Accounting standards convergence dynamics

Description
This paper aims to explore convergence of accounting standards across worldwide adopted
measures to investigate whether countries that have not completely adopted International Accounting
Standards across the globe have displayed a tendency to act so.

Accounting Research Journal
Accounting standards convergence dynamics: International evidence from club
convergence and clustering
Nicholas Apergis Christina Christou Christis Hassapis
Article information:
To cite this document:
Nicholas Apergis Christina Christou Christis Hassapis , (2014),"Accounting standards convergence
dynamics", Accounting Research J ournal, Vol. 27 Iss 3 pp. 226 - 248
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Accounting standards
convergence dynamics
International evidence from club convergence
and clustering
Nicholas Apergis and Christina Christou
Department of Banking and Financial Management, University of Piraeus,
Piraeus, Greece, and
Christis Hassapis
Department of Economics, University of Cyprus, Nicosia, Cyprus
Abstract
Purpose – This paper aims to explore convergence of accountingstandards across worldwide adopted
measures to investigate whether countries that have not completely adopted International Accounting
Standards across the globe have displayed a tendency to act so.
Design/methodology/approach – The newpanel convergence methodology, developed by Phillips
and Sul (2007), is employed.
Findings – The empirical fndings suggest that countries form distinct convergent clubs, albeit on a
limited prevalence, yielding support to the notion that on a global basis frms and countries have
initiatedprocesses that will eventuallyleadthemto a uniformpatternof employingcommonaccounting
standards.
Practical implications – These fndings have substantial implications on a frm level, mainly for
differences in accounting quality as well as for differences in their cost of capital, thus leading the
regulatory authorities to opt for further improvements in fnancial reporting.
Originality/value – The novelties of this paper frst, stem from the fact that it is the frst time in the
relevant literature that an empirical study attempts to formally measure whether the accounting world
exhibits a tendency for accounting standards convergence or whether tactics and policies remain
stagnant, acquiring drastic policy measures to speed up the convergence process. In addition, this study
employs the implementation of the new methodology of panel convergence testing. This methodology
has several appealing characteristics.
Keywords IFRS, Convergence, Club convergence methodology, Global frms
Paper type Research paper
JEL classifcation – M41, C33
The authors highly appreciate two referees of this journal for their constructive comments that
enhanced the quality of this study. The authors also appreciate Donggyu Sul for making the
Gauss code available to them. Asample code can be downloaded fromDonggyu Sul’s homepage:http://homes.eco.auckland.ac.nz/dsul013/. The authors also express our gratitude to Nicholas
Koumbiadis and Robert Rikards for constructive comments that improve the frst picture of this
paper. The usual disclaimer applies.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1030-9616.htm
ARJ
27,3
226
Accounting Research Journal
Vol. 27 No. 3, 2014
pp. 226-248
©Emerald Group Publishing Limited
1030-9616
DOI 10.1108/ARJ-06-2013-0031
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1. Introduction
As of January 1, 2005, all publicly listed frms in the European Union are required to
prepare fnancial statements in accordance with International Financial Reporting
Standards (IFRS)-although a number of frms were already preparing their fnancial
statements even from 2000, while more and more frms in Asia are turning to the IFRS
standards. USAfrms are the only remaining entities in the world not yet adopting IFRS
(Hail et al., 2010). As more countries converge to IFRS, the accounting and fnancial
community is getting increasingly interested in evaluating the benefts associated with
IFRS adoption (Ball, 2006; Cynthia and Murphy, 2009). Nevertheless, even for those
countries that have adopted IFRS directly, certain differences may exist during the
implementation of the IFRS regime. Given these differences, it is essential to have
reliable evidence of the progress in achieving worldwide convergence.
A primary objective of the International Accounting Standards Board (IASB) is to
develop a high-quality systemof accounting standards that will ensure transparent and
comparable information regarding the quality of fnancial statements reporting. To this
end, the IASBhas adopted a number of steps to remove alternative accounting practices
and, thus, to require accounting measurements that refect a frm’s economic position
and performance (Ball et al., 2003). The application of such international accounting
practices is expected to lead to higher accounting information quality and,
consequently, to a lower equity cost of capital (Ewert and Wagenhofer, 2005). They
present a rational expectations model which provides empirical evidence that
accounting earnings refect better a frm’s underlying economic position and, thus, are of
higher quality.
The current worldwide evidence documents those frms which have not adopted
international accounting practices, display less earnings management, more timely loss
recognition and more value relevance of accounting amounts vis-a`-vis those frms that
have considered the IFRS regime. More specifcally, the former frms display a higher
variance of net income changes, a higher ratio of the variances relevant to net income
and cash fows changes, a lower extent of correlation between accruals and cash fows
and, fnally, a lower frequency of small positive net income levels. Moreover, the IFRS
regime is expected to facilitate growth, not only for the frms themselves, but also for
bilateral activities involving international transactions (Daske et al., 2008). Anumber of
studies argue that the adoption of the IFRS regime is expected to reduce information
costs in an economy, especially as trade and capital fows become more and more
globalized: it is cheaper for capital market participants to become familiar with one set
of international standards versus several local standards. (Leuz, 2003; Brath, 2008).
Beneish and Yohn (2008) explored the effect of the adoption of IFRS on the tendency of
investors to under-invest in foreign equities, given the pre-determined home bias effect
considered in the relevant literature. Their empirical fndings highlight that the quality
of information that investors receive is higher, placing the home bias effect in dispute.
Gaston et al. (2010) also examine the quantitative impact of the IFRS adoption on
fnancial reporting by Spain and the UK, by comparing the information content
disclosed under IFRS vis-a`-vis the information content under local generally accepted
accounting principles (GAAP) systems. Their empirical fndings reveal that the
quantitative impact is signifcant. Karampinis and Hevas (2013) investigate whether the
adoption of IFRS in Greece tends to change tax-induced incentives for fnancial earnings
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management. They document that although tax pressure is considered as a signifcant
negative factor of discretionary accruals, this pressure dissipated in the IFRS era.
Overall, the benefts of a unifed accounting standards system are related to the
reduction of the information asymmetry associated with potential fnancial market
investors and to the promotion of free fows of global investment; at the same time, it is
related to the achievement of substantial benefts for all capital markets stakeholders,
i.e. investors, frms and auditors (Dikova et al., 2010).
