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					                (Non)Convergence in International Accrual Accounting


                                           Peter Joos
                       Morgan Stanley and MIT Sloan School of Management
                                         pjoos@mit.edu


                                           Peter Wysocki *
                                   MIT Sloan School of Management
                                          wysockip@mit.edu



                                         Current Draft: June 2007




                                                  Abstract

We present empirical evidence of systematic and persistent differences in the properties of firms’
working capital accruals across 20 countries between 1991 and 2005. These persistent
differences exist in spite of claims of growing harmonization and convergence in international
accounting standards and practices. We hypothesize that the persistent accruals differences
across countries arise from continuing international differences in the economic and institutional
(versus standards-based) determinants of accruals. Our empirical evidence supports this
hypothesis. Our findings suggest that the international harmonization of accounting standards
will not result in the convergence of accrual accounting properties if differences in institutional
and real operating environments persist across countries.



JEL classification: G15, G30, M41, M42

Key Words:       Accrual accounting; Convergence; Harmonization; International accounting;
                 Operating decisions




*
  Corresponding author: Peter Wysocki, Massachusetts Institute of Technology, Sloan School of Management, 50
Memorial Drive - E52-325, Cambridge, MA 02142, USA. We wish to thank Donal Byard, S.P. Kothari, Christian
Leuz and seminar participants at the University of North Carolina, the FEA Conference, the EAA Congress, and the
FIRRE Euroconference for helpful comments and suggestions.
1. Introduction

        This paper presents empirical evidence showing systematic and persistent differences in

the properties of firms’ working capital accruals around the world. Our study is motivated by

recent claims that implicit or explicit international harmonization in accounting standards has

lead to greater uniformity in accounting numbers across countries (see, for example, Land and

Lang, 2002). However, our evidence suggests that on-going differences in institutional and

operating factors across countries can limit the convergence in the properties of firms’ reported

accounting numbers.1 We suggest that this lack of convergence in accounting numbers is driven

by continuing international differences in the underlying economics affecting firms’ underlying

operations. Our study attempts to provide a better understanding of these economic determinants

of accruals properties across countries. Overall, we show that, while global standards have

arguably become more similar over the past 15 years, there still remain large differences in

operating and institutional factors that drive accounting properties across countries. Our findings

are important because researchers and policymakers may draw incorrect inferences about the

observed outcomes of international accounting harmonization if they do not understand and

account for the fundamental economic drivers of differences in firms’ actual operating decisions.

        We examine accounting convergence by comparing the properties of firms’ working

capital accruals across 20 countries between 1991 and 2005. In particular, we examine the

sensitivity of a firm’s specific working capital accruals (i.e., changes in accounts receivable,

changes in inventory, or changes in accounts payable) to economic shocks that are captured by

changes in a firm’s sales.2 Despite the growing harmonization of international accounting


1
  McLeay and Jaafar (2007) and Beuselinck, Joos, and Van der Meulen (2007) also find persistent differences in
     accounting methods and earnings ratios across European countries leading up to the adoption of IFRS.
2
  Jones (1991) was one of the first studies to hypothesize and show an empirical link between accruals and changes
     in a firm’s sales.
                                                             2
standards, we find persistent differences in the sensitivity firms’ accruals to sales shocks across

countries between 1991 and 2005. There is no evidence of a convergence in these accruals

properties across countries over this time period. We hypothesize that the persistent accruals

differences arise from continuing international differences in the operating environment and

institutional (versus standards-based) determinants of accruals. Our empirical evidence supports

this hypothesis.

           We start our analysis by noting that a firm’s working capital accruals should be

determined by: 1) mandated rules on accrual accounting, 2) discretionary reporting incentives,

and 3) the operating environment. Whereas previous research studies the relation between

accruals and the first two determinants (e.g., Ball et al., 2000; Hung, 2001; Leuz et al., 2003),

there is a relatively little international evidence on the relation between a firm’s accrual use and

its operating environment.3 We extend prior research by examining how observed accrual

properties are influenced by a firm’s operating and institutional environment, over and above the

effects of mandated accounting rules and discretionary reporting incentives.

           We first present descriptive evidence on the properties of working capital accruals (i.e.,

changes in non-cash working capital accounts) and the level of working capital accounts across

20 countries in two major sub-periods (1991-1998 and 1999-2005).                          We find systematic

differences in both the sensitivity of firms’ accruals to sales shocks and in the level of working

capital accounts across countries. Moreover, we find no evidence of across-country convergence

in neither this accruals property nor in the level of working capital accounts between 1991 and

2005.      This outcome is in spite of the apparent harmonization of accounting rules across


3
    For example, Dechow (1994) studies the effect of a firm’s operating environment on the properties of earnings,
    accruals and cash flows in the US capital market context. Guenther and Young (2000) link real economic activity
    to accounting numbers in an international context.

                                                              3
countries during this time period. The persistent cross-country differences in both the properties

of working capital accruals and the level of working capital accounts motivate our more detailed

investigation into the links between these two phenomena.

       Our research design develops an empirical model of working capital accruals that

accounts for operating environment effects.        To incorporate the effect of the operating

environment on accruals, we start from the observation that accruals (i.e., changes in working

capital accounts) should be related to the absolute level of a firm’s working capital accounts. For

example, greater use of customer financing arrangements (i.e., accounts receivable) increases the

likelihood of large year-to-year changes in this receivables account.         Therefore, a firm’s

propensity to hold high levels of receivables, payables and inventory accounts will directly affect

the magnitude of a firm’s periodic accrued revenues and expenses. The question then becomes

why certain firms choose to maintain higher levels of receivables, payables and inventories than

other firms? The economic factors underlying a firm’s operating environment ultimately dictate

these decisions. For example, firms in different industries choose drastically different inventory

management techniques or different systematic customer and supplier financing arrangements.

Country-specific institutional factors can also affect trade credit utilization (payables and

receivables) and optimal inventory stockpiles. The effects of these industry and institutional

factors flow through and indirectly affect the magnitude of working capital accruals in firms’

reported net income.

       We next undertake regression analyses to better understand the relation between the level

of firms’ working capital accounts and the sensitivity of firms’ working capital accruals to sales

shocks. Given that greater levels of receivable, payable and inventory accounts increase the

likelihood of large year-to-year changes in these accounts, we hypothesize that the sensitivity of


                                                    4
firms’ receivable, payable and inventory accruals to sales shocks will be positively related to the

level of these working capital accounts. We test this prediction based on a model similar to the

Jones (1991) model, but with the modification that the sensitivity of accruals to sales shocks

varies conditionally with a firm’s working capital level. We call this model the “Conditional

Jones Model”. Consistent with our expectation, we find that the sensitivity of specific working

capital accruals (such as changes in account receivable) to sales shocks is increasing in the level

of the associated working capital account (i.e., the level of accounts receivable). In addition, we

find evidence consistent with Barton and Simko (2002) that a working capital change in also

related to the starting level of a working capital account.

       After documenting that working capital accruals are related to the level of a working

capital account, it should not be surprising that properties of working capital accruals have not

converged across countries because the underlying levels of working capital accounts have also

not converged between 1991 and 2005. This evidence suggests that operating and institutional

factors affecting working capital levels may ultimately restrict convergence of firms’ accruals

use across countries. To further investigate this issue, we examine the determinants of firms’

working capital levels across countries. We hypothesize and find supporting evidence that

country features that affect customer financing and trade credit (i.e., factors that affect working

capital accounts related to receivables and payables) or that affect inventory holdings (i.e.,

factors that affect working capital accounts related to inventories) are sig nficantly related to the

these working capital accounts. Taken together, the evidence suggests that, despite recent

harmonization in accounting practices and standards, international differences in accrual

accounting are likely to persist without convergence in operating environments across countries.




