Earnings management and debt

Faculty of Economics and Applied Economics

Earnings management and debt Piet Sercu, Heidi Vander Bauwhede en Marleen Willekens

DEPARTMENT OF ACCOUNTANCY, FINANCE AND INSURANCE (AFI)

AFI 0619

Earnings management and debt⇤ Piet Sercu† , Heidi Vander Bauwhede‡ and Marleen Willekens§ This version: October 2006

The order of listing the authors is by alphabet only. We gratefully acknowledge useful comments, on this paper or predecessors, from Linn Barkess, Ann Gaeremynck, Nancy Huyghebaert, Jere Francis, Robert Knechel, Martina Vandebroek, Staf Van Herck, and other participants at the International Symposium on Auditing Research (Sydney 2002), the University of Florida Conference on Corporate Governance and Assurance (Gainsville Florida 2002) and the KU Leuven workshop series in Accounting and Finance. ⇤

† KU Leuven, Department of Accounting, Finance and Insurance, Naamsestraat 69, B- 3000 Leuven; Tel: +32 1632 6756; Fax: +32 1632 6632; email: [email protected].

VLGMS and KU Leuven, Department of Accounting, Finance and Insurance, sestraat 69, B- 3000 Leuven; Tel: +32 1632 6930; Fax: +32 1632 6632; [email protected]. ‡

Naamemail:

Tilburg University, Faculty of Economics and Business Administration and KU Leuven, Department of Accounting, Finance and Insurance, Naamsestraat 69, B- 3000 Leuven; Tel: +32 1632 6932; Fax: +32 1632 6632; email: [email protected]. §

Abstract Like others before, we find that in our sample of Belgian non-listed firms earnings management (EM) is positively related to leverage. In light of the virtual absence of debt covenants, this regressor cannot just act as a proxy for the risk of violating restrictive covenants in loan or bond contracts (the stock explanation in the US/UK literature); rather, leverage must be proxying for general costs of financial distress. Our main empirical finding is that debt is heterogeneous in this respect: judging by the amount of EM it triggers, in our sample bank debt seems to be perceived as more alarming than trade credit. There are two implications about the relative costs and benefits of bank debt. First, rent extraction by banks is not of the order of magnitude to make the banker as lenient as a regular supplier, in case of financial stress. Second, the quality signal the firm obtains via the bank loan does not make EM redundant; in fact, the signal even fails to reduce the need for EM to the level judged necessary in the case of trade debt. Key words: earnings management, governance, relationship banking, rent extraction, signaling, cost of financial distress Data availability: all data used in this study are publicly available

Earnings management and debt

Introduction Studies of earnings management (EM) often find a positive association between the level of debt and the amount or likelihood of earnings management, and the stock phrase is this reflects a desire to avoid violations of the bond or loan covenant. This paper points out that the situation must be more subtle than this. First, while in our empirical work we do find the usual association between

EM

and debt, our sample is one of unlisted Belgian firms where

covenants are rare and, if they exist, far less detailed than in e.g. the US. Second, we find that the incidence of

EM

is strongly related to bank debt and hardly to commercial debt. To

interpret all this we invoke finance theories: the general costs of financial distress (Altman, 1984; Titman, 1984), the supplier’s quasi-equity stake (Petersen and Rajan, 1997; Wilner, 2000), the bank’s rent extraction (Sharpe, 1990; Rajan, 1992), and signaling (Diamond, 1991). Our first and most obvious point is that, for current purposes, the

EM

literature should

adopt the standard view in Corporate Finance theory: the firm’s objective is to avoid general costs of financial distress, a concept that is much wider than penalties from violating bond covenants. One implication is that the purpose of

EM

may be to reassure employees or cus-

tomers rather than lenders. Our second point is that, in terms of financial-distress costs, debt is not homogenous with respect to the expected cost of financial distress. In our sample, the prime types of debt are supplier credit and bank loans, and the latter are more likely to cause financial distress than A/P. The main reason is that, although banks probably have some quasiequity stake in the lending relation via their rent extraction, this is likely to be dwarfed by the supplier’s quasi-equity stake arising from future profitable sales to the debtor. If suppliers accordingly tend to be more lenient, then ceteris paribus a firm with relatively more bank debt should be sending out more reassuring than a firm with relatively more

A/P.

EM

signals to potentially worried stakeholders

(The list of stakeholders includes not just customers and

employees, but also suppliers, banks, and potentially even some of the shareholders.) But, beside creating distress costs, increased debt can also have a positive signaling e↵ect if the other stakeholders view the lenders as better informed. This positive signal, in itself, lowers the need for

EM.

Again, this e↵ect is unlikely to be the same for all kinds of debt: bank loans

are the prime candidates for providing the stronger signal. Recall that we found that

EM

is

2


more strongly associated with bank credit. We accordingly interpret our evidence as meaning that, on balance, one dollar of bank debt still needs to be balanced by more reassurance (via EM)

than one dollar of trade debt despite the positive signaling e↵ect.

In the remainder of this introduction, we spell out these arguments in greater detail, motivate our test design, and review our findings. In general,

EM

is the practice of taking into account the impression made on stakeholders

when deciding on the allocation of accruals, that is, the non-cash items in the

P&L

statement.

In Anglo-Saxon studies (typically on listed firms)1 earnings management is empirically linked to higher debt, and particularly earnings-increasing accruals decisions. Similarly, there is more EM

in years preceding bond issues or new bank loans. One frequently cited explanation in

all these papers is that earnings are increased to avoid the cost of violating bond covenants. Others argue that

EM

serves to fool gullible “outside” lenders into subscribing at terms they

would not have accepted otherwise. We raise the question whether the “covenant” aspect should not be broadened to the more general concept of avoiding cost of financial distress, including all ex ante and ex post costs, whether financial or not. After all, the cost of violating bond covenants and the risk premia charged by bondholders are just two examples of distressrelated costs; there is no a priori reason to exclude other such costs, like credit spreads in banks’ interest rates, or wage risk premia charged by employees that dislike unsafe jobs, or sales revenue lost when after-sales service etc become uncertain. To answer this question we look at financial-statement data of small, unlisted companies, which are easily available in Belgium. These firms are financed by their owners, banks, and suppliers, who may all be far from perfectly informed but still are more “inside” than the proverbial small private investor. Also, these companies have no public bonds outstanding, which rules out bond covenants; and in Belgium bank financing does not typically come with the long list of restrictions one usually sees in the US either.2 Yet we observe the same leverage-related elsewhere. We conclude that