The objective of this paper is to investigate convergence of accounting standards
levels across 27 countries all over the globe and spanning the period 2000-2012. The
fndings will be the basis of more realistic policy recommendations that could be put
forward, in an effort to eliminate such differences on a worldwide basis. The empirical
fndings could provide additional information to the users of fnancial reporting by
helping themto assess the quality and comparability of the current convergence pattern.
The convergence of accounting practices is a decisive strategic factor for global capital
markets. The reason is simple: high-quality information is essential to high-quality
markets.
The novelties of this paper stem from the fact that it is the frst time in the relevant
literature that an empirical study attempts to formally measure whether the accounting
world exhibits a tendency for accounting standards convergence or whether tactics and
policies remain stagnant, demanding for drastic policy measures to speed up the
convergence process. In addition, this study makes use of the newmethodology of panel
convergence testing, recommended by Phillips and Sul (2007). The philosophy of the
methodological approach is the club convergence hypothesis, suggested by Fischer and
Stirbock (2004), which claims that certain countries or regions or frms which belong in
a club move from a disequilibrium position to its club-specifc steady-state position.
This methodology has several appealing characteristics. To begin with, no specifc
assumptions concerning the stationarity of the variable of interest and/or the existence
of common factors are necessary. Nevertheless, this convergence test could be
interpreted as an asymptotic cointegration test without suffering fromthe small sample
problems of unit root and cointegration testing. In addition, the methodology is based on
a quite general form of a nonlinear time-varying factor model which takes into account
that countries experience transitional dynamics. Finally, an additional novelty of the
paper is that it tests for convergence by using a number of alternative methodologies
that measure accounting standards to provide robust support to the studies’ fndings.
The rest of the paper is organized as follows. Section 2 reviews the recent empirical
literature on international accounting standards. Section 3 presents the new
methodology employed. Section 4 discusses the results of the empirical analysis, while
Section 5 summarizes the paper, suggests possible venues for future research and offers
some policy implications.
2. Literature review
The fexibility of IFRS principles-based standards allows frms to continue handling
accounting information given to the public and to potential investors, thus reducing
accounting quality. In this major strand of the literature on the effects of the IFRS
regime, this type of fexibility has been a main concern of securities markets regulators
(Breeden, 1994), while Street and Gray (2001) and Ball et al. (2003) argue that lax
enforcement leads to limited compliance with the standards and, therefore, to their
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limited effectiveness. With respect to the latter study, frms in Asian countries follow
accounting standards largely derived fromcommon lawand thus are very close to IFRS.
Empirical fndings of their study show that in these Asian frms the quality level of
timely loss recognition is no better vis-a`-vis frms in other parts of the world that follow
the code law system. Moreover, Bradshaw and Miller (2005) study non-USA frms that
follow USA domestic accounting standards and yet the characteristics of their
accounting practices are far from being similar to those by US frms. Peng et al. (2008)
show that accounting standards convergence is documented across Chinese frms.
Jeanjean and Stolowy (2008) fnd that the pervasiveness of earnings management
increased in Australia, the UKand France, even after the adoption of IFRS, while Ahmed
et al. (2010) fnd that mandatory adoption of IFRS leads to higher earnings smoothing,
more aggressive reporting of accruals and, fnally, to reduced levels in timeliness of loss
recognition. Following the adoption of IFRS by Greek frms, Tsalavoutas et al. (2010)
provide evidence against any signifcant changes in the value relevance of equity book
values and earnings. Zeghal et al. (2011) examine whether the mandatory adoption of the
IFRS regime in France is associated with lower earnings management. Based on a large
sample of 353 frms, their results display that the new accounting regime is associated
with a reduction in the level of earnings management, especially for frms with good
corporate governance and for those that depend heavily on foreign fnancial markets.
Clarkson et al. (2011) argue that there are no changes in price relevance for frms
operating in countries under either the Code Law regime or the Common Law regime.
Landsman et al. (2012) examine whether the information content of earnings
announcements increases in countries that have adopted an IFRS regime. Their
empirical fndings suggest that that this information content strongly increases in IFRS
regimes across a sample of 16 countries. They also identifed three mechanisms through
which this increase is attributed to: reduced reporting lags, increasing analysts
following and increasing foreign investment. Finally, Dimitropoulos et al. (2013)
examine the impact of the IFRS adoption on the quality of accounting information
within the Greek manufacturing setting. They provide convincing evidence that the
implementation of the IFRS regime contributes to less earnings management, to more
timely loss recognition and to greater value relevance of accounting fnancial
statements. By contrast, Misirlioglu et al. (2013) examine whether the mandatory
adoption of the IFRS regime by Turkish listed frms played a signifcant role or not in
the measurement of disclosures. They provide strong evidence that most of the items
supposed to be disclosed in an IFRS regime were not disclosed.
A different strand of the literature investigates the potential association between
accounting standards and informational asymmetries. Easley and O’Hara (2004) model
the impact of information characteristics on the cost of capital. Their results confrmthe
direct impact of accounting information on the frm’s cost of capital. Yip and Young
(2009) and Horton et al. (2010) provide evidence that the adoption of IFRS reduces the
asymmetry of information and has a positive effect on asset prices. Finally,
Bruggenmann et al. (2009) and Yu (2009) show that the mandatory adoption of IFRS
contributed to higher levels of trading activity across individual investors and higher
volumes of investment in capital markets due to lower asymmetric information costs
related to the cost of equity capital.
Studies comparing IFRS to domestic accounting standards report mixed results
about their quality. In particular, Garrido et al. (2002) use a longitude study – that
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employs Euclidian distances – to research formal convergence. Their methodology
suffers from the drawback that such distances can show the difference between the
items compared, but cannot refect similarities or dissimilarities concerning the items
under comparison. Ashbaugh and Pincus (2001) investigate whether convergence in
international accounting standards is capable of forecasting analysts’ attempts to
forecast frms’ earnings. Eccher and Healy (2003) fnd that accounting information
based on IFRS is not more value-relevant than that based on Chinese accounting
standards for frms that can be owned by foreign investors, attributing these differences
to the lack of effective controls and infrastructure to monitor the application of IFRS.
Tarca (2004) compares reporting practices between domestic and international settings
for a sample of countries. Her empirical fndings show that a growing number of frms,
even in the US market, adopt the IFRS methodology. Van Tendeloo and Vanstraelen
(2005) show that German frms applying IFRS do not exhibit differences in earnings
management vis-a`-vis frms that apply German accounting standards. Consistent with
their fndings, the study by Daske (2006) also fnds the absence of evidence regarding
cost capital reductions for the same German frms. Fontes et al. (2005) recommend the
Spearman’s coeffcient approach to assess the process of convergence between any two
sets of accounting standards. Their results document that their assessment
methodology has comparative advantages over distance methodologies.
