





















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  
 

 
 
   










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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 

46 

53 

152 

106 

149 

174 

167 

123 

77 

90 

131 

83 

81 

129 

182 

106 

156 

Brazil 

97 

114 

174 

131 

93 

134 

118 

138 

149 

250 

309 

346 

165 

158 

255 

189 

196 

Chile 

71 

61 

104 

72 

71 

97 

99 

100 

154 

151 

140 

120 

106 

171 

261 

305 

338 

Colombia 

84 

66 

103 

110 

85 

99 

88 

79 

90 

126 

148 

137 

121 

151 

227 

123 

136 

Denmark 

76 

93 

123 

97 

99 

140 

117 

119 

120 

110 

125 

119 

122 

188 

300 

373 

N/a 

Germany 

81 

88 

135 

112 

140 

191 

178 

149 

125 

157 

231 

178 

172 

205 

322 

305 

669 

Hong Kong 

103 

112 

158 

96 

128 

194 

193 

134 

159 

151 

189 

139 

125 

236 

202 

104 

220 

Ireland 

75 

82 

128 

127 

149 

145 

150 

157 

117 

123 

194 

179 

155 

152 

264 

235 

320 

Japan 

65 

81 

129 

129 

127 

138 

127 

99 

97 

94 

145 

98 

97 

127 

140 

95 

110 

Mexico 

62 

60 

81 

79 

70 

67 

54 

44 

27 

33 

50 

65 

69 

94 

93 

72 

74 

New Zealand 

40 

66 

171 

98 

128 

151 

129 

74 

63 

91 

87 

110 

108 

124 

167 

119 

157 

Pakistan 

68 

71 

76 

70 

84 

92 

70 

62 

80 

51 

54 

81 

79 

104 

123 

96 

192 

Singapore 

63 

69 

130 

117 

126 

151 

161 

122 

99 

117 

182 

184 

201 

288 

326 

224 

283 

Sweden 

79 

68 

94 

83 

89 

106 

98 

96 

107 

104 

108 

101 

111 

105 

116 

102 

124 

United States 

67 

80 

139 

126 

148 

157 

158 

138 

92 

113 

145 

142 

153 

189 

326 

175 

184 

Belgium 

65 

75 

126 

181 

140 

140 

135 

128 

115 

99 

91 

83 

88 

90 

278 

168 

138 

Canada 

63 

68 

155 

132 

149 

232 

225 

181 

152 

188 

233 

244 

332 

333 

464 

277 

278 

China 

67 

67 

144 

129 

109 

152 

186 

114 

112 

138 

247 

289 

375 

581 

575 

399 

518 

Croatia 

48 

38 

36 

57 

68 

98 

140 

131 

140 

182 

172 

190 

159 

130 

281 

180 

219 

Greece 

71 

75 

103 

96 

118 

117 

124 

97 

101 

130 

118 

98 

100 

79 

71 

63 

59 

France 

75 

116 

160 

139 

207 

250 

279 

248 

191 

224 

310 

317 

250 

256 

309 

251 

341 

India 

49 

53 

142 

109 

109 

163 

185 

133 

97 

71 

74 

73 

57 

73 

100 

60 

81 

Italy 

69 

60 

86 

105 

122 

143 

137 

164 

117 

106 

129 

78 

115 

126 

173 

114 

122 

South Korea 

91 

83 

141 

147 

149 

167 

163 

131 

82 

128 

189 

161 

145 

257 

204 

176 

269 

The Netherlands 

60 

49 

102 

131 

124 

124 

133 

143 

95 

84 

83 

74 

65 

88 

126 

N/a 

N/a 

Spain 

77 

80 

100 

99 

119 

141 

178 

134 

125 

128 

120 

110 

116 

137 

197 

144 

156 

United Kingdom 

74 

70 

155 

139 

232 

228 

305 

222 

182 

204 

543 

476 

368 

431 

307 

185 

294 

Russia 

101 

94 

122 

89 

112 

141 

146 

169 

233 

206 

184 

216 

198 

284 

491 

334 

577 

Nigeria 

N/a 

N/a 

N/a 

N/a 

N/a 

N/a 

N/a 

N/a 

N/a 

N/a 

128 

93 

82 

92 

125 

94 

93 

Mean 

71 

75 

124 

111 

123 

147 

152 

130 

118 

130 

167 

158 

149 

185 

242 

181 

233 

Median 

70 

71 

129 

110 

123 

142 

143 

131 

114 

125 

145 

120 

121 

151 

227 

172 

192 

Change in Mean 

N/a 

4 

49 

-13 

12 

24 

4 

-22 

-12 

13 

37 

-10 

-9 

37 

56 

-61 

53 

Change in Median 

N/a 

1 

58 

-19 

13 

19 

1 

-12 

-17 

11 

21 

-25 

0 

30 

76 

-55 

21 

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. 
 
