amiram (2012).pdf
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JOURNAL OF INTERNATIONAL ACCOUNTING RESEARCH American Accounting AssociationVol. 11, No. 2 DOI: 10.2308/jiar-502822012pp. 57–81
Financial Information Globalization andForeign Investment Decisions
Dan Amiram
ABSTRACT: This paper investigates the association between the adoption of
international accounting standards and foreign investment decisions. Prior research
suggests that information asymmetries between local and foreign investors and
behavioral biases caused by unfamiliarity of the foreign markets contribute to investors
preferring to invest in their home markets. Because one of the goals of the adoption of
international accounting standards is to establish a high-quality, internationally familiar
set of accounting standards, I predict that foreign investments will increase in countries
that adopted International Financial Reporting Standards (IFRS) after the adoption and
that this increase is driven by the familiarity of IFRS. I find that foreign equity portfolio
investments (FPI) increase in countries that adopt IFRS. More importantly, I find that this
relation is driven by foreign investors from countries that also use IFRS. Moreover, the
effect of accounting familiarity is more pronounced when investor and investee countries
share language, legal origin, culture, and region. I also find that countries with lower
corruption and better investor protection experience larger increases in FPI after they
adopt IFRS relative to other IFRS users. These findings are consistent with thehypothesis that familiar accounting information drives foreign investment decisions.
Keywords: foreign portfolio investments; international accounting; IFRS; familiarity;
information asymmetry; home bias; foreign bias; cross-border investments.
I. INTRODUCTION
I
nformation asymmetries between local and foreign investors and differences in investment
environments are the common explanations for the fact that investors tend to invest in their
domestic market rather than diversify their portfolios with foreign investments. This paper empirically investigates what role, if any, the globalization of one of the most important sources of
financial information, accounting information, plays in cross-border investment decisions.
Dan Amiram is an Assistant Professor at Columbia University.
I thank Jeff Abarbanell, Robert Bushman, Mary Margaret Frank, Wayne Landsman, Mark Lang, Chris Lundblad, KatieMcDermott, Mark Maffett, Ed Owens, Jana Raedy, Mike Welker (editor), Frank Warnock, Chris Williams, Emanuel Zur,and two anonymous reviewers for helpful discussions and comments. This study was conducted as part of the second-year requirements of the doctoral program at The University of North Carolina at Chapel Hill and also benefited fromcomments made by workshop participants there.
Editor’s note: Accepted by Michael Welker.
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Information asymmetries between local and foreign investors can negatively affect foreign
investments (Brennan and Cao 1997; Portes and Rey 2005; Van Nieuwerburgh and Veldkamp
2009). These asymmetries can help local investors to react with more timeliness and accuracy than
foreign investors and thus to exploit asymmetries at the expense of the foreign investors. This in
turn can cause foreign investors to invest less in the foreign market. Prior research also claims that
familiarity—also known as the competence effect—with the investment environment can cause
investors to feel more competent about the foreign market and thus to increase their investments
abroad (Heath and Tversky 1991; Huberman 2001; Graham et al. 2009). ‘‘Familiarity’’ is defined as
an increase in investor understanding of the business environment, leaving the quality of
information constant. Graham et al. (2009) note that when people feel skillful or knowledgeable in
an area, they would rather bet on their own judgment or invest. But when people do not feel
competent, they prefer not to bet.
In the last decade more than 100 countries around the world have permitted or required the use
of International Financial Reporting Standards (IFRS) as their official accounting system.1 IFRS are
issued by the International Accounting Standards Board (IASB) with the goal of being
internationally accepted, high-quality accounting standards.2 The growing use of IFRS can affect foreign investors’ decisions in at least two ways. First, IFRS are in general of higher quality than
local financial reporting standards (Barth et al. 2008) and thus may reduce information asymmetry
between foreign and local investors and thus increase investment. Second, the use of familiar
accounting standards can increase foreign investors’ confidence in their ability to assess the foreign
market and thus can lead them to invest more in the market. 3
Foreign investments in equity can be divided into two large subgroups—foreign portfolio
investments (FPI) and foreign direct investments (FDI). Foreign portfolio equity investments typically
are defined as a purchase of ownership, but not control, in a domestic corporation by a foreign entity or
an individual. This contrasts with the definition of FDI, which requires control over the acquired
corporation. FPI represents a growing portion of global investment; it is even larger than FDI for U.S.investors in most recent years. I focus my investigation on equity FPI investments because, unlike FDI
investors, FPI investors depend solely on standardized information such as accounting data.
Using a large sample of FPI across countries and years, I find a positive relation between the
adoption of IFRS and foreign portfolio investment. More importantly, I predict and find that
investors from countries that use IFRS increase their FPI in countries that adopt IFRS more than
investors from countries that do not use IFRS. I also find that, after controlling for this accounting
familiarity effect, IFRS has only a small effect on foreign investments. This finding supports the
hypothesis that investors seek a familiar accounting language when making their investment
decisions. In addition, I find that the familiarity of accounting standards acts in a similar fashion to
other familiarity factors such as language, geographical region, legal origin, and colonial past. Thefamiliarity of accounting standards has an incremental effect on FPI over and above these other
1 Leuz and Wysocki (2008) suggest using the worldwide adoption of IFRS as a significant event to test the cross-country and macroeconomics effects of accounting.
2 IAS were issued by the International Accounting Standards Committee (IASC) until 2001. The IASB, thesuccessor to the IASC, issues IFRS, which include standards issued not only by the IASB, but also by the IASC,some of which have been amended by the IASB. My sample includes data from both periods. Throughout thispaper, I use the acronym IFRS to describe both IAS and IFRS.
3 McKinsey Global Investor Opinion Survey (McKinsey & Co. 2002) revealed some of the preferences of international investors for a global set of accounting standards. According to the survey, 90 percent of theparticipants thought a single global set of accounting standards is desirable, and 42 percent of them identifiedinternational accounting standards as a ‘‘very important factor ’’ for investment decisions. This percentageexceeded the percentage of investors who identified market liquidity and property rights laws as important factorsfor investment decisions One purpose of this paper is to evaluate empirically whether investors act in a way that
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familiarity factors, and accounting familiarity increases the effects of these other factors. Finally, I
predict and find that countries with lower corruption and better property rights laws experience
higher benefits from the use of IFRS than other adopters. These results are consistent with claims
that accounting standards alone are not enough to achieve positive economic outcomes ( Ball 2006).
The paper makes several contributions to the accounting, finance, and economics literature.
First, it highlights the effect of familiarity of accounting standards on investment decisions. The
findings thus complement findings by Huberman (2001) that familiarity with a corporation’s
headquarters location spurs investments, and Graham et al. (2009) that increased competence
influences trading. It also adds to the evidence on competence effects documented in the behavioral
literature (Heath and Tversky 1991). Second, this study provides evidence that a significant portion
of the increase in foreign investment after the adoption of IFRS is associated with familiarity. This
evidence adds to our knowledge on the ways through which IFRS affects foreign investments.