By contrast, a number of recent studies provide evidence that the quality of
accounting information is not managed by the adoption of a specifc accounting
regime, but by market forces and institutional factors (Ball et al., 2003; Ball and
Shivakumar, 2005). Their main fnding is that the adoption of a particular
accounting system does not seem to enhance the quality of accounting information
provided to potential investors and thus to reduce agency conficts regarding
groups of investors and/or shareholders. What really matters is the impact of legal
institutions on auditors; performance.
3. Methodologies of accounting standards
A crucial concept for investigating convergence in accounting standards is the
appropriate approach of accounting measurement, i.e. calculating accounting numbers
through the measurement of stock values coming fromthe balance sheet. We followthe
methodological approaches offered in the relevant literature on the employment of
specifc metrics that consider accounting standards convergence, i.e. the earnings
management approach.
This approach measures accounting information quality using various earnings
management metrics. The literature has used a formal approach of measuring earnings
management, i.e. earnings smoothing. Regarding earnings smoothing, frms with less
earnings smoothing exhibit higher earnings volatility (Leuz et al., 2003; Lang et al.,
2005). Therefore, we make use of two measures of earnings volatility: volatility in net
income changes scaled by total assets and the ratio of volatility in net income changes to
volatility in cash fow changes. Moreover, the second ratio disaggregates across
fnancing cash fows, investing cash fows and operating activities cash fows. We will
examine whether frms in our sample display earnings smoothing metrics convergence,
as IFRS frms have less discretion to smooth earnings.
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4. Econometric methodology
In this section, we outline the methodology proposed by Phillips and Sul (2007)
(henceforth PS) to test for convergence in a panel of countries. We also briefy discuss the
clustering algorithm that allows us to classify countries into convergent clubs.
4.1 Testing for convergence
We make use of panel data for a variable X
it
, where i ?1,...Nand t ?1,...T, with N, Tthe
number of countries and the sample size, respectively. Often X
it
is decomposed into two
components, one systematic, g
it
, and one transitory a
it
X
it
? g
it
? a
it
(1)
PS transform (1) in a way that common and idiosyncratic components in the panel are
separated. Specifcally:
X
it
?
?
g
it
? a
it
?
t
?
?
t
? ?
it
?
t
, for all i, t (2)
In this way, the variable of interest, X
it
, is decomposed into two components, one
common, ?
t
, and one idiosyncratic, ?
it
, both of which are time-varying components. ?
it
is
assumed to converge, for each country i, to some limiting value ?
i
for that country. The
average difference between ?
it
and ?
i
is assumed to decline over time at a rate
proportional to 1/(t
?
log(t ?1)) for some ??0. The convergence hypothesis is that every
country converges to the same limit, ?
i
? ?. This formulation enables testing for
convergence by testing whether the factor loadings ?
it
converge. To do so, PS defne the
relative transition parameter, h
it
, as:
h
it
?
X
it
1
N
?
i?1
N
X
it
?
?
it
1
N
?
i?1
N
?
it
(3)
which measures the loading coeffcient ?
it
in relation to the panel and, as such, the
transition path for the economy i relative to the panel average. The relative transition
curves depict the relative transition coeffcients h
it
, calculated from Equation (3).
Having extracted the trend component fromthe series denoted as X
ˆ
it
(our data series
are trending, therefore, we had to apply the PS methodology on the trend components of
the series, which were extracted using the Hodrick – Prescott flter), we calculate the
estimated transition paths as h
ˆ
it
?
X
ˆ
it
1
N
?
i?1
N
X
ˆ
it
. Next, we construct the cross-sectional
variation ratio H
1 /
H
t
, where:
H
t
?
1
N
?
i?1
N
(h
ˆ
it
?1)
2
(4)
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To defne a formal econometric test, PS assume the following functional form for the
transition distance H
t
:
H
t
?
A
L(t)
2
t
2?
as t ¡ ? (5)
where A is a positive constant, L(t) is a slowly varying and increasing function
diverging at infnity, such as log (t ?1) , and ? denotes the speed of convergence. The
null hypothesis of convergence for all i, takes the form:
H
0
: ?
i
? ?
and ? ? 0 (6)
against the alternative:
H
A
: ?
i
?
or ? ? 0 (7)
PS run the following log t regression:
log
?
H
1
H
t
?
? 2log L(t) ? c ? blog t ? u
t
, (8)
where L(t) ? log(t ? 1). The standard errors of the estimates are calculated using a
heteroskedasticity and autocorrelation consistent estimator for the long-run variance of
the residuals. We employ the quadratic spectral kernel and determine the bandwidth by
means of the Andrews (1991) data-dependent procedure. By employing the conventional
t-statistic t
b
, the null hypothesis of convergence is rejected if t
b
? ?1.65 . In practice,
this regression is run after a fraction of the sample is removed. PS recommend starting
the regression at some point t ? ?rT? , where ?rT? is the integer part of rT, and
r ?0.3. [1]
Given that rejection of the null hypothesis for the panel as a whole does not imply the
absence of club convergence, PS go one step beyond and develop an algorithm for club
convergence. We next briefy outline the basic steps of the respective algorithm.
4.2 Club convergence algorithm
Step 1 (Ordering) Order the members of the panel according to the last observation.
Step 2 (Core group formation) Calculate the convergence t-statistic, t
k
, for sequential
log t regressions based on the k highest members (Step 1) with 2 ? k ? N. The core
group size is chosen on the basis of the maximum of t
k
with t
k
? ?1.65.
Step 3 (Club membership) Select countries for membership in the core group (Step 2)
by adding one at a time. Include the newcountry (member) if the associated t-statistic is
greater than zero (conservative choice). Make sure that the club satisfes the criterion for
convergence.
Step 4 (Recursion and stopping) The countries not selected in the club formed in step
3, form a complementary group. Run the logt regression for this set of countries. If it
converges, then these countries form a second club. If not, Steps 1 to 3 are repeated, to
reveal some sub-convergent clusters. If no core group is found (Step 2), then these
countries display a divergent behavior.