                                                      5
       The remainder of the paper is organized as follows. We discuss the sample construction

and descriptive evidence on accruals, working capital accounts, and institutional variables across

countries in section 2. Section 3 present empirical tests on differences in (i) the sensitivity of

working capital accruals to sales shocks, and (ii) working capital levels across 20 countries in

two sub-periods (1991-1998 and 1999-2005).        In section 4, we analysis the economic and

institutional determinants of both the accruals sensitivities to sales shocks and working capital

levels across countries. Section 5 presents a summary of robustness tests and section 6

concludes.



2. Descriptive evidence

2.1 Data and Sample Description

       Our source of financial accounting data is the Worldscope database, which contains

historical financial data from home-country annual reports of publicly-traded companies around

the world. We focus on a sample of manufacturing, retail and wholesale firms (covered by 2-

digit SIC codes in the range 30-39 and 50-59) to ensure comparability in working capital

accounts. We use a constant sample approach to rule out the confounding effects of changes in

sample composition changes. Each sample firm must have continuous financial data from 1991-

2005, including sales, operating income, accounts receivable, accounts payable and inventory.

We limit the sample to 20 countries for which we have measures of accrual accounting standards

based on a metric calculated in Hung (2001). In addition, a country must have at least 20 firms

meeting the time-series data availability criteria.       The final sample consists of 3,130 firm

observations for the fiscal years 1991-2005 across 20 countries (giving a final sample of 46,950

firm-year observations).


                                                      6
         We focus on working capital accruals because we are specifically interested in the effect

of firms’ operating environment on reported accounting numbers. The main working capital

accounts are accounts receivable, inventory, and accounts payable, and the short-term accruals

that arise from these accounts. Working capital accruals lend themselves well to study this

relation because they are an immediate expression of a firm’s operating environment.

Specifically, they are short-term accruals and directly related to firm’s operating cycle or

working capital decisions (Dechow 1994). To control for scale effects, we scale the working

capital and accruals variables by total sales.4 We calculate the levels of each of these working

accounts as and define working capital accruals to sales, mean accounts receivable to sales

[AccountsReceivable/Sales], accounts payable to sales [AccountsPayable/Sales], and inventory to

sales [Inventory/Sales].



2.2 Descriptive Results

         Table 1 presents the number of firm-year observations per country as well as descriptive

statistics of firm characteristics and the relevant accounting variables. Not surprisingly, there is

significant variation in the number of firm-year observations across countries due to differences

in capital market development, country size, and the availability of complete financial

accounting data. For each country, we report the median value of firm sales in $US so as to

reduce the influence of extreme observations and to allow for direct firm-size comparisons

across countries. Based on the large differences in the median firm size across countries, the

main financial variables in the analysis are scaled by sales or lagged sales.5 Consistent with


4
  An alternate deflator could be total assets. However, as noted in Wysocki (2004), scaling international accounting
   data by total assets is problematic given across-country differences in asset recognition rules and choices. As a
   robustness check, we also perform our analyses using total assets as a scaling variable (see section 5).
5
  Scaling by lagged sales does not affect the results.
                                                              7
Leuz et al. (2003), we find substantial cross-country variation in absolute accrual across

countries. In addition, we find a large corresponding cross-country variation in the three main

working capital accounts (accounts receivable, inventory, and accounts payable). However,

there does not appear to be a strong pattern in the changes in total work capital accruals (WCAcc)

or for individual working capital accrual accounts (AP, Inv, and AP) across countries. This is not

really surprising given that these short-term accruals tend to reverse and should roughly average

to zero.

       Table 2 presents descriptive correlations for the main variables. We examine the

correlation between firm’s: (i) scaled accounts receivable, inventory, and accounts payable

levels, (ii) absolute value of the scaled short-term accruals (i.e., absolute changes in working

capital accounts), and (iii) the scaled short-term accruals (i.e., raw changes in working capital

accounts). As noted in Table 1, the accruals accounts have means close to zero. Therefore, to

capture the extent of use of accruals (see, Leuz, Nanda, and Wysocki, 2003), we examine

absolute accruals and their correlations with the levels of working capital accounts. The bottom

half of the correlation matrix reports Pearson correlations of the raw variables and the top half of

the correlation matrix reports Pearson correlations for the country-mean adjusted values of the

variables (mean adjustment is calculated prior to taking absolute values). The correlation

evidence suggests a strong relation between the levels of the working capital accounts and the

variability of these working capital (accruals) accounts as captured by the absolute changes in

these accounts. It should be noted that there appears to be a small, but often significant, negative

relation between the level of a working capital account and the change in that account. This is

consistent with the findings of Barton and Simko (2002) which suggest that prior cumulative

accruals which affect the level of a working capital account can constrain future accrual


                                                     8
movements. In our sample, it suggests that high cumulative accruals in the past (reflected in the

level of a working capital account) tend to lead to lower accruals in the future.

       Table 3 provides summary information on the economic, accrual accounting standards

and institutional variables for each country in the sample. We use two main measures to capture

country-level economic influences on the levels of working capital accounts such as usse of trade

credit/customer financingf and the need to maintain inventories. To capture the availability of

bank loans to corporations, we calculate the percentage of domestic credit provided to the private

sector as a fraction of GDP (taken from the IMF’s International Financial Statistics 2001). This

variable, referred to as LendDevelop in later analyses, is based on the average credit to GDP ratio

in each country 1990 and 1999. We observe that countries such as Switzerland and Hong Kong

rate high on this metric while Denmark and Italy rate low. This measure is intended to capture

the availability of short term financing to both firms (as a substitute for AP) and customers (as a

substitute for AR). Second, to capture the stability of business relationships between firms, we

use an index of the risk of contract repudiation from La Porta et al. (1998). In later analyses we

refer to this variable as ContractStability. This index is intended to capture whether firms can

enter into long-term, specific, just-in-time contractual business relationships with each other.

The table shows that countries such as Switzerland and Japan rate high on this metric while

South Africa and Malaysia rate low.

       To capture the other determinants of working capital accrual properies, we measure the

effects of mandated accounting rules by using an index of the degree accrual accounting in a

country’s accounting standards. This index is constructed by Hung (2001) for a sample of 21

countries. The index, referred to as AccountingStandards in later analysis, measures the extent

to   which   a   country’s    accounting    rules   allow   firms   to   recognize   current   cash


                                                     9
receipts/disbursements as revenues/expenses in future or past periods.            The index was

constructed using countries’ stated accounting standards in 1993. Table 3 shows that countries

like United States and the UK rate high on this standards-based measure of accruals, while

Germany and Switzerland rate low. This characterization may appear surprising given prior

empirical evidence on the use of accruals in common law and code law countries (see, for

example, Ball et al. 2000 and Leuz et al. 2003). However, we emphasize that this illustrates that

there is a difference between stated accounting rules and actual accounting practice.

       Finally, we include two institutional variables used in prior research in the empirical

model to capture earnings management incentives. Similar to Leuz et al. (2003), we use the

measures of outside investor protection laws and enforcement to capture managers’ incentives

and ability to use large absolute discretionary accruals to mask firm performance. The outside

investor rights variable (InvestorRights) is the “anti-director rights” index created by La Porta et

al. (1998) and captures the aggregate rights of minority shareholders. Countries like Belgium,

Germany and Italy rate low on this index while Canada, the United Kingdom and the United

States have a high rating. The legal enforcement measure (Enforcement) is the mean score

across three variables, each ranging from zero to ten: (1) an index of the efficiency of a country’s

legal system, (2) an index of the rule of law in a country, and (3) the level of corruption in a

country. We see that Scandinavian countries have the highest level of enforcement while

countries like Indonesia, Pakistan and the Philippines have the lowest.