Contemporaneous studies by Florou and Pope (2012), Yu (2010), DeFond et al. (2011), and
Khurana and Michas (2011) also find an increase in foreign investments following the adoption of
IFRS, but they focus on decreased information asymmetry and increased comparability of
accounting information as drivers of the association between investments and IFRS. Third, contraryto these contemporaneous studies, I use a dataset that contains information on essentially all foreign
portfolio investments in a country and thus provide more general evidence. These contemporaneous
studies focus on subsamples of foreign portfolio investments and thus subsamples of the sample I
use. Some of these studies focus on mutual funds. Some focus on available data on institutional
investors. Others focus on U.S. investors. Because investors from these subsamples may be affected
by different economic forces, these foreign investors may increase investments while other foreign
investors decrease investments. Beneish et al. (2009) contemporaneously examine the effect of
mandatory IFRS adoption on cross-border investment in equity and debt markets using an
aggregated version of the dataset I use. They fail to find evidence that IFRS affects FPI in the equity
market, which may be due to the aggregate level of their tests.The paper continues as follows: Section II provides more information about FPI, IFRS, and the
relations between FPI holdings, home bias, information, and accounting. Section III develops the
hypotheses and empirical tests. Section IV provides detail on the data and sample selection. Section
V describes the empirical results, and Section VI contains sensitivity tests. Section VII concludes.
II. INSTITUTIONAL BACKGROUND AND MOTIVATION
Institutional Background
Foreign equity investments can be divided into two large subgroups—foreign portfolio
investments and foreign direct investment. The International Monetary Fund (IMF) considersinvestments of more than 10 percent of the controlling rights to be FDI and those of less than 10
percent to be FPI. At any point in time, the balance that a domestic resident holds in foreign equity
is considered to be a foreign holding. I examine the consequences of the globalization of accounting
information on FPI rather than FDI for several reasons. First, FPI is not as ‘‘sticky’’ as FDI and
tends to change in reaction to significant market events. Second, foreign direct investors have more
ability to change the activities of the investee firms in ways that will fit their needs. Third and most
importantly, foreign direct investors usually have large holdings in the acquired company and are
better positioned to demand and get the information they need. Foreign portfolio investors, in
contrast, may rely more on accounting data and their own interpretation of this information.
Moreover, as will be discussed below, the bilateral nature of the FPI dataset and the dataset
structure is better suited for examining the familiarity effect between local and foreign investors.
The cost of focusing on FPI is that extra caution is needed when one generalizes the results to
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Although foreign portfolio investments make important contributions to the recipient
economies and represent a growing part of the total foreign investments (FDI and FPI), there is
little evidence on how accounting information affects foreign portfolio investors’ decisions in a
multinational setting and how the globalization of accounting information affects FPI.4
Motivation
Prior literature refers to the tendency of domestic investors to invest in their home market as home
bias. Several explanations for home bias have been offered, such as barriers to capital flows,
withholding taxes, political risk, regulation, information asymmetries, and familiarity, but there is little
evidence regarding how global integration of financial information can mitigate this phenomenon. The
foreign investment literature relates directly to the home bias literature and uses similar arguments to
explain the barriers to and determinants of foreign investments (Amiram and Frank 2012).
Brennan and Cao (1997) suggest that foreign investors are less informed than locals and thus
react more slowly to market developments. This information asymmetry between foreign and
domestic investors can lead to lower foreign investments because of the foreign investor’sdisadvantage. Portes and Rey (2005) discuss the information relevant to evaluating investments in
financial assets such as corporate equity. They find a strong association between information
proxies such as telephone call traffic, geographical distance, and investment bank presence and
equity portfolio transactions. They also claim that accounting practices and corporate culture,
among other factors, help to mitigate the information asymmetries that cause the bias.
French and Poterba (1991) suggest that ‘‘familiarity effect ’’ shapes foreign investment decisions.
They speculate that investors may invest less in foreign markets because they know less about these
markets and their institutions and firms. Heath and Tversky (1991), Huberman (2001), and Graham et
al. (2009) are good examples of the behavioral explanation for the familiarity effect, which suggests
that people invest in familiar stocks, while ignoring the principles of portfolio theory. Heath and
Tversky (1991) argue that people feel more competent to bet in environments they feel they know more
about. Huberman (2001) extends this idea to financial markets and finds that investors invest in more
familiar stocks. Graham et al. (2009) find that investors who feel more competent have more
internationally diversified portfolios. Taken together, these findings suggest that differences in
information between local and foreign investors and lack of familiarity obstruct foreign investment.
Accounting helps firms to communicate information to investors. More precise and familiar
accounting information may mitigate the information asymmetry and familiarity barriers for
investments. The objective of financial reporting is to provide useful information for investment
decisions (IASC 2001). Accounting standards do not discriminate between information provided to
domestic and foreign investors, which means that domestic and foreign investors should receive the
same information from the firm. However, it is the investor’s responsibility to learn and understandthe accounting standards that are used in a foreign country. Countries can mandate which accounting
standards their domestic firms use, i.e., domestic standards, U.S. generally accepted accounting
standards (GAAP), IFRS, or let the individual firms decide which ones they want to use. Although
each set of standards has advantages and disadvantages, which can result in differences in accounting
quality, an important question is whether such differences can mitigate the asymmetric information
and familiarity barriers and thus increase FPI.5 Ex ante, it is plausible that, because IFRS are the most
4 See Errunza (2001) and Stulz (1999) for reviews on the contributions and costs of foreign investments. Graetz andGrinberg (2003) indicate that FPI has become a larger part of international portfolios. In the U.S., for example, the
market value of FPI was higher than that of FDI in most years since 1990.5 See Barth et al. (2008) for the accounting quality differences of accounting amounts from applying local and
International Accounting Standards (IAS) and Barth et al (2012) for accounting quality differences of accounting
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commonly used accounting standards worldwide, their use can increase FPI by reducing information
asymmetry and making the investment environment more familiar to foreign investors.
III. HYPOTHESIS AND EMPIRICAL SPECIFICATION
Adoption Hypothesis
As noted above, information asymmetry and familiarity of the investment environment are
important determinants of cross-border investments. Because the adoption of IFRS can reduce
information asymmetry and increase familiarity, I hypothesize the following:
H1: All else equal, investors from foreign countries hold more equity portfolio investments in
countries that adopt IFRS after the adoption, relative to countries that do not use IFRS.
To examine my hypothesis, I exploit data on the foreign portfolio holdings of 73 countries
from the Coordinated Portfolio Investment Survey (CPIS) of the IMF. As described below, these
data provide the allocation of equity portfolio investment holdings for each of the 73 reporting
(investor) countries in 240 target (investee) countries and territories. This dataset is available for the
year 1997 and for the years 2001 through 2006.6 Using different sources of information, I
determine, as described later, the year that countries in my sample adopted IFRS.
Combining these two datasets and different control variables allows me to estimate Equation
(1) below, which follows the natural log, double fixed-effects specification of Lane and Milesi-
Ferretti (2008). Lane and Milesi-Ferretti (2008) develop an analytical model, based on a
generalization of Obstfeld and Rogoff’s (1996) gravity of trade model, which explains bilateral FPI.