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5. Empirical analysis
5.1 Data description
We select both frms that have adopted the IFRS system(IFRS) and frms that have not
adopted the IFRS systemon a country basis, spanning the period 2000-2012. Many frms
around the globe adopted IFRS accounting standards mostly within that period (either
on a volunteer basis or on a mandatory basis). Firm-level data (on an annual basis)
across countries are obtained from Datastream. The empirical analysis makes use of
cash fows, total assets and net income data. To establish data on a comparable basis,
these values are calculated as the sum (across frms) of the US dollar
capitalization-weighted values for the relevant individual frms. The Data Table
presents the country breakdown of our sample, indicating a wide range of countries. A
fnal note is that although there are specifc country blocks, i.e. the European countries,
which adopted the IFRS around 2005, our analysis commences at 2000 for two reasons:
because the methodological approach needs a time dimension, and, more importantly,
these countries had already started making preparations for adopting the IFRS regime
well before their formal introduction in 2005 (Table I).
Table I.
Data table
Countries No. of frms IFRS NIFRS
Australia 718 ?
Austria 44 ?
Belgium 67 ?
Canada 583 ?
China 1,191 ?
Denmark 54 ?
Finland 94 ?
France 388 ?
Germany 408 ?
Greece 46 ?
Hong Kong 790 ?
Italy 132 ?
Japan 2,738 ?
Malaysia 568 ?
The Netherlands 92 ?
Philippines 163 ?
Portugal 38 ?
Russia 27 ?
Singapore 373 ?
South Africa 209 ?
South Korea 665 ?
Spain 11 ?
Sweden 189 ?
Switzerland 157 ?
Turkey 28 ?
United Kingdom 716 ?
United States 3,585 ?
Notes: IFRS ?frms adopted IFRS; NFRS ?frms not adopted IFRS
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5.2 Club convergence and clustering: earnings management and volatility based on
squared residuals from ARMA models
The analysis begins with an examination of panel tests for unit roots to determine the order
of integration for the respective variables and to confrm the presence of trends in the
variables under study. Levin et al. (2002) set forth a panel based Augmented Dickey-Fuller
test (ADF) test that assumes homogeneityinthe dynamics of the autoregressive coeffcients
for all panel units. Onthe other hand, Imet al. (2003) propose apanel unit root test that allows
for heterogeneity in the dynamics of the autoregressive coeffcients for all panel units.
Alternatively, Maddala and Wu (1999) employ nonparametric panel unit root tests with the
advantage of permitting as much heterogeneity across units as possible through the use of
Fisher-ADF and Fisher-PP panel unit root tests. The Levin et al. (2002), Im et al. (2003),
Fisher-ADF and Fisher-PP approaches test the null hypothesis of a unit root with the
alternative hypothesis of the absence of a unit root. As displayed in Table II, the panel unit
root tests showthat eachvariable displays the presence of trendat the 1per cent signifcance
level.
5.2.1 Volatility in net income changes scaled by total assets. Table III reports results of
the panel convergence methodology for volatility in net income changes scaled by total
assets based on squared residuals. The frst row shows the results of the full
convergence logt test, i.e. convergence among all countries, and the club clustering
algorithm. The null hypothesis of full convergence is rejected at the 5 per cent level for
the time period under scrutiny. Specifcally, the point estimate of b is ?1.839 (t-statistic:
?34.283). Rows 2 to 3 display the formation of two different convergence clubs. In other
words, the empirical fndings showthat there exist two groups of countries, each with 14
and 8 countries, respectively, apparently characterized by different phases of
international accounting convergence. Row 4 identifes a non-converging group of
countries, i.e. Canada, China, Philippines, Russia, and the USA, which seem not to
belong to any of the predetermined clubs, i.e. they are the countries that have not
adopted the IFRS regime (Data Table), with b-coeffcient ?2.153 and t-static equal to
?4.889. Once again, the empirical fndings display that for all sub-clubs there is no
evidence to support mergers of the original clubs.
Phillips and Sul (2009) argue that their convergence club methodology tends to
overestimate the number of clubs than their true number. To avoid this
overdetermination, they run the algorithm across the sub-clubs to assess whether any
evidence exists in support of merging clubs into larger clubs. The results of the new
converging tests are also reported in Table III. The empirical fndings display that for all
sub-clubs there is no evidence to support mergers of the original clubs.
5.2.2 Ratio of volatility in net income changes to volatility in cash fow changes.
Tables IV to VII present clustering results in terms of the ratio of volatility in net income
changes to volatility in cash fowchanges, both on an aggregated basis (Table IV), and on a
disaggregated basis, i.e. fnancing cash fows, investing cash fows and operating activities
cashfows (Table V, Table VI andTable VII, respectively). Table IVdocuments that the null
hypothesis of full convergence for the aggregatedmetric andfor the full sample is rejectedat
the 5 per cent level. The point estimate of b (t-statistic in parenthesis) is ?1.403 (?8.969).
Onceagain, Canada, China, Philippines, RussiaandtheUSAarethenon-IFRScountrieswith
b-coeffcient equal to?1.517andcorrespondingt-statistic ?1.747. Inthis case, twoclubs are
formed, with their pattern very close to those clubs found in Table III. Their corresponding
t-statistics are ?0.822 and ?1.351, respectively.
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Table V presents results for the disaggregated metric with reference to fnancing
cash fows. Once again, the club algorithm recommends the rejection of full
convergence with a value of t-statistic equal to ?25.259. This time, however, three
clubs are formed with each containing 3, 19 and 5 countries, respectively. The third
club contains the non-IFRS countries with a corresponding t-statistic equal
to ?1.121.
Table II.