3. Empirical tests

3.1. Relation between accruals and sales changes across countries/sub-periods




                                                    10
        Table 4 presents empirical tests of the relation between specific accruals (i.e., accounts

receivables accruals, inventory accruals, or accounts payable accruals) and economic shocks to

sales (i.e., sales changes). We capture the sensitivity measure based on a version of the Jones

(1991) where accruals are modeled as:



           ΔWCt/Salest-1 =  + Σcountry,subperiod*Dcountry,subperiod ΔSalest/Salest-1 + ε   (1)


where ΔWCt is the annual change in a given working capital account (ΔAccountsReceivable,

ΔInventory, or ΔAccountsPayable). The variables are scaled by lagged total sales. The

regressions includes 40 interactive variables, Dcountry,subperiod *ΔSalest/Salest-1, one for each of the

20 country by 2 sub-period combinations. The first sub-period covers 1991-98 and second sub-

period covers 1999-2005. The regression includes fixed effects for the industries in the sample:

materials manufacturing (SIC 30-33 & 39), machinery (SIC 34, 35 & 37), electrical

manufacturing (SIC 36 & 38), retail (SIC 52-59), and wholesale (SIC 50 & 51). Country-year

fixed effects are also included in the regressions.

        We present estimates of the country/sub-period sensitivity for 8 major countries and a

summary of the absolute changes in  for all 20 countries. The patterns suggest the following:

While there is significant variation in the estimated sensitivities () across countries, there does

not appear to be convergence in country sensitivities from the first sub-period (1991-1998) to the

second sub-period (1999-2005). The “Change Signif.” columns present the p-value from an F-

test of change in country slope coefficient between the sub-periods. Overall, there is rarely a

significant change in across sub-periods. If there was a meaningful convergence, then one would

expect that the majority of estimated sub-period ’s would have a smaller absolute value in the

second sub-period. However, this is not the case. In fact, we find that the absolute countries ’s

                                                        11
were at least as large or larger in 12 of the 20 countries for the AR accruals, 10 of the 20

countries for the AP accruals, and 13 of the 20 countries for the Inventory accruals. Overall,

there is no evidence of convergence of this accruals property over the full sample period.



3.2. Level of Working Capital Accounts in Different Countries and Sub-Periods


       Table 5 presents regression estimates of differences in the levels of scaled working

capital accounts for the full sample of 46,950 firm-year observations covering two sub-periods

(1991-1998 and 1999-2005). Based on a similar approach to Land and Lang (2002), we estimate

regression model the following regression model to test for differences in average working

capital account levels in each country and each sub-period:




                      WCt/Salest = Σcountry,subperiod*Dcountry,subperiod + ε   (2)




where WCt is the end-of-year level of a given working capital account (AccountsReceivable,

AccountsPayable, or Inventory). The variables are scaled by total sales and adjusted by

subtracting the sub-period mean of each scaled variable. There are 40 country/sub-period

indicator variables (Dcountry,subperiod). The “coefficient value” reported in the table captures the

average country deviation of the variable from the mean of all countries in a given sub-period.

The regression includes fixed effects for the industries in the sample: materials manufacturing

(SIC 30-33 & 39), machinery (SIC 34, 35 & 37), electrical manufacturing (SIC 36 & 38), retail

(SIC 52-59), and wholesale (SIC 50 & 51). The “Change Signif.” columns have the p-value from

F-test of change in country effect between the sub-periods.


                                                       12
       We present estimates of the country/sub-period deviations in working capital levels for

the same 8 major countries as well as a summary of the changes in working capital levels for all

20 countries across the two sub-periods. Again, the patterns suggest the following: There is

significant variation in the levels of the working capital accounts across countries, but is not a

convergence in average country working capital levels from the first sub-period (1991-1998) to

the second sub-period (1999-2005). The “Change Signif.” columns present the p-value from an

F-test of change in country slope coefficient between the sub-periods. Again, there is rarely a

significant change in working capital levels across sub-periods. We find that the working capital

levels are at least as large (or larger) in 12 of the 20 countries for the level of accounts

receivable, 10 of the 20 countries for the accounts payable level, and 12 of the 20 countries for

the inventory level. Overall, there is no evidence of convergence of these working capital

account levels.



3.3. Estimates of the “Conditional Jones Model”

       As we noted earlier, we hypothesize that working capital accruals (i.e., changes in

working capital accounts) should be related to the absolute level of a firm’s working capital

accounts.   For example, greater use of customer financing arrangements (i.e., accounts

receivable) increases the likelihood of large year-to-year changes in this receivables account.

Therefore, a firm’s propensity to hold high levels of receivables, payables and inventory

accounts should directly affect the magnitude of a firm’s periodic accrued revenues and

expenses. The descriptive correlation evidence in Table 2 suggests that there appears to be a

relation between working capital accounts levels and absolute changes in these accounts.




                                                   13
       We more directly test this relation by specifying a version of the Jones (1991) model for

working capital accruals where the sensitivity of accruals changes conditional on the level of a

given working capital account. We first estimate the base-line Jones (1991) model for working

capital accruals:

                         ΔWCt/Salest-1 =  +  ΔSalest/Salest-1 + ε     (3-a)


where ΔWCt is the annual change in a given working capital account (ΔAccountsReceivable,

ΔInventory, or ΔAccountsPayable). The variables are scaled by lagged total sales. We then

extend the model to allow for the sensitivity of accruals-to-sales changes () to vary as a function

of the level of working capital. The working capital levels are measured at the beginning of the

year (i.e., at the end of year t-1). The “conditional” version of the Jones (1991) is specified as:



ΔWCt/Salest-1=+1ΔSalest/Salest-1+2(ΔSalest/Salest-1)(WCt-1/Salest-1)+3WCt-1/Salest-1+ε (3-b)


       The results of the estimates of both the standard model (model 3-a) and the “Conditional

Jones Model” (model 3-b) are presented in Table 6. As expected, the estimates of the standard

model for the receivables, payables and inventory accruals show a significant positive relation

between the accruals and contemporaneous changes in sales. In the pooled sample, changes in

sales explain between 19% and 26% of the variation in the individual accruals accounts. Next,

we estimate model (3-b). The results are striking and show that the sensitivity of accruals to sales

shocks is significantly increasing in the starting level of the corresponding working capital

account. In addition, the incremental explanatory power of the models increases by up to 5%.

Interestingly, we also find a significant negative relation between the beginning of period level of

a working capital account and the change in that account (ie, the current period accrual). Again,

                                                     14
this finding is consistent with Barton and Simko (2002) and suggests that prior cumulative

accruals which affect the level of a working capital account can constrain future accrual

movements. In our sample, high cumulative accruals in the past (reflected in the level of a

working capital account) predict lower accruals in the future. Overall, these results suggest that

working capital level choices are important determinants of accrual properties.



4. Institutional Determinants of Accruals and Working Capital Levels

4.1. Sensitivity of accruals to sales shocks (role of working capital levels and country factors)

           The empirical patterns presented in the previous sections suggest that both working

capital levels and the working capital accruals properties have not converged across our sample

countries between 1991 and 2005. These patterns contrast the recent evidence in Land and Lang

(2002).6 Therefore, our objective is to provide a better understanding of factors that affect the

properties of firms’ accruals in an international setting. The starting point of our analysis is the

observation that firms’ accruals should be affected by three determinants: 1) mandated

accounting rules, 2) discretionary reporting incentives, and 3) operating environment effects.

