Research PaperOpen Access

HIERARCHICAL EFFECTS OF PUBLIC HEALTH RESPONSES AND FISCAL POLICY MEASURES ON TAX MORALE OF ENTREPRENEURIAL FIRMS DURING THE COVID-19 PANDEMIC IN NIGERIA

LUKMAN RAIMI

Department of Business Administration (Entrepreneurship), Universiti Brunei Darussalam, Brunei

E-mail Address: Lukman.raimi@ubd.edu.bn

Corresponding author.

Search for more papers by this author

LATEEF A. AGBETUNDE

Department of Accountancy, Yaba College of Technology, Nigeria

Search for more papers by this author

, and

BASIRAT OLAIDE RAIMI

School of Social Entrepreneurship, Roxbourg Institute of Social Entrepreneurship, La Sarraz, Switzerland

E-mail Address: laideraimi@gmail.com

Search for more papers by this author

https://doi.org/10.1142/S2737566823500056Cited by:0 (Source: Crossref)

Abstract

The purpose of this paper is to examine the hierarchical effects of Public Health Responses (PHRs) and fiscal policy measures (FPMs) on the tax morale (TM) of entrepreneurial firms in Nigeria during the COVID-19 pandemic. The theoretical basis for the study is based on social contract theory (SCT) and fiscal exchange theory (FET), which oblige entrepreneurs to pay taxes to the state in return for public goods and social services. Using a cross-sectional survey design, we collected primary data from 177 professional managers of entrepreneurial firms in Nigeria using online questionnaires. The sampled firms were accessed with the support of the Lagos Chamber of Commerce. Data collected at a point in time reflecting respondents’ views on the impact of PHRs and FPMs on TM were analyzed using hierarchical regression analysis. Three estimates emerged from the hierarchical regression analyses. The first estimation suggests that the PHRs have a significant positive effect on the TM of entrepreneurial firms. The second estimate suggests that the FPMs do not have a significant positive effect on the TM of entrepreneurial firms. The third estimation found that the interactive effects of PHRs and FPMs do not have a significant positive effect on the TM of entrepreneurial firms. The study, although modest, contributes to the literature on the entrepreneurial behavior of firms and tax morals in a period of economic uncertainty. The study provides valuable insights and validates the social contract and fiscal exchange theories in the recessionary period.

Keywords:

1. Introduction

Undoubtedly, the COVID-19 pandemic caught the world unprepared as it quickly spread to several countries and threatened the public health of the global community (Han et al., 2020). The boomerang impact of the pandemic on all aspects of life has been felt in all countries, businesses, and people, but some are less affected by the devastating effects than others (Lund et al., 2021). The nature of the pandemic makes it the most devastating public health crisis since World War II (World Bank, 2020a). Impacts include loss of life, jobs and livelihoods, a global economic recession, the cancellation of international flights, and the disruption of supply chains in multiple sectors of the global economy (Dzigbede and Pathak, 2020; Osland et al., 2020). Additionally, the International Monetary Fund (2020) predicted a sharp slowdown in global economic growth and forecasted worse growth outcomes for developing countries, particularly in Africa. Additionally, the International Labour Organization (2021) reported that the scale of disruption in the labor market was unprecedented: working hours fell 8.8% in the fourth quarter of 2020, equivalent to 255 million full-time jobs.

To mitigate the ravaging impact of the COVID-19 pandemic, especially the rising number of infections, and the low number of recoveries and deaths, the governments of different countries have been forced to adopt Public Health Responses (PHRs) and Fiscal Policy Measures (FPMs) to curb the devastating health risks and economic consequences. Specifically, these measures include lockdowns, social distancing requirements, and financial support for businesses. However, these twin measures have had a mixed impact on tax morale (TM), particularly for entrepreneurs in developing countries.

The PHRs and FPMs have appeared in academic and professional discourses since the beginning of the pandemic and have also become important concepts in academic research (Dzigbede and Pathak, 2020; Moriarty et al., 2020; Tabari et al., 2020; Ozili and Arun, 2020). PHRs are part of the proactive strategy governments around the world are pursuing to end the COVID-19 pandemic (Guest et al., 2020). As a health-oriented policy, it includes response areas such as (a) collecting data on infected people to clarify the level of risk for prevention, treatment, and measures to contain the outbreak, and (b) improving health protection measures and ensuring equitable access to quality health care for all citizens (Boyle et al., 2020; Tabari et al., 2020). The FPM, on the other hand, is a government policy that aims to increase spending, reduce taxes, and provide subsidies to keep the economy going (Dzigbede and Pathak, 2020). PHRs were bolstered by FPMs as the health crisis turned into an economic recession. The devastating effects of the pandemic also increased uncertainty and stock market indices reacted negatively, prompting hasty consumption and investment decisions (Ozili and Arun, 2020). Also, FPMs aim to boost the operations of entrepreneurs in agriculture, manufacturing, entertainment, technology, and services to avert a possible economic recession, increase gross domestic product (GDP), preserve jobs and increase job opportunities during and after the pandemic (Raimi, 2021).

Based on the above, this study examines the hierarchical effects of public health and fiscal policies on the TM of entrepreneurial firms during the COVID-19 pandemic in Nigeria. The importance of TM at a time of a public health crisis of monumental proportions like the COVID-19 pandemic cannot be overstated. TM is a critical part of any successful tax system as it can affect the level of tax collection. Timely payment of tax is a legal and civic obligation of individuals and firms. With or without a public health crisis, the government needs tax revenue for the delivery of public goods, social services, and the provision of emergency response to mitigate the devastating effects of a public health crisis like the COVID-19 pandemic and prevent financial frictions (Loayza and Pennings, 2020; Dougherty and de Biase, 2021). Therefore, low income tax revenues reduce the government’s ability to provide public goods and social services, save lives and livelihoods, and slows the financial response to public health emergencies (Adekoya et al., 2021; Dougherty and de Biase, 2021).

Entrepreneurial firms for the purposes of this study are registered small, medium, large, and multinational companies that are members of the Lagos Chamber of Commerce and Industry (LCCI). They are entrepreneurial firms because they have five dimensions of entrepreneurial orientation (EO), namely: risk-taking, proactiveness, innovativeness, autonomy, and competitive aggressiveness (Johnson-Deberry, 2023; Kabir and Abubakar, 2023). This study attempts to fill the knowledge gap created by the lack of literature on the hierarchical effects of PHRs and FPMs on the TM of entrepreneurial firms during a pandemic period. In addition to the introduction (Sec. 1), there are four other sections. Section 2 discusses the concepts of PHRs, FPMs and TM, including theoretical foundations and research hypotheses. Section 3 explains the research methods. Section 4 presents the results and discusses the results. Section 5 concludes with a summary of the results, implications, limitations, and future research directions.

2. Literature Review

2.1. Conceptual review

The three mutually reinforcing concepts that form the building block of this study are public health responses, FPMs, and TM.

2.2. Public health responses (PHRs)

The term PHRs refers to the various public health interventions deployed to control, manage, and mitigate the impact of COVID-19 on people’s lives, economic conditions, and general well-being (Boyle et al., 2020; Dzigbede and Pathak, 2020). PHRs predate the current pandemic, as they are widely used to address public health crises and the related problems faced by high-risk individuals, survivors, and frontline healthcare workers (Mak et al., 2009; Glasser et al., 2011). Furthermore, PHRs represent a three-step coping strategy that includes public health leadership, rapid innovation, and bold political will (Guest et al., 2020). In addition, PHRs ensure better emergency preparedness and response to public health crises now and in the future (Boyle et al., 2020; Garg et al., 2020; Gold et al., 2020). There are different views on PHRs in literature. First, PHRs are described as broadly covering three response areas, namely (a) collecting data on those infected to clarify the level of risk for prevention, treatment and containment measures during the outbreak, and (b) improving public health response and ensuring equal access to quality medical care for all citizens (Boyle et al., 2020). While Tabari et al. (2020) suggest that PHRs cover four main areas, namely surveillance, public education, crowd control and care facilities.

2.3. Fiscal policy measures (FPMs)

FPMs are government interventions largely focused on public spending and taxes. In a normal period without economic crises, governments need taxes to meet their fiscal obligations (Kneller et al., 1999; Alesina and Ardagna, 2010). Currently, the devastating pandemic has impacted many countries’ income forecasts and spending plans, leading to a significant drop in tax revenues. Therefore, governments have been forced to deploy FPMs to save jobs and keep businesses afloat (Clemens and Veuger, 2020; Edelberg and Sheiner, 2020). The use of FPMs in a recession is supported by Keynesian economic theory. During the recession phase, expansionary FPMs will restore equilibrium to boost the level of economic activity. However, in the inflation phase, strict FPMs are used in both developed and developing countries to pragmatically contain and manage inflation in a recessionary situation (Alesina and Giavazzi, 2013; Ramey, 2019).