EM

EM

as others found

is about more than bond covenants and uninformed outside

financiers: stated most broadly, it tells us that at least some of the stakeholders do worry about the potential financial distress, and that some of them— not necessarily including the

1

See e.g. Becker et al., 1998; Beneish, 1997 and 1999; DeFond and Park, 1997; DeFond and Jiambalvo, 1991, 1993, and 1994; Dhaliwal et al., 1982; Peasnell et al., 1999; Sweeney, 1994; Warfield, 1995; Young, S.,1998. 2

Traditionally, banks used callable credit lines rather than covenanted term loans to discipline firms. Nowadays covenants are more frequent, but the standard corrective measure is a mandatory increase of equity; this is not expensive, in terms of agency and transaction costs, as firms are family-owned.

3


lenders themselves—are taken in by the prettified picture presented in the accounts. The second issue is whether all debt is equally costly, for this purpose. Given the sample, we compare only bank debt and commercial credit (see e.g. Biais & Gollier, 1997; Burkart and Ellingse, 2004; Fisman & Love, 2003; Huyghebaert, 2006; Jain, 2001; Petersen & Rajan, 1997; Wilner, 2000.) One crucial fact is that suppliers have an equity-like stake in the company: upon bankruptcy they stand to lose not just the value of outstanding invoices but also the present value of profits from later sales to that customer. Another fact is that suppliers’ recovery rates are quite low anyway; Franks and Sussman (2005) even report a zero median. As a result, a trade creditor is less likely to send for the baili↵s him- or herself, and is more inclined to provide extra credit if the customer’s liquidity problems seem to be temporary. In an extreme example, one Belgian

B2B

company that sells a lot to very small firms in emerging countries

even has a special program (dubbed fin tonic) to help distressed customers renegotiate bank loans and

P&E-vendor

debt, find new banks, convert debt into equity, etc.3 Also, bank loans

tend to be for longer maturities than accounts payable; so the likelihood that they might ever lead to financial distress is higher. Everything else being the same,

EM

should therefore be

primarily related to financial debt. At least two arguments could soften or even reverse this ceteris paribus prediction. First, the “hold-up” literature on financial debt implies that banks might have a similar quasi-equity stake in their customers. Such a stake would notably arise if the bank has a monopoly position and uses it to extract rents (Sharpe, 1990; Rajan, 1992). It is, indeed, widely accepted that, for small companies that have outgrown the friends, family and fools financing stage but still have no established reputation, relationship banking is the only economically sensible solution.4 But building the relationship and reducing the original information asymmetry does take time. As a result, the argument goes, the incumbent house bank acquires a certain monopoly position which it abuses to “hold up” the borrower. This could be especially relevant for our small unlisted firms, as they have no access to stock or bond markets. The bank’s monopoly rents increase the cost of pulling the plug on a debtor, in the same way as a supplier’s profit margin does. So regarding equity stakes the di↵erence between bank and trade debt is, at best, one

3 4

Deceuninck Plastics, Financial Management Team of the year 2005; www.fm.be.

Overcoming the information asymmetries requires too much expertise and e↵ort to be economically justified to the many share- or bondholders that might otherwise have provided a small part of the financing. Concentrated financing avoids this problem. If, in addition, this lender already has a long-standing relation with the borrower, the marginal cost of evaluating the borrower becomes quite small.

4


of degree rather than a fundamental one. One could reply that a supplier’s profit margins on sales are generally much larger than a banker’s excessive credit spreads on loans, thus making bank debt still most risky from the financial-distress point of view. But there is a second consideration that could reduce a borrower’s need to reassure stakeholders, via EM, about bank debt: in itself, a bank’s willingness to lend provides a signal of quality (Diamond, 1991). True, also suppliers could be quite well informed via frequent on-the-spot visits and the flow and evolution of orders (Biais and Gollier, 1997; Smith, 1987). But outsiders might still be less reassured by new commercial credit than by extra bank loans. Indeed, outsiders realize that suppliers may have been swayed by their larger quasi-equity stake, and that their credit is shorter-lived too. Banks, on the other hand, have access to the day-to-day payment flows, a source of information that is likely to beat even the supplier’s first-hand data on the borrower; and banks typically grant loans only after a formal credit re-evaluation. In addition, there are regular follow-up evaluations. Suppliers, by contrast, often do not use formal evaluation procedures for all their customers, or they follow them up less frequently. Indeed, they are not perceived as being generally professional at this, and they themselves often tend to count on banks’ vetting abilities. In short, a bank’s fiat is likely to be more reassuring than a supplier’s, and it therefore reduces the need to resort to EM, everything else being the same. Again, the signaling advantage from bank debt is potentially quite important for the small firms in our sample, where information asymmetries are rife. In light of these considerations it is less obvious that bank debt should still be the component that triggers more

EM.

So we let the numbers speak. We find a significant positive association

with bank debt and increases therein, and only a statistically unclear link with similar variables for trade debt. We infer that, on balance, in this respect bank debt must have been perceived as more alarming than A/P—at least in the current sample. The indirect inferences are twofold. First, even though our firms have no genuine alternative beside bank debt, any rent extraction by these lenders is not of the order of magnitude to make the banker as lenient as a supplier, in the event of financial distress. Second, the positive signal that the firm obtains from the bank loan is insufficient to render

EM

less necessary than in the case of trade debt.

The remainder of the paper is organized as follows. We first provide a brief description of the hypotheses and the test variables. Our testing and sample-selection procedures are presented in Section 2, along with some descriptive statistics. Our main empirical results and sensitivity checks follow in Section 3. Section 4 concludes.


1

5

Hypotheses and Proxies

In this section we proceed with a presentation of our hypotheses and proxy variables, and the estimation procedure.

