159

Finance, Accounting and Business Analysis

Volume 6 Issue 2, 2024

http://faba.bg/

ISSN 2603-5324

DOI:

https://doi.org/10.37075/FABA.2024.2.06

Economic policy uncertainty, financial reporting quality, and accounting

enforcement: International evidence

Catalin Robert Mos

Faculty of Economics and Business Administration, Babes-Bolyai University, Cluj-Napoca, Romania

Info Articles

Abstract

History Article:

Submitted 2 August 2024

Revised 3 November 2024

Accepted 12 November 2024

Purpose: Given recent developments around the world, the purpose of

this article is to explore the association between financial reporting

quality and economic policy uncertainty. Additionally, we investigated

whether accounting enforcement acts as a mediating factor between the

two.

Design: To achieve the purpose, we used a large sample consisting of

284 908 firm-year observations from 29 countries. We estimate the

quality of financial reporting using traditional accruals models. For

economic policy uncertainty, we rely on the index developed by Baker

et al. (2016). Accounting enforcement was quantified using the strength

of the auditing and reporting standards. Furthermore, for robustness

tests, we use alternative measures for all these variables. We ran an OLS

regression with country and industry fixed effects.

Findings: We found that uncertainty is negatively associated with the

quality of financial reporting. Accounting enforcement plays a key role

in reducing this negative association. For the baseline model, for one unit

of change in accounting enforcement, the negative association between

financial reporting quality and economic policy uncertainty is reduced

between 10.41% and 17.54%. For the alternative measures, the decrease

is between 1.14% and 6.93%. Our results are consistent and robust.

Practical Implications: This study is important for capital markets and

policy makers, since the last 3 years were characterized by high

uncertainty. Therefore, the present study provides evidence of the

disruptive impact of uncertainty on financial reporting quality.

Furthermore, we introduced in discussion the role of accounting

enforcement and, therefore, propose a possible instrument available for

policy makers to counter the effects of uncertainty.

Originality: Compared to existing research, the present study expands

the period of analysis until 2022; therefore, it covers the periods with the

highest uncertainty. Combined with the large number of countries, the

observations ensure the relevance of the findings. The present study is

also one of the first that introduces in discussion the role of accounting

enforcement, which is an important topic in accounting research

Paper Type: Research Paper

Keywords:

financial reporting,

uncertainty, accounting

enforcement

JEL: M41, M42, M48

Address Correspondence:

E-mail: catalin.mos@econ.ubbcluj.ro, moscatalin5@gmail.com

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

160

INTRODUCTION

The last few years have been marked by macroeconomic uncertainty. This was heightened by a series

of consecutive events, namely the coronavirus pandemic, Ukraine's aggression, the energy shortage and the

inflation crisis. There is an emerging body of literature that attempts to understand the association between

uncertainty and firm outcomes.

Uncertainty worsens the economic environment, delays important investment decisions, and

increases financing and production costs (Arouri et al. 2016). The capital market and investors are affected

as well, uncertainty leads to high volatility of stock prices, decrease in returns, and underpricing of initial

public offerings (Liu and Zhang 2015; Arouri et al. 2016; Connolly et al. 2005; Dzielinski 2012; Boulton

2022). Stanton and Roelich (2021) note that in this context, it is difficult for investors to make decisions

because the outcome cannot be reasonably predicted. Therefore, for an efficient decision-making process,

investors seek to obtain firm-related information to a greater extent. Walters et al. (2023) and Andrei et al.

(2023) provide evidence in this regard, investors are more responsive to available firm information, and their

learning process intensifies when uncertainty rise.

Financial reporting and annual reports offer comprehensive information about the firm, are part of

the control mechanisms (Shivakumar 2013), and attenuate the information asymmetry between

management and investors (Kraft et al. 2012; Healy and Palepu 2001). Considering damaging effects of high

uncertainty and the race of investors to get as much information as possible about companies, financial

reporting quality (FRQ) becomes a significant aspect. Through a faithful representation of the performance

in the financial statements, investors could learn about the risk associated with their holding, assess how

business operations are affected, review the performance, and decide. The question that arises is how much

the investors could rely on FRQ in times of high uncertainty?

This study provides additional evidence on this subject. One of the key articles in the literature is that

by Baker et al. (2016) that provides an appropriate measure for uncertainty. This index covers two sides of

uncertainty economic and political. The economic policy uncertainty index (EPU) allows us to observe the

association between EPU and FRQ using a large international sample. Our study contributes in several ways

to the literature. A high proportion of previous studies analyze uncertainty in the context of US firms. Our

analysis focusses on 29 countries, which to the best of our knowledge is one of the largest samples. Therefore,

our results provide strong evidence that uncertainty is negatively associated with FRQ. This feature of our

sample give us enough variability between macro-attribute (uncertainty) and micro-attribute (FRQ) to

capture the full impact. Furthermore, our study covers the period between 2020 and 2022 when the

uncertainty increases with 72% compared with the average value of the last 10 years. Unlike previous

research, whose sample mostly ends in 2015-2018, our study expands the length of the sample to the period

with the most profound uncertainty, allowing us to better understand this phenomenon.

The chair of Security Exchange Commission (SEC) in the US emphasizes that in times of high

uncertainty, the SEC is particularly focused on protecting investors (Reuters, 2023). Accounting

enforcement (ENF) is one of the instruments used to protect investors. Accounting enforcement is an activity

carried out by state institutions to ensure correct applicability of accounting standards in the preparation of

financial statements. Christensen et al. (2013), Brown et al. (2015), Ernstberger et al. (2012), Böcking et al.

(2015), and Windisch (2021) show that accounting enforcement is positively associated with FRQ. However,

the effect of accounting enforcement in the context of uncertainty has not yet been tested in the literature.

The second objective of our study is to address and analyze this point. In this regard, we rely on the strength

of auditing and reporting standards index and introduce an interaction term between EPU and ENF in our

regression analysis.

Our results suggest that the uncertainty is negatively associated with FRQ. Furthermore, we observe

that accounting enforcement has the ability to reduce this negative association. Our results are robust to

different measures of FRQ, alternative measures of accounting enforcement and uncertainty, controlling for

economic conditions, and controlling for firm characteristics. Additionally, we included in our regression

analysis country and industry fixed effects which allow us to control for potential unobserved effects.

Together, the conclusions of this study are valid and emphasize the negative consequences of uncertainty.

Our findings are of interest to investors and policymakers. In the first place, we show that uncertainty

declines the firm information environment because of negative association between uncertainty and FRQ.

This affects the trust of investors in financial reporting, which is one of the pillars that guarantee the

functioning of the capital market. However, policy makers can counteract the uncertainty effects by

strengthening accounting enforcement. Therefore, this study not only provides evidence of the negative

effects of uncertainty on FRQ, but also discusses the available instrument to attenuate these effects.

The remaining of this paper is structured as follows. In Section 2 we provide the theoretical

background for this study. Section 3 shows the methodology applied in this study, Section 4 presents the

findings, and the conclusions are drawn in Section 5.

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

161

LITERATURE REVIEW AND HYPOTHESIS

Uncertainty

Economic policy uncertainty generates serious shocks in the capital markets and triggers investors.

Graham et al. (2005) surveyed more than 400 executives about the incentives behind the reported earnings.

The authors highlight that investors hate uncertainty, management is concerned about this, and CFOs prefer

to smooth earnings to reduce the uncertainty. Starting from this theory, a new topic emerged in the literature

about FRQ in times of uncertainty.

El Ghoul et al. (2021), Yung and Root (2019), Goncalves et al. (2022), and Kurniawan et al. (2023)

analyze the impact of high uncertainty on FRQ using cross-country samples while Bermpei et al. (2021),

Dhole et al. (2021), Jin et al. (2019), Dai and Ngo (2020), Nagar et al. (2018), Jain et al. (2021), Shin (2019),

and Jiang et al. (2022) explore the effects of uncertainty for US firms. We can observe that the previous

literature investigates mostly the United States. This can be argued by the fact that the most widely used

measure of uncertainty in previous studies was initially developed for the US in 2016 and subsequently

expanded to other countries. There are limited studies in previous research with cross-country samples.

Evaluation of the association between FRQ and uncertainty implies a combination of macro- (uncertainty)

and micro- (FRQ) features. Therefore, the sample consisting only of firms from one country does not allow

enough variability to support solid conclusions. On the other hand, cross-country sample enables to consider

other macro characteristics such as institutional settings.

In terms of sample period, previous research covered the period until 2015-2018 (El Ghoul et al. 2021;

Yung and Root 2019; Goncalves et al. 2022; Bermpei et al. 2021; Jin et al. 2019; Dai and Ngo 2020; Nagar

et al. 2018; Jain et al. 2021; and Jiang et al. 2022). The fact that previous research does not capture 2020,

2021, and 2022 constitutes a significant gap that needs to be addressed. These three years can be

distinguished by intense increase in uncertainty compared with the previous decade and therefore enhance

applicability of the results, allow proper detection of relationships, and increase the accuracy of the model.