2.4. Tax morale (TM)

TM refers to citizens’ attitudes towards compliance, evasion, or avoidance of tax payment obligations (Ramona-Anca and Larissa-Margareta, 2013). TM also describes an individual’s intrinsic motivation to pay or evade taxes (Torgler and Schneider, 2004;Torgler, 2006; Torgler and Schneider, 2007; Palil et al., 2013; Andriani et al., 2020). From a governance perspective, TM is a moral obligation and not a legal matter (Torgler and Schneider, 2009). In the recessionary phase, the government is expected to cut taxes and provide financial stimulus to restore economic balance. Therefore, tax breaks and the provision of subsidies are powerful fiscal tools to stimulate economic activity in times of economic shocks. In the absence of tax-paying capacity, it is the government’s responsibility to initiate policy interventions to help citizens meet the changing needs of labor markets and to foster the resilience of people preparing for the post-war era prepared for the pandemic (OECD, 2021).

From the above, the conceptual framework in Fig. 1 explains the hierarchical impact of PHRs and FPMs on TM in the pandemic period. The framework assumes that PHRs and FPM, if properly deployed by the government, could plausibly stimulate TM. Taxes paid by corporate citizens (corporations) and collected by governments are enshrined in a social contract. This social and fiscal exchange relationship is based on the agreement that the government will provide public goods and social services to corporate citizens as benefits for positive TM, and that in return corporate citizens will pay income taxes to the state in order to be able to perform public services (Armah-Attoh and Awal, 2013; Beale and Wyatt, 2017).

2.5. Theoretical framework

Both social contract theory (SCT) and fiscal exchange theory (FET) provide logical explanations for the above conceptual framework. The two theories when linked together posit that a social contract exists between the state and citizens, whereby the former provide certain responsibilities, which the latter is expected to pay for. In other words, a social contract allows for the imposition of duties/responsibilities on both parties for mutually beneficial relationships. The role of government is to provide public goods and social services to corporate citizens. In return, corporate citizens (as beneficiaries of goods and services) are expected to pay personal income taxes needed to fund public goods. Taxes are the prices citizens pay in return for public goods enjoyed by corporate citizens (Ostrom and Ostrom, 2019). Any situation where either party fails to fulfil its share of the statutory duty in the fiscal exchange relationship is considered a breach of the social contract.

As rational entities managed by humans, corporate citizens (businesses) value the benefits of public goods and social services compared to the value of the taxes they pay. It makes sense that citizens’ perceptions of the benefits accruing from PHRs and FPMs affect TM (willingness to pay taxes) when the benefits do not match the tax paid. Beyond the provision of public goods and social services as benefits to corporate taxpayers, TM is influenced by a chain of socio-cultural, and psychological factors (Ranđelović, 2017;Ungureanu and Ciocanea, 2021).

2.6. Previous studies and hypothesis development

Building on previous studies, this section discusses the effect of PHRs and FPMs on corporate TM.

2.6.1. Public health responses and tax morale in COVID-19

Around the world, international financial institutions, in collaboration with governments, have responded quickly to address the challenges of COVID-19 through proactive PHRs. PHRs are necessary to protect businesses as the pandemic has left global economic activity vulnerable to pandemic-induced recessionary shocks. The IMF (2020) recommended PHRs such as (i) supporting health priorities; (ii) protecting distressed businesses; (iii) protection of data subjects; and (iv) protecting revenue streams. In addition, amid the devastating effects of the pandemic, the Chinese government injected 1.2 trillion yuan (US$174 billion) in liquidity into the economy (Leng and Goh, 2020), which was followed by further fiscal stimulus. The total liquidity injection in China has been estimated at RMB 8 trillion (Funke and Tsang, 2020). In Europe, governments launched fiscal packages to prevent corporate defaults and increase government spending. European countries used their existing well-developed social safety nets such as sick pay, unemployment benefits, social assistance, and active labor market policies to mitigate the impact of the pandemic while enforcing PHRs (Yeo and Kim, 2020).

Like other continents, Africa has implemented PHRs that include emergency declarations on lockdowns, lockdowns, restrictions on movement, capacity-building training for medical/health workers, provision of personal protective equipment (PPE) for health workers, strengthening national surveillance in areas of contact tracing, etc. Quarantine, isolation and treatment, expansion of rapid testing/diagnostic facilities and aggressive dissemination of public health information campaigns to reduce the spread of infections and deaths (World Health Organization, 2020).

In addition, the World Bank provided the Ghanaian government with an amount of US$100 million to expand emergency response systems and containment efforts (World Bank, 2020b). All these interventions aimed to improve PHRs and boost economic activity. In Nigeria, the PHRs include the release of over 1 billion naira in emergency funds to the Nigerian Centers for Disease Control, the establishment of a N500 billion COVID-19 Crisis Intervention Fund earmarked for upgrading healthcare facilities, and the creation of 774,000 jobs. Fan et al. (2020) explained from the Chinese experience that in the early phase of the PHRs aimed at containing the spread of the pandemic, the government faced financial and logistical challenges that health care affected the smooth distribution of materials across the country. As a result, policymakers introduced a preferential tax against the pandemic, which has been well-received by taxpayers and organizations. Entrepreneurial firm responses to the pandemic could be explained by conditional lead-lag relationships (Matos et al., 2021). Leading companies added $275 billion to their expected economic profit pool in a year despite the pandemic, while laggards lost $373 billion (McKinsey and Company, 2020).

The effects of these PHRs on TM in the existing literature are mixed. Some studies reported that entrepreneurial firms were more willing to pay taxes during the pandemic because they recognized the importance of PHRs in stemming the spread of the devastating virus. In contrast, some other entrepreneurial firms that have had to close or experience a significant drop in sales have been less willing to pay taxes because governments have introduced the automatic suspension of corporate income tax (CIT) payments (Zulkarnaen et al., 2020). In other words, the leading companies were those that were able to meet their tax obligations after receiving supportive interventions from the PHRs, while the laggards were those that were unable to meet their tax obligations due to the devastating impact of the pandemic on their operations.

In Kosovo, it was found that all state interventions during the pandemic slowed down local economic growth, which had a negative impact on tax revenues (due to the low morale of entrepreneurs), GDP, economic activity, business activity and employment levels (Sekiraqa et al., 2021). In comparison, Indonesia was found to have low TM in response to PHRs, as MSME owners could not distinguish between tax incentives and government incentives. The various individual and corporate tax breaks have had an impact on TM and are therefore proving to be crude and mostly ineffective FPMs that call for desirable reforms amid the ongoing pandemic. A better approach is to keep the tax system as it is, but there is a need to develop more targeted alternative non-tax responses to create an effective and fair income tax system (Sadiq and Krever, 2021). Based on the above findings, it could be hypothesized that:

H₁1:

Public health responses have a significant positive effect on the tax morale of entrepreneurial firms during the COVID-19 pandemic in Nigeria.

2.6.2. Fiscal policy measures and tax morale in COVID-19

At the peak of the pandemic, instead of levying taxes during the pandemic, governments introduced FPMs to allow authorities to provide aid and tax incentives, including tax cuts, to save jobs and keep businesses afloat (Clemens and Veuger, 2020; Edelberg and Sheiner, 2020). Various countries made FPMs available to their citizens and businesses. For example, the United States paid unemployment claims to 28 million people (Barrero et al., 2020). Additionally, under the CARES Act Paycheck Protection Program (PPP), the U.S. government provided bailouts to businesses to alleviate the challenges of payroll, leasing, rent, mortgage, and utility payments (Bartik et al., 2020). Similarly, the Nigerian government has introduced a number of FPMs such as the N50 billion Household and SME Credit Facility; credit support for the healthcare industry; a one-year moratorium on borrowers to repay all credit interventions, interest rate cut from 9% to % per year; conditional money transfers, reduction in pump price per liter, suspension of the increase in electricity tariffs, waiver of import duties on medicines, and protective equipment to treat COVID 19 (Nevin, 2020). Intervention by FPMs is necessary to support the recovery of SMEs from the impact of COVID-19 (Women Entrepreneurs Finance Initiative, 2020). Firms supported by governments through FPMs are expected to be able to meet their tax obligations.