They test their model using the CPIS bilateral data.7 The model suggests that the incentive to hold
foreign portfolio investments comes from the need to hedge against changes in costs of trade
imports. If prices and quantity of imports from a foreign country are increasing, foreign portfolio
investments will ensure that the domestic investor, who is also the importer, can offset some of theincrease in import costs by gaining return on his/her investments. Thus the model establishes a
relation between FPI and imports, and uses log transformation to express this relation in a linear
equation that can be estimated using the standard OLS assumptions. This basic model allows me to
establish the association between foreign portfolio investments and the adoption of IFRS. The
estimating equation is thus:
Log of Equity FPI ijt ¼ b0 þ b1 IFRS INVESTEE jt þ b2k CONTROLSijt
þ FixedEffect INVESTORi þ FixedEffect INVESTEE j þ eit : ð1Þ
The dependent variable in Equation (1), Log of Equity FPI ijt , is the natural log of the level of
equity holdings of country i (investor country) in country j (investee country) at time t .8
IFRSINVESTEE jt , the variable of interest, is an indicator variable that takes the value of 1 if country j
uses IFRS at time t , and 0 otherwise, i.e., it takes the value of 1 in the year of and the years after
6 The data are available at: http://www.imf.org/external/np/sta/pi/cpis.htm. Further description of the dataset can befound in Bertaut and Kole (2004) and Lane and Milesi-Ferretti (2008). Seventy-three (240) residence (source)countries is the maximum number of countries available in the data.
7 The log specification is a direct result of the Lane and Milesi-Ferretti (2008) analytical model. My empiricalspecification follows the study’s empirical specification. Desai and Dharmapala (2011) use a similar empiricalmodel with U.S. Treasury data for U.S. investments. Portes and Rey (2005) also use a similar specification.Amiram and Frank (2012) use the same methodology and data.
8 To keep observations with FPI in country j equal to 0 in the log form, I add 1 to all the FPI data, which isconsistent with Lane and Milesi-Ferretti (2008). Desai and Dharmapala (2011) apply a similar procedure.Inferences are not sensitive to the elimination of the zero observations (more than 4 000 country-year
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Based on the Lane and Milesi-Ferretti (2008) model and results, I expect the coefficient on
LIMPORT to be positive. I also expect the degree of openness in the investee country,
OPENESS INVESTEE , to positively explain FPI. However, during my sample period, most economies
are relatively open for capital movement, which can cause the coefficient on this index to be
insignificant. All the risk measures should correlate negatively with FPI. However, because the riskmeasures are highly correlated, it would not be surprising that individual risk coefficients are
indistinguishable from zero. Similarly, collinearity can affect the market cap, LMCAP INVESTEE , and
the GDP, LGDP INVESTEE , variables’ coefficients.
Familiarity Hypothesis
The next step in my analysis is to determine whether familiarity of information is associated with
higher levels of foreign portfolio investments. If the familiarity of IFRS significantly determines
foreign investment decisions, then investors from countries that are familiar with IFRS (i.e., those
countries that use IFRS) will increase their equity portfolio investments in countries that adopt IFRS
more than investors from countries that do not use IFRS. This leads me to the second hypothesis:
H2: All else equal, investors from countries that adopt IFRS have higher holdings of equity
portfolio investments in countries that also adopt IFRS than investors from countries that
do not adopt IFRS.
To test this hypothesis I use a variation of Equation (1) and estimate the following equation:
Log of Equity FPI ijt ¼ c0 þ c1 IFRS INVESTEE jt þ c2 IFRS
INVESTORit þ c3 IFRS
BOTH t
þ c4k CONTROLSijt þ FixedEffect INVESTORi þ FixedEffect
INVESTEE j
þ lit ; ð2Þ
where IFRS INVESTEE , as in Equation (1), is an indicator variable that takes the value of 1 if theinvestee country is an IFRS user, and 0 otherwise, i.e., in the years after the adoption. IFRS INVESTOR
is an indicator variable that equals 1 if the investor country is an IFRS user, and 0 otherwise, i.e., in
the years after the adoption. The variable of interest, IFRS BOTH , is the interaction between
IFRS INVESTEE and IFRS INVESTOR, and it equals 1 when both countries are IFRS users. As in
Equation (1), CONTROLS is a vector of control variables identical to those used in Equation (1).
FixedEffect INVESTOR and FixedEffect INVESTEE are investor and investee country fixed effects.
Standard errors are clustered by investor and year.
Equation (2) tests whether foreign equity portfolio holdings from countries that use IFRS
increase their investments in investee countries that adopt IFRS more than other investor countries.
Finding that c3 . 0 suggests that familiarity of accounting standards plays a role in internationalportfolio investment decisions. Finding c1 ¼ 0 can be interpreted as lack of ability of the use of IFRS to explain FPI after controlling for the familiarity effect. In addition, this test further mitigates
the concern of correlated omitted variables since it is difficult to attribute acceptance of this
hypothesis to institutional factors.
Synergy Hypothesis
The literature suggests that accounting standards alone cannot produce economic outcomes
(e.g., Bushman and Smith 2001; Ball 2006; Barth et al. 2008). This argument can be applied
directly to this study. If a country lacks the institutional environment that allows accounting
standards to achieve their goals, it is less likely that IFRS will increase foreign investments. For
example, if a country is corrupt or has weak property rights laws, it is less likely that foreign
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hand, in countries with strong institutional environments, the use of IFRS should be more
beneficial. This line of reasoning leads to predicted cross-sectional differences in the effects of the
use of IFRS on foreign investments, which are formally stated in the third hypothesis:
H3: All else equal, countries that have low corruption and good property rights laws will
experience a larger increase in FPI when they adopt IFRS than countries that do not havelow corruption and good property rights law.
To test this hypothesis I estimate the following Equation (3):
Log of Equity FPI ijt ¼ c0 þ c1 IFRS INVESTEE jt þ c2STRONGINST jt
þ c3 IFRS INVESTEE jt
STRONGINST jt þ c4k CONTROLSijt
þ FixedEffect INVESTORi þ FixedEffect INVESTEE j þ lit : ð3Þ
IFRS INVESTEE , as in Equation (1), is an indicator variable that takes the value of 1 if the investee
country is an IFRS user, and 0 otherwise. STRONGINST is a variable that changes with the different
specifications to take the values of the underlying strong institution variable (corruption index or property rights index). The corruption index data are obtained from the Transparency International
website, and the property rights index was obtained from the Heritage Foundation Index of
Economic Freedom website.10 One reason to use the corruption and property rights indices is that
they are time varying and thus allow for time-varying controls for institutional features in the
research design. In addition, they are likely to capture the institutional environment in a country. In
all specifications, STRONGINST will take higher values when there is a stronger institution (high
corruption index means less corruption in the country). The variable of interest is the interaction
between IFRS INVESTEE and STRONGINST . As in Equation (1), CONTROLS is a vector of control
variables identical to those used in Equation (1). FixedEffect INVESTOR and FixedEffect INVESTEE are
investor and investee country fixed effects. Standard errors are clustered by investor and year.Equation (3) tests whether indeed IFRS acts in synergistic fashion with other institutional
features in the economy such as lower corruption and stronger property rights laws. This test
identifies predictable cross-sectional differences in the benefits of the use of IFRS. Finding that c3. 0 provides evidence that supports H3.