We extend prior research by focusing on how observed accruals are determined by a firm’s

operating environment, over and above the effects of mandated accounting rules and

discretionary reporting incentives.

           We model operating environment effects by relying on the fact that a firm’s use of high

levels of receivable, payable and inventory accounts will directly affect the magnitude of its

periodic accrued revenues and expenses. More importantly, management’s choice to maintain

high or low levels of receivables, payables and inventories is strongly influenced by a firm’s

operating environment. For example, firms in different industries choose drastically different
6
    Guenther (2002) also provides competing explanations to the Land and Lang (2002) results.
                                                              15
inventory management techniques or different systematic customer and supplier financing

arrangements. In addition to industry factors, country-specific institutional factors can also

affect trade credit utilization (payables and receivables) and optimal inventory stockpiles. In

other words, we view a firm’s working capital choices as a proxy for its underlying economic

environment. We therefore estimate an empirical model of the estimated sensitivity of a firm’s

accruals (i.e., beta from model (4)) as a function of the level of a firm’s receivables, payables and

inventories. In addition, we include other institutional determinants of this accruals property.

Each firm’s accruals sensitivity is estimated using a firm-specific time-series regression of

accruals on changes in sales:

                          ΔWCt/Salest-1 =  + ΔSalest/Salest-1 + ε    (4)


       Table 7 presents the regression results of estimates of the determinants of accruals

sensitivities for each of the major short-term accruals accounts based on the following model:

       i =  + 1ARi/Salesi + 2APi/Salesi + 3Invi/Salesi +

                1AccountingStandardsj + 1InvestorRightsj + 2Enforcementj + i              (5)

where Accrual Accounting Standards represents the stated accrual accounting standards in a

country and the degree to which the standards move away from a cash measure of performance

(from Hung 2001). Anti-Director is the “anti-director rights” index created by La Porta et al.

(1998). It is an aggregate measure of minority shareholder rights and ranges from zero to six.

Enforcement is measured as the mean score across three legal enforcement variables used in La

Porta et al. (1998): (1) the efficiency of the judicial system, (2) an assessment of rule of law, and

(3) the corruption index. All three variables range from zero to 10.



                                                     16
          The empirical results in Table 7 show that, in all cases, a firm’s  is significantly and

positively related to the cross-section mean of the level of the corresponding working capital

account. Additionally, the coefficients on the two proxies for discretionary reporting incentives

are also highly significant with the expected sign, consistent with earlier research by Leuz et al.

(2003).



4.2. Country-level determinants of working capital levels

          Based on the extant literature, we select three different types of operating environment

variables that might determine the level of working capital accounts. First, we include firm-level

variables that capture characteristics such as growth, profitability or size related to working

capital accounts. Second, we include industry dummies in the regression. Industry affiliation is

an important determinant of a firm’s operating environment an it captures factors such as

operating cycles, inventory methods, and the propensity to extend or receive trade credit.

Finally, we include two country-level variables that measure particular aspects of the financial

environment that affect firms’ working capital account choices. The first variable measures the

extent of private sector lending development in the countries in the sample. This measure

reflects the availability of short-term financing from traditional financial institutions to both

firms (as a substitute for AP) and customers (as a substitute for AR). The relevant finance

literature has discussed and documented the existence of this substitution effect (e.g., Petersen

and Rajan 1997; Fisman and Love 2001). If a country has a poorly-developed private lending

sector, then firms will tend to substitute to trade credit financing and rely more on receivable and

payable financing methods. The second institutional variable captures business contract stability

in a particular country. We assume that the more stable the country’s contractual environment is,


                                                     17
the more likely firms will engage in long-term contractual relations with customers and

suppliers. Other things equal, this will lead to firms to maintain lower inventory levels because

contracts with their suppliers are enforceable and more reliable.      A good example of this

phenomenon is the just-in-time (JIT) inventory technique that arises from stable supplier-

customer relationships in Japan. We discuss the choice of proxies for these determinants and the

specification of the first-stage regressions in more detail below.


       We estimate three separate regressions for each of the main working capital accounts

(accounts receivable, accounts payables, and inventories) to allow for variation in the economic

determinants across the three equations. We assume the economic determinants of accounts

receivable and accounts payable are based on similar “trade credit” factors. We therefore

estimate the following two regressions:



       ARi/Salesi =  + 1Growthi + 2Margini + 3Log(Sizei) +

                               1LendDevelopj + ijIndustryij + i                      (6)



       APi/Salesi =  + 1Growthi + 2Margini + 3Log(Sizei) +

                               1LendDevelopj + ijIndustryij + i                       (7)



where Growthi is the mean annual sales growth for firm i over the sample period, Margini is the

mean operating profit margin (operating income/sales) for firm i over the sample period, and

Log(Sizei) is the natural logarithm of average US$ Sales for firm i. We expect more working

capital with greater growth, less working capital with higher margin and less working capital for

larger firms. The variable LendDevelopj was defined earlier and is a country-level variable that

                                                     18
captures private sector lending development in country j. We expect lower levels of receivable

and payable accounts (trade credit financing) when LendDevelop is larger, consistent with the

substitution argument discussed earlier. We also include 19 industry dummies in the regression

to control for differences in accounts receivable and accounts payable policies across the twenty

2-digit SIC industries in the sample.

       We separately estimate the inventory working capital account equation as follows:



       Invi/Salesi =  + 1Growthi + 2Margini + 3Log(Sizei) +

                               1ContactStabilityj + ijIndustryij + i                    (6)



where the variables are as before in equations (4) and (5) except for ContactStabilityj which

captures business contract stability in country j (see also Table 2). We expect the coefficient on

ContactStabilityj to be negative. Our reasoning is that if there are more stable contractual norms

in a country firms will be more willing (or likely) to enter into long-term JIT relationships with

their suppliers. In other words, we assume that when the country is characterized by higher

contractual stability, firms will require lower inventory levels inventory as potential insurance

against suppliers not delivering the inputs used in the production process on time. We think of

this variable as a necessary, but not necessarily sufficient, condition for this type of contractual

relation between firms. As an illustration, the descriptive results in Table 3 for this variable

shows that Japan scores very high on this dimension.

       Table 8 shows the results of the regressions of the working capital accounts on their

potential economic determinants.        The results in this table are based on a cross-sectional

regression including all firms and all countries. We find that the results are consistent with our


                                                    19
expectations, with one exception: the sign on our growth variable is significantly negative in all

regressions and highly significant in the accounts receivable and accounts payable regressions.

One potential explanation is that the other variables included in the regression already

incorporate growth in the regression (e.g., size can be seen as a growth proxy) so that the

remaining effect of this proxy is negatively or insignificantly related to the working capital

accounts. Note also that the coefficients on both variables that describe characteristics of the

macro-economic setting in which the firms operate are significant with the predicted sign.

Finally, our regressions better explain accounts receivable and inventory than accounts payable

levels as measured by the regression R2’s.

       Overall, the results confirm that working capital accruals are a major determinant of a

firms’ working capital levels. In addition, our results in Table 8 working capital accruals will not

become more similar across different countries so long as the business practice of firms with

respect to working capital is distinct across these countries.         Even if implicit or explicit

incentives are present to harmonize accounting practices, the resulting accounting numbers will

remain a function of distinct real working capital practices and decisions by management.