1.1

Abnormal accruals as a measure of earnings management

Our main tests rely on logit analysis, with the event being defined as Y = 1 when there is substantial upward

EM,

and Y = 0 when there is substantial negative management. This

requires a measure of earnings management. Following a time-honored tradition, we take discretionary accruals (DAc), scaled by lagged total assets. We start from the total-accruals number (that is, the non-cash items in net income, where to some extent judgment is used), and then subtract from that figure the level of total accruals chosen by the average comparable firm. Earnings management, in short, is measured by “abnormal” or “unexpected” accruals, where the term “abnormal” is not intended to carry any moral or legal overtones. Because of our definition of “normal” accruals as the choice by the average comparable firm rather than some absolute standard, we can identify earnings management only in a relative sense, vis-` avis the average firm. Still, for convenience we use terms like “upward earnings management”, dropping qualifiers like “unusual” or “unexpected” or “abnormal”. We establish comparability using a Jones (1991)-Kasznik (1999) type regression equation nested with DeAngelo’s (1986, 1988) martingale model: we run sector- and year-specific regressions of total-accruals data, with as right-hand side variables the level of depreciable assets, the change in cash revenue from sales, the changes in operating cash flow and earnings and, to capture any missing firmspecific items, last year’s total accruals. (The firm-specific factors picked up by lagged accruals are substantial and cope with recent criticisms of Jones-based DAc proxies.) Regressand and regressors are all scaled by the firm’s lagged total assets (TA). The details are provided in the appendix. Despite a satisfactory sample size available for modeling “normal” accruals—over 16,000 complete records—our measure of normal accruals is not perfect, implying observation errors in DAc which are possibly correlated with the test variables. We reduce this problem in two ways. First, we rank the data on the basis of estimated DAc and remove all records corresponding to the middle 50 percent. Second, in the remaining data we ignore the size of the estimated abnormal accruals, reducing them to an indicator which equals +1 (0) whenever earnings management is deemed to be upward (downward). Eliminating the middle block of

6


data substantially reduces the chance that we have the sign of earnings management wrong, while working with an indicator avoids the bias towards zero that follows from selecting a sample on the basis of the left-hand-side variable. The resulting {+1, 0} data serve as the

inputs of a standard logistic regression. Consistent with the above errors-in-variables logic, our results become less significant whenever we use either all DAc data or use the DAc numbers themselves rather than their {+1, 0} transforms. Beside logistic regression, we have also used piecewise linear regression equations. The results are largely comparable, except for one instance mentioned in the discussion of the results.

1.2

Earnings management and leverage: test hypotheses and test variables

Because of an obligation to file annual statements with a central depository, Belgian data are available on, in principle, all but the smallest firms. This sample is interesting because the potential for rent extraction is definitely larger than in a population of large, listed companies that is the usual source of data in this field. Also, the absence of bond covenants and the closer relation with the financiers allow us to test whether the usual explanations of leverage-related EM

need to be broadened. Under null-form, the hypothesis is

Hypothesis 1 In a sample where bond covenants are absent and financiers are quasi-insiders, EM

is empirically unrelated to leverage

with as alternative hypothesis a positive association. Next we ask the question whether one dollar of bank debt triggers as much

EM

as one dollar of trade credit. The null is

Hypothesis 2 In the logistic regression analysis of

EM,

the coefficient for (scaled) bank debt

is the same as that for (scaled) trade debt with a two-sided alternative. To test these hypotheses we include in the logistic regression the following variables: financial debt over lagged total assets, FinD i,t /TAi,t

1;

an indicator variable, I( FinD i,t ), stating

whether in the year following the annual report a new bank loan is granted or an existing one increased; accounts payable relative to lagged total assets, AP i,t /TAi,t

1;

and a dummy,

I( AP i,t ), indicating a rise, if any, in trade credit over the next year.5 The dummies indicating

5 For both variables we also used filtered versions that ignored rises below p percent, but this made no di↵erence.

7


a subsequent rise in bank or trade debt are added to capture possible pro-active

EM,

including window dressing aimed at the lender. If leverage remains associated with

possibly EM

even

when there are no bond covenants, we expect a significant coefficient for at least some of the above variables. In addition, if trade debt and bank debt are perfect substitutes as far as financial-distress costs are concerned, we expect the same coefficient for FinD i,t /TAi,t FinD i,t /TAi,t

1.3

1,

1

as for

meaning that sum total of debt is the relevant variable.

Control variables

Equity issues

IPO’s

and

SOE’s

are associated with

EM

(Friedlan, 1994; Shivakumar, 2000). So

even if it sounds unlikely that equity placements would give rise to

EM

in companies where

the largest shareholder usually is an insider, we can not entirely rule out the possibility. Note that since debt and equity sum to total assets, we cannot include the fraction of equity as a separate regressor. This in turn implies that the coefficients for the debt fractions have to be seen as di↵erentials relative to the coefficient for equity, if any.6 To have at least an indication whether

EM

is associated with equity issues too we include an increased-equity dummy as a

control variable, I( Eqi,t ), indicating whether or not new stock is issued within one year after the annual report. We take this as a serious indication of the presence/absence of equity-related EM

because for trade debt and bank debt the coefficients for the balance-sheet fractions turn

out to be in perfect agreement with the coefficients of the corresponding dummies. Sta↵- and customer-related costs of financial distress We now turn to relations with other stakeholders. From Altman (1984) or Titman (1984) and others, the indirect costs of financial distress include the loss of human capital when edgy employees leave, the deterioration of supplier relations, and a loss of customers if the product is a capital good that normally comes with after-sales service, repairs, a warranty, etc. Thus, to reassure the employees and customers, higher-debt firms would typically be more tempted to counterbalance their leverage with higher earnings. We use the wage bill scaled by total assets, Wages i,t /TAi,t

1

as a main

test variable for the firm’s dependence on employees. The variable proxying for sensitivity of sales to financial-distress rumors, lastly, is a dummy that indicates a durable-goods product,

6

Indeed, let the shares of A/P, bank debt, and equity be denoted by Xi , i = {1, 2, 3}. These sum to almost unity: the only other meaningful type of debt besides bank loans and AP is advances by the owner(s), which we consider to be part of equity, and the remainder, tax and social-security debts, is very small. So if Y = a+bX1 + cX2 + dX3 + e with X3 = 1 X2 X1 , then the reduced relation becomes Y = (a + d) + (b d)X1 + (c d)X2 + e: we can estimate only the di↵erential e↵ect.