Panel unit root tests
Variables LLC IPS Fisher-ADF Fisher-PP
Volatility in net income changes scaled by
total assets ?2.14 ?2.31 11.65 14.39
Ratio of volatility in net income changes to
volatility in cash fow changes ?1.63 ?2.15 7.19 12.64
Ratio of volatility in net income changes to
volatility in cash fow changes-fnancing ?1.32 ?2.14 1.25 2.11
Ratio of volatility in net income changes to
volatility in cash fow changes-investing ?1.12 ?1.24 3.20 5.32
Ratio of volatility in net income changes to
volatility in cash fow changes-operating
activities ?1.13 ?1.27 3.23 5.56
Volatility in net income changes scaled by
total assets – absolute value of residuals ?1.15 ?1.22 3.08 5.17
Ratio of volatility in net income changes to
volatility in cash fow changes – absolute
value of residuals ?1.12 ?1.26 3.09 5.11
Ratio of volatility in net income changes to
volatility in cash fow changes-fnancing –
absolute value of residuals ?1.16 ?1.21 3.11 5.12
Ratio of volatility in net income changes to
volatility in cash fow changes-investing –
absolute value of residuals ?1.10 ?1.29 3.21 4.53
Ratio of volatility in net income changes to
volatility in cash fow changes-operating
activities ?1.17 ?1.35 3.86 4.18
Volatility in net income changes scaled by
total assets – GARCH estimates ?1.24 ?1.39 3.65 4.82
Ratio of volatility in net income changes to
volatility in cash fow changes – GARCH
estimates ?1.31 ?1.44 4.52 4.25
Ratio of volatility in net income changes to
volatility in cash fow changes-fnancing –
GARCH estimates ?1.30 ?1.48 4.58 4.85
Ratio of volatility in net income changes to
volatility in cash fow changes-investing –
GARCH estimates ?1.26 ?1.46 4.71 4.64
Ratio of volatility in net income changes to
volatility in cash fow changes-operating
activities – GARCH estimates ?1.37 ?1.62 4.83 4.92
Note: All unit root tests include an intercept and trend
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Table III.
Volatility in net income
changes scaled by total
assets – squared residuals
approach
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany,
Greece, Hong Kong, Italy, Japan, Malaysia,
The Netherlands, Philippines, Portugal,
Russia, Singapore, South Africa, South
Korea, Spain, Sweden, Switzerland, Turkey,
UK, US
?34.283 ?1.839
1st club Austria, Belgium, Denmark, Finland,
France, Germany, Greece, Italy, The
Netherlands, Portugal, South Korea, Spain,
Sweden, UK
0.916 0.390
2nd club Australia, Hong Kong, Japan, Malaysia,
Singapore, South Africa, Switzerland,
Turkey
?0.227 ?0.011
Non-converging Canada, China, Philippines, Russia, US ?4.889 ?2.153
Club Tests of club merging
1 Club 1 ?2 ??0.057* (-6.41)
Note: *denotes statistical signifcant at the 5 per cent level, while it rejects the null hypothesis of
convergence. Figures in parentheses denote t-statistics
Table IV.
Ratio of volatility in net
income changes to
volatility in cash fow
changes – squared
residuals approach
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany,
Greece, Hong Kong, Italy, Japan, Malaysia,
The Netherlands, Philippines, Portugal,
Russia, Singapore, South Africa, South
Korea, Spain, Sweden, Switzerland, Turkey,
UK, US
?8.969 ?1.403
1st club Australia, Austria, Belgium, Denmark,
Finland, France, Germany, Greece, Hong
Kong, Italy, The Netherlands, Portugal,
South Africa, Spain, Sweden, Switzerland,
UK
?0.822 ?0.098
2nd club Japan, Malaysia, Singapore, South Korea,
Turkey
?1.351 ?0.301
Non-converging Canada, China, Philippines, Russia, US ?1.747 ?1.517
Club Tests of club merging
1 Club 1 ?2 ??0.057* (?6.41)
Note: Similar to Table III
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Table V.
Ratio of volatility in net
income changes to
volatility in cash fow
changes-fnancing –
squared residuals
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Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany,
Greece, Hong Kong, Italy, Japan, Malaysia,
The Netherlands, Philippines, Portugal,
Russia, Singapore, South Africa, South
Korea, Spain, Sweden, Switzerland, Turkey,
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?25.259 ?2.059
1st club Australia, South Korea, Switzerland ?1.569 ?0.947
2nd club Austria, Belgium, Denmark, Finland,
France, Germany, Greece, Hong Kong, Italy,
Japan, Malaysia, The Netherlands,
Portugal, Singapore, South Africa, Spain,
Sweden, Turkey, UK
?0.787 ?0.084
3rd club Canada, China, Philippines, Russia, US ?1.121 ?0.311
Club Tests of club merging
1 Club 1 ?2 ??0.057* (?6.41)
2 Club 2 ?3 ??0.073* (?5.95)
Note: Similar to Table III
Table VI.
Ratio of volatility in net
income changes to
volatility in cash fow
changes-investing –
squared residuals
approach
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany,
Greece, Hong Kong, Italy, Japan, Malaysia,
The Netherlands, Philippines, Portugal,
Russia, Singapore, South Africa, South
Korea, Spain, Sweden, Switzerland, Turkey,
UK, US
?2.329 ?0.444
1st club Australia, Austria, Belgium, Denmark,
Finland, France, Germany, Greece, Hong
Kong, Italy, Japan, The Netherlands,
Portugal, South Africa, Spain, Switzerland,
UK
1.290 1.071
2nd club Malaysia, South Korea, Sweden, Turkey 0.282 0.116
3rd club Canada, China, Philippines, Russia, US ?0.998 ?1.128
4th club Singapore 0.094 0.130
Club Tests of club merging
1 Club 1 ?2 ??0.057* (?6.41)
2 Club 2 ?3 ??0.073* (?5.95)
3 Club 3 ?4 ??0.104* (?6.48)
Note: Similar to Table III
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Tables VI and VII report convergence results of the ratio of volatility in net income
changes to volatility in cash fow changes, when they are proxied as investing and
operating activities, respectively. Both tables reject the full sample convergence (with
corresponding t-statistic values of ?2.329 and ?3.835, respectively), while they provide
support to the formation of four converging clubs, although their structure is not similar.
In Table VI and in terms of the non-converging group, the results display consistency
for Canada, China, Philippines, Russia and the USA, signaling once again that these
countries continue to followtheir own domestic accounting standards. Across Tables IV
to VII the empirical fndings confrm the absence of merging across the original clubs.
6. Robustness tests: club convergence and clustering: earnings
management and volatility based on the absolute value of the residuals
from ARMA models
6.1. Volatility in net income changes scaled by total assets
Table VIII reports results for the newmeasure of volatility in net income changes scaled
by total assets based on the absolute value of residuals. The frst rowshows that the null
hypothesis of full convergence is rejected at the 5 per cent level for the time period under
scrutiny. Specifcally, the point estimate of b is ?1.673 (t-statistic: ?13.981). Rows 2 to 3
display the formation of two different convergence clubs, indicating that there exist two
groups of countries, with 23 and 5 countries, respectively. These empirical fndings
clearly document the separation between IFRS-adopting and non-IFR-adopting
countries.
Table VII.