5. Robustness checks

       We undertake a number of robustness checks to ensure stability of our results. First, we

estimate the regressions using total assets as a scaling variable rather than total sales. Our main

specification use total sales to avoid the problems of differences in accounting recognition of

assets. However, our results are still robust to using total assets as a deflator. In particular, there

is no convergence in either working capital accounts or accruals between 1991-1998 and 1999-

2005 sub-periods.



                                                      20
       Second, we re-estimate the sub-period analyses using different windows to define the

sub-periods. In particular, we use 1991-1993 to define the first sub-period and 2002-2005 to

define the second sub-period. The results are unchanged. Similar to Land and Lang (2002), we

also adjust for autocorrelation in the residuals from the regressions in Tables 4-6. The inferences

in our statistical tests are unchanged after applying the Cochrane-Orcutt adjustment.

       Third, we relax the constant sample restriction and re-run our analyses on a larger non-

constant sample of firms. In this sample, we only require that the firm have reported profits for at

least 1 year prior to entering the sample for the first time. We again find similar results for those

of the main constant sample; there is no evidence of convergence in the accruals properties nor in

the level of working capital accounts.


6. Summary and Conclusion


       In this paper we investigate whether international differences in firms’ operating

strategies are associated with across-country patterns of working capital accruals. Our starting

point is the empirical observation that working capital accruals properties have not converged

across 20 major countries over the period 1991-2005, despite claims of growing harmonization

in international accounting standards and practices. We hypothesize that the persistent accruals

differences arise from continuing international differences in the microeconomic and institutional

(versus standards-based) determinants of accruals. To evaluate this hypothesis, we estimate an

empirical working capital accruals model that contains three determinants of accruals: 1)

mandated accounting rules, 2) discretionary reporting incentives, and 3) operating environment

effects. We focus on working capital accruals in this analysis because they are an immediate




                                                     21
expression of a firm’s operating environment: they are short-term in nature and directly related to

operating cycle or working capital decisions of the firm.

       To incorporate the effect of the operating environment on working capital accruals, we

develop and estimate an empirical model where working capital accruals properties are

hypothesized to be fundamentally affected by the level of a firm’s working capital investments.

Quite simpIn particular, Our result show that In addition, we document how the level of various

working capital accounts is directly affected by the economics underlying a firm’s operating

environment. Our results show that operating environment factors are important determinants of

the level of working capital across countries. Moreover, the significant international differences

in these operating environment factors persist between 1991 and 2005. In particular, we observe

no convergence in the level of these working capital accounts across countries during the sample

period. Our results therefore show that the absence of convergence in operating environment

factors and working capital accounts is mirrored in the persistent cross-country differences in

accrual levels over sample period.

       Taken together, the results indicate that working capital accruals are a function of the

three determinants specified in the empirical tests, namely operating environment effects,

discretionary reporting incentives, and accounting standards. Our findings have implications for

regulators, practitioners and international accounting researchers engaged in the current debate

on the international harmonization of accounting standards and practice. Commentators have

highlighted the importance of adopting harmonized rules such as IFRS or US GAAP, along with

the necessary conditions of strong audit environments and accounting standard enforcement

mechanisms to create convergence of accounting practice around the world.               Our study

emphasizes that the differences in the underlying economics of business practice across countries


                                                    22
limits the harmonization of worldwide accounting practice.     Even if managers’ explicit or

implicit reporting incentives converge across countries, actual accounting outcomes can still

differ because of continuing international differences in firms’ operating environments.

Relatedly, our findings also imply that international accounting researchers need to carefully

control for operating environment differences when analyzing the convergence of accounting

outcomes in multi-country studies




                                                 23
                                        TABLE 1: Descriptive Statistics of Firm Characteristics by Country

The full sample consists of 3,130 firms with continuous financial accounting data between 1991 and 2005 across 20 countries. A firm’s primary SIC must be in the manufacturing,
wholesale or retail sector to be included in the sample. Accounting information is obtained from the Worldscope database. Firm size is measured as average total US$ sales over
the sample period. Working capital accruals (WCAcc) are calculated as AccountsReceivable + Inventory - Accounts Payable.

                                    Median Firm            Mean            Mean            Mean           Mean                 Mean             Mean              Mean
                           #         Size: Sales          AR/Sales        AP/Sales       Inv/Sales      WCAcc/Sales          ΔAR/Sales         ΔAP/Sales        ΔInv/Sales
 Country                 Firm         $US MM                (%)             (%)            (%)            (%)                  (%)               (%)               (%)
 AUSTRALIA                 47            1,541              17.3%           10.2%         15.0%            -0.05%               0.30%            -0.09%           -0.29%
 BELGIUM                   36            1,722              20.7%           11.7%         15.3%             0.08%               0.46%             0.31%           -0.50%
 CANADA                   107            1,440              15.7%           14.3%         15.1%             0.14%              -0.14%             0.22%            0.23%
 DENMARK                   67             589               17.9%            7.4%         17.8%            -0.11%               0.37%            -0.50%           -0.05%
 FINLAND                   40            1,784              20.0%            8.4%         15.4%             0.07%              -0.07%             0.12%            0.28%
 FRANCE                   202            2,781              25.2%           15.2%         19.6%             0.18%              -0.04%             0.47%           -0.09%
 GERMANY                  228            2,913              17.4%            8.4%         17.1%             0.26%               0.21%             0.01%            0.29%
 HONG KONG                 23            1,619              16.9%           16.5%         16.5%            -0.14%              -0.11%             0.04%           -0.18%
 IRELAND                   21             891               17.1%           11.9%         12.2%            -0.21%               0.10%            -0.44%           -0.51%
 ITALY                     62            1,906              36.3%           22.6%         30.8%             0.09%               0.12%             0.07%           -0.11%
 JAPAN                    894            2,219              25.6%           16.3%         13.7%             0.08%               0.49%             0.10%           -0.52%
 NETHERLANDS               79            2,685              16.8%            9.9%         15.4%            -0.17%              -0.36%             0.24%           -0.10%
 NORWAY                    21            1,962              19.1%            7.9%         13.5%             0.13%               0.33%             0.08%           -0.30%
 SINGAPORE                 20             513               22.1%           12.3%         14.0%             0.19%               0.29%            -0.21%            0.19%
 SOUTH AFRICA              34            1,214              18.8%           15.7%         14.3%            -0.14%              -0.45%             0.50%           -0.38%
 SPAIN                     38             566               32.0%           15.5%         18.8%             0.09%              -0.02%            -0.19%            0.53%
 SWEDEN                    59            1,932              18.6%            9.5%         18.1%            -0.05%              -0.45%             0.12%            0.21%
 SWITZERLAND               57            1,879              19.1%            8.7%         18.9%             0.27%               0.34%             0.10%            0.41%
 UK                       432            1,451              18.3%           10.9%         15.4%            -0.17%              -0.09%            -0.16%           -0.11%
 USA                      663            6,467              15.4%            7.6%         12.7%             0.02%              -0.05%             0.06%           -0.11%
 Mean                     154            1,903              20.5%           12.0%         16.5%             0.03%               0.06%             0.04%           -0.06%
 Median                    58            1,753              18.7%           11.3%         15.4%             0.08%               0.04%             0.07%           -0.10%
 Max                      894            6,467              36.3%           22.6%         30.8%             0.27%               0.49%             0.50%            0.53%
 Min                       20             513               15.4%            7.4%         12.2%            -0.21%              -0.45%            -0.50%           -0.52%
                                 TABLE 2: Sample Correlations of Key Working Capital and Accruals Variables

The full sample consists of 3,130 firms with continuous financial accounting data between 1991 and 2005 across 20 countries (46,950 firm-year observations). A firm’s primary
SIC must be in the manufacturing, wholesale or retail sector to be included in the sample. Accounting information is obtained from the Worldscope database. Working capital
accruals (WCAcc) are calculated as AccountsReceivable + Inventory - AccountsPayable. The bottom half of the correlation matrix reports Pearson correlations of the raw
variables. The top half of the correlation matrix reports Pearson correlations for the country-mean adjusted values of the variables (mean adjustment is calculated prior to taking
absolute values). * indicates significant correlations at 0.05 level or better.

