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Financial Reporting Differences Around the World: What Matters?

Helena Isidro*

Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal

Dhananjay Nanda†

School of Business Administration, University of Miami

Peter Wysocki†

School of Business Administration, University of Miami

July 2016

Abstract

The international financial reporting literature identifies a multitude of country attributes that each appear to

explain financial reporting differences around the world. We first show that a single underlying factor

explains across-country variation in 6 reporting quality measures used in the international literature. We then

examine 72 country attributes and show that they are highly correlated and that 4 underlying factors explain

most of the variation in these attributes across countries. Furthermore, individual country attributes provide

essentially no incremental explanatory power for international reporting diversity over these 4 factors, which

collectively explain over 70% of the variation in reporting differences. Our findings highlight the very high

causal density of country attributes and thus the difficulty in attributing international reporting diversity to

specific institutions and policies. We conclude with a discussion of possible future directions for research on

financial reporting around the world.

Keywords: IFRS, International accounting, Complementarities, Correlation, Factor analysis,

Financial reporting, Accounting quality, Multiple testing

* Helena Isidro: ISCTE-IUL Instituto Universitário de Lisboa, Avenida das Forças Armadas, 1649-026, Lisboa, Portugal. Tel:+351

217 903 480. Email: helena.isidro@iscte.pt

† Dhananjay Nanda: School of Business Administration, University of Miami, Coral Gables, FL 33146. Email:

dnanda@bus.miami.edu

† Peter Wysocki: School of Business Administration, University of Miami, Coral Gables, FL 33146. Email:

pwysocki@bus.miami.edu

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1.1.1.1. Introduction

The international accounting literature has associated a multitude of country-level attributes

with cross-country variation in several financial reporting outcomes around the world. These

country attributes include geographic features (e.g. country latitude), legal institutions (e.g. legal

origin), religious affiliation (e.g. percentage catholic, religiosity), cultural development (e.g.

masculinity, societal trust) and economic outcomes (e.g. per capita GDP, market capitalization,

stock market participation).1 Can all of these attributes individually affect country-level reporting

outcomes? Can existing empirical methods and data be used to distinguish between the many

proposed and competing ‘theories’ of the determinants of international reporting diversity? What

truly determines or influences the quality of reported financial numbers across countries?

This study takes a comprehensive look at the existing panapoly of country-level attributes

proposed in prior empirical studies that appear to be individually associated with variation in

several financial reporting quality measures around the world.2 First, we define and summarize the

empirical measures of country-level financial reporting quality previously used in the literature. We

show that, while there are numerous measures, they collectively seem to capture a single underlying

(latent) construct of financial reporting quality. We then survey more than a hundred empirical

studies published in the last two decades comparing country attributes and identify 72 different

variables that have been used to measure differences in economic, cultural, institutional and societal

1 The extant literature often uses the term “institutions” (see, for example, Leuz and Wysocki [2016]) to describe

possible country determinants of financial reporting and disclosure. We use the broader term “country attributes”

thoroughout our paper to capture a broader set of empirically-observable country features including institutions, but

also features such as exogenous physical geography and endogenous economic outcomes. 2 Examples of these variables include the quality of reporting standards (e.g. Christensen et al. [2015], Core et al.

[2014], Daske et al. [2013], Armstrong et al. [2010], Barth et al. [2008], Daske et al. [2008]), enforcement regulation

(e.g. Christensen et al. [2013], Brown et al. [2014], legal rules (e.g. Gupta et al. [2008], Hail and Leuz [2006]), investor

protection (e.g. DeFond et al. [2007], Leuz et al. [2003]); economic development (e.g. Chen et al. [2015]); political

institutions (e.g. Li et al. [2016], Batta et al. [2015], Boutchkova et al. [2012], Bushman and Piotroski [2006], Riahi-

Belkaoui [2004a]), and social values (Pevnzer et al. [2015], Nanda and Wysocki [2015]; McGuire et al. [2012],

Kanagaretnam et al. [2011]).

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development across countries. Using data from 35 countries, we first empirically explore which, if

any, of the variables are incrementally associated with financial reporting quality variation beyond

an underlying core set of latent factors derived from the remaining variables.3 Based on this

analysis, we conclude that there are fundamental barriers for researchers to empirically isolate the

effect of individual country attributes in explaining reporting and disclosure diversity around the

world because (a) the country-level measures are highly correlated suggesting strong and arguably

inseparable interdependencies among country-level institutions and features, and (b) the relatively

few country observations in the world limits empiricists’ ability to statistically isolate the effects of

individual country attributes. While accounting researchers have suspected the existence of these

problems, this is the first study to systematically document the fundamental inferential problems

facing comparative country analyses that arise from: (i) the plethora of candidate “theories” and

associated country variables that potentially explain global reporting and disclosure outcomes, (ii)

the static or generally slow moving nature of country attributes, and (iii) as listed above, the high

correlation among these possible explanatory variables (i.e. high causal density) and the limited

number of country observations in the world. In summary, we lack of degrees of freedom in current

cross-country studies with too many correlated candidate theories/attributes/variables and there are

too few empirical observations to parse the possible competing effects. We further highlight that

this is not really a problem of incorrect empirical tools (i.e., multiple regression vs structural

modelling vs qualitive comparative analysis), but an issue of statistical inference based on too little

data used to identify an effect from too many interwined country-level attributes.

It should be noted that the ‘zoo’ of possible global reporting and disclosure determinants

and outcomes increases each time a seemingly ‘new’ country variable is reported to be significantly

3 Restricting the sample to 35 countries is necessitated because we require data for all financial reporting characteristics

and for most of the previously identified country attributes. Our sample largely overlaps with the countries used in

many prior international accounting studies.

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associated with a financial reporting diversity measure across countries, or a ‘new’ financial

reporting quality variable is found to co-move with previously identified country attributes.

Unfortunately, each new variable is either empirically tested in isolation, or at best, in combination

with a few other country variables, making it difficult to evaluate whether a proposed variable

incrementally explains reporting outcomes or is simply associated with the set of previously

correlated attributes. To help guide future discoveries of “seemingly” new country-level attributes

and their relation with “seemingly” new international reporting diversity measures, we outline a

benchmark correlation analysis where any newly-proposed variable should provide incremental

explanatory power for reporting outcomes beyond 4 core empirically derived latent factors that

capture the joint explanatory power of previously-documented country attributes for reporting

outcomes around the world. We show that the 4 latent factors collectively explain a substantial

amount of the observed cross-country variation in financial reporting outcomes. Further, our

empirical analyses suggests that few, if any, existing individual country attributes provide any

significant explanatory power for reporting outcomes beyond these 4 underlying factors.

An additional issue that we highlight in our examination is that inferences should be based

on a multiple testing framework rather than on a single test perspective since many international

research studies attempt to explain the same international cross-section of reporting and disclosure

differences. A multiple testing framework corrects for the bias towards false discoveries that result

from simultaneously testing multiple hypotheses using the same data. The upshot being: to evaluate

the significance of a new country attribute in explaining financial reporting diversity, a researcher

must account for tests that document correlations between financial reporting outcomes and other

country-level atrributes.4

4 Similar problems exist in the empirical asset pricing literature in finance where a multitude of studies have proposed

and tested hundreds of isolated “return predicting signals” without acknowledging the existence and possible

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We make two key contributions highlighting both the high correlation among reporting

quality measures as well as the high causal density of country attributes that have been used in past

research to explain the cross-country variation in these measures. We first show that commonly

used country-level reporting quality measures are highly correlated and a single factor explains

most of the cross-country variation in these measures. The six measures that we choose are 1)

abnormal returns around earnings announcement (Nguyen and Truong [2013]), 2) abnormal volume

around earnings announcement (Nguyen and Truong [2013]), 3) return synchronicity (Nguyen and

Truong [2013]), 4) reporting transparency (Leuz et. al. [2003]), 5) disclosure quality (Bushman, et.

al. [2004]), and 6) asymmetric timeliness (Bushman and Piotroski [2006]). A single principal

component underlying the six variables explains ninety percent of the cross-country variation in our

35-country sample.

Our second contribution is that we systematically tabulate the very high correlations among

many country-level variables used in international studies. These high correlations are indicative of

strong and significant comovement among country attributes. On average each variable has a

correlation of more than 0.5 with five or six other measures, and a correlation of more than 0.7 with

three or four of them. Further, the ability of country-level measures to explain other country-level

measures is quite high. For example, block premium, regulatory quality, legal origin, and latitude

(a proxy for economic development from La Porta et al. [1999]) explain 74% of private control of

self-dealing, a measure extensively used to represent the level of shareholder protection in a

country. Similarly, public enforcement of securities regulation, latitude, religious fractionalization,

and power distance explain about 65% of number of analysts in a country, a measure often used to

assess a country’s public firms’ informational environment quality (Byard et al. [2011]). To

examine whether the high correlations between these variables capture a set of underlying latent

correlations among these signals. Harvey et al. [2016], Green et al. [2014] and Green et al. [2013] highlight these

problems in the empirical asset pricing literature and provide evidence on the robustness of the prior findings.

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factors in an economically meaningful way, we perform a factor analysis on all 72 variables. Our

factor analysis attempts to resolve the dimensionality problem that arises in international studies

because the number of possible explanatory variables far exceeds the number of observations

(countries) typically investigated. Based on their statistical significance (eigenvalues) we retain four

latent factors from our factor analysis. The four factors that we retain explain about 60% of the

variation in country attributes and the first two factors alone explain more than a third of the

variation.

Next, we analyze the relation between our identified latent factors based on country

attributes and the single “reporting quality” latent factor derived from six financial reporting

measures. We assess the four latent factors’ ability to explain variation in the latent financial

reporting quality factor across 35 countries. We find that the four factors representing country-level

characteristics explain a significant portion of the international variation in financial reporting

outcomes. A regression of the reporting quality factor on the four latent country-level factors has an

R2 of almost 70%. In particular, the first two latent factors explain more than half of the variation in

reporting outcomes across countries. Additionally, most of the 72 country-level variables in the

literature are significantly associated with variation in the financial reporting factor when tested

individually and in isolation. However, the explanatory power of each of these variables essentially

evaporates when one controls for the 4 latent factors, computed while excluding the variable

examined, that aggregate other country-level characteristics along with a multiple testing

adjustment to test statistics.5 Our findings highlight the difficulty in inferring empirically whether

differences in any single country characteristic affects observed differences in financial reporting

outcomes across countries. Collectively, our empirical evidence suggests that financial reporting

5 As described in section 3, we use both the Bonferroni and Holm adjustments to account for multiple testing issues.

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outcomes are likely influenced by an inextricably intertwined set of interrelated country-level

features.

In the concluding section of this study, we discuss how this study’s findings can help push

the boundaries of future international research examining the association between country-level

features and economic outcomes. Overall, our analyses show that the paucity of country-level

observations coupled with the very large number of previously-identified and highly-related

country attributes sets a high bar for researchers to document novel associations between individual

country attributes and reporting and other outcomes. However, on the upside, our evidence also

shows that country attributes collectively (as captured by our four factors) explain a substantial

portion of the cross-country variation in reporting quality outcomes. Thus, we first suggest that

researchers who are investigating a ‘new’ country attribute should benchmark the attribute against

the four empirical factors presented in this paper. This benchmarking exercise can help provide

insights into both the possible incremental explanatory power (for various accounting or other

economic outcomes) of the ‘new’ country attribute and also how the attribute ‘fits’ with other

previously-identified groups of country attributes and institutions. Second, we suggest that future

research should more explicitly acknowledge the interdependencies among the many attributes and

institutions that exist in countries and regions. So, rather than focusing on an isolated country

attribute, studies would be better served by acknowledging the portfolios of correlated country

attributes and policies that work together as a ‘package’. Third, the evidence in this paper highlights

the opportunities to better understand how and why portfolios of country attributes and institutions

work together to influence financial reporting and other economic outcomes.

The remainder of our paper is organized as follows. The next section describes the data, and

our examined country characteristics and financial reporting variables. Section 3 discusses our

empirical methods and results. Section 4 explores the potential of alternative empirical methods’

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ability to overcome the limitations of traditional regression analyses. Section 5 presents our out-of-

sample case analyses to highlight the generalizability of our findings. Section 6 presents our

conclusions and suggestions regarding future research directions.

2. Financial Reporting and Country Attributes

2.1. Financial reporting measures

The international financial reporting literature has used a variety of measures to examine

reporting quality diffrences across countries. Consistent with this literature, we use a

multidimensional approach to study firm financial reporting outcomes at the country level. We

examine the following six measures at the country-level that reflect financial reporting attributes of

public firms domiciled in those countries. Reporting transparency and disclosure quality are

accounting-based measures that reflect firms’ reporting and disclosure choices. Specifically,

reporting transparency is the negative of the opacity score of Leuz [2010], which is an update to

the Leuz et al. [2003] earnings management score and only includes data from reported financial

statements. Disclosure quality is the disclosure index created by the Center for Financial Analysis

and Research based on the inclusion or exclusion of 90 items in firms’ annual reports (reported in

Bushman et al. [2004]). Abnormal return, abnormal volume and return synchronicity are market-

based measures that capture investors’ reaction to the release of periodic financial reporting

information (reported in Nguyen and Truong [2013]). Abnormal return and abnormal volume are

market adjusted returns and trading volume measured over the three-day window around firms’

earnings announcements, respectively. These variables reflect how investors respond to the public

dissemination of accounting earnings information. Similarly, Return synchronicity is the weighted

average R-squared of regressions of firm returns around earnings announcements on market returns,

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multiplied by minus one. It measures the extent that earnings information is impounded in stock

prices over the long-term. Asymmetric timeliness is reported in Bushman and Piotroski [2006] and

measures the relation between accounting information and market information. It is intended to

reflect managers’ accounting choices in recognizing bad news in earnings as seen by market

participants. We redefine the measures so that higher values indicate higher reporting quality and

standardized them to a zero mean and a standard deviation of one. Appendix 1 - Panel A provides a

brief description of the reporting quality variables, data sources, and examples of studies that use

these measures in an international setting.

2.2. Country attributes

We collect a comprehensive set of variables used in the financial reporting literature over

the last two decades, that represent multiple country characteristics for a sample of 35 countries

(see Table 1 for the list of countries). We collect the relevant variables from publicly available

sources for periods between 1995 and 2012 (see Appendix 1 – Panel B for definitions of data

sources of the country variables).6 We select variables that have been extensively used to explain

country-level differences in geographic, institutional, cultural and economic features, and that are

publicly available. These variables represent geographic features (e.g. latitude), economic

development (e.g. GDP per capita), capital market development (e.g. market capitalization to

GDP), legal institutions (e.g. law and order), political systems (e.g. democracy), tax mechanisms

(e.g. assessment of tax evasion), securities regulation (e.g. securities regulation disclosure

requirements), sources of financing (e.g. bank money in private sector to GDP), reporting and

auditing enforcement (e.g. enforcement of accounting standards), investor and creditor rights (e.g.

anti-director rights), foreign investment (e.g. foreign institutional holdings), analyst activity (e.g.

6 In most cases, the data sources provide only time invariant values for certain country variables. In cases where time

series data are available, we use the average time-series value of the variable.

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number of analysts), audit quality (e.g. Big4 market share), dissemination of information (e.g.

media), cultural values (e.g. individualism), social attitudes (e.g. trust), religious practices (e.g.

catholic), and linguistic properties (e.g. English proficiency). To deal with differences in scale and

missing values we standardize all variables and replace missing observations with the sample

mean.7 Appendix 1 - Panel A describes the 72 country-level measures included in our study and

their corresponding data sources. We also cite studies that use each country-level variable to

explain international variation in financial reporting quality measures. To choose the 35 countries

we considered countries that are economically and socially important and for which data is

available from public sources for the country-level variables.8

3. Empirical analysis

3.1. Determining the dimensionality of international financial reporting quality

The summary statistics for the individual financial reporting measures (unstandardized) are

reported in Panel A of Table 1. We observe substantial cross-country variation in financial

reporting measures, but we also note that these measures are highly correlated and this suggests that

they may represent common constructs (Panel B). To identify the common constructs we perform a

principal factor analysis. Factor analysis uses the correlation patterns among the observed financial

reporting variables to identify unobserved latent factors. We conduct the factor analysis using the

principal components method and the squared multiple correlation between the variable and all

other variables for the prior communality estimates.9 We then perform a linear transformation on

7 Results are unaffected by this choice, albeit weaker due to the smaller sample size.

8 One could plausibly increase the number of countries in the sample and increase the degrees of freedom available to

test associations between country attributes and financial reporting outcomes by relaxing our data availability

constraint. However, for many countries some of the variables are either unavailable from realiable sources, or

incorrectly measured and this would increase measurement error. Tabulated data for the publicly-available country

variables are available from the authors upon request. 9As the proportion of common variance among variables is not known in advance, factor analysis requires an initial

estimate. We set the squared multiple correlation as the prior communality. This criterion is widely used in the

literature, but for robustness we also repeat the factor analysis using two alternatives: i) the maximum absolute

correlation, and (ii) a random correlation.We obtain a similar solution with one latent reporting factor.

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the factor solution applying varimax rotation. Table 2 presents the results. The factor analysis

suggests one single underlying financial reporting factor. The single factor explains about 89% of

the total cross-country variation in reporting measures, and all variables have high loadings on the

single factor (see Panel A of Table 2). The generally used rule of selecting factors with eigenvalues

of one or higher (Kaiser [1960]) and the reliability score of 0.858 (Cronbach [1951]) confirms the

one factor solution.10

Panel B of Table 2 presents the standardized scores of the financial reporting

latent factor for all the countries. Figure 1 graphically displays the factor scores. Ireland, United

Kingdom and United States have the highest reporting quality factor scores whereas Taiwan,

Pakistan and Greece have the lowest factor scores.

3.2. The correlations and potential interdependencies among country attributes

To demonstrate the high interdependency between the country attributes, we begin with a

regression analysis of country attributes on other country attributes.11

In Table 3 we report the

adjusted R2 of 72 separate regressions for each country-level variable regressed on 4 other country-

level variables that yield the highest adjusted R2. The purpose of this exercise is to assess the

maximum ability of various country-level variables to explain other country-level variables. In each

regression we remove any variable that is mechanically related to both the dependent and the

independent variables or that represents a similar construct so that the resulting adjusted R2s are not

mechanically inflated.12

The adjusted R2s reported in Table 3 are generally very high ranging from

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The common rule of thumb is that the reliability measure should be at least 0.5 with many researchers suggesting a

minimum value of 0.7. 11

We also perform a correlation analysis for the country attributes. In Appendix 2 - Panel A we list all 72 previously

identified variables and the set of other variables most correlated with each of these proxies for country characteristics.

