Embed Size (px)
Citation preview
International Evidence on the Matching between Revenues and Expenses*
Wen He
School of Accounting
University of New South Wales
Kensington, Sydney, NSW 2052, Australia
Yaowen Shan
School of Accounting
University of Technology, Sydney
Broadway, Sydney, NSW 2007, Australia
Abstract
This study investigates the time-series trend and determinants of matching between revenues
and expenses in a sample of 42 countries. We find that the decline in matching, documented
by Dichev and Tang (2008), is not unique to the U.S. but a world-wide phenomenon. Our
results show that matching is weaker in countries with: (1) more widely use of accrual
accounting; (2) a larger portion of firms reporting significant special items; (3) slower
economic growth; (4) more research and development activities; (5) larger service sectors;
and (6) stronger investor protection. There is no evidence that mandatory IFRS adoption
affects matching. Changes accounting and economic factors collectively explain the
downward trend in matching. Overall, the results suggest that both accounting and economic
factors are important determinants of matching over time and across countries.
Key words: Matching; accounting principles; revenues and expenses; IFRS; international
markets
JEL Classification: G15, M41
August 2014
* We are grateful to helpful comments from Ilia Dichev, Brian Roundtree, Stephen Taylor, Terry Walter and
workshop participants at the University of Technology, Sydney. Yaowen Shan gratefully acknowledges the
financial support by UTS-BRG grants.
1
1. Introduction
Matching of revenues to expenses is a fundamental principle in accounting, and
proper matching ensures earnings to reliably capture a firm’s profitability.1 However, Dichev
and Tang (2008; hereafter DT) document a significant decline in the contemporaneous
revenue-expense relationship of large U.S. companies over the past 40 years, suggesting that
matching has declined. Though the evidence appears unambiguous, researchers disagree on
why matching has declined and how accounting standard setters should respond. One view is
that the decline is attributable to changes in accountings standards, such as a shift from an
income-statement approach to a balance-sheet approach to determine earnings (DT), and the
passage of SAB101 in 2000 that resulted in an increase in recognition of deferred revenues
(Prakash and Sinha 2012). Accordingly, this view suggests that these accounting changes led
to deterioration in the quality of accounting information, which contradicts standard setters’
objective of making accounting information more useful. The other view, however, believes
that changes in economic activities are responsible for the decline in matching. For example,
Donelson, Jennings and McInnis (2011; hereafter DJM) show that the decline is primarily
due to increasing incidences of large special items, which in turn is caused by changes in
economic activities rather than changes in accounting. Srivastava (2011) shows that the shift
in the U.S. economy towards industries with higher period costs and more research and
development activities has contributed to the decline in matching.
In this study we examine the trend and determinants of matching between revenues
and expenses using a sample of 42 countries. Our examination in the international setting is
important for at least three reasons. First, it helps us better understand the reasons behind the
decline in matching in the U.S. Existing U.S. studies mostly rely on time-series analysis and
1 The matching principle requires a firm’s expenses to be recognized in the same period in which the revenues
are earned. Dichev and Tang (2008) provide theoretical predictions and empirical evidence that the mismatch
between revenues and expenses (poor matching) is likely to increase earnings volatility and decrease earnings
persistence, implying lower quality of accounting information.
2
make inference based on the coincidence of the decline in matching and changes in
accounting or economic activities over time. While such time-series analysis is informative, it
could not rule out the possibility of spurious correlation and there are potentially an unlimited
number of events that can be correlated with the time-series trend in matching. In contrast,
our examination using international data provides a unique setting where large cross-country
variations in accounting standards and economic activities enable us to conduct more
powerful tests and draw robust conclusions on the determinants of matching. Our results
therefore contribute to the current debate in the U.S. by providing out-of-sample evidence.
Second, there is little empirical evidence on matching in non-U.S. markets,2 despite the fact
that matching remains one of the fundamental accounting principles in the accounting
conceptual framework in many countries. Our international evidence thus fills in the void in
the literature and is informative to standard setters around the world concerning about the
quality of accounting information. Third, a number of countries mandatorily adopted
International Financial Reporting Standards (IFRS) in recent years. Accordingly, we use the
adoption of IFRS as a natural experiment to examine the relation between significant
accounting changes and matching between revenues and expenses. To our knowledge, we are
among the first to document evidence on the association between IFRS adoption and the
quality of matching.
Following Dichev and Tang (2008), we measure matching as the contemporaneous
relation between revenues and expenses. Our sample includes 42 countries for which we can
estimate matching annually from 1991 to 2010. We find that the decline in matching is not
unique to the U.S., but a world-wide phenomenon over the past two decades. The average
matching estimate has decreased significantly from 0.886 in 1990s to 0.801 in 2000s. The
result holds for a subsample of 13 countries that have non-missing matching estimates every
2 One exception is Jin, Shan and Taylor (2012) who examine matching in Australia.
3
year during the sample period. In each of these 13 countries we observe a decrease in
matching, and the decrease is statistically significant for 11 countries.
We then examine whether accounting standards, economic activities and country-
level governance attributes explain cross-country differences in matching. For accounting
standards we consider the extensiveness of accrual accounting and the adoption of IFRS. The
extensiveness index of accrual accounting reflects the extent to which accrual accounting is
used in a country’s accounting standards. Accrual accounting could improve matching as
revenues and expenses are recognised as they are earned or incurred, regardless of the
receipts or payments of cash.3 However, accrual accounting could also lead to poor matching
because a large amount of accruals, as a reflection of managerial estimations and/or
discretions, is likely to contain large estimation errors or it allows managers to
opportunistically shift revenues or expenses over accounting periods. Therefore it remains an
empirical question as to how accruals accounting affects matching.
The adoption of IFRS represents one of the most significant changes in accounting
regime in many countries. Matching could decline after its adoption because IFRS follows a
balance-sheet approach and allows a larger scope for fair value accounting (DT). However,
there is some evidence that the adoption of IFRS restricts managerial opportunistic behaviour
and increases accounting quality (Barth, Landsman and Lang 2008), implying better quality
of matching under IFRS. We test these conjectures in our empirical tests.
We examine a number of economic factors as determinants of matching, including the
proportion of firms reporting large special items, economic growth, the weight of service
industry in a country’s GDP and the intensity of R&D activities. DJM show that changes in
economic activities, such as increasing competition, lead to rising incidences of large special
items that are responsible for the decline in matching in the U.S. In addition, large special
3 For example, depreciation allows the cost of a machine to be spread out in its useful life, which helps match
revenues generated by the machine to depreciation expenses over time.
4
items tend to be recognized in recessions or economic downturns. Srivastava (2011), on the
other hand, finds that the matching decline in the U.S. is parallel to an increase in the weight
of service industry in the U.S. economy and an increase in the outlay of research and
development (R&D). Both increases lead to higher period costs (relative to production costs)
that have little relation to current revenues, resulting in a decline in matching.
We also consider whether country-level governance quality affects matching between
revenues and expenses. A growing body of literature has shown that a country’s legal system
and investor protections have a significant impact on its accounting system and properties of
financial reporting. In particular, in countries with a common law legal origin and stronger
investor protections, accounting is more conservative (Ball, Kothari and Robin 2000,
Bushman and Piotroski 2006) and earnings are less managed (Leuz, Nanda and Wysocki
2003). While conservative accounting could result in poor matching (DT), less managerial
discretion could improve matching. We test the effect of investor protection on matching
empirically.
Our results reveal a negative association between matching and the extensiveness of
accrual accounting in a country’s accounting standards, consistent with the view that
estimation errors and opportunistic earnings manipulation associated with accrual accounting
hinder the matching between revenues and expenses. This is also in line with DT’s findings
that the decline in matching is only evident in accrual-based revenues and expenses, but not
in cash-based revenues and expenses. Regarding the adoption of IFRS, we use a difference-
in-difference approach and compare IFRS adopters and non-adopters in the periods before
and after 2005, a year in which the majority of adopters mandated IFRS. However, we do not
find that the IFRS adoption has a significant impact on matching in our sample countries.