The uncertainty is estimated in three ways. Dai and Ngo (2020), Jain et al. (2021), and Goncalves et

al. (2022) use the elections to quantify the uncertainty. During election years, uncertainty about the future

policies of the incoming government tends to increase. Shin (2019) relays on market shocks to capture

uncertainty, while the rest of the authors use the index developed by Baker et al. (2016).

Most of the findings suggest that uncertainty produces negative effects on FRQ. On the other hand,

El Ghoul et al. (2021) find positive effects, and the authors show that the capacity of accounting to measure

performance is significantly better under high uncertainty. However, there are some differences between the

study by El Ghoul et al. (2021) and other research that can lead to contradictory findings. El Ghoul et al.

(2021) use the Nikolaev model to estimate FRQ. This model is more complex compared to the other models,

but it has some limitations acknowledged by the authors. The model does not allow to estimate the FRQ at

firm-year level; therefore, it is challenging to evaluate the association between FRQ and uncertainty over

time which is a major disadvantage. Furthermore, the sophistication of the model may reduce the focus on

management discretional behavior, which is the objective of earnings management models. Another point

is the inclusion of year-fixed effects in the model. Controlling for year-fixed effects underestimate the results

due to collinearity between year-fixed effects and uncertainty.

There are two prevalent explanations in the literature for the association between FRQ and

uncertainty. The first one agrees that in times of high uncertainty, investors are more engaged in obtaining

firm specific financial information. In this case, management incentives are to improve performance and

avoid small losses by using earnings management (Shin 2019; Dai and Ngo 2020; Jiang et al. 2022; Brempei

et al. 2021). On the contrary, Jin et al. (2019) and Nagar et al. (2018) emphasize that in periods of high

uncertainty, the information asymmetry between management and investors increases. Consequently,

management is likely to smooth the earnings because it is difficult for investors to detect earnings

management.

Our first hypothesis considers the impact that uncertainty has on the economic environment, the

investor reaction, and the management incentives. As presented above, management incentives are to reduce

investor concern, reduce the volatility of earnings, and present a better financial situation. We argue that

uncertainty, which is produced by a crisis such as the 2008 financial crisis or the pandemic crisis, produces

a decline in the economy. This decline is reflected in the performance of the companies; therefore, the

management is incentivized to use earnings management. Furthermore, we acknowledge the gaps in the

literature presented above and the limited evidence for the recent years.

H1. Uncertainty leads to a decrease in FRQ worldwide.

Accounting enforcement

Jiang et al. (2022) and Cui et al. (2021) and El Ghoul et al. (2021) introduce in discussion the role of

external monitoring in times of high uncertainty. They demonstrate that strong external monitoring

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

162

mitigates the effects of uncertainty over FRQ. The studies are based on external monitoring under the form

of analyst coverage, institutional investors, and auditors. Accounting enforcement (ENF) is a component of

external monitoring with notable sanctioning power. Market and investors react to the announcement of

enforcement results. Dee et al. (2011), Ernstberger et al. (2012), Christensen et al. (2020), Dechow et al.

(1996) and Curtis (2016) demonstrate that sanctions lead to decrease in firm valuation and increase in the

cost of capital.

A relevant description of accounting enforcement is provided by Hope et al. (2003). In the absence

of proper accounting enforcement, even the best accounting standards remain only rules on the paper. The

goal of accounting enforcement institutions is to act in the best interest of investors by overseeing and

inspecting the financial statements and the work performed by auditors. For example, the mission of the

Public Company Accounting Oversight Board (PCAOB) in the United States is to protect investors and

further the public interest in the preparation of informative, accurate and independent audit reports (PCAOB

2023). The European Securities Market Authority (ESMA), an institution of the European Union,

emphasizes in its last accounting enforcement report that the purpose of this activity is to improve future

financial reporting and compliance with accounting standards (ESMA 2023).

In the literature, there is a consensus among researchers that accounting enforcement is beneficial for

FRQ. Brown et al. (2015), Christensen et al. (2013), Brown et al. (2015), Ernstberger et al. (2012), Böcking

et al. (2015), and Windisch (2021) indicate that accounting enforcement plays a substantial role in securing

adequate applicability of accounting and auditing standards. Consequently, investors will benefit from

proper financial reports. However, there is no work in the previous literature that analyses accounting

enforcement in the context of uncertainty. We expect that for countries with strong accounting enforcement,

the impact of uncertainty on FRQ will not be as intense as for countries with weak accounting enforcement.

This is because the non-compliance with accounting and auditing standards is sanctioned and penalized in

two ways, by enforcement institutions and by the market and investors. This leads to our second hypothesis.

H2. In countries with strong accounting enforcement, the effects of uncertainty on FRQ are less

pronounced.

METHODOLOGY

Uncertainty

Our uncertainty measure is the index developed by Baker et al. (2016). The economic policy

uncertainty (EPU) consists of three components. The first uses the newspaper’s coverage of topics related to

economic uncertainty, the second covers uncertainty about changes in tax legislation and monetary policies,

while the last component deals with uncertainty about macroeconomic forecasts. Baker et al. (2016)

conducted several tests to verify the reliability and accuracy of the methodology used. Analysis of the

relationship between the EPU index and other uncertainty measures and audit of the reasonability of the

newspapers included in the index show that the methodology was appropriate. As indicated by Baker et al.

(2016), there is a strong correlation between EPU and other indicators of capital market uncertainty (implied

stock market volatility) therefore, the index is a strong candidate for our study. Brempei et al. (2021), Yung

and Root (2019), Jiang et al. (2022), and Nagar et al. (2018) discuss that this index is helpful in analysing

the effects of EPU on firm outcomes, in our case, FRQ. Furthermore, they highlight that the index shows

large spikes around serious events that cause uncertainty, a feature that is important for our research design.

The value of EPU is collected for each of the 29 countries in the sample from the EPU website. However,

for the Netherlands there are no data for 2021 and 2022, for Denmark there are no data for 2022, and for

Nigeria there are no data for the period between 2005 and 2016. We eliminate from the final sample the

observations belonging to these countries and periods. The EPU is determined monthly. To obtain the value

for each country year we use the arithmetic mean of the monthly value. Finally, we use in the regression

analysis the change in the natural logarithmic value of the EPU from year to year. Table 1 shows the raw

data on EPU extracted from the EPU website (https://www.policyuncertainty.com/).

The minimum value for the EPU is noted for Mexico in 2014 (27) while the maximum value is

observed for Germany in 2022 (669). The most notable changes in mean and median are recorded in 2008

(change in mean: 49, change in median 58), in 2020 (change in mean: 56, change in median 76), and in 2021

(change in mean: -61, change in median -55). Aside from the significant changes, we can observe that the

EPU fluctuates over the years, there are periods of growth (2010-2012 and 2015-2016) and periods of decline

(2013-2014 and 2017-2018).

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

163

Table 1. Raw data on EPU

Countries

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Australia

152

106

149

174

167

123

131

129

182

106

156

Brazil

114

174

131

134

118

138

149

250

309

346

165

158

255

189

196

Chile

104

100

154

151

140

120

106

171

261

305

338

Colombia

103

110

126

148

137

121

151

227

123

136

Denmark

123

140

117

119

120

110

125

119

122

188

300

373

N/a

Germany

135

112

140

191

178

149

125

157

231

178

172

205

322

305

669

Hong Kong

103

112

158

128

194

193

134

159

151

189

139

125

236

202

104

220

Ireland

128

127

149

145

150

157

117

123

194

179

155

152

264

235

320

Japan

129

127

138

127

145

127

140

110

Mexico

New Zealand

171

128

151

129

110

108

124

167

119

157

Pakistan

104

123

192

Singapore

130

117

126

151

161

122

117

182

184

201

288

326

224

283

Sweden

106

107

104

108

101

111

105

116

102

124

United States

139

126

148

157

158

138

113

145

142

153

189

326

175

184

Belgium

126

181

140

135

128

115

278

168

138

Canada

155

132

149

232

225

181

152

188

233

244

332

333

464

277

278

China

144

129

109

152

186

114

112

138

247

289

375

581

575

399

518

Croatia

140

131

140

182

172

190

159

130

281

180

219

Greece

103

118

117

124

101

130

118

100

France

116

160

139

207

250

279

248

191

224

310

317

250

256

309

251

341

India

142

109

163

185

133

100

Italy

105

122

143

137

164

117

106

129

115

126

173

114

122

South Korea

141

147

149

167

163

131

128

189

161

145

257

204

176

269

The Netherlands

102

131

124

133

143

126

N/a

Spain

100

119

141

178

134

125

128

120

110

116

137

197

144

156

United Kingdom

155

139

232

228

305

222

182

204

543

476

368

431

307

185

294

Russia

101

122

112

141

146

169

233

206

184

216

198

284

491

334

577

Nigeria

N/a

128

125

Mean

124

111

123

147

152

130

118

130

167

158

149

185

242

181

233

Median

129

110

123

142

143

131

114

125

145

120

121

151

227

172

192

Change in Mean

N/a

-13

-22

-12

-10

-9

-61

Change in Median

N/a

-19

-12

-17

-25

-55

Source: Authors’ own processing after Baker et al. (2016)

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

164

Financial reporting quality

The conceptual accounting framework lists several characteristics of qualitative financial information,

such as relevance and faithful representation. The general theory says that management should act in the

best interest of shareholders and prepare financial information compatible with the above characteristics.