We tabulate the list of variables that are very highly correlated (absolute correlation of 0.7 or higher), highly correlated

(absolute correlation between 0.5 and 0.7), and moderately correlated (absolute correlation between 0.3 and 0.5). We

note that all measures have correlations of 0.3 or higher with at least one other variable proxying for country

charcteristics. On average each measure exhibits correlations of 0.3 or higher with 10 other variables (see Appendix 2

Panel B). Moreover, many measures have very high correlations with other measures. On average each measure has

correlations of 0.7 or higher with 3 or 4 other variables, and correlations of 0.5 or higher with 5 or 6 other variables. 12

An example of a mechanically related variable is secrecy, which is the combination of three other variables

(uncertainty avoidance plus power distance minus individualism). An example of two measures that represent the same

institutional characteristic is political stability and low political risk.

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0.33 for the audit spending variable to 0.97 for control of corruption. The high joint explanatory

power is in line with the high pair-wise correlations between variables tabulated in Appendix 2 and

it also confirms our observed interdependencies between country-level attributes. These

interdependencies between country attributes make it difficult to causally isolate, or even

incrementally associate, the effect of any single country-level attribute on observed financial

reporting outcomes (or, more generally, other economic outcomes). Although isolating individual

effects is difficult to achieve because of the degree and complexity of the interactions between

country variables, we argue that the relations and potential complementarities between constructs

should be recognized in empirical examinations that relate economic outcomes to country-level

characteristics in international settings. Another practical implication is the dimensionality problem

(see, for example, Leuz and Wysocki [2016]). The fact that there are more country-level variables

than country observations with reliable and available data leads to estimation problems. Moreover,

using time-series variation in the data is unlikely to resolve the empirical problem because many

country-level features tend change very slowly, if at all, or co-move with other country attributes.

3.3. Factor analysis of country-level variables

As a first step toward better understanding and addressing the aforementioned

dimensionality problem, we describe patterns in the country-level variables and investigate whether

the observed patterns are explained by a much reduced and empirically-tractable set of (latent)

factors that capture common variation in the country variables. As with the financial reporting

quality metrics, we analyse common variation in the country attributes using factor analysis. Factor

analysis takes into account the correlation patterns among the country attributtes to identify

unobserved latent factors. Factor analysis also helps address the high dimensionality problem as it

significantly reduces the number of possible country-level variables that explain variation in

another economic outome (such as financial reporting). We apply factor analysis using the principal

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components method and extract four orthogonal factors using varimax rotation.13

The four-factor

outcome represents a balance between (i) explaining a large proportion of the variation, (ii)

retaining factors with substantial incremental explanatory power, and (iii) finding a parsimonious

solution.14 Table 4 summarizes the results of our factor analysis. The four factors explain about

58% of the total variation, with the first two factors alone explaining a significant portion (80%) of

that variation (Table 4 - Panel A).15

We also note that adding a fifth factor adds little in terms of

variation explained (less than 5%) and that the incremental explanatory power of a factor declines

in the number of factors. Table 4 - Panel B presents the loadings of each country-level variable on

the latent factors. To facilitate interpretation we only present factor loadings higher than 0.4. A

large number of country-level variables load highly in the first factor that accounts for 31% of the

total variation. This first factor comprises a mix of measures related to a country’s legal and

governance systems (e.g. regulatory quality and rule of law), economic welfare (e.g. Gdpc and

bank money in private sector to GDP), legal rights (e.g. creditor rights), and also social attributes

representing more informal institutions (e.g. trust and ethnic fractionalization). This heterogeneous

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The squared multiple correlations cannot be used for the prior communality estimates because there are more

variables than country observations (i.e. our correlation matrix is singular). For simplification, we set prior

communality estimates equal to one, but we repeat the factor analysis assuming the largest absolute correlation or a

pseudo-random number uniformly distributed between 0 and 1. We also use the iterated principal factor method with

the largest absolute correlation for the prior communality estimates. In all cases the four factor solution continues to

explain more than half of the variation in the country variables. To simplify the factor structure and to obtain an

interpretable final solution, we apply an orthogonal rotation (varimax) of the factors. Other rotation methods result in

similar factor loadings and country scores. Specifically, we performed a quartimax rotation (orthogonal) and a promax

rotation (oblique). 14

In selecting the number of factors, we also consider commonly used criteria such as the Kaiser (1960) rule - selecting

factors with eigenvalues larger than one, and the Cattell (1966) screen test – selecting factors above the point of

inflection in a plot of eigenvalues. 15

The correlations that form the basis of the factor estimates can potentially suffer with the presence of two discrete

variables (legal origin and class action lawsuit). An alternative to analyse the relation between discrete and continuous

variables is the non-parametric Spearman rank correlation, i.e. essentially defining all variables as discrete, or the

polyserial correlation which is the inferred latent correlation between a continuous variable and a ordered categorical

variable (Drasgow [1986]). We compare the Pearson, Spearman and polyserial correlations of the two variables with all

other variables and conclude that they are similar. The correlations (Pearson) between the average Pearson, Spearman

and polyserial correlations are: (i) for legal origin - Pearson / Polyserial = 0.988, Pearson / Polyserial = 0.954,

Spearman/Polyserial = 0.947; and (ii) for class action suit - Pearson / Polyserial = 0.945, Pearson / Polyserial = 0.874,

Spearman/Polyserial = 0.858. Another alternative is to exclude the two discrete variables from the factor analysis. The

four latent factors based on only 70 variables continue to explain 58% of the cross-country variation.

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mix suggests that economic, legal, political, and societal features are interconnected and cannot be

examined in isolation. The Cronbach’s measure of reliability presented at the bottom of Panel B is

very close to one (0.97) indicating that the set of measures are highly correlated with the latent

variable, and that they represent a cohesive factor.16 Therefore, the effect of one stand-alone

country-level variable on reporting outcomes potentially reflects variation in other variables or in

the group as a whole.

The second factor extracted from our factor analysis captures correlated variables such as

creditor and investor rights (e.g. creditor rights and private control of self-dealing index), securities

regulation (e.g. securities regulation liability standards, and securities regulation disclosure

requirements), capital market size (e.g. market cap. to GDP), and legal origin. Social characteristics

such as English proficiency and uncertainty avoidance are also associated with capital market

development. The Cronbach’s measure of reliability is also close to one (0.931). Factor 3 has high

loadings in measures related to the political process (legislative competition and number of veto

players), and some financial and tax reporting system characteristics (book tax independence, tax

compliance, and enforcement of accounting standards). The reliability measure for factor 3 meets

the recommended cut-off value of 0.7. Factor 4 captures the openness or closeness of society

particularly in relation to external investment. This factor is characterized by high negative loadings

on measures representing US institutional holdings, US cross-listing, audit spending and English

proficiency, and high positive loadings on long-term orientation, Buddhist, and bank money in

private sector to GDP. These characteristics are typical of closed economies.

Table 4 - Panel C presents the standardized factor scores for each country. For consistency

across factors we revert the scores of factor 4 so that it represents openness rather than closeness of

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For the purpose of computing the measure of reliability, we assign variables only to the factor where they load the

highest. This avoids assigning arbitrary weights to variable loadings. But we note that the majority of the variables load

highly in only one factor.

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society. Pakistan and the Philippines have the lowest factor 1 score (general institutional

development) whereas Finland and Sweden have the highest score. Figure 1 displays factor 1 scores

for the countries in our sample. In Appendix 3 we report the country factor scores for an extended

list of 47 countries. The US has the highest rank in terms of political systems and reporting

enforcement. It has the fifth most developed capital market and ranks 17th in the first factor

representing general institutional development.

3.4. Relation between country-level variables and financial reporting

In this section, we examine the robustness of the empirical associations between individual

country-level variables and the financial reporting factor. Specifically, we estimate the statistical

significance and incremental explanatory power of individual variables in explaining variation in

financial reporting quality (q) when (i) regressions include only an individual country-level

explanatory variable, and (ii) regressions include both an individual country variable and four latent

country factors. We start by comparing the estimated t-statistics for a country-level explanatory

variable (vi) using menthods (i) and (ii) above. Table 5 compares the t-statistics for each individual

country variable vi obtained from estimating two regression models: (i) a reporting quality (q)

regression using only an individual country-level explanatory variable vi (unidimensional model q

= a + c*vi), and (ii) a reporting quality (q) regression using both an individual country-level

explanatory variable vi reporting quality (q) in combination with four factors extracted from a factor

analysis using all country-level measures excluding the identified individual variable,

(multidimensional model q = a+b1*f1+b2*f2+b3*f3+b4*f4+c*vi). By comparing the t-statistics

obtained in these two models we examine two important aspects. First, whether a certain country-

level variable is associated with variation in financial reporting (a research design typically applied

in previous international studies). Second, after considering the country features that co-move with

a given variable, whether that country variable still significantly explains variation in observed

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financial reporting outcomes. The main result shown in Table 5 is the large drop in the number of

statistically significant variables in the second model. The first column in Table 5 shows that

individually the majority of country-level variables (45 of the 72 variables) are statistically

significantly associated with the variation in financial reporting quality and in the direction

typically identified in the extant literature. In contrast, the second column for the multidimensional

model shows only eight significant measures. This result highlights the interdependency between

the country-level measures and is in line with the high correlations observed in Table 3.

We next apply a multiple testing approach to the significance levels and estimate t-statistics

that control for family-wise error rate based on either the Bonferroni adjustment or the Holm

adjustment. The Bonferroni adjustment controls for the probablility of a single false discovery by

equally penalizing each p-value by the number of tests conducted in the same sample (72). The

Holm correction uses a sequential approach that is based on the rank order of the p-values and thus

is less restrictive than the Bonferroni adjustment. For both types of multiple testing adjustments we

observe that only 15 of the country-level variables are statistically significant in the univariate

model (column 1 of Table 5), and only 1 variable is significant in the multivariate model (column 2

of Table 5). 17

Overall, this striking empirical evidence suggests that essentially none of the

individual country attributes are found to be statistically significant incremental determinants of

international reporting after one accounts for 4 latent factors that distill the explanatory power of

other known and documented country attributes.

In Table 6 we perform a similar analysis to that described for Table 5 but examine the

explained variation. We start by comparing the variation in the financial reporting factor that is

17

To assess the possibility that the only seemingly significant variable (Individualism) is a false positive, we perform

Monte Carlo simulations (10,000 iterations) for the multiple regression model that includes Indvidualism as an

explanatory variable and obtain parameter estimates for Individualism under several different data generation processes.

For many of the simulations, we fail to reject the null hypothesis that the estimated coefficient on Individualism is

statistically different from zero. Thus, this simulation evidence suggests that even the apparent significance of

Individualism may be a statistical fluke.

17

explained by the 4 latent country-level factors with the variation explained by each of the 72

individual country-level variables. If the country latent factors reasonably capture the multifaceted

structures that affect financial reporting, then we expect these factors to explain a substantial

portion of cross-country variation in financial reporting. Further, if an individual country variable

conveys explanatory power in excess of that explained by the identified country latent factors, then

the single variable likely represents a distinct country-level feature that separately explains

reporting quality outcomes (q). We compare the explanatory power of the following three models.

Model 1 is the unidimensional model above, i.e. a regression of the individual variable vi on the

reporting quality factor q (q = a + c*vi). Model 2 is a regression of four factors extracted from

factor analysis using all country-level variables while excluding the identified individual variable

on the reporting quality factor q (q = a+b1*f1+b2*f2+b3*f3+b4*f4). We label Model 3 as the

multidimensional model above, i.e. a regression of four factors extracted from factor analysis using

all country-level variables while excluding the identified individual variable, and the individual

variable vi on the reporting quality factor q (q = a+b1*f1+b2*f2+b3*f3 +b4*f4+c*vi). Two patterns

emerge from Table 6. First, we observe a considerable increase in the explanatory power when

moving from model 1 to model 2. The average adjusted R2 increases from 16% to 72%. Second, the

explanatory power is unchanged as we move from model 2 to model 3. The four factors used in

model 2 capture a significant part of the cross-country variation in financial reporting. Hence, most

of the individual variables considered in isolation add little, if any, explanatory power to the model

because their effect is captured by correlated country attributes. Moreover, we find that only one

variable (individualism) has significant additional explanatory power in a multiple testing context.

18

We now turn to the four original latent factors as reported in Table 4. We examine the

association between these latent factors and the financial reporting factor.18

We assign the countries

to high, medium and low groups for each country factor and compare the mean and median values

of the financial reporting factor across the groups. The univariate results reported in Panel A of

Table 7 show that financial reporting quality improves as we move from the low quality group to

the high quality group which supports the premise of a positive association between the quality of

country-level characteristics and firms’ reporting outcomes. Regression results are presented in

Panel B of Table 7. All four country factors are positively associated with financial reporting

outcomes. In particular, factor 1 representing several characteristics associated with economic and

social development and country factor 2 with high loadings of variables related to capital market

development are significantly associated with reporting quality at the 1% level, in all model

specifications. The explanatory power of the country factors is quite high. In general, adjusted R2’s

are higher than 50% and the adjusted R2 for models including all four country factors are higher

than 70%. Hence, the four country factors that capture the interrelatedness of various country-level

features explain a substantial and significant portion of the international variation in financial

reporting outcomes.19

4. Can other empirical methods crack the country comparison problem?

In this section, we examine other candidate empirical methods that potentially address the

limitations of correlation and regression analyses in isolating which country attributes affect

financial reporting diversity around the world. As discussed in the prior sections, the four

18

The regression analyses preformed in this study are based on strictly linear models with no interactive effects. Prior

research suggests possible interactive effects between individual institutional variables (see, for example, Leuz et al.

[2003]). While non-linear specifications and interactive effects may result in greater explanatory power for reporting

quality, the use of non-linear and interactive approaches would not affect the main conclusions of this study.

Specifically, the high correlations among individual country variables make it difficult, if not impossible, to separate

and ascribe the effects of individual country variables (or interactions among individual country variables). 19

As a robustness test we repeated the analysis excluding the US. Our conclusions regarding the country factors,

reporting quality factor, and regressions results do not change.

19

fundamental problems facing comparative country analyses arises from: (i) the limited number of

country observations, (ii) the static or generally slow moving nature of country attributes, (iii) the

plethora of candidate “theories” and associated country variables that potentially explain reporting

outcomes, and (iv) the high correlation among these possible explanatory variables (i.e., high causal

density). In summary, the lack of degrees of freedom in multiple regression analyses arising from

too many candidate theories/attributes/variables and too few empirical observations make it

difficult to separate the competing effects.

4.1 Alternate approaches to constructing factors

Our previous analysis took a purely statistical approach by using factor analysis to identify

the latent factors from 72 country attributes, which results in factors that are difficult to label or

define since several seemingly unrelated variables load on the same factor. Further, in the interest

of parsimony we restrict the number of factors used in our empirical analysis to only four.

However, there are potentially more factors that could explain reporting quality variation across

countries if a researcher exercises her judgment in identifying distinct measures ex-ante based on

intuition. In this section we modify our approach by explicitly and ex-ante categorizing the 72

country-level variables based on distinct common categories used in the international literature:

Economic, Sociological, Geopolitical, Regulatory, and Legal categories. We then examine whether

this categorization better explains cross-country variation in attributes as well as incrementally

explains cross-country reporting quality differences. Our categorization is described in Table 8 -

Panel A.

We further analyze the within-category correlations among variables and find these to be

substantial and significant (bottom of Table 8 – Panel A). Because of the high within-category

correlations, and for parsimony, we perform factor analyses within each category. The independent

within-category factor analyses results in 10 factors with significant eigenvalues; two Economic

20

factors, four Sociological factors, a Geopolitical factor, two Regulatory factors and a Legal factor.

Collectively, these ten factors explain 58% of the total cross-country variation in attributes.

We then determine the power of the category factors individually and

collectively in explaining cross-country reporting quality variation using regression analysis

(Columns 1-6 of Table 8 - Panel B). We find that all categories except geopolitical load

significantly in these regressions, although the explanatory power of each category varies. The two

economics factors explain almost half of the variation in reporting quality, the four sociological

factors 57%, the two regulatory factors 55% and the legal factor (the single variable legal origin)

25%. In a regression of the reporting quality factor on all ten category factors (column 6 of Table 8)

the adjusted R2 is 78%, which is marginally higher than the 72% adjusted R2 obtained using only

four factors described in section 3. From these results, we infer that an ex-ante classification of

country attributes in an effort to easily label them or enhance their explanatory power is limited in

its ability to overcome the limited observations and high causal density problems in cross-country

research. We confirm this intuition by examining the correlations among all the category factors

(Table 8 - Panel C). These high correlations attest to our conclusions that (i) the category-based

factors do explain much of the cross-country variation in reporting quality, but (ii) they are highly

correlated and thus suffer from the same issues that affect cross-country studies that relate

individual attributes to financial reporting and economic outcomes.

4.2 Alternate empirical methodologies

Several scholars have proposed using alternative empirical methods to help test hypotheses

regarding which variables are associated with or even cause observed reporting outcomes around

21

the world. A recent survey by Gow, Larcker and Riess [2016] suggests that structural equation

modelling (SEM) may provide a practical path forward for empirical accounting research using

observational data. In addition, Wysocki [2011] suggests structural equation modelling as a

possible tool to help identify the associations between reporting quality and other economic

outcomes around the world. One important advantage of SEM over standard regression analysis is

that it allows for financial reporting quality to be both an outcome itself and a possible causal

determinant of other economic outcomes (such as financial market development or cost of capital).

However, a major limitation impeding the valid use of structural equation modelling for

international comparative analyses is the lack of formal and well-defined theories to guide the

correct implementation of pathway models for country-level data. While this problem is not

insurmountable, it suggests much work for future accounting researchers.

However, the application of SEM methods for international comparative analyses is still

stymied by exactly the same empirical issues facing correlation and regression; it is an issue of too

many competing and highly correlated country attributes and variables and too few country

observations. So, while one could potentially develop a formal testable theory, apply SEM methods,

and find evidence that a variable (say, investor protection) appears to explain reporting quality, the

problem remains that there are dozens of other highly correlated variables (with associated stories,

ad hoc models or even full blown structural models) that would have indistinguishable and

confounding empirical effects. The lack of observations and the high correlation among the

candidate explanatory theories/variables implies that SEM faces the same problems that are

highlighted in sections 2 and 3 of our study.

Scholars in accounting (see, for example, Leuz and Wysocki [2016]) and in other fields

recommend Qualitative Comparative Analysis (QCA) and Fuzzy Set Analysis as empirical tools that

potentially help identify the correlation structure of observational data and to qualitatively test the

22

consistency of the data with deterministic hypotheses (see, for example Schneider and Wagemann,

[2012], Grant et al. [2010], Vaisey [2007], and Ragin [2000]). QCA is an empirical tool that

evaluates the relation between observed dichotomous outcome variable(s) and all possible Boolean

combinations of other (explanatory) variable sets. QCA examines which set combinations or

configurations are most likely to be present when an outcome variable of interest has a “high”

value. Fuzzy Set analyses extend the QCA methodology beyond dichotomous variables to include

continuous variables (that range from 0 to 1). In an on-line appendix (On-Line Appendix 1), we

provide a simple case example showing the use of QCA/Fuzzy Set analysis on the dataset of 35

country observations used in sections 2-4 of this study.