Ratio of volatility in net
income changes to
volatility in cash fow
changes-operating
activities – squared
residuals approach
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany,
Greece, Hong Kong, Italy, Japan, Malaysia,
The Netherlands, Philippines, Portugal,
Russia, Singapore, South Africa, South
Korea, Spain, Sweden, Switzerland, Turkey,
UK, US
?3.835 ?1.288
1st club Austria, Belgium, Denmark, Finland,
France, Germany, Greece, Italy, The
Netherlands, Portugal, Spain, Sweden
?1.455 ?0.656
2nd club South Africa, Turkey, UK 0.765 0.352
3rd club Hong Kong, Japan, Malaysia, Singapore,
South Korea
?1.204 ?0.750
4th club Australia, Switzerland ?0.871 ?0.383
Non-converging Canada, China, Philippines, Russia, US ?3.814 ?2.453
Club Tests of club merging
1 Club 1 ?2 ??0.036* (?5.18)
2 Club 2 ?3 ??0.048* (?5.53)
3 Club 3 ?4 ??0.064* (?6.81)
Note: Similar to Table III
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Once again, the empirical fndings display that for all sub-clubs there is no evidence to
support mergers of the original clubs.
6.2. Ratio of volatility in net income changes to volatility in cash fow changes
Tables IX-XII present clustering results for the ratio of volatility in net income changes
to volatility in cash fow changes, both on an aggregated (Table IX) and on a
disaggregated basis (Tables X, XI, and XII, respectively). The picture remains similar to
the previous case. More specifcally, Table VIII documents that the null hypothesis of
full convergence for the aggregated metric and for the full sample is rejected at the 5 per
cent level. The point estimate of b (t-statistic in parenthesis) is ?2.685 (?3.514). Canada,
China, Philippines, Russia and the USA remain as the non-IFRS countries with
b-coeffcient equal to ?0.391 and corresponding t-statistic ?10.377. Two clubs are
formed, with corresponding t-statistics ?1.342 and 3.160, respectively, highlighting
again the even countries that have adopted the IFRS regime are characterized by
different stages of the adoption process.
Table X presents the results for the disaggregated metric with reference to the
fnancing cash fows. The club algorithmrecommends rejection of full convergence with
a value of t-statistic equal to ?17.646, while only one club is formed with 23 countries,
while a non-converging group is present, with Canada, China, Philippines, Russia and
the USA, with a corresponding t-statistic equal to ?13.761.
Tables XI and XII report convergence results of the ratio of volatility in net income
changes to volatility in cash fowchanges, with cash fows being measured as investing
and operating activities, respectively. Both tables reject full sample convergence (with
corresponding t-statistic values of ?5.274 and ?6.824, respectively). The frst table
provides support to the formation of two converging clubs and a non-converging club
Table VIII.
Volatility in net income
changes scaled by total
assets – absolute value of
residuals
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany,
Greece, Hong Kong, Italy, Japan, Malaysia,
The Netherlands, Philippines, Portugal,
Russia, Singapore, South Africa, South
Korea, Spain, Sweden, Switzerland, Turkey,
UK, US
?13.981 ?1.673
1st club Australia, Austria, Belgium, China,
Denmark, Finland, France, Germany,
Greece, Hong Kong, Italy, Japan, Malaysia,
The Netherlands, Portugal, Singapore,
South Africa, South Korea, Turkey, Spain,
Sweden, Switzerland, UK
?10.025 ?0.132
2nd club Canada, China, Philippines, Russia, US ?1.136 ?0.936
Club Tests of club merging
1 Club 1 ?2 ??0.093* (?6.08)
Note: Similar to Table III
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(the regular non-IFRS countries), while Table XII displays the formation of three clubs,
still denoting consistency for both the European country club and the non-IFRS club.
The results across Tables IXto XII display that for all sub-clubs there is no evidence
to support mergers of the original clubs.
7. Robustness tests: club convergence and clustering: earnings
management and volatility based on Generalized Autoregressive
Conditional Heteroscedasticity (GARCH) estimates
7.1. Volatility in net income changes scaled by total assets
Table XIII reports results for the newmeasure of volatility in net income changes scaled
by total assets. This time we employ the GARCH methodology to account for a
Table IX.
Ratio of volatility in net
income changes to
volatility in cash fow
changes – absolute value
of residuals
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany,
Greece, Hong Kong, Italy, Japan, Malaysia,
The Netherlands, Philippines, Portugal,
Russia, Singapore, South Africa, South
Korea, Spain, Sweden, Switzerland, Turkey,
UK, US
?3.514 ?2.685
1st club Austria, Belgium, Denmark, Finland,
France, Germany, Greece, Italy, The
Netherlands, Portugal, Singapore, South
Africa, South Korea, Spain, UK
?1.342 ?0.971
2nd club Australia, Hong Kong, Japan, Malaysia,
Sweden, Switzerland, Turkey
3.160 1.207
Non-converging Canada, China, Philippines, Russia, US ?10.377 ?0.391
Club Tests of club merging
1 Club 1 ?2 ??0.069* (?5.42)
Note: Similar to Table III
Table X.
Ratio of volatility in net
income changes to
volatility in cash fow
changes-fnancing –
absolute value of residuals
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany, Greece, Hong
Kong, Italy, Japan, Malaysia, The Netherlands,
Philippines, Portugal, Russia, Singapore, South
Africa, South Korea, Spain, Sweden, Switzerland,
Turkey, UK, US
?17.646 ?1.712
1st club Australia, Austria, Belgium, Denmark, Finland,
France, Germany, Greece, Hong, Kong, Italy, Japan,
Malaysia, Netherlands, Portugal, Singapore, South
Africa, South Korea, Spain, Sweden, Switzerland,
Turkey, UK
2.848 0.707
Non-converging Canada, China, Philippines, Russia, US ?13.761 ?0.401
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time-varying ratio of net income changes scaled by total assets. The GARCH
methodological approach is highly popular in empirical investigations of fnancial and
accounting relationships given that the estimated conditional volatility can serve as a
proxy for uncertainty. In addition, this particular uncertainty measure generates
superior estimates, especially at longer horizons. The frst row shows that the null
Table XI.
