Pandemic and Tax Avoidance: A Cross-Country Analysis

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Economic Modelling 124 (2023) 106300

Available online 5 April 2023

Pandemic and tax avoidance: Cross-country evidence

^☆

Jun Zhu

^a

, Kung-Cheng Ho

^b

, Sijia Luo

^c

, Langchuan Peng

^d^,^*

aSchool of Public Finance and Taxation, Nanjing University of Finance and Economics, Nanjing, Jiangsu Province, China

bPearl River Delta Collaborative Innovation, Center of Scientific Finance and Industry, Institute of Regional Finance, Guangdong University of Finance & Economics, Guangzhou, Guangdong Province, China

cMonash Business School, Monash University, Victoria, Australia

dInstitute of Economics and Finance, Nanjing Audit University, Nanjing, Jiangsu Province, China

A R T I C L E I N F O Handling Editor: Sushanta Mallick JEL classification:

H25 H26 O50 Keywords:

Pandemic Tax avoidance Formal institution Informal institution

A B S T R A C T

Tax avoidance is appealing to firms since it increases their cash flow by reducing tax payments, which can then be used to expand operations and invest in research and development. The existing literature explores numerous factors that contribute to firms’ tax avoidance behavior, but it fails to consider the impact of a pandemic. Based on a sample of 42 countries between 1989 and 2020, we investigate the relationship between pandemic presence and firms’ tax avoidance activities. We find that tax avoidance activities increase during pandemics, which is reflected in decreased effective tax rates (ETRs). Furthermore, this influence is strengthened by adopting In- ternational Financial Reporting Standards and societal trust level. Channel analysis shows that the findings are realized through worse firm performances, reduced economic growth, lower country-level stability, and more interest obligations. These findings offer up-to-date perspectives and recommendations to policymakers in crafting suitable and effective schemes for post-pandemic recovery.

1. Introduction

During the past two decades, the world has experienced several epidemics and pandemics (e.g., SARS and H1N1), each causing severe health and economic consequences. The COVID-19 outbreak was char- acterized as a pandemic by the World Health Organization (WHO) on March 11, 2020. As of December 2, 2022, the cumulative number of confirmed cases reported is over 640 million worldwide, including more than 6.6 million deaths. As the most contagious, widespread global public health crisis in recent history, COVID-19 has threatened every country. WHO calls for worldwide cooperation and emphasizes that “no country will be safe until we’re all safe.” Various measures, including social distancing, travel restrictions, and vaccination, have been taken in response to the enormous impacts resulting from pandemics. These measures have substantial influences on firms and individuals worldwide.

Prior literature discusses the effects of a public health crisis on macroeconomic indicators, such as gross domestic product (GDP)

growth (Maliszewska et al., 2020), inflation (Cavallo, 2020), unem- ployment (Bartik et al., 2020; Chodorow-Reich and Coglianese, 2021), and national income (Fan et al., 2018). The impacts of the pandemic on firms are reflected in the sharp revenue drops resulting from shrinking consumer demands (Shinozaki and Rao, 2021). This study explores the impact of worldwide pandemics on firms’ tax avoidance behavior to extend this area of literature. Tax avoidance is appealing to firms because it increases their cash flow by reducing tax payments, which can then be used to expand their operations, invest in research and development, or acquire new equipment.

There are two possible reasons for firms to increase tax avoidance activities during pandemics. First, pandemics affect many aspects of society (e.g., administrative business shutdowns and restrictions on social contact), and these influences can deteriorate firms’ economic and financial conditions. Prior literature shows that when firms experience financial distress, they tend to engage in more tax avoidance activities (Hackbarth et al., 2006; Richardson et al., 2015; Alm et al., 2020).

During pandemics, the associated costs of tax avoidance activities could

☆Peng acknowledges financial support from the National Natural Science Foundation of China (Grant No. 72203103). Zhu acknowledges financial support from the Important Project of the National Social Science Foundation of China (Grant No. 21&ZD094). Ho acknowledges financial support from the National Natural Science Foundation of China (Grant No. 71903199) and Guangdong Provincial Department of Education Innovation Team Project (Grant No. 2018WCXTD001). We thank the editor, the associate editor, the copy editor, and two anonymous referees for their helpful comments and suggestions.

* Corresponding author. 86 West Yushan Road, Pukou District, Nanjing, Jiangsu Province, China..

E-mail addresses: [email protected] (J. Zhu), [email protected] (K.-C. Ho), [email protected] (S. Luo), [email protected] (L. Peng).

Contents lists available at ScienceDirect

Economic Modelling

journal homepage: www.journals.elsevier.com/economic-modelling

https://doi.org/10.1016/j.econmod.2023.106300

Received 10 January 2022; Received in revised form 29 March 2023; Accepted 31 March 2023

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be marginal compared to the potential gains, which may cause firms to prioritize such activities (Brondolo, 2009). This could help reduce current income tax payments and increase liquidity ratios. Second, firm managers may perceive that the tax authority is less stringent in enforcing tax laws during pandemics, making it less risky or more socially acceptable to participate in tax avoidance activities (Brondolo, 2009). Additionally, governments may announce various fiscal and monetary policies in response to pandemics to mitigate the crisis’s adverse effects, such as the deterrence of tax payments, subsidies to firms that continue to employ a large number of workers, and other discretionary fiscal policies (Alm et al., 2020; Funke and Tsang, 2020;

Kaplan et al., 2020).