However, like SEM methods, QCA/Fuzzy Set analyses also suffer from the same empirical

issues facing correlation and regression analyses; namely, too many competing and highly

correlated country attributes and variables and too few country observations. Again, one could

outline a formal testable theory, apply QCA/Fuzzy Set methods, and seem to find qualitative

evidence of a pattern of 5 or 6 country variables that “appear together” when reporting quality is

high. However, the problem with using QCA/Fuzzy Set methods for international comparative

analyses is that there are about 35 country observations, but there are 72+ highly correlated

variables that would have indistinguishable and inseparable empirical effects.

5. Out of sample tests: The N+1th

explanatory variable

An important issue affecting international studies is the difficulty in isolating the impact of

an individual institutional variable on accounting outcomes (Leuz and Wysocki [2016]). Many

studies proposing a relation between a country-level variable and firms’ reporting outcomes do not

assess whether the proposed feature has explanatory power over and above the existing set of

relations with other previously documented country-level characteristics, and how that feature is

related to the broad set of known country characteristics. We shed some light on this issue by

23

performing the following analysis. We select a variable that is not included in the set of our 72

institutional measures and examine its incremental explanatory power on financial reporting quality

measures over and above the four institutional factors derived from the factor analysis. As an

example, we use the economic freedom index recently proposed by Chen et al. [2015] to explain

firms’ investment decisions. Chen et al. [2015] find that greater economic freedom enhances

investment efficiency, which translates into higher earnings and book value multiples. The authors

also document that the effect of economic freedom is distinct from the effect of GDP, legal origin,

law enforcement, investor protection, and quality of a country’s accounting system. In Table 8 -

Panel A we present the correlations between our four country factors and the economic freedom

index reported in Chen et al. [2015]. The correlations with factor 1 and factor 2 are large and

statistically significant. In Table 8 - Panel B, we present regression results of the relation between

economic freedom, the four country factors, and reporting quality. The results of model (1), when

our country factors are excluded, are suggestive of a positive association between economic

freedom and financial reporting quality. However, when we include the multidimensionality of

country attributes represented by the four latent factors in the models, the statistical association

between economic freedom and financial reporting quality is no longer statistically significant.

Further, the explanatory power is substantially larger for models with the four country factors than

for the model with only economic freedom as an independent variable. These results indicate that

the country-level construct that economic freedom represents is already embedded in other country

features. Although the fact that the four factors capture a large number of country-level features

makes it difficult for any new measure to have incremental effects, the empirical results suggest

that the general patterns and complementarities between country’s institutions, endowments and

other features should be recognized when assessing the role of country-level characteristics on

economic outcomes.

24

We also test whether the high explanatory power of the country factors is exclusive of the

reporting outcomes represented in our financial reporting quality factor. We select an alternative

measure of reporting incentives from Chen et al. [2015]: market value of equity to earnings

(MV/E). This measure captures both the properties of information as prepared by managers and the

use of financial information by market participants. Country factor 2 representing institutional

features linked to capital markets is strongly associated with MV/E and explains 40% of the

variation in that variable (see Table 8 - Panel C). The results confirm the idea that country-level

institutional dimensions collectively affect firms’ reporting and economic outcomes. Moreover, the

effect of a given country-level variable is difficult to assess because of the many complementarities

and interactions between many co-existing features.

To further investigate this point, we examine other reporting outcomes. First, we estimate

the model that explains cross-country differences of the quality of accounting standards measured

as the difference between domestic standards and IFRS. The measure is developed by Bae et al.

(2008) and counts the differences between domestic standards and IFRS across 21 key accounting

items. In contrast to MV/E, this measure reflects only the quality of the information prepared by

managers. Second, we estimate the model that explains cross-country differences in analyst forecast

errors measured as the average absolute analyst forecast error for forecasts of earnings of current

year, one year and two years ahead (reported in Dhaliwal et al. [2012]). Our estimation results are

presented in Table 10 - Panel A. Higher “distance to” IFRS standards is mostly (negatively)

associated with institutions related to capital market development represented in factor 2. Analyst

forecast errors are negatively associated with factor 2 and with factor 3, which captures financial

reporting enforcement, taxation and political systems.20

We also test the relation between the two

outcome variables and the reporting quality factor and find a significant association. Overall, our

20

For the analyst forecast error variable we re-estimate the factor analysis excluding “number of analysts” given the

potential mechanical relation between the two variables

25

results suggest that the multidimensional set of country-level attributes affect a diverse set of

reporting outcomes and that there appear to be complementarities in reporting outcomes.

Several studies have used the properties of the accounting information to explain economic

outcomes such as capital market development, international cross-listing, corporate control,

ownership structure (e.g. La Porta et al. [1998], Dyck and Zingales [2002], Covrig et al. [2007], and

Fernandes and Ferreira [2008]). As an illustration, we test whether the reporting quality factor and

the country-level factors explain market capitalization. We obtain new country-level factors from

factor analysis excluding all variables related to capital market development (e.g. market

capitalization to GDP, listed firms to population, IPO’s to GDP, securities regulation). We report

the estimation results for the first two factors in Table 10 Panel B. We find that reporting quality is

positively related with capital market development and explains about 10% of the variation. The

two country-level factors are also significantly associated with capital market development.

Moreover, the reporting quality and capital market development association is statistically

insignificant when the country-level factors are included as additional regressors.

6. Conclusions and possible future research directions

The extant body of evidence in the empirical international accounting literature suggests

that a multitude of country-level characteristics and attributes each seem to be individually

associated with reporting outcomes around the world. This study empirically examines the multi-

dimensionality of and likely complementarities between country-level variables and how they

jointly relate to international variation in reporting and disclosure outcomes. We address the

incomplete picture in the existing accounting literature where the effects of ‘new’ country-level

variables on accounting outcomes are generally established without acknowledging or controlling

for the effects of numerous other known and documented country attributes.

26

First, we provide evidence that a single latent financial reporting factor appears to explain

most of the cross-country variation in six commonly used empirical measures of financial reporting

quality. We then investigate a comprehensive set of 72 country-level variables proposed in the

extant literature to explain international variation in economic outcomes, particularly accounting

quality. We find that country-level variables such as the quality of laws, governance and political

systems, financial and auditing enforcement mechanisms, capital market development, social

values, and culture and religious beliefs are highly correlated with each other and have high

combined explanatory power for each other. The strong co-movement among these variables

creates two problems for empirical researchers examining the relation between economic outcomes

and country-level features. First, because the country-level variables are highly correlated, it is

difficult to attribute an outcome to any one specific variable. Second, because there are fewer

country observations than there are measures of country attributes, it would appear to be difficult, if

not impossible, to control for and isolate the individual effects of all possible country attributes in

empirical studies.

In order to provide a framework and a feasible empirical methodology to better understand

the correlations between country-level variables and their relation to accounting quality, we

perform a factor analysis and identify four underlying factors that explain the common variation in

individual country-level variables across 35 countries. Moreover, after accounting for these four

factors in regression analyses, individual country variables have little, if any, ability to explain

variation in financial reporting outcomes around the world.21

Our findings suggest that accounting

outcomes, like other economic outcomes, are determined by a multidimensional and intertwined set

21

For clarity and for correct interpretation of this methodology, it is worth repeating that when evaluating the

incremental explanatory power of a given individual country variable (for example, investor protection), the four

factors are first estimated exluding the country variable of interest (for example, investor protection). Then, nested

regressions are estimated with accounting quality as the dependent variable and the four factors plus the given

individual country variable are used as explanatory variables.

27

of country-level conditions and their interactions. Hence, in order to determine the potential effect

of any ‘new’ country-level variable on observed financial reporting outcomes, one must explicitly

acknowledge and account for their association with the previously empirically identified set of

country-level attributes. Further, we highlight that the four-latent factors derived from country

attributes collectively explain a large portion of the differences in financial reporting outcomes

around the world.

In summary, there is a severe lack of degrees of freedom in current cross-country studies

with a plethora of correlated candidate theories/attributes/variables and too few empirical

observations to reasonably separate the confounding effects. We further highlight that this is not

really a problem of incorrect empirical tools (i.e., multiple regression vs structural modelling vs

qualitative comparative analysis), but an issue of too little data generated from too many

interrelated effects. We use 35 country observations in our empirical analyses to create and test a

dataset with as broad a set as possible of previously identified county attributes. It should be noted

that some international studies undertake analyses using data based on firm- or industry-level

observations to increase the sample size to hundreds, if not ten of thousands, of observations.

However, almost all comparative international empirical studies test “theories” and apply treatment

effects based on one or a few country-level variables. Therefore, using firm or industry observations

does not address or solve the fundamental problem identified in this study because the question of

interest is based on country-level (treatment) effects and we systematically document that a given

country variable is highly correlated with dozens of other country attributes. Thus, any claimed

explanatory power of a single country variable is very likely to be subsumed by other highly

correlated country attributes because there are only so many country observations. While the use of

times-series observations and changes analyses may help isolate and possibly separate the effects of

various competing country-level attributes or policies (see, for example, Christensen et al. [2013],

28

and Barth and Israeli [2013]), existing studies only account for a few examples of other competing

attributes and variables. However, as highlighted in this study, there are dozens of existing

competing theories/effects and at least 72 (and growing) documented country variables that are not

only highly correlated, but co-evolving over time.

6.1 Discussion of possible future directions for international research

A key insight of this study is that we formally document the high degree of correlation

among individual country attributes and thus the difficulty in separating the effects of individual

attributes. These findings have both retrospective and prospective implications for international

accounting research. Accounting researchers, doctoral students and policymakers can use the

evidence and data presented in this study to help interpret prior empirical studies on the

determinants and outcomes of financial reporting and disclosure around world. For example, many

existing international accounting studies apply conditional tests and regressions to test a particular

hypothesis on the determinants or outcomes of high quality financial reporting. For example,

research examining the association between variable X and reporting outcome Y may find that the

link is most pronounced when country attribute Z1 is high and this empirical evidence is then used

to argue that X “causes” Y.22 However, the evidence in this study suggests that many country

attributes (say, Z2, Z3, etc.) are often very highly correlated with country attribute Z1. Thus, it is

difficult to claim that the Z1-mediated effect of X on Y conclusively supports a particular

hypothesis when so many other plausible competing hypotheses with associated country variables

(Z2, Z3, etc.) provide equally compelling and empirically indistinguishable evidence that supports

22

Examples of this approach include Lang et al. [2004] who study the effect of analyst following mediated by variation

in investor protection across countries, or Pevzner et al. [2015] that investigates how societal trust affects market

reaction to earnings announcements as mediated by investor protection or disclosure requirements across countries.

29

alternate mechanisms for the documented determinants and outcomes of firms' financial reporting

policies.

On the upside, our findings can help guide future research exploring the determinants and

outcomes of international financial reporting. First, researchers who are investigating a ‘new’

country attribute can benchmark this attribute against the four empirical factors presented in this

paper. This benchmarking exercise can help provide insights into both the possible incremental

explanatory power (for various accounting or economic outcomes) of the ‘new’ country attribute

and also how the attribute ‘fits’ with other previously-identified groups of country attributes and

institutions.

Second, we suggest that future research should more explicitly acknowledge the

interdependencies among the many attributes and institutions that exist in countries and regions.

For example, rather than attempting to document that an isolated country attribute is associated with

variation in financial reporting quality across countries, studies would be better served by

acknowledging the portfolios of correlated country attributes and policies that work together as a

‘package’. For example, a study might model the effect of auditors on financial reporting outcomes

and then determine if this effect is more or less pronounced in countries that have a portfolio of key

institutions (for example, the correlated institutions that load strongly on Factor 1 in Table 4 of this

paper including strong rule of law, low political risk, high regulatory quality, low risk of

repudiation of contracts and expropriation by governments, high political stability, strong protection

of property rights, high judicial independence and efficiency). In other words, support for a

particular hypothesized link between auditors and financial reporting outcomes could be buttressed

if there is also supporting empirical evidence that a portfolio of strong institutions in a country

supports, or possibly substitutes for, the impact of auditors. While this portfolio approach may not

generate the type of simple policy prescriptions that some audiences may desire (i.e., countries only

30

need to change a single policy or ‘fix’ a single institution), it more reasonably reflects the realities

of how many institutions, attributes and features co-exist and interact in the real world.

Furthermore, this portfolio approach does not blindly emphasize one institutional mechanism while

ignoring other important and potentially complementary mechanisms affecting financial reporting

and other economic outcomes.

Third, the evidence in this paper highlights the opportunities to better understand how and

why portfolios of country attributes and institutions work together to influence financial reporting

and other economic outcomes. While advancing our understanding of these complementary effects

is very important, empirical researchers will still face the problem of the limited number of country

observations relative to the number of correlated country attributes. One possible step to addressing

the limited number of country observations available for international comparative analyses is to

also take advantage of within-country variation in provincial/state attributes and institutions. For

example, many countries exhibit large regional variation in laws, culture, language, history and

physical endowments within their borders.

Fourth, the recent accounting literature has correctly highlighted the need to find better-

identified empirical settings (such as natural experiments) to help isolate the stand-alone effect of a

particular economic variable or mechanism (see, for example, Leuz and Wysocki [2016]).

However, given that country attributes and institutions display very high correlations and appear to

work as systems, it may be a misguided strategy to search for “well-identified” research

experiments solely focused on single country attributes or stand-alone mechanisms. While a

researcher may be able to find an exogenous shock, the strong links among a country’s attributes

and institutions mean that: (i) any shock is unlikely to only impact a single economic mechanism

and thus would violate the exclusion restrictions necessary to draw any causal inferences about

stand-alone economic attributes or mechanisms, and (ii) even if a shock only directly affects a

31

single country attribute, the complementary links between country attributes and institutions

suggests a subsequent system-wide rebalancing of numerous attributes and institutions that would

make it difficult to isolate the role of any one institution or mechanism (also confounded by the

many variables in the system compared relatively few event observations).

Finally, we see future opportunities for international accounting researchers to better

capitalize on the unique features and advantages of empirical settings focused on single countries or

regions. While the prior international empirical accounting literature has produced a litany of

‘replication’ studies based on data from specific (non-U.S.) countries, these studies often

mechanically replicated earlier U.S. studies.23

These mechanical replications generally generated

little interest because they: (i) failed to motivate and explain why or how the findings should be

similar or different from those previously found in the U.S., and (ii) failed to capitalize on the

unique features and events in a country that could provide important experimental insights

unavailable in a U.S. research setting. Therefore, we recommend that international accounting

researchers strive to identify important research questions with potentially broad and generalizable

implications and then seek out powerful or unique experimental settings in specific countries or

regions that could not be addressed using the institutional environment or data available in the U.S.

In conclusion, we return to the three questions that motivated this study: Can all of these

attributes individually affect country-level reporting outcomes? Can existing empirical methods and

data be used to distinguish between the many proposed and competing ‘theories’ of the

determinants of international reporting diversity? What truly determines or influences the quality of

reported financial numbers across countries? At this time, the answers appear to be “the empirical

23

We acknowledge the important role for replication studies in empirical accounting research (as well as other

academic research fields) in documenting the validity and robustness of claimed initial discoveries. However, as

outlined above, the types of “replication” studies found in the prior international accounting literature often failed to

advance our understanding of why and how certain U.S. findings did or did not exist in non-U.S. samples.

32

evidence suggests no”, “unlikely”, and “we do not yet have a complete picture – more innovative

research is needed”.

33

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40

Table 1 - Summary statistics of 6 financial reporting outcomes for 35 countries Panel A – Descriptive statistics by country

Country

Reporting

transparency

Disclosure

quality

Asymmetric

timeliness

Abnormal

return

Abnormal

volume

Return

synchronicity

Argentina -0.391 68 0.401 3.79 0.135 0.510

Australia -0.078 80 0.343 5.41 0.664 2.033

Austria -0.808 62 -0.093 4.33 0.213 1.603

Belgium -0.682 68 0.181 4.55 0.766 1.777

Brazil -0.658 56 0.152 3.53 0.250 1.249

Canada -0.162 75 0.377 6.03 0.960 2.582

Switzerland -0.504 80 0.303 4.97 0.993 2.192

Chile -0.358 78 0.017 3.31 -0.110 1.200

Germany -0.620 67 0.22 3.88 0.329 1.872

Denmark -0.530 75 0.244 5.64 1.357 1.817

Spain -0.792 72 0.314 4.07 0.622 1.352

Finland -0.26 83 0.11 5.07 1.254 1.757

France -0.536 78 0.04 5.56 0.919 2.022

United Kingdom -0.133 85 0.276 8.94 1.744 1.814

Greece -0.881 61 0.087 3.49 -0.010 1.015

Hong Kong -0.521 73 0.256 6.49 1.286 1.306

Indonesia -0.715 65 0.046 3.43 0.436 1.351

India -0.537 61 0.156 5.11 0.860 1.263

Ireland -0.199 81 0.495 9.34 1.914 1.999

Israel -0.329 74 0.23 3.97 1.108 0.795

Italy -0.826 66 0.135 4.05 0.994 1.474

Japan -0.802 71 0.107 4.82 1.002 1.384

Mexico -0.502 71 0.466 4.8 0.514 0.915

Malaysia -0.643 79 0.125 3.93 0.331 1.230

Netherlands -0.482 74 0.177 7.59 1.362 1.815

New Zealand -0.121 80 0.419 5.42 0.916 1.913

Pakistan -0.706 73 -0.085 2.68 -0.100 0.756

Philippines -0.552 64 0.231 4.32 0.074 1.055

Portugal -0.88 56 0.263 3.68 0.327 1.781

Singapore -0.601 79 0.13 6.11 1.194 1.332

Sweden -0.168 83 0.486 5.26 1.424 1.532

Thailand -0.506 66 0.337 3.55 0.247 1.173

Taiwan -0.639 58 0.158 3.25 0.081 0.618

United States -0.228 76 0.312 7.48 1.654 2.142

South Africa -0.307 79 0.051 3.42 0.278 1.595

Panel B - Correlations

(1) (2) (3) (4) (5) (6)

(1) Reporting transparency 1

(2) Disclosure quality 0.706* 1

(3) Asymmetric timeliness 0.539* 0.259 1

(4) Abnormal return 0.539* 0.537* 0.440* 1

(5) Abnormal volume 0.485* 0.583* 0.431* 0.868* 1

(6) Return synchronicity 0.380* 0.423* 0.223 0.565* 0.552* 1

Table 4 reports summary statistics for 6 reporting quality variables used in the literature for 35 countries.

Panel A reports mean values of reporting quality variables (unstandardized) by country. Panel B reports

Pearson correlations. Variable definitions are provided in Appendix 1. The symbol * indicates statistical

significance at the 0.05 level.

