Capital market development and the (perceived) strength of

auditing and reporting standards

Henry L. Friedman

UCLA Anderson School of Management

August 2016

Abstract: Do stronger auditing and reporting standards (ARS) contribute to the development of

capital markets? Using a survey-based measure that captures executives’ beliefs about ARS in a

panel of nearly 1,000 country-years, this study finds that stronger ARS are robustly associated

with greater equity market development, but are significant in explaining only cross-country,

rather than within-country, variation in credit market development. Furthermore, trust in

politicians complements the perceived strength of ARS, though generalized trust does not. In

countries with high trust in politicians, stronger ARS predict within-country variation in credit

market development and have greater associations with equity market development.

110 Westwood Plaza, D4.02, Los Angeles, CA 90095; henry.friedman@anderson.ucla.edu; phone: (310)

206-1503. I thank David Aboody, Asher Curtis, Jack Hughes, Jaewoo Kim (discussant), Sarah McVay,

Hyung I. Oh (discussant), Bugra Ozel, Suhas Sridharan and seminar participants at London Business

School, Ohio State, Rice, the 2014 UCLA Spring Accounting Mini Conference, the 2015 Tel Aviv

International Conference in Accounting, the 2015 AAA Annual Meeting, and the 2016 FARS Midyear

Meeting. I also thank UCLA’s Anderson School of Management, Center for Global Management, and

Fink Center for Finance and Investments for financial support. Amalia Merino, Tiffany Park, and Justin

Wang provided valuable research assistance.

Capital market development and the (perceived) strength of

auditing and reporting standards

Abstract:

Do stronger auditing and reporting standards (ARS) contribute to the development of capital

markets? Using a survey-based measure that captures executives’ beliefs about ARS in a panel of

nearly 1,000 country-years, this study finds that stronger ARS are robustly associated with

greater equity market development, but are significant in explaining only cross-country, rather

than within-country, variation in credit market development. Furthermore, trust in politicians

complements the perceived strength of ARS, though generalized trust does not. In countries with

high trust in politicians, stronger ARS predict within-country variation in credit market

development and have greater associations with equity market development.

JEL Codes: G15, G18, G38, M41

Keywords: Financial auditing standards; financial reporting standards; equity market

development; credit market development

1

1 Introduction

Auditing and reporting standards (ARS) are a primary lever used by regulators to reduce

information asymmetry that potentially inhibits the development of both credit and equity markets.

Notwithstanding the prominence of credit markets, the extent to which ARS contribute to their

development has yet to be fully examined empirically.1 I address this gap with evidence that

exploits both cross-country and within-country variation across nearly 1,000 country-years.

Turning to equity markets, I re-examine the association between ARS and equity market

development shown in prior studies exploiting primarily cross-sectional variation (La Porta et al.,

2006; La Porta et al., 1998), as cross-sectional results are susceptible to country-level omitted

variables and reverse causality, i.e., that ARS develop in response to well-developed equity

markets (Isidro et al., 2016; Leuz and Wysocki, 2016).2 The panel analysis here includes country-

level fixed effects, lagged dependent variables, and time varying proxies for macroeconomic and

institutional features that influence ARS, mitigating these concerns and allowing for more robust

estimation of the degree to which (perceptions of) stronger financial auditing and reporting

standards facilitate the development of both credit and equity markets.

I use the perceived strength of auditing and reporting standards (PSARS), taken from the

World Economic Forum’s Executive Opinion Survey (EOS), as the main measure of the strength

of ARS.3 Since 2002, the EOS has annually asked thousands of executives worldwide to rate the

1 Credit markets tend to be larger than equity markets, operate in more countries than public stock markets, and

facilitate economic growth beyond that attributable to equity markets (Levine and Zervos, 1998). Descriptive statistics

in Table 5 show that national credit markets tend to be larger than equity markets. 2 As discussed below, a number of studies also exploit panel variation from national shocks (e.g., the introduction of

IFRS) to investigate the effects of reporting standards on capital market development, although there is disagreement

in the literature as to the degree to which these shocks are confounded by concurrent institutional changes and reverse

causality (Barth and Israeli, 2013; Christensen et al., 2013; Leuz and Wysocki, 2016). 3 The terminology of “auditing and reporting standards” is used here because it is consistent with the questions asked

in the EOS (See Table 1). It is not meant to refer to specific written standards (e.g, the FASB’s Accounting Standards

Codification, the IASB’s International Accounting Standards, or PCAOB Auditing Standards).

2

strength of financial auditing and reporting standards in their home county. Their country-year

average answers provide the basis for the PSARS measure. Supporting construct validity, PSARS

is strongly and significantly positively correlated with proxies for disclosure and accounting

standards used in prior literature (e.g., the CIFAR index), as well as with firms’ use of high-quality

accounting standards (either US GAAP or IFRS), analyst following, and high-quality auditor

choice (i.e., use of a Big-4 auditor). PSARS. In contrast to measures used in prior studies, PSARS

is based on the perceptions of executives who are accessing capital markets, offering a wider

perspective on ARS than measures based on specific characteristics of reports and standards per

se.4 In a broad sense, PSARS directly captures the credibility of the financial auditing and reporting

system, and tends to decline after high-profile domestic accounting failures (see Figure 1),

consistent with Giannetti and Wang (2016). As proxies for credit and equity market development,

I use standard measures that capture annual lending from banks, annual lending to the private

sector (i.e., non-government entities), equity market capitalization, and equity trading activity

(e.g,. Beck and Levine, 2002; Djankov et al., 2007; Levine and Zervos, 1998).

In tests focused on credit market development, I find evidence for associations between

PSARS and credit market development in the cross-section, but these associations are not robust

to controls for country effects or lagged levels of credit market development. A possible reason

for the lack of significance in the panel estimates is that there is insufficient unexplained variation

in PSARS after controlling for country-level factors that are highly correlated with both PSARS

4 An additional benefit of relying on a perception-based measure is that perceptions are a necessary mediator if

information quality is to affect markets through agents’ economic actions, since their actions are guided by beliefs.

For example, for risk related to information quality to be priced, the marginal investor must perceive variation in this

quality across firms and use this belief about variation as an input in her financial decisions. This is not necessarily a

behavioral (i.e., non-Bayesian) explanation as beliefs can be based on rational Bayesian updating. Additionally, survey

respondents’ perceptions of standards’ strength is likely to incorporate enforcement and the standards in effect rather

than just the standards as written. As shown in Section 3.2, the PSARS measure is significantly correlated, in the

expected direction, with prior measures of disclosure and reporting quality.

3

and capital market development. Contradicting this explanation, in regressions of equity market

development measures on lagged PSARS, I find that lagged PSARS has significant explanatory

power over and above macroeconomic and capital market factors as well as country and time

indicators. The equity-market results imply that the unexplained variation in PSARS is, in fact,

sufficient to help predict variation in capital market development.

Overall, the evidence does not suggest that better financial auditing and reporting standards

facilitate credit market development, on average, but does suggest that prior results relating

disclosure standards to equity market development are robust to controls for cross-sectional

country-level variation. The credit market results contrast with the suggestion in Djankov et al.

(2007, p. 325) of a credit-market-motivated “role for government in facilitating information

sharing.” The pattern of results is furthermore consistent with a muted effect of public information

on lenders, like banks, who rely on private information and should be less affected by

improvements in public financial auditing and reporting standards (Rajan and Zingales, 1998). The

results are also plausibly consistent with debt being less information-sensitive than equity.

Additional analyses examine cross-sectional variation in the relation between PSARS and

capital market development, motivated by the potential interaction between the quality of

information provided by reporting systems and the trust that investors have in regulators,

managers, and other market participants. With greater trust, market participants should be less

likely to believe that the system is rigged (due to unscrupulous politicians and bureaucrats) or that

financial reports are faulty (due to unethical managers). Conceptually, trust contributes to the

credibility of the financial auditing and reporting system (Gipper et al., 2016). I find that trust in

politicians in particular complements PSARS in facilitating both equity and credit market

development. For both credit and equity market indicators, I find positive and significant

4

associations between lagged PSARS and current capital market development in regimes with

above-median trust in politicians, and that the associations between PSARS and subsequent capital

market development tend to be stronger when public trust in politicians is high. The significance

of PSARS in regimes with high trust in politicians is consistent with enforcement facilitating the

positive effects of reporting standards (e.g., Christensen et al., 2013), as more trustworthy

politicians are likely to be better enforcers. In contrast, splits based on trust in managers and

generalized trust provide mixed results, highlighting the importance of domain-specific trust.

The empirical evidence in this paper is demonstrated in regressions that: control for

variation across countries and across years; control for within-country variation associated with

macroeconomic fundamentals and existing financial market development; and cluster standard

errors by country to account for correlated errors across compatriot observations. As such, the

findings related to PSARS are not subject to the types of concerns about correlated omitted

variables that arise in studies that make inferences based on cross-sectional measures of accounting

and disclosure standards, like the CIFAR index. PSARS is lagged relative to the dependent

variables, mitigating reverse-causality concerns. Additional analyses show that the results are

robust to alternative estimation methods (i.e., System GMM as in Blundell and Bond, 1998), and

additional controls capturing variation in broader institutional protections potentially correlated

with reporting quality (i.e., property rights), cultural-ethical norms that can improve reporting

quality, and growth expectations. These additional controls also help mitigate concerns about

common response bias driving the main results, as the additional controls come from the same

survey instrument as PSARS.

Despite the robustness along several dimensions, the causal effect of an increase in the

strength of standards on capital market development cannot be unambiguously established absent

5

an exogenous and un-confounded shock to the strength of ARS. I cannot rule out the possibility

that, for example, regulators strengthen ARS in anticipation of a positive future shock to capital

market development. However, this channel seems unlikely to drive results, given that the

empirical strategies exploit lead-lag relations while using controls that are plausibly associated

with such expectations. Furthermore, a priori arguments suggest that changes in standards are

generally reactionary rather than anticipatory, designed to attract market participants (Coffee,

2001) in the wake of shocks that here are captured by controls for lagged macroeconomic and

institutional characteristics.

The results contribute primarily to our understanding of factors influencing capital market

development. Several studies based on cross-sectional regressions have shown that legal

protections for investors are associated with capital market development (Djankov et al., 2007;

Spamann, 2010), and that accounting and disclosure quality are strongly associated with equity

market development, (La Porta et al., 2006; La Porta et al., 1998). This study provides more robust

evidence that stronger ARS are associated with larger and more liquid stock markets, while casting

some doubt on the existence of a positive average effect of ARS on credit market development.

Daske et al. (2008) and Leuz and Wysocki (2008, 2016) highlight the paucity of research relating

market-level outcomes to reporting and disclosure standards like ARS, which are important

because market-level outcomes capture externalities and market-wide effects potentially omitted

from firm-level studies. The analysis in this study specifically helps address this gap in the

literature by providing evidence on the associations between PSARS and macro-level measures

related to both economic and capital market development. This study also provides evidence that

the potential effects of PSARS on capital market development depend on public trust in politicians,

suggesting that the returns to investments in PSARS will vary with cultural factors.

6

As a secondary contribution, this paper presents a relatively novel country-year measure

of the strength of ARS to the literature.5 Such a measure can be useful across a number of contexts

because time-series variation allows for tests using PSARS to exploit panel methods that avoid

country-level confounds. Additionally, the breadth of coverage of the PSARS measure (over 100

countries) allows for research on accounting and auditing standards to extend beyond the oft-used

CIFAR sample of around 40-50 countries. An additional benefit of PSARS in the international

setting is that it is possible to capture variation that is potentially influenced by observable

fundamentals (e.g., GDP), but is not measured using firms’ accounting systems. Dechow, Ge, and

Schrand (2010) identify this measurement issue as a significant problem in empirical work

involving financial reporting quality.

2 Related Literature and Hypothesis development

Several studies examine the determinants and implications of ARS and disclosure

standards (see Leuz and Wysocki (2016) for a review focused on regulation). These studies

generally utilize either the introduction of IFRS (e.g., Daske et al., 2008), or cross-sectional

measures available at a single point in time, including the CIFAR index (e.g., La Porta et al., 1998;

Levine et al., 2000), the PWC Opacity Index (Gelos and Wei, 2005), S&P Transparency scores

(Khanna et al., 2004), disclosure measures based on disclosures in prospectuses (La Porta et al.,

2006), and disclosure rules promulgated by exchanges (Frost et al., 2006). Jackson and Roe (2009)

show, using cross-country variation, that equity market development is positively associated with

the resources available to securities regulators (i.e., staffing levels and budgets). As Holthausen

5 Questions from the EOS related to those that form the basis for PSARS have been used to measure corporate opacity

in the cross section of countries in Gelos and Wei (2005) and Jin and Myers (2006), and cross-sectional variation in

regulatory quality in Christensen et al. (2016). Bushee and Friedman (2016) use the PSARS questions as part of a

proxy for disclosure standards.

7

(2009) points out, inferring causality from cross-country analyses is difficult because it is

impossible to rule out reverse causality (i.e., variation in financial development causes variation

in standards and enforcement). Furthermore, only so many controls can be included in samples

with 30-50 cross-sectional country-level observations.

