paper were collected and made available by the
European Commission, Directorate General Economic and Financial
Affairs, BACH database. BACH (Bank for the Accounts of Companies
Harmonised) is a database containing harmonised annual accounts
statistics of non-financial enterprises for 11 European countries, plus
Japan and the United States. In this paper 10 European countries are
used; the eleventh European country in the database (Finland) had too
many missing observations. An overview over the countries used in this
paper and their codes are given in the appendix. The BACH database is
composed by institutions from the twelve European Union Member States
and European Commission and OECD. Members are Oesterreichische
Nationalbank, Bank Nationale de Belgique, Bank of England, Statistics
Finland, Banque de France, Deutsche Bundesbank, Bank of Greece,
Central Bank of Ireland, Centrale dei Bilanci, Centraal Bureau voor de
Statistiek, Banco de Portugal, and Banco de España.
The accounts are 'harmonised' through a common layout for balance
sheet, profit and loss accounts, statements of investments and statements
of depreciation. They are based on the Fourth Council Directive
(78/660/EEC of July 1978). All data are given in amounts in the local
currency and in current prices, i.e. in 1000 NLG for the Netherlands,
1000 BEF for Belgium, 1000 FRF in France, 1 million ESP in Spain, 1000
PTE in Portugal, 1 million ITL in Italy, 1000 ATS in Austria, 1000 DKK in
Denmark, 1 million SEK in Sweden and 1 million DM in Germany.
More information about the database can be found on the website of
the European Commission of Economic and Financial Affairs:
http://europa.eu.int/comm/economy_finance/indicators/bachdatabase/bachdatabase_whatisbach_en.htm
In each country, we have taken the most detailed sectoral information
possible. This means the sectors with BACH code 1 (energy and water,
including refining industry), BACH code 3 (building and civil
engineering), BACH code 5 (transport and communication) and BACH
code 6 (other services) on a one-digit level. The sectors with BACH code 4
(trade) are available on a two-digit level. Finally, the sectors with BACH
6
code 2 (manufacturing industry) are available on a three-digit level.3 A
conversion table from BACH to NACE codes is given in the appendix.
Using industry average data has the advantage over the obvious
alternative of individual data that idiosyncratic elements and
measurements errors can be assumed to average out. Moreover, industry
average data are more often publicly available, as the BACH database
illustrates, and better suited to construct homogeneous time-series. The
data used here have a panel character. This substantially enhances the
possibilities to analyse these data. The disadvantage of this type of data is
also obvious: average behaviour, if it exists at all, may be hard to explain.
However, Ng et al. (1999) report that trade credit terms show wide
variation across industries but little variation within industries. To the
extent that this observation is also valid for the data at hand, this justifies
the analysis of industry averages.
3.2 Descriptive analysisTo give an impression of the variation and structure in the data, we first present a
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
31
32
33
34
39
43
45
46
49
35
1
Country Code
AR/TA
AP/TL
Figure 1: Accounts receivable over total assets (AR/TA) and accounts payable over total liabilities (AP/TL) for sector BACH code 2 in 1996. 4
graph of the percentages of accounts payable and receivable in the various countries. The amount of accounts payable in the total liabilities and the amount of accounts receivable in total assets in 1996 for sector 2 are given in Figure 1 above. In this randomly chosen year, the percentage of accounts payable varies between the countries from around 10 to over 40%, while the percentage of accounts receivable ranges from around 5 to over 30%. When we look at the whole dataset, the maximum percentages of accounts payable in any year in any sector ranges from 30% (in the Netherlands) to 83% (in Denmark). Similar numbers for the percentage of accounts receivable are 27% (in Denmark) to 58% (in Spain).
3 Some sectors were not available for the following countries: Denmark (sectors 5, 6, 41, 42, 44), Germany (sectors 1, 5, 6, 42, 44) and Portugal (sectors 6, 42, 43, 44). 4 In 1992 for the Netherlands (country code 31).
7
Next, we present a table with the same ratios per country and
industry. Table 3 shows this information across different industries
(codes 1, 3, 5, 6) and within the manufacturing industry (211-234).