In sub-Saharan Africa, governments implemented FPMs during the pandemic to increase the liquidity of their economies, increase the availability of funds for commercial banks and introduce debt relief, which is considered insufficient given the enormous impact of the pandemic (Fletcher, 2020). However, there is no evidence of the impact of FPMs on TM.

Empirical results on the effect of FPMs on corporate TM are mixed. TM is falling in countries where governments have deliberately granted business tax/VAT payment deferrals and self-assessment payment deferrals to entrepreneurs during the pandemic (Aladejebi, 2020; Collier et al., 2020). Despite huge government tax stimulus under the FPMs in Indonesia at the height of the COVID pandemic totalling IDR 63.16, the Directorate-General for Taxes (DGT) reported that the country was able to achieve the 100% government tax revenue target by the 2021 budget due to taxpayers’ obligation to pay taxes (Kumala and Abu Bakar, 2022). It has been reported that contractor firms have deferred all payments, including taxes, rather than cutting labor costs, cutting expenses, and negotiating contracts and terms because their finances are relatively weak to stay afloat (Thorgren and Williams, 2020). Furthermore, Popescu and Šebestová (2022) found that because of the COVID-19 pandemic shock, OECD countries’ FPMs negatively impacted the TM of entrepreneurial firms, thereby leading to corporate tax evasion and tax havens.

In Bangladesh, Islam et al. (2021) reported that despite PHRs and FPMs the COVID-19 pandemic negatively affected income level, and the government’s tax collection (an indication of low TM). In the United States however, FPMs have been observed to have created a leaky social safety net during the pandemic that has been grossly abused by entrepreneurial firms, resulting in low tax compliance. Consequently, it is argued that, in the short term, an excess profit tax should be levied on companies that have benefited from the pandemic, while a progressive corporate income tax and VAT should be imposed on benefiting companies in the short term (Avi-Yonah, 2020). In Indonesia, it was noted the government’s fiscal response to TM is poor because MSME owners could not distinguish between tax incentives and government incentives, and many MSMEs were unaware of the benefits of tax incentives and government alternative tax incentive policies in overcoming economic problems MSMEs faced during the COVID-19 pandemic (Wijoyo et al., 2021).

In Serbia, at the height of the COVID-19 pandemic, the owners of medium-sized companies paid lump-sum tax payments during the epidemic, which negatively impacted day-to-day operations, turnover, and the supply chain (Beraha and Đuričin, 2020). Not only do FPMs negatively impact TM, Ozili and Arun (2020) also found that the adoption of FPMs during the COVID-19 crisis negatively impacted levels of economic activity, including major capital market indices. However, expansive FPMs in the form of increased government spending had a positive effect on the level of economic activity. In Europe, Christl et al. (2021) found that monetary compensation schemes (FPMs) played an important role in cushioning the impact of the COVID-19 shock by 35.2% in EU countries that experienced a sharp drop in market incomes during the 2020 pandemic. In other words, the inequality-enhancing nature of the pandemic in market incomes has been largely offset by the tax and benefit systems put in place in most EU member states. In addition, over 30 million people in Germany, France, the UK, Spain, and Italy have applied for government support for their wages due to the negative impact of the pandemic on livelihoods and employment (Jackson et al., 2020).

In Latin America and the Caribbean, Obando and Aguilar (2020) found that governments in the region provided taxpayers with FPMs to maintain tax revenues, but TM was low during the health crisis due to a lack of fiscal space, cooperative compliance, and scarce economic flows due to the suspension of audits and debt collections, as well as rapid digitization in the region, among others. From the above review, it can be deduced that entrepreneurial firms are highly vulnerable to economic shocks during a recession as they suffer from limited financial and infrastructural resources, declining cash flows, supply chain disruptions, and limited ability to cope and adapt to unplanned situations (Jomo and Chowdhury, 2020; Raimi, 2021) who tend to limit their ability and ability to work at full capacity to increase their revenue streams. It could therefore be hypothesized that:

H₁2:

The fiscal policy measures of the government have a significant positive effect on the tax morale of entrepreneurial firms during the COVID-19 pandemic in Nigeria.

2.6.3. Interactive effects of PHRs and FPMs

The interactive effects of PHRs and fiscal policies on TM are complex as there is scanty evidence in the literature. Dzigbede and Pathak (2020) found that targeted government spending (PHRs) and revenue enhancement measures (FPMs) partially offset economic shocks, positively cushioned the emergence of poverty and inequality, and had prospects of stimulating support for long-term economic resilience. In times of economic stability, TM is higher in more developed countries with stronger legal systems, less corruption, and higher levels of government intervention, both in the form of taxes and spending (Williams and Krasniqi, 2017).

From this finding, it could be logically stated that if PHRs are effective in containing and managing the spread of the virus and entrepreneurial firms can carry out their operations, TM could improve. However, if firms are unable to continue business operations because of public health concerns, TM could suffer. In the same way, If FPMs are effective in providing financial support to firms and helping them recover from the economic impact of the pandemic, TM could also improve. However, if the firms feel that the subsidy is insufficient or they are not entitled to the subsidy, TM can suffer.

Based on the foregoing, the interactive effects of PHRs and FPMs on TM is likely to be influenced by a number of factors, including the severity of the pandemic, the effectiveness of public health responses, adherence to democratic norms, and human rights and humanity (Schunk and Wagner, 2021; McKeown, 2022), Awareness of beneficiaries, the generosity of fiscal policies, and the perceived fairness of those policies in relation to life and livelihood, among others (Herbert and Marquette, 2021; Raimi, 2021; Settele and Shupe, 2021; Haaland et al., 2023). Based on the above, it could therefore be hypothesized that:

H₁3:

The interactive effects of both fiscal policy measures and public health responses do not have a significant effect on the tax morale of entrepreneurial firms during the COVID-19 pandemic in Nigeria.

3. Methods

3.1. Data, variables, and measures

The firm-level primary data was collected from contractor companies in Lagos, Nigeria. The scope of the topics addressed was in the period of the COVID-19 pandemic of 2020–2021. The entrepreneurial companies on which the focus was focused were small companies, medium-sized companies, large corporations, and multinational companies, falling under the Lagos Chamber of Commerce (LCCI). Two independent variables (IVs) are public health responses and fiscal policies. The only dependent variable (DV) is TM, while control variables are coping strategy, firm type, firm size, and firm performance. In the following model specification, 11 measures are included, excluding the error term.

3.2. Research design, population, and sample

This study used a cross-sectional survey design to collect primary data from professional managers of selected entrepreneurial firms at a given point in time (2020–2021). From a target population of 2,300 entrepreneurial firms registered with the LCCI, a sample of 384 firms was drawn using Partens’ formula (1950). Access to the sampled firms was gained through the support of the Chamber of Commerce. The sampled companies were selected using the random sampling technique and completed the online survey questionnaire, giving all 384 companies an equal chance of being selected. At the end of the survey, 177 responses were received from companies. The collected data was organized, streamlined, and analyzed using hierarchical regression analysis.

3.3. Hierarchical regression analysis and model specifications

Hierarchical regression analysis is a useful statistical technique to study the relationship or causality between a dependent variable and multiple independent variables (de Jong, 1999; Lai et al., 2022). Some scholars describe it as an extension of multiple regression analysis used to determine the relationship between a dependent variable and two or more independent variables (Raab et al., 2023). The process of entering independent variables into the hierarchical regression model follows a specific order based on the research theory model or empirical evidence. Its advantages over ordinary least squares analysis and multiple regressions stem from the fact that it allows for the control of the effects of certain independent variables on the dependent variable while examining the effects of other independent variables and moderating/mediating variables (de Jong, 1999; Radmacher and Martin, 2001). In addition, it provides a useful framework for studying the complex relationships between the independent variable and the dependent variable (Hung et al., 2023).

The model specification for a hierarchical regression analysis is shown below.