Addressing concerns related to inferences from purely cross-sectional measures, Djankov

et al. (2008) and Djankov et al. (2007) construct time-varying indices. Their evidence suggests

that better information, if available to investors, facilitates capital market development. However,

these studies focus on specific types of information availability: the index in Djankov et al. (2008)

focuses on disclosure related to specific self-dealing transactions that can allow managers to

expropriate value from shareholders and debtors; and the index in Djankov et al. (2007) focuses

on the availability of information about firms’ and individuals’ creditworthiness provided by

private credit bureaus and public credit registries. In contrast, this study uses a proxy that captures

the strength of ARS, more closely related to firms’ periodic reports of ongoing financial

performance. This channel captures broader information than disclosures of self-dealing

transactions and applies specifically to information about firms.

Studies focusing on governance and accounting standards often exploit specific

institutional changes (e.g., the adoption of IFRS or the introduction of SOX in the US or corporate

governance initiatives in the EU) or firm-level auditor choices.6 Inferences based on these can be

potentially confounded by cross-sectional variation in governance norms, contemporaneous

institutional changes, firm-level unobservables, or a firm’s ability to select its auditor (Ball, 2009;

Christensen et al., 2013). Studies in the accounting and finance literatures also frequently use

6 Studies exploiting the adoption of IFRS include Leuz and Verrecchia (2000) and Daske et al. (2008). Lang et al.

(2012) use auditor choice as one of several proxies for firm-level transparency. Christensen et al. (2016) exploit the

staggered implementation of the EU Market Abuse and Transparency Directives to show that securities regulation

enhances firm-level liquidity.

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proxies for accounting quality related to firm-level earnings characteristics and relations between

earnings and other fundamentals, including earnings smoothing, earnings predictability, and

timely loss recognition (Bhattacharya et al., 2003; Lang et al., 2012). Several proxies use stock

returns as a benchmark for the information that should be reflected in earnings, which here would

require an inappropriate implicit assumption of homogeneous price efficiency across

heterogeneously-developed equity markets (see Holthausen (2003), Section 6, for a brief

discussion). Dechow et al. (2010) discuss issues with earnings quality proxies in international

settings, including problems related to measurement noise and the effects of institutional

heterogeneity. They note that institutional differences can imply that higher values of earnings-

and accruals- based proxies could be interpreted differently across countries (i.e., in some cases

reflecting harmful opportunism, in other cases reflecting beneficial transparency).

The PSARS measure used here is available annually at the country-year level for a broad

panel of countries from 2002 through 2013, allowing for both within- and across-country

identification untethered from any particular institutional changes or idiosyncratic firm-specific

decisions. PSARS is based on executives’ perceptions of their surroundings rather than firms’

optimizing choices. This mitigates concerns about selection-based endogeneity, as managers do

not select their beliefs. Furthermore, its construct validity does not rely on assumptions about

market efficiency, and higher values can consistently be interpreted as better.

Regarding outcomes, ARS and related securities regulation have been linked to firm-

specific characteristics including share liquidity, idiosyncratic volatility, and synchronicity

(Christensen et al., 2016; Daske et al., 2008). Using the adoption of IFRS as a positive shock to

the strength of standards, Leuz and Verrecchia (2000) and Daske et al. (2008), for example, show

that the adoption of IFRS is associated with increased liquidity. Jin and Myers (2006) find a

9

negative cross-sectional relation between disclosure transparency and synchronicity, which they

argue reflects the degree of firm-specific information in prices (Morck et al., 2000). Gipper et al.

(2016) use changes in earnings response coefficients around PCAOB inspections to show that

increased auditor oversight can enhance the credibility of accounting earnings to equity investors.

2.1 Hypothesis development

When investors expect better information quality, facilitated by stronger ARS, their need to

price protect should be lower. Better information can reduce uncertainty, potentially leading to

less discounting of future risky cash flows. Additionally, better financial auditing and reporting

can reduce information asymmetry. This can help level the playing field between investors and

managers, between directors and management, and across investor types. The reduction in

information asymmetry can mitigate losses due to agency problems and loosen financial

constraints caused by investors price-protecting. From a behavioral perspective, beliefs in stronger

ARS can facilitate beliefs in well-functioning capital markets and thereby encourage household

participation (Giannetti and Wang, 2016) and trade (Gipper et al., 2016). For creditors, better

contractible information provided by financial reports can also improve the state-contingent

allocation of control rights, lowering the need to price protect at origination (Zhang, 2008). Lower

price protection in credit markets should translate into more loans being granted and expansions

in credit markets. For equity-market investors, lower price protection should be associated with

more trading activity, because price protection acts as a transaction cost when impounded into bid-

ask spreads. Lower price protection should also facilitate higher valuations, easier firm access to

capital, and potentially a greater number of firms listing their equity on the market. These should

all lead to larger domestic stock markets, as measured by market capitalization.7 Based on these

7 There could also be a dampening effect due to compliance costs of more extensive ARS.

10

mechanisms, consistent with prior literature linking institutional features to capital market

development (Djankov et al., 2008; La Porta et al., 2006; La Porta et al., 1998), I propose the first

hypotheses:

Hypothesis 1 PSARS is positively associated with credit market development.

Hypothesis 2 PSARS is positively associated with equity market development.

Financial auditing and reporting standards do not exist in a vacuum. There are additional

features that can facilitate the potential effects of PSARS on capital market development, including

trust that the primary players will dutifully carry out their responsibilities related to auditing and

reporting. Politicians exercise control and oversight over the regulatory regime, while managers

bear primary responsibility for financial reporting quality. When politicians and managers are

more trustworthy, financial reporting can be expected to be more reliable, amplifying the positive

effect of stronger standards on capital market development. Prior research finds that generalized

trust (i.e., trust in others in general) matters in capital markets, in that it is positively associated

with stock market participation (Guiso et al., 2008) and cross-border investment (Guiso et al.,

2009). Bjørnskov (2011) finds that social trust complements the negative effects of formal

institutions on corruption, suggesting that trust can also enhance the ability of high-quality ARS

(an institutional feature) to reduce costs related to corruption or information asymmetry.

Furthermore, more trusting investors might be more likely to believe financial reports and respond

to perceived changes in ARS. These observations lead to the third, fourth, and fifth hypotheses:8

8 Hypotheses 3-5 are based on a complementary relation between trustworthiness and the strength of ARS. If these

are instead substitutes, then trustworthiness would be expected to be negatively related to the association between

PSARS and capital market development. I expect a complementary relation as trustworthy politicians and managers

are an important part of ensuring that strong ARS translate into reliable information. If investors, are more trusting in

general, then the substitutive relation may play a larger role. Across countries, Aghion et al. (2010) find that trust is

negatively correlated with product and labor market regulations that inhibit entrepreneurial entry and constrain

employers. ARS, in contrast, relate specifically to corporate communication, where the receiver’s trust in the sender

is necessary for the communication to be credible and thereby influence the receiver’s decisions (Sobel, 1985).

11

Hypothesis 3 The association between PSARS and capital market development is concentrated

and stronger in settings in which people have high trust in politicians.

Hypothesis 4 The association between PSARS and capital market development is concentrated

and stronger in settings in which people have high trust in managers.

Hypothesis 5 The association between PSARS and capital market development is concentrated

and stronger in settings in which people are more trusting in general.

3 Sample construction and descriptive statistics

3.1 Source and definition of PSARS

The measure of the strength of auditing and reporting standards, PSARS, comes from the

EOS conducted by the World Economic Forum (WEF), which is used as an input to and reported

in the WEF’s annual Global Competitiveness Reports (GCR). The EOS is conducted annually and,

since 2002, has included a question on the strength of ARS. The questions, shown in Table 1, vary

slightly in wording but generally ask respondents to rate the strength of financial auditing and

reporting standards related to company financial performance in the respondents’ countries on a

scale from 1 (extremely weak) to 7 (extremely strong – the best in the world).

Each annually-published GCR provides country-year averages of the responses to these

questions. Gelos and Wei (2005), Jin and Myers (2006), and Fernandes and Ferreira (2009) use

related scores from 1999 and 2000 to measure country-level opacity related to disclosure

standards.9 Christensen et al. (2016) use the scores from 2002/2003 as a secondary proxy for

regulatory quality related to transparency and market abuse. Bushee and Friedman (2016) use the

9 They use responses to “The level of financial disclosure required is extensive and detailed (1 = strongly disagree; 7

= strongly agree)”, which was asked in 1999 and 2000 only. This question about financial disclosure was not asked

after 2000, but the responses to it are highly correlated with the PSARS questions from 2002-2013. Bushee and

Friedman (2016) use the disclosure questions from 1999 and 2000, the PSARS questions from 2002-2009, and the

CIFAR index from the mid-1990s in their index of disclosure quality.

12

scores as part of a proxy for the quality of disclosure standards. I use the country-year responses

as a measure of the strength of ARS, broadly defined. This definition is consistent with the use of

the survey responses in prior work and a natural interpretation of the survey questions.

The EOS is administered in the first quarter of each year in a generally increasing number

of countries over time. In each year survey responses are collected from thousands of executives

through a network of the WEF’s partner institutions. Most respondents are CEOs or at a similar

executive level. The 2013 EOS was administered to over 13,000 executives in 148 countries.10

Values for PSARS from 2006-2013 are downloadable from the data platform on the GCR

website.11 Pre-2006 values were hand collected from the appendices of published GCRs. Because

the WEF does not make the raw survey data available, PSARS values are the WEF-reported

country-year averages.12 Reported country-year scores prior to 2006 were computed simply as

equal-weighted averages. In 2007 the GCR began reporting country-year scores as moving

(weighted) averages based on responses from the current and prior years’ surveys. Surveys from

each year are weighted to emphasize current-year responses while taking into account the country-

specific number of responses in both years. In 2008, the GCR began computing country-year

averages using a weighting process based on respondents’ firms’ sectors (Agriculture,

Manufacturing industry, Non-manufacturing industry, and Services), where the weight applied to

responses is based on the sector’s contribution to GDP. Reported scores since 2008 are a result of

sector-weighting at the country-year level and averaging the current and prior years’ responses to

10 Respondents come from a range of industries, sectors, and firm sizes. The 2013 GCR is titled “The Global

Competitiveness Report 2013-2014,” but corresponds to data from 2013 and before, as it was posted online on

September 3, 2013. Greater detail on the EOS can be found in each annual GCR. 11 Accessed at http://www.weforum.org/issues/competitiveness-0/gci2012-data-platform/ on February 6, 2014. 12 In private correspondence, a WEF Associate Director responsible for coordinating the EOS process confirmed that

“we do not share the respondent level data as it is proprietary to the Forum.”

13

form a moving average score. This moving averaging process is expected to weaken the power of

country-year regression analyses by smoothing variation in PSARS across country-years.

The main sample is based on country-years with PSARS scores reported in the GCRs. The

GCR reports data at the entity-year level, where entities are generally countries, but there are

exceptions (e.g., Hong Kong). I use the term “country” for clarity even though the term “entity”

might be technically more descriptive. All analyses are conducted at the country-year level.

Based on the timing of the surveys, PSARS taken from the GCR in year t is matched to

independent variables measured in year 𝑡 − 1. So, PSARS taken from the 2010 GCR is matched to

2009 values of GDP, capital market development, etc. In other words, the 2009 value for PSARS

for each country is taken from the 2010 GCR, which itself is based on a weighted average of survey

responses received in early 2009 and 2010. This matching procedure is consistent with the WEF’s

matching procedure in calculating their Global Competitiveness Index in that they match surveys

from year 𝑡 to data (e.g., GDP) corresponding to year 𝑡 − 1.

PSARS evolves slowly, leading to significant autocorrelation.13 The regression analyses

presented below address this autocorrelation in two ways. First, standard errors are clustered by

country to account for nonzero correlation across country-specific residuals. Second, all models

on which inferences are based are estimated with country fixed effects. In some tables, I present

regression estimates without country fixed effects, since juxtaposition of estimates with and

without country fixed effects provides information about associations identified using across-

versus within-country variation. The across-country variation is more comparable to prior studies

using static measures of financial auditing and reporting quality.

13 The AR(1) coefficient 𝜌 from estimating 𝑃𝑆𝐴𝑅𝑆𝑘,𝑡 = 𝛼 + 𝜌𝑃𝑆𝐴𝑅𝑆𝑘,𝑡−1 + 𝜀𝑘,𝑡 is 0.96 with a standard error of 0.01.

The coefficient 𝜑 from the estimating Δ𝑃𝑆𝐴𝑅𝑆𝑘,𝑡 = 𝛼 + 𝜑Δ𝑃𝑆𝐴𝑅𝑆𝑘,𝑡−1 + 𝜀𝑘,𝑡 is 0.00 with a standard error of 0.042.

14

Figure 1 shows time-series plots of PSARS for the United States, Italy, Ireland, and India

from 2002 through 2013. Their PSARS time-series are shown to provide a better understanding of

potential its drivers. The United States is mainly shown for reference, since there were numerous

events that plausibly influenced PSARS in this time period. From 2002 through 2013, there were

several accounting scandals (e.g., WorldCom, Enron, HealthSouth, Lehman Brothers) in addition

to regulatory responses aimed at improving financial auditing and reporting quality (e.g., the

Sarbanes-Oxley and Dodd-Frank Acts). Other countries provide starker examples. Italy’s PSARS

score dropped considerably from 2003 to 2004, around the time of the Parmalat accounting

scandal. Ireland from 2008 through 2011 shows a decline of similar magnitude, coincident with

the Anglo Irish Bank accounting scandal around 2008. This decline also coincides with the global

financial crisis and macroeconomic slowdown, illustrating the need to control in subsequent tests

for macroeconomic factors that can influence PSARS. India’s time-series of PSARS scores shows

a modest decline from 2009 to 2011 around the time of the Satyam accounting scandal.