S
C 1 3 5 6 21
1
21
2
21
3
22
1
22
2
22
3
23
1
23
2
23
3
23
4
31 ar/
ta
10 24 6 15 12 12 3 15 6 7 9 12 10 13
ap/
tl
10 26 8 10 14 15 7 18 8 21 15 13 13 16
32 ar/
ta
10 29 16 9 15 11 15 27 24 18 18 28 20 24
ap/
tl
16 32 21 10 20 14 27 32 34 38 26 36 26 36
33 ar/
ta
8 32 4 19 24 21 20 31 31 19 20 28 25 34
ap/
tl
7 30 6 14 34 29 31 32 37 34 32 34 34 39
34 ar/
ta
7 49 7 11 16 17 30 36 41 24 23 34 29 35
ap/
tl
15 48 11 12 27 29 41 34 47 54 38 33 34 45
39 ar/
ta
10 35 11 40 31 26 32 37 38 29 30 36 33 37
ap/
tl
17 30 24 46 28 30 38 38 41 42 34 37 37 40
43 ar/
ta
4 22 15 6 11 9 14 14 11 9 13 19 13 13
ap/
tl
8 15 21 11 20 10 16 14 17 18 21 15 16 16
45 ar/
ta
11 24 NA NA 17 12 13 21 17 13 12 14 17 15
ap/
tl
11 23 NA NA 21 18 14 18 22 27 16 16 15 16
46 ar/
ta
5 14 9 6 8 8 6 9 7 4 12 18 5 11
ap/
tl
5 14 13 8 24 12 14 12 9 9 22 23 8 18
49 ar/
ta
NA 11 NA NA 10 8 7 16 13 7 12 18 15 17
ap/ NA 14 NA NA 22 16 17 17 15 31 27 28 22 18
8
tl
35
1
ar/
ta
5 28 6 NA 17 13 28 27 28 24 21 22 17 25
ap/
tl
7 30 8 NA 30 22 44 30 43 23 24 30 24 30
Table 3: Overview over percentage of accounts receivable over total assets (ar/ta) and accounts payable over total liabilities (ap/tl) for all sectors (S) in all countries (C) in the year 1996. Exception: for the Netherlands (31) 1996 was not available, so 1992 is used.
This breakdown represents roughly the lowest aggregation level available
in the data. Based on this, we calculated the largest difference between
the percentages of accounts payable / accounts receivable between the
different sectors (codes 1, 3, 5, 6), and within sector 2 (manufacturing).
Manufacturing is the only sector with enough sub sectors to make this
operation meaningful. This calculation was done for every country
resulting in two numbers for every item (accounts receivable or accounts
payable) for every country. The general conclusion from these
calculations is that the difference within sectors is smaller than the
difference between sectors. Although this analysis is crude and far from
conclusive, it does indicate support for the findings from Ng. et al., that
the differences between sectors are larger than within sectors.
4 Model and estimation resultsThe empirical model is constructed to reflect, as far as the data allow, the
theoretical determinants discussed in section 2. The following influences
and their proxy-variables are included in the model:
- Informational asymmetry: A high degree of informational asymmetry
is associated with, among other things, high monitoring costs, which
make debt financing relatively unattractive. However, trade partners
avoid monitoring costs by collecting information in normal course of
business, which gives them, other things equal, an advantage vis-à-vis
informational asymmetry. This makes the hypothesized effect on
accounts payable positive. A corresponding effect on accounts
receivable is less obvious and therefore not formulated. The variable
investments in daughter companies (as a fraction of total assets) is
used as a proxy variable for informational asymmetry. Firms and
industries that are characterized by a high degree of cross investments
are relatively intransparent, necessitating high monitoring costs.
- Taxes: As was argued before, trade credit is expected to flow from
firms in high tax brackets to those in low brackets. So low tax rates are
9
hypothesized to be associated with high levels of accounts payable and
low levels of receivables. As no direct measure of the tax rate is
available, the alternative non-debt tax shields represented by
depreciation charges are used to approximate (inversely) the tax effect.