                        AR/              Inv/             AP/          |ΔAR|/       |ΔInv|/        |ΔAP|/           |ΔWC|/            ΔAR/          ΔInv/             ΔAP/
                        Sales            Sales           Sales          Sales        Sales          Sales            Sales            Sales         Sales             Sales
  AR/Sales                               0.09*           0.19*          0.16*         0.04          0.07*             0.10*          -0.19*          -0.04           -0.08*
  Inv/Sales             0.16*                            0.08*          0.03         0.26*          0.06*             0.12*           -0.04         -0.17*           -0.05*
  AP/Sales              0.34*            0.13*                          0.06*         0.02          0.20*             0.09*            0.04          0.03            -0.13*
|ΔAR|/Sales             0.31*            0.09*           0.14*                       0.17*          0.18*             0.29*          -0.06*          0.04            -0.08*
|ΔInv|/Sales            0.07*            0.42*           0.08*          0.24*                       0.19*             0.24*          -0.07*         -0.08*            -0.03
 |ΔAP|/Sales            0.22*            0.10*           0.39*          0.37*        0.25*                            0.24*          -0.05*          -0.02           -0.07*
|ΔWC|/Sales             0.29*            0.32*           0.26*          0.61*        0.53*          0.47*                                            0.05*             0.02
 ΔAR/Sales               -0.07           0.05*            0.01         -0.05*         0.02           0.03            -0.07*                          0.28*            0.12*
 ΔInv/Sales              -0.03          -0.08*            0.04          0.02          -0.03         0.07*            -0.05*           0.42*                           0.19*
 ΔAP/Sales               -0.02           0.02            -0.03          -0.04        -0.07*         -0.06*            0.02            0.58*          0.32*




                                                                                             25
                                                TABLE 3
                             Institutional Characteristics of Sample Countries
This table preent country-level institutional characteristics that are hypothesized to affect firms’ operating choices and accrual
accounting choices. The sample consists of 20 countries. Private Sector Lending Development is measured as the % of domestic
credit provided to the private sector as a fraction of GDP (average of values in 1990 and 1999, Source: International Financial
Statistics 2000). Business Contract Stability is an index of the risk of contract repudiation from La Porta et al.(1998). Accrual
Accounting Standards represents the stated accrual accounting standards in a country and the degree to which the standards move
away from a cash measure of performance (from Hung 2001). Outside Investor Rights is the “anti-director rights” index created
by La Porta et al. (1998). It is an aggregate measure of minority shareholder rights and ranges from zero to six. Legal
Enforcement is measured as the mean score across three legal variables used in La Porta et al. (1998): (1) the efficiency of the
judicial system, (2) an assessment of rule of law, and (3) the corruption index. All three variables range from zero to 10.



     Country                 Private Sector           Business           Accrual             Outside             Legal
                                Lending               Contract          Accounting           Investor         Enforcement
                                Develop               Stability         Standards             Rights
AUSTRALIA                          74.70                8.71                0.82                  4                  9.5
BELGIUM                            56.50                9.48                0.68                  0                  9.4
CANADA                             83.25                8.96                0.82                  5                  9.8
DENMARK                            43.50                9.31                0.55                  2                 10.0
FINLAND                            69.40                9.15                0.55                  3                 10.0
FRANCE                             89.50                9.19                0.64                  3                  8.7
GERMANY                           105.05                9.77                0.41                  1                  9.1
HONG KONG                         162.25                8.82                0.64                  5                  8.9
IRELAND                            68.40                8.96                0.82                  4                  8.4
ITALY                              57.85                9.17                0.45                  1                  7.1
JAPAN                             157.95                9.69                0.55                  4                  9.2
NETHERLANDS                        93.45                9.35                0.73                  2                 10.0
NORWAY                             82.90                9.71                0.82                  4                 10.0
SINGAPORE                         106.40                8.86                0.64                  4                  8.9
SOUTH AFRICA                      108.65                7.27                0.68                  5                  6.4
SPAIN                              83.75                8.40                0.77                  4                  7.1
SWEDEN                            117.50                9.58                0.59                  3                 10.0
SWITZERLAND                       171.05                9.98                0.32                  2                 10.0
UK                                119.70                9.63                0.82                  5                  9.2
UNITED STATES                     119.20                9.00                0.86                  5                  9.5
             TABLE 4 – Relation between Accruals (Accounts Receivables, Inventory, or Accounts Payable Accruals)
                                      and Sales Changes Across Countries/Sub-Periods
This table presents estimates from regression model (1) for a sample of 3,130 firms (46,950 firm-year observations). ΔWCt is annual change in a given working
capital account (ΔAccountsReceivable, ΔInventory, or ΔAccountsPayable). The variables are scaled by lagged total sales. The regressions includes 40 interactive
variables, Dcountry,subperiod *ΔSalest/Salest-1, one for each of the 20 country by 2 sub-period combinations. The first sub-period covers 1991-98 and second sub-
period covers 1999-2005. The regression includes fixed effects for industries in the sample: materials manufacturing (SIC 30-33 & 39), machinery (SIC 34, 35 &
37), electrical manufacturing (SIC 36 & 38), retail (SIC 52-59), and wholesale (SIC 50 & 51). Country-year fixed effects are also included in regressions. The
“Change Signif.” columns present the p-value from an F-test of change in country slope coefficient between the sub-periods.