41

Table 2 – Factor analysis of 6 financial reporting measures for 35 countries

Panel A – Rotated factor loadings and statistics of the factor analysis

Factor loadings Statistics of the factor analysis

Abnormal return 0.868 Eigenvalue: 3.211

Abnormal volume 0.868 Variation explained: 0.889

Reporting transparency 0.864 Cronbach’s measure of reliability : 0.858

Disclosure quality 0.748

Return synchronicity 0.728

Asymmetric Timeliness 0.587

Panel B – Standardized scores for financial reporting factor

Country Financial Reporting

score

Country Financial Reporting

score

Ireland 2.214 Japan -0.270

United Kingdom 2.083 Belgium -0.302

United States 1.543 Italy -0.511

Sweden 1.168 Malaysia -0.532

Canada 0.986 Spain -0.543

Netherlands 0.967 Argentina -0.561

New Zealand 0.914 Germany -0.612

Finland 0.817 Chile -0.642

Australia 0.797 Thailand -0.650

Hong Kong 0.620 Philippines -0.752

Denmark 0.586 Indonesia -0.900

Singapore 0.462 Austria -1.048

Switzerland 0.422 Brazil -1.058

France 0.302 Portugal -1.140

Israel 0.215 Taiwan -1.230

Mexico -0.150 Pakistan -1.335

India -0.162 Greece -1.449

South Africa -0.249

Table 2 reports the results of factor analysis on the 6 financial reporting outcome

variables for 35 countries The factor analysis is performed using the principal

components method and the squared multiple correlation between the variable and all

other variables for the prior communality estimates. As the proportion of common

variance among variables is not known in advance, factor analysis requires an initial

estimate. We set the squared multiple correlation as the initital prior communality. We

then perform a linear transformation on the factor solution applying varimax rotation.

Panel A reports factor loadings and statistics of the factor analysis. Panel B reports

standardized factor scores the estimated single reporting quality measure for 35

countries. Variable definitions are provided in Appendix 1.

42

Table 3 - Adjusted R2 of regressions of country variable on the other most correlated country

variables

Short name Dependent variable Adjusted R2 4 independent variables providing max adj. R

2

AntiDir Anti-director rights 0.584 ProprR, PolitStab, Latitude, Big4Share

LTaxEv Assessment of tax evasion 0.809 BTaxInd, ReligFract, CCorr, LegalO

AuditSpend Audit spending 0.330 DisclReg, USlisting, UncAvoid, ClassAction

BankPriv Bank money in private s. to GDP 0.744 MarkCap, LRepContr, Masculin, Big4Share

Big4Share Big4 market share 0.427 BankPriv, ForeignInv, ReligFract, Trust

BlockPr Block premium 0.567 DisclReg, USlisting, Religness, Masculin

BTaxInd Book tax independence 0.482 Latitude, InstHoldDom, ReligFract, Law

Budhist Buddhist 0.577 PublCtr, Protestant, EnglProf, LegalO

Catholic Catholic 0.660 InstHoldDom, IndividH, Trust, LegalO

ClassAction Class action lawsuit 0.546 MarkCap, PublicEnf, LegislComp, ReligFract

CCorr Control of corruption 0.974 InstHoldDom, Law, Protestant, RegQ

CCorrL Corruption 0.864 ProprR, PolitStab, UShold, IndividH

CreditR Creditor rights 0.862 BankPriv, AntiDir, LegalO, JudEff

Democracy Democracy 0.667 LegislComp, IndividH, IndividW, EnglProf

InstHoldDom Domestic institutional holdings 0.388 Latitude, UShold, IndividH, UncAvoid

EnforAccS Enforcement of accg. standards 0.651 DisclReg, LibStand, IndividH, Big4Share

EnforAudS Enforcement of audit standards 0.843 EnforAccS, AntiDir, Gdpc, BTaxInd

EnglProf English proficiency 0.645 LRepContr, ForeignInv, IndividH, LegalO

EthFract Ethnic fractionalization 0.615 LangFract, Religness, UncAvoid, Big4Share

PrivCtrEA Ex ante private control self-dealing 0.745 CreditR, PolitStab, IntHoldFor, JudEff

PrivCtrEP Ex post private control self-dealing 0.648 BTaxInd, AuditSpend, NrAnal, LegalO

IntHoldFor Foreign institutional holdings 0.486 BankPriv, LegislComp, Latitude, ClassAction

ForeignInv Foreign investment to GDP 0.599 MarkCap, ReligFract, Protestant, CCorr

Gdpc GDPc 0.881 LRepContr, LegislComp, Latitude, JudEff

HierIndep Hierarchy vs independence 0.417 DisclReg, BTaxInd, LegislComp, LangFract

IndividH Individualism 0.786 Gdpc, Democracy, ForeignInv, EnglProf

IndividW Individualism in income 0.576 BankPriv, OwnConc, PolitConn, USlisting

InfoKnow Information and knowledge 0.689 Gdpc, PolitStab, LangFract, Masculin

IPO IPOs to GDP 0.525 ListedF, PolitStab, InstHoldDom, CCorr

JudEff Judicial efficiency 0.835 CreditR, BankPriv, Gdpc, IndividW

JudIndep Judicial independence 0.943 ProprR, UShold, Trust, LegalO

LangFract Language fractionalization 0.688 ProprR, BlockPr, PolitStab, EthFract

EnglProx Language proximity to English 0.906 UShold, LTorient, HierIndep, EnglProf

Latitude Latitude 0.815 PublicEnf, ReligFract, PowerD, NrAnal

LawO Law and order 0.933 ProprR, LRepContr, PolitStab, CCorr

LegalO Legal origin 0.795 CreditR, DisclReg, Gdpc, PowerD

43

Table 3 - Adjusted R2 of regressions of country variable on the other most correlated country

variables

(cont.)

Short name Dependent variable Adjusted R2 4 independent variables providing max Adj R

2

LegislComp Legislative competition 0.516 Gdpc, Democracy, PolitConn, HierIndep

ListedF Listed firms to population 0.642 OwnConc, PublicEnf, Religness, LegalO

LTorient Long-term orientation 0.604 LRepContr, Democracy, Religness, CCorr

LPolitR Low political risk 0.952 LRepContr, PolitStab, LTorient, LegalO

LRepContr Low repudiation contracts by gov. 0.893 ProprR, Gdpc, PolitConn, UncAvoid

LExprR Low risk expropriation by gov. 0.943 MarkCap, LRepContr, Big4Share, ClassAction

MarkCap Market cap. to GDP 0.713 BankPriv, ListedF, ForeignInv, InfoKnow

Masculin Masculinity 0.440 Gdpc, Latitude, IndividW, CCorr

Media Media 0.752 PublicEnf, Gdpc, InfoKnow, EnglProf

Muslim Muslim 0.805 Democracy, PolitStab, Latitude, UShold

NrAnal Number of analysts 0.654 PublicEnf, Latitude, ReligFract, PowerD

Veto Number of veto-players 0.719 Democracy, LegislComp, Law, CCorr

OReligion Other religion 0.524 ListedF, Law, CCorr, JudEff

OwnConc Ownership concentration 0.553 ListedF, DisclReg, Religness, ClassAction

PolitScore Political score 0.939 Democracy, Latitude, Law, CCorr

PolitStab Political stability 0.853 ProprR, AntiDir, LangFract, Big4Share

PolitConn Politically connected firms 0.573 BankPriv, Gdpc, LegislComp, IndividW

PowerD Power distance 0.730 DisclReg, Democracy, InfoKnow, LegalO

PrivCtrIndex Private control of self-dealing index 0.743 BlockPr, Latitude, RegQ, LegalO

ProprR Property rights 0.949 JudIndep, UShold, RegQ, Trust

Protestant Protestant 0.659 ForeignInv, Law, Trust, CCorr

PublCtr Public control of self-dealing 0.346 BlockPr, Democracy, Law, RegQ

PublicEnf Public enforcement sec.regulation 0.673 ListedF, Latitude, LTorient, NrAnal

RegQ Regulatory quality 0.957 DisclReg, LibStand, Protestant, CCorr

ReligFract Religion fractionalization 0.605 BTaxInd, Latitude, Law, Big4Share

Religness Religiousness 0.781 LegislComp, EthFract, LTorient, CCorr

Law Rule of law 0.968 Democracy, USlisting, Protestant, CCorr

Secrecy Secrecy 0.830 MarkCap, Gdpc, Protestant, EnglProf

DisclReg Securities reg.disclosure req. 0.931 LibStand, EthFract, RegQ, JudEff

LibStand Securities reg.liability standards 0.939 DisclReg, EthFract, RegQ, CCorr

SSecRegul Strengh of securities regulation 0.878 BlockPr, DisclReg, PublicEnf, ClassAction

TaxComp Tax compliance 0.557 PublicEnf, PolitConn, EthFract, CCorr

Trust Trust 0.656 Latitude, PolitConn, Religness, UncAvoid

UncAvoid Uncertainty avoidance 0.704 DisclReg, LegislComp, Protestant, EnglProf

USlisting US cross-listing 0.791 UShold, LTorient, Big4Share, LegalO

UShold US institutional holdings 0.805 ListedF, PolitStab, USlisting, PowerD

Table 3 reports the adjusted R2 of each regression for a given country variable regressed on 4 explanatory

country variables chosen from a list of 72 possible country variables (the variable descriptions are provided

in Appendix 1). The selected 4 explanatory variables are the variables that maximize the explanatory power

of the regression for a given depedendent variable (ie, maximal adjusted R2 using 4 country variables).

44

Table 4 – Factor analysis of 72 country variables for 35 countries (description at bottom of Table 4)

Panel A: Factor analysis statistics - Variation explained by candidate factors

Eigenvalue Variation explained Cumulative variation explained

Country factor 1 22.261 0.309 0.309

Country factor 2 10.868 0.151 0.460

Country factor 3 5.049 0.070 0.530

Country factor 4 3.699 0.051 0.582

Country factor 5 3.229 0.045 0.627

Panel B: Rotated factor loadings

Country variables Factor 1 Factor 2 Factor 3 Factor 4

Control of corruption 0.938

Rule of law 0.938

GDPc 0.928

Low political risk 0.914

Law and order 0.907

Regulatory quality 0.901

Low repudiation of contracts by gov. 0.891

Corruption 0.888

Political score 0.887

Political stability 0.883

Low risk of expropriation by gov. 0.875

Property rights 0.843

Judicial independence 0.802 0.428

Judicial efficiency 0.794

Latitude 0.749

Media 0.735

Information and knowledge 0.722

Enforcement of audit standards 0.702

Individualism 0.696 0.540

Trust 0.635

Democracy 0.585

Protestant 0.571

Assessment of tax evasion 0.554 0.625

Foreign institutional holdings 0.530

Bank money in private sector to GDP 0.530 0.452

Tax compliance 0.507 -0.484

Creditor rights 0.457 0.768

Politically connected firms -0.508

Individualism in income -0.533

Language fractionalization -0.540

Muslim -0.569

Ethnic fractionalization -0.580

Secrecy -0.694

Power distance -0.707

Religiousness -0.847

45

Table 4 – Factor analysis of country variables (cont.)

Panel B: Rotated factor loadings (cont.)

Country variables Factor 1 Factor 2 Factor 3 Factor 4

Private control of self-dealing index 0.855

Legal origin 0.836

Securities regulation liability standards 0.830

Securities regulation disclosure requir. 0.816

Strength of securities regulation 0.761

Ex ante private control of self-dealing 0.756

Ex post private control of self-dealing 0.745

Listed firms to population 0.647

Anti-director rights 0.643

Market cap. to GDP 0.636

English proficiency 0.574 -0.433

Public enforcement securities regulation 0.556

Language proximity to English 0.527

Religion fractionalization 0.479 0.434

Other religion 0.454

Foreign investment to GDP 0.430

IPOs to GDP 0.420

Block premium -0.540

Catholic -0.592

Uncertainty avoidance -0.756

Book tax independence 0.658

Legislative competition 0.627

Class action lawsuit 0.618

Number of analysts 0.541

Domestic institutional holdings 0.514

Number of veto-players 0.479

Enforcement of accounting standards 0.425

Hierarchy vs independence -0.489

Long-term orientation 0.737

Buddhist 0.440

Audit spending -0.536

US institutional holdings -0.591

US cross-listing -0.641

Ownership concentration

Public control of self-dealing

Masculinity

Big4 market share

Cronbach’s alpha measure of reliability 0.970 0.931 0.727 0.701

46

Table 4 – Factor analysis of country variables (cont.)

Panel C – Standardized scores for the four latent country factors

Country

factor 1

Country

factor 2

Country

factor 3

Country

factor 4

Score Rank Score Rank Score Rank Score* Rank

Finland 1.409 1 -0.300 19 -1.062 31 0.543 9

Sweden 1.296 2 -0.324 21 0.122 15 0.454 12

Switzerland 1.296 3 -0.067 18 -0.147 20 -0.756 26

Denmark 1.295 4 0.118 15 -0.420 25 1.007 7

Netherlands 1.092 5 -0.339 22 0.248 14 0.497 11

Austria 1.043 6 -1.320 32 -0.622 28 0.180 16

Germany 0.953 7 -0.961 28 0.556 11 -1.043 32

New Zealand 0.802 8 0.792 8 0.027 16 1.156 5

Ireland 0.773 9 0.538 11 -0.895 30 2.027 1

United Kingdom 0.687 10 1.337 4 0.875 6 -0.272 22

Japan 0.643 11 -0.412 23 -0.384 24 -2.363 35

Australia 0.592 12 0.972 7 1.262 3 0.450 13

Belgium 0.533 13 -0.836 27 -0.106 19 -0.987 31

France 0.473 14 -0.612 25 0.625 10 -1.427 33

Canada 0.470 15 1.022 6 2.130 2 0.114 17

Hong Kong 0.446 16 2.033 2 -1.236 33 -0.107 21

United States 0.362 17 1.030 5 2.438 1 -0.020 20

Spain 0.129 18 -0.680 26 0.256 13 -0.323 23

Italy 0.082 19 -0.972 29 0.370 12 -0.967 30

Portugal 0.059 20 -1.250 31 -0.311 22 -0.013 19

Singapore 0.019 21 2.310 1 -2.489 35 -0.420 24

Chile -0.186 22 -0.482 24 -1.092 32 1.100 6

Israel -0.197 23 0.449 13 -0.043 17 1.328 4

Taiwan -0.240 24 0.111 16 -0.060 18 -1.805 34

Greece -0.276 25 -1.403 34 -0.274 21 -0.685 25

South Africa -0.830 26 0.757 9 0.668 9 0.723 8

Argentina -0.950 27 -1.448 35 -0.365 23 1.685 2

Mexico -0.972 28 -1.371 33 -0.620 27 1.441 3

Brazil -1.024 29 -1.225 30 0.781 7 0.531 10

Malaysia -1.286 30 1.538 3 -0.434 26 -0.908 29

Thailand -1.359 31 0.478 12 -0.884 29 -0.894 28

India -1.419 32 0.609 10 1.240 4 0.272 15

Indonesia -1.804 33 -0.013 17 -1.748 34 -0.883 27

Philippines -1.856 34 -0.301 20 0.711 8 0.063 18

Pakistan -2.055 35 0.220 14 0.882 5 0.301 14

Table 4 reports the results of factor analysis on 72 country variables for 35 countries. The factor analysis is

performed using the principal components method. The squared multiple correlations cannot be used for the prior

communality estimates in this sample because there are more variables than country observations (i.e. our

correlation matrix is singular). Thus, for tractability, we set prior communality estimates equal to one, The four-

factor outcome represents a balance between (i) explaining a large proportion of the variation, (ii) retaining

factors with substantial incremental explanatory power, and (iii) finding a parsimonious solution. Panel A reports

statistics for the factor analysis. Panel B reports the variable loadings on the four latent country factors (for

clarifity only loadings higher than 0.4 are printed). Panel C reports standardized factor scores for the 35 countries.

Variable definitions are provided in appendix 1. * For consistency across factors we revert the sign of factor.

47

Table 5 – T-statistics from regressions of financial reporting factor (q) on country variable (vi) from simple

unidimensional regression (q = a + c*vi) and multidimensional regressions that exclude the variable of interest from

the factors (q = a+b1*f1+b2*f2+b3*f3 +b4*f4+c*vi) Country variable (vi) Unidim.

t-stat on vi

Multidim.

t-stat on vi

Country variable (vi) Unidim

t-stat on vi

Multidim

t-stat on vi

Anti-director rights 1.59 0.64 Market Cap. to GDP 2.25* -0.63

Assessment of tax evasion 3.60* -1.59 Masculinity -0.37 0.03

Audit spending 3.12* 2.00 Media 2.85* 1.38

Bank money in private sector to GDP 1.81 -0.64 Muslim -1.89 -0.76

Big4 market share 3.64* 0.75 Number of analysts 2.64* 2.06*

Block premium -2.40* -0.68 Number of veto-players -0.18 -1.27

Book tax independence 0.28 -0.70 Other religion 0.32 -1.39

Buddhist -0.94 0.09 Ownership concentration -3.33* -1.77

Catholic -0.97 1.00 Political score 3.44* 0.21

Class action lawsuit 0.37 -0.51 Political stability 2.88* -0.78

Control of corruption 4.78*#† -2.30* Politically connected firms -1.22 1.88

Corruption 4.42*#† -0.91 Power distance -2.99* 0.61

Creditor rights 5.23*#† -0.38 Private control of self-dealing index 2.54* 0.36

Democracy 1.67 0.48 Property rights 3.89*#† -2.08*

Domestic institutional holdings -0.66 0.08 Protestant 3.49* 1.12

Enforcement of accounting standards 3.13* 0.87 Public control of self-dealing -1.23 -2.9*

Enforcement of audit standards 5.46*#† 1.98 Public enforcement sec. regulation 1.11 0.94

English proficiency 3.97*#† 0.01 Regulatory quality 4.64*#† -1.12

Ethnic fractionalization -1.30 -0.3 Religion fractionalization 1.67 -1.76

Ex ante private control of self-dealing 1.83 0.79 Religiousness -2.4* 0.79

Ex post private control of self-dealing 2.80* -0.35 Rule of law 4.35*#† -1.84

Foreign institutional holdings 2.55* 1.51 Secrecy -7.90*#† -2.37*

Foreign investment to GDP 1.92 0.35 Securities reg. disclosure requirements 2.65* 2.38*

GDPc 4.38*#† 1.66 Securities reg. liability standards 2.69* 1.94

Hierarchy vs independence -1.76 -0.79 Strengh of securities regulation 2.25* 1.49

Individualism 5.56*#† 4.03*#† Tax compliance 1.80 1.69

Individualism in income -1.01 1.02 Trust 3.56* 1.38

Information and knowledge 2.30* 0.24 Uncertainty avoidance -4.26*#† -2.04

IPOs to GDP 2.38* 0.50 US cross-listing 0.72 2.91*

Judicial efficiency 5.02*#† -0.37 US institutional holdings 0.62 1.97

Judicial independence 5.21*#† -0.86 Nr. significant country variables 45 (15) 8 (1)

Language fractionalization -1.21 -1.04 Table 5 reports t-statistics for the coefficient “c” on an

individual country variable vj in the following regression

models for 35 countries: (i) a regression of reporting quality

factor q on country variable vj (unidimensional model: q= a +

c*vi), and (ii) a regression using four country latent factors

(from a factor analysis that excludes the individual country

variable vi) plus the individual country variable vi

(multidimensional model: q=a+b1*f1+b2*f2+b3*f3

+b4*f4+c*vi). Variable definitions are provided in Appendix

1. The symbol * indicates statistical significance at the 0.05

level, the symbol # indicates statistical significance at the 0.05

level for Bonferroni adjusted t-stats, and the symbol †

indicates statistical significance at the 0.05 level for Holm

adjusted t-stats.