IV. DATA AND SAMPLE
Dependent Variable: Foreign Portfolio Investments Data
The International Monetary Fund provides data on worldwide holdings of foreign portfolio
investment at the Coordinated Portfolio Investment Survey (CPIS) website (http://www.imf.org/ external/np/sta/pi/cpis.htm).11 The CPIS reports bilateral data on foreign equity portfolio asset
holdings by the residence of the issuer. The first CPIS was conducted at the end of 1997, when 29
economies participated. Since 2001, the survey has been conducted annually and contains holdings as
of the end of 2006 for 73 source countries. For each source, the survey reports holdings in
approximately 240 destination countries or territories. Participants in the CPIS follow definitions and
classifications that are mutually consistent by following the methodology set out in the IMF Balance
of Payment Manual (IMF 1993).12 Prior research suggests most FPI flows to public corporations
10 The corruption index is available at: http://www.transparency.org/research/cpi/ . The property rights index is
available at: http://www.heritage.org/index/download11 The data were last downloaded on August 3, 2008.12 See further discussion about the benefits and limitations of the data in Lane and Milesi-Ferretti (2008) and Bertaut
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because it is more difficult for foreign investors to purchase nontraded foreign equities. The CPIS
dataset is recognized as the most valuable and accurate source of foreign portfolio investments data,
and it allows me to examine my research question in a worldwide setting with bilateral relations.
Experimental Variable: IFRS Adoption Data
Determining the year in which a country adopted IFRS is not a trivial task and requires several
research design choices and a few sources of information. My aim is to capture the date in which
foreign investors face a critical mass of IFRS adopters in a foreign country. Thus, in most countries,
it will be the date on which a country mandated the use of IFRS for domestically traded
corporations. In a very few cases, I consider the adoption date to be the date on which a country
permits the use of IFRS and on which there is evidence that supports a significant use of IFRS in
that country. While misclassification of the adoption date is likely to make it harder to find an
association between IFRS adoption and FPI, I conduct several sensitivity tests to alleviate the
concern that the results are driven by misclassification and my research design choices. First, I
randomly choose an adoption date for countries that adopt IFRS in my sample and re-estimate myregression. I find no significant association between IFRS and FPI using this method. This suggests
that the results are not driven by random country characteristics. Second, I change the classification
to only countries that mandatorily adopted IFRS, which are almost all of the countries in my
sample, and find that my inferences below are unchanged.
My main source for the year that a country started using IFRS (adoption date) is Deloitte’s
IAS Plus website.13 The website provides information on 162 countries from which 85 jurisdictions
require the use of IFRS for all listed companies. The vast majority of the IFRS adoption dates were
obtained from the country-specific web pages in the IAS Plus website.14 Other adoption dates were
obtained from the International Federation of Accountants (IFAC) website. This website provides
surveys conducted by IFAC to its 157 members for which they provide information about their
accounting system, and some of them provide information of IFRS year of adoption. The last
source for adoption dates data is the World Bank and the IMF ‘‘Reports of the Observance of
Standards and Codes’’ (ROSC), which provides reviews of the accounting systems of several
countries.15 According to the IAS Plus website, many jurisdictions that maintain their own local
GAAP claim that their local GAAP is ‘‘based on,’’ ‘‘similar to,’’ or ‘‘converged with’’ IFRS. Often,
not all IAS/IFRS have been adopted locally. Since the website does not compare national or
regional accounting standards to IFRS in detail, it reports only on the direct use of IFRS in
individual countries or regions. Direct use means that the basis of preparation note and the auditor’s
report refer to conformity with IFRS. I classify a country as an IFRS adopter if one of the sources of
adoption information that I use indicates that the auditor report in that country refers to compliance
with IFRS. For example, when the countries in the EU adopted IFRS in 2005, the auditor report stated that the financial report was prepared in accordance to IFRS as adopted by the EU. In this
case, the EU countries will be classified as adopters because the audit report refers to IFRS.16
Countries around the world vary in the way they implement IFRS (IAS Plus). Some countries
adopt IFRS for all corporations domiciled in that country. Some adopt IFRS only for public firms,
and some allow certain sectors not to adopt. Because prior research finds that foreign portfolio
investments are concentrated in the largest public corporations in a country, I define the country as
13 See global use of IFRS table at: http://www.iasplus.com/country/useias.htm14 IAS Plus is considered to be the most reliable source of IFRS adoption dates, and the IASB itself relies on it as a
data source (Ramanna and Sletten 2010). The data were collected on June 17, 2008.15 IFAC surveys are available at: http://www.ifac.org/ComplianceAssessment/published_surveys.php. ROSC
reports are available at: http://www worldbank org/ifa/rosc more html
Financial Information Globalization and Foreign Investment Decisions 65
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an adopter if public companies are the ones that adopt IFRS. The quality of implementation also
may vary across countries. If the quality of IFRS varies across countries, this may cause the IFRS
adoption effect to be less pronounced in certain countries. I use this notion to test H2.
In almost all countries, I classify the date of adoption as the date on which the country
mandated the use of IFRS for traded corporations. But a few countries, including Switzerland(2005) and Austria (2002), required mandatory adoption of either IFRS or U.S. GAAP for their
firms. In these cases, when there is evidence in either Worldscope or prior research that a significant
amount of traded corporations adopted IFRS, I used this year as the adoption year. 17 Another
example is Germany, which permitted IFRS before the mandatory adoption in 2005, and prior
research suggests that a significant number of firms voluntarily adopted IFRS (Leuz and Verrecchia
2000). Inferences are unaltered if I use only the dates of mandatory adoption in a country. This is
expected, because very few countries are classified based on this exception.
My final list contains only countries for which I have a date of IFRS adoption and countries that
never adopted IFRS. Countries that use IFRS but the date of adoption is not available were excluded
from the sample. Thus the final list contains 105 countries, 60 that adopted IFRS by the end of my
sample period (2006) and 45 that did not. Table 1 provides the full list of sample countries. 18
Sample Selection
I collect and merge all data on foreign portfolio equity investments available in the CPIS. This
step results in a potential of 122,640 country-year observations ([73 reporting countries in 2006]3
[240 possible destinations] 3 [7 years that the survey was conducted]). Merging the year of
adoption data and control variables, and deleting observations with missing holdings data, results in
a dataset of 19,608 country-year observations. Most of the observations are eliminated because
information about the use of IFRS is not available (68,985 observations), but many of these
observations had missing values anyway. By eliminating observations with missing controlvariables, I obtain the final sample that contains 13,992 country-year observations. Table 2 presents
the final 53 investor and 81 investee countries that are included in my main analysis. The countries
in the sample are heterogeneous: they are located on different continents and have different sizes
and different economic characteristics. Table 3 presents descriptive statistics for the data. In the
sample, foreign holdings comprise, on average, 7 percent of the market capitalization of the
investee country ($545 81 out of $646,185). As noted by Lane and Milesi-Ferretti (2008), thispercentage of holdings suggests that the data capture a significant portion of the total foreign
portfolio investments, unlike other datasets. Generally, for most variables, there is evidence of
skewness, as indicated by differences between sample means and medians. Estimation using natural
log helps to mitigate this problem.
V. RESULTS
Testing the Adoption Hypothesis
Table 4 presents the results of the estimation of Equation (1) using OLS. Model 1 includes
investor and investee fixed effects, while Model 2 does not. As predicted, in both models, there is a
17 If the Worldscope dataset shows that at least 20 percent of the firm-level observations in a country have adoptedIFRS in a certain year, or any other source suggests that there is a ‘‘significant adoption’’ in a country in a certain
year, then I classify that country as an IFRS adopter before the official mandatory adoption date.18 In a few countries, the domestic exchange allows foreign issuers to file financial reports based on IFRS. If this fact
makes domestic investors more familiar with IFRS then it should be harder to detect any foreign investments
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positive significant relation between countries that adopt IFRS and holdings of foreign portfolio
investments in that country after the adoption. The coefficient on IFRS INVESTEE is 0.25 with t-
statistic of 4.09 in Model 1, and 0.22 with t-statistic of 2.19 in Model 2.