8


or, more generally, a good where the firm’s continued existence is material.7 Taxes Belgium has no separation of income statements for tax versus reporting purposes, a feature that provides a strong and permanent incentive to downplay earnings. The incentive to postpone taxes should be stronger the higher the tax rate. True, the corporate tax rate is, for all practical purposes, constant and flat;8 but the e↵ective tax rate can still be below the posted rate if the firm has accumulated losses and if tax payment, therefore, is postponed. The e↵ect can be substantial: if the accumulated tax credits would shelter several years of future income, the tax cost of moving forward an income is zero if that income would anyhow have to be reported within the sheltered period. We represent the presence of deductible losses from the past as a dummy, I(T axi,t ), which equals unity if the firm did pay taxes last year and, therefore has no inherited tax credits from the past. We use an indicator transform of a lagged variable because contemporaneous taxes are based on reported profits including but what we need to know is whether they would have paid taxes without

EM.

EM;

Our prior is

that I(T axi,t ) is associated with a negative coefficient: a tax-paying status reduces the chance of upward profit management. Firm Size, log(TAi,t

1 ),

could stand for visibility (which deters from managing too openly),

complexity (possibly leading to relatively more unusual-looking accounting decisions), or diversification (reducing the total relative impact of many accounting decisions and thus making EM

harder to detect). In sum, our prior regarding this coefficient is di↵use.

Auditor status External auditors attest the credibility of firms’ financial statements and also have a legal responsibility, albeit secondary, towards stakeholders regarding the quality and fairness of the information in those statements. Most empirical auditing studies hypothesize that big international auditors are higher-quality auditors than other auditors: larger audit firms have more to lose—quasi-rents (DeAngelo, 1981) or brand-name reputation (Klein and Le✏er, 1981)—if and when audit failure occurs.9 Francis et al. (1999) and Becker et al. (1998)

7 I(Dur ) is set equal to unity for NACE sectors manufacture of chemicals and allied products (257), manufacture of metal articles (31), mechanical engineering (32), manufacture of office machinery and data processing machinery (33), electrical engineering (34), manufacture of motor vehicles(35), manufacture of other means of transport (36), instruments engineering (37), manufacture of floor covering (438), timber work (463), wooden furniture (467), other manufacturing industries (49), and building and civil engineering (50). These are the old codes as used at the time. 8 For incomes below EUR 50,000 the average tax rate slowly rises towards the standard rate, but to achieve this the marginal rate has to exceed the standard rate, making the situation ambiguous. Also, there are too few firms in this income bracket to obtain any statistical power. 9

See for example Carpenter and Strawser (1971), Simunic (1980), Francis (1984), Palmrose (1986), Francis

9


provide evidence that big auditors constrain earnings management more than other auditors, at least in publicly held American firms. We use a dummy indicating that the auditor is one of the (then six) international giants, I(Big6i,t ) = 1. Other measures of auditor professionalism, like the number of employees or turnover produced similar results. Operating cash flow and earnings Operating cash flow, (OCF i,t /TAi,t

1 ),

is likely to be nega-

tively related to income-boosting. One reason is that changes in credit terms, if any, convert cash income into accruals or vice versa; a second reason is management’s desire to smooth out other variations in cash earnings. We further include earnings before taxes (Earni,t /TAi,t

1 ),

scaled by lagged total assets, to control for potential misspecification that may occur in tests of earnings management for firms with extreme financial performance (Dechow et al., 1995).

2

Sample selection and descriptive statistics

2.1

Data

From all one-digit-NACE sectors with at least 56 observations, 10 percent of the records were randomly chosen to be part of the test sample. The remaining 90 percent (at least 50 records per industry⇥year) were used for estimation of the normal accruals for that sector and year (see Appendix). The total test sample initially contained over 3100 records. From these 3100 test observations we deleted (i) firm-years for which the auditor could not be identified or where the firm changed auditors (there is evidence that firms have negative discretionary accruals in the last year with the original auditor; see, for example, DeFond and Subramanyam 1998); (ii) observations that bore on listed companies; (iii) firm-years with missing data for the variables in our accruals expectations model or explanatory model, occasionally including some lagged variables, like TAi,t

1,

and leading figures used in

FinD i,t etc. Table 2 gives a breakdown

of our final sample by industry. Of the 1302 records, 555 relate to manufacturing and 747 to services, a normal balance for small companies. Table 3 presents the descriptive statistics for our sample prior to and after deleting the 50 percent records corresponding to moderate accruals management. These samples are indicated by their sample sizes, 1302 and 561; as there are no noteworthy di↵erences we mostly discuss the 1302-sample results.

and Simon (1987), Simunic and Stein (1987), Francis and Wilson (1988), Palmrose (1988), Simon and Francis (1988), DeFond (1992), Francis et al. (1999).

10


Table 1: Sample Selection Procedure company characteristic Random Drawings from Total Sample Lost observations: Firm-years without auditor data or of firms that changed auditors Firm-years of publicly held companies Firm-years whose industry- and year-matched portfolios had less than 50 obs Firm-years with missing data for discretionary accruals calculation Firm-years with missing data for variables of explanatory regression Remaining number of firm-years Distribution of records over years: Number of firm-years in year 1994 Number of firm-years in year 1995 Number of firm-years in year 1996 Total Distribution of firms by number of years of presence: Number of firms with one year of complete data (incl. leading & lagging) Number of firms with two years of complete data (incl. leading & lagging) Number of firms with three years of complete data (incl. leading & lagging) Total

number 3137 -963 -15 -613 -126 -118 1302 344 517 441 1302 99 249 235 583

Key. Table 1 gives an overview of the sample selection procedure for the event sample that we use in the logistic probability model. Firms in our sample have to satisfy the following criteria: (i) submit full financial statements, (ii) have these statements audited by an auditor and (iii) be an industrial or commercial company (NACE codes 0-7) in an industry that has at least 100 companies in each of the sample years, so that at least 50 are available for estimation of the DAc regressions and 50 for the test sample (including records with incomplete data). We obtain our sample from the Belfirst CD-ROM, June 1999. Our analysis bears on the period 1994-6. Of the about 18,000 observations in the sample as defined by the above three criteria, an event sample of over 3000 candidate firm-years was randomly selected to test the main hypothesis, while the about 15,000 remaining observations were used for the estimation of the accruals regression relation.