However, the management behavior could be driven by other incentives, and the reporting process is twisted.

Investors and analysts often use earnings to evaluate the activity of the company. Earnings include a

component that is a management estimate, the accruals, which are not reflected in cash flows. The

researchers attempted to estimate the discretionary management behavior applied in the preparation of

financial statements by looking at the accruals related to earnings.

Accrual-based models are widely used in the literature. These models aim to separate abnormal

accruals from reasonable business accruals. Management uses abnormal accruals to manipulate firm

performance, usually to improve it. Dechow et al. (2010) pointed out that business reasonable accruals reflect

the fundamental firm performance, whereas abnormal accruals unveil the discretionary behavior applied by

management in preparation of the financial information. The authors also note that discretionary accruals

reduce the usefulness of the decision-making process. Therefore, we can link these models to the usefulness

of financial information or to a faithful representation of firm performance.

Accrual-based models regress total accruals with firm attributes that predict reasonable business

accruals. The residuals from regressions are abnormal accruals, accruals that cannot be explained by firm

attributes. The standard Jones model (Jones, 1991) considers sales growth and property plant and equipment

as primary firm attributes. Dechow et al. (1995) modified the standard Jones model by considering only

credit sales, which could be more easily misshaped by the management. Kothari et al. (2005) also added the

performance of the firm to the model, which is an important firm attribute, as well, that can explain the

evolution of total accruals. Dechow and Dichev (2002) consider that accruals should eventually translate

into payments in the future and propose a model that considers present past and future cash flow. In the

context of the capital market, where investors make decisions based on firm performance, we consider these

models appropriate for our research. We label these models FRQ1, FRQ2, FRQ3, and FRQ4.

ACC

=α

+α

it-1

+α



∆REV

+α



∆PPE

+ε

(1)

ACC

=α

+α

it-1

+α



∆REV

∆AR

+α



∆PPE

+ ε

(2)

ACC

=α

+α

it-1

+α

󰇡

∆REV

∆AR

󰇢+α

󰇡

∆PPE

󰇢+α

ROA

+ε

(3)

WC

=α

+α

CFO

it-1

+α

CFO

+α

CFO

it+1

+ α

∆REV

+α

PPE

+ ε

(4)

Table 2 describes the variables used in our FRQ models.

Table 2. Description of variables for FRQ models

Variable

Description

ACCit

Change in non-cash current assets – change in current liabilities,

change in the current portion of long-term debt – depreciation and

amortization expense scaled by lagged total assets for firm i in year t

WCit

Change in receivables + change in inventory – change in accounts

payables – change in income tax payable + change in other assets

scaled by lagged total assets for firm i in year t

TAit

Total assets of firm i in year t

Δ REVit

Change in sales of firm i in year t

Δ ARit

Change in trade receivables of firm i in year t

Δ PPEit

Change in gross property, plant, and equipment of firm i in year t

CFOit

Cash flow from operations of firm i in year t scaled by lagged total

assets of firm i in year t

ROAit

Net income/total assets of firm i in year t

Source: Authors’ own processing

The models are estimated cross-sectionally at the industry-year level. In line with the literature, we

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

165

required at least 10 observations for each industry-year

. The larger the residuals from the regressions, the

lower the FRQ is.

Accounting enforcement

Our accounting enforcement measure is represented by the strength of the auditing and reporting

standards included in the Global Competitiveness Report prepared by the World Economic Forum (WEF).

The index is derived from a survey of business leaders who were asked to evaluate the strength of their

country’s accounting and auditing standards. Business leaders are considered well-suited to assess their

country's environment, including aspects of accounting enforcement (World Economic Forum 2019).

Boolaky et al. (2015) pointed out that this index shows perceptions of a country’s competitiveness from the

perspective of auditing and reporting standards. This competitiveness is given by the expected outcome of

accounting enforcement, namely correct application of accounting and auditing standards, investor

protection, and useful, timely, and comparable information. We collect data from the World Bank database.

In our regression analysis, we use the change in the strength of auditing and reporting standards. However,

data is only available for the period from 2006 to 2019. For the years 2020 and 2021, we applied the average

index value derived from the 2006–2019 data. To mitigate this aspect, in an additional test, we use another

measure for accounting enforcement.

Sample

We extracted financial data about companies from Refinitiv. We selected only companies listed on a

stock exchange for countries with the available EPU index. We carefully analyzed the database and

performed additional work to prepare it. We eliminate companies that do not report relevant figures to

compute the FRQ at least for three consecutive years. The final sample consists of 284,908 firm-year

observations.

Tables 3 and 4 show the distribution of our sample per country and industry.

Table 3. Description of variables for FRQ models

Country

No. of observations

Country

No. of observations

Japan

47,114

Italy

2,659

United States

43,018

Greece

1,863

China

41,529

Chile

1,775

India

29,704

Russia

1,740

South Korea

25,615

Spain

1,517

Hong Kong

20,347

Mexico

1,395

United Kingdom

10,106

New Zealand

1,294

Canada

10,094

Denmark

1,123

Australia

9,597

Belgium

1,096

Singapore

6,589

The Netherlands

740

France

6,210

Croatia

704

Germany

5,845

Nigeria

415

Sweden

5,513

Colombia

301

Pakistan

3,776

Ireland

168

Brazil

3,061

Source: Authors’ own processing

We use Global Industry Classification Standard from Refinitiv, detailed information is provided in section

‘Sample’

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

166

Table 4. Sample distribution per industry

Industry

No. of

observ

ations

Industry

No. of

observ

ations

Industry

No. of

observ

ations

Industry

No. of

observ

ations

Machinery

15 862

Semiconductors

Semiconductor

Equipment

5 946

Independent Power

and Renewable

Electricity

Producers

2 532

Automobiles

1 347

Chemicals

14 728

Media

5 857

Energy Equipment

& Services

2 486

Gas Utilities

1 333

Metals & Mining

13 033

Trading

Companies &

Distributors

5 721

Personal Care

Products

2 438

Diversified

REITs

1 137

Real Estate

Management &

Development

12 327

Household

Durables

5 603

Paper & Forest

Products

2 253

Health Care

Technology

1 110

Electronic

Equipment,

Instruments &

Components

12 176

Biotechnology

5 361

Aerospace &

Defense

2 227

Retail REITs

1 064

Food Products

10 681

Health Care

Equipment &

Supplies

5 231

Transportation

Infrastructure

2 221

Office REITs

923

Textiles, Apparel

& Luxury Goods

10 170

Entertainment

4 613

Ground

Transportation

2 129

Water

Utilities

919

Software

9 517

Professional

Services

4 138

Technology

Hardware, Storage

& Peripherals

2 071

Passenger

Airlines

806

Construction &

Engineering

8 834

Health Care

Providers &

Services

4 027

Broadline Retail

2 054

Household

Products

696

Hotels,

Restaurants &

Leisure

8 450

Communicatio

ns Equipment

4 025

Distributors

1 960

Multi-

Utilities

651

Pharmaceuticals

8 405

Consumer

Staples

Distribution &

Retail

3 729

Interactive Media &

Services

1 822

Wireless

Telecommun

ication

Services

619

Oil, Gas &

Consumable

Fuels

8 297

Building

Products

3 496

Leisure Products

1 734

Residential

REITs

567

Automobile

Components

7 709

Construction

Materials

3 221

Diversified

Telecommunication

Services

1 704

Industrial

REITs

465

Electrical

Equipment

7 095

Containers &

Packaging

2 818

Air Freight &

Logistics

1 623

Specialized

REITs

407

IT Services

6 523

Diversified

Consumer

Services

2 709

Life Sciences Tools

& Services

1 529

Hotel &

Resort

REITs

370

Commercial

Services &

Supplies

6 353

Beverages

2 602

Industrial

Conglomerates

1 478

Health Care

REITs

339

Specialty Retail

6 314

Electric Utilities

2 557

Marine

Transportation

1 431

Tobacco

335

Source: Authors’ own processing

The largest number of observations are from Japan (47 114), the United States (43 018), China (41

529), India (29 704), and South Korea (25 615). The top 5 industries, representing 25% of our sample, are

machinery (15 862), chemicals (14 728), metals and mining (13 033), real estate (12 327), and electronic

equipment (12,176). We extracted from Refinitiv the industry classification determined by the Global

Industry Classification Standard (GICS). According to MSCI, the GICS was created to help investors

understand the key business activities of listed companies (MSCI 2023). This is a four-tier hierarchical

classification; we use the third tier which consists of 74 industries. However, we eliminate the financial

industry (Banks, Capital Markets, Financial Services, Insurance, Consumer Finance, and Mortgage

Investment Trusts) which results in 68 industries in our sample.