The risk factor coefficients are not significantly different from zero. This can be the result of the
high correlation between the risk factors. When I aggregate all risk factors into one aggregated
TABLE 1
Adoption of IFRS
Adopters Non-Adopters
Armenia Norway Albania Taiwan
Australia Paraguay Argentina Thailand
Austria Poland Azerbaijan Togo
Bahrain Portugal Bangladesh Tunisia
Belgium Romania Belarus United States
Belize Serbia Benin Uruguay
Bosnia Singapore Bhutan Uzbekistan
Botswana Slovak Republic Brazil Vietnam
Bulgaria Slovenia Brunei Darussalam
Croatia South Africa Burkina Faso
Cyprus Spain Burundi
Czech Republic Sri Lanka Cambodia Denmark Suriname Canada
Estonia Sweden Chile
Finland Switzerland China, P.R.
France Tajikistan Colombia
Germany Tanzania Côte d’Ivoire
Greece Trinidad and Tobago Cuba
Greenland Turkey Ecuador
Guatemala United Arab Emirates Ghana
Hungary United Kingdom Guam
Iceland Venezuela Hong Kong
Ireland India Italy Indonesia
Jamaica Iran
Jordan Iraq
Kazakhstan Israel
Latvia Japan
Liechtenstein Korea
Lithuania Mali
Luxembourg Moldova
Malta Niger
Morocco Pakistan
Namibia Philippines
The Netherlands Russia
New Zealand Saudi Arabia
Nicaragua Syria
The table presents the countries in the sample separated into IFRS adopters and non-adopters. Adopters are countries that effectively adopted IFRS by the end of 2006.
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TABLE 2
Participant Countries
Country A–K INVESTOR INVESTEE Country L–Z INVESTOR INVESTEE
Argentina X X Latvia X X
Armenia X Lithuania X
Australia X X Luxembourg X X
Austria X X Malta X X
Bahrain X X Moldova X
Bangladesh X Morocco X
Belgium X X Namibia X
Bhutan X The Netherlands X X
Botswana X New Zealand X X
Brazil X X Norway X X
Bulgaria X X Pakistan X X
Canada X X Paraguay XChile X X Philippines X X
China X Poland X X
Colombia X X Portugal X X
Croatia X Romania X X
Cyprus X X Russia X X
Czech Republic X X Saudi Arabia X
Côte d’Ivoire X Serbia X
Denmark X X Singapore X X
Ecuador X Slovak Republic X X
Estonia X X Slovenia X
Finland X X South Africa X XFrance X X Spain X X
Germany X X Sri Lanka X
Ghana X Sweden X X
Greece X X Switzerland X X
Guatemala X Tanzania X
Hong Kong X X Thailand X X
Hungary X X Trinidad and Tobago X
Iceland X X Tunisia X
India X X Turkey X X
Indonesia X X United Arab Emirates X
Iran X United Kingdom X X
Ireland X X United States X X
Israel X X Uruguay X X
Italy X X Uzbekistan X
Jamaica X Venezuela X X
Japan X X Vietnam X
Jordan X
Kazakhstan X X
Korea X X
Total 53 81
The table presents the countries that are used in the main analysis. Investor countries are countries that report their FPI
holdings in the CPIS dataset in at least one year of my sample. Investee countries are countries that at least one investor country reported that it holds FPI in it in at least one year of my sample.
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between the aggregated measure of risk and FPI.19 In addition, untabulated results from specifications
in which individual risk factors are included generally generate significant negative coefficients for
each. Some of the coefficients on control variables in the regressions are not significant, probably
because of the high correlation between the variables in the regression. An untabulated joint F-test
shows that control variables as a whole are statistically different from zero. The high R2 (70 percent)
in the full model mitigates some of the concerns about uncontrolled factors.
The results of Equation (1) estimation provide evidence that foreign investors increase their
investments in IFRS reporting countries. These findings may have several explanations, among themthe belief that IFRS standards lead to better-quality accounting information and that investors are now
more familiar with the information that firms that use IFRS provide. The results of the estimation of
Equation (2), which relates to my second hypothesis, may shed additional light on this issue.
Testing the Familiarity Hypothesis
Main Test of the Familiarity Hypothesis
Equation (2) is designed to test whether investors from countries that use IFRS are more
sensitive to the adoption of IFRS in the investee countries than investors from non-IFRS adopting
countries. The results of the tests of this hypothesis are presented in Table 5. In this estimation, the
TABLE 3
Descriptive Statistics
Variable n Mean Median Std. Dev.
Log of equity FPI 13,992 2.68 1.40 2.97
HOLDING 13,992 545 3.04 1,439
IFRS INVESTEE 13,992 0.25 0.00 0.43
IFRS INVESTOR 13,992 0.24 0.00 0.43
IFRS BOTH 13,992 0.13 0.00 0.33
LGDP INVESTEE 13,992 11.67 11.80 1.85
MCAP INVESTEE 13,992 646,185 73,250 2,215,628
LMCAP INVESTEE 13,992 24.64 25.02 2.78
LPOP INVESTEE 13,992 2.86 2.76 1.65
EU INVESTEE 13,992 0.39 0.00 0.49
IMPORT 13,992 2,458 239 10,518
LIMPORT 13,992 5.07 5.48 3.02OPENESS INVESTEE 13,992 1.24 2.00 1.51
CORPSSCORE INVESTEE 13,097 5.72 5.50 2.47
POLRISK INVESTEE 13,992 2.17 2.00 0.78
ECORISK INVESTEE 13,992 2.26 2.00 0.79
LEGRISK INVESTEE 13,992 1.99 2.00 0.89
TAXRISK INVESTEE 13,992 2.04 2.00 0.71
OPERISK INVESTEE 13,992 2.26 2.00 0.79
SECRISK INVESTEE 13,992 2.21 2.00 0.91
The descriptive statistics presented above are taken from a pooled sample of countries from the year 1997 and the years
2001–2006.See Appendix A for detailed variable definitions.
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variable of interest is IFRS BOTH . As in the estimation of Equation (1), Models 1 and 2 use OLS
estimation. Model 1 includes investor and investee fixed effects, and Model 2 does not. As
expected, in both models there is a positive significant relation between foreign portfolio
investments and the fact that both the investor and investee use IFRS. The coefficients on IFRS BOTH
in Model 1 and 2 are 0.24 and 0.39, respectively, with t-statistics of 2.38 and 2.97, respectively.After controlling for IFRS BOTH , the main effect, i.e., the coefficient on IFRS INVESTEE , is not
statistically different from zero. This is consistent with the notion that a significant portion of the
increase in FPI comes from the familiarity to other IFRS users and not necessarily from IFRS
quality or a decrease in information asymmetry affecting all foreign investors. Equation (2)
estimation results provide evidence that foreign investors seek to invest in countries that employ
accounting standards they are familiar with.
A Closer Look at Familiarity
Findings reported above indicate that investors from countries that use IFRS tend to invest
more in countries that also use IFRS (i.e., the positive coefficient on IFRS BOTH in Equation (2)).