Two caveats must be pointed out before proceeding. First, the EOS focuses on opinions of

executives, mainly CEOs. While their opinions are important, they are far from the only

participants in capital markets or the only producers and users of financial reports. That being said,

executives are likely to be wealthy individuals who are investors in capital markets, and are likely

to use accounting information and disclosures of rivals when formulating strategy (e.g.,

acquisitions, expansions, production, etc.). Therefore, while executives might be thought of

primarily as preparers, their role as users of financial information should not be disregarded.14

Because executives are preparers of financial reports, they may answer with positive bias in an

14 On using surveys of executives in empirical studies, see Dichev et al. (2013), Graham et al. (2005), and Graham

and Harvey (2001) for arguments in favor and Nelson and Skinner (2013) for arguments against.

15

attempt to influence survey-readers to view local accounting and auditing standards as stronger or

more favorable. To the extent that such biases are country-specific and constant, their effects will

be absorbed by country fixed effects in the empirical tests. Second, the EOS measures executive

opinions with noise. This noise could be due to measurement noise (e.g., common response bias)

or other factors influencing executives’ opinions (e.g., general optimism or a desire to influence

survey readers). While the analysis attempts to control for factors that plausibly influence

managerial opinions, this noise could still attenuate statistical relations between proxies and is

expected to work against positive findings in the empirical tests. Robustness checks address

concerns about biases in estimates due to noise in the survey-based proxy.

3.2 Relations between PSARS and extant proxies for reporting quality

In this section I focus on correlations between PSARS and previously-used measures of

accounting, auditing, financial reporting, and disclosure standards and quality. Given the relative

novelty of the PSARS measure, these correlations are presented to provide further support for

PSARS’s construct validity.

First, I explore correlations between country-mean PSARS and several measures used in

prior literature to capture financial reporting or disclosure quality available at the country level.

These prior measures include the extensively-used CIFAR score (e.g., Bushman et al., 2004), the

prospectus disclosure index (La Porta et al., 2006), the O-factor opacity index constructed by

PriceWaterhouseCoopers (Gelos and Wei, 2005), the S&P Transparency and Disclosure Survey

mean country score (Khanna et al., 2004), and the anti-self-dealing business disclosure index

available from the World Bank (Djankov et al., 2008).15 Consistent with PSARS capturing

15 Brown et al. (2014) construct proxies for the quality of the audit environment (BPT_AUDIT) and the degree of

accounting enforcement activity (BPT_ENFORCE) for cross-sections of approximately 50 countries in years 2002,

2005, and 2008. Based on the 149 country-year observations with data available for PSARS, BPT_AUDIT, and

16

variation in financial reporting quality, country-mean PSARS values are positive and significantly

correlated with CIFAR, S&P Transparency, Prospectus Disclosure, and Self-Dealing Disclosure,

and negatively correlated with opacity as captured by PwC’s opacity index. A benefit of the PSARS

score over these measures is that the PSARS score is available at the country-year level, allowing

for time-series variation, and for over 100 countries, representing broader coverage.16

Second, I examine simple correlations between PSARS and proxies for earnings quality,

the fraction of listed firms using Big-N auditors to audit their financial statements, the fraction of

listed firms choosing to report with “high-quality” accounting standards (i.e., US GAAP or IFRS),

and analyst following. These proxies are based on annual firm-level data from Compustat and

IBES. Firm-level data based on fiscal years ending in January through August (September through

December) of year t are matched with country-level PSARS corresponding to year t (year t+1).

Firm-level data is winsorized at the first and 99th percentiles, consistent with prior research. A

benefit of the PSARS measure in the international setting, relative to alternative proxies examined

in this section, is that it is possible to capture variation that is potentially influenced by observable

fundamentals (e.g., GDP), but is not measured using firms’ accounting systems.17

The earnings or accruals quality proxies are calculated as country-year medians of:

absolute accruals (Absolute Accruals), signed accruals (Signed Accruals), timely loss recognition

BPT_ENFORCE, the Pearson correlation between PSARS and BPT_AUDIT (BPT_ENFORCE) is 0.497 (0.457) with

a p-value of less than 0.001. In a regression of PSARS on BPT_AUDIT, BPT_ENFORCE, country indicators, and year

indicators, with standard errors clustered at the country level, neither of the coefficients on the BPT proxies is

significantly different from zero at the 15 percent level or better. This result suggests that most of the covariance

between PSARS and the BPT measures is attributable to country- or year-level variance that would be absorbed by

country- and year-level fixed effects in the main regressions. 16 While the Self-Dealing Disclosure index is also available at the country-year level, it focuses on disclosure related

to self-dealing transactions only rather than financial auditing and reporting standards for ongoing listed companies

more broadly and is available only from 2005 rather than from 2002. As this study uses lagged variables in small-T

(i.e., few years), large-N (i.e., many countries) regressions, the extra years of coverage are particularly useful in

contributing power to statistical tests. 17 Dechow, Ge, and Schrand (2010) identify this measurement issue as a significant problem in empirical work

involving financial reporting quality.

17

(TLR), the Dechow and Dichev (2002) accruals quality measure (DD AQ), the Francis et al. (2005)

measure of discretionary accruals quality (FLOS AQ), and two discretionary smoothing proxies

that Lang et al. (2012) calculate (SMTH1 and SMTH2). Details regarding the construction of the

earnings quality proxies can be found in the Appendix.

Three additional proxies are the median number of analysts making annual earnings

forecasts in the 90 days prior to a firm’s annual earnings announcement (Analysts Following; from

IBES), the fraction of firms preparing their annual financial statements using US GAAP or IFRS

(High Acct Std Frac; from Compustat), and the fraction of firms employing Big-N auditing firms

as the primary auditors of their annual reports (Big N Frac; from Compustat).

Correlations between PSARS and proxies for earnings or accruals quality, analyst

following, accounting standard adoption, and auditor choice are shown in Table 3. Overall, PSARS

is significantly positively correlated with Signed Accruals, Analyst Following, High Acct Std Frac,

and Big N Frac. PSARS has a significantly negative Pearson correlation with Absolute Accruals,

with a negative but insignificant Spearman correlation. PSARS is not significantly correlated with

country-year median values of the earnings quality proxies. The correlations presented in Table 3

are consistent with PSARS capturing greater auditing and financial reporting quality, although

potentially a dimension that is orthogonal to the features captured by common earnings quality

proxies. The correlations are also consistent with Dechow et al. (2010)’s observation that earnings

quality proxies in international settings are noisy, due in part to potentially heterogeneous

interpretations across countries and institutional settings (i.e., in some cases high accruals are more

likely to reflect information, in other cases they are more likely to reflect obfuscation). In contrast,

because it is based on survey responses, PSARS is likely to have a consistent interpretation across

countries and institutional settings.

18

3.3 Definitions and sources of dependent and control variables

Variables representing macroeconomic fundamentals include GDP, GDP Growth,

Unemployment, Inflation, and FDI, which are taken from the World Bank’s World Development

Indicators data set. GDPk,t is GDP per capita in year t for country k measured using current US

dollars.18 GDP Growthk,t is the year-on-year percent change in GDP. Unemployment is the

unemployment rate in percent, and Inflationk,t is the annual inflation rate in percent based on the

local consumer price index. FDIk,t is foreign direct investment (net inflows) as a percent of GDP.

Proxies for institutional quality are taken from the International Country Risk Guide

(ICRG). Monthly values for Corruptionk,t, Law and Orderk,t, Bureaucracy Qualityk,t, and

Investment Profilek,t reported by ICRG are averaged to form annual values. These and similar

indicators have been used extensively in prior research on institutional factors (e.g., La Porta et

al., 1998). At the time of data collection, the ICRG variables were available in a monthly time

series through April 2010. Analyses with ICRG data therefore exclude years after 2010.

Proxies for capital market development also come from the World Development Indicators

data set: Bank Creditk,t is domestic credit provided by the banking sector as a percentage of GDP

in year t; Private Creditk,t is domestic credit provided to the private sector (not just from banks) as

a percentage of GDP in year t; Stock Market Capk,t is the equity market’s total capitalization as a

percentage of GDP in year t; and Stock Trading Volumek,t is the value of listed company stock

traded during year t divided by year-t GDP.

Additional proxies are taken from the EOS and the World and European Values Surveys

(WVS), to facilitate tests of Hypotheses 3, 4, and 5, and robustness checks on the tests of

Hypotheses 1 and 2. The additional proxies from the EOS include PTIP (public trust in politicians),

18 GDP per capita measured in constant US dollars is available for a smaller set of country-years. In regression tests,

year indicators will absorb variation in GDP driven by $US inflation over years.

19

EBOF (ethical behavior of firms), Property Rights, and Capacity for Innovation. Each of these is

available at the country-year level and has coverage similar to that of PSARS. The numerical scores

for these proxies are country-year average responses to prompts in the EOS as reported in GCRs.19

As with PSARS, reported values from the GCR from year t are matched to macroeconomic data in

year 1t . General Trust is taken from the WVS as the fraction of survey respondents in each

country indicating that they believe most people can be trusted. The WVS surveys are not

conducted every year, so General Trust is used as a country-level variable.

3.4 Sample descriptive statistics

Table 4 presents a list of the countries with PSARS available, the number of years available,

and the average country values for PSARS and GDP. The upper limit of 12 observations per

country is based on the number of years for which the EOS included the PSARS question as of the

time of data collection. Finland and New Zealand have the highest average values of PSARS, at

6.21 and 6.18, respectively. Myanmar and Angola have the lowest, at 2.29 and 2.48, respectively.

The number of observations for each country ranges from 1 for Myanmar and Bhutan to 12 for

much of the developed and developing world. Some cross-country comparisons of average PSARS

values (e.g., the United States’ 5.66 compared to South Africa’s 6.28) might defy expectations.

These cross-country differences in means highlight the importance of controlling for country

effects that can mitigate variation based on, for example, country-specific respondents’ biases.

19 The prompts are scored on Likert scales ranging from one to seven. The prompt from the 2013 EOS for PTIP is, “In

your country, how would you rate the ethical standards of politicians? [1 = extremely low; 7 = extremely high].” The

prompt for EBOF from the 2013 EOS is, “In your country, how would you rate the corporate ethics of companies

(ethical behavior in interactions with public officials, politicians and other firms)? [1 = extremely poor—among the

worst in the world; 7 = excellent—among the best in the world].” Property Rights are taken as average responses to

“In your country, how strong is the protection of property rights, including financial assets? [1 = extremely weak; 7 =

extremely strong].” The prompt from the 2013 EOS for Capacity for Innovation is, “In your country, to what extent

do companies have the capacity to innovate? [1 = not at all; 7 = to a great extent].”

20

Descriptive statistics are presented in Table 5. There are 1,443 country-year observations

with PSARS available. These country-years form the basis of the sample. PSARS has a sample

mean of 4.72 (on a one to seven scale) and a standard deviation of 0.90.20 PSARS does not appear

to be skewed, as the median is 4.70, close to the mean. The institutional quality variables from the

ICRG are available only for 1034 country-years, as coverage stops in 2010. Bank Credit and

Private Credit have means of 73.48 and 62.97 percent, respectively.21 Mean Stock Market Cap is

57.52, while mean Stock Trading Volume is 47.82. In general, the descriptive statistics suggest that

credit markets playing a larger role in private sector finance.

Univariate correlations across country-years are shown in Table 6. PSARS is positively

correlated with proxies for macroeconomic and institutional development (e.g., GDP,

Bureaucracy Quality, Corruption, Law and Order, and Investment Profile).22 PSARS is also

positively correlated with measures of capital market development (Stock Market Cap, Stock

Trading Volume, Bank Credit, Private Credit), other proxies from the EOS (Public Trust in

Politicians, Ethical Behavior of Firms, Property Rights, and Capacity for Innovation), and

General Trust from the WVS.23 PSARS is not significantly correlated with FDI. Again, these

20 The means reported in Table 5 are means of country-year observations. The means reported at the bottom of Table

4 are means of country-means, and therefore do not match exactly. 21 Due to data constraints, the following 32 countries are not used in the regressions: Angola, Bhutan, Bolivia, Brunei

Darussalam, Burundi, Cape Verde, Chad, Côte d'Ivoire, Gabon, The Gambia, Guinea, Guyana, Haiti, Iran, Kenya,

Lao PDR, Lebanon, Liberia, Libya, Mozambique, Myanmar, Nigeria, Oman, Puerto Rico, Rwanda, Senegal,

Seychelles, Sierra Leone, Suriname, Swaziland, Timor-Leste, and Yemen. Several countries (42 out of 147, or roughly

29%) have missing values for Stock Market Cap and Stock Trading Volume, in most cases because they lack stock

markets. Because these countries tend to be less developed (average GDP per capita of $3,848 for these countries

versus $12,501 for all sample countries), they are less likely to be covered by the EOS, and they represent only 285

out of 1443 country-year observations (less than 20%). The 18 countries with credit market data but without equity

market data are: Albania, Algeria, Azerbaijan, Bosnia and Herzegovina, Burkina Faso, Cambodia, Cameroon,

Dominican Republic, Ethiopia, Guatemala, Honduras, Lesotho, Madagascar, Mali, Mauritania, Mauritius, Moldova,

and Nicaragua. Negative values for Bank Credit are driven by government holdings of international reserves as

deposits in banks other than the central bank, as deposits have a negative effect on Bank Credit. 22 Recall that higher values of Corruption represent country-years less susceptible to corruption, consistent with

ICRG’s reporting methodology. 23 The positive correlation between PSARS and the proxies for trust (Public Trust in Politicians, Ethical Behavior of

Firms, and General Trust) contrasts with the result in Aghion et al. (2010) that trust is negatively associated with

21

correlations are susceptible to confounds from omitted correlated variables (i.e., GDP is correlated

with everything) and inflated test statistics due to multiple observations of the same country, both

of which are addressed in the regression analyses.