Language proximity to English 3.42* -0.02

Latitude 2.48* 0.65

Law and order 3.21* -0.58

Legal origin 3.52* -0.49

Legislative competition 0.26 -1.31

Listed firms to population 2.25* -1.80

Long-term orientation -1.10 -1.92

Low political risk 3.53* 0.36

Low repudiation of contracts by govern. 3.46* 0.43

Low risk of expropriation by govern. 3.85*#† 1.11

48

Table 6 – Adjusted R2’s from nested regressions of financial reporting factor (q) on country-level variable (vi)

and 4 factors – Model (1): q = a + c*vi , Model (2): q = a+b1*f1+b2*f2+b3*f3 +b4*f4 , Model (3): q =

a+b1*f1+b2*f2+b3*f3 +b4*f4+c*vi

Explanatory country variable (vi) (1) (2) (3) (1) (2) (3)

Anti-director rights 0.04 0.72 0.71 Market Cap. to GDP 0.11 0.72 0.72

Assessment of tax evasion 0.26 0.73 0.74 Masculinity -0.03 0.71 0.70

Audit spending 0.20 0.69 0.72 Media 0.17 0.72 0.73

Bank money in private sector to GDP 0.06 0.71 0.70 Muslim 0.07 0.72 0.71

Big4 market share 0.27 0.71 0.71 Number of analysts 0.15 0.73 0.75 *

Block premium 0.12 0.73 0.72 Number of veto-players -0.03 0.72 0.72

Book tax independence -0.03 0.72 0.72 Other religion -0.03 0.71 0.72

Buddhist 0.00 0.73 0.72 Ownership concentration 0.23 0.72 0.74

Catholic 0.00 0.72 0.72 Political score 0.24 0.72 0.71

Class action lawsuit -0.03 0.72 0.71 Political stability 0.18 0.72 0.72

Control of corruption 0.39 0.72 0.76 * Politically connected firms 0.01 0.74 0.76

Corruption 0.35 0.72 0.72 Power distance 0.19 0.72 0.71

Creditor rights 0.44 0.73 0.72 Private control of self-dealing index 0.14 0.72 0.71

Democracy 0.05 0.72 0.71 Property rights 0.29 0.72 0.75 *

Domestic institutional holdings -0.02 0.71 0.70 Protestant 0.25 0.70 0.71

Enforcement of accounting standards 0.21 0.72 0.72 Public control of self-dealing 0.01 0.72 0.78 *

Enforcement of audit standards 0.46 0.72 0.74 Public enforcement sec. regulation 0.01 0.71 0.71

English proficiency 0.30 0.73 0.72 Regulatory quality 0.38 0.72 0.72

Ethnic fractionalization 0.02 0.72 0.71 Religion fractionalization 0.05 0.72 0.74

Ex ante private control of self-dealing 0.06 0.71 0.71 Religiousness 0.12 0.72 0.71

Ex post private control of self-dealing 0.17 0.72 0.71 Rule of law 0.35 0.72 0.74

Foreign institutional holdings 0.14 0.71 0.72 Secrecy 0.64 0.69 0.73 *

Foreign investment to GDP 0.07 0.73 0.72 Securities reg. disclosure requirements 0.15 0.72 0.75 *

GDPc 0.35 0.72 0.73 Securities reg. liability standards 0.16 0.72 0.74

Hierarchy vs independence 0.06 0.72 0.71 Strengh of securities regulation 0.11 0.71 0.73

Individualism 0.47 0.70 0.80 *#† Tax compliance 0.06 0.72 0.73

Individualism in income 0.00 0.73 0.73 Trust 0.26 0.71 0.72

Information and knowledge 0.11 0.72 0.71 Uncertainty avoidance 0.33 0.70 0.73

IPOs to GDP 0.12 0.72 0.71 US cross-listing -0.01 0.69 0.75 *

Judicial efficiency 0.42 0.72 0.72 US institutional holdings -0.02 0.70 0.73

Judicial independence 0.43 0.72 0.72 Average Adjusted R2 0.16 0.72 0.72

Language fractionalization 0.01 0.72 0.72 Table 6 reports adjusted R2’s for the following regression

models for 35 countries. Model 1: regression of reporting

outcome factor q on country variable vi (q = a + c*vi); model

2: regression of reporting outcome factor q on 4 country

factors (from a factor analysis that excludes the individual

country variable vj) (q=a+b1*f1+b2*f2+b3*f3+b4*f4 ); and

model 3: regression of reporting outcome factor q on four

country factors from a factor analysis that excludes the

individual country variable vi plus the individual country

variable vi on reporting outcome factor q (q =

a+b1*f1+b2*f2+b3*f3+b4*f4+c*vi). Variable definitions are

provided in appendix 1. The symbol * (#) [†]indicate a

statiscally higher adjusted R2 of model 3 (compared to model

2) at the 0.05 level (with Bonferroni adjustment) [with Holm

adjustment].

Language proximity to English 0.24 0.72 0.72

Latitude 0.13 0.72 0.71

Law and order 0.21 0.72 0.71

Legal origin 0.25 0.72 0.72

Legislative competition -0.03 0.73 0.73

Listed firms to population 0.11 0.73 0.75

Long-term orientation 0.01 0.67 0.70

Low political risk 0.25 0.72 0.71

Low repudiation of contracts by govern. 0.24 0.72 0.71

Low risk of expropriation by govern. 0.29 0.72 0.72

49

Table 7 – The relation between country latent factors and financial reporting factor

Panel A: Descriptive statistics by factor-groups

Mean Median

Low Medium High High-

Low

Low Medium High High

- Low

Country factor 1 -0.752 0.149 0.658 *** -0.701 0.258 0.817 ***

Country factor 2 -0.643 -0.080 0.789 *** -0.587 -0.028 0.797 ***

Country factor 3 0.042 -0.171 0.140 -0.210 -0.406 -0.162

Country factor 4 -0.439 0.185 0.278 ** -0.522 0.229 0.215 **

Panel B: Regression analysis of country factors on financial reporting factor

(1) (2) (3) (4)

Country factor 1 0.557*** 0.557*** 0.557*** 0.557***

(5.581) (6.268) (5.969) (6.426)

Country factor 2 0.502*** 0.502*** 0.502***

(4.173) (4.784) (5.849)

Country factor 3 0.165* 0.165*

(1.992) (2.040)

Country factor 4 0.317***

(3.109)

Adjusted R2 0.318 0.588 0.608 0.719

Table 7 reports univariate and regression results. Panel A reports mean and median values of the reporting

outcome factor by terciles of country factors (description of the single financial reporting factor can be

found in Table 2 and description of 4 estimated country factors can be found in Table 4). Panel B reports

estimation results of regressions of four latent country factors on financial reporting outcome factor.

Heteroskedasticity adjusted t-statistics are presented in parentheses. The symbols ***,**,and * indicate

statistical significance at the 0.01, 0.05 and 0.1 level, respectively, based on two tailed tests.

50

Table 8 – Categories of country variables

Panel A – Variables in each category

Economic Geopolitical Sociological Regulatory Legal

Assessment of tax evasion

Audit spending

Bank money in private

sector to GDP

Big4 market share

Block premium

Domestic institutional

holdings

Foreign institutional

holdings

Foreign investment to GDP

GDPc

Information and knowledge

IPOs to GDP

Listed firms to population

Low political risk

Market Cap. to GDP

Media

Number of analysts

Ownership concentration

Political stability

Politically connected firms

Tax compliance

US cross-listing

US institutional holdings

Democracy

Latitude

Legislative competition

Number of veto-players

Buddhist

Catholic

English proficiency

Ethnic fractionalization

Hierarchy vs independence

Individualism

Language fractionalization

Language proximity to English

Long-term orientation

Masculinity

Muslim

Other religion

Power distance

Protestant

Religion fractionalization

Religiousness

Secrecy

Trust

Uncertainty avoidance

Individualism in income

Judicial efficiency

Judicial independence

Law and order

Low repudiation of contracts by govern.

Low risk of expropriation by govern.

Political score

Property rights

Rule of law

Corruption

Anti-director rights

Book tax independence

Class action lawsuit

Control of corruption

Creditor rights

Enforcement of accounting standards

Ex ante private control of self-dealing

Ex post private control of self-dealing

Private control of self-dealing index

Public control of self-dealing

Public enforcement securities regulation

Regulatory quality

Securities Regulation Disclosure

Requirements

Securities regulation liability standards

Strength of securities regulation

Enforcement of audit standards

Legal Origin

Average Maximum Pairwise

Correlation Among

“Economic” Variables

= 0.624

Average Maximum Pairwise

Correlation Among “Geopolitical”

Variables

= 0.641

Average Maximum Pairwise

Correlation Among “Sociological”

Variables

= 0.617

Average Maximum Pairwise Correlation

Among “Regulatory” Variables

= 0.805

51

Panel B – Explaining financial reporting using category-derived country factors

Economic

(1)

Geopolitical

(2)

Sociological

(3)

Regulatory

(4)

Legal

(5)

All categories

(6)

Economic factor1 0.540*** 0.590**

(5.130) (2.498)

Economic factor2 0.407*** 0.125

(4.125) (1.426)

Geopolitical factor 0.225 -0.050

(1.338) (-0.433)

Sociological factor1 -0.250** -0.340**

(-2.095) (-2.117)

Sociological factor2 0.545*** 0.344***

(5.471) (3.410)

Sociological factor3 0.457*** 0.338**

(4.780) (2.484)

Sociological factor4 -0.054 -0.148

(-0.505) (-1.310)

Legal 0.501*** 0.019

(3.431) (0.094)

Regulatory factor1 0.629*** -0.348

(6.369) (-1.338)

Regulatory factor2 0.364*** 0.533***

(3.080) (3.718)

Adjusted R2 0.467 0.027 0.573 0.549 0.251 0.784

52

Panel C – Correlations between category-derived country factors

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

(1) Financial reporting factor 1

(2) Economic factor1 0.563* 1

(3) Economic factor2 0.425* 0 1

(4) Geopolitical factor 0.235 0.479* -0.230 1

(5) Sociological factor1 -0.261 -0.753* 0.089 -0.586* 1

(6) Sociological factor2 0.569* 0.151 0.321 0.175 0 1

(7) Sociological factor3 0.477* 0.406* 0.114 0.102 0 0 1

(8) Sociological factor4 -0.057 -0.014 0.271 -0.160 0 0 0 1

(9) Legal 0.523* -0.065 0.502* 0.027 0.307 0.590* 0.319 0.246 1

(10) Regulatory factor1 0.656* 0.839* 0.325 0.401* -0.615* 0.406* 0.415* 0.219 0.288 1

(11) Regulatory factor2 0.380* -0.337* 0.554* -0.294 0.513* 0.396* 0.018 0.246 0.729* 0 1

Table 8 reports country variables that are pre-classified into 5 categories (economic, geopolitical, sociological, legal and regulatory). Panel A describes the

variables included in each category. Panel B reports estimation results of regressions of financial reporting quality factor on categories of country factors

(derived from factor analysis of pre-classified factors). Heteroskedasticity adjusted t-statistics are presented in parentheses. The symbols ***,**,and *

indicate statistical significance at the 0.01, 0.05 and 0.1 level, respectively, based on two tailed tests. Panel C reports pairwise correlations. The symbol *

indicates statistical significant at 5% level.

53

Table 9 – Out-of-sample tests for a ‘new’ country attribute and a ‘new’ reporting outcome

Panel A: Correlation between new country attribute (economic freedom index) and country factors

Factor 1 Factor 2 Factor 3 Factor 4

0.714*** 0.480** -0.167 0.080

Number of countries = 27

Panel B: Explaining financial reporting quality with a ‘new’ country variable

(Economic freedom index)

(1) (2) (3) (4)

Economic freedom 0.662*** -0.299 -0.205 -0.176

(5.607) (-1.219) (-0.955) (-0.997)

Country factor 1 0.871*** 0.821*** 0.785***

(3.503) (3.636) (4.078)

Country factor 2 0.708*** 0.699*** 0.652***

(3.758) (4.403) (4.878)

Country factor 3 0.322*** 0.303***

(4.289) (3.846)

Country factor 4 0.194**

(2.212)

Adjusted R2 0.416 0.656 0.759 0.792

Panel C: Explaining a ‘new’ financial reporting outcome (MV/E) with country factors

(1) (2) (3) (4) (5) (5)

Factor 1 -0.132 -0.144 -0.118 0.136 0.226

(-1.196) (-1.169) (-0.959) (0.668) (0.972)

Factor 2 -0.663*** -0.654*** -0.687*** -0.630*** -0.455*** -0.376**

(-4.187) (-4.117) (-4.863) (-4.972) (-2.932) (-2.164)

Factor 3 -0.196 -0.159 0.177

(-1.002) (-0.872) (1.135)

Factor 4 -0.355*** 0.034

(-3.341) (0.337)

Economic

freedom -0.034 -0.077

(-0.154) (-0.330)

BV/E 0.664*** 0.799***

(5.375) (3.941)

Adjusted R2 0.399 0.405 0.416 0.532 0.752 0.754

Table 9 reports univariate and regression results. Panel A reports Pearson correlation between the economic freedom index (see

appendix 1 for definition) and country factors derived from factor analysis of 72 country variables. Panel B reports estimation

results of regressions of country factors and economic freedom on the reporting outcome factor. Panel C repors estimation results of

regressions using country factors and economic freedom on the outcome variable MV/E (market-to-earnings). Heteroskedasticity

adjusted t-statistics are presented in parentheses. The symbols ***,**,and * indicate statistical significance at the 0.01, 0.05 and 0.1

level, respectively, based on two tailed tests.

54

Table 10 – Explaining other economic and financial reporting outcomes

Panel A: Outcome variables – IFRS difference and unexpected earnings

IFRS

difference

Unexpected

earnings

Factor 1 -0.168 -0.084

(-1.306) (-0.891)

Factor 2 -0.693*** -0.546***

(-5.572) (-3.830)

Factor 3 -0.015 -0.176**

(-0.162) (-2.251)

Factor 4 -0.197 -0.134

(-1.389) (-0.909)

Adjusted R2 0.486 0.246

Observations 35 29

Panel B: Outcome variable – capital market development

(1) (2)

Reporting outcome factor 0.381** -0.202

(2.655) (-1.140)

Factor 1 excluding market development 0.308**

(2.839)

Factor 2 excluding market development 0.698***

(2.926)

Adjusted R2 0.107 0.347

Observations = 35

Table 10 reports estimation results of regressions of country factors derived from factor analysis and

reporting outcome factor on economic outcomes. Panel A reports estimation results for IFRS

difference (score measuring differences between domestic standards and IFRS in 21 key accounting

items – Bae et al. 2008), and unexpected earnings (average absolute analyst forecast errors for

forecasts of the current year, one and two-years ahead - Dhaliwal et al 2012). Panel B reports

estimation results for capital market development as a proportion of GDP. Heteroskedasticity adjusted

t-statistics are presented in parentheses. The symbols ***,**,and * indicate statistical significance at

the 0.01, 0.05 and 0.1 level, respectively, based on two tailed tests.

55

Figure 1 – Country scores for financial reporting factor

Greece

Pakistan

Taiwan

Portugal

Brazil

Austria

Indonesia

Philippines

Thailand

Chile

Germany

Argentina

Spain

Malaysia

Italy

Belgium

Japan

South Africa

India

Mexico

Israel

France

Switzerland

Singapore

Denmark

Hong Kong

Australia

Finland

New Zealand

Netherlands

Canada

Sweden

United StatesUnited Kingdom

Ireland

-1.75 -1.50 -1.25 -1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50

56

Figure 2 – Country scores for factor 1

PakistanPhilippines

IndonesiaIndia

ThailandMalaysia

BrazilMexico

ArgentinaSouth Africa

GreeceTaiwan

IsraelChile

SingaporePortugalItaly

SpainUnited States

Hong KongCanadaFrance

BelgiumAustralia

JapanUnited Kingdom

IrelandNew Zealand

GermanyAustria

NetherlandsDenmarkSwitzerlandSweden

Finland

-2.25 -2 -1.75 -1.5 -1.25 -1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1 1.25 1.5 1.75

57

Appendix 1 – Variable definitions and data sources

Panel A – Financial reporting variables

Variable name Brief description Source References

Abnormal return Abnormal returns over the three-day announcement

window around annual earnings announcements

Nguyen and Truong [2013] Core et al. [2015], Pevnzer et al. [2015], Wang

[2014], Landsman et al. [2012], Choy and Zhang

[2011], Armstrong et al. [2010], DeFond et al.

[2007], Lang et al. [2004]

Abnormal volume Abnormal trading volume over the three-day

announcement window around annual earnings

announcements

Nguyen and Truong [2013] Pevnzer et al. [2015], Wang [2014], Landsman et al.

[2012]

Return synchronicity Weighted average R2 of regressions of firm stock

returns around earnings announcement on the

country’s market return, multiplied by minus one

Nguyen and Truong [2013] Karolyi et al. [2012], Brochet et al. [2016], DeFond et

al. [2007]

Disclosure quality Center for Financial Analysis and Research index of

disclosure practices in the country. The index based

on the disclosure of 90 items in firms’ 1995 annual

reports

Bushman et al. [2004] Pevnzer et al. [2015], Nanda and Wysocki [2015],

Nguyen and Truong [2013], Karolyi et al. [2012],

Dhaliwal et al. [2012], Landsman et al. [2012], Kim

and Shi [2012], Choy and Zhang [2011], Hope et al.

[2008], Bushman et al. [2004], Francis et al. [2005],

Bhattacharya et al. [2003], Hope [2003], DeFond et

al. [2007], Dyck and Zingales [2004], Doidge et al.

[2004], La Porta et al. [1998]

Reporting transparency Aggregate score of the following four earnings

management metrics multiplied by minus one: 1)

earnings smoothing 2) the correlation between

accounting accruals and operating cash flows, 3)

magnitude of accruals, and 4) small loss avoidance

Leuz et al. [2003], Leuz [2010] Pevnzer et al. [2015], Srinivasan et al. [2015], Nanda

and Wysocki [2015], Choy and Zhang [2011],

DeFond et al. [2011], Daske et al. [2008], Doupnik

[2008], Francis and Wang [2008], DeFond et al.