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

167

Empirical model and control variables

Our empirical model and the summary of the variables are presented below.

FRQ=α

+α

EPU+α

ENF+α

SIZE+α

LEV+α

ROA+α

DCE+α

AUD+α

RES+ ε

(5)

Table 5. Summary of variables

Variable

Description

Type of variable

Source of data

FRQ

Quality of Financial Reporting

Dependent variable

Refinitiv

EPU

Change in Economic Policy Uncertainty

Focus variable

Baker et al. (2016)

ENF

Change Strength of auditing and reporting

standards

Focus variable

World Bank (2023)

SIZE

Natural logarithm of the market capitalization

of the company

Control variable

Refinitiv

LEV

Leverage, determined as total debt/total equity

Control variable

Refinitiv

ROA

Net income divided by total assets

Control variable

Refinitiv

DCE

Dummy variable if the total equity is negative

or not

Control variable

Refinitiv

AUD

Dummy variable if the auditor is from Big4 or

not

Control variable

Refinitiv

RES

Dummy variable if the financial statements

contain a restatement or not

Control variable

Refinitiv

Source: Authors’ own processing

The auditors exert a significant influence on FRQ. Their responsibility is to provide additional

assurance to the shareholders, and is expected that, following the audit tests, they will detect the abnormal

accruals. Subsequently, management will correct the financial statements. The Big 4 network is widely

spread throughout the world, and its audit practices are mostly consistent within the network. There is a

consensus that they perform higher quality audits than nonBig 4 auditors (DeFond and Zhang 2014; Che et

al. 2020; Krishnan 2003; Krishnan 2003; Behn et al. 2008; Carver et al. 2011). Their industry specialists,

their capacity to attract well-prepared people, resources, and audit tools represent an advantage compared

to non-Big 4 auditors. We control for auditor by including a dummy variable (AUD) that is equal to 1 if the

firm is audited by Big-4 and 0 otherwise.

Restatements occur when a material error is discovered in financial statements. Both international

accounting standards (IAS) and United States accounting standards (USGAAP) state that a restatement

should be properly presented and disclosed in the financial statements. A restatement could be an indication

of weak internal control around the preparation of financial statements. Given this, we could expect that the

restatements will indicate a lower FRQ. We included in our model a dummy variable (RES) which equals

1 if the company issue a restated financial statement and 0 otherwise.

Management incentives are an important determinant of FRQ. Meeting debt covenants is essential

for management, as it ensures the continuity of financing from the banks. Anagnostopoulu and Tsekrekos

(2017), Gu et al. (2005), and Lazzem and Jilani (2018) provide strong evidence that highly leveraged firms

engage in earning management and have lower FRQ. Furthermore, Gu et al. (2005) found that the variability

of accruals is positively associated with increased leverage. Dechow et al. (2010) discussed that, for highly

levered firms, the management takes discretionary actions to avoid violating a covenant. We include

leverage (LEV) as a control variable in our model, determined as the total debt divided by the total equity.

Dechow et al. (2010) point out that small firms mostly have a deficient control over financial reporting

due to fixed costs. Therefore, small companies will engage in earnings management more frequently. We

control the size of the company; our SIZE variable is determined as the natural logarithm of the market

capitalisation of the company.

Dechow et al. (2010) noted that poor performance could provide an incentive for management to

engage in discretionary actions. The purpose of management is to create value for shareholders. This value

is created through good results and performance; therefore, management is less interested in manipulating

the results of a firm that performed well. DeFond and Park (1997) suggested that to reduce the threat of

being dismissed, the management of firms with current poor performance but with expected good

performance in the future has incentive to manipulate the financial statements. Additionally, Keating and

Zimmerman (2000) noted that managers change the accounting policies to offset the poor performance of

the firm. We control performance by including the return on assets (ROA) and a dummy variable in our

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

168

model, which takes 1 if the company reported negative equity and 0 if otherwise.

RESULTS

Descriptive statistics

The following table shows the descriptive statistics for our variables.

Table 6. Summary of statistics

Variable

Mean

Std. Dev.

Min

Max

FRQ1

5.7936

8.8039

0.0372

61.4595

FRQ2

5.6782

8.459

0.0357

57.7571

FRQ3

5.0831

7.1852

0.0371

47.0644

FRQ4

6.4209

8.5185

0.0677

57.3992

EPU

-7.1733

16.234

-43.5854

32.1983

ENF

9.9027

13.8725

-42.292

42.8672

SIZE

18.8668

2.3415

13.0787

24.3003

LEV

0.2442

0.2418

1.4634

ROA

-0.0319

0.288

-2.0467

0.2751

DCE

0.0426

0.2019

AUD

0.4578

0.4982

RES

0.0911

0.2878

Table description: This table presents the summary statistics for our variables. We multiply the FRQ values

by 100 to facilitate the interpretation of the results. To be able to correctly interpret the coefficients for EPU

and ENF we normalise their value between -50 and 50 using the min-max method. The summary statistics

are winsorized at 1%.

The FRQ takes values between 0.0357 (FRQ2) and 61.4595 (FRQ1), and we can observe variability

in our measures of FRQ. The mean of EPU is situated at -7.1733 and the standard deviation is 16.2340.

There is a high variation of EPU, the minimum is 43.5854 while the maximum is situated at 32.1983. This

is an important feature of this research, since our sample captures periods with low uncertainty and extreme

uncertainty. The mean of ENF is 9.9027, the minimum is -42.292 while, the maximum is 42.8672.

Regression Analysis

Table 7 illustrates the regression output for FRQ1, FRQ2, FRQ3 and FRQ4.

For the interpretation of the results, we will refer to the positive association between the earnings

management measures and the EPU as a negative association between the FRQ and the EPU. The bigger

the residuals from earnings management regressions presented in section ‘Financial reporting quality’ the lower

the FRQ is. Therefore, the positive association means that earnings management increases and FRQ

decreases. The results show that FRQ is negatively associated with EPU. The coefficient is statistically

significant in all four models at a level of 1%. A change with one unit in EPU will cause a decrease in FRQ

of 0.00599 in Model 1, 0.00838 in Model 2, 0.00562 in Model 3, and by 0.00394 in Model 4. The results

validate our first hypothesis, EPU deteriorates the FRQ. This is consistent with Yung and Root (2019),

Goncalves et al. (2022), Bermpei et al. (2021), Dhole et al. (2021), Jin et al. (2019), Dai and Ngo (2020),

Nagar et al. (2018), Jain et al. (2021), and Jiang et al. (2022).

There is a positive association between ENF and FRQ. The coefficient is statistically significant at

the 1% level in all models. For a change with one unit in ENF, the FRQ increases by 0.0107 in Model 1,

0.0101 in Model 2, 0.0106 in Model 3, and by 0.00794 in Model 4. We can conclude that ENF strengthens

FRQ, which is consistent with Christensen et al. (2013), Brown et al. (2015), Carson et al. (2021), Ernstberger

et al. (2012), Böcking et al. (2015), Florou et al. (2020), Florou and Shuai (2022), Li et al. (2022), and

Windisch (2021). The results of this regression are in line with those obtained by Mos (2024a) in a paper

that investigates the role of accounting standards, and industry characteristics in mediating the association

between uncertainty and financial reporting quality and with a paper that investigates the same association

but for European Union (EU) settings Mos (2024b).

Firms that restate their financial statements also have a lower FRQ, as expected. As we explained in

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

169

the previous section, a restatement means weak internal control around the preparation of financial

statement. Therefore, the earning management could be undetected by internal controls. Firms audited by

BIG 4 have a higher FRQ than others, which is consistent with the literature. Large firms report a higher

FRQ. Due to their exposure to the market and analysts, large firms are more prudent in using discretionary

accruals. Leveraged firms report a lower FRQ due to financial constraints and pressure to meet financial

covenants. Taken together, our control variables are in line with the literature which validate our approach.

The adjusted R squared is situated around 10%; this is comparable to the adjusted R squared obtained

by Bermpei et al. (2021), Goncalves et al. (2022), Yung and Root (2019), Jain et al. (2021), and El Ghoul et

al. (2021).