Model:

$TM = α + β_{1} G 1 + β_{2} G 2 + β_{3} G 3 + β_{4} G 4 + β_{5} G 5 + β_{6} G 6 + β_{7} G 7 + β_{8} A 2 + β_{9} A 3 + β_{10} B 3 + β_{11} GB 4 + ε .$ $TM = α + β_{1} G 1 + β_{2} G 2 + β_{3} G 3 + β_{4} G 4 + β_{5} G 5 + β_{6} G 6 + β_{7} G 7 + β_{8} A 2 + β_{9} A 3_{} + β_{10} B 3 + β_{11} GB 4 + ε .$

Key:

	$TM = Tax$ $TM = Tax$ Morale,
	$GI = Control$ $GI = Control$ measures of the governments,
	$G 2 = The$ $G 2 = The$ treatments of people infected,
	$G 3 = Care$ $G 3 = Care$ and incentives given to medical staff,
	$G 4 = COVID$ $G 4 = COVID$ -19 relief materials, palliatives, and cash assistance to the public,
	$G 5 = Tax$ $G 5 = Tax$ reliefs given to the business organizations by government,
	$G 6 = Debt$ $G 6 = Debt$ rescheduling by government,
	$G 7 = Support$ $G 7 = Support$ assistance to businesses,
	$A 2 = Firm$ $A 2 = Firm$ type,
	$A 3 = Firm$ $A 3 = Firm$ size,
	$B 3 = Firm$ $B 3 = Firm$ performance,
	$B 4 = Coping$ $B 4 = Coping$ strategy, and
	$ε = Error$ $ε = Error$ term
	A-priori expectation: $0 < β_{1 - 7} < 1$ $0 < β_{1 - 7} < 1$

•	PHRs refer to various public health measures to mitigate the impact of the COVID-19 pandemic. The measurements of the PHRs in the survey instrument are G1, G2, and G3.
•	FPMs refer to various fiscal policy measures to mitigate the economic impact of the COVID-19 pandemic. The measurements of FPMs are G4, G5, G6, and G7 in the survey instrument.
•	TM refers to voluntary and timely tax assessment, voluntary tax compliance, and timely payment of taxes. The measurements of TM in the questionnaire are C1, C2, C3, and C4.

4. Analysis/Discussion

4.1. Diagnostic tests: Reliability, validity, auto-correlation, and normality tests

The result of the reliability test performed to determine the reliability of the survey instrument was 80.7% reliability as shown in Table 1. George and Mallery (2003) provided a benchmark for deciding reliability. They indicated that Cronbach’s alpha reliability coefficient of $> 0.9$ $> 0.9$ was excellent, $> 0.8$ $> 0.8$ was good, and $> 0.7$ $> 0.7$ was acceptable. Therefore, considering that Cronbach’s alpha value for the questionnaire is greater than 0.700, the instrument is reliable, replicable, and acceptable.

**Table 1. Test of reliability (Scale Reliability Statistics).**
	Mean	SD	Cronbach’s $α$ $α$
Scale	2.86	0.434	0.807

Source: Authors’ Computation (2021).

The Durbin–Watson (DW) statistical test auto-correlation showed a value of $-$ $-$ 0.0123 and a DW statistic of 2.00. The test confirmed a negative autocorrelation. Our results for DW as shown in Table 2 fall within the acceptable threshold; hence, there are no correlations among the independent variables.

**Table 2. Durbin–Watson Test for autocorrelation.**
Autocorrelation	DW statistic	$p$ $p$
$-$ $-$ 0.00983	2	0.97

Source: Authors’ Computation (2021).

The collinearity statistical test was conducted. The collinearity statistical test was conducted as shown in Table 3 below. The tolerance and variance inflation factor (VIF) detects whether the independent variables (G1, G2, G3, G4, G5, G6, and G7) are highly correlated with one another in the model. The benchmark for tolerance is 1-R2, while the benchmark for the VIF is greater than or equal to 1. VIF above 4 or tolerance below 0.25 indicates that multicollinearity might exist. Our results for tolerance and VIF fall within the acceptable threshold in statistics; hence, multicollinearity does exist among the independent variables.

**Table 3. Collinearity statistics.**
	VIF	Tolerance
G1	1.82	0.55
G2	1.9	0.527
G3	1.53	0.656
G4	1.64	0.609
G5	2.01	0.498
G6	1.89	0.53
G7	2.28	0.439
Firm type	1.06	0.942
Firm performance	1.06	0.946
Coping strategies	1.08	0.925

Source: Authors’ Computation (2021).

We used Kaiser–Meyer–Olkin (KMO) to test the sample adequacy and suitability of the variables in our model for factor analysis. As a rule of thumb, KMO values between 0.8 and 1 indicate sampling is adequate, while KMO values below 0.6 indicate sampling is insufficient and remedial action should be taken (Glein, 2023). In the end, the overall KMO score of 0.8 presented in Table 4 ranges from 0.8 to 1. We have therefore confirmed that our sample is adequate, and our data are suitable for exploratory factor analysis.

**Table 4. KMO measure of sampling adequacy.**
	MSA
Overall	0.8
G1	0.848
G2	0.803
G3	0.803
G4	0.815
G5	0.842
G6	0.817
G7	0.792
TM	0.5
Firm type	0.464
Firm performance	0.573

Source: Authors’ Computation (2021).

In this study, numerical and graphical methods were used to test the normality of the data. The Shapiro–Wilk normality test indicated that the data were normally distributed. The $P$ $P$ value of 0.576 was greater than 0.05. In addition, normality is established in the Q–Q plot as the data points are close to the diagonal line as shown in Fig. 2.

4.2. Demographics

The results in Table 5 show that 39 respondents (22%) were line managers, 50 (28.2%) were middle-level managers, 25 (14.1%) were top-level managers, and 63 (34.6%) were business owners/CEO. These results suggested that respondents to the questionnaire had adequate experience and were in relevant positions sufficient to give reliable, relevant and informed opinions on the effect of PHRs and FPMs of government on TM in Nigeria.

Regarding firm size and operation, the demographics showed that 86 (48.6%) are small-scale businesses, 58 (32.8%) are medium-scale businesses, 25 (14.1%) operate large-scale businesses and 8 (4.5%) are multinational firms. Likewise, 120 respondents (68.6%) work with firms with 1–50 employees, 16 (9.1%) work with firms with 51–100 employees, 15 (8.6%) are from firms with 101–250 employees, 8 (4.0%) come from firms with 251–500 employees, and 15 (8.6%) work with firms with more than 500 employees.

Sectoral analysis revealed that 126 (71.2%) of respondents work with services and retail companies, 21 respondents (11.9%) are from the manufacturing sector, seven respondents (4.0%) work with construction and real estate firms, seven (4.0%) work with oil, gas, and energy firms, 11 respondents (6.2%) work with telecommunication and ICT firms, and four respondents (2.3%) work with other unlisted sectors. The results showed that the respondents are spread across industrial sectors.

**Table 5. Demographics.**
Items	Frequency	Percent (%)
Managerial Level
Line Manager	39	22.0%
Middle-Level Manager	50	28.2%
Top-Level Manager	25	14.1%
CEO/Business Owner	63	35.6%
Firm size: Operation
Small Scale Business	86	48.6%
Medium Scale Business	58	32.8%
Large Scale Business	25	14.1%
Multinational Business	8	4.5%
Firm size: Employees
1–50	124	70.1%
51–100	16	9.0%
101–250	14	7.9%
251–500	8	4.5%
Above 500	15	8.5%
Industrial Sector
Oil, Gas and Energy Industry	8	4.5%
Manufacturing	21	11.9%
Construction & Real Estate Industry	7	4.0%
Telecoms & ICT	11	6.2%
Services & Retail	126	71.2%
Others	4	2.3%

Source: Authors’ Computation (2021).

4.3. Inferential analysis

4.3.1. Correlation analysis

From Table 6, the correlation test revealed that GI is $r = 0.111$ $r = 0.111$ , $P$ $P$ value of $0.070 < 0.05$ $0.070 < 0.05$ , G2 is $r = 0.127$ $r = 0.127$ , $P$ $P$ value of $0.046 < 0.05$ $0.046 < 0.05$ , G3 is $r = - 0.088$ $r = - 0.088$ , $P$ $P$ value of $0.877 < 0.05$ $0.877 < 0.05$ , G4 is $r = - 0.082$ $r = - 0.082$ , $P$ $P$ value of $0.139 < 0.05$ $0.139 < 0.05$ , G5 is $r = - 0.045$ $r = - 0.045$ , $P$ $P$ value of $0.275 < 0.05$ $0.275 < 0.05$ , G6 is $r = - 0.056$ $r = - 0.056$ , $P$ $P$ value of $0.222 < 0.05$ $0.222 < 0.05$ , and G7 is $r = - 0.085$ $r = - 0.085$ , $P$ $P$ value of $0.131 < 0.05$ $0.131 < 0.05$ .