[2007], Burgstahler et al. [2006], Lang et al. [ 2006],

Riahi-Belkaoui [2004a], Riahi-Belkaoui [2004b],

Bhattacharya et al. [2003], Land and Lang [2002],

Hung [2001]

Asymmetric timeliness Average country-level association between firms’

earnings and negative stock returns

Bushman and Piotroski [2006] Christensen et al. [2015], Nanda and Wysocki [2015],

Barth et al. [2012], Francis and Wang [2008], Barth

et al. [2008], Lang et al. [2006], Ball et al. [2003],

Ball et al. [2000], Basu [1977]

58

Appendix 1 – Variable definitions and data sources (cont.)

Panel B – Country variables

Variable name Short name Brief description Source References

Anti-director rights AntiDir Revised anti-director rights index Djankov et al. [2008],

La Porta et al. [1998]

Chen et al. [2015], Nguyen and Truong [2013], Kim and Shi [2012], Hope

et al. [2011], Leuz [2010], Francis and Wang [2008], Frost et al. [2006],

Bushman et al. [2004], Dyck and Zingales [2004], Doidge et al. [2004],

Haw et al. [2004], Hope [2003], Leuz et al. [2003], Hung [2001]

Assessment of tax

evasion

LTaxEv Score of low prevalence of tax evasion Djankov et al. [2008],

La Porta et al. [1998]

Dyck and Zingales [2004]

Audit spending AuditSpend Fees of country's ten largest accountig firms

as percentage of GDP

Mueller, Gernon and

Meek [1994]

Hope [2003], Ali and Hwang [2000]

Bank money in

private sector to GDP

BankPriv Claims of deposit money banks on private

sector to GDP

Beck, Demirguç-Kunt

and Levine, [2001]

Beck et al. [2003], Bushman and Piotroski [2006], Bushman et al. [2004]

Big4 market share Big4Share Market share of big 4 audit firms Francis and Wang

[2008]

Nanda and Wysocki [2015], Hope et al. [2008]

Block premium BlockPr Difference between price paid by the control

block and market price

Djankov et al. [2008] Dyck and Zingales [2004]

Book tax

independence

BTaxInd Required book-tax conformity multiplied by

minus one

Li et al. [2016] Ali and Hwang [2000]

Buddhist Budhist Percentage of population that are buddhist Stulz and Williamson

[2003],

CIA Worldfact book

[2003, 2010]

Pevnzer et al. [2015]

Catholic Catholic Percentage of population that are catholic Stulz and Williamson

[2003],

CIA Worldfact book

[2003, 2010]

Pevnzer et al. [2015], Siegel et al. [2011], Dyck and Zingales [2004], Beck

et al. [2003]

Class action lawsuit ClassAction Indicator if class-action lawsuit is available Leuz [2010]

Control of corruption CCorr Score of control of corruption (average 1995-

2013)

Kaufmann, Kraay and

Mastuzzi.

Worldwide

Governance Indicators

Pevnzer et al. [2015], Dhaliwal et al. [2012], Armstrong et al [2010],

Riahi-Belkaoui[2004b], Leuz et al. [2003], La Porta et al. [1998]

Corruption CCorrL Effectiveness of control of corruption La Porta et al. [1998] Dang et al. [2016], Nanda and Wysocki [2015], Dhaliwal et al. [2012],

Karoli et al [2012], Kim and Shi [2012], Kanagaretnam et al. [2011], Kim

et al. [2011], Gupta et al. [2008], Hope et al. [2008], Hope [2003], Leuz et

al. [2003], Morck et al. [2000]

Creditor rights CreditR Legal protection of creditors and borrowers World Economic

Forum

Chen et al. [2015], Kanagaretnam et al. [2011], Kim et al. [2011]

Democracy Democracy Democracy score (autocracy multiplied by

minus one)

Boutchkova et al.

[2012]

Bushman et al. [2004]

Domestic institutional

holdings

InstHoldDom Holdings by domestic instutitional investors

as a proportion of firms’ market value

Covrig, DeFond and

Hung [2007]

Florou and Pope [2012], Callen et al. [2005]

59

Economic freedom EcoFreed Economic freedom index Chen, Chen and Jin

[2015]

Riahi-Belkaoui [2004b]

Enforcement of

accounting standards

EnforAccS Score of accounting and market enforcement

(average 2002, 2005, 2008)

Brown, Prieato and

Tarca [2014]

Christensen et al. [2013], Alford et al. [2003]

Enforcement of audit

standards

EnforAudS Score of auditing enforcement (average

2002, 2005, 2008)

Brown, Prieato and

Tarca [2014]

English proficiency EnglProf Score for the speaking portion of the TOEFL

exam

Brochet, Naranjo and

Yu [2016]

Ethnic

fractionalization

EthFract One minus the Herfindhal index of ethnicity Alesina et al. [2003] Beck et al. [2003], Siegel et al. [2011]

Ex ante private

control of self-dealing

PrivCtrEA Index of ex-ante private control of self-

dealing

Djankov et al. [2008] Leuz [2010],

Ex post private

control of self-dealing

PrivCtrEP Index of ex-post private control of self-

dealing

Djankov et al. [2008] Leuz [2010]

Foreign institutional

holdings

IntHoldFor Percentage of holdings by foreign

instutitional investors

Covrig, DeFond and

Hung [2007]

Yu and Wahid [2014], Karolyi et al. [2012], DeFond et al. [2011], Florou

and Pope [2012], Callen et al. [2005]

Foreign investment to

GDP

ForeignInv Net inflows of investment to acquire 10

percent or more of voting stock in a foreign

enterprise (average 1995-2013)

World Bank

Development

Indicators

Bushman et al. [2004]

GDPc Gdpc GDP per capita (average 1995-2013) World Bank

Development

Indicators

Chen et al. [2015], Core et al. [2014], Srinivasan et al. [2015], Christensen

et al. [2013], Boutchkova et al. [2012], Karolyi et al. [2012], DeFond et al.

[2011], Kanagaretnam et al. [2011], Kim et al. [2011], Lang et al [2010],

Siegel et al. [2011], Leuz [2010], Djankov et al. [2008], Hope et al. [2008],

Bushman et al. [2004], Dyck and Zingales [2004], Doidge et al. [2004],

Riahi-Belkaoui [2004b], Bhattacharya et al. [2003], Leuz et al. [2003],

Land et al. [2002], La Porta et al. [1998]

Hierarchy vs

independence

HierIndep 100+%(should follow instructions)-

%(must be convinced first)

World Values Survey Pevzner et al. [2015]

Individualism IndividH Hosftede individualism score Hofstede [2010] Pevnzer et al. [2015], Nanda and Wysocki [2015], Nguyen and Truong

[2013], Kanagaretnam et al. [2011], Han el al. [2010], Doupnik [2008],

Hope et al. [2008], Ding et al. [2005], Hope [2003]

Individualism in

income

IndividW Index equal to 100+%(completly agree we

need large income difference)-%(completly

agree with income should be equal)

World Values Survey Pevzner et al. [2015]

Information and

knowledge

InfoKnow Score of information and knowledge based

on 9 dimensions (average 2002-2012)

Global Democracy

Rankings

Bushman et al. [2004]

IPOs to GDP IPO Ratio of the equity issued by newly listed

firms to its GDP

Djankov et al. [2008],

La Porta et al. [2006]

Leuz et al. [2003]

Judicial efficiency JudEff Score of judicial efficiency La Porta et al. [1998] Nanda and Wysocki [2015], Kanagaretnam et al. [2013], Dhaliwal et al.

[2012], Kim and Shi [2012], Kanagaretnam et al. [2011], Kim et al.

[2011], Gupta et al. [2008], Hope et al. [2008], Bushman and Piotroski

[2006], Bushman et al. [2004], Dyck and Zingales [2004], Doidge et al.

[2004], Haw et al. [2004], Hope [2003], Leuz et al. [2003]

60

Judicial independence JudIndep Score of judicial independence World Economic

Forum

Bushman and Piotroski [2006]

Language

fractionalization

LangFract One minus the Herfindhal index of language

measure

Alesina et al. [2003] Siegel et al. [2011]

Language proximity

to English

EnglProx The distance between English and the main

language based on a 5-point classification

system, multiplied by minus one

Brochet, Naranjo and

Yu [2016]

DeFond et al. [2011], Frost et al. [2006]

Latitude Latitude Geographic latitude La Porta et al. [1999] Siegel et al. [2011], Beck et al. [2003]

Law and order LawO Score of law and order La Porta et al. [1998] Chen et al. [2015], Brochet et al.[2016], Core et al. [2014], Nanda and

Wysocki [2015], Kanagaretnam et al. [2013], Nguyen and Truong [2013],

Dhaliwal et al. [2012], Kim and Shi [2012], Hope et al. [2011],

Kanagaretnam et al. [2011], Kim et al. [2011], Gupta et al. [2008], Hope et

al. [2008], Hail and Leuz [2006], Frost et al. [2006], Brown and Higgins

[2005], Riahi-Belkhaoui [2004a], Dyck and Zingales [2004], Haw et al.

[2004], Hope [2003], Leuz et al. [2003], Beck et al. [2003]

Legal origin LegalO Indicator of legal origin: Common,

Civil/French,Civil/German,Civil/Scandinavia

Stulz and Williamson

[2003],

La Porta et al. [2006]

Dang et al. [2015], Pevnzer et al. [2015], Chen et al. [2015], Siegel et al.

[2011], Armstrong et al [2010], Leuz [2010], Francis and Wang [2008],

Bushman and Piotroski [2006], Bushman et al. [2004], Doidge et al.

[2004], Dyck and Zingales [2004], Haw et al. [2004], Lang et al. [2004],

Beck et al. [2003], Bhattacharya et al. [2003], Lang et al. [2003a], Leuz et

al. [2003], Beck et al. [2001], Hung [2001], Ball et al. [2000]

Legislative

competition

LegislComp Index of the number of parties competing in

legislative elections

Beck, Demirguç-Kunt

and Levine [2003]

Beck et al. [2001]

Listed firms to

population

ListedF Listed firms per 1000 population World bank

Development

Indicators

Frost et al.[2006], Leuz et al. [2003]

Long-term orientation LTorient Hosftede long-term orientation score Hofstede [2010] Doupnik [2008]

Low political risk LPolitR Index of political risk multiplied by minus

one

Boutchkova et al.

[2012]

Bushman and Piotroski [2006]

Low repudiation of

contracts by govern.

LRepContr Score of repudiation of contracts by the

government multiplied by minus one

La Porta et al. [1998] Dang et al. [2016], Nguyen and Truong [2013], Kim and Shi [2012],

Karoli et al [2012], Kanagaretnam et al. [2011], Kim et al. [2011], Morck

et al. [2000]

Low risk of

expropriation by

govern.

LExprR Score of expropriation by the government

multiplied by minus one

La Porta et al. [1998] Dang et al. [2016], Kanagaretnam et al. [2013], Nguyen and Truong

[2013], Karoli et al [2012], Kim and Shi [2012], Kanagaretnam et al.

[2011], Kim et al. [2011], Bushman and Piotroski [2006], Bushman et al.

[2004], Morck et al. [2000]

Market Cap. to GDP MarkCap Market capitalization to GDP (average 1995-

2013)

World Bank

Development

Indicators

Brochet et al. [2016], Karolyi et al. [2012], DeFond et al. [2011], Djankov

et al. [2008], Frost et al. [2006], La Porta et al. [2006], Bushman et al.

[2004], Dyck and Zingales [2004], Leuz et al. [2003], La Porta [1997]

Masculinity Masculin Hosftede masculinity score Hofstede [2010] Pevnzer et al. [2015], Kanagaretnam et al. [2011], Han el al. [2010],

Doupnik [2008], Ding et al. [2005], Hope [2003]

Media Media Average rank of the media development

(print and television)

Bushman et al. [2004] Kanagaretnam et al. [2013], Frost et al. [2006], Dyck and Zingales [2004],

Haw et al. [2004]

61

Muslim Muslim Percentage of population that are muslim Stulz and Williamson

[2003],

CIA Worldfact book

[2003, 2010]

Pevnzer et al. [2015], Beck et al. [2003]

Number of analysts NrAnal Number of financial analysts follow firms Bae, Tan and Welker

[2008]

Pevnzer et al. [2015], Srinivasan et al. [2015], Choy and Zhang [2011],

Landsman et al. [2012], Covrig et al. [2007], DeFond et al. [2007], Frost et

al. [2006], Bushman et al. [2004], Lang et al. [2004], Hope [2003], Land

and Lang [2002]

Number of veto-

players

Veto Number of veto-players in the political

decision-making process

Beck et al. [2003] Li et al. [2016], Beck et al. [2001]

Other religion OReligion Percentage of population from religions

other than catholic, protestant, buddhist, or

muslim

Stulz and Williamson

[2003], La Porta,

Lopez-de-Silanes and

Schleifer [2006]

Pevnzer et al. [2015], Beck et al. [2003]

Ownership

concentration

OwnConc Percentage of common shares owned by top

three shareholders in the 10 largest firms

Djankov et al. [2008],

La Porta et al. [2006]

Chen et al. [2015], Boutchkova et al. [2012], Hope et al. [2008],

Burgstahler et al. [2006],Bushman and Piotroski [2006], Bushman et al.

[2004], Dyck and Zingales [2004], Leuz et al. [2003]

Political score PolitScore Index of political quality based on 8

dimensions(average 2002-2012)

Global Democracy

Rankings

Boutchkova et al. [2012]

Political stability PolitStab Score of political stability (average 1995-

2013)

Kaufmann, Kraay and

Mastuzzi

Worldwide

Governance Indicators

Boutchkova et al. [2012]

Politically connected

firms

PolitConn Percentage of firms connected to polititians Faccio [2006] Batta et al. [2014], Riahi-Belkhaoui [2004b]

Power distance PowerD Hosftede power distance score Hofstede [2010] Pevnzer et al. [2015], Nanda and Wysocki [2015], Kanagaretnam et al.

[2011], Han et al. [2010], Doupnik [2008], Hope et al. [2008], Ding et al.

[2005], Hope [2003]

Private control of self-

dealing index

PrivCtrIndex Index of ex-ante and ex-post private control

of self-dealing

Djankov et al. [2008] Pevnzer et al. [2015], Siegel et al. [2011], Leuz [2010], Bushman and

Piotroski [2006]

Property rights ProprR Score of property rights World Economic

Forum

Li et al. [2016], Bushman et al. [2004], Beck et al. [2003], La Porta et al.

[1999]

Protestant Protestant Percentage of population that is protestant Stulz and Williamson

[2003],

La Porta et al. [2006]

Pevnzer et al. [2015], Siegel et al. [2011]

Public control of self-

dealing

PublCtr Index of public enforcement of anti self-

dealing

Djankov et al. [2008] Leuz [2010]

Public enforcement

securities regulation

PublicEnf Index of public enforcement (La Porta) La Porta et al. [2006] Dhaliwal et al. [2012], Leuz [2010], Francis and Wang [2008], Bushman

and Piotroski [2006]

Regulatory quality RegQ Score of regulatory quality Kaufmann, Kraay and

Mastuzzi. Worldwide

Governance Indicators

Dang et al. [2015], Christensen et al. [2013], Leuz et al. [2013]

Religion

fractionalization

ReligFract One minus the Herfindhal index of religion

measure

Alesina et al. [2003] Siegel et al. [2011]

62

Religiousness Religness Principal component of religious attendance

and importance of religion in life

World Values Survey McGuire et al. [2012]

Rule of law Law Score of the quality of the rule of law

(average 1995-2013)

Kaufmann, Kraay and

Mastuzzi.

Worldwide

Governance Indicators

Srinivasan et al. [2015], Nanda and Wysocki [2015], Landsman, Maydew

and Thornock [2012], Armstrong et al [2010], Byard et al. [2010], Daske

et al. [2008]

Secrecy Secrecy Uncertainty avoidance plus power distance

minus individualism

Hope et al. [2008] Nanda and Wysocki [2015]

Securities regulation

disclosure

requirements

DisclReg Index of disclosure requirements by security

laws

La Porta et al. [2006] Pevnzer et al. [2015], Core et al. [2014], Nanda and Wysocki [2015],

Kanagaretnam et al. [2011], Leuz [2010], Francis and Wang [2008], Hail

and Leuz [2006], Hope at al. [2006], Haw et al. [2004], Leuz et al. [2003]

Securities regulation

liability standards

LibStand Index of the procedural difficulty in

recovering losses

La Porta et al. [2006] Srinivasan et al. [2015], Leuz [2010], Francis and Wang [2008]

Strengh of securities

regulation

SSecRegul The strength of securities regulation

mandating and enforcing disclosures. Mean

of disclosure requirements index, liability

standard index, and public enforcement

index

Hail and Leuz [2006] Bhattacharya et al. [2003]

Tax compliance TaxComp Tax avoidance spread multiplied by minus

one

Li et al. [2016] Dyck and Zingales [2004], Haw et al. [2004]

Trust Trust Index equal to 100+%(most people can be

trusted)-%(can't be too careful)

World Values Survey Pevzner et al. [2015], Nanda and Wysocki [2015], La Porta et al. [2006]

Uncertainty avoidance UncAvoid Hosftede uncertainty avoidance score Hofstede [2010] Pevnzer et al. [2015], Brochet et al. [2016], Nanda and Wysocki [2015],

Nguyen and Truong [2013], Kanagaretnam, et al. [2011], Han el al.