We introduce an indicator variable for the five pandemics within our sample period. If the corresponding pandemic hits a country, the indicator variable is assigned a value of 1. We then construct a composite variable, Pandemics, which aggregates the five indicator variables.

Specifically, during the pandemic years, this variable takes the value of the corresponding indicator variable, while for all the other years in our sample period, it takes a value of 0. Based on a sample of 42 countries between 1989 and 2020, we document a negative relationship between pandemic occurrence and cash effective tax rate (ETR). The negative association is strengthened by the adoption of International Financial Reporting Standards (IFRS) and societal trust level. Furthermore, we show that this negative association is formed through deteriorating firm performances, reduced economic growth, lower country-level stability, and more obligation of interests. To ensure the robustness of our findings, we conduct three additional tests: investigating five pandemics separately, removing three countries with the most firm-year observations, and using alternative tax avoidance measures. To alleviate the endogeneity concern, we re-estimate our model by generalized method of moments (GMM) and two-stage least square (2SLS) regression, which support our baseline findings.

The contribution of this study is threefold. First, this study adds evidence to the literature about the association between public health and economics, an emerging and heated field. Our novelty lies in that, instead of focusing on one specific public health event, we choose a relatively long sample period and comprehensively consider five pandemics altogether, increasing the generality of our findings. Though our sample does not directly include COVID-19, our findings draw from previous global public health crises, which is helpful for understanding firms’ reactions to COVID-19.

Second, we add to the literature on tax avoidance. Previous studies explored the determinants of corporate tax avoidance, focusing on the effects of executive characteristics (Dyreng et al., 2010; Law and Mills, 2017; Koester et al., 2017), firm characteristics (Gupta and Newberry, 1997; Chen et al., 2010; Lisowsky, 2010; Kerr, 2019), and market characteristics (Cai and Liu, 2009; Arena et al., 2021). For example, tax avoidance is found to be associated with ownership structure (McGuire et al., 2014), labor investment inefficiency (Taylor et al., 2019), and government corruption (Sun, 2021). Conversely, our study considers the influence of worldwide exogenous shocks, shedding light on the importance of the macro-environment on corporate tax avoidance activities.

Third, this study shows the roles of formal and informal institutions in the relationship between public health crises and corporate tax avoidance. Instead of regarding these formal and informal institutions as direct determinants (Xia et al., 2017; Kanagaretnam et al., 2018b; Kerr, 2019; Zeng, 2019), we innovatively consider their moderation roles in the pandemic background and stress that the influence of these formal and informal institutions should also be taken into consideration by policymakers in setting the recovery scheme after the pandemic.

The remaining part of this study is organized as follows. Section 2 summarizes related literature and proposes our hypothesis, Section 3 introduces the statistical description of our data, and Section 4 presents empirical results. Section 5 provides additional tests, while Section 6 concludes the study.

2. Literature review and hypothesis 2.1. Pandemic crisis

Prior literature has documented the various political, social, and economic impacts of pandemics. Almond (2006) finds that, under the influence of pandemics, individuals display lower educational attain- ment, higher rates of physical disability, lower income, and lower socioeconomic status. Keogh-Brown et al. (2010) find significant GDP losses in European countries. Furthermore, the pandemic could generate an unconventional growth trap (Chakraborty et al., 2010) and accelerate the tendencies of de-globalization and de-dollarization (Tokic, 2020).

The sovereign credit risk significantly increases after the pandemic (Hao et al., 2022). Cottafava et al. (2022) estimate the socioeconomic impact of lockdowns and other restrictive policies during the pandemic, while several other studies discuss the environmental effects (Cottafava et al., 2022; Mohanty and Sharma, 2022).

Recent studies show the impacts of pandemics on firms and capital markets. The flu impacted the stock market by decreasing trading and volatility, reducing returns, and increasing bid–ask spreads (McTier et al., 2013). Firms with greater cash reserves and higher market-to-book ratios experience fewer negative returns (Carter et al., 2022). Firms that engage in more corporate social responsibility activities and with less entrenched executives experience a lower pandemic-induced drop in stock returns (Ding et al., 2021). Concerning the stock market, research shows that the pandemic could amplify the returns’ volatility, decrease market liquidity, and raise the overall risk (Ftiti et al., 2021).

2.2. Tax avoidance

Tax avoidance usually involves planning and schemes designed to reduce current tax payments. Tax codes are usually complicated and highly technical, and subject to subjective interpretations. Firms thus have incentives to search for and exploit the ambiguities and loopholes in the tax system to significantly reduce their tax payments (Atwood et al., 2012). There are many ways to reduce tax payments, such as reducing foreign income taxes through income shifting and transfer pricing, claiming larger research and development (R&D) credits, and investing in assets that apply to accelerated depreciation schedules.