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TA
it
󰇢+α
3
󰇡
∆PPE
it
TA
it
󰇢+α
4
ROA
it
+ε
it
   

(3) 

WC
it
=α
0
+α
1
CFO
it-1
+α
2
CFO
it
+α
2
CFO
it+1
+ α
2
∆REV
it
+α
3
PPE
it
+ ε
it
 

(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
1
. 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  
   
                                                      
1
 We use Global Industry Classification Standard from Refinitiv, detailed information is provided in section 
‘Sample’ 





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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
0
+α
1
EPU+α
2
ENF+α
3
SIZE+α
4
LEV+α
5
ROA+α
6
DCE+α
7
AUD+α
8
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 

0 

1.4634 

ROA 

-0.0319 

0.288 

-2.0467 

0.2751 

DCE 

0.0426 

0.2019 

0 

1 

AUD 

0.4578 

0.4982 

0 

1 

RES 

0.0911 

0.2878 

0 

1 

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 

284 908 

284 908 

284 908 

Country fixed effects 

Yes 

Yes 

Yes 

Yes 

Industry fixed effects 

Yes 

Yes 

Yes 

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 

284 908 

284 908 

284 908 

Country fixed effects 

Yes 

Yes 

Yes 

Yes 

Industry fixed effects 

Yes 

Yes 

Yes 

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’.  

DE
it
TA
it
it
=α
0
+α
1
1
TA
it-1
+α
2

REV
it
TA
it
+ε
it
 

(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) 

 

RM 

RM 

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*** 

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 

284 908 

Country fixed effects 

Yes 

Yes 

Industry fixed effects 

Yes 

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 

- 

1 

- 

- 

1 

- 

- 

1 

- 

- 

1 

- 

- 

1 

- 

- 

1 

Brazil 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

Chile 

- 

- 

- 

1 

1 

- 

- 

1 

- 

- 

- 

1 

1 

1 

1 

1 

- 

Colombia 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

Denmark 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

Germany 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

Hong Kong 

- 

1 

1 

- 

- 

- 

1 

- 

- 

- 

1 

1 

- 

- 

- 

1 

1 

Ireland 

- 

1 

- 

- 

- 

1 

- 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

Japan 

- 

- 

- 

1 

1 

- 

- 

- 

1 

- 

- 

1 

- 

- 

- 

1 

- 

Mexico 

1 

- 

- 

1 

- 

- 

1 

- 

- 

1 

- 

- 

1 

- 

- 

- 

- 

New Zealand 

- 

- 

1 

- 

- 

1 

- 

- 

1 

- 

- 

1 

- 

- 

1 

- 

- 

Pakistan 

- 

- 

1 

- 

- 

- 

- 

1 

- 

- 

- 

- 

1 

- 

- 

- 

- 

Singapore 

1 

- 

- 

- 

- 

1 

- 

- 

- 

1 

- 

1 

- 

- 

1 

- 

- 

Sweden 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

United States 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

Belgium 

- 

1 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

- 

1 

- 

- 

- 

Canada 

1 

- 

1 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

1 

- 

China 

- 

- 

1 

- 

- 

- 

- 

1 

- 

- 

- 

- 

1 

- 

- 

- 

- 

Croatia 

- 

1 

- 

1 

1 

1 

- 

- 

- 

1 

1 

- 

- 

- 

- 

- 

- 

Greece 

- 

1 

- 

1 

1 

- 

1 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

France 

- 

1 

- 

- 

- 

- 

1 

- 

- 

- 

- 

1 

1 

1 

1 

- 

1 

India 

- 

- 

- 

1 

- 

- 

- 

- 

1 

- 

- 

- 

- 

1 

- 

- 

- 

Italy 

1 

- 

1 

- 

- 

- 

- 

1 

- 

- 

- 

- 

1 

- 

- 

- 

- 

South Korea 

- 

1 

1 

- 

- 

- 

1 

- 

- 

- 

1 

1 

- 

- 

1 

- 

1 

The Netherlands 

1 

- 

- 

- 

1 

- 

1 

- 

- 

- 

- 

1 

- 

- 

- 

1 

- 

Spain 

- 

- 

1 

- 

- 

1 

- 

- 

- 

- 

1 

- 

- 

1 

- 

- 

- 

United Kingdom 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

Russia 

- 

1 

1 

- 

- 

1 

1 

- 

- 

- 

1 

- 

- 

1 

- 

1 

- 

Nigeria 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

- 

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 

284 908 

284 908 

284 908 

284 908 

Country fixed effects 

Yes 

Yes 

Yes 

Yes 

Yes 

Industry fixed effects 

Yes 

Yes 

Yes 

Yes 

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 

RM 

EPU 

0.00541*** 

0.00790*** 

0.00494*** 

0.00312** 

0.0542*** 

 

(5.35) 

(8.35) 

(5.97) 

(3.17) 

(15.09) 

RQ 

-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 

284 908 

284 908 

284 908 

284 908 

Country fixed effects 

Yes 

Yes 

Yes 

Yes 

Yes 

Industry fixed effects 

Yes 

Yes 

Yes 

Yes 

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. 
 
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