Table 7. Regression results for EPU

(1)

(2)

(3)

(4)

FRQ1

FRQ2

FRQ3

FRQ4

EPU

0.00599***

0.00838***

0.00562***

0.00394***

(5.95)

(8.92)

(6.85)

(4.03)

ENF

-0.0107***

-0.0101***

-0.0106***

-0.00794***

(-9.07)

(-8.77)

(-10.98)

(-6.88)

SIZE

-0.468***

-0.457***

-0.379***

-0.282***

(-33.44)

(-33.49)

(-34.59)

(-23.73)

LEV

1.949***

1.931***

1.411***

0.646***

(14.16)

(14.45)

(13.48)

(5.04)

ROA

-2.853***

-2.806***

-1.713***

-2.609***

(-17.97)

(-18.20)

(-14.85)

(-15.96)

DCE

1.757***

1.698***

3.141***

0.516**

(9.48)

(9.47)

(19.98)

(3.19)

AUD

-0.621***

-0.609***

-0.366***

-0.665***

(-12.60)

(-12.66)

(-9.13)

(-13.42)

RES

0.465***

0.418***

0.462***

0.605***

(7.39)

(6.96)

(8.99)

(9.57)

R-squared

0.1224

0.1273

0.1263

0.0824

No. of observations

284 908

Country fixed effects

Yes

Industry fixed effects

Yes

Table description: This table presents the regression results for regression results for EPU. In each case, we

employed an OLS regression with fixed effects. In the interaction terms, we center EPU and ENF by

subtracting the mean value. In each model, the standard errors are clustered at the firm level. The t-values

are in parentheses. The significance levels at 10%, 5% and 1% are represented by *, **, and ***, respectively.

Our results suggest that the EPU exacerbates earning management and reduces the FRQ. In times of

high uncertainty, it seems that management incentives prevail over accounting principles and the public

mission of accounting. Next, we attempt to identify several reasons why the EPU is negatively associated

with FRQ.

Peng et al. (2020) indicate that good news related to earnings diminishes the overall uncertainty.

When the EPU increases, investors, analysts, and creditors tend to become more pessimistic. This could

mean a decrease in corporate ratings, a withdrawal of investor support, and a shortage of financial resources

for companies. Then the management incentives and pressures are to reduce the uncertainty of the firm

prospects. Meeting or even exceeding the analyst earnings forecast is a useful tool for management to create

good news related to earnings. Upward earnings management will help them achieve this.

Arouri et al. (2016) discussed how EPU affects business operations. Supply chains, production costs,

and earnings are affected by EPU. Therefore, the profitability of the company will decrease. Shin (2019)

pointed out that the market reacts more negatively to small losses under high uncertainty. Consequently,

management incentives are to avoid small losses at any cost. Increase profitability by applying discretionary

behavior in determining accruals seems the best option available.

Accounting enforcement and EPU

In this section, we analyze the possible role of high accounting enforcement in countering the effects

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

170

of EPU. For this purpose, we introduce in regression an interaction term between EPU and ENF

(EPU#ENF). Table 8 shows the results of the regression.

The coefficient of EPU#ENF is negative in all models and is statistically significant at 1% level. This

means that accounting enforcement can reduce the negative association between FRQ and EPU. An

increase with one unit in the ENF will lead to a decrease in the negative association between FRQ and EPU

by 0.00756 in Model 5, 0.000712 in Model 6, 0.000680 in Model 7, and by 0.00768 in Model 8. In relative

terms, accounting enforcement reduces the negative association between FRQ and EPU by 17.38% in Model

5, 10.41% in Model 6, 16.39% in Model 7, and 17.54% in Model 8.

Table 8. Regression results with the interaction term between EPU and ENF

(5)

(6)

(7)

(8)

FRQ1

FRQ2

FRQ3

FRQ4

EPU

0.00435***

0.00684***

0.00415***

0.00285**

(4.31)

(7.28)

(5.04)

(2.88)

ENF

-0.0102***

-0.00970***

-0.0102***

-0.00768***

(-8.82)

(-8.53)

(-10.68)

(-6.71)

EPU#ENF

-0.000756***

-0.000712***

-0.000680***

-0.000500***

(-9.31)

(-9.08)

(-10.20)

(-6.39)

SIZE

-0.466***

-0.455***

-0.377***

-0.281***

(-33.28)

(-33.33)

(-34.42)

(-23.60)

LEV

1.940***

1.922***

1.402***

0.639***

(14.09)

(14.38)

(13.40)

(4.99)

ROA

-2.854***

-2.807***

-1.714***

-2.610***

(-17.98)

(-18.21)

(-14.87)

(-15.97)

DCE

1.769***

1.709***

3.152***

0.524**

(9.55)

(9.53)

(20.06)

(3.24)

AUD

-0.629***

-0.616***

-0.373***

-0.670***

(-12.75)

(-12.81)

(-9.30)

(-13.52)

RES

0.466***

0.419***

0.463***

0.606***

(7.41)

(6.98)

(9.01)

(9.58)

R-squared

0.1227

0.1275

0.1267

0.0825

No. of observations

284 908

Country fixed effects

Yes

Industry fixed effects

Yes

Table description: This table presents the regression results for regression results with the interaction term

between EPU and ENF. In each case, we employed an OLS regression with fixed effects. In the interaction

terms, we center EPU and ENF by subtracting the mean value. In each model, the standard errors are

clustered at the firm level. The t-values are in parentheses. The significance levels at 10%, 5% and 1% are

represented by *, **, and ***, respectively.

We discussed in previous sections that EPU induces pessimistic sentiment in the market. This

sentiment leads to a decrease in the market value of companies and has caused investors to overreact to bad

news, especially those related to earnings. Accounting errors discovered following accounting enforcement

inspections and actions also lead to a negative reaction from the capital market (Ernstberger et al. 2012;

Christensen et al. 2020; Dechow et al. 1996; Curtis (2016). Additionally, we discussed in previous sections

that errors related to auditors made publicly by the accounting enforcement institution produce negative

reactions in the capital market (Dee et al. 2011). In countries where accounting enforcement is well

implemented, the finalization of the process consists of announcing the results. These results are made

known to the press and the market. These results usually comprise the accounting errors and the firms where

the errors were found. Taking into account these facts, we can build the following argument for our results.

We acknowledge that the market is pessimistic and that investors react more prudently to firm information

in times of high EPU. Pessimistic sentiment and negative market evolution are general conditions under

high EPU. The announcement of negative outcome of the accounting enforcement is limited to few firms

annually in each country. This event, in times of high EPU, will only aggravate the general pessimism and

condition of the market. Therefore, these firms will face more severe consequences and negative reactions

from the market. The explanations and reasons for the obtained results align with those presented by Mos

(2024b) in the context of the European Union (EU). In that study, the author employed alternative measures

of uncertainty, specifically tailored to the EU's unique circumstances.

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

171

Additional tests

Another measure for FRQ

Real earnings management (RM) is another widely used model to estimate FRQ. Compared with

earnings management, this model is designed to identify the discretional behavior of management when

they choose to cut certain expenses to achieve the desired profitability instead of correlated them with the

actual needs of the firm. We follow the approach illustrated by Cohen et al. (2008). The RM is the residuals

from the below model where DE is discretionary expenses which incorporate general administrative

expenses and research and development expenses. The remaining notations are already defined in Section

‘Methodology’.



=α

+α

it-1

+α



REV

+ε

(5)

The residuals are the deviation from the predicted discretionary expenses. A negative or low value of

the residuals from the RM means low FRQ. To ease the interpretation of the results, we multiplied the

residuals by -1. Therefore, in line with earnings management models, we expect a positive association

between RM and EPU. Table 9 presents the results of the regressions.

Table 9. Regression results with the interaction term between EPU and ENF

(9)

(10)

EPU

0.0577***

0.0534***

(16.35)

(15.73)

ENF

-0.0362***

-0.0352***

(-6.81)

(-6.60)

EPU#ENF

-0.00195***

(-5.94)

SIZE

1.623***

1.629***

(20.64)

(20.71)

LEV

0.0102

-0.0145

(0.01)

(-0.02)

ROA

13.45***

(11.90)

(11.89)

DCE

-23.50***

-23.46***

(-19.90)

(-19.87)

AUD

2.978***

2.957***

(11.14)

(11.06)

RES

1.728***

1.731***

(6.47)

(6.49)

R-squared

0.2188

0.2189

No. of observations

284 908

Country fixed effects

Yes

Industry fixed effects

Yes

Table description: This table presents the regression results for the regression results for RM with the

interaction term between EPU and ENF. In both cases, we used an OLS regression with fixed effects. In the

interaction terms, we center EPU and ENF by subtracting the mean value. In each model, the standard

errors are clustered at the firm level. The t-values are in parentheses. The significance levels at 10%, 5% and

1% are represented by *, **, and ***, respectively.

The results are as expected; the RM is positively associated with EPU, which further validates our

previous results. The coefficient of EPU is 0.0577 signifying that when uncertainty increases by one unit,

the FRQ decreases by 0.0577. The coefficient is statistically significant at the 1% level. In periods with high

uncertainty, the actual discretionary expenses are lower than the predicted ones. The management uses the

discretionary expenses as an instrument to improve the firm performance. Regarding accounting

enforcement, we observe, similar to previous results, a negative and statistical significant coefficient. For

one unit change in ENF, the association between EPU and RM decreases by 0.00195. Therefore, even if we

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

172

use another measure for FRQ, accounting enforcement retains its role in countering the effects of

uncertainty. In relative terms, this translates to a 3.65% decrease in the negative association between RM

and EPU.