3.5 PSARS and macro/institutional determinants

This section explores macroeconomic, capital market, and institutional fundamentals as

contemporary determinants of PSARS, potentially because they influence demand for accounting

and disclosure quality. The main goal here is to identify covariates that will form the set of relevant

control variables to use in the main tests presented in Section 4. Secondary goals are: i) to

demonstrate the importance of controlling for cross-sectional variation when investigating

relations between institutional and capital market features; and ii) to show how PSARS, which is

based on surveys, might be affected by market characteristics not examined in Section 3.2.

The estimates in Table 7 are from regression estimates of the following equation

, , ,k t t k t k tPSARS Γ X (1)

where Xk,t is a vector of proxies for macroeconomic fundamentals, institutional quality, and capital

market development. Regressions presented in Table 7 are estimated on variables standardized to

be mean-zero and unit-variance, so that the coefficients can be interpreted as expected standard

deviation changes in expected PSARS with standard-deviation changes in underlying variables.

All regressions shown in Table 7 include year fixed effects to account for global shocks, and the

even-numbered regressions also include country-level fixed effects to account for unobservable

factors at the country level. The odd-numbered models do not include country fixed effects in order

to allow for comparisons of regression coefficients estimated using all variation with coefficients

regulation. However, Aghion et al. (2010) focus on regulation that inhibits entry and constrains employers, while the

focal regulation here relates to the production and transmission of information by firms, which is likely to have a more

complementary relation with trust because trust facilitates and complements communication.

22

estimated from only within-country variation. Standard errors are clustered at the country level to

adjust for correlation in ,k t across same-country observations.

The regressions in Table 7 overall suggest that much of the variation in PSARS can be

explained by GDP, a state variable related to economic recession or growth (Unemployment,

Inflation, or GDP Growth), the size of the stock market relative to the country’s GDP, and country

and year fixed effects. These factors are also plausibly associated with subsequent capital market

development (Garcia and Liu, 1999), meaning that it is crucial to control for them when

investigating potential effects of PSARS on capital market development. The results in Table 7

further highlight the importance of using panel data to investigate the effects of PSARS on capital

market development, as a cross-sectional study bears a significant risk of attributing variation in

capital market development to variation in accounting, auditing, or disclosure factors when it is

plausibly driven by other country-specific features. As the inclusion of country fixed effects makes

associations between PSARS and each of FDI, Bureaucracy Quality, Corruption, Law and Order,

and Investment Profile insignificant, I do not include these as controls in the later tests. The

exclusion of the institutional quality proxies taken from the ICRG is further motivated by the

limitation their inclusion places on sample size, since they are available only through 2010. Other

potential controls that are static at the country level (e.g., legal origin) are omitted because their

variation is absorbed by the country fixed effects.

4 Effects of PSARS on capital market development

4.1 Least-squares fixed-effects panel regressions

Table 8 presents regressions that relate PSARS to proxies for stock market development,

based on estimates of the following equation,

23

, , 1 ,t ,*k t t P k t k k tCMD PSARS Γ X (2)

where CMD is one of the following proxies for capital market development: Private Credit, Bank

Credit, Stock Market Cap, or Stock Trading Volume. Xk,t is a vector of controls including

log(GDP), GDP Growth, Unemployment, Inflation, CMDk,t-1, and country and year fixed effects.

The control variables are selected based on the regressions presented in Table 7, with the goal of

including controls for macroeconomic and institutional features that are plausibly associated with

both PSARS and CMD (i.e., Table 7 helps identify potential confounds). The R2 statistics from the

regressions presented in Table 7 are high, ranging from 0.744 in Model 1 to 0.949 in Model 4,

implying that the 𝛽𝑃 coefficient in (2) is estimated based on a potentially small fraction of the total

variation in PSARS. Econometrically, this is a reasonable approach given a greater concern for

type 1 error (incorrectly rejecting the null) than for type 2 error (incorrectly failing to reject the

null). The fact that the R2 statistics presented in Table 7 are so high is precisely why researchers

should be concerned about associations between reporting quality and capital market development

being attributable to, for example, unobservable country-level factors.

The model estimates presented in the odd-numbered columns of Table 8 exclude country-

level fixed effects and CMDk,t-1, allowing the estimates of 𝛽𝑝 to depend on cross-sectional

variation. These models are presented for comparison with the even-numbered models to show

how the estimated effects of PSARS depend on whether cross-sectional variation is exploited.

Standard errors in all models are clustered by country, and all variables are standardized to be

mean-zero and unit-variance.

Models 1 through 4 focus on debt markets, presenting regressions with Private Credit and

Bank Credit as dependent variables, respectively. In Models 1 and 3, which exploit cross-sectional

variation, the coefficients on PSARS are positive and significant at the one percent levels (𝛽𝑝 =

24

0.332 in Model 1 and 𝛽𝑃 = 0.311 in Model 3). In Models 2 and 4, however, the inclusion of

lagged dependent variables and country fixed effects as regressors yields coefficients on PSARS

that are insignificantly different from zero. The insignificance in Models 2 and 4 could be

attributable to insufficient unexplained variation in PSARS after controlling for country-level

factors that are highly correlated with both PSARS and capital market development. However, the

significant results from equity market development regressions, presented in Models 5 through 8

and discussed below, suggest that insufficient unexplained variation in PSARS is not the culprit,

as this would tend to cause insignificant results there as well. Overall, the results do not support a

robust relation between PSARS and credit market development.

Shifting to equity market development, Models 5 and 6 have Stock Market Cap as the

dependent variable. The coefficient on lagged PSARS in Model 5 is positive ( 0.528)P and

significant at the one percent level. In Model 6, the coefficient on lagged PSARS in remains

positive, but is smaller ( 0.091)P and significant at the ten percent level. Notably, the

coefficient in Model 6 is less than one fifth of the magnitude of the coefficient in Model 5.

In Models 7 and 8, Stock Trading Volume is the dependent variable, and the coefficient on

lagged PSARS is positive and significant at the one percent level (𝛽𝑃 = 0.328 in Model 7 and

𝛽𝑃 = 0.218 in Model 8). The regression results presented in Models 5 through 8 suggest a positive

effect of PSARS on subsequent levels of stock market development that is robust to country and

time effects, plausible macroeconomic determinants, and the pre-existing level of stock market

development. Furthermore, they suggest that cross-sectional regressions of equity market

development proxies on PSARS plausibly overstate both the magnitude and the significance of the

association.

25

4.2 PSARS and trust

This section presents results involving tests of Hypotheses 3, 4, and 5, which suggest that

the association between PSARS and capital market development should be stronger in settings with

greater trust in politicians, managers, and in general. These tests are operationalized in estimates

of the following regression equation:

, , , 1 , , 1 , , 1 , , 1 ,t ,* * * *k t t P LowT k t LowT k t P HighT k t HighT k t k k tCMD PSARS PSARS 1 1 Γ X (3)

where CMD is one of the four indicators for capital market development, , , 1LowT k t1 is an indicator

for below-median trust, and , , 1HighT k t1 is similarly an indicator for above-median trust. The

indicator variables correspond to trust in politicians, trust in managers, or generalized trust, as

captured by EOS questions about public trust in politicians (PTIP) and the ethical behavior of

firms (EBOF), and the WVS questions about trust in others. As in prior regressions, Xk,t represents

a vector of controls related to macroeconomic factors and lagged CMD. , , 1HighT k t1 is also included

in regressions using PTIP and EBOF splits, as these are time-varying and will not be absorbed by

fixed effects. Essentially, equation (3) corresponds to the Models in Table 8, but with the

coefficient on PSARSk,t-1 allowed to vary across a median split of indicators for trust.

Table 9.A shows estimates of equation (3) with the coefficient on PSARS varying with the

median-split on PTIP, which captures public trust in politicians. Models 1 and 2 have, respectively,

Private Credit and Banking Credit as dependent variables. In these models, ,P HighT is positive

and significant at the five and ten percent levels, while ,P LowT is not significantly different from

zero. Additionally, in both models, , ,P HighT P LowT is positive and significant at the five percent

level, supporting Hypothesis 3, and providing evidence supporting Hypothesis 1 in high-trust

regimes. Model 3 has Stock Market Cap as the dependent variable. The coefficient on

26

, 1 , , 1*k t LowT k tPSARS 1 is near zero and not significant, while the coefficient on

, 1 , , 1*k t HighT k tPSARS 1 is positive and significant at the five percent level. The coefficients are

significantly different from each other at the ten percent level, providing additional support for

Hypothesis 3. With Stock Trading Volume as the dependent variable, in Model 4, ,P LowT and

,P HighT are positive and significant at the five and one percent levels, respectively. ,P HighT is

significantly higher than ,P LowT (p < 0.05), further supporting Hypothesis 3.

Table 9.B shows estimates of equation (3) with the coefficient on PSARS varying with the

median-split on EBOF, which captures perceptions of the ethical behavior of firms, used here as a

proxy for trust in managers. In Model 1, with Private Credit as the dependent variable, ,P HighT is

positive and significant at the five percent level, but not significantly higher than ,P LowT . In Model

2, which uses Banking Credit as the dependent variable, neither ,P HighT nor ,P LowT is

significantly different from zero, and they are not significantly different from each other. In Model

3, with Stock Market Cap as the dependent variable, ,P HighT is positive and significant at the ten

percent level and ,P LowT is insignificantly different from zero, but the coefficients are not

significantly different from each other. In Model 4, however, with Stock Trading Volume as the

dependent variable, ,P HighT is positive and significantly different from both zero and ,P LowT at

the one percent levels. Overall the estimates in Table 9.B provide moderate, mixed support for

Hypothesis 4.

Table 9.C shows estimates of equation (3) with the coefficient on PSARS varying with the

median-split on General Trust at the country level, which captures beliefs about whether others

can be trusted, or whether individuals should exercise care, in general. In Models 1, 2, and 3 with

27

Private Credit, Banking Credit, and Stock Market Cap as the dependent variables, neither ,P HighT

nor ,P LowT is significantly different from zero or each other. In Model 4, with Stock Trading

Volume as the dependent variable, ,P HighT is positive and significantly different from zero and

,P LowT at the one and five percent levels, respectively. The estimates in Table 9.C provide support

for Hypothesis 5 only with respect to equity trading volume, i.e., that generalized trust

complements PSARS only with respect to market development capture by aggregate transactions

with anonymous counterparties.

Overall, a comparison of the panels suggests that the effects of PSARS on capital market

development vary with trust in politicians and managers, principal players in regulated financial

markets, but not with trust in general. In particular, the positive effects of PSARS on both equity

and credit market development are concentrated in settings characterized by greater trust in

politicians, and higher in these settings than when trust in politicians is low. Trust in politicians

therefore complements PSARS in facilitating multiple facets of capital market development,

plausibly due to the importance of politicians in the regulatory and enforcement processes.

4.3 System GMM

The results presented above suggest positive effects of PSARS on stock market

development. Due to concerns of coefficient bias driven by correlated omitted variables and

reverse causality, regressions in Table 8 include country fixed effects and dependent variables.

However, the lagged dependent variables are by construction correlated with the error terms, which

introduces a bias in coefficients on the order of 1/T, where T is the number of years in the data set

(see Nickell (1981), Baltagi (2013), and Roodman (2009)).

To address such bias, I use System GMM based on Blundell and Bond (1998) and Arellano

and Bover (1995) to estimate coefficients and standard errors. The System GMM estimator

28

accounts for endogeneity of the right hand side variables and provides consistent estimates in

panels like the one here, with relatively large N (number of countries) and small T (Levine et al.,

2000). The equation to be estimated is

, , 1 , , ,*k t k t P k t k t k t k tCMD CMD PSARS Γ X (4)

where CMD is either Stock Market Cap, Stock Trading Volume, Private Credit, or Banking Credit,

X is a vector of macroeconomic indicators as in the regressions shown in Table 8, and αk and αt

are country and year fixed-effects.24 I briefly describe the estimator here and refer interested

readers to Roodman (2009), on which much of this discussion is based.

The data are first transformed using forward orthogonal deviations (Arellano and Bover,

1995).25 The Blundell and Bond (1998) estimation technique involves estimating two equations.