This section compares the behavior of IFRS BOTH to other familiarity factors, such as common
TABLE 4
The Adoption Hypothesis
Variable
Model 1 Model 2
Estimate t Estimate t
IFRS INVESTEE 0.25 4.09*** 0.22 2.19**
LIMPORT 0.22 6.63*** 0.43 9.24***
LGDP INVESTEE 1.26 2.11** 0.12 1.00 LMCAP INVESTEE 0.17 2.00** 0.16 2.34**
LPOP INVESTEE 0.36 0.21 0.22 2.15**OPENESS INVESTEE 0.03 0.77 0.10 2.04**
POLRISK INVESTEE 0.14 1.22 0.20 1.13 ECORISK INVESTEE 0.09 0.76 0.03 0.22 LEGRISK INVESTEE 0.15 1.25 0.72 3.12***
TAXRISK
INVESTEE
0.12 0.56 0.04 0.25OPERISK INVESTEE 0.11 0.92 0.29 1.28SECRISK INVESTEE 0.12 1.57 0.04 0.36
Investor and investee fixed effects Yes No
R2 0.70 0.43
n 13,992 13,992
*, **, *** Indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively.This table presents the results of the estimation of Equation (1):
Log of Equity FPI ijt ¼ b0 þ b1 IFRS INVESTEE jt þ b2k CONTROLSijt þ FixedEffect
INVESTORi þ FixedEffect
INVESTEE j
þ eit :
The dependent variable is the log of foreign portfolio holdings of country i in country j. IFRS INVESTEE is an indicator variable that takes the value 1 if country j uses IFRS.All other variables are described in Appendix A. Intercept is included but not presented. Model 1 does not include fixedeffect, and Model 2 does.Tested hypotheses are indicated in bold.
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(COMCOLONY ), shared language (COMLANG), and shared legal origin (COMLEGORIG). Each
of these four familiarity variables is an indicator variable that takes the value of 1 if the investor and
investee share the familiarity factor. Whether the countries share a common language and whether
they share a common colonizer is obtained from the CEPII website. The legal origin of a country
was obtained from La Porta et al. (2008). To examine the interaction between these other familiarity
factors and the IFRS familiarity factor, I construct a familiarity index ( FAMILIARITYINDEX ). The
familiarity index is the sum of the four familiarity indicator variables as in La Porta et al. (1998).
Results from estimating variations of Equation (2) tabulated in Model 1 of Table 6 show that
IFRS familiarity ( IFRS BOTH ) behaves in a similar way to the other four factors described above
(coefficient of 0.33 with a t-statistic of 4.96), i.e., it has strong positive effect on FPI, over and
above the other familiarity factors and with similar magnitude to the other familiarity factors. In
Model 2, I combine the familiarity effects as described above and show that the FAMILIAR-
ITYINDEX explains the FPI holdings between countries (coefficient of 0.56 with t-statistic of
12.47). The interesting result of this subsection comes from estimating the interaction between the
familiarity index and IFRS BOTH . The coefficient on the interaction between FAMILIARITYINDEX
TABLE 5
The Familiarity Hypothesis
Variable
Model 1 Model 2
Estimate t Estimate t
IFRS INVESTEE 0.07 0.91 0.03 0.21
IFRS INVESTOR 0.15 2.50** 0.05 0.60 IFRS BOTH 0.24 2.38** 0.39 2.97***
LIMPORT 0.22 6.57*** 0.43 9.22***
LGDP INVESTEE 0.98 1.59 0.13 1.04 LMCAP INVESTEE 0.16 2.08** 0.16 2.29**
LPOP INVESTEE 0.26 0.16 0.23 2.23**OPENESS INVESTEE 0.04 0.94 0.11 2.25**
POLRISK INVESTEE 0.16 1.43 0.20 1.12
ECORISK
INVESTEE
0.08 0.67 0.02 0.16 LEGRISK INVESTEE 0.16 1.43 0.71 3.08***TAXRISK INVESTEE 0.11 0.55 0.06 0.33OPERISK INVESTEE 0.10 0.90 0.28 1.24
SECRISK INVESTEE 0.12 1.58 0.04 0.40
Investor and investee in fixed effects Yes No
R2 0.70 0.43
n 13,992 13,992
*, **, *** Indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively.This table presents the results of the estimation of Equation (2):
Log of Equity FPI ijt ¼ c
0 þ c
1 IFRS INVESTEE
jt þ c
2 IFRS INVESTOR
it þ c
3 IFRS BOTH
it þ c
4k CONTROLS
ijt þ FixedEffect INVESTORi þ FixedEffect
INVESTEE j þ lit :
The dependent variable is the log of foreign portfolio holdings of country i in country j. IFRS INVESTEE is an indicator variable that takes the value 1 if country j uses IFRS. IFRS INVESTOR is an indicator variable that takes the value 1 if country i uses IFRS. IFRS BOTH is an indicator variable that takes the value 1 if both country i and country j use IFRS.All other variables are described in Appendix A. Intercept is included but not presented.Tested hypotheses are indicated in bold.
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which is consistent with the hypothesis that IFRS familiarity adds to the benefits of other familiarity
effects in increasing FPI. The coefficient on IFRS BOTH remains positive and significant even in the
presence of the familiarity index (coefficient of 0.21 with t-statistic of 2.93).
Testing the Synergy Hypothesis
Model 1 in Table 7 presents the result of the estimation of Equation (3) where STRONGINST is
the corruption index. As predicted, the coefficient on the interaction term STRONGINST
TABLE 6
A Closer Look at Familiarity
Variable
Model
All Factors
1
Familiarity Index
2
Interaction
3
Estimate t Estimate t Estimate t
IFRS BOTH 0.33 4.96*** 0.21 2.93***
FAMILIARITYINDEX 0.56 12.47*** 0.53 11.62***
FAMILIARITYINDEX IFRS BOTH 0.19 2.78***COMREGION 1.26 8.87***
COMCOLONY 0.45 2.92***
COMLANG 0.66 3.89***
COMLEGORIG 0.13 1.47
LIMPORT 0.05 1.74* 0.09 3.03*** 0.09 2.91*** LGDP INVESTEE 1.66 2.67** 1.82 2.92*** 1.51 2.49**
LMCAP INVESTEE 0.18 1.96** 0.22 2.17** 0.18 2.06**
LPOP INVESTEE 0.33 0.20 0.22 0.13 0.08 0.05OPENESS INVESTEE 0.03 0.76 0.03 0.55 0.03 0.69
POLRISK INVESTEE 0.13 1.15 0.10 0.87 0.15 1.37 ECORISK INVESTEE 0.01 0.11 0.05 0.40 0.01 0.13 LEGRISK INVESTEE 0.14 1.22 0.09 0.70 0.14 1.22TAXRISK INVESTEE 0.20 0.95 0.36 1.54 0.20 0.94OPERISK INVESTEE 0.13 1.05 0.11 0.91 0.13 1.06
SECRISK INVESTEE 0.12 1.54 0.12 1.43 0.09 1.22
Investor and investee fixed effects Yes Yes YesR2 0.73 0.72 0.72
n 13,512 13,512 13,512
*, **, *** Indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively.This table presents the results from estimation of variation of Equation (2).The dependent variable is the log of foreign portfolio holdings of country i in country j. IFRS BOTH is an indicator variablethat takes the value 1 if both country i and country j use IFRS. The familiarity factors are common geographical region of the investor and investee (COMREGION ), shared colonial past (COMCOLONY ), shared spoken language (COMLANG),and same legal origin (COMLEGORIG). Each of these four familiarity variables is an indicator variable that takes thevalue of 1 if the investor and investee share the familiarity factor (i.e., are in the same geographic area, shared colonialpast, share common spoken language, and have the same legal origin). The familiarity index ( FAMILIARITYINDEX ) isthe sum of the four familiarity indicator variables.All other variables are described in Appendix A. Intercept is included but not presented. Model 1 contains all familiarityfactors, Model 2 contains only the familiarity index, and Model 3 includes the familiarity index and IFRS BOTH .Tested hypotheses are indicated in bold.