• Regressand. The mean (median) absolute level of discretionary accruals (| DAc |) is about 4.9 (3.1) percent of lagged total assets. This is the same order of magnitude as earnings

themselves, and about half the typical cashflow. In the 1302 sample, income-decreasing earnings management was somewhat more prevalent (54.3 percent) than income-increasing action, a minor oddity that disappears in the 651 sample. • Financing. The mean (median) amount of financial debt is 17.32 (10.25) percent of total assets. Note the implied right-skewness. The first-quartile value is still essentially zero; that is, at least one quarter of the firms has no bank debt. Even for the third-quartile firm, bank debt amounts to just 30 percent of TA. Mean (median) trade credit (AP/TA) is over 28 (24) percent of total assets. Note that this exceeds average bank debt, possibly because of the

11


Table 2: Industry classification of firm-year observations Description (1-digit level) Chemical industry

Metal

Other manufact

Building and civil engineering Distribution, hotels, catering, repair

Transport and communication

Total

NACE Code 24 25 — 31 32 34 — 41 42 43 45 46 47 48 — 50 — 61 63 64 65 66 67 — 72 76 77 —

Description (2-digit level) Manufacture of non-metallic mineral products Chemical Total Manufacture of metal articles Mechanical engineering Electrical engineering Total Food drink and Tobacco Food drink and Tobacco Textile industry Leather industry Timber and wooden furniture Paper, printing and publishing Processing of rubber and plastics Total General building and engineering Total Wholesale distribution Agents Retail distribution Retail distribution Hotels and catering Repair consumer goods, vehicles Total Other (than railway) transport Supporting services to transport Travel [...], storage, warehousing Total

# firmyears

# firms

55 37 92 42 45 12 99 31 41 36 10 23 43 20 204 160 160 478 22 41 41 19 19 620 53 10 64 127 1302

21 18 39 25 17 5 47 16 17 15 4 11 19 9 91 70 70 211 11 19 19 8 14 282 23 4 27 54 583

lower risk to the borrower (see Introduction). The quartile values likewise demonstrate that supplier credit is much more widespread than bank debt: virtually all firms use it. About half of the firms increase their reliance on bank debt in the next year, and likewise for trade debt. Equity increases, by contrast, are rare, with an incidence of just 5 percent. • Control variables. For the typical firm, wages amount to one third of assets, but with a healthy variation across firms. About one quarter of the firms are in a durable-goods sector. 71 percent of our sample firm-years paid taxes in the prior year, and thus have no tax-loss carry-forwards. In terms of asset value, our firms tend to be small: the median company in the sample has less than EUR 5m in assets, and even the third-quartile firm just achieves EUR 10m. 37 percent of our firm-year observations are audited by big-6 auditors. This is fairly consistent with the anecdotal evidence on the market shares held by big-6 and other

12


Table 3: Regression variables: descriptive statistics Variable

Nobs

Mean

St.Dev.

Min.

Q1

Median

Q3

Max

| DAC |

1302

0.049

0.055

0

0.014

0.031

0.066

0.571

DACSIGN

1302 651

0.457 0.501

1 1

FinD/TA

1302 651 1302 651 1302 651 1302 651 1302 651

0.173 0.171 0.500 0.467 0.282 0.276 0.499 0.467 0.051 0.048

1302 651 1302 651

0.323 0.349 0.220 0.228

1302 651 1302 651 1302 651 1302 651 1302 651

0.713 0.691 16.96 15.81 0.372 0.398 0.077 0.084 0.052 0.060

0.498 0 0 0 1 0.500 0 0 1 1 Financing-related regressors 0.191 0 0.001 0.103 0.306 0.193 0 0 0.098 0.311 0.500 0 0 1 1 0.499 0 0 0 1 0.205 0 0.132 0.242 0.381 0.200 0 0.132 0.241 0.376 0.500 0 0 0 1 0.499 0 0 0 1 0.219 0 0 0 0 0.213 0 0 0 0 Regressors related to other stakeholders 0.307 0 0.132 0.257 0.439 0.353 0 0.135 0.276 0.455 0.415 0 0 0 0 0.419 0 0 0 0 Control Variables 0.452 0 0 1 1 0.462 0 0 1 1 56.50 0.026 2.598 4.914 10.718 66.13 0.026 2.298 4.467 9.169 0.483 0 0 0 1 0.490 0 0 0 1 0.196 -1.255 -0.019 0.067 0.152 0.240 -1.255 -0.037 0.074 0.189 0.115 -0.407 0.002 0.029 0.094 0.138 -0.407 0.001 0.040 0.114

I(FinD) AP /TA I( AP ) I( Eq) Wages/TA I(Dur) I(Tax ) TA E+6 I(Big6) OCF /TA Earn/TA

0.920 0.920 1 1 0.988 0.987 1 1 1 1 3.462 3.462 1 1 1 1 940.18 940.18 1 1 1 2.082 1.140 1.140

Key. | DAC | = absolute value of discretionary accruals (DAc) ;DACSIGN = 1 if discretionary accruals are

positive, = 0 otherwise; FinD/TA is financial debt (bank debt) over total assets; I( FinD) = 1 if bank debt is increased within one year of the financial report, = 0 otherwise; AP/TA is accounts payable over total assets; I( AP ) =1 if trade credit is increased within one year of the financial report, = 0 otherwise; I( Eq) =1 if equity is increased within one year of the financial report, = 0 otherwise; Wages/TA is wages paid over total assets; I(Dur ) = 1 if the firm produces durable goods, 0 otherwise; I(Tax ) = 1 if the firm had no tax-loss carry-forwards, 0 otherwise; TA = total assets, in millions of EUR; I(Big6) = 1 if the auditor is Big6, = 0 otherwise; OCF /TA = scaled operating cash flow; Earn/TA = scaled earnings.

auditors in the private client segment of the Belgian audit market. Statistically, the closer the fraction is to 1/2, the higher the power of a test; so in that sense our sample is more promising than the typical US listed-firm sample, where the fraction of Big-6-audited firms is too close to unity. Mean (median) cash flow amounts to over 7.79 (6.67) percent of lagged total assets. Mean (median) earnings, lastly, are about 5 (2.86) percent of lagged total assets.