Another measure for uncertainty

Dai and Ngo (2020), Jain et al. (2021), and Goncalves et al. (2022) use in their studies a dummy

variable for the years with elections to estimate the uncertainty. In election years, there is an increase in

uncertainty because the new elected political power will usually change certain aspects of the fiscal and

monetary policy. Furthermore, we can argue that this casts a major uncertainty on the budgeting process,

which is an important part of planning the business. The inability to know possible future changes in

legislation may affect the accuracy of forecasts.

To measure the uncertainty using the elections we rely on Database of Political Institutions prepared

by Carlos et al. (2020) . However, their database contains data only until 2020. For 2021 and 2022 we

checked if there were elections for countries in our sample. Furthermore, for United States, China, South

Korea, and Hong Kong, we collected information regarding the elections since the database does not contain

information about these countries.

We use a dummy variable that equals 1 if there were elections in a specific year for a specific country

in our sample. Table 10 presents detailed information on the years with elections for each country and year.

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

173

Table 10. Descriptive statistics for the variable ELECT (dummy variable which takes value 1 for years with elections)

Countries

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Australia

Brazil

Chile

Colombia

Denmark

Germany

Hong Kong

Ireland

Japan

Mexico

New Zealand

Pakistan

Singapore

Sweden

United States

Belgium

Canada

China

Croatia

Greece

France

India

Italy

South Korea

The Netherlands

Spain

United Kingdom

Russia

Nigeria

Source: Authors’ own processing based on Carlos et al. (2020)

174

Table 11 shows the results of the regression

Table 11. Regression results for ELECT

(11)

(12)

(13)

(14)

(15)

FRQ1

FRQ2

FRQ3

FRQ4

RMS

ELECT

0.229***

0.165***

0.123***

0.220***

1.320***

(6.77)

(5.14)

(4.54)

(6.43)

(10.35)

ENF

-0.0115***

-0.0108***

-0.0113***

-0.00894***

-0.0402***

(-9.74)

(-9.38)

(-11.67)

(-7.74)

(-7.55)

EPU#ENF

-0.00580***

-0.00671***

-0.00853***

-0.0111***

-0.0151**

(-3.53)

(-4.31)

(-6.27)

(-6.52)

(-2.75)

SIZE

-0.469***

-0.459***

-0.380***

-0.281***

1.606***

(-33.49)

(-33.62)

(-34.57)

(-23.65)

(20.44)

LEV

1.953***

1.938***

1.415***

0.646***

0.0612

(14.19)

(14.51)

(13.53)

(5.04)

(0.07)

ROA

-2.857***

-2.813***

-1.719***

-2.614***

13.41***

(-17.99)

(-18.24)

(-14.90)

(-15.99)

(11.86)

DCE

1.753***

1.689***

3.137***

0.519**

-23.57***

(9.46)

(9.42)

(19.96)

(3.20)

(-19.96)

AUD

-0.613***

-0.600***

-0.362***

-0.663***

3.062***

(-12.42)

(-12.46)

(-9.02)

(-13.37)

(11.44)

RES

0.467***

0.418***

0.462***

0.607***

1.732***

(7.42)

(6.96)

(9.00)

(9.60)

(6.48)

R-squared

0.1224

0.1271

0.1263

0.0826

0.2185

No. of observations

284 908

Country fixed effects

Yes

Industry fixed effects

Yes

Table description: This table presents the regression results for regression results for ELECT which is another

measure of uncertainty. The regressions also include the interaction term between ELECT and ENF. In all

cases, we used an OLS regression with fixed effects. In interaction terms, we center the ENF by subtracting

the mean value. In each model, the standard errors are clustered at the firm level. The t-values are in

parentheses. The significance levels at 10%, 5% and 1% are represented by *, **, and ***, respectively.

The results show that even if we measure the uncertainty in another way, the findings of our research

are still valid, and we reach the same conclusion. The ELECT is positively associated with real earnings

management and, therefore, negatively associated with FRQ. The coefficient is statistically significant in all

models. In election years the uncertainty increases and leads to a decrease in FRQ by 0.229 in Model 11,

0.165 in Model 12, 0.123 in Model 13, 0.220 in Model 14, and by 1.320 in Model 15. With respect to

accounting enforcement we observe the same pattern, the coefficient of the interaction term is negative and

statistically significant at the level of 1% in Models 11-14 and the level of 5% in Model 15. In the election

years when uncertainty increases, accounting enforcement reduces the negative association between ELECT

and FRQ by 0.00580 in Model 11, 0.00671 in Model 12, 0.00853 in Model 13, 0.0111 in Model 14, and by

0.0151 in Model 15. In relative terms, the decrease is 2.53% in Model 11, 4.07% in Model 12, 6.93% in

Model 13, 5.05% in Model 14, and 1.14% in Model 15. Another important aspect is that ELECT reflect

mainly the political uncertainty. Therefore, strong accounting enforcement institutions guarantee the FRQ

even when the government and the administration of the country change. This is a vital element for the

functioning of capital markets.

Another measure for ENF

Accounting enforcement is a reflection of the quality of regulatory environment. This is the reason

why in prior research, many of the scholars include rule of law in their studies as a measure for accounting

enforcement (for example, Daske et al. 2008 and Hope 2003). As a robustness test, we use another measure

of accounting enforcement that is appropriate for our research. The regulatory quality index (RQ)

determined by the World Bank (2023) captures the ability of the government to formulate and implement

policies and regulations related to the private sector. Compared with rule of law, regulatory quality is a more

suitable measure for accounting enforcement because it promotes the implementation of regulations

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

175

specifically for the firms and business sector rather than for all categories as rule of law. Therefore, RQ is a

more refined version of the rule of law applicable to firms. Table 12 shows the results of the regressions.

Table 12. Regression results for RQ

(16)

(17)

(18)

(19)

(20)

FRQ1

FRQ2

FRQ3

FRQ4

EPU

0.00541***

0.00790***

0.00494***

0.00312**

0.0542***

(5.35)

(8.35)

(5.97)

(3.17)

(15.09)

-0.00373*

-0.00183*

-0.00729***

-0.00491**

-0.0575***

(-2.36)

(-1.23)

(-5.62)

(-3.09)

(-8.10)

EPU#RQ

-0.000564***

-0.000563***

-0.000470***

-0.000791***

-0.00107**

(-5.78)

(-6.34)

(-6.01)

(-8.07)

(-2.79)

SIZE

-0.471***

-0.460***

-0.382***

-0.284***

1.619***

(-33.66)

(-33.72)

(-34.79)

(-23.94)

(20.61)

LEV

1.950***

1.933***

1.409***

0.641***

-0.0265

(14.15)

(14.46)

(13.46)

(5.00)

(-0.03)

ROA

-2.846***

-2.800***

-1.707***

-2.604***

13.48***

(-17.91)

(-18.15)

(-14.78)

(-15.91)

(11.92)

DCE

1.754***

1.693***

3.141***

0.519**

-23.47***

(9.46)

(9.44)

(19.97)

(3.21)

(-19.88)

AUD

-0.620***

-0.608***

-0.363***

-0.667***

2.999***

(-12.57)

(-12.64)

(-9.05)

(-13.45)

(11.22)

RES

0.504***

0.456***

0.501***

0.643***

1.853***

(8.04)

(7.61)

(9.76)

(10.20)

(6.96)

R-squared

0.1223

0.1271

0.1262

0.0826

0.2188

No. of observations

284 908

Country fixed effects

Yes

Industry fixed effects

Yes

Table description: This table presents the regression results for the regression results for RQ as another

measure for accounting enforcement with the interaction term between EPU and RQ. The table also includes

the results for real earnings management (RM) defined previously. In all cases, we used an OLS regression

with fixed effects. In the interaction terms, we center EPU and RQ by subtracting the mean value. In each

model, the standard errors are clustered at the firm level. The t-values are in parentheses. The significance

levels at 10%, 5% and 1% are represented by *, **, and ***, respectively.

The results are similar to those already obtained and highlights again the importance of accounting

enforcement in reducing the negative impact of EPU on FRQ. The coefficient of the interaction term is

negative and statistically significant at 1% in all models. The results suggest that when accounting

enforcement increases by one unit the negative impact of EPU on uncertainty decreases by 0.000564 in

Model 16, 0.000563 in Model 17, 0000470 in Model 18, and by 0.00107 Model 20.

CONCLUSIONS

Our study investigates the effects of uncertainty on the quality of financial reporting. We use data

from 29 countries. Based on 284,908 firm-year observations, we find that uncertainty is negatively associated

with the FRQ. We provide evidence that accounting enforcement is an efficient tool to counteract the effects

of uncertainty on FRQ. The findings show that the accounting enforcement reduces the negative association

between FRQ and uncertainty. Our findings are robust to other measures for FRQ, uncertainty, and

accounting enforcement.