Apart from G3, the six other correlation coefficients are positive with $P$ $P$ values that are less than 0.5, and the result is a strong indication that the synergistic effect of public health responses and four FPMs (GI, G2, G3, G4, G5, G6, and G7) have positive significant relationships with tax morals.

**Table 6. Correlation matrix.**
		TM	G1	G2	G3	G4	G5	G6	G7
TM	Pearson’s r	—
	$p$ $p$ value	—
G1	Pearson’s r	0.111	—
	$p$ $p$ value	0.07	—
G2	Pearson’s r	0.127	0.581	—
	$p$ $p$ value	0.046	$< 0.001$ $< 0.001$	—
G3	Pearson’s r	$-$ $-$ 0.088	0.444	0.527	—
	$p$ $p$ value	0.877	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	—
G4	Pearson’s r	0.082	0.464	0.34	0.291	—
	$p$ $p$ value	0.139	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	—
G5	Pearson’s r	0.045	0.417	0.368	0.393	0.513	—
	$p$ $p$ value	0.275	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	—
G6	Pearson’s r	0.058	0.414	0.423	0.336	0.308	0.547	—
	$p$ $p$ value	0.222	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	—
G7	Pearson’s r	0.085	0.418	0.459	0.319	0.501	0.599	0.611	—
	$p$ $p$ value	0.131	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	$< 0.001$ $< 0.001$	—

Note: * $p < 0.05$ $p < 0.05$ , ** $p < 0.01$ $p < 0.01$ , *** $p < 0.001$ $p < 0.001$ , one-tailed.

Source: Authors’ Computation using Jamovi Software (2021).

4.3.2. Hierarchical regression analysis

The correlation test is useful for determining the strength and direction of the linear relationship between two continuous variables. Hierarchical regression is required to test the effects of PHRs and FPMs on TM. Model estimates in Table 7 suggest that the $p$ $p$ -values of two out of three measures of public health responses (G2 and G3) are above the 0.05 significance level. These results suggest that the quality of treatment of infected people (Q2) as well as the quality of care and incentives for medical staff (G3) have a significant impact on the TM of entrepreneurial firms. But the control measures introduced by the health authorities (G1) do not have a significant impact on the TM of entrepreneurial companies. We, therefore, accept the alternative hypothesis and conclude that PHRs have a significant positive effect on entrepreneurial firm TM during the COVID-19 pandemic in Nigeria.

**Table 7. Effect of PHRs on TM during the COVID-19 pandemic.**
Model Coefficients — TM
Predictor	Estimate	SE	$t$ $t$	$p$ $p$
Intercept	48.59	2.317	20.97	$< 0.001$ $< 0.001$
G1	1.07	0.94	1.14	0.257
G2	2	1.032	1.94	0.054
G3	$-$ $-$ 2.44	0.922	$-$ $-$ 2.64	0.009
R	0.237
R²	0.0564
RMSE	10.3
F	3.45
df1	3
df2	173
p	0.018

Source: Authors’ Computation (2021).

Model estimates in Table 8 suggest that the $p$ $p$ -values of the four measures of fiscal policy (Q4, Q5, Q6, and Q7) are above the 0.05 significance level. These results suggest that support materials, cash assistance, business tax breaks, debt restructuring, and other support incentives do not have a significant impact on entrepreneurial firm tax compliance.

We accept the null hypothesis and conclude that FPMs do not have a significant positive effect on entrepreneurial firms’ TM during the COVID-19 pandemic in Nigeria.

**Table 8. Effect of FPMs on the TM during the COVID-19 pandemic.**
Model Coefficients — TM
Predictor	Estimate	SE	$t$ $t$	$p$
Intercept	48.219	2.3	21.006	$< 0.001$
G4	0.818	1.21	0.679	0.498
G5	$-$ 0.395	1.18	$-$ 0.336	0.737
G6	0.221	1.13	0.195	0.846
G7	0.751	1.32	0.57	0.570
R	0.0999
R²	0.00998
RMSE	10.6
F	0.433
df1	4
df2	172
p	0.784

Source: Authors’ Computation (2021).

Model estimates in Table 9 suggest that the $p$ -values of FPMs and public health responses (PHR) are above the 0.05 significance level. The $p$ -values of the three control variables (firm type, firm performance, and coping strategies) are also above the significance level of 0.05. These results suggest that fiscal and public health policies do not have a significant impact on entrepreneurial firms’ tax compliance.

Overall, we accept the null hypothesis and conclude that the synergy of public health and fiscal policies does not have a significant positive effect on entrepreneurial firms’ TM during the pandemic.

**Table 9. Effect of PHRs and FMPs on TM during the COVID-19 pandemic.**
Model Coefficients — TM
Predictor	Estimate	SE	$t$	$p$
Intercept	50.9463	3.83	13.3008	$< 0.001$
G1	0.8053	1.019	0.79	0.431
G2	1.765	1.084	1.6275	0.106
G3	$-$ 2.4302	0.956	$-$ 2.5417	0.012
G4	0.6594	1.243	0.5303	0.597
G5	0.0926	1.189	0.0779	0.938
G6	$-$ 0.0586	1.154	$-$ 0.0507	0.960
G7	0.4184	1.355	0.3089	0.758
Firm type	0.5519	0.947	0.5827	0.561
Firm performance	$-$ 0.3844	0.563	$-$ 0.6823	0.496
Coping strategies	$-$ 1.4368	0.875	$-$ 1.6415	0.103
R	0.282
R²	0.0795
Adjusted R²	0.0241
RMSE	10.2
F	1.43
df1	10
df2	166
p	0.169

Source: Authors’ Computation (2021).

4.3.3. Discussion of findings

Overall, three results clearly emerged from the hierarchical regression model estimations. According to the results hierarchical effects of public health responses (PHRs) and FPMs on the TM of entrepreneurial firms in Nigeria during the COVID-19 pandemic. Let us examine each result. First, it was found that the PHRs have a significant positive effect on the TM of entrepreneurial firms.

The first finding is inconsistent with most previous findings except the isolated one in China. Fan et al. (2020) found that in the Chinese experience in the early phase of the pandemic PHRs were introduced to contain the spread of the virus along with a preferential tax against the pandemic, which was well-received by taxpayers and organizations. This is because the tax-paying firms in China recognized the importance of PHRs in stemming the spread of the devastating virus. However, in most countries, the PHRs have a negative effect on TM. In Kosovo, it was found that all state interventions during the pandemic had a negative impact on tax revenues due to the low morale of entrepreneurs and low economic activity (Sekiraqa et al., 2021). In comparison, Indonesia was found to have low TM in response to PHRs, because the MSME owners could not distinguish between tax incentives and government incentives (Sadiq and Krever, 2021).

Second, the study found that the FPMs do not have a significant positive effect on the TM of entrepreneurial firms in Nigeria during the COVID-19 pandemic. This finding is consistent with studies of Aladejebi (2020), and Collier et al. (2020) that found that TM fell in countries where governments have deliberately granted business tax/VAT payment deferrals and self-assessment payment deferrals to entrepreneurs during the pandemic. Furthermore, Popescu and Šebestová (2022) found that because of the COVID-19 pandemic shock, OECD countries’ FPMs negatively impacted the TM of entrepreneurial firms, thereby leading to corporate tax evasion and tax havens. Also in the US, it was found that FPMs created a leaky social safety net during the pandemic that has been grossly abused by entrepreneurial firms, resulting in low-tax compliance.

Third, the study found that the interactive effects of PHRs and FPMs do not have a significant positive effect on the TM of entrepreneurial firms during the COVID-19 pandemic. This is consistent with several studies. In the Latin American and Caribbean context, it was found that the interactive effects of PHRs and FPMs do not have a significant positive effect on the TM of entrepreneurial firms because as consumption and economic activity decreased, there were massive drops in fiscal revenue collection in the form of VAT and other taxes (Obando and Aguilar, 2020). Generally, in times of economic stability, TM is found to be higher in more developed countries with stronger legal systems, less corruption, and higher levels of government intervention, both in the form of taxes and spending (Williams and Krasniqi, 2017).