We find measures of economic activities are strongly associated with matching across
countries. Specifically, matching is weaker in countries where more companies report
5
significant special items, where GDP growth rates are low, where there are more R&D
activities, and where service sector accounts for a larger portion in the economy. These
results support the view that economic activities are important determinants of matching.
Institutional factors are also found to have a significant effect on matching.
Contemporaneous revenue-expense relation is weaker in countries with a common law legal
origin and stronger investor protections. However, in these countries, there is a stronger
association between past expenses and current revenues, implying expenses are more likely
to be recognized before the associated revenues. If, as DT suggest, the extent to which
“expenses lead revenues” can be used as a measure of conservatism, our results imply more
conservative accounting in countries with strong investor protections. This is consistent with
Ball, Kathori and Robin (2000) and Bushman and Piotroski (2006) that asymmetric loss
recognition, a commonly-used measure of accounting conservatism, is greater in countries
with stronger investor protections. Our results also imply that more conservative accounting
is associated with a poorer quality of matching between current revenues and current
expenses, consistent with DT’s evidence from the U.S. where deterioration in
contemporaneous revenue-expense relations coincides with an increase in the relationship
between past expenses and current revenues.
Finally, we examine whether the accounting and economic factors are able to explain
the decline in matching around the world. Once we control for these factors in regressions,
we find the downward trend in matching disappears, and both accounting and economic
factors have independent and incremental effects on matching. The result therefore suggests
that the decline in matching is mainly attributable to changes in both accounting and
economic factors.
This study makes several contributions to the literature. First, it is among the first to
provide empirical evidence on matching for a large sample of countries outside of the U.S.
6
This evidence is important for the understanding of cross-country differences in the
properties of accounting information and for the understanding of the determinants of these
differences (Ball, Kathori and Robin 2000, Ball, Robin and Wu 2001, Bushman and Piotroski
2006).
Second, our results show that both accounting and economic factors are important
determinants of matching, and changes in these factors are able to explain the observed
decline in matching. Our results contribute to the current debate on whether matching
decline in the U.S. is attributable to changes in accounting standards or changes in economic
activities. Our evidence provides useful insights and novel evidence to this debate, although
we do not aim to resolve this debate.
Third, we find no evidence that the mandatory adoption of IFRS affects matching in
our sample countries. This adds to a growing literature examining the effect of IFRS adoption
(e.g., Daske et al. 2008, DeFond et al. 2011, Landsman et al. 2012). The evidence would be
of interest to policy makers in counties (e.g., U.S.) that are considering adopting IFRS as the
principle accounting standards.
The rest of the paper proceeds as follows. Section 2 reviews related studies and
develops the hypotheses. Section 3 describes research design and Section 4 reports the
empirical results. We conclude in Section 5.
2. Hypothesis Development
Matching has long been recognized as a fundamental principle in accounting. For
example, in their classic book Paton and Littleton (1940) refer to matching as “the principal
concern” and “the fundamental problem” of accounting. Proper matching of revenues to
expenses incurred in generating such revenues is essential for earnings, the difference
between revenues and expenses, to appropriately capture profits earned in the accounting
7
period. In contrast, poor matching could lead to over-stated or under-stated accounting profits,
resulting in reduced usefulness of earnings as a measure of a firm’s performance. However,
in the past several decades, matching has been found to decline in the U.S., and researchers
disagree on the reasons for the decline.
DT is the first study to document a downward time-series trend in matching. They
show that the contemporaneous relationship between revenues and expenses decreased, while
the relation between current revenues and lagged and future expenses increased in the period
from 1967 to 2003. DT further show that there is no temporal decline matching between
cash-based revenues and expenses, in sharp contrast to a significant decline in matching
between accrual-based revenues and expenses. Based on this evidence, DT attribute the
decline in matching mainly to accounting factors such as the shift in accounting standards
away from matching as the foundation of financial reporting and toward a balance-sheet-
based model. Prakash and Sinha (2012) find that the passage of SAB101 in 2000 resulted in
an increase in recognition of deferred revenues, potentially causing lower matching after
2000.
DJM also document a decline in contemporaneous revenues-expenses relation, but
they find that the decline is primarily driven by a low correlation between revenues and
special items and an increase in the incidence of large special items over time. They further
show that changes in economic activities, such as increasing competition pressure, are likely
to play an essential role in the increasing incidence of special items. Srivastava (2011)
provides evidence that decline in matching is associated with increasing research and
development expenses and higher period costs relative to variable costs in the U.S. industries.
Overall, these studies challenge DT’s explanation of accounting standard changes and
suggest changes in economic activities as the key driver of the decline in matching.
8
In their discussion, DT (page 1427) note that there could be three possible reasons for
poor matching. The first one is economic or business factors, such as fixed costs in operation,
poor traceability of costs, and assets impairment or corporate restructuring that result in large
special item losses. The second reason is accounting rules. For example, R&D expenses are
required to be expensed regardless of traceability. The third is the managerial discretion, such
as shifting revenues or expense across accounting periods to smooth earnings or take a “big
earnings bath”.
These reasons, however, are likely to simultaneously affect matching. For example,
globalization of markets has caused influx of foreign goods and increasing use of outsourcing,
resulting in more competition, more bankruptcies, and more restructuring. One manifestation
of these significant economic changes in financial statements is the increasing number of
large special items and accounting losses. At the same time, U.S. accounting standards have
experienced several changes over the same time period, which is likely to affect matching
between revenues and expenses. Furthermore, the incentives for managers to alter reported
accounting numbers could also change over time in response to factors such as investor
sentiment (Rajgopal, Shivakumar and Simpson 2007) and regulations (e.g., Sarbanes-Oxley
Act). Simultaneity of these forces makes it difficult to disentangle their individual effect
from a pure time-series analysis as in prior studies, and a cross-country study with multiple
time-series would offer useful insights.
In our international setting, we first consider the effect of accounting standards on the
degree of matching success across countries. In particular we examine whether matching is
systematically associated with the use of accrual accounting in a country’s accounting
standards. As Hung (2001) documents, almost every country uses accruals accounting, but to
different degrees. For example, accruals accounting are more widely used in British-
American countries (e.g., Australia, Canada, the U.K. and the U.S.) than in continental
9
European countries. The effect of accrual accounting on matching, however, is ambiguous.
On the one hand, accruals accounting improves matching because the key advantage of
accrual accounting over cash accounting is better matching of revenues and expenses
regardless of payments of receipts of cash. For example, depreciation allows the cost of fixed
assets to be spread over their useful life, matching the revenues generated in future periods.
As another example, in Finland, the local GAAP allows R&D expenditures to be capitalized,
which possibly enhances matching between R&D expenses and future revenues generated
from R&D activities. On the other hand, many accruals need to be estimated or forecast,
allowing managers to use discretion to shift expenses across accounting periods. For example,
managers could under-estimate depreciation expenses for a few years and report a large
write-down of asset value in one year. This practise will reduce the degree of matching of
revenues and depreciation expenses. Based on the above discussion, we state our first
hypothesis in the null form as follows.
H1: The use of accruals accounting does not affect the matching of revenues to
expenses.
In 2000s a number of countries adopted IFRS, which represents one of the most
significant regulatory changes in accounting history. It is unclear how the IFRS adoption
would affect matching. One view is that matching could improve after the adoption because
IFRS are more restrictive than local GAAP in many aspects and thus will reduce managerial
manipulation (Barth, Landsman and Lang 2008). The other view, however, predicts the
opposite because IFRS follows a balance-sheet approach and uses fair-value accounting that
will result in more gain or losses (expenses) from asset revaluation rather than from revenue
generating activities. These views lead to our second hypothesis, stated in null form as
follows.
H2: The adoption of IFRS does not affect the matching of revenues to expenses.