[2010], Doupnik [2008], Ding et al. [2005], Hope [2003]

US cross-listing USlisting Percentage of American Depositary Receipt

(ADR) trading on a U.S. exchange

Bradshaw, Bushee

and Miller [2004]

Daske et al. [2013], Hope et al. [2013], DeFond et al. [2011], Hail and

Leuz [2009], Daske et al. [2008], DeFond et al. [2007], Lang et al. [2004] ,

Lang et al. [2003a], Lang et al. [2003b]

US institutional

holdings

UShold Total market value of shares owned by U.S.

institutions as a proportion as firms’ market

value

Bradshaw, Bushee

and Miller [2004]

Srinivasan et al. [2015], Florou and Pope [2012], Callen et al. [2005]

63

Appendix 2 – Correlations between country characteristics

Panel A: Correlations between variables

Variable name Short name Absolute correlation >= 0.7 0.5 =< Absolute correlation < 0.7 0.3 =< Absolute correlation < 0.5

GDPc Gdpc

Law, LRepContr, LawO, LExprR, CCorr,

LPolitR, RegQ, CCorrL, Media, PolitStab,

JudEff, ProprR, EnforAudS, Religness,

InfoKnow, PolitScore, JudIndep, Secrecy

IndividH, Latitude, PowerD, Trust,

LTaxEv, BankPriv, EnforAccS

IntHoldFor, CreditR, EthFract, Protestant, NrAnal, IPO,

Democracy, OwnConc, Muslim, TaxComp, LangFract,

PolitConn, IndividW, Big4Share, ListedF, LTorient,

EnglProx, BlockPr

Rule of law Law

CCorr, RegQ, ProprR, LawO, LPolitR,

LRepContr, LExprR, Gdpc, JudIndep,

CCorrL, PolitStab, JudEff, PolitScore,

Religness, LTaxEv, EnforAudS

Secrecy, Media, InfoKnow, IndividH,

Latitude, PowerD, CreditR, Trust,

BankPriv, EthFract, IntHoldFor

Muslim, Protestant, IndividW, Democracy, TaxComp,

EnforAccS, PolitConn, ListedF, IPO, OwnConc,

LangFract, NrAnal, PrivCtrEP, BlockPr, EnglProx,

Big4Share, MarkCap, ForeignInv, ReligFract

Regulatory quality RegQ

CCorr, Law, ProprR, LPolitR, LRepContr,

LawO, PolitStab, Gdpc, CCorrL, LExprR,

JudIndep, JudEff, PolitScore, LTaxEv

Religness, EnforAudS, Media, Secrecy,

InfoKnow, CreditR, BankPriv, PowerD,

IndividH, Muslim, Latitude, ListedF,

Trust

IntHoldFor, EthFract, EnforAccS, IPO, Protestant,

ForeignInv, TaxComp, IndividW, MarkCap, LangFract,

Democracy, Big4Share, PolitConn, PrivCtrEP, NrAnal,

EnglProx, ReligFract, OwnConc

Control of corruption CCorr

Law, RegQ, ProprR, LPolitR, CCorrL,

LawO, JudIndep, PolitStab, Gdpc,

LRepContr, LExprR, JudEff, PolitScore,

Religness, LTaxEv, Secrecy

EnforAudS, Media, InfoKnow, PowerD,

CreditR, IndividH, Latitude, Trust,

Protestant, BankPriv, IntHoldFor

Muslim, EthFract, ListedF, IndividW, PolitConn,

TaxComp, EnforAccS, Big4Share, LangFract, Democracy,

OwnConc, NrAnal, MarkCap, ForeignInv, IPO, EnglProx,

PrivCtrEP, ReligFract, BlockPr, UncAvoid, EnglProf

Law and order LawO

LPolitR, PolitStab, LExprR, LRepContr,

Law, CCorr, Gdpc, RegQ, CCorrL,

ProprR, PolitScore, Religness, JudEff,

JudIndep

Media, EnforAudS, IndividH, InfoKnow,

Latitude, Secrecy, Trust, LTaxEv,

BankPriv, PowerD, Muslim

LangFract, NrAnal, EnforAccS, Democracy, Protestant,

EthFract, IntHoldFor, OwnConc, IPO, CreditR, TaxComp,

Big4Share, IndividW, PolitConn, UShold, ReligFract

Judicial efficiency JudEff

CCorr, Law, JudIndep, CCorrL, ProprR,

Gdpc, RegQ, CreditR, LExprR, LawO,

Religness, LRepContr

Secrecy, LPolitR, PolitStab, EnforAudS,

LTaxEv, IndividH, PolitScore, InfoKnow,

Media, PowerD, ListedF

BankPriv, Latitude, Trust, Protestant, IndividW, EnglProx,

PolitConn, PrivCtrEP, EnforAccS, Big4Share, EthFract,

OwnConc, EnglProf, ReligFract, MarkCap, TaxComp,

LegalO, IPO, UncAvoid, Muslim, ForeignInv, Democracy,

LangFract, OReligion

Corruption CCorrL

CCorr, Law, ProprR, LPolitR, RegQ,

LRepContr, JudEff, LawO, JudIndep,

Gdpc, PolitStab, LExprR, PolitScore,

EnforAudS, Religness

IndividH, Secrecy, Media, Latitude,

PowerD, CreditR, InfoKnow, LTaxEv,

Protestant, BankPriv, Muslim,

Democracy

PolitConn, Trust, IndividW, EnforAccS, NrAnal, ListedF,

ReligFract, Big4Share, LangFract, MarkCap, IntHoldFor,

TaxComp, EthFract, IPO, OwnConc, BlockPr, EnglProx

Judicial independence JudIndep

ProprR, CCorr, Law, RegQ, JudEff,

CCorrL, LRepContr, CreditR, LExprR,

Secrecy, Gdpc, LTaxEv, LawO

LPolitR, PolitStab, PolitScore, Religness,

EnforAudS, Trust, PowerD, BankPriv,

IndividH, Protestant, InfoKnow, Media,

LegalO

Latitude, EnglProx, IntHoldFor, PrivCtrEP, ListedF,

EnforAccS, UncAvoid, OwnConc, EnglProf, IndividW,

MarkCap, ReligFract, Catholic, Democracy, TaxComp,

BlockPr, PolitConn, PrivCtrIndex, EthFract, ForeignInv,

Muslim, Big4Share

Property rights ProprR

JudIndep, Law, CCorr, RegQ, LRepContr,

CCorrL, LExprR, JudEff, PolitStab,

LPolitR, LawO, Gdpc, LTaxEv

Religness, CreditR, PolitScore,

EnforAudS, BankPriv, Secrecy, Media,

Trust, IndividH, InfoKnow

Protestant, PowerD, IntHoldFor, MarkCap, Latitude,

ListedF, IndividW, ReligFract, IPO, PrivCtrEP, EnglProx,

OwnConc, Muslim, PolitConn, EnforAccS, ForeignInv,

TaxComp, EthFract, UncAvoid, BlockPr, Democracy,

LegalO, LTorient, Big4Share, UShold, EnglProf

64

Panel A: Correlations between variables (cont)

Bank money in private

sector to GDP BankPriv

LRepContr, MarkCap, ProprR, LTaxEv,

JudIndep, RegQ, Law, CCorr, LExprR,

Gdpc, CreditR, Religness, LawO, CCorrL,

LTorient

JudEff, EnforAudS, LPolitR, PolitStab, Media,

ReligFract, ListedF, InfoKnow, IndividW, EnforAccS,

IPO, TaxComp, USlisting, BlockPr, IntHoldFor, Trust,

LibStand, OwnConc, PolitScore

Low repudiation of

contracts by gov. LRepContr

LExprR, LawO, Law, Gdpc, ProprR,

RegQ, LPolitR, CCorr, PolitStab, CCorrL,

JudIndep, EnforAudS, Religness,

PolitScore, JudEff

Media, BankPriv, InfoKnow, Secrecy,

IndividH, LTaxEv, Trust, Latitude,

PowerD, EnforAccS, CreditR

IntHoldFor, Muslim, Democracy, LTorient, TaxComp,

NrAnal, OwnConc, IPO, EthFract, Protestant, LangFract,

IndividW, PrivCtrEP, Big4Share, ReligFract, UShold,

BlockPr, ListedF

Low risk of

expropriation by gov. LExprR

LRepContr, LawO, Law, Gdpc, CCorr,

RegQ, LPolitR, ProprR, CCorrL, PolitStab,

JudIndep, EnforAudS, PolitScore, JudEff,

Religness

Media, IndividH, Secrecy, InfoKnow,

Latitude, LTaxEv, Trust, BankPriv,

PowerD, IntHoldFor, NrAnal, EnforAccS

CreditR, Democracy, Muslim, OwnConc, LangFract,

Big4Share, TaxComp, Protestant, LTorient, EthFract,

IPO, IndividW, PrivCtrEP, ReligFract

Assessment of tax

evasion LTaxEv

RegQ, ProprR, CCorr, CreditR, JudIndep,

Law

JudEff, PrivCtrEP, BankPriv, LRepContr,

CCorrL, LExprR, ListedF, Gdpc, LawO,

ReligFract, PolitStab, LegalO, MarkCap,

LPolitR, Secrecy

PrivCtrIndex, EnforAudS, LibStand, Big4Share,

OwnConc, UncAvoid, BlockPr, Religness, InfoKnow,

Catholic, Media, PowerD, Trust, IntHoldFor, IPO,

DisclReg, EnglProx, EnforAccS, ForeignInv, AntiDir,

EnglProf, Protestant, PrivCtrEA, TaxComp, SSecRegul,

PolitScore

Low political risk LPolitR

PolitStab, LawO, Law, CCorr, RegQ,

LRepContr, Gdpc, CCorrL, LExprR,

ProprR, PolitScore, Religness

JudEff, JudIndep, Media, Muslim,

Latitude, EnforAudS, IndividH,

InfoKnow, Secrecy, LangFract, EthFract,

Democracy, LTaxEv

PowerD, BankPriv, Protestant, Trust, TaxComp,

IntHoldFor, IndividW, IPO, PolitConn, NrAnal, CreditR,

EnforAccS, Big4Share, OwnConc

Political stability PolitStab

LPolitR, LawO, CCorr, Law, RegQ,

LRepContr, CCorrL, ProprR, Gdpc,

LExprR, PolitScore

Religness, JudEff, JudIndep, Media,

Muslim, LangFract, Latitude, EnforAudS,

LTaxEv, EthFract, InfoKnow

IndividH, Secrecy, BankPriv, TaxComp, Trust,

Democracy, Protestant, PowerD, IndividW, IPO,

Big4Share, IntHoldFor, PolitConn, CreditR, ForeignInv,

NrAnal, UShold, MarkCap

Foreign institutional

holdings IntHoldFor LExprR, Latitude, CCorr, Law

RegQ, PolitScore, Gdpc, LRepContr, ProprR, InfoKnow,

JudIndep, LPolitR, LawO, Trust, LTaxEv, PolitStab,

CCorrL, Secrecy, Religness, BankPriv, NrAnal, PowerD,

Media, EnforAudS, TaxComp, Protestant, IndividH,

EthFract

Information and

knowledge InfoKnow Gdpc

Religness, CCorr, Law, PowerD, LawO,

LRepContr, RegQ, LExprR, CCorrL,

JudEff, LPolitR, Latitude, EnforAudS,

PolitScore, Media, JudIndep, PolitStab,

Secrecy, Trust, ProprR

EthFract, LangFract, IntHoldFor, Protestant, IndividH,

LTaxEv, BankPriv, TaxComp, PolitConn, EnforAccS,

OwnConc

Media Media Gdpc

LRepContr, LawO, LExprR, LPolitR,

RegQ, Law, CCorr, CCorrL, PolitStab,

EnforAudS, Religness, ProprR,

PolitScore, JudEff, InfoKnow, Latitude,

JudIndep

IPO, NrAnal, IndividH, Secrecy, BankPriv, Trust,

PowerD, EnforAccS, EthFract, LTaxEv, LangFract,

Muslim, TaxComp, ListedF, Protestant, Democracy,

IntHoldFor, OwnConc, MarkCap, LTorient

65

Panel A: Correlations between variables (cont)

Protestant Protestant

Trust, Secrecy, CCorr, JudIndep,

PolitScore, CCorrL, PowerD, IndividH

Law, Latitude, Religness, ProprR, LPolitR, Gdpc, LawO,

JudEff, RegQ, InfoKnow, PolitStab, UncAvoid, Masculin,

LRepContr, EnforAudS, LExprR, Catholic, Media,

CreditR, OwnConc, IntHoldFor, Big4Share, LTaxEv

Religiousness Religness

PolitScore, CCorr, Law, Gdpc,

LawO, LRepContr, CCorrL, JudEff,

LExprR, LPolitR

RegQ, PolitStab, ProprR, InfoKnow,

JudIndep, EthFract, Media, Latitude,

Trust, EnforAudS, PowerD, Democracy,

IndividH, BankPriv

PolitConn, IndividW, Secrecy, Protestant, Muslim,

LangFract, TaxComp, OwnConc, LTorient, LTaxEv,

CreditR, ListedF, IntHoldFor, BlockPr, PublicEnf

Trust Trust

Protestant, Secrecy, JudIndep, Gdpc,

PowerD, Religness, CCorr, LawO, Law,

LExprR, LRepContr, Latitude, PolitScore,

ProprR, InfoKnow, RegQ

IndividH, CCorrL, LPolitR, JudEff, PolitStab,

EnforAudS, Media, Catholic, UncAvoid, IntHoldFor,

TaxComp, LTaxEv, OwnConc, BlockPr, CreditR,

BankPriv, Big4Share, Masculin

Creditor rights CreditR JudIndep, LegalO, JudEff, LTaxEv

ProprR, PrivCtrIndex, Secrecy,

PrivCtrEP, ListedF, UncAvoid, CCorr,

CCorrL, EnforAudS, RegQ, Law,

EnglProf, EnglProx, MarkCap,

PrivCtrEA, BankPriv, AntiDir,

LRepContr

LibStand, ReligFract, DisclReg, LExprR, Catholic, Gdpc,

BlockPr, EnforAccS, ForeignInv, IndividH, Religness,

LawO, LPolitR, Trust, Big4Share, OReligion, PolitStab,

PowerD, SSecRegul, OwnConc, Protestant, TaxComp,

IPO

Market cap. to GDP MarkCap

ListedF, BankPriv, ForeignInv, CreditR,

LibStand, LTaxEv, IPO, DisclReg

ProprR, PrivCtrIndex, SSecRegul, UncAvoid, RegQ,

PrivCtrEA, JudEff, BlockPr, JudIndep, LegalO,

ReligFract, CCorrL, PrivCtrEP, CCorr, AntiDir,

OReligion, EnforAudS, EnglProx, Big4Share, Catholic,

Law, Media, PolitStab, EnglProf

Listed firms to

population ListedF

MarkCap, CreditR, ForeignInv, LTaxEv,

OReligion, PrivCtrEP, PrivCtrIndex,

RegQ, JudEff

IPO, LegalO, EnforAudS, SSecRegul, CCorr, JudIndep,

ProprR, LibStand, Law, BankPriv, DisclReg, PrivCtrEA,

CCorrL, PublicEnf, UncAvoid, EnglProf, AntiDir,

EnforAccS, Religness, Media, Gdpc, Catholic, BlockPr,

LRepContr

Anti-director rights AntiDir

PrivCtrIndex, PrivCtrEA, LegalO,

UncAvoid, CreditR

LibStand, DisclReg, PrivCtrEP, Latitude, SSecRegul,

MarkCap, ListedF, LTaxEv, Catholic

Ex ante private control

of self-dealing PrivCtrEA PrivCtrIndex

LegalO, AntiDir, CreditR, LibStand,

DisclReg, PrivCtrEP, Latitude

UncAvoid, PolitConn, MarkCap, SSecRegul, ListedF,

Catholic, ForeignInv, PublicEnf, LTaxEv, BlockPr,

EnglProf, Democracy

Ex post private control

of self-dealing PrivCtrEP PrivCtrIndex

LegalO, LibStand, DisclReg, CreditR,

LTaxEv, SSecRegul, ListedF, PrivCtrEA

BlockPr, JudIndep, AntiDir, JudEff, OReligion, EnglProf,

OwnConc, ProprR, Catholic, EnforAudS, Law,

UncAvoid, MarkCap, RegQ, PublicEnf, EnforAccS,

CCorr, LExprR, ForeignInv, LRepContr, EnglProx

Private control of self-

dealing index PrivCtrIndex PrivCtrEA, PrivCtrEP, LegalO

CreditR, LibStand, DisclReg, AntiDir,

SSecRegul, ListedF

UncAvoid, LTaxEv, MarkCap, Catholic, BlockPr,

ForeignInv, PublicEnf, EnglProf, OReligion, Latitude,

PolitConn, ReligFract, JudIndep, EnglProx

66

Panel A: Correlations between variables (cont)

Foreign investment to

GDP ForeignInv MarkCap, ListedF

RegQ, CreditR, PrivCtrIndex, PrivCtrEA, ProprR, CCorr,

UncAvoid, PolitStab, InstHoldDom, JudEff, LTaxEv,

PrivCtrEP, EnglProx, JudIndep, Law, IPO, EnglProf,

BTaxInd

Catholic Catholic LegalO, OReligion, BlockPr

CreditR, PrivCtrIndex, UncAvoid, Trust, USlisting,

PrivCtrEP, PrivCtrEA, LibStand, DisclReg, LTaxEv,

JudIndep, OwnConc, Protestant, ListedF, SSecRegul,

MarkCap, Muslim, AntiDir

Other religion OReligion Catholic, ListedF

PrivCtrEP, SSecRegul, IPO, PrivCtrIndex, CreditR,

MarkCap, LegalO, DisclReg, JudEff

Uncertainty avoidance UncAvoid

LegalO, CreditR, Secrecy, EnglProf,

LibStand, DisclReg, EnglProx,

SSecRegul, AntiDir

PrivCtrIndex, PrivCtrEA, LTaxEv, Catholic, Protestant,

MarkCap, JudIndep, PublicEnf, AuditSpend, Trust,

ListedF, PrivCtrEP, Big4Share, ProprR, ForeignInv,

BlockPr, JudEff, CCorr, IPO, ReligFract

Legal origin LegalO CreditR, PrivCtrIndex

PrivCtrEP, DisclReg, EnglProf, Catholic,

LibStand, UncAvoid, PrivCtrEA,

EnglProx, AntiDir, LTaxEv, SSecRegul,

JudIndep

Secrecy, ListedF, MarkCap, JudEff, BlockPr, EnforAudS,

OwnConc, ReligFract, OReligion, PublicEnf, ProprR

Latitude Latitude PolitScore

PowerD, Gdpc, IndividH, CCorrL,

Democracy, LawO, Law, LPolitR, CCorr,

Religness, PolitConn, LExprR, Secrecy,

PolitStab, LRepContr, InfoKnow, Trust,

NrAnal, RegQ, IntHoldFor, Media,

PrivCtrEA, EnforAudS

JudIndep, Protestant, JudEff, ProprR, IndividW, AntiDir,

PublicEnf, Muslim, LangFract, PrivCtrIndex, EthFract,

LegislComp, SSecRegul, EnforAccS

Democracy Democracy PolitScore, Muslim

Latitude, IndividH, PolitConn,

LegislComp, Religness, IndividW,

LPolitR, CCorrL, PowerD

Law, LawO, LRepContr, EthFract, LExprR, Gdpc,

PolitStab, RegQ, EnforAudS, CCorr, Secrecy, JudIndep,

LangFract, ProprR, PublicEnf, Media, JudEff, HierIndep,

PrivCtrEA

Political score PolitScore

Latitude, Democracy, Law,

LPolitR, CCorr, Religness,

CCorrL, IndividH, LawO, Gdpc,

RegQ, PolitStab, LExprR,

LRepContr, PowerD

Muslim, ProprR, JudIndep, Secrecy,

PolitConn, JudEff, EnforAudS, Media,

IndividW, Protestant, Trust, InfoKnow,

EthFract

IntHoldFor, LangFract, NrAnal, PublicEnf, EnforAccS,

TaxComp, OwnConc, Big4Share, Budhist, BankPriv,

LTaxEv

Legislative

competition LegislComp Veto, Democracy

HierIndep, PolitConn, Latitude, LTorient, IndividW,

IndividH, ClassAction

Politically connected

firms PolitConn

PolitScore, IndividW, Latitude,

Democracy, Muslim

Religness, CCorrL, LegislComp, PowerD, CCorr,

PrivCtrEA, Law, JudEff, Gdpc, IndividH, EthFract,

LPolitR, ProprR, RegQ, PolitStab, BTaxInd, EnforAudS,

LawO, JudIndep, InfoKnow, PrivCtrIndex, Secrecy,

LangFract, EnglProf, EnglProx

67

Panel A: Correlations between variables (cont)

