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 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. ACC it =α 0 +α 1 1 TA it-1 +α 2  ∆REV it TA it +α 3  ∆PPE it TA it +ε it (1) ACC it =α 0 +α 1 1 TA it-1 +α 2  ∆REV it TA it + ∆AR it TA it +α 3  ∆PPE it TA it + ε it (2) ACC it =α 0 +α 1 1 TA it-1 +α 2 󰇡 ∆REV it TA it + ∆AR it 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’ 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 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. 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