Summarizing the discussion of the results, some scholars noted that government interventions for entrepreneurial firms during the COVID-19 pandemic through PHRs and FPMs had negative impacts on TM and macroeconomic variables globally for several reasons (Avi-Yonah, 2020; Didier et al., 2021; Raimi, 2021). First, the pandemic is fundamentally different from previous crises and therefore requires stronger political responses. Second, the policy direction of state intervention is to preserve corporate relationships with workers, suppliers, customers, and creditors to avoid inefficient bankruptcies and long-term damaging economic effects. Third, the main preoccupation for businesses during the pandemic is ensuring the minimum expenses necessary to withstand the pandemic while using credit to stay afloat until the crisis subsides. Fourth, the existing legal and regulatory infrastructure is ill-equipped to deal with an exogenous systemic shock such as a pandemic. After all, it was easy to route loans to firms, but these loan interventions are difficult to implement as they involve different trade-offs (Didier et al., 2021). For instance, a leaky social safety net during the pandemic has been grossly abused by entrepreneurial firms, resulting in low tax compliance (Avi-Yonah, 2020).

5. Conclusion, Implications, Limitations, and Recommendations

This study examined the hierarchical effects of PHRs and FPMs on the TM of entrepreneurial firms in Nigeria during the pandemic. At the end of the study, three findings emerged in hierarchical order. First, PHRs were found to have a significant positive effect on entrepreneurial firm TM. Second, it was found that FPMs do not have a significant positive effect on the TM of entrepreneurial firms. When both interventions are combined, the third result shows that the interactive effects of PHRs and FPMs on TM do not have a significant positive effect on TM of entrepreneurial firms during the period of the COVID-19 pandemic in Nigeria.

5.1. Theoretical implications

The current research has two theoretical implications. By combining SCT and FET to explain how the government stabilizes the economy through PHRs and FPMs, the current study strengthened and expanded the explanatory and predictive capacities of both theories in times of stability and turbulence. We have therefore expanded the application and adaptability of SCT and FET. Second, the insightful results, albeit modest, add to the literature on corporate behavior and TM at a time of economic uncertainty in the context of developing countries during the time of the COVID-19 pandemic.

5.2. Practical implications

The first practical implication of the overall negative relationships underscores the need for policymakers to proactively employ the right PHRs and FPMs to stimulate economic activity, safeguard jobs, stimulate aggregate demand, and ultimately strengthen the fiscal morale of entrepreneurial firms. This is in full agreement with the government’s role in the social contract in the fiscal exchange relationship.

Second, the study confirms the Nigerian government’s strong willingness to support MSMEs in the post-COVID-19 era through the widely acclaimed PHRs and FPMs, but both measures are woefully inadequate to impact TM because of several financial concerns of entrepreneurial firms at the peak of the pandemic.

Third, from a business perspective, to keep the economy afloat and the entrepreneurial activity of entrepreneurial firms, policymakers need timely and robust PHRs and FPMs to drive the economic sustainability plan in a recessionary period precipitated by the COVID-19 pandemic.

Fourth, the law obligates entrepreneurial firms to pay taxes to the government in return for public goods and services to corporate organizations, it is therefore important for the government to promote tax policies that boost the tax morals of entrepreneurial firms, responsible financial transactions, and ethical behavior in a recessionary period.

5.3. Limitations and future research directions

Despite the laudable theoretical and practical implications outlined above, this study, like previous studies that focused on the devastating impact of the COVID-19 pandemic on TM and compliance, has some limitations. First, the results of this study are based on firm-level primary data collected from selected entrepreneurial firms. Using the secondary data, where available, can provide more in-depth analysis and insights than current insights. Second, the study sampled 177 entrepreneurial firm managers in Lagos, Nigeria. Therefore, the views emerging from a sample size of 177 entrepreneurial firms may be insufficient and not representative of the viewpoints of entrepreneurial firms in Nigeria. Future research might consider a larger sample size to make it easier to generalize and predict the research results. Furthermore, there were no mediating or moderating factors for the effect of PHRs and FPMs on TM in this study. Future studies might consider introducing moderating or mediating factors as these may provide more clarity about the direction of causality. Finally, quantitative research based on the use of a questionnaire to collect data was employed in this study. This positivist paradigm sometimes disregards certain information and limits respondents’ opinions on the sensitive phenomenon being studied. Future studies could explore a mixed research method on the convergence of results between quantitative and qualitative research methods. Overall, this study is unique as it empirically opens the space for further studies on the hierarchical effects of PHRs and FPMs on TM in the developing country context.