13


The correlation matrix in Table 4 does not indicate any severe multicollinearity problems.

3

Empirical Results

It is likely that the control variables do not account for all relevant heterogeneities in the sample. Unidentified heterogeneity would not render the coefficient estimates inconsistent provided the heterogeneity is unrelated to the regressor variables, but correlation still needs to be taken into account to allow correct statistical inference. We accordingly report results that use Stata’s “cluster” option. The first block in Table 5 reports our empirical results for the full regression. The fundingrelated variables are listed first. As expected, there is no statistically convincing relation between our equity variable—the indicator of a stock issue in the year following the reporting date—and the amount of positive abnormal accruals; indeed, the estimated coefficient is even weakly negative. This is consistent with the notions that, for our sample of unlisted firms, owners are insiders and that other stakeholders need not be reassured (by

EM)

when equity

rises. By implication there is no evidence that the coefficients for the trade- or bank-debt fractions should be viewed as di↵erential e↵ects relative to equity rather than as total e↵ects. We next consider the indicators for rises in debt and then the fractions themselves. For the indicators of increased use of trade and bank credit the estimated coefficients are positive, as expected if earnings management is to some extent pro-active. But while for Accounts Payable the p value is rather ambiguous, at 9 percent one-sided, we do get a clearer alpha (6 percent) for the indicator of an increase in bank debt. As mentioned, we have tested whether it helps to filter out minor rises in AP when constructing the indicator of a rise in trade credit, but such filtering does not change the answer. When looking at the financing ratios themselves, the di↵erence between the types of funding becomes much more marked. We note a statistically unambiguous positive link between the reliance on bank debt and earnings-boosting accounting choices (p < 0.005 percent), while for trade credit the relationship is weak by all usual standards (p = 27 percent). The absence of a strong relation for trade credit is not due to lack of variation in that regressor: in fact, from Table 3, reliance on trade credit shows slightly more variability across firms than does the use of bank debt. Nor is the insignificance due to multicollinearity between the two debt variables: indeed, the correlation between them is a moderate –0.15, see Table 4. The tentative interpretation that suppliers may be more lenient than are banks is also consistent with the more intense use of trade credit, already noted in Table 3. The Wald

-0.036 0.060 0.011 -0.071 0.117 -0.142 -0.033 -0.228 0.290 0.002 -0.009 0.051

0.061 0.022 0.079 0.074 -0.054 -0.050 -0.050 -0.104 0.010 -0.005 0.034

I( Eq) 0.354 0.078 -0.120 0.022 0.024 0.084 -0.054 -0.013 0.029 0.037 0.023

I( AP ) 0.124 0.121 0.011 0.133 -0.048 -0.017 -0.033 -0.115 0.000 0.016 0.043

I( Fin) 0.782 0.575 0.047 -0.152 0.032 -0.114 0.012 -0.088 0.136 -0.096 -0.063

AP /TA 0.072 0.043 0.002 0.774 -0.200 -0.136 -0.347 -0.299 -0.044 0.075 -0.054

Fin/TA 0.003 0.059 0.583 0.001 0.000 -0.033 0.226 0.277 -0.014 0.067 0.053

I(Tax ) 0.000 0.173 0.547 0.225 0.417 0.001 0.113 -0.003 -0.045 -0.112 0.187

Wages 0.400 0.205 0.032 0.663 0.004 0.001 0.401 0.498 -0.012 0.039 -0.070

OCF 0.000 0.204 0.171 0.400 0.769 0.000 0.000 0.004 0.084 0.052 -0.053

Earn 0.000 0.008 0.750 0.003 0.025 0.000 0.000 0.939 0.000

0.148 -0.149

I(Big6) 0.962 0.802 0.457 0.993 0.001 0.267 0.725 0.251 0.762 0.032

0.101

T A, 106 0.825 0.894 0.347 0.677 0.014 0.057 0.088 0.004 0.327 0.185 0.000

I(Dur ) 0.198 0.392 0.564 0.271 0.106 0.169 0.176 0.000 0.073 0.178 0.000 0.010

defined in Table 3, apart from a compressed notation in the top line. The lower triangle provides the correlations, the upper one the p-values for zero correlation.

Key. ”Select” refers to an indicator that the estimated amount of EM is large (first or last quartile) so that the observation is included. The other variables are as

select I( Eq) I( AP ) I( FinD) AP /TA FinD/TA I(Tax ) Wages/TA OCF /TA Earn/TA I(Big6) TA E+6 I(Dur )

select

Table 4: Correlation matrix


14

– + +

– + ? ? ?

I(Tax ) Wages/TA I(Durbl )

OCF /TA Earn/TA I(Big6) TA E+6 constant

-9.055 19.860 -0.216 0.000 -0.083

-1.428 0.199 0.429

-0.255 0.286 0.319 0.301 1.592

estim

1.364 2.267 0.201 0.000 0.343

0.250 0.274 0.236

0.429 0.210 0.203 0.484 0.547

-6.640 8.760 -1.080 -0.820 -0.240

-5.710 0.730 1.810

-0.600 1.360 1.570 0.620 2.910

0.000 0.000 0.282 0.412 0.808

0.000 0.234 0.035

0.276 0.087 0.059 0.268 0.002

p-val

-291.441 0.354

first-pass regression std t-stat

-291.561 0.354

0.000 0.000 0.277 0.418 0.781

0.000 0.227 0.037

0.090 0.056 0.273 0.002

-292.470 0.352

0.585

0.776 -291.561 0.354

0.000 0.000

0.000 0.192 0.033

0.101 0.054 0.314 0.002

0.000 0.000 0.205

0.000 0.195 0.049

0.092 0.055 0.244 0.002

trimming stages p-val p-val p-val

-0.082

0.333

1.362 2.242

0.231

0.462 -8.959 19.603

0.251

0.204 0.202 0.483 0.538

-0.250

-6.580 8.740

2.000

-5.680

1.360 1.590 0.410 2.800

final regression std t-stat

-1.423

0.277 0.321 0.196 1.510

coe↵

-293.249 0.350

0.805

0.000 0.000

0.022

0.000

0.087 0.056 0.343 0.003

p-val

p values are based on the Wald

2

test, not the t-ratios, and we show one-sided probabilities whenever relevant. 5,

p-values for the interim regressions with gradual deletion of insignificant variables; and estimated coefficients, SE;s, T-tests, and p-values for the final regression. The

p-value, one-sided being indicated by + or –, two-sided by “?”. The table shows estimated coefficients, SE;s, T-tests, and p-values for the first-pass regression; the

function of the regressors listed in Table 3. The column ”sign?” indicates the direction of the alternative hypothesis and the corresponding interpretation of the