Tax avoidance substantially impacts a firm’s total factor productivity and economic growth (Célimène et al., 2016; Bournakis and Mallick, 2021). Prior literature documents various determinants of corporate tax avoidance. At the firm level, tax avoidance is associated with several characteristics, such as the firm’s capital structure, asset mix, perfor- mance (Gupta and Newberry, 1997; Fernández-Rodríguez et al., 2021), leverage, profitability, size, and foreign-source income (Lisowsky, 2010). The ownership structure could also affect firms’ tax avoidance decisions; due to their unique agency problem, family firms (Chen et al., 2010) and dual-class firms (McGuire et al., 2014) engage less tax avoidance than their counterparts. At the country level, scholars identify several factors in this area, such as the country’s tax system characteristics (Atwood et al., 2012; Li et al., 2020), the complexity of the tax code (Richardson, 2016), media independence (Kanagaretnam et al., 2018a), societal trust (Kanagaretnam et al., 2018b), and tax morale (Richardson, 2016). A recent stream of tax avoidance research discusses the determinants at the individual manager level. Higher-ability managers pursue more tax avoidance activities (Koester et al., 2017), while managers with military experience engage in less tax avoidance activities (Law and Mills, 2017); however, little research considers the effects of exogenous events on corporate tax avoidance activities. One excep- tion might be Richardson et al. (2015), who discover more engagement in tax avoidance activities during global financial crises. Our study adds to this strand of literature by investigating how firms respond to pandemics in tax avoidance activities.

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2.3. Hypothesis

Pandemics heavily impact almost every industry. Due to travel restrictions and social distancing, public transportation, the hotel business, and tourism are affected more than other industries. Lockdown policies significantly impact consumer living and spending behavior, reflected by a redirection of demand away from activities requiring proximity between people (Maliszewska et al., 2020) and a consistent increase in precautionary savings (Jord`ae et al., 2022). This decrease in consumer demand also results in firms’ decreasing revenue or closing during pandemics (Hassan et al., 2020). The deteriorating economic and financial conditions change a firm’s tax avoidance decision. Firms engage in more tax avoidance activities since marginal benefits (miti- gating decreasing revenue or avoiding closure) exceed marginal costs, and they also experience higher loan spreads and smaller loan sizes (Gong et al., 2021). Pandemics are also positively correlated with low returns to assets and low increases in workers’ real wages (Jord`ae et al., 2022). Exacerbating operating performances indicate lower taxable income, driving down tax payments. Thus, firms experience higher interest expenses, which works as a tax deduction. Overall, GPD growth slows during pandemic periods, and firms experience increasing financial pressure due to lower operating revenue and higher interest payments; thus, we predict a reduction of explicit taxes.

H1. Firms are involved in more tax avoidance activities during pandemic periods.

3. Data

3.1. Sample construction

Firm-level data for the current analysis originate from the Thomson Reuters Worldscope database, which contains information on all pub- licly listed firms in 287 countries. We drop certain observations to apply our empirical analysis. (1) We exclude countries in which financial reports are not available; (2) we exclude firms with variables used in our estimation lasting for less than 200 consecutive days; (3) we exclude firms with missing control variables (e.g., the economic freedom index has been available since 1995). After these steps, we obtain 265,652 observations from 42 countries between 1989 and2020.¹

3.2. Variable definition 3.2.1. Tax avoidance

To measure firms’ tax avoidance activities, we employ the cash effective tax rate (ETR), which is calculated as cash taxes paid divided by pretax book income before special items. The use of cash ETR as an indicator of tax avoidance is well-established in the literature, with numerous studies using it to examine the reduction of explicit tax payments (Kerr, 2019; Koester et al., 2017; McClure et al., 2018; Amiram et al., 2019; Dyreng et al., 2017; Dyreng et al., 2019; Guenther et al., 2019). Wang et al. (2019) provide a comprehensive review of the use of ETR to study corporate tax avoidance. This metric captures a firm’s ability to decrease its tax liability by implementing tax planning strategies, exploiting favorable provisions in tax laws, or engaging in illegal tax evasion techniques. Furthermore, we also examine two additional long-term measures, namely, the three-year and five-year cash ETRs, because changes in a firm’s annual cash ETR may be due to economic and societal fluctuations. As a result, the three-year and five-year cash ETRs represent a firm’s long-term tax obligations. Specifically, Cashetr3 is defined as the sum of cash taxes paid in periods from t to (t+2) divided

by pretax book income before special items in periods from t to (t+2);

Cashetr5 is defined as the sum of cash taxes paid in periods from t to (t+4) divided by pretax book income before special items in periods from t to (t+4). For the above three variables, their numerators and denominators must be positive, and ratios greater than one are reset to one (Koester et al., 2017).

3.2.2. Pandemic crisis

Referring to the ninth volume of Disease Control Priorities published by the World Bank, the five notable epidemics and pandemics within our sample period are SARS in 2003, H1N1 in 2009, MERS in 2012, Ebola in2014,²and Zika in 2015. We introduce five indicator variables for each of the five pandemics. If the WHO determines that a pandemic or epidemic impacts a country, the corresponding indicator variable equals 1; otherwise, it is 0. Our variable of interest, Pandemic, is a composite variable of these five indicator variables. For example, if Year =2009, Pandemic takes the value of the binary indicator H1N1; if Year =2012, Pandemic takes the value of the binary indicator MERS. For all the other years within our sample period, Pandemic equals 0.

To graphically illustrate the impact of pandemics worldwide, we plot the following heat map based on the five pandemics used in our study.

Darker colors indicate increased occurrences. The plot shows that North America, Western Europe, and Eastern Asia experienced more pandemics than other countries and regions during our sample period.