                                  ΔWCt/Salest-1 =  + Σcountry,subperiod*Dcountry,subperiod ΔSalest/Salest-1 + ε         (1)
                                   ΔAccountsReceivable/Sales                  ΔAccountsPayable/Sales                         ΔInventory/Sales
 Country       Sub-period                                Change                                 Change                                 Change 
                                 Value         Signif.      Signif.        Value         Signif.      Signif.        Value        Signif.       Signif.
Australia       1991-1998         0.28        (<0.05)*                      0.15        (<0.05)*                      0.10       (<0.05)*
                1999-2005         0.24        (<0.05)*       (0.09)         0.19        (<0.05)*       (0.06)         0.12       (<0.05)*        (0.25)
 Canada         1991-1998         0.l4        (<0.05)*                      0.10        (<0.05)*                      0.09       (<0.05)*
                1999-2005         0.17        (<0.05)*       (0.24)         0.13        (<0.05)*       (0.32)         0.15       (<0.05)*       (0.04)*
  France        1991-1998         0.13        (<0.05)*                      0.25        (<0.05)*                      0.19       (<0.05)*
                1999-2005         0.12        (<0.05)*       (0.56)         0.20        (<0.05)*      (0.04)*         0.l6       (<0.05)*        (0.17)
Germany         1991-1998         0.10        (<0.05)*                      0.07        (<0.05)*                      0.08       (<0.05)*
                1999-2005         0.13        (<0.05)*       (0.21)         0.08        (<0.05)*       (0.57)         0.10       (<0.05)*        (0.32)
   Italy        1991-1998         0.33        (<0.05)*                      0.23        (<0.05)*                      0.19       (<0.05)*
                1999-2005         0.31        (<0.05)*       (0.33)         0.27        (<0.05)*       (0.07)         0.24       (<0.05)*       (0.03)*
  Japan         1991-1998         0.21        (<0.05)*                      0.09        (<0.05)*                      0.04        (0.08)
                1999-2005         0.18        (<0.05)*       (0.19)         0.10        (<0.05)*       (0.63)         0.06       (<0.05)*        (0.35)
   U.K.         1991-1998         0.14        (<0.05)*                      0.07        (<0.05)*                      0.13       (<0.05)*
                1999-2005         0.17        (<0.05)*       (0.23)         0.09        (<0.05)*       (0.29)         0.14       (<0.05)*        (0.59)
   U.S.         1991-1998         0.20        (<0.05)*                      0.16        (<0.05)*                      0.11       (<0.05)*
                1999-2005         0.20        (<0.05)*       (0.96)         0.18        (<0.05)*       (0.11)         0.10       (<0.05)*        (0.70)
  Change in ||’s across       || larger/unchanged in 12/20 countries   || larger/unchanged in 13/20 countries   || larger/unchanged in 11/20 countries
      sub-periods               in 1999-2005 compared to 1991-1998        in 1999-2005 compared to 1991-1998        in 1999-2005 compared to 1991-1998
              TABLE 5 – Level of Working Capital Accounts (AR, Inv, or AP) in Different Countries and Sub-Periods
This table presents regression estimates for model (2) for the full sample of 46,950 firm-year observations covering two sub-periods (1991-1998 and 1999-2005).
WCt is the end-of-year level of a given working capital account (AccountsReceivable, AccountsPayable, or Inventory). The variables are scaled by total sales and
adjusted by subtracting the sub-period mean of each scaled variable. There are 40 country/sub-period indicator variables (Dcountry,subperiod). The “coefficient value”
reported in the table captures the average country deviation of the variable from the mean of all countries in a given sub-period. The regression includes fixed
effects for the industries in the sample: materials manufacturing (SIC 30-33 & 39), machinery (SIC 34, 35 & 37), electrical manufacturing (SIC 36 & 38), retail
(SIC 52-59), and wholesale (SIC 50 & 51). The “Change Signif.” columns have the p-value from F-test of change in country effect between the sub-periods.

                                                 WCt/Salest = Σcountry,subperiod*Dcountry,subperiod + ε        (2)
                                     AccountsReceivable/Sales                     AccountsPayable/Sales                           Inventory/Sales
 Country        Sub-period       Coeffic.       Coeffic.      Change        Coeffic.       Coeffic.      Change        Coeffic.       Coeffic.      Change
                                  Value         Signif.       Signif.        Value         Signif.       Signif.        Value         Signif.       Signif.
Australia       1991-1998         -3.2%         (0.04)*                      -1.9%          (0.26)                      -1.4%          (0.23)
                1999-2005          1.0%          (0.23)       (0.02)*        -2.2%          (0.19)        (0.36)        -0.9%          (0.28)        (0.19)
 Canada         1991-1998         -3.8%         (0.04)*                       2.6%          (0.08)                      -1.3%          (0.25)
                1999-2005         -4.2%         (0.03)*        (0.19)         1.8%          (0.22)        (0.06)        -0.7%          (0.41)        (0.28)
  France        1991-1998          3.4%         (0.04)*                       4.2%          (0.02)                       4.8%        (<0.01)*
                1999-2005          4.1%         (0.03)*        (0.27)         4.4%          (0.01)        (0.58)         5.1%        (<0.01)*        (0.45)
Germany         1991-1998         -3.0%         (0.07)                       -1.1%          (0.36)                      -0.6%          (0.49)
                1999-2005         -2.2%         (0.15)         (0.12)        -1.7%          (0.29)        (0.11)        -1.8%          (0.31)        (0.17)
   Italy        1991-1998         10.7%        (<0.01)*                       8.6%         (<0.01)*                      8.3%        (<0.01)*
                1999-2005         11.5%        (<0.01)*        (0.09)        11.3%         (<0.01)*     (<0.01)*        10.2%        (<0.01)*       (0.05)*
  Japan         1991-1998          3.1%         (0.02)*                       4.6%         (<0.01)*                     -4.8%        (<0.01)*
                1999-2005          4.0%         (0.02)*       (0.02)*         5.3%         (<0.01)*       (0.05)        -3.9%        (<0.01)*        (0.16)
   U.K.         1991-1998         -2.2%         (0.16)                       -1.5%          (0.22)                      -1.2%          (0.37)
                1999-2005         -2.7%         (0.09)         (0.21)        -1.9%          (0.14)        (0.27)        -0.8%          (0.56)        (0.57)
   U.S.         1991-1998         -4.2%         (0.01)*                      -3.5%         (0.04)*                      -3.8%         (0.03)*
                1999-2005         -4.4%        (<0.01)*        (0.13)        -3.7%         (0.03)*        (0.23)        -4.3%         (0.01)*        (0.32)
  Change in ||’s across        || larger/unchanged in 12/20 countries    || larger/unchanged in 10/20 countries    || larger/unchanged in 12/20 countries
      sub-periods                in 1999-2005 compared to 1991-1998         in 1999-2005 compared to 1991-1998         in 1999-2005 compared to 1991-1998

                                                                                      28
                       TABLE 6: Sample Estimates of “Conditional Jones Model” of Working Capital Accruals
                                    (ΔAccountsReceivable, ΔInventory, or ΔAccountsPayable)
This table presents estimates from regression model (3) for a sample of 3,130 firms (46,950 firm-year observations). ΔWCt is annual change in a given working
capital account (ΔAccountsReceivable, ΔInventory, or ΔAccountsPayable). The variables are scaled by lagged total sales.

                                                 ΔWCt/Salest-1 =  +  ΔSalest/Salest-1 + ε          (3-a)
                   ΔWCt/Salest-1 =  + 1ΔSalest/Salest-1 + 2(ΔSalest/Salest-1) (WCt/Salest-1) + 3WCt/Salest-1 + ε                 (3-b)
       Dependent Variable                    ΔAccounts Receivable/                     ΔAccounts Payable/                       ΔInventory/
                                                    Sales                                    Sales                                 Sales
Constant                                       0.00             -0.03*                 -0.01            -0.02*               0.00              -0.02
                                              (0.37)            (0.04)                (0.29)            (0.14)              (0.72)            (0.18)
ΔSalest/Salest-1                              0.22*              0.13*               0.16*              0.09*               0.15*             0.11*
                                             (<0.01)            (<0.01)             (<0.01)            (<0.01)             (<0.01)           (<0.01)
(ΔSalest/Salest-1)* (ARt/Salest-1)                               0.46*
                                                                (<0.01)
ARt/Salest-1                                                     -0.25*
                                                                (<0.01)
(ΔSalest/Salest-1)* (APt/Salest-1)                                                                      0.33*
                                                                                                       (<0.01)
APt/Salest-1                                                                                            -0.14*
                                                                                                       (<0.01)
(ΔSalest/Salest-1)* (Invt/Salest-1)                                                                                                           0.27*
                                                                                                                                             (<0.01)
Invt/Salest-1                                                                                                                                 -0.19*
                                                                                                                                             (<0.01)
Adjusted R2                                    26%                31%                  19%               22%                24%                28%
Number of Observations                        46,950            46,950                46,950           46,950              46,950             46,950

                                                                                 29
 TABLE 7: Determinants of Firm-Level Sensitivity of Accruals to Sales Shocks for Specific
   Working Capital Accruals ((ΔAccountsReceivable, ΔInventory, or ΔAccountsPayable)
The table presents coefficients and two-sided p-values (in parentheses) from firm-level regressions of average absolute working
capital accruals on predicted accruals determinants. The sample consists of 3,130 firm observations from 20 countries.
Sensitivities () for each firm are from firm-specific estimates of regression model (4) using 15 years of data. Accrual Accounting
Standards represents the stated accrual accounting standards in a country and the degree to which the standards move away from
a cash measure of performance (from Hung 2001). Anti-Director is the “anti-director rights” index created by La Porta et al.
(1998). It is an aggregate measure of minority shareholder rights and ranges from zero to six. Enforcement is measured as the
mean score across three legal enforcement variables used in La Porta et al. (1998): (1) the efficiency of the judicial system, (2) an
assessment of rule of law, and (3) the corruption index. All three variables range from zero to 10.