The findings of this study are critical for investors and policy makers. We show that uncertainty is a

key determinant of FRQ, and both investors and policymakers should acknowledge this. Furthermore, given

all the recent events around the world, the uncertainty will last much longer than previously expected, and

we should know how to deal with it. Accounting enforcement is an efficient instrument, strengthening it will

prevent the decrease in FRQ when uncertainty rise.

This study contributes to the literature in many ways. We used a large sample consisting of firms from

29 countries and 284,908 which will result in reasonable variability that supports our findings. Furthermore,

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

176

accounting enforcement was analyzed for the first time in this study. This is an important topic that enriches

the existing literature on accounting enforcement which is one of the main determinants of FRQ.

Note

The current study partially adopts methodologies from two works by Mos (2024a; 2024b). The first examines

economic policy uncertainty in the EU using measures specifically designed by EU authorities for the

European context. The second explores how international accounting standards and industry characteristics

mediate the relationship between economic policy uncertainty and financial reporting quality. Those studies

are appropriately cited in this work and indirectly through this statement.

REFERENCES

Anagnostopoluou S. C., and A. E. Tsekrekos. 2017. The effect of financial leverage on real and accrual-

based earnings management. Accounting and Business Research, 191-236, DOI:

https://doi.org/10.1080/00014788.2016.1204217.

Andrei D., H. Friedman H., and N. B. Ozel. 2023. Economic uncertainty and investor attention. Journal of

Financial Economics, 149: 179-217, DOI: https://doi.org/10.1016/j.jfineco.2023.05.003.

Arouri M., C. Estay, C. Rault, and D. Roubaud. 2016. Economic policy uncertainty and stock markets:

Long-run evidence from the US. Finance Research Letters, 18: 136-141, DOI:

https://doi.org/10.1016/j.frl.2016.04.011.

Baker S., N. Bloom, and S. Davis. 2016. Measuring economic policy uncertainty. The Quarterly Journal of

Economics, 131: 1593-1636, DOI: https://doi.org/10.1093/qje/qjw024.

Behn B. K., J. H. Choi, and T. Kang. 2008. Audit quality and properties of analyst earnings forecasts. The

Accounting Review, 83: 327-349, DOI: https://doi.org/ 10.2139/ssrn.1013214.

Böcking H. J., M. Gros, and D. Worret. 2015. Enforcement of accounting standards: how effective is the

German two-tier system in detecting earnings management? Review of Managerial Science, 9:431-485,

DOI: https://doi.org/ 10.1007/s11846-014-0159-z.

Boolaky P. K., and C. Krishnamurti. 2015. Determinants of the Strength of Auditing and Reporting

Standards. Australian Accounting Review, 25: 292-308, DOI: https://doi.org/ 10.14453/aabfj.v7i4.3.

Boulton T. J. 2022. Economic policy uncertainty and international IPO underpricing. Journal of International

Financial Markets, Institutions and Money, 81, DOI: https://doi.org/10.1016/j.intfin.2022.101689.

Brempei T., A. N. Kalyvas, L. Neri, and A. Russo. 2021. Does economic policy uncertainty matter for

financial reporting quality? Evidence from the United States. Review of Quantitative Finance and

Accounting, 58: 795-845, DOI: https://doi.org/10.1007/s11156-021-01010-2.

Brown P., J. Preitato, and A. Tarca. 2015. A Comparison of Between-Country Measures of Legal Setting

and Enforcement of Accounting Standards. Journal of Business Finance & Accounting, 42:1-50, DOI:

https://doi.org/10.1111/jbfa.12112.

Carlos S., C. Cesi, and K. Philip. 2020. The Database of Political Institutions 2020 (DPI2020). The Database

of Political Institutions 2020 (DPI2020), DOI: http://dx.doi.org/10.18235/0003049.

Carson E., P. T. Lamoreaux, R. Simnett, U. Thuerheimer, and A. Vanstraelen. 2021. Establishment of

National Public Audit Oversight Boards and Audit Quality. Forthcoming, Available at

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3049828.

Carver B. T., C. W. Hollingsworth, and J. D. Stanley. 2011. Recent Auditor Downgrade Activity and

Changes in Client's Discretionary Accruals. Auditing: A Journal of Practice and Theory, 30: 33-58, DOI:

https://doi.org/10.2308/ajpt-10053.

Che L., O. K. Hope, and J. C. Langli. 2020. How Big-4 Firms Improve Audit Quality. Management Science,

66: 1-21, DOI: https://doi.org/10.1287/mnsc.2019.3370.

Chen F., O. K. Hope, Q. Li, and X. Wang. 2018. Flight to Quality in International Markets: Investors’

Demand for Financial Reporting Quality during Political Uncertainty Events. Contemporary

Accounting Research, 35:117-155, DOI: https://doi.org/10.1111/1911-3846.12355.

Christensen H. B., L. Hail, and C. Leuz. 2013. Mandatory IFRS reporting and changes in enforcement.

Journal of Accounting and Economics, 56:147-177, DOI: https://doi.org/10.1016/j.jacceco.2013.10.007.

Christensen H. B., Y. L. Liu, and M. Maffett. 2020. Proactive Financial Reporting Enforcement and

Shareholder Wealth. Journal of Accounting and Economics, 69, DOI:

https://doi.org/10.1016/j.jacceco.2019.101267.

Cohen D. A., A. Dey, and T. Z. Lys. 2008. Real and accrual-based earnings management in the pre-and

post-Sarbanes-Oxley periods. The Accounting Review, 83(3): 757-787, DOI:

https://doi.org/10.2308/accr.2008.83.3.757.

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

177

Connolly R., C. Stivers, and L. Sun. 2005. Stock market uncertainty and the stock-bond return relation.

Journal of Financial and Quantitative Analysis, 40: 161-194, DOI:

https://doi.org/10.1017/S0022109000001782.

CPAB. 2023. Canadian Public Accountability Board. Retrieved from CPAB.ca: https://cpab-ccrc.ca/who-we-

are, 15 April.

Cui X., S. Yao, Z. Fang, and H. Wang. 2021. Economic policy uncertainty exposure and earnings

management: evidence from China. Accounting and Finance, 61:3937-3976, DOI:

https://doi.org/10.1111/acfi.12722.

Curtis, N. 2016. The impact of SEC investigations and accounting and auditing enforcement releases on

firms’ cost of equity capital. Review of Quantitative Finance and Accounting, 47: 57-82, DOI:

https://doi.org/ 10.1007/s11156-014-0494-9.

Dai L., and P. Ngo. 2020. Political Uncertainty and Accounting Conservatorism. European Accounting

Review, 30: 77-307, DOI: https://doi.org/10.1080/09638180.2020.1760117

Daske H., L. Hail, C. Leuz, and R. Verdi. 2008. Mandatory IFRS Reporting around the World: Early

Evidence on the Economic Consequences . Journal of Accounting Research, 46(5); 1085-1142, DOI:

https://doi.org/10.1111/j.1475-679X.2008.00306.x.

Dechow P. M., R. G. Sloan, and A. P. Sweeney. 1995. Detecting Earnings Management. The Accounting

Review, 70: 193-225.

Dechow P., W. Ge and C. Schrand. 2010. Understanding earnings quality: A review of the proxies, their

determinants and their consequences. Journal of Accounting and economics, 50(2-3): 344-401, DOI:

https://doi.org/10.1016/j.jacceco.2010.09.001.

Dechow P. M., and I. D. Dichev. 2002. The Quality of Accruals Earnings: The Role of Accrual Estimation

Errors. The Accounting Review, 77: 35-99, DOI: http://dx.doi.org/10.2139/ssrn.277231.

Dechow P. M., R. G. Sloan, and A. P. Sweeney. 1996. Causes and consequences of earnings manipulation:

An analysis of firms subject to enforcement actions by the SEC. Contemporaty Accounting Research, 13:

1-36, DOI: https://doi.org/10.1111/j.1911-3846.1996.tb00489.x

Dee C. C., A. Lulseged, and T. Zhang. 2011. Client Stock Market Reaction to PCAOB Sanctions Against

a Big 4 Auditor. Contemporary Accounting Research, 28: 263-291, DOI: https://doi.org/10.1111/j.1911-

3846.2010.01044.x.

DeFond M., and J. Zhang. 2014. A review of archival auditing research. Journal of Accounting and Economics,

58: 275-326, DOI: https://doi.org/10.1016/j.jacceco.2014.09.002.

DeFond M. L., and C. W. Park. 1997. Smoothing income in anticipation of future earnings. Journal of

Accounting and Economics, 23: 115-139, DOI: https://doi.org/10.1016/S0165-4101(97)00004-9.

Dhole S., L. Liu, G. J. Lobo, and S. Mishra. 2021. Economic policy uncertainty and financial statement

comparability. Journal of Accounting and Public Policy, 40, DOI:

https://doi.org/10.1016/j.jaccpubpol.2020.106800.

Dzielinski M. 2012. Measuring economic uncertainty and its impact on the stock market. Finance Research

Letters, 9: 167-175, DOI: https://doi.org/10.1016/j.frl.2011.10.003.