3.2.3. Control variables

Following prior literature on tax avoidance (Cai and Liu, 2009;

Dyreng et al., 2010; Atwood et al., 2012; Koester et al., 2017), our firm-level control variables include firm size (SIZE), leverage (LEV), change in month turnover (DTURN), return on assets (ROA), percentage of PPE (TANG), sales growth rate (GROWTH), stock return volatility (VOL), cash ratio (CASH), and R&D expenditure (RD).

Prior studies find that firms’ tax avoidance levels correlate with market characteristics, such as market competition (Cai and Liu, 2009) and litigation risk (Arena et al., 2021); thus, we include market competition (HHI) and high-litigation industries (LITIGATION) as our industry-level control variables.

We also include country-level control variables in our empirical analysis. They consider the economic situation and governance quality, including the GDP growth rate (GGDP), inflation rate (INFLATION), and economic freedom (FREEDOM) (Kaufmann et al., 2011). Considering the cultural differences among countries, we also add four cultural di- mensions developed by Hofstede (2001): power distance (POWER DIS- TANCE), uncertainty avoidance tendency (UNCERTAINTY AVOIDANCE), masculinity versus femininity (MASCULINITY), and indulgence versus restraint (INDULGENCE). Table 1 presents details for all control variables used in this study.

3.3. Descriptive statistics

Table 2 reports our sample’s descriptive statistics, showing the mean, standard deviation, VIF, and Pearson correlation coefficients of all variables used in our estimation. The mean value of Pandemics is 0.13, indicating that 13% of our observations come from pandemic periods.

All VIF values are less than 3, indicating no multicollinearity issue in our sample. The variable Pandemics is positively correlated with Cashetr, supporting our H1. Among firm-level characteristics, SIZE, LEV, TANG, VOL, CASH, and RD are negatively associated with Cashetr, while ROA is positively associated with Cashetr. Two industry-level characteristics, HHI, and LITIGATION, are negatively related to Cashetr. Among country-level characteristics, INFLATION, FREEDOM, MASCULINITY, and UNCERTAINTY AVOIDANCE are negatively related to Cashetr,

1 The calculation of Cashetr5 requires a 4-period-lagged term; thus, total sample period is 1989–2020, while country and firm level variables cover 1989–2016.

2 The West Africa Ebola virus outbreak occurred from 2013 to 2016, but the peak and international response efforts began in 2014.

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while GGDP and INDULGENCE show a positive correlation.

We also report Pearson correlation coefficients between the variables used in our estimation, which can help us identify the strength and di- rection of the linear correlation between any two continuous variables.

The results in Table 2 show that, among firm-level characteristics, firm size, leverage, tangible assets percentage, stock return volatility, cash ratio, and R&D expenditure are positively related to tax avoidance activities. In contrast, ROA is negatively related to tax avoidance activities;

in terms of the industry environment, firms operating industries char- acterized by high product market competition and high litigation are positively associated with tax avoidance activities. Finally, Pandemics and Cashetr are negatively correlated, which provides preliminary support for our hypothesis.

4. Empirical results 4.1. Baseline regression

We use the pooled OLS regression shown in model (1) to explore the effect of Pandemics on corporate tax avoidance.

Cashetri,c,t=β₀+β₁Pandemicsc+γ∑

Xi,c,t+εi,c,t (1)

where the country is indexed by c, firm by i, and year by t. Cashetr is one Table 1

Variable definition.

Variable Definition

Cashetr Cash taxes paid (txpd) divided by pretax book income

before special items (pi-spi). We require positive values for the numerator and denominator, and ratio values greater than one are reset to one (Koester et al., 2017).

Cashetr3 Sum of cash taxes paid (txpd) in periods t through t+2 divided by pretax book income before special items (pi-spi) in periods t through t+2. We require positive values for the numerator and denominator, and ratio values greater than one are reset to one (Koester et al., 2017).

Cashetr5 Sum of cash taxes paid (txpd) in periods t through t+4 divided by pretax book income before special items (pi-spi) in periods t through t +4. We require positive values for the numerator and denominator, and ratio values greater than one are reset to one (Koester et al., 2017).

Cashetr_ADJ Difference between the cash ETR for firm i and the mean cash ETR for the corresponding set of firms belonging to the same industry and asset-size quintile in year t (Balakrishnan et al., 2012).

Cashetr3_ADJ The industry-year mean-adjusted tax avoidance measure is computed as the firm’s TAXAVOID minus the firm’s industry-year mean TAXAVOID. The extent of tax avoidance is increasing in this measure (Kanagaretnam et al., 2018).

Cashetr5_ADJ Industry-year mean-adjusted measure of tax avoidance, computed as the firm’s TAXAVOID in periods t through t + 4 minus the firm’s industry-year mean TAXAVOID in periods t through t +4. The extent of tax avoidance is increasing in this measure.

Country level variables

Pandemics A composite variable defined based on the following five

indicator variables.

Five modern health crises considered in this variable are SARS (2003), H1N1 (2009), MERS (2012), Ebola (2014), and Zika (2016).

SARS An indicator variable, equals to one if the country is

announced by WTO to be hit by SARS in 2003. Specifically, for the following countries, SARS =1: AUS, CAN, CHE, CHN, DEU, ESP, FRA, GBR, HKG, IDN, IND, IRL, ITA, KOR, KWT, MAC, MNG, MYS, NZL, PHL, ROU, RUS, SGP, SWE, THA, USA, VNM, ZAF

H1N1 An indicator variable, equals to one if the country is

announced by WTO to be hit by H1N1 in 2009.