Ratio of volatility in net
income changes to
volatility in cash fow
changes-investing –
absolute value of residuals
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany, Greece, Hong
Kong, Italy, Japan, Malaysia, The Netherlands,
Philippines, Portugal, Russia, Singapore, South
Africa, South Korea, Spain, Sweden, Switzerland,
Turkey, UK, US
?5.274 ?0.749
1st club Australia, Austria, Belgium, Denmark, Finland,
France, Germany, Greece, Hong Kong, Italy, Japan,
The Netherlands, Portugal, Singapore, South
Africa, Spain, Sweden, Switzerland, Turkey, UK
?1.366 ?0.186
2nd club Malaysia, South Korea ?1.012 ?2.421
Non-converging Canada, China, Philippines, Russia, US ?12.972 ?2.652
Club Tests of club merging
1 Club 1 ?2 ??0.119* (?7.35)
Note: Similar to Table III
Table XII.
Ratio of volatility in net
income changes to
volatility in cash fow
changes-operating
activities – absolute value
of residuals
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany,
Greece, Hong Kong, Italy, Japan, Malaysia,
The Netherlands, Philippines, Portugal,
Russia, Singapore, South Africa, South
Korea, Spain, Sweden, Switzerland, Turkey,
UK, US
?6.824 ?2.935
1st club Austria, Belgium, Denmark, Finland,
France, Germany, Greece, Italy, The
Netherlands, Portugal, South Africa, Spain
0.758 0.306
2nd club Australia, Hong Kong, Japan, Malaysia,
Singapore, South Korea, Sweden,
Switzerland, Turkey, UK
1.514 0.360
3rd club Canada, China, Philippines, Russia, US ?1.465 ?2.507
Club Tests of club merging
1 Club 1 ?2 ??0.064* (?5.89)
2 Club 2 ?3 ??0.075* (?5.31)
Note: Similar to Table III
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hypothesis of full convergence which is rejected at the 5 per cent level. Specifcally, the
point estimate of b is ?0.593 (t-statistic: ?42.481), while row 2 identifes the standard
IFRS group of countries (which convergence) with b-coeffcient ?0.617 and t-statistic
?1.181. Finally, row 3 identifes the non-IFRS group of countries, i.e. Canada, China,
Philippines, Russia and the USA, with b-coeffcient ?1.266 and t-statistic equal to
?32.709. Once again, the empirical fndings reject any support for mergers of the
original clubs.
7.2. Ratio of volatility in net income changes to volatility in cash fow changes
Tables XIV-XVII report clustering results in terms of the ratio of volatility in net income
changes to volatility in cash fowchanges, both on an aggregated basis (Table XIV), and
on a disaggregated basis, i.e. fnancing cash fows, investing cash fows and operating
activities cash fows (Tables XV, XVI, and XVII, respectively). Once again, the picture
remains consistent and very similar to the previous case. More specifcally, Table XIV
documents that the null hypothesis of full convergence for the aggregated metric and for
the full sample is rejected at the 5 per cent level. The point estimate of b (t-statistic in
parenthesis) is ?0.619 (?46.787). Canada, China, Philippines, Russia and the USA are
the non-converging countries with b-coeffcient equal to ?0.619 and corresponding
t-statistic ?46.787. Two clubs are formed, with corresponding t-statistics ?0.071 and
5.954, respectively. These empirical fndings display again a strong picture of
convergence, yielding support to the convergence hypothesis, especially, for the
European group of countries.
Table XV presents results for the disaggregated metric with reference to the
fnancing cash fows. The club algorithmrecommends rejection of full convergence with
a value of t-statistic equal to ?36.898, while two clubs are formed with 7 and 15
countries, respectively. Our regular non-IFRS group is still present with a corresponding
t-statistic equal to ?33.849.
Tables XVI and XVII report convergence results of the ratio of volatility in net
income changes to volatility in cash fow changes when cash fows are proxied by
investing and operating activities, respectively. Both tables reject full sample
convergence (with corresponding t-statistic values of ?28.164 and ?38.782,
Table XIII.
Volatility in net income
changes scaled by total
assets–GARCH estimates
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany,
Greece, Hong Kong, Italy, Japan, Malaysia,
The Netherlands, Philippines, Portugal,
Russia, Singapore, South Africa, South
Korea, Spain, Sweden, Switzerland, Turkey,
UK, US
?42.481 ?0.593
1st club Australia, Austria, Belgium, Denmark,
Finland, France, Germany, Greece, Hong
Kong, Italy, Japan, Malaysia, The
Netherlands, Portugal, Singapore, South
Africa, South Korea, Spain, Sweden,
Switzerland, Turkey, UK
?1.181 ?0.617
Non-converging Canada, China, Philippines, Russia, US ?32.709 ?1.266
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respectively). Both tables provide support to the presence of two converging clubs,
although their content does not look quite similar. In terms of the non-converging group
(Table XVI), the results display consistency for Canada, China, Philippines, Russia and
the USA, with a t-statistic value equal to ?41.864.
Finally, the empirical fndings across Tables XIVto XVdisplay that for all sub-clubs
there is no evidence to support mergers of the original clubs.
Table XIV.
Ratio of volatility in net
income changes to
volatility in cash fow
changes – GARCH
estimates
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany, Greece, Hong
Kong, Italy, Japan, Malaysia, Netherlands,
Philippines, Portugal, Russia, Singapore, South
Africa, South Korea, Spain, Sweden, Switzerland,
Turkey, UK, US
?46.787 ?0.619
1st club Austria, Belgium, Denmark, Finland, France,
Germany, Greece, Hong Kong, Italy, Japan,
Netherlands, Portugal, South Africa, Spain, Sweden,
Switzerland, UK
?0.071 ?0.007
2nd club Australia, Malaysia, Singapore, South Korea,
Turkey
5.954 0.757
Non-converging Canada, China, Philippines, Russia, US ?46.787 ?0.619
Club Tests of club merging
1 Club 1 ?2 ??0.119* (?5.97)
Note: Similar to Table III
Table XV.
Ratio of volatility in net
income changes to
volatility in cash fow
changes-fnancing –
GARCH estimates
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany, Greece, Hong
Kong, Italy, Japan, Malaysia, The Netherlands,
Philippines, Portugal, Russia, Singapore, South
Africa, South Korea, Spain, Sweden, Switzerland,
Turkey, UK, US
?36.898 ?0.921
1st club Australia, Japan, Malaysia, Singapore, South
Africa, South Korea, Turkey
3.362 0.380
2nd club Austria, Belgium, Denmark, Finland, France,
Germany, Greece, Hong Kong, Italy, The
Netherlands, Portugal, Spain, Sweden, Switzerland,
UK
3.913 0.267
Non-converging Canada, China, Philippines, Russia, US ?33.849 ?1.142
Club Tests of club merging
1 Club 1 ?2 ??0.098* (?7.73)
Note: Similar to Table III
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8. Conclusions and policy implications
Motivated by the lack of literature analyzing convergence issues in terms of various
accounting systems, this paper tested for accounting standards convergence across 27
countries. To this objective, the novel methodology of Phillips and Sul (2007) was
employed. The advantages of this methodological approach enabled us to provide more
Table XVI.