References

Adekoya, A.A., L.A. Agbetunde and O.O. Akinrinola (2021). COVID-19 Pandemic and Internally Generated Revenues in Local Governments: Nigeria Experience. International Journal of Applied Economics, Finance and Accounting, 9(2), pp. 63–75. Google Scholar
Aladejebi, O. (2020). Managing Small Businesses in Nigeria during COVID-19 Crisis: Impact and Survival Strategies. IOSR Journal of Business and Management, 22(8), pp. 24–34. Google Scholar
Alesina, A. and S. Ardagna (2010). Large Changes in Fiscal Policy: Taxes Versus Spending. Tax Policy and the Economy, 24(1), pp. 35–68. Crossref, Google Scholar
Alesina, A. and F. Giavazzi (eds.) (2013). Fiscal Policy after the Financial Crisis. University of Chicago Press: Chicago, USA. Crossref, Google Scholar
Andriani, L., R. Bruno, E. Douarin and P. Stepien-Baig (2020). Is Tax Morale Culturally Driven?. Journal of Institutional Economics, pp. 1–18. Google Scholar
Armah-Attoh, D. and M. Awal (2013). Tax Administration in Ghana: Perceived Institutional Challenges. Afrobarometer Briefing Paper, 124(1), pp. 1–9. Google Scholar
Avi-Yonah, R. (2020). COVID-19 and Fiscal Policies: COVID-19 and US Tax Policy: What Needs to Change?. Intertax, 48(8/9), pp. 790–793. Google Scholar
Bartik, A.W., M. Bertrand, Z.B. Cullen, E.L. Glaeser, M. Luca and C.T. Stanton (2020). How are Small Businesses adjusting to COVID-19? Early Evidence from a Survey., Working Paper No. 26989, National Bureau of Economic Research Massachusetts Avenue Cambridge, MA 02138. Google Scholar
Beale, T. and P. Wyatt (2017). Predicting taxpayer behaviour and compliance: An economic analysis of Jamaica’s property tax system, Paper Prepared for Presentation at The “2017 World Bank Conference On Land and Poverty”, The World Bank-Washington DC. March, 20–24, 2017. Google Scholar
Beraha, I. and S. Đuričin (2020). The Impact of COVID-19 Crisis on Medium-Sized Enterprises in Serbia. Economic Analysis, 53(1), pp. 14–27. Crossref, Google Scholar
Boyle, C.A., M.H. Fox, S.M. Havercamp and J. Zubler (2020). The Public Health Response to the COVID-19 Pandemic for People with Disabilities. Disability and Health Journal, 13(3), p. 100943. Crossref, Google Scholar
Christl, M., S. De Poli, F. Figari, T. Hufkens, A. Papini and A. Tumino (2021). The Cushioning Effect of Fiscal Policy in the EU during the COVID-19 Pandemic (No. 02). JRC Working Papers on Taxation and Structural Reforms. Google Scholar
Clemens, J. and S. Veuger (2020). Implications of the COVID-19 Pandemic for State Government Tax Revenues. National Tax Journal, 73(3), pp. 619–644. Crossref, Google Scholar
Collier, R.S., A. Pirlot and J. Vella (2020). Tax Policy and the COVID-19 Crisis. University of Oxford, Saïd Business School, Centre for Business Taxation. Google Scholar
de Jong, P.F. (1999). Hierarchical Regression Analysis in Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 6(2), pp. 198–211. Crossref, Google Scholar
Didier, T., F. Huneeus, M. Larrain and S.L. Schmukler (2021). Financing Firms in Hibernation during the COVID-19 Pandemic. Journal of Financial Stability, 53, p. 100837. Crossref, Google Scholar
Dougherty, S. and P. de Biase (2021). Who Absorbs the Shock? An Analysis of the Fiscal Impact of the COVID-19 Crisis on Different Levels of Government. International Economics and Economic Policy, 18, pp. 517–540. Crossref, Google Scholar
Dzigbede, K.D. and R. Pathak (2020). COVID-19 Economic Shocks and Fiscal Policy Options for Ghana. Journal of Public Budgeting, Accounting & Financial Management, 32(5), pp. 903–917, https://doi.org/10.1108/JPBAFM-07-2020-0127. Crossref, Google Scholar
Edelberg, W. and L. Sheiner (2020). What could additional fiscal policy do for the economy in the next three years? The Brookings Institution, October, 9. Available at https://www.brookings.edu/wp-content/uploads/2020/10/fiscalstimulus_technicalappendix_FINAL_updated10.21.pdf. Google Scholar
Fan, Y., S. Yang and P. Jia (2020). Preferential Tax Policies: An Invisible Hand behind Preparedness for Public Health Emergencies. International Journal of Health Policy and Management, 11(5), pp. 547–550, https://doi.org/10.34172/ijhpm.2020.139. Google Scholar
Fletcher, B. (2020). The economic impact of COVID-19 on sub-Saharan Africa. Available at https://www.controlrisks.com/our-thinking/insights/the-economic-impact-of-covid-19-on-sub-saharan-africa. Google Scholar
Funke, M. and A. Tsang (2020). The People’s Bank of China’s Response to the Coronavirus Pandemic: A Quantitative Assessment. Economic Modelling, 93, pp. 465–473. Crossref, Google Scholar
Garg, S., L. Kim, M. Whitaker, A. O’Halloran, C. Cummings, R. Holstein , … and A. Fry (2020). Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019—COVID-NET, 14 States, March 1–30, 2020. Morbidity and Mortality Weekly Report, 69(15), p. 458. Crossref, Google Scholar
Glasser, J.W., N. Hupert, M.M. McCauley and R. Hatchett (2011). Modelling and Public Health Emergency Responses: Lessons from SARS. Epidemics, 3(1), pp. 32–37. Crossref, Google Scholar
Glein, S. (2023). Kaiser-Meyer-Olkin (KMO) Test for Sampling Adequacy. From StatisticsHowTo.com: Elementary Statistics for the rest of us! Available at: https://www.statisticshowto.com/kaiser-meyer-olkin/ [accessed 19 January 2022]. Google Scholar
Gold, J.A., K.K. Wong, C.M. Szablewski, P.R. Patel, J. Rossow, J. Da Silva , … and B.R. Jackson (2020). Characteristics and Clinical Outcomes of Adult Patients Hospitalized with COVID-19—Georgia, March 2020. Morbidity and Mortality Weekly Report, 69(18), p. 545. Crossref, Google Scholar
Guest, J.L., C. Del Rio and T. Sanchez (2020). The Three Steps Needed to End the COVID-19 Pandemic: Bold Public Health Leadership, Rapid Innovations, and Courageous Political Will. JMIR Public Health and Surveillance, 6(2), p. e19043. Crossref, Google Scholar
Han, Q., Q. Lin, S. Jin and L. You (2020). Coronavirus 2019-nCoV: A Brief Perspective from the Front Line. Journal of Infections, 80, pp. 373–377, https://doi.org/10.1016/j.jinf.2020.02.010. Crossref, Google Scholar
Herbert, S. and H. Marquette (2021). COVID-19, governance, and conflict: emerging impacts and future evidence needs. DK4D Emerging Issues Report 34. Brighton, UK: Institute of Development Studies, https://opendocs.ids.ac.uk/opendocs/bitstream/handle/20.500.12413/16477/EIR34_COVID-19_governance_and_conflict_emerging_impacts_and_future_evidence_needs.pdf?sequence=1. Google Scholar
Hung, T.K., M. Tian and S.L. Lee (2023). The relationship between characteristics of the source of knowledge and knowledge transfer performance: Moderated mediation examination of political skill. Management Decision (ahead-of-print). Google Scholar
International Labour Organization (2021, January). ILO Monitor: COVID-19 and the World of Work. Seventh edition. Report, World of Bank, https://www.ilo.org/global/topics/coronavirus/impacts-and-responses/WCMS_767028/lang--en/index.htm [accessed 22 February 2022]. Google Scholar
International Monetary Fund (2020). World Economic Outlook Update, International Monetary Fund, June 24, 2020. Google Scholar
Islam, M.R., S. Suraiya, N.M. Zayed, K.B.M.R. Hasan, M.S. Bipasha and V. Nitsenko (2021). Assessing the Effect of the COVID-19 Pandemic on the Government Revenues: A Study on Individual Taxpayers of Bangladesh. Scientific Bulletin of National Mining University (5), pp. 154–159. Google Scholar
ITU (2015). SME Competitiveness Outlook 2015: Connect, Compete and Change for Inclusive Growth. Available at https://intracen.org/resources/publications/sme-competitiveness-outlook-2015-connect-compete-and-change-for-inclusive [accessed on 16 April, 2022]. Google Scholar
Jackson, J.K., M.A. Weiss, A.B. Schwarzenberg and R.B. Nelson (2020). Global Economic Effects of COVID-19. Report Number R46270, Congressional Research Service, pp. 1–110, https://apps.dtic.mil/sti/pdfs/AD1152929.pdf [accessed 10 March 2022]. Google Scholar
Johnson-Deberry, D. (2023). Understanding the Impact of Digital Marketing Capabilities and Female-Owner Behaviors on the Performance of Entrepreneurially Oriented Firms in the Digital Economy. Doctoral dissertation, The University of North Carolina at Charlotte. Google Scholar
Jomo, K.S. and A. Chowdhury (2020). Covid-19 Pandemic Recession and Recovery. Development, 63(2), pp. 226–237. Crossref, Google Scholar
Kabir, I. and Y.A. Abubakar (2023). Public policy responses to COVID-19 and the survival of ethnic minority businesses (EMBs): Does entrepreneurial orientation (EO) matter?. International Journal of Entrepreneurial Behavior & Research, 29(3), pp. 561–586. Crossref, Google Scholar
Kneller, R., M.F. Bleaney and N. Gemmell (1999). Fiscal Policy and Growth: Evidence from OECD Countries. Journal of Public Economics, 74(2), pp. 171–190. Crossref, Google Scholar
Kumala, R. and I. Abu Bakar (2022). Evaluation of the Utilization of Tax Incentive Policies during the Covid-19 Pandemic in the Framework of National Economic Recovery. Ilomata International Journal of Tax and Accounting, 3(1), pp. 13–21, https://doi.org/10.52728/ijtc.v4i1.422. Crossref, Google Scholar
Lai, J., Y. Zou, J. Zhang and P.R. Peres-Neto (2022). Generalizing Hierarchical and Variation Partitioning in Multiple Regression and Canonical Analyses using the Rdacca. hp R Package. Methods in Ecology and Evolution, 13(4), pp. 782–788. Crossref, Google Scholar