Specifically, for the following countries, H1N1 =1: AGO, ALB, AND, ARE, ARG, ASM, AUS, AUT, AZE, BDI, BEL, BGD, BGR, BHR, BHS, BIH, BLR, BLZ, BMU, BOL, BRA, BRB, BRN, BTN, BWA, CAN, CHE, CHL, CHN, CIV, CMR, COD, COG, COL, CPV, CRI, CUB, CYM, CYP, CZE, DEU, DMA, DNK, DOM, DZA, ECU, EGY, ESP, ETH, FIN, FJI, FRA, FSM, GAB, GBR, GEO, GHA, GRC, GRD, GTM, GUM, GUY, HND, HRV, HTI, HUN, IDN, IND, IRL, IRN, IRQ, ISL, ISR, ITA, JAM, JOR, JPN, KAZ, KEN, KHM, KIR, KNA, KOR, KWT, LAO, LBN, LBY, LCA, LIE, LKA, LSO, LUX, MAR, MDA, MDG, MDV, MEX, MHL, MKD, MLI, MLT, MMR, MNE, MNG, MOZ, MUS, MWI, MYS, NAM, NGA, NIC, NLD, NOR, NPL, NRU, NZL, OMN, PAK, PAN, PER, PHL, PLW, PNG, POL, PRI, PRT, PRY,PSE, QAT, ROU, RUS, RWA, SAU, SDN, SGP, SLB, SLV, SRB, STP, SUR, SVK, SVN, SWE, SWZ, SYC, TCD, THA, TJK, TLS, TON, TTO, TUN, TUR, TUV, TZA, UGA, URY, USA, VCT, VEN, VNM, VUT, WSM, YEM, ZAF, ZMB, ZWE

MERS An indicator variable, equals to one if the country is

announced by WTO to be hit by MERS in 2012.

Specifically, for the following countries, MERS =1: ARE, AUT, CHN, DEU, DZA, EGY, FRA, GBR, GRC, IRN, ITA, JOR, KOR, KWT, LBN, MYS, NLD, OMN, PHL, QAT, SAU, THA, TUN, TUR, USA, YEM

Ebola An indicator variable, equals to one if the country is

announced by WTO to be hit by Ebola in 2014. Specifically, for the following countries, Ebola =1: ESP, GBR, GIN, ITA, LBR, MLI, NGA, SEN, SLE, USA

Zika An indicator variable, equals to one if the country is

announced by WTO to be hit by Zika in 2016. Specifically, for the following countries, Zika =1: ABW, ARG, ATG, BHS, BLZ, BOL, BRA, BRB, CAN, CHL, COL, CRI, CUB,

Table 1 (continued)

Variable Definition

CYM, DMA, DOM, ECU, GRD, GTM, GUY, HND, HTI, JAM, KNA, LCA, NIC, PAN, PER, PRI, PRY, SLV, SUR, TCA, TTO, URY, USA, VCT, VIR

GOV Government stability from ICRG

TRUST Societal trust score from World Values Survey

IFRS An indicator variable equals to one if the country adopts

IFRS and zero otherwise.

Country-level control variables

GGDP GDP growth rate (Meng and Yin, 2019).

INFLATION Inflation rate: annual rate of change on consumer price index. Source: World Bank.

FREEDOM Economic Freedom of the World (EFW) datasets,

Worldwide Governance Indicators (Kaufmann et al., 2011).

POWER DISTANCE Power distance (Hofstede, 2001).

MASCULINITY Masculinity (Hofstede, 2001).

UNCERTAINTY

AVOIANCE Uncertainty avoidance (Hofstede, 2001).

INDULGENCE Indulgence (Hofstede, 2001).

Industry-level control variables

HHI The Herfindahl Hirschman index: the sum of squared market shares of all firms in the industry.

LITIGATION Indicator variable equals to one if a firm operates in a high- litigation industry (SIC codes 2833–2836, 3570–3577, 3600–3674, 5200–5961, 7370–7374, 8731–8734) and zero otherwise.

Firm-level control variables

SIZE Natural log of totl assets.

LEV Debt to total assets.

DTURN The average monthly share turnover over the current fiscal

year period minus the average monthly share turnover over the previous fiscal year period, where monthly share turnover is calculated as the monthly trading volume divided by the total number of shares outstanding during the month.

ROA Return on assets: net income divided by the book value of total assets.

TANG The ratio of property, plant, and equipment (PPE) to the

book value of total assets.

GROWTH The sales growth rate, calculated as the ratio of the

difference between sales in the current year and prior year to sales in the prior year.

VOL Standard deviation of weekly stock returns.

CASH Cash divided by the book value of total assets

RD R & D expenditure divided by the book value of total assets

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of the effective tax rate measures: Cashetr, Cashetr3, and Cashetr5. The Pandemics variable is a composite defined in Section 3.2.2, and X de- notes a series of firm-level, industry-level, and country-level control variables discussed above. We control for the year, industry, and county fixed effects. This study includes country-specific characteristics because, as shown in Fig. 1, they document the relevance of geographic dispersion in conducting empirical analysis regarding firms’ behavior.

These three types of fixed effects (time, industry, and country) could alleviate the unobservable and fixed influence of possible omitted variables, such as possible differences in time, industry, and country characteristics.