- Interest rate: the arbitrage argument specifies that trade credit flows
from firms with a low interest rate to firms with a high one. This means
a positive effect on accounts payable and a negative effect on accounts
receivable. However, the endogenous interest rate used here (total
interest paid/total liabilities) also reflects cross sectional differences in
riskiness, which makes the total effect ambiguous.
- Industry dummy variable: are included to capture industry specific
effect not represented by other variables. Dummy variables for the first
digit sectors are included (see the appendix for an overview of the first
digit sectors). The “last” available sector dummy (usually for sector 6)
is omitted to avoid perfect multicollinearity in combination with the
intercept. No hypotheses are formulated regarding the sign or the size
of the influence of these dummies.
- Size: sales and costs of goods sold are used as a size variable, but in a
flexible specification (see below) that allows for scale effects. Different
size variables are used to avoid bias caused by differences in gross
margin. As trade credit is expected to flow predominantly from larger
better-established firms to the smaller, newer ones, the ratio of
accounts payable to size is expected to decrease with size. The
opposite effect is expected for accounts receivable: the ratio of
receivables to size is expected to increase with size. This means that a
positive intercept is expected for receivables and a negative intercept
for payables.
The following specification is used to estimate the relations and test the
hypotheses:
(1)
where:
- Y = variable to be explained (accounts receivable and accounts
payable)
- Size = size variable (turnover and cost of goods sold for receivables
and payables respectively)
- invd = investments in affiliated and daughter companies,
10
- depr = depreciation charges,
- inr = interest payments on debt,
- ta= total assets,
- tl = total liabilities minus trade credit,
- dumsi is a dummy for sector i
- a, bi = coefficients to be estimated.
Note that in the above equation:
scale effects are expressed as the intercept a. If a > 0 then the ratio
of AR or AP to size decreases with size, while the reverse is true if a <
0. The former is expected for accounts payable, the latter for accounts
receivables.
if a, b2-4 and the dummy variables are all = 0, then the equation boils
down to the ratios of AR and AP to size. This facilitates an easy
interpretation of the results.
for the non-size variables a multiplicative specification is used
because they are expected to influence the b1 coefficients rather than
the absolute amount of debt. An exponentional specification is chosen
for the dummy variables, which can take zero value.
Equation (1) is not linear and the results are provided with the non-linear
least squares procedure in the SPSS package, using the Levenberg-
Marquardt algorithm. The hypotheses are summarized in Table 4 below
in terms of the expected signs of the coefficients of the variables:
Expected signs
Ascale effect
B1size
B2inf.
asymm.
B3tax
B4interest
AR Negative Positive Negative NegativeNegative
AP Positive Positive Positive Positive Positive
Table 4: Hypothesized signs for accounts receivable (AR) and accounts
payable (AP)
The estimation results based on the pooled panel data are given in table
5.