10
Aiming to measure and capture business activities in firms, accounting numbers are
ultimately driven by economic factors. Changes in economic activities should have a
fundamental effect on outputs of the accounting system. For this reason, it is plausible that
matching between revenues and expenses is likely to be affected by variations in economic
activities (DJM, Srivastava 2011). We consider two economic factors as potential drivers of
matching success. The first factor is the growth rate of macro economy that could affect
matching in several ways. First, economic growth affects the weight of variables costs in total
expense. In an economic expansion period, firms increase production activities and variable
costs account for a large portion of total expenses. In recessions, production activities and
variable costs decline, but fixed costs are sticky and remain high, resulting in a lower portion
of variable costs in total expense. Since variable costs can be better matched to revenues than
fixed costs, one could expect matching between revenues and total expenses to be higher in
economic expansions than in recessions. Second, in recessions, firms are more likely to write
down or write off their assets, resulting in large negative special items that have little
correlation with revenues in that accounting period. Third, recessions also see intensified
industry competition and more corporate restructuring that lead to large restructuring
expenses, which also have little relation to revenues in that accounting period. These
arguments predict better matching between revenues and expense in periods with higher
economic growth. We therefore state our third hypothesis in null form as follows.
H3: Economic growth does not affect matching between revenues and expenses.
The second economic factor is the composite of economy. Srivastava (2011)
documents that the decline in matching in the U.S. coincides with an increase in aggregate
R&D spending and a growing service sector in the U.S. economy. Since R&D expenditures
are required to be expensed immediately regardless of their implication on future revenues,
more R&D spending is expected to result in a lower contemporaneous relation between
11
revenues and expenses. Firms in service sectors usually have high fixed-to-variable cost ratio
(Brignall et al. 1991). Service firms also have high period costs such as marketing expense,
accounting and auditing fees, litigation and lobbing, expensed as incurred regardless of their
association with revenues (Srivastava 2011). These features will result in lower matching
between revenues and expenses in service industries. Based on this discussion, we state our
fourth and fifth hypotheses in null form as follows.
H4: R&D spending does not affect matching between revenues and expenses.
H5: The weight of service section does not affect matching between revenues and
expenses.
There is a growing literature on how country-level institutional factors affect the
properties of reported earnings (e.g. Ball, Kathori and Robin 2000). In particular Leuz, Nanda
and Wysocki (2003) document that reported earnings are less smoothed or managed in
countries with stronger investor protection. If stronger investor protection restricts managerial
discretion and earnings management, we could expect higher matching between revenues and
expenses in stronger investor protection countries. On the other hand, Bushman and Piotroski
(2006) find that accounting is more conservative in countries with stronger investor
protection. If conservative accounting results in accelerated recognition of expenses or
delayed recognition of revenues (DT), we might expect lower matching in stronger investor
protection countries. This discussion leads to our sixth hypothesis in the null form as follows:
H6: A country’s investor protection does not affect matching between revenues and
expenses.
3. Variables, Sample and Data
3.1 Measures of matching
12
Following DT, we estimate the relation between revenues and expenses using the
following multivariate regression:
REVit= a + b1EXPit-1 + b2EXPit + b3EXPit+1 + e i t (1)
where REVit is the revenues for firm i in year t, and EXPit is total expenses, computed as the
difference between revenues and earnings after tax. Both revenues and expenses are deflated
by total assets in year t. We estimate Equation 1 for each country-year and use the coefficient
on current-period expenses (b2) as the country-specific measure of matching. Relative to
simple correlation coefficients, the multivariate specification in Equation 1 has an advantage
in that it controls for the strong auto-correlation in expenses. This is especially important if
researchers are interested in the relation between revenues and non-contemporaneous
expenses. Furthermore, since past, present and future expenses have roughly the same
variation, the coefficients in Equation 1 indicate the incremental correlations between
revenues and expenses. Both DT and follow-up studies use this specification and take b2 as a
quantitative measure of matching.
DT note that the coefficient of past expenses, b1, indicates the extent to which past
expenses “lead” current revenues. They propose to use b1 as a new measure of accounting
conservatism because recognizing expense before their associated revenues seems to capture
the very essence of conservatism. Lee (2011) provides some evidence supporting using b1 to
measure conservatism.
3.2 Accounting factors
There are significant differences in accounting standards across countries. We focus
on the use of accrual accounting, as DT find some evidence that accrual accounting could be
responsible for the decline in matching in the U.S. To measure the extent to which accrual
accounting is used in a country’s accounting standards, we use an accrual index developed by
13
Hung (2001) who examined 11 accrual-related accounting standards in 1993. If a country
applied accrual method to a particular accounting standard, it scored one. The index is the
sum of the scores, with higher scores indicating a wider use of accrual accounting. The index,
however, is only available for 24 countries, and is based on accounting standards in 1993. In
2000s when a number of countries adopted IFRS, the accrual index is no longer a valid
measure of accrual accounting across countries. Therefore, our empirical test on the accrual
index uses data prior to a country’s adoption of IFRS.
We supplement this accrual index with an alternative measure based on total accruals.
If accrual accounting is widely used in a country, we would expect a higher magnitude of
reported total accruals. For each country-year, we use the median of absolute value of total
accruals to capture the degree of accrual accounting. Following Sloan (1996) we use balance-
sheet approach to construct total accruals, which is available every year for all countries in
our sample.
To examine the effect of mandatory adoption of IFRS on matching, we use a
difference-in-difference approach to control for any time-series change in matching
experienced by all countries in the sample period. This control is important as a number of
changes in economic activities could lead to a decline in matching in every country.
Specifically we bench mandatory adopting countries against those that do not adopt IFRS,
and examine whether the difference in matching between adopters and non-adopters changes
after IFRS adoption. In empirical tests we create an indicator variable, IFRS, which takes
value of 1 for countries that adopted IFRS in 2000s. Since most of the countries adopted
IFRS in 2005, we construct an indicator variable, POST, for years after 2005. We then
regress country-year measure of matching on IFRS, POST, and an interaction term between
IFRS and POST. The coefficient of IFRS captures the difference in matching between
14
adopters and non-adopters prior to 2005. Of interest is the coefficient of the interaction term
which captures whether this difference has changed after 2005.
3.3 Economic factors
We consider the following measures of economic activities in a country. The first
measure is economic growth, captured by annual GDP growth rates. The second measure is
the proportion of firms reporting large special items in the annual financial statements.
Following DJM, we define large special items as those no less than 1% of total assets. DJM
show that the frequency of reporting large special items is closely related to a number of
economic events including employee growth, merger and acquisition activity, discontinued
operations, operating losses and sales growth. So the frequency of large special items could
serve as a parsimonious measure of economic activities.
The third measure is the weight of service sector in a country’s economy, calculated
as the ratio of service sector value added to GDP. Srivastava (2011) argues that decline in
matching in the U.S. is attributable to the increasing weight of service sector in the U.S.
economy. Our fourth measure is the intensity of R&D activity, captured by the number of
patents granted by the U.S. to non-U.S. citizens in a country, deflated by the country’s
population (in 100,000s). The rationale for using the number of patents is that more R&D
activities are expected to produce a larger number of patents recognized by the U.S.
We acknowledge that some of these measures of economic activities also have much
to do with accounting. For example, a key reason why R&D activities affect matching is the
accounting rule that requires most of R&D costs be expensed immediately, regardless of
whether they can bring in future revenues. Furthermore, although large special items may
capture the consequences of some economic activities, DT suggest that special items
themselves indicate poor matching because many special items arise because of assets
revaluation or unusual charges, which manifest a lack of relationship between revenues and
15
expenses. These issues highlight the difficulty in disentangling the effect of accounting and
economic factors, since any accounting number is an outcome of both economic activities
and application of accounting rules.
3.4 Governance factors
Prior studies have shown that country-level governance factors, particularly investor
protections, have a significant impact on the outcomes of financial reporting (Ball, Kothari
and Robin 2000, Bushman and Piotroski 2006). Following this literature, we use three
measures of investor protection in our empirical tests. The first measure is an indicator
variable taking value of one for countries with a common law legal origin. La Porta et al.
(1998) find evidence suggesting that common law countries generally have stronger legal
protection for investor rights. The second measure is the anti-director rights index, developed
by La Porta et al. (1998). Based on six rules of investor voting (voting by mail, voting
without blocking of shares, and calling an extraordinary meeting) and minority protection
(proportional board representation, pre-emptive rights, and judicial remedies), the index
captures the strength of minority shareholder protections and has been widely used in the
literature. The third measure is the anti-self dealing index as in Djankov et al. (2008) who
construct the index based on legal rules that governs a specific self-dealing transaction. The
index captures the strength of legal protections of minority shareholders against expropriation
by corporate insiders.