Table 3 presents the regression results, indicating that the coefficients of Pandemics are significantly negative, supporting our H1 that firms pay less tax during pandemic periods. In other words, firms are involved in more tax avoidance activities. In addition, the absolute value of coefficients decreases as the formation window increases, suggesting that the effect of a pandemic on tax avoidance is diminishing with time.

The results are consistent with our previous speculation; governments often implement fiscal policies to stimulate the economy during pandemics, so firms may use such policies to decrease their tax burdens.

4.2. The moderation role of formal or informal institution

This section evaluates two candidates for moderation roles in our study by constructing and estimating the following regression function (2). These moderation variables can influence the impact of the pandemic on tax avoidance.

CASHETRi,c,t=ϑ0+ϑ1INSTITUTIONc,t∗Pandemicsc+ϑ2Pandemicsc

+ϑ3INSTITUTIONc,t+γ Xi,c,t+εi,c,t (2) Equation (2) introduces the institution variable and its interaction term with Pandemics. INSTITUTION is one of the institution variables, IFRS or TRUST, and the coefficient of interaction term captures the moderation role of formal or informal institutions.

These two candidates explored are the adoption of IFRS and societal trust. IFRS is a set of international accounting standards regulating how a firm’s transactions and other operation incidents should be recorded in the financial statements. IFRS was established to create a common accounting language worldwide, so international business and opportunities can be encouraged. Adopting IFRS can correlate with high transparency in financial reporting since the firm’s financial report is written in a “worldwide acceptable language.” The adoption of IFRS is well known to be a determinant of tax avoidance (Kerr, 2019; Zeng, 2019).

Societal trust is our second moderation role. Various studies have identified the correlation between societal trust and tax avoidance (Xia et al., 2017; Kanagaretnam et al., 2018b). Trust is a foundation in every economic transaction (Arrow, 1974), including following the tax code;

therefore, the extent to which firms exploit the loopholes and prefer- ential policies in the tax system should be correlated with societal trust.

4.2.1. Adoption of IFRS

Adopting IFRS significantly improves information transparency and flexibility. In this study, IFRS is an indicator variable, equal to one if a country has adopted IFRS and zero otherwise. Table 4 presents the regression results. The coefficients of IFRS are positive, following prior literature, indicating that adopting IFRS effectively reduces corporate tax avoidance activities (Kerr, 2019). The coefficients of interaction terms have negative signs, indicating that the adoption of IFRS can magnify the negative correlation between pandemics and ETR. The relationship between IFRS and tax avoidance is mixed in the literature.

Some scholars find that improving transparency could reduce tax avoidance activities (Kerr, 2019); however, some find an opposite or no correlation between the two (Joshi et al., 2020). Several opposing factors jointly determine the two’s correlation. For example, transparency Table 2 Descriptive statistics. Mean STD VIF (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (1) Cashetr 0.09 0.25 (2) Pandemics 0.13 0.33 1.15 ¡0.01 (3) SIZE 7.26 3.28 1.43 ¡0.02 ¡0.05 (4) LEV 0.50 0.23 1.26 ¡0.01 ¡0.01 0.25 (5) DTURN 0.06 0.41 1.01 0.00 0.00 0.00 ¡0.03 (6) ROA 0.05 1.36 1.01 0.01 0.00 0.04 0.02 0.00 (7) TANG 0.30 0.23 1.17 ¡0.02 ¡0.04 0.15 0.01 0.00 0.01 (8) GROWTH 0.16 0.60 1.02 0.00 ¡0.03 ¡0.07 ¡0.04 0.04 0.00 ¡0.03 (9) VOL 0.10 0.24 1.01 ¡0.03 0.00 ¡0.07 ¡0.04 0.01 ¡0.01 0.01 0.02 (10) CASH 0.11 0.15 1.36 ¡0.05 ¡0.01 ¡0.12 ¡0.35 0.04 ¡0.04 ¡0.25 0.03 0.01 (11) RD 0.02 0.09 1.07 ¡0.02 0.00 ¡0.11 ¡0.08 0.00 ¡0.06 ¡0.10 0.02 0.01 0.18 (12) HHI 0.10 0.14 1.14 ¡0.03 ¡0.01 ¡0.14 ¡0.07 0.02 ¡0.01 0.03 0.01 0.05 0.03 ¡0.01 (13) LITIGATION 0.23 0.42 1.09 ¡0.02 0.01 ¡0.10 ¡0.12 0.01 ¡0.01 ¡0.19 0.03 0.01 0.14 0.16 ¡0.05 (14) GGDP 0.07 0.02 1.99 0.06 ¡0.17 0.05 ¡0.09 0.05 0.00 0.06 0.07 0.03 0.00 ¡0.05 0.01 ¡0.02 (15) INLFATION 0.09 0.01 1.48 ¡0.09 0.09 0.04 0.03 0.00 ¡0.01 ¡0.12 ¡0.04 ¡0.06 0.11 0.03 ¡0.08 0.06 ¡0.22 (16) ECONOMIC FREEDONM 69.54 9.62 1.80 ¡0.05 0.01 ¡0.27 ¡0.04 ¡0.02 ¡0.02 ¡0.12 0.03 0.00 0.04 0.07 0.01 0.03 ¡0.44 0.42 (17) POWER DISTANCE 56.84 19.28 2.04 0.00 ¡0.01 0.13 ¡0.18 0.04 ¡0.01 0.16 0.01 0.03 0.07 ¡0.09 0.02 ¡0.08 0.47 ¡0.28 ¡0.50 (18) MASCULINITY 62.42 19.26 1.47 ¡0.10 ¡0.06 0.29 0.09 ¡0.02 0.01 ¡0.02 ¡0.06 ¡0.06 0.10 ¡0.01 ¡0.29 0.02 ¡0.22 0.28 0.03 ¡0.11 (19) UNCERTAINTY AVOIANCE 58.62 24.54 1.75 ¡0.16 ¡0.08 0.35 0.04 0.00 ¡0.01 0.03 ¡0.06 ¡0.03 0.10 ¡0.01 ¡0.07 ¡0.01 ¡0.32 0.29 0.10 0.03 0.39 (20) INDULGENCE 48.62 17.80 2.04 0.05 0.04 ¡0.32 0.11 ¡0.05 0.01 ¡0.13 0.02 0.00 ¡0.15 0.07 0.00 0.03 ¡0.35 0.12 0.50 ¡0.66 ¡0.11 ¡0.29 Bold font indicates statistical significance at the 10% level.