CountryYears
A B1 B2 B3 B4 R2 N
Netherla
nds (31)
AR 3.58 105 s
(0.72 105)
0.011 s
(0.005)
-0.066 s
(0.023)
-0.452 s
(0.095)
-0.170
(0.103)
0.92
1
216
1981-
1992
AP 2.17 105 s
(0.38 105 )
0.152 s
(0.015)
-0.055 s
(0.017)
0.245 s
(0.072)
0.100
(0.079)
0.94
8
11
Belgium
(32)
AR 2.40 106
(2.59 106 )
0.009 s
(0.003)
NA -1.028 s
(0.087)
0.117 s
(0.052)
0.98
6
198
1989-
1999
AP 4.98 106 s
(1.52 106)
0.164 s
(0.027)
NA -0.105 s
(0.040)
-0.072
(0.037)
0.99
2
France
(33)
AR -1.76 106
(1.40 106)
0.034 s
(0.011)
-0.171 s
(0.031)
-0.521 s
(0.077)
0.017
(0.046)
0.86
2
336
1984–
1999
AP -6.54 105
(6.68 105)
0.003 s
(0.000)
-0.094 s
(0.020)
0.148 s
(0.042)
-0.090 s
(0.027)
0.95
6
Spain
(34)
AR 4.89 104 s
(1.21 104)
0.120 s
(0.040)
0.041
(0.035)
-0.372 s
(0.064)
0.134 s
(0.042)
0.88
7
306
1983–
1999
AP 4.16 104 s
(0.63 104)
1.018 s
(0 .253)
-0.110 s
(0.027)
0.349 s
(0.045)
-0.137 s
(0.031)
0.94
4
Italy (39) AR -2.45 106 s
(0.45 106)
0.377 s
(0.081)
0.036
(0.032)
-0.201 s
(0.065)
0.070 s
(0.032)
0.92
4
324
1982–
1999
AP -0.87 105
(2.99 105)
0.573 s
(0.118)
0.064 s
(0.032)
0.035
(0.082)
-0.024
(0.031)
0.93
2
12
CountryYears
A B1 B2 B3 B4 R2 N
Austria
(43 5)
AR 2.88 105
(2.22 105)
0.003 s
(0.001)
NA -0.973 s
(0.078)
NA 0.85
8 360
1980–
1999
AP 1.09 106 s
(0.15 106)
0.023 s
(0.008)
NA -0.571 s
(0.093)
NA 0.83
6
Denmark
(45 6 7)
AR 3.97 105
(2.32 105)
0.203
(0.142)
-0.016
(0.060)
0.483 s
(0.196)
-0.357 s
(0.155)
0.87
9
183
1983–
1998
AP 7.81 105 s
(1.29 105)
2.117
(1.377)
-0.021
(0.049)
0.902 s
(0.223)
-0.084
(0.154)
0.93
5
Sweden
(46)
AR -1764.630
(942.807)
0.034 s
(0.011)
-0.032
(0.062)
-0.453 s
(0.112)
0.213 s
(0.060)
0.87
9
126
1991–
1997
AP -337.050 s
(325.902)
0.052 s
(0.008)
-0.067 s
(0.028)
-0.238 s
(0 .053)
0.153 s
(0.028)
0.97
1
Germany
(49)
AR 435.667
(369.841)
0.001 s
(0.000)
-0.227 s
(0.043)
-1.193 s
(0.080)
0.209 s
(0.068)
0.95
1
143
1987–
1997
AP 462.935 s
(163.293)
0.048 s
(0.010)
-0.270 s
(0.032)
-0.139 s
(0.040)
0.006
(0.044)
0.97
5
Portugal
(351 2)
AR 2.66 107 s
(0.37 107)
0.015 s
(0.005)
-0.068 s
(0.027)
-0.524 s
(0.101)
-0.169 s
(0.041)
0.98
2
140
1990–
1999
AP 2.07 107 s
(0.72 107)
0.142 s
(0.066)
0.023
(0.033)
-0.051
(0.127)
-0.059
(0.044)
0.98
4Table 5: Results of the non-linear regression for 10 European countries. Standard errors are given in brackets. If a variable is significant different from zero at 5% level, this is indicated with a ‘s’.
Inspecting Table 5 we note the following. The hypothesized opposite
scale effects are not found in the data: the switch in the signs of the
intercepts does not occur at all. The intercepts in payables are positive,
as expected, and significantly so in 7 of the 10 cases. A significantly
negative intercept is found in one case. To a lesser degree, the same
results are found in receivables, however: positive intercepts in 7 cases,
5 Other value adjustments and provisions are included in the depreciation
charges. 6 Other charges are added to interest paid on financial debts. 7 Trade credit (accounts payable) is constructed from total short-term debt minus other short-term debt.
13
and significantly positive in 3. The hypothesized negative intercept is
found only in 3 cases, 1 of which is significantly < 0.
In all the tested countries, the size variables are positive though not
always significantly so. In nine out of the ten countries, the size variable
for both accounts receivable and accounts payable is significant.
The signs of the coefficients of the information asymmetry proxy,
investments in affiliated and daughter companies, are predominantly
negative, both in the results for payables and receivables. The
hypothesized positive effect on payables is found in 2 cases, only once of
which is significantly > 0. Again, no significant opposite effects are found.