El Ghoul S., O. Guedhami, Y. Kim, and H. J. Yoon. 2021. Policy Uncertainty and Accounting Quality.

The Accounting Review, 96: 233-260, DOI: https://doi.org/10.2308/TAR-2018-0057.

Ernstberger J., M. Stich, and O. Vogler. 2012. Economic Consequences of Accounting Enforcement

Reforms: The Case of Germany. European Accounting Review, 21: 217-251, DOI:

https://doi.org/10.1080/09638180.2011.628096.

ESMA. 2023. 2022 Corporate reporting enforcement and regulatory activities. ESMA Communication Office.

Florou A., S. Morricone, and P. F. Pope. 2020. Proactive financial reporting enforcement: Audit fees and

financial reporting quality effects. The Accounting Review, 95: 167-197, DOI:

https://doi.org/10.2308/accr-52497.

Florou A., and S. Yuan. 2022. Public Audit Oversight and Audit Pricing: Evidence from the EU. European

Accounting Review, forthcoming, DOI: https://doi.org/10.1080/09638180.2022.2145981.

Gonçalves T., V. Baross, and G. Serra. 2022. Political elections uncertainty and earnings management: Does

firm size really matter? Economic Letters, 214, DOI: https://doi.org/10.1016/j.econlet.2022.110438.

Graham J.R., C. R. Harvey, and S. Rajgopal. 2005. The economic implications of corporate financial

reporting. Journal of Accounting and Economics, 40: 3-73, DOI:

https://doi.org/10.1016/j.jacceco.2005.01.002.

Gu Z., C. W. Lee, and J. G. Rosett. 2005. What Determines the Variability of Accounting Accruals? Review

of Quantitative Finance and Accounting, 24:313-334, DOI: https://doi.org/10.1007/s11156-005-6869-1.

Healy P. M., and K. G. Palepu. 2001. Information asymmetry, corporate disclosure, and the capital markets:

A review of the empirical disclosure literature. Journal of Accounting and Economics, 31: 405-440, DOI:

https://doi.org/10.1016/S0165-4101(01)00018-0.

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

178

Hope O. K. 2003. Disclosure Practicies, Enforcement of Accounting and Analyst Forecast Accuracy: An

International Study. Journal of Accounting Research, 41: 235-272, DOI: https://doi.org/10.1111/1475-

679X.00102.

Jain A., D. Jackson, and H. Sakaki. 2021. Political, economic, financial uncertainty, and real earnings.

Journal of Corporate Accounting and Finance, 32: 52-66, DOI: https://doi.org/10.1002/jcaf.22485.

Jiang L., J. A. Pittman, and W. Saffar. 2022. Policy Uncertainty and Textual Disclosure. Accounting Horizons,

36:113-131, DOI: https://doi.org/10.2308/HORIZONS-2019-515.

Jin J. Y., K. Kanagaretnam, Y. Liu, and G. J. Lobo. 2019. Economic policy uncertainty and bank earnings

opacity. Journal of Accounting and Public Policy, 38: 199-218, DOI:

https://doi.org/10.1016/j.jaccpubpol.2019.05.002.

Jones J. J. 1991. Earnings Management During Import Relief Investigations. Journal of Accounting Research,

29: 193-228, DOI: https://doi.org/10.2307/2491047.

Keating A. S., and J. L. Zimmerman. 2000. Depreciation-policy changes: tax, earnings management, and

investment opportunity incentives. Journal of Accounting and Economics, 28: 359-389, DOI:

https://doi.org/10.1016/S0165-4101(00)00004-5.

Kothari S. P., A. Leone, and C. E. Wasley. 2005. Perofmance Matched Discretionary Accruals Measures.

Journal of Accounting and Economics, 39:163-197, DOI: https://doi.org/10.1016/j.jacceco.2004.11.002.

Kraft A., Fu R., and H. Zhang. 2012. Financial reporting frequency, information asymmetry, and the cost

of equity. Journal of Accounting and Economics, 54: 132-149, DOI:

https://doi.org/10.1016/j.jacceco.2012.07.003.

Krishnan G. V. 2003. Audit Quality and the Pricing of Discretionary Accruals. Auditing: A Journal of Practice

and Theory, 22: 109-126, DOI: https://doi.org/10.2308/aud.2003.22.1.109.

Krishnan G. V. 2003. Does big 6 auditor industry expertise constrain earnings management? Accounting

Horizons, 17: 1-16, DOI: https://doi.org/ 10.2308/acch.2003.17.s-1.1.

Kurniawan F., H. T. Amanati, A. H. Nugroho, and N. A. Pusparini. 2023. Effects of policy and economic

uncertainty on investment activities and corporate financial reporting: a study of developing countries

in Asia-Pacific. Asian Review of Accounting, DOI: https://doi.org/10.1108/ARA-12-2022-0290.

Lazzem S., and F. Jilani. 2018. The impact of leverage on accrual-based earnings management : the case of

listed French firms. Research in International Business and Finance, 44: 350-358, DOI:

https://doi.org/10.1016/j.ribaf.2017.07.103.

Li L., B. Qi, A. Robin, and R. Yang. 2022. The effect of enforcement action on audit fees and the audit

reporting lag. Accounting and Business Research, 52: 38-66, DOI:

https://doi.org/10.1080/00014788.2020.1808441.

Liu L., and T. Zhang. 2015. Economic policy uncertainty and stock market volatility. Finance Research Letters,

15: 99-105, DOI: https://doi.org/10.1016/j.frl.2015.08.009.

Mos C, 2024a. Economic Policy Uncertainty, Financial Reporting Quality, and Audit Fees: Examining the

Role of Industry Characteristics and International Accounting Standards. Audit Financiar, Vol.22,

No. 4: 791-802, DOI: http://dx.doi.org/ 10.20869/AUDITF/2024/176/029

Mos C, 2024b. Uncertainty, financial reporting quality and accounting enforcement: Evidence from the

European Union. Journal of Accounting and Management Information Systems, Vol.23, No. 3: 616-

642, DOI: http://dx.doi.org/10.24818/jamis.2024.03008

MSCI. 2023. MSCI.com. Retrieved from https://www.msci.com/our-solutions/indexes/gics, 14 April.

Nagar V., J. Schoenfeld, and L. Wellman. 2018. The Effect of Economic Policy Uncertainty on Investor

Information Asymmetry and Management Disclosures. Journal of Accounting and Economics, 67: 36-57,

DOI: https://doi.org/10.1016/j.jacceco.2018.08.011.

PCAOB. 2023. Public Company Accounting Oversight Board. Retrieved from PCAOB.org:

https://pcaobus.org/about/mission-vision-values, 25 April.

Peng Z. 2020. Asymmetric impact of earnings news on investor uncertainty. Journal of Business Finance and

Accounting, 47: 3-26.

Reuters. 2023. SEC says it is focused on prosecuting misconduct that might threaten investors, markets.

Retrieved from https://www.reuters.com/markets/us/us-sec-says-focused-prosecuting-misconduct-

that-might-threaten-investors-markets-2023-05-04/, 4 May.

Shin J. E. 2019. Asymmetric Investor Reaction around Earnings Benchmark under Economic Uncertainty.

Asia-Pacific Journal of Financial Studies, 48: 98-122, DOI: https://doi.org/ 10.1111/ajfs.12246.

Shivakumar L. 2013. The role of financial reporting in debt contracting and in stewardship. Accounting and

Business Research, 43: 362-383, DOI: https://doi.org/10.1080/00014788.2013.785683.

Staton M. C. B., and K. Roelich. 2021. Decision making under deep uncertainties: A review of the

applicability of methods in practice. Technological Forecasting & Social Change, 171, DOI:

https://doi.org/10.1016/j.techfore.2021.120939.

Catalin Mos / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

179

Walters D. J., U. Gülden, T. David, E. Carsten, and F. R. Craig. 2023. Investor Behavior Under Epistemic

vs. Aleatory Uncertainty. Management Science, 69: 2761-2777, DOI:

https://doi.org/10.1287/mnsc.2022.4489.

Windisch D. 2021. Enforcement, Managerial Discretion, and the Informativeness of Accruals. European

Accounting Review, 30: 705-732. DOI: https://doi.org/10.1080/09638180.2020.1771393.

World Bank. 2023. World Bank Data Bank. Retrieved from World Bank Data Bank:

https://databank.worldbank.org/

World Economic Forum. 2019. The Global Competitiveness Report 2019. World Economic Forum.

Yung K., and A. Root. 2019. Policy uncertainty and earnings management: International evidence. Journal

of Business Research, 100: 255-267, DOI: https://doi.org/10.1016/j.jbusres.2019.03.058.

Zhang B. 2019. Economic Policy Uncertainty and Investor Sentiment: linear and nonlinear causality

analysis. Applied Economics Letters, 26: 1264-1268, DOI:

https://doi.org/10.1080/13504851.2018.1545073.