The first equation is (4) using variables transformed using forward orthogonal deviations, where

lagged levels of variables (e.g., 𝑥𝑘,𝑡−𝑗 with 𝑗 ≥ 1) are used as instruments for transformed current

variables, 𝑥𝑘,𝑡∗ . The second equation is (4), not transformed, using lagged transformed variables

(e.g., 𝑥𝑘,𝑡−𝑗∗ , with 𝑗 ≥ 1) as instruments for current non-transformed variables, 𝑥𝑘,𝑡. The estimator

exploits the moment conditions implied by the use of these instruments for both equations.

Three features of System GMM are important to highlight before continuing. First, the

estimated errors should not display second-order autocorrelation. Empirically, additional lags of

24 Equation (4) uses contemporary PSARS as a right-hand-side (RHS) variable because the System GMM estimation

treats the RHS variables as endogenous, while Equations (2) and (3) use lagged PSARS because fixed effects least

squares regressions treat RHS variables as exogenous. 25 Like first differencing, this transformation eliminates the individual error components. Unlike first differencing, it

is robust to gaps in panel data and keeps lagged variables orthogonal to contemporaneous error terms. The

transformation involves subtracting the average of all future available observations of a variable from the current value

of the variable. Let 𝑥𝑘,𝑡 be the original variable and 𝑥𝑘,𝑡∗ be the transformed value based on forward orthogonal

deviations. Then 𝑥𝑘,𝑡∗ = √

𝑇𝑘,𝑡

𝑇𝑘,𝑡+1(𝑥𝑘,𝑡 − ∑

𝑥𝑘,𝑠

𝑇𝑘,𝑡𝑠>𝑡 ), where 𝑇𝑘,𝑡 is the number of future available observations of 𝑥𝑘,𝑡.

Transformed values are available for all but the last observations of 𝑥𝑘,𝑡. With forward orthogonal deviations, lagged

values of 𝑥𝑘,𝑡 (e.g., 𝑥𝑘,𝑡−𝑗 with 𝑗 ≥ 1) are not used in the computation of 𝑥𝑘,𝑡∗ , meaning they are not by construction

correlated with the contemporaneous error terms, 𝜀𝑘,𝑡.

29

the dependent variable can be included as independent variables until the higher-order error

autocorrelation disappears. Second, System GMM estimation allows for the researcher to specify

the maximum number of lagged variables to include as instruments in a regression. In tests below,

I either restrict the number of lags to two or leave them unrestricted. Third, I adopt the Windmeijer

(2005) correction to adjust for potentially downward-biased standard errors.

System GMM estimates of the parameters in equation (4), with Stock Market Cap and

Stock Trading Volume as the dependent variables, are presented in Table 10. Overall the System

GMM estimates shown in Table 10 corroborate the inferences from Table 8, that PSARS is

associated with stock market development.26 System GMM estimates with Private Credit and

Banking Credit as the dependent variables (not reported) result in coefficients on PSARS that are

positive but not significant. The inference from these results is the same as the inference from the

earlier estimates in Table 8, that there is insufficient evidence to reject the null of no association

between PSARS and credit market development.

4.4 Additional controls from the Executive Opinion Survey

The goal has been to present main analyses that are robust to or directly address

endogeneity concerns related to correlated omitted variables and reverse causality. Absent a source

of exogenous variation, concerns remain that the coefficient on PSARS is biased by, for instance,

institutional quality, corporate culture, or growth expectations. Common response bias in the EOS

responses is also a potential problem. To address these concerns, this section presents estimates of

26 The coefficient magnitudes are not directly comparable to the coefficients shown in Table 8 because those

regressions were estimated on standardized variables. For comparison, based on the coefficient of 10.8 in Model 1 of

Table 10, a one standard-deviation change in PSARS (0.9) is associated with an expected change in Stock Market Cap

of approximately 0.9 * 10.8 = 9.72, which is approximately 0.15 standard deviations (9.72/64.85 = 0.15). This

estimate, 0.15, is quite near the estimated coefficient of 0.091 from Table 8, Model 6. The estimated coefficient in

Table 10, Model 2, which uses all potential lagged variables as instruments, is also positive and significant at the one

percent level (𝛽𝑃 = 9.55). This estimated coefficient corresponds to a 0.13 standard deviation change in Stock Market

Cap with a standard deviation change in PSARS, which also comports well with the estimates in Model 8 of Table 8.

30

Model 6 from Table 8 supplemented with additional explanatory variables taken from the EOS. I

use Property Rights to control for legal protections and institutional quality, EBOF to capture a

positive dimension of managerial culture, and Capacity for Innovation to capture growth

expectations. Including measures from the EOS also helps address common response bias

concerns, as this bias should influence both PSARS and the additional controls, implying

covariance that would not be used to estimate 𝛽𝑝 in the regressions.

In Model 1, which includes Property Rights, the coefficient on PSARS remains positive

and significant at the ten percent level, and the coefficient on Property Rights is negative but not

significant, potentially because of collinearity between Property Rights and the other independent

variables. The coefficient on PSARS in Model 2 is positive and significant at the five percent level

with the addition of lagged EBOF as a supplementary control. In Model 3, with Capacity for

Innovation as the additional explanatory variable, the coefficient on PSARS is positive and

significant at the ten percent level, and roughly the same as in Model 6 of Table 8.

The estimates presented in Table 11 support the inference from Table 8, Model 6, and help

provide assurance that the results supporting Hypothesis 2 are not attributable to common response

bias on the EOS, or to PSARS capturing other constructs, like property rights, culture, or growth

prospects. Unreported analyses replicating Table 11, but with Stock Trading Volume as the

dependent variable, support the inference from Model 8 of Table 8.

5 Discussion and Conclusion

This study uses a panel measure, PSARS, to examine the potential effects of stronger

financial auditing and reporting standards on both credit and equity market development.

Consistent with the need to exploit a panel setting, I find that a large degree of variation in PSARS

can be explained by country and year fixed effects, GDP, an additional macroeconomic state

31

variable (e.g., GDP growth) and stock market development. The large degree of variation in PSARS

attributable to fixed effects and macroeconomic indicators calls into question the ability to draw

robust inference from cross-sectional studies of associations between proxies for auditing and

reporting quality and capital market development (e.g., La Porta et al., 1998).

The main findings are as follows. First, PSARS is not significantly associated with credit

market development on average, suggesting that the strong positive cross-sectional association is

attributable to possibly-unobservable country-level factors. However, in regimes with high trust in

politicians, PSARS is associated with subsequent credit market development. Second, PSARS is

associated with subsequent levels of equity market development, on average. The strongest results

obtain when equity market development is measured by trading volume relative to GDP, consistent

with market-level PSARS lowering transactions costs related to information-driven price

protection. Third, the associations between PSARS and equity market development are more

positive in regimes characterized by high trust in politicians and managers than in regimes with

low trust in politicians and managers. These associations are suggestive of potential positive

effects of PSARS on capital market development and a complementarity between PSARS and

public trust in government and corporate officials in promoting capital market development.

The stronger association between PSARS and equity market development relative to credit

market development may be due in part to the following mechanism. In credit markets, private

information can play an important role in facilitating relationship-based lending (Dang et al., 2014;

Petersen and Rajan, 1994). As such, reporting and disclosure practices that transform private

information into public information can reduce the rents available to banks from prior

informational advantages. This would dampen the positive effect of public information on credit

markets driven by better public information facilitating lower price protection. The positive

32

association between PSARS and credit market development in settings with high trust in politicians

is consistent with the dampening effect of relational lending being concentrated in settings with

lower trust in authority figures.

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35

Appendix: Construction of earnings quality proxies

Signed Accruals is defined as the country-year median of firm-year accruals (earnings

before extraordinary items minus operating cash flows) divided by lagged total assets. Absolute

Accruals is |Signed Accruals|. The model used to estimate timely loss recognition (TLR) relies only

on the time-series properties of earnings. As Dechow et al. (2010) note, return-based measures

implicitly assume that stock markets are efficient and, an international panel, homogeneously

efficient, which contradicts the premise here of heterogeneity in market development. TLR is

therefore based on the earnings time-series measure from Basu (1997),

, 0 1 , 1 0 , 1 1 , 1 , 1 ,earnings earnings earningsj t j t j t j t j t j tD D (5)

where , , , 1earnings earnings earningsj t j t j t , Dj,t-1 is an indicator for ,earnings j t < 0, and

earningsj,t is earnings before extraordinary items. Regression (5) is estimated for each firm-year

(j,t) using firm-year observations from t-4 through t (up to 5 years; no less than 3), where t is the

calendar year containing the fiscal year end. Firm-year TLRj,t is 0 1 0/ from a single

regression. Country-year TLRi,t is the country-year median of TLRj,t.

Dechow and Dichev (2002) estimation errors (DD AQ) are calculated as the median

absolute residual from the following equation estimated cross-sectionally with firm-year

observations by country-year.

, 1 , 1 2 , 3 , 1j t j t j t j t tWC CFO CFO CFO (6)

where ,j tWC is the change in working capital from year t-1 to year t, and CFOj,t is operating cash

flow in year t. Variables in (6) are scaled by lagged total assets. ,j tWC is calculated as

, , , , , ,j t j t j t j t j t j tWC RECT INVT AP TXP ACO (7)

where ,j tRECT is the change in accounts receivable,

,j tINVT is the change in inventory, ,j tAP

36

is the change in accounts payable, ,j tTXP is the change in income taxes payable, and

,j tACO is

the change in other current assets.

The second measure of accruals quality, FLOS AQ, is taken from Francis et al. (2005).

First, the following regression is estimated cross-sectionally in each country-year using firm-year

observations:27

, 0 1 , 1 2 , 3 , 1 4 , 5 , ,j t j t j t j t j t j t j tTCA CFO CFO CFO REVT PPENT (8)

where TCAj,t is total current accruals, CFOj,t is cash flows from operations, ,j tREVT is the change

in revenues, PPENTj,t is net property, plant, and equipment, j denotes firm, and t denotes year.

Variables in equation (8) are scaled by total assets reported for year t-1. TCAj,t is calculated as the

change in current assets minus the change in current liabilities minus the change in cash plus the

change in the current portion of long-term debt. Firm-year residuals are used to calculate FLOS

AQ TOTAL, which is the standard deviation of the residuals for firm j from years t-4 through t (up

to 5 years, no less than 3). Next, FLOS AQ TOTAL is decomposed into discretionary and non-

discretionary components using the following regression:

, 1 , 2 , 3 ,

4 , 5 , ,

log

log( )

j t Ind Year j t j t j t

j t j t j t

FLOS AQ TOTAL AT CFO REVT

NegEarn OC

(9)

where j denotes firm, t denotes year, Ind and Year are 2-digit SIC and year indicators,

respectively, ,log j tAT is the log of total assets, ,j tCFO is the standard deviation of cash from

operations scaled by lag total assets for the last 5 (no less than 3) years, ,j tREVT is the standard

deviation of revenue scaled by lag total assets for the last 5 (no less than 3) years, ,j tNegEarn is

27 This corresponds to equation (1) from Francis et al. (2005). They focused on the US, and estimated their model at

the industry-year level.

37

the fraction of the last 5 (no less than 3) years in which the firm reported losses before extraordinary

items, and ,log( )j tOC is the log of the operating cycle in days defined as days accounts receivable

plus days inventory. Firm-year discretionary accruals, FLOS AQ are taken as the residuals, , ,j t

from the pooled panel estimate of equation (9). Country-year measures of FLOS AQ are defined

as country-year median values of the firm-specific observations within each country-year.

Measures of earnings smoothing are defined based on Bhattacharya et al. (2003), Lang et

al. (2012), and Leuz et al. (2003). First, two measures of smoothing at the firm-year level are

calculated as:

, , 1

,

, , 1

/1

/

j t j t

j t

j t j t

earnings ATSMTH TOTAL

CFO AT

and (10)

, , ,2 ,j t j t j tSMTH TOTAL Corr ACC CFO (11)

where earningsj,t is earnings before extraordinary items, ATj,t is total assets, CFOj,t is cash flows

from operations, ,j tACC is the change in accruals where accruals, ACCj,t, are defined as

earningsj,t minus CFOj,t, ,j tCFO is the change in cash flows from operations, tx is the

standard deviation of xt, and ,t tCorr x y is the correlation between xt and yt. SMTH1 TOTALj,t is

the standard deviation of scaled earnings divided by the standard deviation of scaled cash flows.

SMTH2 TOTALj,t is the correlation between changes in accruals and changes in cash flows. Both

smoothing measures are calculated in each firm-year based on firm-specific time series with

observations from t-4 through t (at most 5 and at least 3 observations). Lower values of SMTH1

TOTALj,t and more negative values of SMTH2 TOTALj j,t represent greater smoothing, interpreted

as lower reporting quality. Lang et al. (2012) decompose their measures of smoothing into

discretionary and non-discretionary components. Following their methodology, I estimate the

38

discretionary components as the residuals from the following regression with firm-year

observations:

, 1 , 2 , 3 , 4 ,

5 , 6 , 7 , 8 , 9 , ,

log

% log

j t Ind Year j t j t j t j t

j t j t j t j t j t j t

SMTHx TOTAL AT Leverage BM REVT

Loss OC SG OPLEV CFO x

(12)

where j denotes firm, t denotes year, Ind and Year are 2-digit SIC and year indicators,

respectively, ,log j tAT is the log of total assets, Leveragej,t is total debt divided by total assets,

BMj,t is the book-to-market equity ratio, ,j tREVT is the standard deviation of sales from

operations scaled by lag total assets for the last 5 (no less than 3) years, ,j tREVT is the standard

deviation of revenue over the last 5 (no less than 3) years, ,% j tLoss is the fraction of the last 5 (no

less than 3) years in which the firm reported losses before extraordinary items, ,log( )j tOC is the

log of the operating cycle in days defined as days accounts receivable plus days inventory, SGj,t is

average sales growth over the past 5 (no less than 3) years, OPLEVj,t is net property, plant, and

equipment divided by total assets, and ,j tCFO is average cash flows from operations divided by

lag total assets over the last 5 (no less than 3) years. Firm-year discretionary smoothing is defined

by estimates of equation (12) using the pooled panel with either x=1 or x=2. SMTH1j,t is ,1j t , and

SMTH2j,t is ,2 j t , the regression errors from (12) estimated with dependent variables SMTH1

TOTAL and SMTH2 TOTAL, respectively. Country-year values of discretionary smoothing,

SMTH1k,t and SMTH2k,t are defined as medians within country-year of firm-year values of the

corresponding variables.