In six out of the available eight countries (for two countries this variable
was not available), this variable has the hypothesized sign for accounts
receivable, of which four are significant.
The hypotheses regarding taxes are supported more often. In four of
the ten countries this hypothesis is supported, with mainly significant
coefficients. In the other countries no switch in sign is found, although
the variable is often significant: in five cases the variable is as expected
for accounts receivable only (not payables). In the remaining country the
sign is only correct for accounts payable. In all, the coefficient of the tax
variable is significant in 18 out of 20 cases. Given the fact that these data
are from many countries with large differences in tax systems, these
results indicate that the tax shield proxy is a good variable to explain
trade credit.
Finally, the hypothesized switch in sign in the effect of interest rate
on accounts receivable and accounts payable was found in 1 out of 9
cases but the coefficients were not significant (in one country the interest
rate on debts was not available). With ten out of the eighteen estimated
coefficients significantly ≠ 0, there is an effect of the interest rate on
trade credit, but the effects on payables and receivables are often not in
the expected direction.
The R2 of the regressions are usually around 0.9, which is high but
not unusual for the specification used.
14
5 ConclusionMost theories of trade credit specify, implicitly or explicitly, different
effects on accounts receivable and payable. This is to a large extent
obvious: the factors that stimulate taking up trade credit from suppliers
(e.g. capital scarcity) deter the supply of it to customers. So an analysis of
both receivables and payables with largely the same model should
produce opposite effects. Surprisingly, more often than not the same
effects are found, as Table 6 below illustrates.
Ascale effect
B2inf.
asymm.
B3tax
B4interest total
same effects 10 6 6 4 26
opposite
effects
0 2 4 5 11
Table 6: Qualitative summary of effects
In sign and significance, the tax effect comes closest to the hypotheses
but even there the rejections are the majority.
Concluding, it appears that the financial explaining trade credit, only give
a partial explanation and that evidently important factors are missing in
the model used here.
15
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17
AppendixDescription of the countries in the BACH database and their codes.
CODE COUNTRY CODE COUNTRY
31 The Netherlands
43 Austria
32 Belgium 45 Denmark
33 France 46 Sweden
34 Spain 49 Germany
39 Italy 351 Portugal
Description of the sectors in the BACH database, and conversion table from BACH sector codes to new NACE codes. CODE SECTOR NACE Rev. 1
1 ENERGY AND WATER (including refining industry)
10 + 11 + 12 + 23 + 40 + 41
2 MANUFACTURING INDUSTRY
21 Intermediate products
211 Extraction of metalliferous ores and preliminary processing of metal
13 + 27.1 + 27.2 + 27.3 + 27.4
212 Extraction of non-metalliferous ores and manufacture of non-metallic mineral products
14 + 26
213 Chemicals and man-made fibres 24
22 Investment goods and consumer durables
221 Manufacture of metal articles, mechanical and instrument engineering
27.5 + 28 + 29.1-6 + 33
222 Electrical and electronic equipment including office and computing equipment
30 + 31 + 32 + 29.7
223 Manufacture of transport equipment 34 + 35
23 Non-durable consumption goods
231 Food, drink and tobacco 15 + 16
232 Textiles, leather and clothing 17 + 18 + 19
233 Timber and paper manufacture, printing 20 + 21 + 22
234 Other manufacturing industries not elsewhere specified (n.e.s)
25 + 36
3 BUILDING AND CIVIL ENGINEERING 45
4 TRADE
41 Wholesale trade, recovery services 51
42 Sale of motor vehicles, wholesale and retail trade
50.1 + 50.3 + 50.4
43 Retail trade 52.1-52.6 + 50.5
44 Hotels-Restaurant 55
5 TRANSPORT AND COMMUNICATION 60 + 61 + 62 + 63 + 64
6 OTHER SERVICES N.E.S 50.2 + 52.7 + 67 + 70 + 71 + 72 + 73 + 74 + 75 + 80 + 85 + 90 + 91 + 92 + 93 + 95
18
19