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indeed works in synergy with strong institutions such as low corruption. The second institutional
characteristic is strong property rights. The index takes higher values when a country has better
property rights. Model 2 in Table 7 presents the result of this estimation. The coefficient on the
interaction term STRONGINST IFRS INVESTEE is positive (0.01) and statistically significant (t-statistic of 3.11). This result is evidence of an incremental IFRS adoption effect on FPI in countries
with other strong institutions.
VI. SENSITIVITY TESTS
Self-Selection
My results might suffer from self-selection bias. In particular, countries may choose to use
IFRS for reasons that are correlated with foreign investments and their institutional environment.
TABLE 7
The Synergy Hypothesis
Variable
Model
STRONGINST ¼CORPSSCORE INVESTEE
H1
1
STRONGINST ¼ PRIGHTS INVESTEE
H1
2
Estimate t Estimate t
IFRS INVESTEE 0.08 0.53 0.22 1.31STRONGINST 0.01 0.11 0.00 0.40
IFRS INVESTEE STRONGINST 0.04 2.03** 0.01 3.11*** LIMPORT 0.26 7.44*** 0.22 6.64***
LGDP INVESTEE 1.24 2.72** 1.25 2.13**
LMCAP INVESTEE 0.32 3.81*** 0.18 2.24** LPOP INVESTEE 0.92 0.69 0.29 0.17OPENESS INVESTEE 0.04 0.96 0.03 0.86
POLRISK INVESTEE 0.17 1.51 0.13 1.16 ECORISK INVESTEE 0.01 0.1 0.10 0.89 LEGRISK INVESTEE 0.01 0.05 0.13 1.17TAXRISK INVESTEE 0.01 0.08 0.17 0.77OPERISK INVESTEE 0.07 0.73 0.09 0.84
SECRISK INVESTEE 0.10 1.51 0.08 1.15
Investor and investee fixed effects Yes Yes
R2 0.71 0.70
n 13,097 13,979
*, **, *** Indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively.This table presents the results of the estimation of Equation (3):
Log of Equity FPI ijt ¼ c0 þ c1 IFRS INVESTEE jt þ c2STRONGINST jt þ c3 IFRS
INVESTEE jt
STRONGINST jt þ c4k X ijt þ wi þ n j þ lit :
STRONGINST in Model 1 takes the values of the corruption index (CORPSSCORE INVESTEE ) in country j. STRONGINST in Model 2 takes the values of the property rights index ( PRIGHTS INVESTEE ) in country k. STRONGINST IFRS INVESTEE
is the interaction between STRONGINST and IFRS INVESTEE .All other variables are defined in Table 5. Intercept is included but not presented.Tested hypotheses are indicated in bold.
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because of the EU directive. In addition, investment happens at the firm level, and the mandatory
adoption of IFRS is exogenous to the firm. Another mitigating factor is the ability of the research
design to use country fixed effects to significantly reduce the likelihood of endogeneity.
To assess the sensitivity of my results to this potential deficiency, I follow a similar procedure
to Leuz and Verrecchia (2000).
20
I estimate, as a first stage, a selection equation using a Probit model. This stage estimates the probability of a country adopting IFRS based on a selection model
equation that resembles the one used by Hope et al. (2006). In the first stage estimation, I regress
IFRS INVESTEE on LMCAP INVESTEE , LGDP INVESTEE , OPENESS INVESTEE , and country fixed effects
(untabulated). This first stage regression yields a high McFadden R2 (0.72), suggesting that the
model captures well the probability of IFRS use. As in Leuz and Verrecchia (2000), the inverse
Mills ratio is calculated and added as an additional explanatory variable to the estimation of
Equations (1) and (2). The results (untabulated) suggest that the inclusion of the inverse Mills ratio
does not alter my results and the inferences in Tables 4 and 5 hold. Note that I do not have an
exogenous variable that determines IFRS adoption but does not determine FPI.
Changes Specification
An alternative test to the fixed effects approach described in H1 is a changes test. The
predictions of the theory of portfolio holdings are stated in levels of holdings and not in changes,
but nevertheless a test that directly implements a changes specification may shed some additional
light on the effects of IFRS adoption on FPI. To do this, I obtain a subsample of countries that
mandatorily adopted IFRS during my sample period. To isolate other elements that affect FPI
holdings, I first obtain the residuals from estimation of a variation of Equation (1) on the entire
sample that includes only the controls and fixed effects from Equation (1). The residuals from this
estimation contain the log of FPI that is not explained by the independent variables. Because
IFRS INVESTEE is excluded from this regression, the residuals also contain the part of Log FPI that isexplained by IFRS INVESTEE .
I then use a simple univariate analysis of the residuals to examine whether countries have
higher FPI holding in year t þ1 compared to year t 1, where t is adoption year. I keep in the sampleonly observations from investor countries that exist in years t 1 and t þ1. This procedure gives me a matched sample for the same investor in the same adopting country for years t 1 and t þ1. Findings(untabulated) show a significant increase in FPI for countries that adopt IFRS from one year before
the adoption to one year after the adoption.
Alternative Specifications
Tobit Specification
Because the sample contains many bilateral zero observations, i.e., some countries do not
invest at all in some other countries, my sample is truncated and clustered at zero. This fact may
imply a problem with OLS assumptions and suggest that I should use a different estimation
technique such as Tobit.21 Tobit estimation yields similar inferences to OLS estimation, i.e., a
strong significant relation between increase in the holding of foreign portfolio equity investments
and the adoption of IFRS, and a strong effect on FPI when both investor and investee countries
adopt IFRS.
20 Larcker and Rusticus (2010) note that no single method can completely solve this problem.21 See Lane and Milesi-Ferretti (2008) for discussion on the advantages and disadvantages of the use of Tobit
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Time Trend
Prior research suggests that FPI are increasing over time. The purpose of this section is to
examine how sensitive the estimation of Equations (1) and (2) is to a possible time trend in the data.
To some extent, it is difficult to explain theoretically the inclusion of a linear time trend in the
specification while economic trends are already captured in other control variables (such as GDP,market capitalization, and imports). To examine the time trend in the data, I obtain the residuals
from the regression used in the Changes Specification section. By construction, these residuals are
orthogonal to the control variables I use. Thus, if the control variables capture the possible trend in
the data, I should not see a trend in the residuals. I plot the mean of the residuals for every year in
my sample. The plot (not presented) shows that, until 2006, there is no trend in the data. There is,
however, an increase in the residual FPI in 2006, which could create biases in my results. This
increase can be explained by the wholesale adoption of IFRS around the world in 2005, as
previously suggested, but also by uncontrolled factors. I deal with this issue in two ways. First, I
add a linear increasing time variable to my estimation to check whether the results of the estimation
of Equations (1) and (2) survive after such inclusion. Second, I delete all observations from 2006and re-estimate Equations (1) and (2). The results (untabulated) of these two tests do not change the
inferences obtained in the main analysis.