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increases the difficulty of exploiting loopholes in the tax code. More- over, because of the asymmetric information between managers and shareholders, tax avoidance activities could be interpreted by shareholders as rent extractions of opportunist managers. Both suggest a negative correlation between the two; however, tax avoidance activities can benefit firms by reducing their tax payments, increasing their value, and enhancing shareholders’ wealth. The adoption of IFRS makes it easier for shareholders to observe managers’ operational strategies and reward them appropriately, particularly during times of pandemics when firms focus on surviving in a challenging economic environment and avoiding bankruptcy. In line with this, our research reveals that the adoption of IFRS exaggerates the negative correlation between pandemics and tax avoidance. Moreover, governments issue various stimuli schemes during pandemics to help firms overcome difficulties. IFRS responds quickly to adjust to the new environment, contributing to the negative relationship between the two.

4.2.2. Societal trust

Social trust is measured using TRUST, a societal trust score from World Values Survey (Guiso et al., 2004; Kanagaretnam et al., 2018a).³ This survey asks residents in countries around the world a series of questions, including the following regarding societal trust: “Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?” Based on the answers from each country’s sampling residents, for country c in year t, we generate a country-level index, TRUSTc,t, defined as 100 +(% “Most people can be trusted”) − (% “Cannot be too careful”). Indexes over 100 indicate that most people trust others, and indexes under 100 indicate that most people are skeptical of others. Table 5 presents the regression results. According to prior literature, TRUST’s coefficients are positive that societal trust is negatively associated with tax avoidance (Kana- garetnam et al., 2018a); however, the coefficients of interaction terms

are significantly negative, indicating that higher societal trust reinforces the negative correlation between pandemics and cash ETR, though the reinforcement effect reduces rapidly with time. This could be because tax avoidance activities are more socially acceptable in times of pandemics (Brondolo, 2009); governments and the public acknowledge and agree that low tax payments are temporary and necessary to survive. In a society with a higher societal trust level, this agreement could be more apparent.

5. Additional tests 5.1. Channel analysis

Our baseline regression indicates that firms pay less tax during pandemic periods. This section discusses the potential channels through which pandemics affect tax avoidance activities; specifically, we consider the effect of pandemics on a group of firm and country-level factors by replacing the dependent variable with firm-level or country- level variables. Specifically, we choose sales growth (GROWTH), return on equity (ROE), and leverage (LEV) as firm-level characteristics and GDP growth (GGDP) and foreign debt (ForDebt) as country-level characteristics. Pandemics often decrease consumer demands; thus, sales growth is expected to decrease or even become negative during pandemics (Richardson et al., 2015). Additionally, resulting from fewer investment opportunities and deteriorated profitability, ROE is expected to reduce. Similarly, we expect more firms to experience reductions in total assets, book value, and market value; thus, the financial leverage increases. Finally, GDP growth and foreign debt reflect a country’s economic stability; the amount of foreign debt also sheds light on political stability, especially during a crisis with worldwide influences.

Table 6 presents the regression results. Pandemic is negatively associated with firm growth, ROE, GDP growth, and foreign debt and positively related to firm leverage. Lower firm growth and ROE suggest that operating performances worsen during pandemics and even experience operating losses. Lower GDP growth and foreign debt indicate a stagnant macroeconomic environment and worse country-level stability. Higher Fig. 1. The occurrence of pandemics worldwide

This heat map is based on the five pandemics used in our paper. Darker color indicates more occurrence. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

3 More can be found at https://www.worldvaluessurvey.org/wvs.jsp.

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firm leverage means higher interest payment, which works as a tax shield and lowers cash ETR; the reduction in cash ETR during pandemics could result from poor firm performances, a stagnant economic environment, low political and economic stability, and increased interest obligation.

5.2. Robustness tests

To ensure the robustness of our baseline finding, we conduct the following three tests. First, we consider each of the five pandemics within our sample period separately by re-estimating Equation (1) with each indicator variable. The regression results are presented in Panel A of Table 7. The coefficients of H1N1 and MERS are insignificant, which can be explained by their relatively low mortality and morbidity, respectively. The coefficients of the other three pandemics are significantly negative, following the baseline regression.

Second, to rule out the concern that our finding is driven by a few countries that account for most of the sample, we exclude the three countries with the most observations (i.e., the US, the UK, and Japan) and re-estimate Equation (1). Panel B of Table 7 presents the results.