39

Figure 1: Time-series plots of PSARS for selected countries

Figure 1 shows plots of PSARS scores by year for the United States, Italy, Ireland, and India. The United

States is shown for reference, while other countries are shown because of the presence of accounting

scandals. The scandals involve Parmalat (Italy in 2003), Anglo Irish Bank (Ireland in 2009), and Satyam

(India in 2009). PSARS is the country-average perceived strength of financial auditing and reporting

standards score from the World Economic Forum’s Executive Opinion Surveys.

3.5

4

4.5

5

5.5

6

6.5

2002 2004 2006 2008 2010 2012

PS

AR

SS

core

Year

PSARS

United

States

Italy

Ireland

India

40

Table 1: Strength of financial auditing and reporting standards questions from the World

Economic Forum’s Executive Opinion Surveys

EOS Year Question

2002 Financial auditing and accounting standards in your country are (1 = extremely weak, 7 = extremely

strong — the best in the world)

2003 Financial auditing and accounting standards in your country are (1 = extremely weak, 7 = extremely

strong, among the best in the world)

2004 Financial auditing and reporting standards regarding companys' financial performance in your

country are (1 = extremely weak, 7 = extremely strong — the best in the world)

2005 Financial auditing and reporting standards regarding company financial performance in your

country are (1 = extremely weak, 7 = extremely strong — among the best in the world)

2006 Financial auditing and reporting standards regarding company financial performance in your

country are (1 = extremely weak, 7 = extremely strong — the best in the world)

2007 Financial auditing and reporting standards regarding company financial performance in your

country are (1 = extremely weak, 7 = extremely strong, the best in the world)

2008 Financial auditing and reporting standards regarding company financial performance in your

country are (1 = extremely weak, 7 = extremely strong, the best in the world)

2009 In your country, how would you assess financial auditing and reporting standards regarding

company financial performance? (1 = extremely weak; 7 = extremely strong)

2010 In your country, how would you assess financial auditing and reporting standards regarding

company financial performance? [1 = extremely weak; 7 = extremely strong]

2011 In your country, how would you assess financial auditing and reporting standards regarding

company financial performance? [1 = extremely weak; 7 = extremely strong]

2012 In your country, how would you assess financial auditing and reporting standards regarding

company financial performance? [1 = extremely weak; 7 = extremely strong]

2013 In your country, how strong are financial auditing and reporting standards? [1 = extremely weak; 7

= extremely strong]

Scores are equal-weighted means of country-year responses for 2002-2006. From 2007 on, reported scores are moving

averages of the current and prior country-year average responses. From 2008 on, country-year averages are weighted

by sector (Agriculture, Manufacturing industry, Non-manufacturing industry, and Services) based on sector

contributions to country-year GDP.

41

Table 2: Correlations between PSARS and country-level proxies

Pearson (spearman) correlations above (below) diagonal

correlation coefficient

1 2 3 4 5 6

p-value

n

PSARS 1 1.0000 0.6187 -0.6509 0.6726 0.4806 0.3048

<.0001 <.0001 0.0004 0.0005 0.0002

147 40 34 23 48 147

CIFAR 2 0.7009 1.0000 -0.6713 0.5394 0.4574 0.2563

<.0001 0.0016 0.0116 0.0030 0.1104

40 40 19 21 40 40

PWC Opacity Index 3 -0.5762 -0.6518 1.0000 -0.7339 -0.3857 -0.1149

0.0004 0.0025 0.0101 0.0517 0.5176

34 19 34 11 26 34

S&P Transparency 4 0.7358 0.5705 -0.7414 1.0000 -0.1103 0.1433

<.0001 0.0069 0.0090 0.6250 0.5141

23 21 11 23 22 23

Prospectus Disclosure 5 0.4417 0.4212 -0.3367 -0.1313 1.0000 0.5003

0.0017 0.0068 0.0926 0.5602 0.0003

48 40 26 22 48 48

Self-Dealing Disclosure 6 0.2913 0.3138 -0.0760 0.1037 0.4475 1

0.0003 0.0486 0.6691 0.6378 0.0014

147 40 34 23 48 147

This table shows cross-sectional correlations using country-level observations. PSARS is the country-average

perceived strength of financial auditing and reporting standards score from the World Economic Forum’s Executive

Opinion Surveys. CIFAR is the disclosure score from the Center for Financial Analysis and Research as reported in

Bushman et al. (2004). PWC Opacity Index is the O-Factor score computed by PriceWaterhouseCoopers as reported

in Gelos and Wei (2005). S&P Transparency is the mean country transparency score based on Standard and Poor’s

Transparency and Disclosure Survey as reported in Khanna et al. (2004). Prospectus Disclosure is the disclosure score

related to IPO prospectuses from La Porta et al. (2006). Self-Dealing Disclosure is the disclosure score related to self-

dealing transactions from the World Bank Business Indicators data set described in Djankov et al. (2008). Pearson

(Spearman) correlations are above (below) the diagonal. Each cell in the table contains the correlation with the p-

value and number of observations used to compute below.

42

Table 3: Correlations between PSARS and Firm-level proxies

Variable 1 2 3 4 5 6 7 8 9 10 11

1 PSARS 1.00 -0.08 0.26 -0.01 0.04 0.00 -0.02 -0.03 0.17 0.09 0.44

2 Absolute Accruals -0.02 1.00 -0.05 -0.03 -0.37 -0.05 -0.08 0.05 0.02 -0.05 -0.03

3 Signed Accruals 0.39 0.12 1.00 -0.03 0.15 0.08 0.03 -0.04 0.09 0.11 0.21

4 TLR -0.04 0.00 -0.15 1.00 0.04 -0.04 -0.01 0.01 0.01 0.04 -0.09

5 DD AQ 0.00 -0.05 0.06 -0.11 1.00 0.23 0.13 -0.08 0.06 0.01 0.03

6 FLOS AQ -0.03 -0.19 0.12 -0.07 0.17 1.00 -0.05 0.12 0.06 0.19 -0.01

7 SMTH1 -0.05 -0.10 0.11 -0.10 -0.03 0.16 1.00 0.09 -0.02 0.17 0.05

8 SMTH2 0.05 -0.07 -0.12 -0.07 0.04 0.06 0.23 1.00 -0.04 0.07 0.02

9 Analyst Following 0.20 0.04 0.20 -0.03 0.05 0.07 -0.02 -0.05 1.00 0.11 0.34

10 High Acct Std Frac 0.11 -0.11 0.16 -0.13 0.22 0.06 0.28 0.08 0.13 1.00 0.18

11 Big N Frac 0.45 0.01 0.31 -0.15 -0.02 0.07 0.09 0.13 0.33 0.20 1.00

This table shows panel correlations using country-year observations. Pearson product-moment (Spearman rank)

correlations are above (below) the diagonal. PSARS is the perceived strength of financial auditing and reporting

standards score from the World Economic Forum’s Executive Opinion Surveys. Absolute Accruals is country-year

median absolute accruals defined as earnings before extraordinary items minus net operating cash flows, scaled by

lagged total assets. Signed Accruals is country-year median accruals defined similarly. TLR is a country-year median

timely loss recognition proxy taken from Basu (1997) based on the time-series of changes in earnings. DD AQ is a

country-year median accrual quality measure based on Dechow and Dichev (2002). FLOS AQ is a country-year median

discretionary accrual quality measure based on Francis et al. (2005). SMTH1 and SMTH2 are discretionary smoothing

proxies based on Lang et al. (2012). Analyst Following is the country-year median number of analysts making forecasts

of firms’ annual earnings in the 90 days before the earnings announcement. High Acct Std Frac is the fraction of firms

preparing their financial reports using US GAAP or IFRS. Big N Frac is the fraction of firms using Big-N auditors as

the primary external auditor on their annual reports. Country-year medians are medians of firm-year values within

country-year, where firms with fiscal years ending in January – August (September – December) of year t are matched

to calendar year t (t-1). Correlations in bold are statistically significantly different from zero at the five percent level.

43

Table 4: List of countries and selected descriptive statistics

Table 4, continued

Country N PSARS GDP

Albania 9 3.99 3,447

Algeria 11 3.42 3,650

Angola 3 2.48 2,414

Argentina 12 3.94 6,813

Armenia 9 4.14 2,749

Australia 12 6.11 39,799

Austria 12 5.93 39,521

Azerbaijan 9 4.04 4,408

Bahrain 10 5.75 17,227

Bangladesh 12 3.61 506

Barbados 8 5.73 15,663

Belgium 12 5.75 37,747

Benin 9 3.60 653

Bhutan 1 4.72 2,399

Bolivia 12 3.42 1,460

Bosnia and Herzegovina 10 3.66 3,761

Botswana 12 5.12 5,350

Brazil 12 4.95 6,850

Brunei Darussalam 6 4.92 34,778

Bulgaria 12 4.34 4,750

Burkina Faso 8 4.16 538

Burundi 8 3.04 196

Cambodia 9 3.54 681

Cameroon 10 3.63 1,027

Canada 12 6.10 38,093

Cape Verde 4 4.02 3,609

Chad 11 2.82 715

Chile 12 5.39 9,251

China 12 4.21 2,877

Colombia 12 4.47 4,432

Costa Rica 12 4.73 5,935

Croatia 12 4.48 11,182

Cyprus 10 5.52 25,849

Czech Republic 12 4.84 14,804

Côte d'Ivoire 6 3.84 1,220

Denmark 12 5.87 49,464

Dominican Republic 12 4.01 4,010

Ecuador 12 3.85 3,553

Egypt 11 4.54 1,975

El Salvador 12 4.51 3,058

Estonia 12 5.57 12,102

Ethiopia 11 3.80 244

Finland 12 6.21 39,605

France 12 5.82 35,205

Gabon 2 4.43 11,513

Gambia, The 11 4.55 489

Table 4, continued

Country N PSARS GDP

Georgia 10 4.14 2,235

Germany 12 5.89 35,790

Ghana 9 4.71 1,008

Greece 12 4.74 22,338

Guatemala 12 4.05 2,434

Guinea 2 3.02 473

Guyana 9 4.08 2,342

Haiti 5 3.10 588

Honduras 12 4.08 1,670

Hong Kong SAR 12 6.02 29,251

Hungary 12 5.23 11,063

Iceland 12 5.55 44,527

India 12 5.34 953

Indonesia 12 4.25 1,923

Iran, Islamic Rep. 4 4.12 5,807

Ireland 12 5.54 46,377

Israel 12 5.73 23,010

Italy 12 4.35 30,998

Jamaica 12 5.34 4,442

Japan 12 5.28 37,623

Jordan 12 5.17 3,141

Kazakhstan 9 4.22 7,437

Kenya 11 4.62 658

Korea, Rep. 9 4.78 19,483

Kuwait 9 5.02 41,557

Kyrgyz Republic 9 3.58 797

Lao PDR 1 3.72 1,417

Latvia 12 4.83 9,292

Lebanon 4 4.48 8,891

Lesotho 8 3.74 936

Liberia 2 4.24 395

Libya 6 3.25 12,165

Lithuania 12 4.91 9,339

Luxembourg 11 5.93 90,575

Macedonia, FYR 11 4.36 3,612

Madagascar 11 3.53 363

Malawi 9 5.03 282

Malaysia 12 5.56 6,774

Mali 11 3.52 550

Malta 11 5.85 17,221

Mauritania 8 3.12 986

Mauritius 12 5.37 6,090

Mexico 12 4.70 8,253

Moldova 5 4.11 1,577

Mongolia 9 3.72 1,973

Montenegro 7 4.40 6,465

44

Table 4, continued

Country N PSARS GDP

Morocco 12 4.31 2,262

Mozambique 11 3.79 368

Myanmar 1 2.29

Namibia 12 5.52 3,826

Nepal 8 3.83 503

Netherlands 12 5.99 41,205

New Zealand 12 6.18 27,399

Nicaragua 12 3.83 1,296

Nigeria 12 4.05 989

Norway 12 5.99 72,236

Oman 7 5.27 19,296

Pakistan 11 4.54 876

Panama 12 4.87 5,791

Paraguay 12 3.48 2,246

Peru 12 4.73 3,854

Philippines 12 4.89 1,598

Poland 12 4.78 9,509

Portugal 12 5.14 18,875

Puerto Rico 7 5.79 25,236

Qatar 9 5.59 67,024

Romania 12 4.18 5,915

Russian Federation 12 3.78 7,611

Rwanda 4 4.56 552

Saudi Arabia 7 5.26 19,316

Senegal 8 4.37 936

Serbia 10 3.87 4,524

Seychelles 2 4.79 12,574

Sierra Leone 2 3.85 568

Singapore 12 6.07 33,967

Slovak Republic 12 4.73 12,969

Slovenia 12 5.01 19,549

South Africa 12 6.28 5,344

Spain 12 5.01 26,657

Sri Lanka 12 5.05 1,692

Suriname 3 3.99 8,644

Swaziland 4 5.05 3,026

Sweden 12 6.11 43,512

Switzerland 12 5.79 58,681

Tanzania 11 4.24 439

Thailand 12 5.02 3,471

Timor-leste 1 2.84 1,068

Trinidad and Tobago 12 4.98 13,534

Tunisia 5 4.99 2,963

Turkey 12 4.50 7,640

Uganda 11 3.84 388

Ukraine 12 3.61 2,344

United Arab Emirates 10 5.24 40,597

United Kingdom 12 6.16 36,850

United States 12 5.66 45,106

Uruguay 12 4.34 7,853

Venezuela 12 3.94 8,035

Vietnam 12 3.68 959

Yemen 3 3.35 1,419

Zambia 10 4.77 951

Zimbabwe 12 5.26 501

Average 9.82 4.60 12,501

This table presents a list of countries reported in Global Competitiveness Report with non-missing PSARS.