                                  ΔWCt/Salest-1 =  + ΔSalest/Salest-1 + ε                   (4)



                                         Estimated country              Estimated country             Estimated country 
                                           of sensitivity of               of sensitivity of              of sensitivity of
                                           ΔAR to ΔSales                   ΔAP to ΔSales                  ΔInv to ΔSales
Constant                                         0.027                          -0.049                          0.053
                                                (0.09)                         (<0.01)                        (<0.01)
Mean Accounts                                    0.621
Receivable/Sales                               (<0.01)
Mean Accounts                                                                      0.433
Payable/Sales                                                                    (<0.01)
Mean Inventory/Sales                                                                                              0.568
                                                                                                                (<0.01)
Accrual Accounting                                 0.063                           0.040                          0.032
Standards                                        (<0.01)                         (<0.01)                        (<0.01)
Anti-Director                                      0.009                           0.027                          0.013
                                                 (<0.01)                         (<0.01)                        (<0.01)
Enforcement                                        0.063                           0.005                          0.040
                                                 (<0.01)                          (0.26)                         (0.02)
Adjusted R2                                         11%                              9%                            15%
Number of Observations                             3,130                           3,130                          3,310
                TABLE 8: Cross-sectional Determinants of Working Capital Levels
The table presents coefficients and two-sided p-values (in parentheses) from firm-level regressions of average absolute working
capital accruals on predicted accruals determinants. The sample consists of 3,130 firm observations from 20 countries. The firm-
specific time-series mean of the working capital accounts (Accounts Receivable, Accounts Payable, and Inventory) are calculated
between 1992 and 2000. Mean annual sales growth is the 9-year mean of annual percentage change in sales. Mean operating
profit margin is calculated as the 9-year average of annual Operating income/Sales. Log(Size) is the natural logarithm of average
US$ Sales. Private Sector Lending Development is measured as the % of domestic credit provided to the private sector as a
fraction of GDP (average of values in 1990 and 1999, Source: International Financial Statistics 2000). Business Contract Stability
is an index of the risk of contract repudiation from La Porta et al. (1998).



Dependent Variable                        Mean Accounts                   Mean Accounts                 Mean Inventory/
                                          Receivable/Sales                Payable/Sales                      Sales
Constant                                       0.331                          0.224                          0.419
                                             (<0.01)                        (<0.01)                       (<0.01)
Mean Annual Sales Growth                      -0.103                         -0.078                        -0.021
                                             (<0.01)                        (<0.01)                         (0.22)
Mean Operating Profit                         -0.098                         -0.194                        -0.110
Margin                                       (<0.01)                        (<0.01)                       (<0.01)
Log(Size)                                     -0.010                         -0.003                          0.006
                                             (<0.01)                         (0.07)                       (<0.01)
Private Sector Lending                        -0.003                         -0.001
Develop                                      (<0.01)                        (<0.01)
Business Contract Stability                                                                                    -0.026
                                                                                                              (<0.01)

Industry Dummies                              Included                       Included                       Included
Adjusted R2                                       32%                            14%                            22%
Number of Observations                           3,310                          3,310                          3,310




                                                                    31
REFERENCES

Ball, R., 2001, Infrastructure Requirements of an Economically Efficient System of Public
     Financial Reporting and Disclosure, Brookings-Wharton Papers on Financial Services, 127-
     169.

Ball, R., 2006, International Financial Reporting Standards (IFRS): Pros and Cons for Investors,
     Accounting and Business Research (Forthcoming).

Barth, M., W. Landsman, and M. Lang, 2006, International Accounting Standards and
    Accounting Quality, University of North Carolina working paper.

Barton, J., Simko, P., 2002. The balance sheet as an earnings management constraint. The
    Accounting Review 77 (Supplement), 1-27.

Burgstahler, D., L. Hail, and C. Leuz, 2006, The Importance of Reporting Incentives: Earnings
    Management in European Private and Public Firms, The Accounting Review, Forthcoming.

Bushman, R., and J. Piotroski, 2006, Financial Reporting Incentives for Conservative
    Accounting: The Influence of Legal and Political Institutions, Journal of Accounting and
    Economic 42, 107-148.

Dechow, P.M., 1994. Accounting Earnings and Cash Flow as Measures of Firm Performance:
    The Role of Accounting Accruals. Journal of Accounting and Economics 18: 3-42.

Fisman, R., Love, I., 2001. Trade credit, financial intermediation development and industry
    growth, Columbia University Graduate School of Business working paper.

Guenther, D., Young, D., 2000. The association between financial accounting measures and real
   economic activity: A multinational study. Journal of Accounting and Economics 29:53-72.

Guenther, D., 2002. Discussion of empirical evidence on the evolution of international earnings.
   The Accounting Review 77 (Supplement), 135-138.

Hail, L., and C. Leuz, 2006, International Differences in the Cost of Equity Capital: Do Legal
     Institutions and Securities Regulation Matter?, Journal of Accounting Research,
     Forthcoming.

Haw, I., B. Hu, L. Hwang, and W. Wu, 2004, Ultimate Ownership, Income Management and
   Legal and Extra-Legal Institutions, Journal of Accounting Research 42, 423-462.

Hung, M., 2001. Accounting standards and value relevance of financial statements: An
   international analysis. Journal of Accounting and Economics 30: 401-420.



                                                  32
Jones, J., 1991. Earnings management during import relief investigations. Journal of Accounting
    Research 29, 193-228.

La Porta, R., F. Lopez-de-Silanes, A. Schleifer, R. Vishny, 1998. Law and finance. Journal of
    Political Economy 106, 1113-1155.

La Porta, R.; F. Lopez-de-Silanes; and A. Schleifer, 2006, What Works in Securities Laws? The
    Journal of Finance 61, 1-32.

Land, J., Lang, M., 2002. Empirical evidence on the evolution of international earnings, The
    Accounting Review 77 (Supplement), 115-133.

Leuz, C., Nanda, D., Wysocki, P., 2001. Investor protection and earnings management: An
    international comparison. MIT Sloan School of Management working paper.

Petersen, M.A., Rajan, R., 1997. Trade credit: theories and evidence. Review of Financial
    Studies 10: 661-691.

Pownall, G., Schipper, K., 1999. Implications of accounting research for the SEC’s
   considersation of international accounting standards for US securities offerings. Accounting
   Horizons 13: 259-280.

Wysocki, P., 2004. Discussion of ultimate ownership, income management, and legal and extra-
   legal institutions. Journal of Accounting Research 42, 463-474.




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