Key. The 561 most extreme of the 1302 DAc observations are converted into indicators (1 if positive, 0 otherwise). The probability of a +1 is modeled as a logistic

loglik pseudo R2

+ + + + +

I( Eq) I( AP ) I( FinD) AP /TA FinD/TA

sign?

Table 5: Logistic probability analysis of Discretionary-Accruals decisions


15

16


test on the di↵erence between the coefficients of bank and trade debt produces

2 (1)

= 4.51,

significant at the 3.32 percent level, two-sided. Thus, it is hardly thinkable that total debt is the relevant variable rather than bank debt as a separate variable. When we purge from the first-pass regression the insignificant variables, the hypothesis that financial and commercial debt are perfect substitutes gets an even clearer rejection. Following textbook procedure we consecutively eliminate, in each step, the variable with the poorest significance, but sparing the main test variables (bank and trade debt, and their

indicators).

In the central and rightmost parts of Table 5 we show the p-values of all intermediate regressions and the results for the final regression. The trade-credit ratio steadily loses significance, its p rising from 0.26 to 0.40 one-sided. Bank debt, on the other hand, remains unambiguously relevant. A Wald test rejects the equality of the two debt coefficients (

2 (1)

= 6.28, p=0.0122

two-sided). This discredits the notion that total debt is the true variable; bank debt is what matters most. We conclude this section with a brief discussion of our results on the control variables in the original regression. There is no good evidence that

EM

is related to labor costs: the link

between the wage-bill variable and DAc is rather weak (p-value 0.24). This finding is consistent with the results of Liberty and Zimmerman (1986) and Konings, Labro and Roodhooft (1998), who likewise fail to establish a link between earnings management and various labor-related variables. When we use an alternative proxy for dependence on labor, a dummy indicating the presence of a works council, we have an even lower significance. In short, according to Table 5 there is no strong positive evidence that earnings management is targeting labor. But for the Durables dummy we do get a significant coefficient of the expected sign: firms with after-sales service and maintenance etc tend to send out more reassuring signals than do other companies. Cash flow comes in with a markedly negative coefficient, signaling active smoothing policies and/or mechanical e↵ects from changed credit terms, which is in line with results by Dechow et al. (1995)—as is our clear positive link with earnings. Lastly, unlike many US tests, we obtain no clear evidence that Big-6 auditors would be more conservative than others: while there may be less income-boosting for Big-6-audited firms, the link is statistically weak. We have used other proxies, like market share, or a top-5 dummy (the fifth-largest firm is local, not one of the international Big 6), but obtained the same verdict. Nor does company size seem to have a clear e↵ect—not surprising, perhaps, in light of the di↵use prior.

17


4

Concluding discussion

It is well established that higher leverage is associated with more earnings management, particularly earnings-increasing accruals decisions. Similarly, there usually is more preceding bond issues, stocks issues or

IPO’s,

EM

in years

and new bank loans. The literature o↵ers two

explanations. Earnings are increased to avoid the cost of violating bond covenants, some say. Others argue that

EM

serves to fool gullible “outside” shareholders and lenders into subscrib-

ing at terms they would not have accepted otherwise. In this paper we o↵er two empirical observations that seem to require a revision, or at least a broadening, of these explanations. The first anomaly is that our relation between leverage and the incidence of EM comes from a wide sample of small Belgian firms that are not listed and have no access to the market of public debt. They are financed by their owners, banks, and suppliers, who all are far better informed than the proverbial small outside investor. To explain widen the class of stakeholders targeted by

EM

EM

in this context one could

to include not just the financiers but also

employees (who prefer safe jobs) and customers (who prefer reliable suppliers if the product requires some form of after-sales service). Simultaneously, one should realize that a clear dichotomy between informed and uninformed stakeholders is an illusion. There are degrees of informedness, with the owner-managers being most knowledgeable, customers typically least informed, and banks, suppliers and employees somewhere in the middle. Our observation of a link between

EM

and debt then indicates a perception among management that at least some

of the relevant stakeholders are at least partly reassured by the prettified picture presented in the accounts. While the first anomaly is easily defused by just widening of the list of potential targets of

EM,

there is a second one that requires more reflection on the role of banks. In our sample,

indeed,

EM

is significantly related to bank debt but not, or only weakly, to

A/P.

Stated di↵er-

ently, managers act as if the stakeholders do need reassurance about indebtedness to banks but not, or less so, to suppliers. The literature o↵ers a convincing explanation. Indeed, in the case of trade credit the creditor stands to lose more, upon bankruptcy, than just the value of outstanding invoices: if the customer firm gets wiped out, the supplier also loses the present value of profits from later sales to that customer. Because of this “equity feature” in trade debt, as some dub it, a trade creditor is less likely to send in the baili↵s him- or herself, and is more inclined to provide extra credit if the customer’s liquidity problems seem to be temporary. But, apparently, the same leniency is not expected from banks. This means that any

18


rent extraction by banks carries, apparently, far less weight than a supplier’s profits. Lastly, our findings also mean that the quality seal that comes with bank loans is not viewed as a sufficient substitute for earnings managment.