Leng, C. and B. Goh (2020, February). China to inject $174 billion of liquidity on Monday as markets reopen. Reuters News. Available at https://www.reuters.com/article/us-china-health-cenbank-idUSKBN1ZW074. Google Scholar
Loayza, N. and S.M. Pennings (2020). Macroeconomic Policy in the Time of COVID-19: A Primer for Developing Countries. World Bank Research and Policy Briefs. World Bank, Washington DC. Available at https://pdfs.semanticscholar.org/743b/8cc6b118771cc8a0621931851b6d456b49e0.pdf [accessed 19 May 2022]. Crossref, Google Scholar
Lund, S., A. Madgavkar, J. Manyika, S. Smit, K. Ellingrud, M. Meaney and O. Robinson (2021). The Future of Work after COVID-19. McKinsey Global Institute. Google Scholar
Mak, I.W., C.M. Chu, P.C. Pan, M.G. Yiu and V.L. Chan (2009). Long-Term Psychiatric Morbidities among SARS Survivors. General Hospital Psychiatry, 31, pp. 318–326. Crossref, Google Scholar
Matos, P., A. Costa and C. da Silva (2021). COVID-19, Stock Market and Sectoral Contagion in US: A Time-Frequency Analysis. Research in International Business and Finance, 57, p. 101400. Crossref, Google Scholar
McKeown, J. (2022). Humanity Over Economy: Biopolitical Responses to the COVID-19 Pandemic in Ghana. United Nations University World Institute for Development Economics Research. Crossref, Google Scholar
McKinsey & Company (2020). The COVID-19 pandemic is widening the gap between leading and lagging industries. Available at https://www.mckinsey.com/featured-insights/coronavirus-leading-through-the-crisis/charting-the-path-to-the-next-normal/the-covid-19-pandemic-is-widening-the-gap-between-leading-and-lagging-industries [accessed 23 March 2022]. Google Scholar
Moriarty, L.F., M.M. Plucinski, B.J. Marston, E.V. Kurbatova, B. Knust, E.L. Murray , … and C.C.S.R. Team (2020). Public health responses to COVID-19 outbreaks on cruise ships—worldwide, February–March 2020. Morbidity and Mortality Weekly Report, 69(12), p. 347. Crossref, Google Scholar
Nevin, A.S. (2020). COVID-19 and the Nigerian Economy: Assessing the Impact, Paper presented at the PwC Nigeria’s Webinar titled “COVID-19: Economic Implication and Policy Responses” held on 8 April 2020, https://pwcnigeria.typepad.com/tax_matters_nigeria/2020/04/pwc-nigerias-covid-19-webinar-economic-implications-and-policy-responses-1.htmlAccessed on Apirl 13, 2022. Google Scholar
Obando, R.R. and J.A. Aguilar (2020). Tax Insights from Latin American and Caribbean in the Context of COVID-19. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 72(1), pp. 128–142. Google Scholar
Osland, J.S., B. Szkudlarek, M.E. Mendenhall and B.S. Reiche (eds.) Advances in Global Leadership, Emerald Publishing Limited, Bingley, pp. 3–56. Google Scholar
Ostrom, V. and E. Ostrom (2019). Public Goods and Public Choices. Routledge, pp. 7–49. Crossref, Google Scholar
Ozili, P.K. and T. Arun (2020). Spillover of COVID-19: impact on the Global Economy. Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3562570. Google Scholar
Palil, M.R., M.R. Akir and W.F.B.W. Ahmad (2013). The Perception of Tax Payers on Tax Knowledge and Tax Education with Level of Tax Compliance: A Study the Influences of Religiosity. ASEAN Journal of Economics, Management and Accounting, 1(1), pp. 118–129. Google Scholar
Parten, M. (1950). Surveys, Polls and Samples. Harper and Brothers, NY. Google Scholar
Popescu, C.R.G. and J.D. Šebestová (2022). Fiscally Responsible Businesses as a Result of COVID-19 Pandemic Shock: Taking Control of Countries’ Tax Systems by Putting an End to Corporate Tax Evasion and Tax Havens. In Handbook of Research on Changing Dynamics in Responsible and Sustainable Business in the Post-COVID-19 Era, IGI Global, pp. 161–192. Crossref, Google Scholar
Raab, D., F. Heitzer, J.C. Liaw, K. Müller, L. Weber, F.G. Flores , … and M. Jäger (2023). Do we still need to screen our patients?—Orthopaedic scoring based on motion tracking. International Orthopaedics, pp. 1–8. Google Scholar
Radmacher, S.A. and D.J. Martin (2001). Identifying Significant Predictors of Student Evaluations of Faculty through Hierarchical Regression Analysis. The Journal of Psychology, 135(3), pp. 259–268. Crossref, Google Scholar
Raimi, L. (2021). Innovative Government-Backed Policy Measures in Nigeria for Sustaining MSMEs Post Pandemic. In N. Baporikar (Eds.), Handbook of Research on Strategies and Interventions to Mitigate COVID-19 Impact on SMEs. IGI Global, pp. 299–317, https://doi.org/10.4018/978-1-7998-7436-2.ch015. Crossref, Google Scholar
Ramey, V. A. (2018). Fiscal Policy: Tax and Spending Multipliers in the U.S. In O. Blanchard and L. H. Summers (Eds.), Evolution or Revolution: Rethinking Macroeconomic Policy After the Great Recession, Cambridge, MA: MIT Press, pp. 71–162. Google Scholar
Ramona-Anca, N. and B. Larissa-Margareta (2013). The Implications of Tax Morale on Tax Compliance Behaviour. Annals of Faculty of Economics, 1(1), pp. 739–744. Google Scholar
Ranđelović, S. (2017). How to Boost Tax Compliance and Tax Morale in Serbia?. Ekonomika preduzeća, 65(1–2), pp. 113–127. Crossref, Google Scholar
Sadiq, K. and R. Krever (2021). Does Tax Policy Fit in the Portfolio of COVID-19 Responses?, Pacific Accounting Review, 33(2), pp. 212–220, https://doi.org/10.1108/PAR-08-2020-0119. Crossref, Google Scholar
Schunk, D. and V. Wagner (2021). “ What Determines the Willingness to Sanction Violations of Newly Introduced Social Norms: Personality Traits or Economic Preferences? Evidence from the COVID-19 Crisis. Journal of Behavioral and Experimental Economics, 93, 101716. Crossref, Google Scholar
Sekiraqa, A. , E. Sekiraça, V. Sekiraqa and S. Ahmeti (2021). The impact of COVID–19 on Collecting of Tax Revenues in Kosovo. In SHS Web of Conferences. EDP Sciences, Vol. 114, p. 01031. Google Scholar
Settele, S. and C. Shupe (2021). Lives or Livelihoods? Perceived Tradeoffs and Policy Demand. The Economic Journal, 132(643), pp. 1150–1178. Crossref, Google Scholar
Tabari, P., M. Amini, M. Moghadami and M. Moosavi (2020). International Public Health Responses to COVID-19 Outbreak: A Rapid Review. Iranian Journal of Medical Sciences, 45(3), pp. 157–169. Google Scholar
Thorgren, S. and T.A. Williams (2020). Staying Alive During an Unfolding Crisis: How SMEs Ward Off Impending Disaster. Journal of Business Venturing Insights, 14, p. e00187. Crossref, Google Scholar
Torgler, B. and F. Schneider (2007). What Shapes Attitudes toward Paying Taxes? Evidence from Multicultural European Countries. Social Science Quarterly, 88(2), pp. 443–470. Crossref, Google Scholar
Torgler, B. (2006). The Importance of Faith: Tax Morale and Religiosity. Journal of Economic Behavior & Organization, 61(1), pp. 81–109. Crossref, Google Scholar
Torgler, B. and F. Schneider (2004). Does Culture Influence Tax Morale? Evidence from Different European Countries (No. 2004-17). CREMA Working Paper. Google Scholar
Torgler, B. and F. Schneider (2009). The Impact of Tax Morale and Institutional Quality on the Shadow Economy. Journal of Economic Psychology, 30(2), pp. 228–245. Crossref, Google Scholar
Ungureanu, M.A. and B.C. Ciocanea (2021). Tax Burden, as Determining Factor of Tax Morale in Romania and European Union. In Organizations and Performance in a Complex World. 26th International Economic Conference of Sibiu (IECS), Vol. 26, Springer International Publishing, pp. 359–371. Google Scholar
Wijoyo, H., M.F. Akbar, A.R. Safii, D. Prasada, A. Yusuf, A. Sudarsono and W. Widiyant (2021). The effect of tax stimulus during the COVID-19 pandemic in improving the performance of taxpayers. Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3873695. Google Scholar
Williams, C.C. and B. Krasniqi (2017). Evaluating the Individual- and Country-Level Variations in Tax Morale: Evidence from 35 Eurasian Countries. Journal of Economic Studies, 44(5), pp. 816–832, https://doi.org/10.1108/JES-09-2016-0182. Crossref, Google Scholar
Women Entrepreneurs Finance Initiative (We-Fi) (2020). Impact of COVID-19 on SMEs. Available at https://we-fi.org/covid-19-response-measures-supporting-smes/. Google Scholar
World Bank (2020a). Global Economic Prospects, World Bank Group, June 8, 2020, p. 15. Google Scholar
World Bank (2020b). “World Bank Group supports Ghana’s COVID-19 response”, Press Release. April 2, 2020. Available at https://www.worldbank.org/en/news/press-release/2020/04/02/world-bank-group-supports-ghanas-covid-19-response (accessed 31 May 2020). Google Scholar
World Health Organization (2020). “African Countries Move from COVID-19 Readiness to Response as many Confirm Cases.” Available at https://www.afro.who.int/health-topics/coronavirus-covid-19 (accessed 27 May 2020). Google Scholar
Yeo, E.G. and S.A. Kim (2020). Emergency Support Measures in Response to Covid-19. Research in Brief, 54, pp. 1–8. Google Scholar
Zulkarnaen, W., E. Erfiansyah, N.N.A. Syahril and D.G. Leonandri (2020). Comparative Study of Tax Policy Related to COVID-19 in ASEAN Countries. International Journal of TEST Engineering & Management, 83(2), pp. 6519–6528. Google Scholar

Vol. 06, No. 02

Metrics

Downloaded 189 times

History

Received 29 September 2022

Revised 31 May 2023

Accepted 31 May 2023

Published: 8 July 2023

Information

This is an open access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords

PDF download