3.5 Data and Sample
We obtain financial statement items from Compustat North America and Compustat
Global Vantage. We require firms to have non-missing data for total assets, revenues and net
profits for consecutive three year periods, as we need to calculate past, current and future
expenses to estimate matching. Our sample period starts from 1990 when accounting data are
more available for non-U.S. companies, and ends in 2011, with estimates of matching
16
available in the 20 year period from 1991 to 2000. Following DJM we exclude financial firms
from the sample. To mitigate the effect of potential data errors and extreme values, we
winsorize revenues and expenses (deflated by total assets) at the 1st and 99th percentiles. To
have a reliable estimate of matching in a year, we require a country to have at least 100 firms
with sufficient data in that year. We also require a country to have estimates of matching for
at least five years. After these filters we obtain a Full sample of 42 countries and 623 country-
year estimates of matching.
One issue with the Full sample is that the number of firms in a country may increase
over time for two reasons. First, databases such as Compustat Global Vantage usually start
their coverage with largest companies, and then gradually include smaller firms in the
databases. Second, more firms go public than delist, increasing the number of public firms
over time. A practical way to maintain a relatively constant sample of firms that are important
to an economy is to focus on large firms only. For example, DT select largest 1,000 U.S.
firms each year to form their sample. Since most non-U.S. markets have less than 1,000 firms
covered by Compustat Global Vantage in most of the years, we instead select the largest 200
companies in a country each year to form the Top 200 sample. If a country has fewer than
200 firms (but more than 100 firms) covered by Compustat Global Vantage, we include all
the firms in that country. This sample selection criterion ensures that we have a large sample
of country-year observations and our estimates of matching are based on a relatively stable
and representative sample of firms.
In our empirical tests, we report the results for both the full sample and the Top 200
sample. Note that the difference between these two samples is that the full sample includes a
larger number of smaller firms and younger firms. To the extent that smaller and younger
firms are more likely to be service firms or have larger R&D expenditures, the difference in
the results for these two samples may reflect the impact of the shift in the real economy
17
toward service sectors and R&D intensive industries. On the other hand, finding consistent
evidence from both samples would provide stronger support for our hypotheses and suggest
that the results are not driven by changes in the composition of the sample.
4. Empirical Results
4.1 Matching over time and across countries
Table 1 reports the time-series average estimate of matching for each country based
on the full sample and the Top 200 sample. We first validate our estimates by comparing our
estimates of matching for the U.S. with those reported in prior studies. As both DT and DJM
estimate matching using the largest 1,000 U.S. firms, we focus on our estimates based on the
Top 200 sample. In the bottom line of Table 1 we estimate the average matching of 0.900
based on the largest 200 U.S. companies for the period from 1991 to 2010. DT report an
average matching of 0.882 for the period from 1986 to 2003 (Table 3, p1437), while DJM’s
estimate is 0.895 for the period from 1986 to 2005 (Table 1, p948). This comparison suggests
that our estimates of matching are comparable to those in prior studies.4
Table 1 reveals that there are large cross-country variations in estimates of matching,
particularly in the full sample. Countries like Australia, Canada, and the U.S. have lowest
estimates of matching, while matching seems to be higher in countries like India, Pakistan,
Peru and Turkey. This variation is smaller in the Top 200 sample, with estimates ranging
from 0.692 in the Philippines to 1.019 in China.
Comparing the two samples, we find that the estimates of matching are always lower
in the full sample than those in the Top 200 sample. For example, the average matching for
the U.S. is estimated to be 0.479 in the full sample, but 0.900 in the Top 200 sample. Since
these two samples differ only in that the full sample includes smaller and younger firms, the
4 Our estimates of the coefficients of lagged and future expenses (b1 and b3, respectively) are also comparable
to those reported in prior studies. For example, we estimate that b1 (b3) is 0.093 (0.027), compared with 0.089
(0.025) as reported by DJM.
18
difference in matching estimates suggest the smaller and younger firms are likely to have a
lower contemporaneous relation between revenues and expenses. This piece of evidence is
consistent with findings in Srivastava (2011) who shows that in the U.S. the average
matching of largest 1,000 firms with at least 10 year operating history is higher than that of
smaller and younger firms.
There may be a few reasons why smaller and younger firms have lower matching
between revenues and expenses. Firstly, these firms are likely to operate in service industries
and have large R&D expenses, as Srivastava (2011) documents a time-series increase in the
aggregate R&D outlays and the weight of service sector in the U.S. economy. Secondly these
firms are more likely to be affected by economic recessions and recognize larger amount of
special items losses. The third reason could be that managers in these firms may have
discretion in the recognition of revenues and expenses since they face less litigation risk and
investor monitoring. However, since both large and small firms in a country use the same set
of accounting standards, the difference in matching between these two groups of firms cannot
be explained by accounting factors alone. It appears that economic factors must be an
important driver of cross-sectional difference in matching.
[Insert Table 1 here]
In Table 2, we examine whether matching changes over time in our sample of
international markets. Panel A reports an average matching estimate across countries for
every year in our sample period. We find that average matching has declined from 1990s to
2000s. In the full sample, average matching decreased from 0.886 in 1990s to 0.801 in 2000s.
The Top 200 sample also shows a decline in matching from 0.936 to 0.883. Both decreases
are statistically significant at 5% level. At the same time we find an increase in the
relationship between current revenues and past expenses, and between current revenues and
19
future expenses. This result is consistent with the U.S. evidence, suggesting that decline in
matching is a universal phenomenon.
Panel A also shows that the number of countries in our sample increases over time, so
there are different sets of countries in the two sub-sample periods, which may invalidate the
results from a simple comparison. To address this concern we focus on a constant sample of
13 countries that appear every year in the 20-year sample period. Panel B of Table 2 reports
the average matching each year for these 13 countries. The average matching for the full
sample decreased from 0.862 in 1990s to 0.691 in 2000s. For the Top 200 sample, average
matching declined from 0.946 to 0.871. These decreases are statistically significant at 1%
level.
In Figure 1, we plot the time-series of matching for the full sample and the Top 200
sample. A visual examination of the figure shows a clear downward trend in the matching for
the full sample. Matching for the Top 200 sample also exhibits an observable, though less
significant, downward trend over time.
Panel C in Table 2 examines the average matching in the two sub-sample periods for
each of these 13 countries. For the full sample we find matching has declined in every
country and the decline is statistically significant at 5% level in 11 countries. For the Top 200
sample matching has declined in 10 counties, and the decline is statistically significant at 5%
level in 3 countries. These results reinforce the conclusion that the decline in matching is not
unique to the U.S. but is a world-wide phenomenon.
Comparing the decline in matching in the two samples, we find that the decline is
usually larger in the full sample. For example, Panel B shows that average matching in the
constant sample of 13 countries declined by 0.171 in the full sample, compared with a decline
of 0.075 in the Top 200 sample. This result suggests that the decline in matching is more
striking in the smaller and younger firms, and that the previously documented decline in the
20
1,000 largest U.S. firms is likely to underestimate the actual decline in matching in the
overall economy.
[Insert Table 2 and Figure 1 here]
4.2 Determinants of matching
We now investigate whether cross-country variations of matching can be explained by
accounting, economic and governance factors. Our empirical strategy is to regress matching
estimates on various determinants using pooled cross-country and time-series samples. One
issue with a pooled sample is potential under-estimates of standard errors. We follow
Petersen’s (2009) suggestion to include year fixed effect in regressions and adjust standard
errors for clustering effect at country level.5 We report two sets of results for estimates of
matching based on the full sample (in Panel A) and the Top 200 sample (in Panel B).