Similar to the baseline regression, the Pandemic coefficients are significantly negative, and the absolute value decreases with the formation window.

Third, to alleviate the measurement errors of tax avoidance, we re- estimate Equation (1) with alternative tax avoidance measures, Cashe- tr_ADJ. We define this adjusted tax avoidance measure as the difference between the firm’s cash ETR and the size-adjusted industry average.

Panel C of Table 7 presents the results, indicating that the Pandemic coefficients are significantly negative.

5.3. Endogeneity concern

Because a pandemic is an exogenous shock, irrelevant to corporate behavior, our finding is unlikely to suffer from reverse causality. For cross-country studies, the primary concern of endogeneity lies in omitted variables. In our setting, it may arise when not accurately controlling for underlying factors that potentially affect the occurrence of pandemics and could also influence tax avoidance. If so, the results regarding the impact of pandemics become spurious and biased; however, it is unlikely that omitted variables drive our findings for the following reasons. First, we consider relevant factors such as GDP growth rate, inflation rate, economic freedom level, HHI, and LITIGA- TION. The results remain robust after including these variables. Second, if any omitted variables exist, biasing the results, they must be able to account for the primary correlation between pandemic crises and tax avoidance.

Furthermore, our variable construction, by nature, alleviates the Table 3

Baseline regression.

Dependent variable Cashetr Cashetr3 Cashetr5

Model 1 Model 2 Model 3

constant −0.5160 ** − 0.8346 *** −0.8176 ***

(-2.10) (-4.91) (-4.72)

Pandemics −0.0789 *** − 0.0274 *** −0.0111 ***

(-21.28) (-10.67) (-4.25)

SIZE 0.0108 *** 0.0138 *** 0.0133 ***

(31.62) (58.30) (55.27)

LEV −0.0117 *** − 0.0419 *** −0.0302 ***

(-3.40) (-17.56) (-12.45)

DTURN −0.0006 − 0.0002 0.0012

(-0.38) (-0.15) (1.05)

ROA 0.0023 *** 0.0027 *** 0.0026 ***

(3.79) (6.58) (6.20)

TANG −0.0634 *** − 0.0437 *** −0.0420 ***

(-18.42) (-18.36) (-17.33)

GROWTH −0.0137 *** 0.0003 −0.0009

(-11.88) (0.37) (-1.08)

VOL −0.0212 *** − 0.0181 *** −0.0161 ***

(-7.39) (-9.09) (-7.95)

CASH −0.0718 *** − 0.0164 *** −0.0032

(-12.67) (-4.20) (-0.80)

RD −0.0382 *** − 0.0476 *** −0.0549 ***

(-4.73) (-8.51) (-9.65)

HHI −0.0546 *** − 0.0362 *** −0.0339 ***

(-8.78) (-8.42) (-7.74)

LITIGATION −0.0163 *** − 0.0083 *** −0.0062 ***

(-8.80) (-6.51) (-4.76)

GGDP −0.3407 *** − 0.1571 *** −0.0489

(-4.52) (-3.01) (-0.92)

INLFATION 2.2924 *** 1.6382 *** 0.8659 ***

(13.41) (13.85) (7.19)

ECONOMIC FREEDONM −0.0007 *** − 0.0005 *** −0.0001

(-3.00) (-2.98) (-0.64)

POWER DISTANCE 0.0061 *** 0.0048 *** 0.0049 ***

(32.33) (36.48) (36.66)

MASCULINITY 0.0010 *** 0.0011 *** 0.0009 ***

(13.55) (20.82) (16.20)

UNCERTAINTY

AVOIANCE 0.0010 *** 0.0011 *** 0.0012 ***

(8.80) (13.36) (15.07)

INDULGENCE 0.0097 *** 0.0084 *** 0.0086 ***

(41.26) (51.67) (51.75)

Year & Industry &

Country Yes Yes Yes

Adj. R² 0.08 0.09 0.08

Obs.

T-statistics are reported in parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

Table 4 Adoption of IFRS.

constant −0.5730 ** − 0.8585 *** −0.7911 ***

(-2.33) (-5.04) (-4.57)

Pandemics*IFRS −0.0112 ** − 0.0125 *** −0.0182 ***

(-2.24) (-3.61) (-5.18)

Pandemics −0.0708 *** − 0.0180 *** 0.0030

(-13.29) (-4.90) (0.80)

IFRS 0.0158 *** 0.0081 *** −0.0030

(5.93) (4.38) (-1.60)

Control variables Yes Yes Yes

Year & Industry &

Country Yes Yes Yes

Adj. R² 0.08 0.09 0.08

Obs. 265,652

Table 5 Societal trust.

constant −0.5524 ** −0.8374 *** − 0.8129 ***

(-2.25) (-4.92) (-4.70)

Pandemics*TRUST −0.0768 *** −0.0194 *** − 0.0085 ***

(-17.55) (-6.40) (-2.74)

Pandemics −0.0584 *** −0.0218 *** − 0.0083 ***

(-15.03) (-8.10) (-3.02)

TRUST 0.0219 *** 0.0146 *** 0.0149 ***

(5.25) (5.06) (5.06)

Control variables Yes Yes Yes

Year & Industry &

Country Yes Yes Yes

Adj. R² 0.08 0.09 0.08

Obs. 265,652