Values reported represent country-means of country-year observations. N is the number of country-year

observations with non-missing PSARS. PSARS is the perceived strength of financial auditing and reporting

standards, taken from the World Economic Forum’s Executive Opinion Survey. GDP is gross domestic

product per capita in US dollars.

45

Table 5: Descriptive statistics

Variable N Mean Std Dev 25th Pctl Median 75th Pctl

PSARS 1443 4.72 0.90 3.99 4.70 5.41

GDP ($US/cap) 1433 13,632 18,091 1,403 4,998 20,165

GDP Growth (%) 1424 4.00 4.51 1.80 4.00 6.28

Unemployment (%) 1054 8.66 5.79 4.80 7.35 10.50

Inflation (%) 1392 6.72 17.56 2.28 4.25 7.97

FDI (% of GDP) 1422 5.19 7.62 1.46 3.30 6.33

Bureaucracy Quality 1034 2.46 1.03 2.00 2.00 3.00

Corruption 1034 2.79 1.22 2.00 2.50 3.50

Law and Order 1034 3.94 1.32 3.00 4.00 5.00

Investment Profile 1034 9.36 2.20 8.00 9.50 11.50

Stock Market Cap (% of GDP) 1135 57.52 64.85 16.97 37.21 76.46

Stock Trading Volume (% of GDP) 1119 47.82 58.19 4.75 24.86 74.75

Bank Credit (% of GDP) 1385 73.48 61.86 30.00 53.71 103.78

Private Credit (% of GDP) 1388 62.97 53.90 23.28 42.39 91.42

Public Trust in Politicians 1431 2.91 1.22 1.99 2.60 3.60

Ethical Behavior of Firms 1432 4.28 0.97 3.59 4.00 4.87

General Trust 1012 0.27 0.15 0.16 0.23 0.35

Property Rights 1431 4.49 1.10 3.66 4.38 5.40

Capacity for Innovation 1431 3.40 0.98 2.70 3.14 3.84

This table presents descriptive statistics for the sample. N is the number of observations. Std Dev is standard

deviation. 25th Pctl and 75th Pctl are the 25th and 75th percentiles of the distribution. PSARS is the perceived

strength of financial auditing and reporting standards, taken from the World Economic Forum’s Executive

Opinion Survey. GDP is gross domestic product per capita in US dollars. GDP Growth is the year-on-year

change in GDP in percent. Unemployment is the unemployment rate in percent, and Inflation is the annual

inflation rate in percent based on the local consumer price index. FDI is foreign direct investment, net

inflows, as a percent of GDStock Market Cap is equity market capitalization as a percentage of GDP. Stock

Trading Volume is the value of listed company stock traded divided by GDP. Bank Credit is domestic credit

provided by the banking sector as a percentage of GDP. Private Credit is domestic credit provided to the

private sector (not just from banks) as a percentage of GDP. GDP, Stock Market Cap, Bank Credit, Stock

Trading Volume, and Private Credit come from the World Bank’s World Development Indicators data set.

Corruption, Law and Order, Bureaucracy Quality, and Investment Profile are institutional quality

indicators from the International Country Risk Guide (ICRG). Public Trust in Politicians (PTIP), Ethical

Behavior of Firms (EBOF), Property Rights, and Capacity for Innovation are additional indicators based

on survey responses from the World Economic Forum’s Executive Opinion Survey. General Trust is the

fraction of respondents indicating that others can be trusted in survey responses from the World Values

Surveys and European Values Surveys.

46

Table 6: Correlations

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1 PSARS 1.00 0.70 -0.23 -0.23 -0.39 0.07 0.75 0.62 0.61 0.71 0.64 0.38 0.64 0.68 0.64 0.84 0.37 0.87 0.63

2 GDP 0.63 1.00 -0.34 -0.23 -0.47 0.08 0.76 0.63 0.67 0.79 0.50 0.43 0.65 0.74 0.58 0.70 0.49 0.72 0.65

3 GDP Growth -0.18 -0.20 1.00 -0.07 0.26 0.22 -0.35 -0.33 -0.17 -0.25 -0.10 -0.12 -0.35 -0.33 -0.10 -0.18 -0.16 -0.18 -0.18

4 Unemployment -0.21 -0.30 -0.08 1.00 0.09 0.00 -0.26 -0.24 -0.30 -0.19 -0.28 -0.28 -0.19 -0.25 -0.33 -0.27 -0.31 -0.26 -0.27

5 Inflation -0.09 -0.15 -0.10 0.00 1.00 0.07 -0.49 -0.42 -0.46 -0.49 -0.38 -0.29 -0.43 -0.48 -0.35 -0.43 -0.30 -0.43 -0.40

6 FDI 0.07 0.13 0.15 -0.04 -0.02 1.00 0.05 0.08 0.13 0.16 0.06 -0.17 -0.02 0.04 0.17 0.10 -0.03 0.09 0.01

7 Bureaucracy Quality 0.76 0.70 -0.25 -0.26 -0.16 0.11 1.00 0.66 0.61 0.72 0.48 0.44 0.70 0.71 0.56 0.71 0.44 0.73 0.69

8 Corruption 0.64 0.68 -0.22 -0.26 -0.24 0.13 0.70 1.00 0.63 0.62 0.42 0.40 0.55 0.58 0.59 0.69 0.44 0.68 0.60

9 Law and Order 0.60 0.66 -0.09 -0.28 -0.22 0.15 0.61 0.66 1.00 0.65 0.39 0.42 0.55 0.63 0.69 0.68 0.55 0.69 0.62

10 Investment Profile 0.63 0.60 -0.10 -0.12 -0.33 0.18 0.66 0.61 0.61 1.00 0.47 0.39 0.55 0.65 0.60 0.71 0.39 0.76 0.59

11 Stock Market Importance 0.53 0.39 -0.01 -0.18 -0.05 0.27 0.33 0.33 0.27 0.30 1.00 0.46 0.58 0.59 0.51 0.59 0.31 0.60 0.44

12 Stock Trading Volume 0.28 0.37 -0.08 -0.24 -0.08 -0.07 0.36 0.32 0.34 0.24 0.27 1.00 0.50 0.48 0.36 0.43 0.59 0.44 0.61

13 Bank Credit 0.58 0.64 -0.29 -0.17 -0.11 0.05 0.67 0.56 0.51 0.48 0.44 0.42 1.00 0.93 0.48 0.61 0.50 0.67 0.62

14 Private Credit 0.63 0.70 -0.28 -0.20 -0.14 0.10 0.69 0.63 0.60 0.57 0.49 0.41 0.95 1.00 0.57 0.65 0.52 0.72 0.65

15 PTIP 0.65 0.68 -0.05 -0.29 -0.16 0.16 0.60 0.68 0.67 0.57 0.46 0.29 0.44 0.55 1.00 0.80 0.55 0.75 0.54

16 EBOF 0.83 0.77 -0.15 -0.29 -0.15 0.11 0.76 0.79 0.69 0.66 0.49 0.37 0.61 0.67 0.84 1.00 0.51 0.90 0.68

17 General Trust 0.41 0.58 -0.09 -0.31 -0.13 0.01 0.45 0.56 0.56 0.35 0.22 0.46 0.44 0.47 0.63 0.61 1.00 0.50 0.64

17 Property Rights 0.86 0.68 -0.11 -0.24 -0.21 0.11 0.74 0.72 0.68 0.75 0.48 0.35 0.64 0.69 0.76 0.89 0.52 1.00 0.67

18 Capacity for Innovation 0.65 0.68 -0.15 -0.28 -0.18 0.02 0.73 0.68 0.63 0.56 0.33 0.52 0.64 0.65 0.58 0.76 0.64 0.71 1.00

This table presents Pearson (Spearman) correlation coefficients below (above) the diagonal. Bold correlations are significantly different from zero

at the one percent level. The unit of observation is the country-year. This table presents descriptive statistics for the sample. N is the number of

observations. Std Dev is standard deviation. 25th Pctl and 75th Pctl are the 25th and 75th percentiles of the distribution. PSARS is the perceived strength

of financial auditing and reporting standards, taken from the World Economic Forum’s Executive Opinion Survey. GDP is gross domestic product

per capita in US dollars. GDP Growth is the year-on-year change in GDP in percent. Unemployment is the unemployment rate in percent, and

Inflation is the annual inflation rate in percent based on the local consumer price index. FDI is foreign direct investment, net inflows, as a percent

of GDP. Stock Market Cap is equity market capitalization as a percentage of GDP. Stock Trading Volume is the value of listed company stock traded

divided by GDP. Bank Credit is domestic credit provided by the banking sector as a percentage of GDP. Private Credit is domestic credit provided

to the private sector (not just from banks) as a percentage of GDP. GDP, Stock Market Cap, Bank Credit, Stock Trading Volume, and Private Credit

47

come from the World Bank’s World Development Indicators data set. Corruption, Law and Order, Bureaucracy Quality, and Investment Profile are

institutional quality indicators from the International Country Risk Guide (ICRG). Public Trust in Politicians (PTIP), Ethical Behavior of Firms

(EBOF), Property Rights, and Capacity for Innovation are additional indicators based on survey responses from the World Economic Forum’s

Executive Opinion Survey. General Trust is the fraction of respondents indicating that others can be trusted in survey responses from the World

Values Surveys and European Values Surveys.

48

Table 7: Potential Macro/Institutional Determinants of PSARS

Dependent Variable PSARS

Variable\Model 1 2 3 4

log(GDP) 0.044 0.723 *** 0.027 0.473 **

(0.108) (0.244) (0.035) (0.204)

GDP Growth 0.057 0.035 0.054 *** 0.041 **

(0.038) (0.023) (0.015) (0.019)

Unemployment 0.063 -0.106 * -0.008 -0.089 (0.083) (0.061) (0.024) (0.057)

Inflation 0.126 *** -0.025 0.050 *** 0.019 (0.035) (0.060) (0.014) (0.070)

FDI -0.035 -0.004 -0.008 -0.002 (0.024) (0.011) (0.008) (0.008)

Bureaucracy Quality 0.346 *** -0.067 0.050 -0.129 (0.089) (0.112) (0.031) (0.093)

Corruption 0.236 *** 0.034 0.054 ** -0.001 (0.057) (0.061) (0.023) (0.054)

Law and Order -0.011 0.112 -0.016 0.085 (0.070) (0.094) (0.022) (0.073)

Investment Profile 0.226 *** 0.080 0.044 0.043 (0.081) (0.072) (0.029) (0.067)

Stock Market Cap 0.249 *** 0.095 ** 0.068 *** 0.084 **

(0.069) (0.045) (0.020) (0.041)

Stock Trading Volume -0.087 * -0.012 -0.040 ** -0.027 (0.045) (0.028) (0.017) (0.026)

Bank Credit -0.030 -0.268 0.004 -0.143 (0.125) (0.190) (0.031) (0.167)

Private Credit 0.074 0.133 -0.007 0.041 (0.124) (0.188) (0.034) (0.170)

lag(PSARS) 0.824 *** 0.375 ***

(0.027) (0.051)

Year FE Yes Yes Yes Yes

Country FE No Yes No Yes

Country Clustered SE Yes Yes Yes Yes

N 698 698 674 674

R-square 0.744 0.936 0.918 0.949

Table 7 presents fixed-effects panel regression estimates of , , ,k t t k t k tPSARS Γ X . PSARS is the

perceived strength of financial auditing and reporting standards, taken from the World Economic Forum’s

Executive Opinion Survey. Xk,t is a vector of explanatory variables defined as follows and potentially

including country indicators (fixed effects). GDP is gross domestic product per capita in US dollars. GDP

Growth is the year-on-year change in GDP in percent. Unemployment is the unemployment rate in percent,

and Inflation is the annual inflation rate in percent based on the local consumer price index. FDI is foreign

direct investment, net inflows, as a percent of GDP. Stock Market Cap is equity market capitalization as a

percentage of GDP. Stock Trading Volume is the value of listed company stock traded divided by GDP.