Table 3 examines the association between matching and a country’s accounting
standards. We first consider the scope of accrual accounting, as captured by an accrual index
and the median absolute value of total accruals. Model 1 and 2 show that both measures of
accrual accounting are negatively and significantly associated with matching, suggesting that
matching is weaker in countries where accrual accounting is more widely used. These results
are consistent in both panels, suggesting the use of accrual accounting affects both large and
small firms. This finding is consistent with DT’s evidence that decline in matching in the U.S.
is only observed for accrual-based revenues and expenses. Taking together the cross-country
evidence in our study and the time-series evidence in DT, we conclude that accrual
accounting is an important determinant of matching.
We then examine the effect of adoption of IFRS on matching. We use a difference-in-
difference approach and investigate if the difference in matching between adopters and non-
5 As a robustness test, we also run regression without year-fixed effect but adjust standard errors for two-way
clustering effect at both country and year level. Our main results remain intact with two-way cluster adjusted
standard errors.
21
adopters changed after 2005 when the majority of adopters mandated IFRS. We use
observations in 2000s to do the test. In Model 3 the coefficient of the interaction term
between IFRS and POST is negative but statistically indifferent from zero, implying that
relative to non-adopters IFRS adopters did not experience a decline in matching after 2005.
For robustness we also examine various specifications such as including year-fixed effects in
regressions, excluding year 2005, or excluding Singapore from the sample. We do not find
significant results in any of these robustness tests. Therefore, we conclude that there is no
evidence that matching declined after the mandatory adoption of IFRS.
[Insert Table 3 here]
In Panel A of Table 4 we investigate the effect of economic activities on matching
estimated from the full sample. Model 1 shows that matching is weaker in countries where a
larger portion of firms report large special items. This result is consistent with the time-series
evidence in DJM that the decline in matching in the U.S. is primarily driven by the increasing
incidences of large special items resulting from various economic activities. In Model 2, we
find a positive association between matching and GDP growth rates, suggesting that
matching is weaker during economic downturns. In Model 3 the coefficient of Royalty is
negative and statistically significant, indicating that matching is weaker in countries with
more intensive R&D activities. Model 4 reveals a negative association between matching and
the weight of service sector in an economy, consistent with the argument of Srivastava (2011)
that service sectors have higher period costs that carry weaker association with current
revenues. In Model 5 we include all four measures of economic activities in the regression to
examine their incremental correlation with matching. The coefficient of Special Items
remains negative and statistically significant, while GDP Growth, Royalty and Service lose
significance in their association with matching.
22
In Panel B, we report the effect of economic factors on matching for the Top 200
sample. Model 1 and 2 show that recognition of large special items and GDP growth rates are
important determinants of matching. However, the coefficients of Royalty and Service in
Model 3 and 4 become statistically insignificant. One reason for this result is that large and
mature firms in the Top 200 sample are less affected by shifts in real economy toward service
oriented and R&D intensive industries. Model 5 reveals that Special Items and GDP Growth
have significant coefficients when we include all the four variables in the regressions. Overall,
the results in Table 4 support the view in DJM and Srivastava (2011) that economic factors
are important cross-sectional determinants of matching.
[Insert Table 4 here]
Table 5 examines the effect of country-level investor protections on matching. In
Panel A the dependent variables are matching (the contemporaneous revenues-expenses
relation) and b1 (the relation between current revenues and lagged expenses) estimated from
the full sample. DT suggest that b1 could be an appropriate measure of conservatism because
it captures the extent to which expenses are recognized prior to revenues. The results show
that all three measures of investor protections have consistently negative and statistically
significant coefficients in the matching regressions, suggesting that matching is weaker in
countries with stronger investor protection. On the other hand, investor protection measures
are positively associated with accounting conservatism, as measured by b1.
In Panel B we re-estimate the regressions using matching and b1 for the Top 200
sample. The measures of investor protection have predicted signs but with lower statistical
significance. To the extent that the relation between revenues and past expenses captures
accounting conservatism as suggested by DT, our result implied that accounting is more
conservative in countries with stronger investor protections. This implication is consistent
23
with findings in Ball, Kothari and Robin (2000) and Bushman and Piotroski (2006) that
losses are recognized in a more timely manner in countries with strong investor protections.
The results in Table 5 suggest that while accounting is more conservative in countries
with stronger investor protection, matching is weaker in such countries. We note that our
cross-country evidence is also consistent with time-series evidence in DT who find that
matching declines when accounting in the U.S. is more conservative. We take these results as
suggesting that there is a trade-off between conservative accounting and quality of matching
between revenues and expenses. We believe that standard setters and users of accounting
information should be aware of this trade-off as it has important implications on the
properties of earnings.
[Insert Table 5 here]
4.3 Time trend in matching
We have documented a downward trend in matching between revenues and expenses,
suggesting the decline in matching is a worldwide phenomenon. In this sub-section, we
explore whether the accounting and economics variables, as examined above, can explain the
time-series decline in matching in an international setting. This analysis will shed light on the
current debate that the decline in the U.S. is attributable to changes in accounting or changes
in economic activities. Our empirical strategy is simple: we investigate whether the time
trend disappears once we control for the accounting and economic factors.6 Table 6 reports
the results from this analysis.
In Panel A, we examine matching estimated from all the firms in the sample.
Consistent with the results in Table 2 and Figure 1, Model 1 shows a coefficient of -0.005 (t-
stat = -2.51) for the time trend, suggesting a downward trend in matching around the globe.
6 We do not consider governance variables for this analysis because these variables are time invariant.
24
The coefficient of the time trend, however, becomes insignificantly different from zero in
Model 2 where we add accounting and economic factors as controls. This result indicates that
there is no clear time trend in matching once we control for the accounting and economic
factors. Models 3 and 4 focus on a constant sample of countries with complete 20 year data
and obtain similar results. The time trend in matching becomes much smaller in magnitude
and only marginally significant after we control for accounting and economic factors in
Model 4. In Panel B, we do not observe a significant time trend in matching when we
estimate matching using the top 200 firms in each country.
Considering the accounting and economics factors, we find Total Accruals and
Special Items have significant coefficients with predicted signs in the models. This result
suggests that both accounting and economic factors have independent effects on matching
and their effects are incremental to each others. Combined with the results in Table 3 and 4,
our evidence implies that both accounting and economic factors are important determinants
of matching across countries and over time. Overall, the results in Tables 6 show that the
downward trend in matching is mainly attributable to the time-varying accounting and
economic factors.
[Insert Table 6 here]
5. Conclusion
This study provides evidence on matching of revenues and expenses in a large sample
of 42 countries. We find that matching around the world has declined in the past two decades,
but the decline is mainly attributable to changes in accounting and economic factors. After
exploring cross-countries differences in a number of institutional factors we show that
matching is weaker in countries with a wider use of accrual accounting, a larger number of
firms reporting large special items, lower economic growth, more R&D activities, larger
25
service sectors and stronger investor protections. We do not find robust evidence that the
quality of matching altered after the mandatory adoption of IFRS.
This study adds to the current debate on why matching declined in the U.S. Our
results suggest that both accounting and economic factors are important determinants of
matching. It thus would be difficult to conclude the decline is due to one factor but not the
other by examining only the time-series coincidence of some changes in accounting and
economic activities. It is likely that both accounting and economic factors changed in the past
decades, which jointly contributes to the decline.
Our study also provides first evidence on the time-series properties of matching for a
large sample of non-U.S. countries. We document an obvious decline in matching around the
world, suggesting that the decline is not unique to the U.S. Furthermore, there is no evidence
that matching declined after the adoption of IFRS. This result would help regulators and
researchers to gauge potential impact of a convergence of the U.S. GAAP to IFRS.
26
References
Ball, R., S.P. Kothari, and A. Robin, 2000. The effect of international institutional factors on
properties of accounting earnings. Journal of Accounting and Economics 29, 1-51.
Barth, M.E., W. R. Landsman, and M. H. Lang, 2008, International accounting standards and
accounting quality. Journal of Accounting Research 46, 467-498.
Grignall, T.J., L. Fitzgerald, R. Silverstro, and R. Johnston, 1991. Product costing in service
organizations. Management Accounting Research 2(4): 227-248.
Bushman, M.R., and J. D. Piotroski, 2006. Financial reporting incentives for consevative
accounting: The influence of legal and political insitutions. Journal of Accounting
and Economics 42, 107-148.