Ratio of volatility in net
income changes to
volatility in cash fow
changes-investing –
GARCH estimates
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany,
Greece, Hong Kong, Italy, Japan, Malaysia,
The Netherlands, Philippines, Portugal,
Russia, Singapore, South Africa, South
Korea, Spain, Sweden, Switzerland, Turkey,
UK, US
?28.164 ?0.714
1st club Hong Kong, Japan, Malaysia, Singapore,
South Korea, Turkey
?0.795 ?0.505
2nd club Australia, Austria, Belgium, Denmark,
Finland, France, Germany, Greece, Italy,
The Netherlands, Portugal, South Africa,
Spain, Sweden, Switzerland, UK
?0.780 ?0.105
Non-converging Canada, China, Philippines, Russia, US ?41.864 ?0.627
Club Tests of club merging
1 Club 1 ?2 ??0.71* (?5.08)
Note: Similar to Table III
Table XVII.
Ratio of volatility in net
income changes to
volatility in cash fow
changes-operating
activities – GARCH
estimates
Countries t-statistic b coeffcient
Full sample Australia, Austria, Belgium, Canada, China,
Denmark, Finland, France, Germany,
Greece, Hong Kong, Italy, Japan, Malaysia,
The Netherlands, Philippines, Portugal,
Russia, Singapore, South Africa, South
Korea, Spain, Sweden, Switzerland, Turkey,
UK, US
?38.782 ?0.603
1st club Australia, Austria, Belgium, Denmark,
Finland, France, Germany, Greece, Hong
Kong, Italy, Japan, Malaysia, The
Netherlands, Portugal, Singapore, South
Africa, South Korea, Spain, Sweden,
Switzerland, Turkey, UK
0.822 0.089
2nd club Canada, China, Philippines, Russia, US ?0.310 ?0.532
Club Tests of club merging
1 Club 1 ?2 ??0.63* (?5.62)
Note: Similar to Table III
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convincing results about the convergence or divergence pattern stemming from the
speed of adopting IFRS.
The empirical fndings suggest that although the countries under consideration do
not form a homogeneous convergence club and are characterized by different
idiosyncratic accounting conditions that are responsible for their convergence behavior,
the number of distinct convergence groups that are formed is limited, yielding support
to the process of convergence on a globalized basis. These empirical fndings receive
robust statistical support from a number of alternative measures of accounting
standards convergence. In addition, there exist a specifc group of countries, i.e. Canada,
China, Philippines, Russia and the USA, characterized consistently as the non-IFRS
group across all tests.
The empirical fndings provide some useful implications for practitioners. In
particular, by showing convergence patterns of accounting standards worldwide, it
launched a call for policymakers and auditors in non-adopting countries to join the IFRS
regime; such adoption fosters lower transaction costs, lower costs of capital market
participation in the adopting country for international investors, ensuring better
transparency in fnancial market investments.
Future research attempts could extend our results to a sample that involves industry
breakdowns. Alternatively, the analysis could provide and explain specifc factors
responsible for the presence of such divergent patterns.
Note
1. Extensive Monte Carlo simulations conducted by Phillips and Sul (2007) show that r ?0.3 is
satisfactory in terms of both size and power.
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About the authors
Dr Nicholas Apergis is Professor in MacroFinance, Department of Banking & Financial
Management, University of Piraeus, Greece. He holds a PhD in Economics from Fordham
University, NewYork. Previous Positions: Visitor Professor, FordhamUniversity and Manhattan
College, Lecturer, University of Macedonia, Greece, Assistant Professor, University of Macedonia,
Greece, Associate Professor, University of Ioannina, Greece, Professor, University of Macedonia,
Greece, Professor, University of Piraeus. Over 160 publications in: Journal of Policy Modeling,
Journal of Banking and Finance, International Advances in Economic Research, Journal of
Economic Studies, Kredit und Kapital, Bulletin of Economic Research, Atlantic Economic Journal,
Weltwirtschaftliches Archiv, Manchester School of Economics, Economics Letters, International
Finance, Energy Economics, Public Choice, Energy Policy, Energies, Applied Financial Economics,
Journal of Economics and Finance. Association Positions and Offces: Editor of International
Journal of Economic Research, ?ember of the Editorial Board in International Advances of
Economic Research, Associate Editor in the Journal of Economics and Finance. Nicholas Apergis
is the corresponding author and can be contacted at: [email protected]
Dr Christina Christou is Assistant Professor in Econometrics, Department of Banking &
Financial Management, University of Piraeus, Greece. She holds a PhD in ?conomics from the
University of Cyprus and a PhD in Physics and Mathematics from Moscow State University,
Lomonosov. She has publications in: International Journal of Business and Management,
Empirical Economics, Investment Analysis and Portfolio Management, The Empirical Economics
Letters, Atlantic Economic Journal, Review of International Economics, Australian Journal of
Agricultural and Resources Economics, Econometric Theory, Economic Letters, Journal of Applied
Physics. Email: [email protected]
Dr Christis Hassapis is an Associate Professor of Economics, in the Department of Economics
at the University of Cyprus and he holds a PhD in Economics from Boston College, USA. He was
an elected member (2004-2007, 2007-2010) of the Board of the University of Cyprus, President of
the University of Cyprus Radio Station (UCY Voice 2004-2010), member of the Board of Diogenis
Incubator for high technology (2004-2009), member of the academic council for the Economic
Research Unit (2004-2008) and member of the Academic council of the Center of Banking and
Finance (2000-09). He was also assistant Dean for the School of Business and Economics at the
University of Cyprus (2004-07). He has publications in: The Economic Journal, The Canadian
Journal of Economics, Oxford Economic Papers, Public Choice, Journal of International Money and
Finance, Quarterly Review of Economics and Finance, Economic Modeling, Journal of Policy
Modeling, Bulletin of Economic Research, Weltwirtschafiches Archiv and the Review of
International Economics. He has also authored several chapters in edited books. Email:
[email protected]
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
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