Appendix: Measurement of Discretionary Accruals (DAc) We measure the extent of earnings management through discretionary accruals. Discretionary accruals are the complement of normal accruals, which are estimated by regression. In the literature, standard accruals expectations models have a low predictive power. We start from the Jones (1991)-Kasznik (1999) regression but substantially boost its performance by adding lagged total accruals as a regressor. That is, we estimate the following year- and industryspecific accruals-expectations model, on all the records that were not randomly selected to be part in our main test sample discussed in the body of the text: TACi,t = ↵j(i),t + TAi,t 1

j(i),t

GPE i,t + TAi,t 1

j(i),t

AdjRev i,t + TAi,t 1

j(i),t

OCF i,t TACi,t 1 +⇣j(i),t +✏i,t , (4.1) TAi,t 1 TAi,t 1

where i refers to a firm and j(i) to the industry of firm i; TACi,t = i’s total accruals in year t; TAi,t = i’s total assets in year t; GPE i,t = i’s gross property plant and equipment in year t; AdjRev i,t = change in i’s revenues minus change in receivables in year t; and

OCF i,t =

change in i’s operating cash flow in year t. Total accruals are computed as working capital accruals minus depreciation. Gross property, plant and equipment is included in the accruals expectations model to account for the part of total accruals that is derived from depreciation accruals, while change in revenues is included to account for bona fide changes in working capital accruals (Jones, 1991). Change in revenues is adjusted for change in accounts receivables to account for the fact that credit sales may be discretionary (Dechow et al., 1995). Change in cash flow from operations is included following Dechow et al. (1994) who report that this variable is significantly related to total accruals (see also Kasznik, 1999). We include lagged total accruals because the components of prior-year total accruals may include information as to the magnitude of this year’s total accruals. The original motivation of Guay et al. (1996) was that standard accruals expectations models might be enhanced by recognizing that (some of the) accruals reverse over time. In contrast, Beneish (1997) argued, the Modified Jones Model does not seem to capture the accruals patterns that are observed in firms that were identified to violate GAAP, suggesting

19


Table 6: Summary statistics of the year- and industry-specific OLS estimates of the accruals expectations model (estimation sample) ↵j(i),t t-stat j(i),t

t-stat j(i),t

t-stat j(i),t

t-stat ⇣j(i),t t-stat Nobs adj R2 White’s p

N 66 66 66 66 66 66 66 66 66 66 66 66 66

Mean 0.001 0.252 -0.017 -1.701 0.020 1.286 -0.731 -23.623 0.701 20.133 251.5 0.752 0.297

Median 0.002 0.234 -0.018 -1.459 0.016 1.296 -0.737 -19.253 0.709 16.979 155.5 0.764 0.277

StDev 0.014 1.413 0.015 1.527 0.028 1.493 0.105 14.274 0.116 13.956 427.3 0.106 0.214

Min -0.036 -2.710 -0.049 -5.060 -0.049 -2.270 -0.970 -87.110 0.267 3.431 50 0.2892 0.0001

Q1 -0.007 -0.784 -0.027 -2.986 0.004 0.326 -0.788 -26.255 0.644 13.110 107 0.697 0.116

Q3 0.011 1.107 -0.006 -0.606 0.039 2.036 -0.671 -15.542 0.774 23.449 203 0.814 0.427

Max 0.01 5.341 0.008 1.867 0.153 5.300 -0.268 -7.540 0.963 85.860 2392 0.971 0.892

%Pos 56.06 7.55 84.85 0 100

Key:TACi,t = total accruals for firm i in industry j(i) and year t; TAi,t = total assets for firm i in industry j in year t; GPE i,t = gross property plant and equipment for firm i in industry j in year t; AdjRev i,t = change in revenues minus change in receivables for firm i in industry j in year t;

OCF i,t = change in operating cash flow

for firm i in industry j in year t. White’s p refer’s to the p-value of the White test on the entire regression.

the need of more firm-specific information.10 In fact, this addition nests the Jones (1991) tradition with an earlier approach by De Angelo (1981), where lagged total accruals are taken as the benchmark for “normal” accruals. The inclusion of this variable has a very powerful e↵ect on the explanatory force: it about doubles the R2 . Note also that the coefficient is clearly positive, lending support to the DeAngelo-Beneish view of continuation rather than the Guay et al. view of reversal. Thus, the introduction of lagged accruals brings in a host of firm-specific circumstances not easily captured by the cross-sectional Jones model, and in that sense goes a long way towards solving recent criticisms against the latter model. Lastly, the variables in our accrual expectations model are scaled by lagged total assets. Of the over 28,000 initial observations used for an industry⇥year-specific normal-accruals model, 17,785 remain. The filter criteria were the same as in the test sample (Section 2.1), with, in addition, a requirement that any industry⇥year sample with less than 50 observations was discarded. The 17,785 remaining observations represent 66 sector⇥year combinations, for each of which the above normal-accruals regression was run. Using these coefficients we next compute, in the event sample, the fitted values for every

10

GPE and Sales are, of course, firm-specific, but the implied depreciation rate being estimated from cross-sections, are industry-wide estimates.

j(i),t

and credit period

j(i),t ,

20


Table 7: Descriptive statistics on total, discretionary, and non-discretionary accruals out of sample (i.e. in the event sample) TAC NAC DAC

N 1554 1554 1554

mean -0.045 -0.042 -0.004

stdev 0.164 0.142 0.077

var 0.027 0.020 0.006

min. -2.086 -1.862 -0.413

Q1 -0.118 -0.108 -0.038

median -0.043 -0.041 -0.003

Q3 0.030 0.026 0.029

max 1.101 0.164 0.571

Key: TAC = Total accruals; NAC = non-discretionary accruals, the out-of-sample fitted values from the regression estimated in table 3; DAC = TAC - NAC = discretionary accruals, the out-of-sample residuals.

test firm, to be interpreted as the normal accruals for a firm with the same characteristics. Discretionary accruals, then, are the out-of-sample residuals. Appendix Table 6 reports summary statistics on the estimated coefficients of discretionary accruals. The explanatory variables are generally significant and have acceptable signs. The explanatory power—a mean (median) adjusted R-squared of 0.7522 (0.7639)—is quite satisfactory. This is mainly thanks to the addition of lagged accruals, without which the explanatory power is just 40 percent. The concomitant reduction of estimation error in discretionary accruals should, in turn, increase the power of our tests of earnings management. Appendix Table 7 reports summary statistics of total accruals, and the estimated discretionary and non-discretionary accruals. The residual variance, var(DAc), amounts to 22 percent of the total variance, var(TAc); thus, out-of-sample the performance is as good as in-sample.

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