Daske, H., L. Hail, C. Leuz, and R. Verdi, 2008. Mandatory IFRS reporting around the world:
Early evidence on the economic consequences. Journal of Accounting Research 46,
1085–1142.
DeFond, M., X. Hu, M. Hung, and S. Li, 2011. The impact of mandatory IFRS adoption on
foreign mutual fund ownership: The role of comparability. Journal of Accounting
and Economics 51, 240–258.
Dichev. I., and V. Tang, 2008. Matching and the changing properties of accounting earnings
over the last 40 years. The Accounting Review 83(6), 1425-60.
Djankov, S., R. La Porta, F. Lopez-De-Silanes, and A. Shleifer, 2008. The law and
economics of self-dealing. Journal of Financial Economics 88, 430-465.
Donelson. D. C., R. Jennings, and J. McInnis, 2011. Changes over time in the revenue-
expense relation: Accounting or Economics? The Accounting Review 86, 945-974.
Jin, K., Y. Shan, and S. Taylor, 2012. Changes over time in the matching between revenues
and expenses: Australian evidence. Working paper, University of Technology,
Sydney.
La Porta, R., F. Lopez-De-Silanes, A. Shleifer, and R. Vishny, 1998. Law and finance.
Journal of Political Economy 106, 1113-1155.
Landsman, W., E. Maydew, and J. Thornock, 2012. The information content of annual
earnings announcements and mandatory adoption of IFRS. Journal of Accounting
and Economics 53(1-2), 34-54.
Lee, J., 2011. Measuring reporting conservatism using the Dichev-Tang (2008) model.
Working paper, Singapore Management University.
27
Leuz, C., D. Nanda, and P. Wysocki, 2003. Earnings management and investor protection:
An international comparison, Journal of Financial Economics 69, 505-527.
Paton, W. A., and A. C. Littleton, 1940, An Introduction to Corporate Accounting Standards,
American Accounting Association Monograph 3.
Prakash, R., and N. Sinha, 2012. Deferred revenues and the matching of revenues and
expenses, Contemporary Accounting Research, forthcoming.
Rajgopal, S., L. Shivakumar, and A. V. Simpson, 2007. A catering theory of earnings
management. Working paper, Emory University.
Srivastava, A., 2011. Why has matching declined? Working paper, Northwestern University.
28
Appendix A: Variable Definitions
Matching Variables
Matching The regression coefficient on current expenses obtained by regressing revenues on
past, current and future expenses on an annual basis by country.
b1 The regression coefficient on past expenses obtained by regressing revenues on past,
current and future expenses on an annual basis by country.
b3 The regression coefficient on future expenses obtained by regressing revenues on
past, current and future expenses on an annual basis by country.
Determinants
Accrual Index A measure of the extent to which accrual accounting is used in a country’s
accounting standards. Data from Hung (2001)
Total Accruals The median of absolute value of total accruals (deflated by total assets) in a country-
year.
IFRS An indicator variable equal to 1 for countries that mandatorily adopted IFRS, and 0
for non-adopting countries
POST An indicator variable equal to 1 for observations after mandatory IFRS adoption, and
0 otherwise. For non-IFRS adopting countries, POST takes value of 1 for years after
2005, and 0 otherwise.
Special Items The ratio of the number of firms with significant special items (above 1% of total
assets) to the total number of companies in a country in the year
Royalty Natural log of total amount (in current dollars) received from foreigners for the
authorized use of intangible, non-produced, non-financial assets and proprietary
rights (such as patents, copyrights, trademarks, industrial processes, and franchises)
and for the use, through licensing agreements, of produced originals of prototypes
(such as films and manuscripts). Data from World Bank.
Service The ratio of value added by service sectors to GDP. Data from World Bank.
GDP Growth The annual growth rate of GDP. Data from World Bank.
Common Law An indicator variable equal to 1 for countries with a common law legal tradition,
and 0 otherwise.
Anti-Self Dealing An index of the strength of anti-self dealing law in a country. Data from Djankov et
al. (2008)
Anti-Director An index of shareholder rights. Data from La Porta et al. (1998).
Time Trend The difference between the year of observation and 1990.
29
Appendix B: Country-level Variables
Country IFRS
Adopter
Accruals
Index
Total
Accruals
Special
Items Royalty
GDP
Growth Service
Common
Law
Anti-
Self
Anti-
Director
Argentina 0 0.07 0.15 18.05 4.56 57.96 0 0.34 4
Australia 1 0.82 0.04 0.27 19.64 3.23 70.41 1 0.76 4
Austria 0
0.05 0.18 19.62 2.05 68.43 0 0.21 2
Belgium 1 0.68 0.05 0.21 21.01 1.78 74.28 0 0.54 0
Brazil 0 0.05 0.13 18.88 3.08 66.78 0 0.27 3
Canada 0 0.82 0.05 0.26 21.46 2.42 66.72 1 0.64 5
Chile 0 0.04 0.06 17.42 3.43 53.13 0 0.63 5
China 0 0.03 0.01 18.95 10.33 38.63 0 0.76
Denmark 1 0.55 0.04 0.15 1.60 72.53 0 0.46 2
Finland 1 0.55 0.04 0.18 20.53 2.53 64.31 0 0.46 3
France 1 0.64 0.04 0.25 21.92 1.56 74.46 0 0.38 3
Germany 1 0.41 0.05 0.18 22.20 1.45 68.03 0 0.28 1
Greece 1 0.03 0.15 17.14 2.48 0 0.22 2
Hong Kong 1 0.64 0.03 0.20 19.22 3.95 90.47 1 0.96 5
India 0 0.02 0.11 17.85 7.04 52.00 1 0.58 5
Indonesia 0 0.05 0.07 17.82 3.81 38.94 0 0.65 2
Ireland 1 0.82 0.05 0.27 20.12 4.47 60.62 1 0.79 4
Israel 0 0.03 0.17 20.11 3.67 1 0.73 3
Italy 1 0.45 0.04 0.18 20.62 0.91 69.99 0 0.42 1
Japan 0 0.55 0.04 0.00 23.02 0.97 65.77 0 0.5 4
Jordan 0 0.01 0.02 8.14 67.56 0 0.16 1
Malaysia 0 0.03 0.08 20.66 5.94 44.05 1 0.95 4
Mexico 0 0.04 0.09 17.35 2.58 64.00 0 0.17 1
Netherlands 1 0.73 0.05 0.14 17.87 2.28 71.76 0 0.2 2
New Zealand 0 0.73 0.04 0.18 21.74 2.68 68.17 1 0.95 4
Norway 1 0.82 0.04 0.19 18.42 2.24 58.80 0 0.42 4
Pakistan 0 0.02 0.06 19.33 4.52 52.09 1 0.41 5
Peru 0 0.03 0.13 16.14 6.33 58.22 0 0.45 3
Philippines 1 0.04 0.09 14.00 4.61 53.32 0 0.22 3
Poland 1 0.03 0.20 15.16 4.00 64.61 0 0.29
Russia 0 0.03 0.18 17.87 4.88 59.24 0 0.44
Singapore 1 0.64 0.02 0.12 19.64 6.46 68.08 1 1 4
South Africa 1 0.68 0.03 0.22 19.58 3.24 65.10 1 0.81 5
South Korea 0 0.03 0.00 17.51 4.59 57.42 0 0.47 2
Spain 1 0.77 0.07 0.22 19.79 2.53 67.24 0 0.37 4
Sweden 1 0.59 0.04 0.15 21.32 2.11 69.56 0 0.33 3
Switzerland 1 0.32 0.05 0.16 1.38 70.04 0 0.27 2
Taiwan 0 0.03 0.00 0 0.56 3
Thailand 0 0.04 0.05 16.71 3.83 47.37 1 0.81 2
Turkey 0 0.03 0.14 4.93 62.29 0 0.43 2
United Kingdom 1 0.82 0.06 0.22 22.85 2.32 72.16 1 0.95 5
United States 0 0.86 0.05 0.32 24.59 2.53 74.97 1 0.65 5
30
Table 1
Matching across Countries
This table reports the average estimates of matching for each country in our sample. The matching measures are
estimated from annual country-specific regression of revenues on past, current and future expenses.