Bank Credit is domestic credit provided by the banking sector as a percentage of GDP. Private Credit is

domestic credit provided to the private sector (not just from banks) as a percentage of GDP. GDP, GDP

49

Growth, Unemployment, Inflation, Stock Market Cap, Bank Credit, Stock Trading Volume, and Private

Credit come from the World Bank’s World Development Indicators data set. Corruption, Law and Order,

Bureaucracy Quality, and Investment Profile are institutional quality indicators from the International

Country Risk Guide (ICRG). All variables have been standardized to be mean-zero and unit-variance.

Heteroscedasticity robust standard errors clustered by country are reported below coefficient estimates.

***, **, * denote statistical significance against a null of zero at the one, five, and ten percent levels,

respectively.

50

Table 8: PSARS and Capital Market Development

Dependent Variable Private Credit Bank Credit Stock Market Cap Stock Trading Volume

Variable\Model 1 2 3 4 5 6 7 8

lag(PSARS) 0.332 *** 0.041 0.311 *** 0.022 0.528 *** 0.091 * 0.328 *** 0.218 ***

(0.086) (0.025) (0.105) (0.026) (0.147) (0.054) (0.125) (0.076)

log(GDP) 0.496 *** 0.168 0.419 *** 0.126 0.105 0.093 0.263 ** -0.108 (0.102) (0.151) (0.122) (0.142) (0.111) (0.259) (0.113) (0.350)

GDP Growth -0.177 *** -0.008 -0.218 *** -0.013 0.154 ** 0.06 *** 0.021 0.012 (0.055) (0.011) (0.058) (0.012) (0.064) (0.023) (0.059) (0.021)

Unemployment -0.056 -0.024 -0.052 -0.006 -0.044 0.023 -0.098 0.009 (0.059) (0.020) (0.069) (0.021) (0.085) (0.033) (0.063) (0.044)

Inflation -0.12 -0.058 ** -0.112 -0.043 -0.013 0.102 -0.028 0.013 (0.073) (0.025) (0.072) (0.027) (0.030) (0.079) (0.032) (0.067)

lag(Dependent Variable) 0.798 *** 0.799 *** 0.449 *** 0.688 ***

(0.036) (0.041) (0.055) (0.090)

Year FE Yes Yes Yes Yes Yes Yes Yes Yes

Country FE No Yes No Yes No Yes No Yes

Country-clustered SE Yes Yes Yes Yes Yes Yes Yes Yes

N 904 903 902 901 831 827 832 825

R-square 0.57 0.981 0.497 0.983 0.31 0.925 0.235 0.902

This table presents regression estimates of , , 1 ,t ,k t t P k t k k tCMD PSARS

Γ X . CMD is one of Private Credit, Bank Credit, Stock Market

Cap, or Stock Trading Volume. Private Credit is domestic credit provided to the private sector (not just from banks) as a percentage of GDP. Bank

Credit is domestic credit provided by the banking sector as a percentage of GDP. Stock Market Cap is equity market capitalization as a percentage

of GDP. Stock Trading Volume is the value of listed company stock traded divided by GDP. PSARS is the perceived strength of financial auditing

and reporting standards, taken from the World Economic Forum’s Executive Opinion Survey. Xk,t is a vector of control variables defined as follows,

potentially including country indicators (fixed effects) and the lagged dependent variable. GDP is gross domestic product per capita in US dollars.

GDP Growth is the year-on-year change in GDP in percent. Unemployment is the unemployment rate in percent, and Inflation is the annual inflation

rate in percent based on the local consumer price index. Stock Market Cap, Bank Credit, Stock Trading Volume, Private Credit, GDP, GDP Growth,

Unemployment, and Inflation come from the World Bank’s World Development Indicators data set. All variables have been standardized to be mean-

zero and unit-variance. Heteroscedasticity robust standard errors clustered by country are reported below coefficient estimates. ***, **, * denote

statistical significance against a null of zero at the one, five, and ten percent levels, respectively.

51

Table 9: PSARS and trust

Private

Credit

Banking

Credit

Stock Market

Cap

Stock

Trading

Volume Dependent Variable:

9.A Trust in Politicians 1 2 3 4

lag(PSARS)*lag(Low PTIP) 0.008 -0.008 -0.007 0.119 **

(0.019) (0.019) (0.064) (0.054)

lag(PSARS)*lag(High PTIP) 0.085 ** 0.064 * 0.154 ** 0.299 ***

(0.036) (0.034) (0.076) (0.103)

N 898 896 823 820

R-square 0.981 0.984 0.926 0.903

Difference in lag(PSARS) coefficients 0.077 ** 0.071 ** 0.160 * 0.181 **

Standard error of difference (0.037) (0.030) (0.090) (0.071)

9.B Trust in Managers 1 2 3 4

lag(PSARS)*lag(Low EBOF) 0.020 -0.006 0.077 0.089 (0.022) (0.019) (0.061) (0.057)

lag(PSARS)*lag(High EBOF) 0.071 ** 0.052 0.123 * 0.309 ***

(0.034) (0.036) (0.067) (0.096)

N 899 897 824 821

R-square 0.981 0.984 0.926 0.904

Difference in lag(PSARS) coefficients 0.052 0.058 0.046 0.220 ***

Standard error of difference (0.035) (0.035) (0.072) (0.076)

9.C General Trust 1 2 3 4

lag(PSARS)*lag(Low General Trust) -0.006 0.021 0.014 0.057 (0.034) (0.045) (0.069) (0.052)

lag(PSARS)*lag(High General Trust) 0.071 0.037 0.113 0.321 ***

(0.043) (0.041) (0.080) (0.121)

N 766 764 735 732

R-square 0.981 0.984 0.925 0.902

Difference in lag(PSARS) coefficients 0.077 0.016 0.099 0.264 **

Standard error of difference (0.060) (0.063) (0.102) (0.124)

This table presents regression estimates of the following equation, with coefficients on controls suppressed:

, , , 1 , , 1 , , 1 , , 1 ,t ,* * * *

k t t P LowT k t LowT k t P HighT k t HighT k t k k tCMD PSARS PSARS

1 1 Γ X . CMD is one

of Private Credit, Bank Credit, Stock Market Cap, or Stock Trading Volume. Private Credit is domestic

credit provided to the private sector (not just from banks) as a percentage of GDP. Bank Credit is domestic

credit provided by the banking sector as a percentage of GDP. Stock Market Cap is equity market

capitalization as a percentage of GDP. Stock Trading Volume is the value of listed company stock traded

divided by GDP. PSARS is the perceived strength of financial auditing and reporting standards, taken from

the World Economic Forum’s Executive Opinion Survey. 1LowT,k,t-1 (1HighT,k,t-1) is an indicator for below-

median (above-median) lagged T, where T is either PTIP (public trust in politicians) EBOF (ethical

behavior of firms), or country-level General Trust. PTIP and EBOF are taken from the World Economic

Forum’s Executive Opinion Survey. General Trust is the fraction of respondents from each country

indicating that others can be trusted in survey responses from the World Values Surveys and European

Values Surveys. Xk,t is a vector of control variables that includes: log(GDP), GDP Growth, Unemployment,

Inflation, lag(CMD), country fixed effects, and year fixed effects, plus lag(High PTIP) and lag(High EBOF)

for the regressions presented in panels A and B, respectively. Controls are defined as follows: GDP is gross

52

domestic product per capita in US dollars. GDP Growth is the year-on-year change in GDP in percent.

Unemployment is the unemployment rate in percent, and Inflation is the annual inflation rate in percent

based on the local consumer price index. Stock Market Cap, Bank Credit, Stock Trading Volume, Private

Credit, GDP, GDP Growth, Unemployment, and Inflation come from the World Bank’s World

Development Indicators data set. All variables have been standardized to be mean-zero and unit-variance.

Heteroscedasticity robust standard errors clustered by country are reported below coefficient estimates.

***, **, * denote statistical significance against a null of zero at the one, five, and ten percent levels,

respectively.

53

Table 10: System GMM estimation

Dependent Variable Stock Market Cap Stock Trading Volume

Variable\Model 1 2 3 4

PSARS 10.80*** 9.550*** 10.06*** 7.427***

(2.89) (3.62) (3.38) (2.83)

lag(Stock Market Cap) 0.860*** 0.874***

(20.46) (20.06)

lag(Stock Trading Volume) 1.130*** 1.139***

(8.56) (8.72)

lag(lag((Stock Trading Volume)) -0.252** -0.248**

(-2.11) (-2.15)

GDP -0.596 -0.939 -2.116 -1.419

(-0.31) (-0.58) (-0.73) (-0.56)

GDP growth 0.696 0.617 -0.0354 0.000193

(1.37) (1.63) (-0.08) (0.00)

CPI -0.186*** -0.202*** -0.126** -0.113***

(-2.87) (-6.89) (-2.29) (-2.79)

Unemployment -0.314 -0.204 -0.589 -0.464*

(-1.09) (-0.77) (-1.35) (-1.79)

Year FE Yes Yes Yes Yes

Number of observations 760 760 667 667

Number of countries 97 97 96 96

Max lags used as instrument 2 Unrestricted 2 Unrestricted

Number of unique instruments 166 334 174 339

AR(1) test statistic -2.567 -2.571 -2.289 -2.309

AR(1) test statistic p-value 0.0103 0.0101 0.0221 0.0210

AR(2) test statistic 0.527 0.524 -1.196 -1.261

AR(2) test statistic p-value 0.598 0.600 0.232 0.207

Table 10 presents estimates of , , 1 , , ,k t k t P k t k t k k tt

CMD CMD PSARS

Γ X based on System GMM

(Blundell and Bond, 1998) with the forward orthogonal deviations transformation (Arellano and Bover,

1995). CMD is one of Stock Market Cap, Stock Trading Volume, Private Credit, or Bank Credit. Stock

Market Cap is equity market capitalization as a percentage of GDP. Stock Trading Volume is the value of

listed company stock traded divided by GDP. Bank Credit is domestic credit provided by the banking sector

as a percentage of GDP. Private Credit is domestic credit provided to the private sector (not just from

banks) as a percentage of GDP. PSARS is the perceived strength of financial auditing and reporting

standards, taken from the World Economic Forum’s Executive Opinion Survey. Xk,t is a vector of control

variables defined as follows. GDP is gross domestic product per capita in US dollars. GDP Growth is the

year-on-year change in GDP in percent. Unemployment is the unemployment rate in percent, and Inflation

is the annual inflation rate in percent based on the local consumer price index. Stock Market Cap, Bank

Credit, Stock Trading Volume, Private Credit, GDP, GDP Growth, Unemployment, and Inflation come

from the World Bank’s World Development Indicators data set. Number of observations are based on pre-

transformation complete observations. Standard errors are based on the Windmeijer (2005) adjusted

covariance matrix, with t statistics in parentheses below estimated coefficients. ***, **, * denote statistical

significance against a null of zero at the one, five, and ten percent levels, respectively.

54

Table 11: Additional controls from the Executive Opinion Survey

Dependent Variable Stock Market Cap

EOS Variable Property

Rights

Ethical Behavior of

Firms

Capacity for

Innovation

Variable \ Model 1 2 3

lag(PSARS) 0.113 * 0.133 ** 0.090 *

(0.064) (0.062) (0.051)

lag(EOS Variable) -0.060 -0.113 * 0.019 (0.056) (0.063) (0.061)

lag(Stock Market Cap) 0.447 *** 0.449 *** 0.448 ***

(0.054) (0.056) (0.054)

log(GDP) 0.088 0.015 0.094 (0.258) (0.238) (0.265)

GDP Growth 0.060 *** 0.065 *** 0.060 ***

(0.023) (0.023) (0.023)

Unemployment 0.014 0.012 0.021 (0.034) (0.033) (0.033)

Inflation 0.101 0.094 0.106 (0.079) (0.080) (0.079)

Year FE Yes Yes Yes

Counry FE Yes Yes Yes

Country-clustered SE Yes Yes Yes

N 823 824 823

R-square 0.925 0.926 0.925

This table presents regression estimates of , , 1 , 1 ,t ,k t t P k t k t k k tSMC PSARS EOS

Γ X . SMC is Stock

Market Cap, defined as equity market capitalization as a percentage of GDP. PSARS is the perceived

strength of financial auditing and reporting standards, taken from the World Economic Forum’s Executive

Opinion Survey. EOS Variable is one of Property Rights, Ethical Behavior of Firms, or Capacity for

Innovation, which are measures based on survey responses taken from the World Economic Forum’s

Executive Opinion Survey. Xk,t is a vector of control variables defined as follows and including country

indicators (fixed effects) and the lagged dependent variable. GDP is gross domestic product per capita in

US dollars. GDP Growth is the year-on-year change in GDP in percent. Unemployment is the

unemployment rate in percent, and Inflation is the annual inflation rate in percent based on the local

consumer price index. Stock Market Cap, Bank Credit, Stock Trading Volume, Private Credit, GDP, GDP

Growth, Unemployment, and Inflation come from the World Bank’s World Development Indicators data

set. All variables have been standardized to be mean-zero and unit-variance. Heteroscedasticity robust

standard errors clustered by country are reported below coefficient estimates. ***, **, * denote statistical

significance against a null of zero at the one, five, and ten percent levels, respectively.