Alternative Dependent Variables
Foreign investors may choose their portfolio weight in a foreign country rather than the level of
their investments. This portfolio weight should be scaled by the weight of the foreign country
market capitalization relative to the world market capitalization, which is the theoretical portfolio
weight based on the international capital asset pricing model. Models that do not use this scaling
approach may suffer from scale bias.22 This approach however may introduce significant noise to
the estimation (Barth and Kallapur 1996). An alternative procedure, which addresses this issue, isto include the theoretical portfolio weight as one of the regressors in the estimated equations.
Untabulated results indicate that the inferences reported in prior sections are unchanged using both
methods, albeit statistically weaker.
European Union Effect
Most members of the European Union (EU) adopted IFRS in 2005. Including investor and
investee fixed effects captures, to some extent, the effects of bilateral investment among the EU
countries. To examine the sensitivity of my results to this potential problem, I add two additional
indicator variables to the estimation. The first takes the value of 1 if the investor country is from the
EU, and the second takes the value of 1 if the investee country is from the EU. The results(untabulated) indicate that the inferences from my main analysis, which is the positive and
significant coefficient on IFRS INVESTEE in Equation (1) and on IFRS BOTH in Equation (2), remain
unchanged.
Subsample Analysis
Removing U.S. and U.K. from the Sample
In light of the limitations of the FPI data that were presented above, there is a possibility that
investments in financial centers and from financial centers distort the results. The problem is that
investment that appears to be investments in the U.S. or the U.K. are actually investments in a third
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country that just flowed through these financial centers. To mitigate the possibility that this data
shortcoming influences my results, I delete all observations that involve the U.S. and U.K., both as
investors and investees. There are significant costs for such elimination, as the U.S. and U.K.
investors account for a large relative proportion of the world portfolio investments and that is in
addition to the loss of approximately 1,500 country-year observations. Untabulated results of theestimations of Equations (1) and (2) after the exclusion of the U.S. and U.K. observations reveal
similar inferences to those presented in Tables 4 and 5.
Removing the Year 1997 from the Sample
As discussed in the CPIS data description, 1997 was the first year the survey was conducted
and contained only 27 participants. To be consistent with prior literature that includes 1997 in the
sample, my main results contain the data from 1997. However, I mitigate the possibility that the
inclusion of the first year of the data, which has a low number of participants and is four years apart
from the main time series of the data, may affect the results by deleting all of the 1997 observations.
Untabulated findings indicate that the exclusion of 1997 has no significant implications for my mainresults, as the coefficients on IFRS INVESTEE in Equation (1) and IFRS BOTH in Equation (2) remain
positive and significantly different from zero.
VII. CONCLUSIONS
The results in this paper provide evidence that accounting information plays a role in foreign
portfolio investment decisions. The results also provide evidence that familiarity of accounting
standards is the source of association between IFRS adoption and foreign investment decisions.
This result can be explained by the ability of the common use of IFRS to increase the competence
of foreign investors by making the investment environment more familiar to them. Moreover, after
controlling for accounting familiarity, I find no increase in FPI following the adoption of IFRS. I
also find that IFRS familiarity is associated with foreign investments in a similar fashion to other
familiarity factors, such as shared geographical region, shared colonizer, shared spoken language,
and common legal origin. The IFRS familiarity effect interacts with these other factors to enhance
investments, and has incremental effects on investments over and above these other factors.
Moreover, the results show IFRS have a greater effect on FPI in investee countries with lower
corruption and stronger property rights.
Although the results survive a wide variety of sensitivity tests, there are several other
explanations that are consistent with them. For example, IFRS users may adopt the standards as part
of a variety of reforms that aim to increase levels of foreign portfolio investments. The
heterogeneous nature of my sample, the inclusion of openness index, and the use of the Heckman
procedure help mitigate this concern. An additional concern is that it is more likely for countries to
adopt IFRS if other countries also adopt the standards (Ramanna and Sletten 2010). It is unlikely
that this issue affects my results because the tests provide evidence that there is a significant
increase in FPI after the adoption of IFRS and that other familiarity factors are likely to capture
these effects. An additional alternative explanation is that IFRS is seen by investors as a set of
accounting standards that provides financial reports with higher quality. However, for the results of
the tests of my second hypothesis to be consistent with this explanation, investors from countries
that adopt IFRS would have to be more affected by better quality information than investors from
non-IFRS countries. Although I do not test for this possibility, there is no reason to believe this is
the case.
If one takes the popular position that FPI are beneficial to the recipient countries, then my
76 Amiram
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global set of accounting standards. The results also give additional insight about which factors
affect global investors and shed additional light on explanations of the home bias phenomenon.
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A P P E N D I X
A
V a r i a b l e s D e fi n i t i o n s
r i a b l e
N a m e
S h o r t D e s c r i p t i o n
D e s c r i p t i o n
r i a b l e s
R e l a t e d t o t h e D e p e n d e n t V a r i a b l e
H O L D I N G
H o l d i n g s o f
e q u i t y f o r e i g n p o r t f o l i o
i n v e s t m e n
t s b y i n v e s t o r i n t h e i n v e s t e e
T h e l e v e l i n m i l l i o n s o f U S D o f h o l d i n g s o f i n v e s t o r c o u n t r y r e s i d e n t s o f e q u i t y
f o r e i g n p o r t f o l i o i n v e s t m e n t s i n t h e i n v e s t e e c o u n t r i e s . S o u r c e : I M F C P I S w e b s i t e .
H O L D I N G P 1
H O L D I N G p l u s 1
E q u a l s H O L D I N G p l u s 1 . T h i s a d d i t i o n o f 1 h a s b e e n m a d e i n o r d e r
n o t t o l o s e
o b s e r v a t i o n s w i t h t h e v a l u e 0 w h
i l e c a l c u l a t i n g t h e n a t u r a l l o g o f h
o l d i n g . S e e
L a n e a n d M i l e s i - F e r r e t t i ( 2 0 0 8 ) . S o u r c e : I M F C P I S w e b s i t e .
L o g o f E q u i t y F P I
N a t u r a l l o g o f H O L D I N G P 1
E q u a l s t h e n a t u r a l l o g o f H O L D I N G
p l u s 1 . T h i s a d d i t i o n o f 1 h a s b e e n m a d e i n
o r d e r n o t t o l o s e o b s e r v a t i o n w i t h t h e v a l u e 0 w h i l e c a l c u l a t i n g t h e
n a t u r a l l o g o f
h o l d i n g . S e e L a n e a n d M i l e s i - F e r r e t t i ( 2 0 0 8 ) . S o u r c e : I M F C P I S w
e b s i t e .
I N V E S T
O R
I n v e s t o r c o u
n t r y
N
a m e o f i n v e s t o r c o u n t r y i n t h e I M
F C P I S d a t a s e t — T h e s e a r e t h e c o
u n t r i e s w h o s e
r e s i d e n t s h o l d e q u i t y f o r e i g n p o r t f o l i o i n v e s t m e n t s i n t h e i n v e s t e e c o u n t r y
r e s i d e n t s .
I N V E S T
E E
I n v e s t e e c o u
n t r y
N
a m e o f i n v e s t e e c o u n t r y i n t h e I M
F C P I S d a t a s e t — T h e s e a r e t h e c o
u n t r i e s w h o s e
r e s i d e n t s ’ e q u i t i e s a r e b e i n g h e l d
b y t h e i n v e s t o r c o u n t r y r e s i d e n t s .
p e r i