REVit= a + b1EXPit-1 + b2EXPit + b3EXPit+1 + e i t (1)
where REVit is the deflated revenues for firm i in year t, and EXPit is total expenses, measured as the difference
between revenues and earnings after tax, divided by average total assets in year t. The measure of matching is
the coefficient on current-period expenses (b2).
Full sample Top 200 sample
Countries Obs Matching b1 b3 Matching b1 b3
Argentina 10 0.863 0.098 0.053 0.863 0.098 0.053
Australia 20 0.551 0.270 0.134 0.793 0.157 0.134
Austria 13 0.843 0.083 0.068 0.843 0.083 0.068
Belgium 13 0.998 0.002 0.009 0.998 0.002 0.009
Brazil 15 0.620 0.201 0.034 0.839 0.118 0.034
Canada 20 0.386 0.277 0.110 0.902 0.111 0.110
Chile 12 0.857 0.107 -0.013 0.857 0.107 -0.013
China 18 0.920 0.076 0.014 1.019 0.007 0.014
Denmark 20 0.851 0.136 0.027 0.851 0.136 0.027
Finland 13 0.850 0.069 0.080 0.850 0.069 0.080
France 20 0.896 0.078 0.022 0.956 0.051 0.022
Germany 20 0.917 0.076 0.012 0.982 0.031 0.012
Greece 12 0.960 0.056 0.010 0.960 0.056 0.010
Hong Kong 19 0.735 0.152 0.095 0.889 0.084 0.095
India 14 1.008 0.005 0.006 0.984 0.014 0.006
Indonesia 15 0.873 0.063 0.076 0.874 0.063 0.076
Ireland 12 0.776 0.215 0.050 0.776 0.215 0.050
Israel 12 0.739 0.104 0.093 0.790 0.087 0.093
Italy 15 0.947 0.037 0.018 0.948 0.034 0.018
Japan 20 0.905 0.090 0.004 0.963 0.034 0.004
Jordan 5 0.920 0.029 0.069 0.920 0.029 0.069
Malaysia 20 0.852 0.096 0.058 0.908 0.072 0.058
Mexico 13 0.937 0.052 0.032 0.937 0.052 0.032
Netherlands 20 0.854 0.103 0.016 0.854 0.103 0.016
New Zealand 12 0.714 0.291 -0.015 0.714 0.291 -0.015
Norway 15 0.755 0.160 0.044 0.755 0.160 0.044
Pakistan 12 1.005 0.014 -0.009 1.005 0.014 -0.009
Peru 9 1.003 0.035 -0.015 1.003 0.035 -0.015
Philippines 12 0.692 0.128 0.117 0.692 0.128 0.117
Poland 12 0.882 0.084 0.023 0.905 0.075 0.023
Russia 8 0.997 0.019 -0.024 0.997 0.019 -0.024
Singapore 20 0.910 0.079 0.004 0.972 0.041 0.004
South Africa 13 0.902 0.098 -0.001 0.934 0.079 -0.001
South Korea 16 0.908 0.059 0.038 0.952 0.039 0.038
Spain 18 0.947 0.045 0.020 0.947 0.045 0.020
Sweden 20 0.842 0.119 0.043 0.926 0.068 0.043
Switzerland 20 0.909 0.054 0.036 0.909 0.054 0.036
Taiwan 12 0.976 0.018 0.020 0.989 0.023 0.020
Thailand 17 0.888 0.093 0.037 0.900 0.088 0.037
Turkey 8 1.008 0.007 -0.031 1.008 0.007 -0.031
United Kingdom 20 0.743 0.139 0.096 0.891 0.075 0.096
United States 20 0.479 0.190 0.027 0.900 0.093 0.027
31
Table 2
Matching over Time
This table reports the average estimates of matching over time The matching measures are estimated from
annual country-specific regression of revenues on past, current and future expenses.
REVit= a + b1EXPit-1 + b2EXPit + b3EXPit+1 + e i t (1)
where REVit is the deflated revenues for firm i in year t, and EXPit is total expenses measured as the difference
between revenues and earnings after tax, divided by average total assets in year t. The measure of matching is
the coefficient on current-period expenses (b2). Prob represents the p-values for testing the difference between
1990-2000 and 2001-2010 for a two tailed t-test. All variables are defined in Appendix A. *** , ** and *
indicate the coefficient is significant at the 1% , 5% and 10% level, respectively.
Panel A. Matching over time for all countries
Full sample Top 200 sample
Year Countries Matching b1 b3 Matching b1 b3
1991 13 0.900 0.084 0.015 0.955 0.037 0.013
1992 14 0.901 0.093 0.004 0.962 0.057 -0.016
1993 16 0.909 0.064 0.025 0.949 0.049 0.007
1994 17 0.864 0.117 0.016 0.888 0.093 0.025
1995 18 0.885 0.074 0.037 0.951 0.043 0.013
1996 22 0.932 0.051 0.011 0.974 0.039 -0.011
1997 23 0.858 0.122 0.015 0.892 0.098 0.012
1998 29 0.855 0.108 0.030 0.912 0.066 0.027
1999 36 0.890 0.068 0.032 0.956 0.042 0.003
2000 36 0.863 0.110 0.001 0.920 0.088 -0.014
2001 37 0.734 0.184 0.053 0.778 0.162 0.053
2002 38 0.755 0.154 0.070 0.814 0.114 0.081
2003 40 0.827 0.094 0.040 0.900 0.079 0.018
2004 41 0.773 0.142 0.056 0.851 0.124 0.026
2005 41 0.825 0.094 0.043 0.901 0.068 0.021
2006 41 0.827 0.070 0.070 0.902 0.046 0.048
2007 41 0.866 0.079 0.022 0.945 0.041 0.015
2008 41 0.809 0.141 0.013 0.896 0.101 0.002
2009 40 0.824 0.105 0.029 0.925 0.054 0.021
2010 39 0.774 0.100 0.101 0.916 0.069 0.030
1991-2000 0.886 0.089 0.019 0.936 0.061 0.006
2001-2010 0.801 0.116 0.050 0.883 0.086 0.032
Difference -0.084*** 0.027* 0.031*** -0.053** 0.025 0.026***
(p-value) 0.000 0.071 0.005 0.014 0.108 0.010
32
Panel B: Matching over time for a constant sample
Full sample Top 200 sample
Year Countries Matching b1 b3 Matching b1 b3
1991 13 0.900 0.084 0.015 0.955 0.037 0.013
1992 13 0.888 0.103 0.006 0.954 0.064 -0.015
1993 13 0.894 0.079 0.026 0.943 0.060 0.004
1994 13 0.847 0.133 0.012 0.878 0.102 0.024
1995 13 0.875 0.072 0.045 0.965 0.031 0.010
1996 13 0.929 0.040 0.021 0.998 0.020 -0.016
1997 13 0.837 0.150 -0.003 0.899 0.106 -0.006
1998 13 0.839 0.134 0.008 0.965 0.041 -0.002
1999 13 0.802 0.117 0.058 0.959 0.047 0.000
2000 13 0.811 0.142 -0.010 0.943 0.083 -0.032
2001 13 0.670 0.183 0.086 0.734 0.170 0.097
2002 13 0.687 0.181 0.070 0.800 0.107 0.108
2003 13 0.703 0.134 0.080 0.897 0.105 0.003
2004 13 0.670 0.192 0.062 0.833 0.143 0.028
2005 13 0.738 0.092 0.089 0.928 0.037 0.029
2006 13 0.797 0.080 0.047 0.948 0.045 0.010
2007 13 0.745 0.150 0.022 0.947 0.067 -0.013
2008 13 0.676 0.197 0.035 0.863 0.116 0.019
2009 13 0.666 0.178 0.057 0.880 0.085 0.033
2010 13 0.557 0.185 0.180 0.880 0.109 0.029
1991-2000 0.862 0.105 0.018 0.946 0.059 -0.002
2001-2010 0.691 0.157 0.073 0.871 0.098 0.034
Difference -0.171*** 0.052*** 0.055*** -0.075*** 0.039*** 0.036***
(p-value) 0.000 0.009 0.003 0.008 0.026 0.018
Panel C: Matching in two subsample periods for a constant sample
Full sample Top 200 sample
Countries 1990s
(A)
2000s
(B) A - B
1990s
(A)
2000s
(B) A - B
Australia 0.624 0.477 0.147** 0.725 0.862 -0.138
Canada 0.497 0.275 0.223*** 0.940 0.865 0.075
Denmark 0.982 0.720 0.262** 0.982 0.720 0.262**
France 0.964 0.827 0.137*** 0.971 0.941 0.030
Germany 0.971 0.864 0.107*** 0.979 0.985 -0.005
Japan 0.940 0.870 0.069** 0.983 0.944 0.038
Malaysia 0.853 0.850 0.003 0.919 0.898 0.022
Netherlands 0.989 0.720 0.269** 0.989 0.720 0.269**
Singapore 0.975 0.846 0.129*** 0.982 0.962 0.019
Sweden 0.960 0.725 0.235** 0.956 0.896 0.060
Switzerland 0.991 0.827 0.163 0.991 0.827 0.163
UK 0.820 0.666 0.154** 0.863 0.919 -0.055
USA 0.643 0.315 0.328*** 1.016 0.784 0.232***
33
Table 3
Determinants of Matching: Accounting Standards
This table reports the effect of accounting standards on matching. All variables are defined in Appendix A.