Muslim Muslim Democracy

PolitScore, LPolitR, PolitStab, RegQ,

CCorrL, PolitConn, LawO

Law, CCorr, EthFract, IndividW, LangFract, LRepContr,

Religness, LExprR, IndividH, Gdpc, ProprR, Latitude,

Media, EnforAudS, JudEff, Catholic, PowerD, JudIndep

Power distance PowerD Secrecy, PolitScore

Latitude, Gdpc, IndividH, InfoKnow,

CCorrL, JudIndep, CCorr, Law, Trust,

RegQ, Religness, LExprR, LRepContr,

JudEff, Protestant, LawO, EnforAudS,

Democracy

LPolitR, ProprR, PolitConn, PolitStab, Media, LTaxEv,

IndividW, CreditR, PublicEnf, LangFract, IntHoldFor,

EthFract, EnglProx, EnglProf, OwnConc, Muslim

Individualism in

income IndividW PolitConn, PolitScore, Democracy

Religness, Law, Muslim, Latitude, CCorr, CCorrL,

JudEff, PolitStab, RegQ, ProprR, LPolitR, JudIndep,

LangFract, LawO, BankPriv, EthFract, PowerD, Gdpc,

IndividH, LExprR, LRepContr, LegislComp, Budhist,

EnforAudS

US institutional

holdings UShold USlisting

LTorient, BlockPr, ProprR, LRepContr, PolitStab, LawO,

IPO

Domestic institutional

holdings InstHoldDom BTaxInd, ForeignInv

US cross-listing USlisting UShold LTorient, BlockPr, Catholic, BankPriv

Masculinity Masculin Protestant, Trust

Long-term orientation LTorient BankPriv

USlisting, LRepContr, Religness, UShold, LExprR,

AuditSpend, LegislComp, PublicEnf, ProprR, Gdpc,

Media

IPOs to GDP IPO MarkCap

Media, ListedF, EnforAudS, RegQ, Gdpc, BlockPr,

PolitStab, LRepContr, LPolitR, Law, ProprR, LawO,

OReligion, LibStand, SSecRegul, OwnConc, JudEff,

EnforAccS, LTaxEv, BankPriv, LExprR, CCorr, CCorrL,

DisclReg, UncAvoid, ForeignInv, CreditR, EnglProx,

UShold

Block premium BlockPr DisclReg, OwnConc, LibStand, Catholic

SSecRegul, PrivCtrEP, CreditR, IPO, PrivCtrIndex,

LTaxEv, USlisting, MarkCap, LegalO, Trust, UShold,

Law, JudIndep, UncAvoid, ProprR, BankPriv, Religness,

EnforAudS, CCorr, PrivCtrEA, CCorrL, TaxComp,

Secrecy, LRepContr, ListedF, Gdpc

Public control of self-

dealing PublCtr PrivCtrIndex

Strengh of securities

regulation SSecRegul

DisclReg, PublicEnf,

LibStand

PrivCtrEP, PrivCtrIndex, UncAvoid,

LegalO, ClassAction

BlockPr, ListedF, MarkCap, LangFract, PrivCtrEA,

OReligion, EnglProf, IPO, AntiDir, ReligFract, CreditR,

EnforAccS, Catholic, OwnConc, Latitude, LTaxEv

Securities regulation

disclosure

requirements DisclReg LibStand, SSecRegul

PrivCtrIndex, LegalO, PrivCtrEP,

PublicEnf, UncAvoid, PrivCtrEA,

BlockPr, MarkCap

CreditR, AntiDir, ListedF, OwnConc, Catholic,

LangFract, LTaxEv, EnglProf, ReligFract, EnforAccS,

IPO, OReligion, EnglProx, HierIndep, Budhist

68

Panel A: Correlations between variables (cont)

Public enforcement

securities regulation PublicEnf SSecRegul DisclReg, LibStand

ClassAction, Latitude, UncAvoid, PrivCtrIndex, ListedF,

PolitScore, PrivCtrEP, PrivCtrEA, Democracy, PowerD, LegalO,

LTorient, TaxComp, Religness

Securities regulation

liability standards LibStand DisclReg, SSecRegul

PrivCtrIndex, PrivCtrEP, LegalO,

PublicEnf, UncAvoid, PrivCtrEA,

MarkCap, BlockPr

CreditR, LTaxEv, AntiDir, ListedF, EnforAccS, Catholic, IPO,

ReligFract, LangFract, EnglProf, OwnConc, EnglProx, BankPriv,

EnforAudS

Tax compliance TaxComp

EthFract, LPolitR, PolitStab, Law, ClassAction, Religness, CCorr,

RegQ, LRepContr, Gdpc, BTaxInd, Trust, LangFract, JudEff,

LawO, LExprR, ProprR, CCorrL, Media, JudIndep, BankPriv,

InfoKnow, PolitScore, IntHoldFor, PublicEnf, CreditR, LTaxEv,

BlockPr

Book tax

independence BTaxInd

ReligFract, TaxComp, PolitConn, IndividH, InstHoldDom,

NrAnal, ForeignInv, EnglProx

Number of veto-

players Veto LegislComp ClassAction

Language

fractionalization LangFract EthFract, PolitStab, LPolitR

LawO, Muslim, InfoKnow, Religness, PolitScore, Gdpc, RegQ,

SSecRegul, CCorr, Law, TaxComp, LExprR, IndividW, DisclReg,

CCorrL, LRepContr, Media, LibStand, Latitude, Democracy,

PowerD, JudEff, PolitConn, EnforAudS

Ethnic

fractionalization EthFract

LangFract, Religness, LPolitR, PolitStab,

PolitScore, Law

TaxComp, RegQ, Muslim, InfoKnow, Gdpc, CCorr, Democracy,

LawO, JudEff, Media, LRepContr, PolitConn, IndividW, LExprR,

ProprR, Latitude, CCorrL, PowerD, JudIndep, IntHoldFor

Class action lawsuit ClassAction SSecRegul PublicEnf, TaxComp, Veto, ReligFract, LegislComp, OwnConc

Religion

fractionalization ReligFract LTaxEv

CreditR, EnglProx, Big4Share, BankPriv, ClassAction, BTaxInd,

ProprR, OwnConc, JudEff, CCorrL, MarkCap, LibStand,

EnglProf, JudIndep, Secrecy, EnforAudS, SSecRegul, LegalO,

DisclReg, CCorr, IndividH, RegQ, PrivCtrIndex, LExprR,

LRepContr, Law, LawO, UncAvoid

Buddhist Budhist EnglProf, IndividW, PolitScore, DisclReg, IndividH

English proficiency EnglProf EnglProx LegalO, Secrecy, CreditR, UncAvoid

Budhist, EnforAudS, IndividH, PrivCtrEP, Big4Share, JudIndep,

JudEff, PrivCtrIndex, SSecRegul, ReligFract, ListedF,

AuditSpend, DisclReg, LibStand, EnforAccS, LTaxEv, PowerD,

PolitConn, CCorr, PrivCtrEA, ForeignInv, MarkCap, ProprR

Language proximity to

English EnglProx EnglProf Secrecy, CreditR, LegalO, UncAvoid

JudIndep, IndividH, EnforAudS, ReligFract, JudEff, ProprR,

Big4Share, LTaxEv, CCorr, EnforAccS, LibStand, DisclReg,

RegQ, Gdpc, MarkCap, Law, ForeignInv, PowerD, PrivCtrIndex,

CCorrL, PrivCtrEP, PolitConn, AuditSpend, BTaxInd, IPO

69

Panel A: Correlations between variables (cont)

Big4 market share Big4Share

Secrecy, LTaxEv, ReligFract, JudEff, EnglProf, CCorr,

RegQ, CCorrL, IndividH, EnforAudS, LExprR, LawO,

PolitStab, HierIndep, UncAvoid, CreditR, EnglProx, Gdpc,

OwnConc, MarkCap, Protestant, ProprR, Law, PolitScore,

Trust, LPolitR, LRepContr, JudIndep

Audit spending AuditSpend UncAvoid, LTorient, EnglProf, Secrecy, EnglProx

Enforcement of audit

standards EnforAudS

Gdpc, IndividH, LExprR, CCorrL,

LRepContr, Secrecy, EnforAccS,

Law

CCorr, RegQ, JudEff, LawO, ProprR,

JudIndep, Media, PolitScore, LPolitR,

CreditR, NrAnal, Religness, PolitStab,

InfoKnow, PowerD, Latitude

BankPriv, IPO, LTaxEv, ListedF, EnglProx, Trust, EnglProf,

Democracy, OwnConc, PrivCtrEP, Protestant, Big4Share,

LegalO, ReligFract, Muslim, PolitConn, MarkCap,

IntHoldFor, BlockPr, HierIndep, LangFract, LibStand,

IndividW

Enforcement of

accounting standards EnforAccS EnforAudS

IndividH, NrAnal, Gdpc, Secrecy,

LExprR, LRepContr

RegQ, LawO, Law, LibStand, CCorrL, JudIndep, CreditR,

CCorr, JudEff, Media, ProprR, IPO, BankPriv, LPolitR,

PolitScore, PrivCtrEP, ListedF, EnglProx, DisclReg,

LTaxEv, EnglProf, InfoKnow, SSecRegul, Latitude

Ownership

concentration OwnConc BlockPr

LTaxEv, LExprR, Gdpc, Religness, HierIndep, LRepContr,

JudIndep, Secrecy, Law, EnforAudS, JudEff, ReligFract,

PrivCtrEP, LawO, ProprR, DisclReg, IPO, IndividH, Trust,

CCorr, LegalO, Catholic, LibStand, CreditR, CCorrL,

Big4Share, InfoKnow, Protestant, SSecRegul, Media, RegQ,

PolitScore, BankPriv, NrAnal, LPolitR, PowerD,

ClassAction

Individualism IndividH Secrecy, EnforAudS, PolitScore

Gdpc, CCorrL, Latitude, LExprR,

PowerD, JudEff, LawO, Law, EnforAccS,

Democracy, CCorr, LRepContr, LPolitR,

JudIndep, NrAnal, RegQ, Religness,

Protestant, ProprR

PolitStab, Trust, Media, EnglProx, Muslim, EnglProf,

InfoKnow, PolitConn, CreditR, Big4Share, OwnConc,

IndividW, BTaxInd, ReligFract, LegislComp, IntHoldFor,

Budhist

Secrecy Secrecy

IndividH, PowerD, JudIndep,

EnforAudS, CCorr, Gdpc

JudEff, Trust, CCorrL, Protestant, Law,

PolitScore, RegQ, CreditR, LExprR,

ProprR, EnglProx, EnglProf, LRepContr,

LawO, UncAvoid, LPolitR, Latitude,

EnforAccS, InfoKnow, LTaxEv

Religness, LegalO, PolitStab, Big4Share, Media, OwnConc,

NrAnal, Democracy, ReligFract, AuditSpend, IntHoldFor,

PolitConn, BlockPr

Hierarchy vs

independence HierIndep

LegislComp, OwnConc, Big4Share, EnforAudS,

Democracy, DisclReg

Number of analysts NrAnal

EnforAudS, IndividH, EnforAccS,

Latitude, LExprR

LawO, Media, Gdpc, LRepContr, PolitScore, CCorrL, Law,

LPolitR, Secrecy, CCorr, RegQ, BTaxInd, IntHoldFor,

OwnConc, PolitStab

70

Panel B: Statistics of the number of correlated variables

Absolute

correlation >= 0.7

0.5 =< Absolute

correlation < 0.7

0.3 =< Absolute

correlation < 0.5

mean 3.7 7.5 16.9

median 2 7 18

min 0 0 1

max 18 23 37

Appendix 2 reports patterns of correlatations between variables representing country characteristics. Panel A reports the most correlated variables with each one of the country variables.

Panel B reports summary statistics on the number of correlated variables for each group of correlations. Variable definitions are provided in appendix 1.

71

Appendix 3 - Standardized scores of country factors for 47 countries Country Factor 1 Factor 2 Factor 3 Factor 4

Finland 1.555 -0.215 -0.335 0.172

Switzerland 1.459 0.096 0.101 -0.344

Sweden 1.405 -0.443 0.937 -0.164

Austria 1.382 -1.199 -0.512 0.762

Norway 1.373 -0.670 1.489 -0.306

Denmark 1.319 0.109 0.681 0.269

Netherlands 1.176 -0.256 0.738 0.424

Germany 1.171 -1.140 0.741 -0.813

Ireland 0.990 1.081 -0.716 2.120

Japan 0.880 -0.541 -0.765 -2.617

Belgium 0.837 -0.729 -0.333 -0.284

New Zealand 0.792 0.986 0.582 0.684

France 0.729 -0.564 0.169 -0.920

United Kingdom 0.696 1.560 0.905 -0.424

Hong Kong 0.662 2.822 -1.827 0.419

Australia 0.624 1.197 1.192 0.258

Canada 0.517 1.243 1.933 0.203

Portugal 0.471 -1.053 -0.846 0.591

Spain 0.401 -0.586 0.087 0.122

Italy 0.335 -0.926 0.099 -0.621

United States 0.315 1.152 2.267 -0.288

Chile 0.260 -0.036 -1.716 1.452

Singapore 0.159 2.804 -1.859 0.015

Czech Republic 0.120 -0.510 -0.368 -0.409

Greece 0.104 -1.398 -0.756 -0.392

Israel 0.064 0.789 -0.207 1.456

Korea 0.046 -0.421 -0.797 -1.562

Poland 0.045 -0.800 -0.415 0.178

Taiwan -0.093 0.128 -0.562 -1.934

Argentina -0.517 -1.150 -0.783 1.786

Mexico -0.530 -1.115 -0.871 1.784

China -0.580 -0.055 -0.744 -1.828

Russian Federation -0.589 -0.519 -1.012 -0.631

Brazil -0.651 -1.190 0.374 0.949

South Africa -0.791 0.840 0.950 0.315

Turkey -0.820 -0.925 -0.419 -0.263

Colombia -0.962 -0.501 -0.391 1.211

Peru -1.053 -0.424 -0.625 1.539

Malaysia -1.077 1.856 -1.054 -0.949

Thailand -1.136 0.612 -1.075 -1.441

India -1.256 0.674 0.839 -0.026

Kenya -1.370 -0.274 1.413 0.219

Philippines -1.591 -0.138 0.248 0.319

Zimbabwe -1.595 -0.019 1.071 0.210

Indonesia -1.647 -0.196 -0.746 -1.114

Nigeria -1.781 0.003 1.795 -0.099

Pakistan -1.848 0.038 1.123 -0.024

72

Online Appendix 1 to “Financial Reporting Differences Around the World: What

Matters?”

Application of Qualitative Comparative Analysis (QCA)/Fuzzy Sets

1) Terminology and brief explanation

Outcome variable = Financial reporting factor (q) as reported in section 2.1 the paper (i.e. factor from

factor analysis applied to six financial reporting characteristics: reporting transparency, disclosure quality,

abnormal return, abnormal volume, return synchronicity, and timeliness).

Predictor sets = Country factor 1, country factor 2, country factor 3, country factor 4 (factors obtained

from factor analysis on 72 country-level attributes as explained in section 3.3 of the paper).

Sets High presence of set Low presence of set

Country factor 1 F1 f1

Country factor 2 F2 f2

Country factor 3 F3 f3

Country factor 4 F4 f4

QCA/Fuzzy Sets evaluates the relation between the financial reporting outcome factor (q) and all possible

Boolean combinations of country factor sets (e.g., F1 . F2, F1 . f2, F2 . f1, f1 . f2, F1 . F3, F1 . f3, … ;

where operator “.” represents the Boolean “and”). QCA/Fuzzy Sets examines which combinations of sets

or configurations are most likely to be present when the financial reporting outcome q is high.

Country observations are represented in terms of the level of membership in a set, for example the level

of membership of the US in country factor 1 is 0.529. In our context the level of set membership can take

any value between 0 (completely exclusive) and 1 (completely inclusive). Hence our setting is a fuzzy set.

2) Fuzzy set analysis

Table 1 – Combinations of country factors that exist when financial reporting outcome is high

Combinations

Y

consistency

N

consistency F value P-value

Country factors

F1 F2 f3 F4 0.978 0.651 11.48 0.002 High factors 1,2 and 4; Low factor 3

F1 F2 F3 f4 0.946 0.695 4.780 0.036 High factors 1,2 and 3; Low factor 4

F1 F2 F3 F4 0.986 0.593 19.98 <0.001 High factors 1,2, 3 and 4

Table 1 reports the combinations that pass the sufficiency condition “if the combination of country factors

exist then financial reporting outcome (q) also exist”. The three combinations pass the commonly used

73

sufficiency tests. First, the probability that “the combination exists when outcome q exists“ or Y

consistency is significantly greater than the probability that “the combination exists when outcome q does

not exist” or N consistency. Second, the Y consistency exceeds the recommended benchmark value of 0.8

(Ragin 2000, 2006).

Because the three significant combinations may overlap we perform a reduction of the proposed solution

(Table 2).

Table 2 – Final solution: country factors combinations leading to high financial reporting outcome

Combinations

Raw

coverage

Unique

coverage Consistency

Country factors

F1 F2 F3 0.499 0.133 0.983 High country factor 1, 2 and 3

F1 F2 F4 0.455 0.089 0.958 High country factor 1, 2 and 4

Total coverage: 0.588

Total consistency: 0.962

The final reduced solution suggests that there are two recipes of country factors that lead to high quality

financial reporting. The first is a high level of country factor 1 (associated with good institutional and

governance systems, and economic and social welfare), high level of country factor 2 (associated with

strong protection of investors’ rights and capital markets development), and high level of country factor 3

(associated with political transparency, and tax and accounting enforcement). The second is a

combination of high country characteristics represented in factors 1, 2 and 4 (openness of society to

external investment). The two recipes together explain 58.8% of the membership in the financial reporting

outcome, a percentage that can be interpreted as R2 in regression models. The overall solution

consistency of 0.962 indicates that few cases deviate from the two patterns identified in the data

(coverage would be 1 if all countries perfectly fit into one of the two recipes).

Comparison of the statistics between the two recipes suggests that having high country factors 1, 2 and 3

is probably a better recipe to achieve high quality financial reporting. The extent to which that recipe

explains financial reporting (raw coverage), the proportion of cases that the recipe alone explains (unique

coverage), and consistency is higher for the first recipe.

In summary, the Fuzzy Set analysis confirms the regression results discussed in the paper. The Fuzzy Set

analysis suggests that a solution with one single country factor is not sufficient to achieve high quality

financial reporting outcomes. Further, country factor 1 and country factor 2 are necessary but not

sufficient for the desired financial reporting outcome. We conclude that an intertwined combination of

many country characteristics needs to exist in a country for high quality financial reporting to exist.