Figures in parentheses are robust t-statistics based on standard errors adjusted for clustering effect at country
level. ***, ** and * indicate the coefficient is significant at the 1%, 5% and 10% level, respectively.
Panel A: Full sample
Variables (1) (2) (3)
Constant 1.323*** 1.040*** 0.778***
(10.41) (12.62) (15.25)
Accrual Index -0.672***
(-3.12)
Total Accruals -2.240**
(-2.49)
IFRS 0.016
(0.27)
Post 0.027
(1.50)
IFRS * Post -0.022
(-0.75)
Year Fixed Effect Yes Yes No
Obs 370 618 370
Adj. R2 0.273 0.081 0.002
Panel B: Top 200 sample
Variables (1) (2) (3)
Constant 1.084*** 1.107*** 0.865***
(17.61) (22.00) (33.23)
Accrual Index -0.200**
(-2.35)
Total Accruals -1.610***
(-3.19)
IFRS -0.014
(-0.37)
Post 0.058***
(2.75)
IFRS * Post -0.014
(-0.47)
Year Fixed Effect Yes Yes No
Obs 370 618 370
Adj. R2 0.137 0.102 0.020
34
Table 4
Determinants of Matching: Economic Characteristics
This table reports the effect of economic factors on matching. All variables are defined in Appendix A. Figures
in parentheses are robust t-statistics based on standard errors adjusted for clustering effect at country level. ***,
** and * indicate the coefficient is significant at the 1%, 5% and 10% level, respectively.
Panel A: Full sample
Variables (1) (2) (3) (4) (5)
Constant 1.021*** 0.877*** 1.044*** 1.157*** 0.916***
(33.66) (16.03) (20.45) (12.43) (8.16)
Special Items -0.791*** -0.656***
(-3.83) (-3.30)
GDP Growth 0.010** -0.001
(2.66) (-0.11)
Royalty -0.026** -0.015
(-2.15) (-1.39)
Service -0.004** 0.001
(-2.35) (0.30)
Year Fixed Effect Yes Yes Yes Yes No
Obs 633 622 520 580 483
Adj. R2 0.173 0.066 0.121 0.088 0.194
Panel B: Top 200 sample
Variables (1) (2) (3) (4) (5)
Constant 1.011*** 0.943*** 0.994*** 0.984*** 0.820***
(33.34) (33.07) (22.46) (13.05) (13.43)
Special Items -0.319*** -0.362***
(-3.11) (-2.83)
GDP Growth 0.008*** 0.009***
(2.91) (3.26)
Royalty 0.002 0.008
(0.036) (1.22)
Service -0.000 0.002
(-0.43) (1.59)
Year Fixed Effect Yes Yes Yes Yes Yes
Obs 633 622 520 580 483
Adj. R2 0.102 0.089 0.080 0.077 0.139
35
Table 5
Determinants of Matching: Investor Protection
This table reports the effect of governance factors on matching. All variables are defined in Appendix A.
Figures in parentheses are robust t-statistics based on standard errors adjusted for clustering effect at country
level. ***, ** and * indicate the coefficient is significant at the 1%, 5% and 10% level, respectively.
Panel A: Full sample
Variables Dependent variable: Matching Dependent variable: b1
(1) (2) (3) (1) (2) (3)
Constant 0.967*** 0.994*** 1.081*** 0.052** 0.026 0.008
(31.68) (26.22) (21.48) (2.64) (1.06) (0.27)
Common Law -0.145** 0.070***
(-2.52) (2.80)
Anti-Self dealing -0.166** 0.102***
(-2.32) (2.70)
Anti-Director -0.051** 0.024***
(-2.68) (3.16)
Year Fixed Effect Yes Yes Yes Yes Yes Yes
Obs 635 635 597 635 635 597
Adj. R2 0.140 0.079 0.137 0.101 0.078 0.096
Panel B: Top 200 sample
Variables Dependent variable: Matching Dependent variable: b1
(1) (2) (3) (1) (2) (3)
Constant 0.968*** 0.969*** 0.995*** 0.021 0.010 0.019
(29.65) (24.41) (27.95) (1.18) (0.41) (0.70)
Common Law -0.028 0.035*
(-1.23) (1.95)
Anti-Self dealing -0.025 0.048
(-0.55) (1.35)
Anti-Director -0.009 0.011**
(-1.33) (2.15)
Year Fixed Effect Yes Yes Yes Yes Yes Yes
Obs 635 635 597 635 635 597
Adj. R2 0.074 0.070 0.076 0.075 0.067 0.072
36
Table 6
Time Trend in Matching
This table reports the effect of time trend, accounting, economic and governance factors on matching. All
variables are defined in Appendix A. Figures in parentheses are robust t-statistics based on standard errors
adjusted for clustering by country. ***, ** and * indicate the coefficient is significant at the 1%, 5% and 10%
level, respectively.
Panel A: Full sample
All countries Countries with 20 observations
Variables (1) (2) (3) (4)
Constant 0.879*** 1.261*** 0.933*** 1.185***
(24.95) (6.73) (20.65) (3.99)
Time trend -0.005** -0.001 -0.015*** -0.008*
(-2.51) (-0.43) (-6.98) (-1.98)
Total Accruals -1.762 1.110**
(-1.50) (-2.31)
Special Items -0.509** -1.044***
(-2.54) (-4.30)
GDP Growth -0.006 -0.013
(-1.21) (-1.50)
Royalty -0.010 -0.017
(-0.92) (-0.73)
Service -0.001 0.005
(-0.43) (0.98)
Obs 635 470 260 185
Adj. R2 0.014 0.141 0.113 0.368
Panel B: Top 200 sample
All countries Countries with 20 observations
Variables (1) (2) (3) (4)
Constant 0.920*** 0.744*** 0.950*** 0.064***
(35.68) (5.83) (26.26) (3.95)
Time trend -0.001 0.000 -0.004 -0.002
(-0.68) (0.08) (-1.25) (-0.66)
Total Accruals -1.649** -2.087**
(-2.02) (-2.32)
Special Items -0.218* -0.296**
-1.69 (-2.09)
GDP Growth 0.002 0.001
(0.48) (0.22)
Royalty 0.012* 0.020*
(1.79) (1.91)
Service 0.000 -0.001
(0.28) (-0.05)
Obs 635 470 260 185
Adj. R2 0.001 0.076 0.017 0.133
37
Figure 1
Matching Over Time
![[XLS] · Web viewIncome Statement Summary Non-operating income Total Airport Revenues Operating aeronautical revenues Ground handling revenues Operating non-aeronautical revenues](https://img.pdfslide.us/doc/110x75/5acac1f37f8b9a7d548e1826/xls-viewincome-statement-summary-non-operating-income-total-airport-revenues-operating.jpg)
