economies

Article
Tax Complexity and Firm Tax Evasion: A Cross-Country Investigation
Prianto Budi Saptono 1, * , Gustofan Mahmud 2,3 , Fauzilah Salleh 4 , Intan Pratiwi 3 , Dwi Purwanto 3
and Ismail Khozen 3

1 Department of Fiscal Administration, Universitas Indonesia, Depok 16424, Indonesia

2 Accounting Department, Swadaya Institute of Communication and Business, Jakarta 13620, Indonesia;
gustofanmahmud@stieswadaya.ac.id
3 Tax Research Department, Pratama Institute for Fiscal Policy and Governance Studies, Jakarta 12530, Indonesia;
intan@pratamaindomitra.co.id (I.P.); dwipurwanto@pratamaindomitra.co.id (D.P.);
khozen@pratamaindomitra.co.id (I.K.)
4 Faculty of Business and Management, Universiti Sultan Zainal Abidin, Kuala Terengganu 21300, Malaysia;
fauzilah@unisza.edu.my
* Correspondence: prianto.saptono@ui.ac.id

Abstract: This paper endeavours to investigate whether a complex tax system influences firms’ propen-
sity toward tax evasion across countries. To achieve the objectives of this study, we utilised the
World Bank Enterprise Survey and the World Bank’s Doing Business databases covering more than
46,000 companies from 83 countries. Our study revealed that the increased time required to pay taxes
and higher total tax payments were associated with a greater extent and incidence of tax evasion
among firms. The consistency of these benchmark regression results was proven through endogeneity
analysis and several robustness tests. Furthermore, our heterogeneity analyses showed that the
effect of tax complexity on firm tax evasion was more prominent in low- and lower-middle-income
countries and also in the primary industry. These findings offer promising evidence for policymakers,
particularly in low- and lower-middle-income countries where the majority of companies operate in
the primary industry. Addressing the complexity of the tax system could potentially mitigate the
adverse impact on tax evasion levels in these countries. Furthermore, our spatial analysis provides
valuable insights, emphasising the potential impact of tax complexity in neighbouring countries and
Citation: Saptono, Prianto Budi,
underscoring the necessity for policymakers in the home countries to strategise on harmonising and
Gustofan Mahmud, Fauzilah Salleh,
Intan Pratiwi, Dwi Purwanto, and
streamlining their tax systems.
Ismail Khozen. 2024. Tax Complexity
and Firm Tax Evasion: A Keywords: tax complexity; firm tax evasion; firm-level data; cross-country dimension
Cross-Country Investigation.
Economies 12: 97.
https://doi.org/10.3390/
economies12050097 1. Introduction
Academic Editor: George R. G. Clarke Enterprises have historically constituted a cornerstone of government revenue streams,
yet paradoxically, they have also been implicated in a significant portion of tax evasion cases
Received: 21 March 2024 (Chang and Lai 2004; Crocker and Slemrod 2005; Nur-Tegin 2008). Governments continue
Revised: 16 April 2024
to pay attention to their evasion behaviour, which appears increasingly complex as busi-
Accepted: 19 April 2024
ness affairs and market globalisation become progressively sophisticated (Beck et al. 2014;
Published: 24 April 2024
Hanlon et al. 2015), particularly after the global financial crisis and the Great Recession
(Moshirian 2011; Slemrod 2019).
The pervasiveness of that issue across nations is not to be underestimated, as it
Copyright: © 2024 by the authors.
erodes the government’s capacity to efficiently allocate resources to provide smooth public
Licensee MDPI, Basel, Switzerland. goods that stimulate business development (Andreoni et al. 1998; Johnson et al. 2000).
This article is an open access article Furthermore, as time unfolds, an unsettling divergence emerges between those who engage
distributed under the terms and in tax evasion and those who conscientiously adhere to tax laws, with the latter group
conditions of the Creative Commons shouldering an increasingly disproportionate tax burden. Consequently, this disparity
Attribution (CC BY) license (https:// fosters incentives for firms to engage in further acts of evasion (Feinstein 1991). A recent
creativecommons.org/licenses/by/ empirical study has unveiled substantial surges in revenue losses from corporate tax evasion
4.0/). in developing countries since the 2000s commodities boom (Cobham and Janský 2018).

Economies 2024, 12, 97. https://doi.org/10.3390/economies12050097 https://www.mdpi.com/journal/economies

Economies 2024, 12, 97 2 of 35

Intuitively, there exists a tacit acknowledgement that a firm’s willingness to meet its
tax obligations is intricately entwined with its alignment with the regulatory framework
established by the government. This inherent inclination to fulfil tax responsibilities,
often referred to as ‘tax morale’, tends to diminish when businesses encounter difficulties
navigating the complex landscape of tax laws. Even if willing to pay, some of them will
exploit the potential for diverse interpretations of tax statutes by (usually) hiring assistance
from tax advisors to choose the lowest-tax options (Frecknall-Hughes et al. 2023). Therefore,
streamlining the tax system may be the most effective route to facilitate good governance,
cornerstone a taxpayer-government ‘fiscal contract’, and curb rampant tax evasion practised
by firms.
Academic papers typically provide evidence that tax complexity can lead to tax non-
compliance. However, most of them are related to individual tax behaviour (e.g., Cuccia
and Carnes 2001; Forest and Sheffrin 2002; Kirchler et al. 2006; Slemrod 2007; Mathieu et al.
2010; Blaufus et al. 2019; Taing and Chang 2020; e Hassan et al. 2021). A few studies bring
the evidence to the business level yet focus only on one country. For example, Eichfelder
and Hechtner (2018) and Musimenta (2020) found that the tax system’s complexity leads to
lower business taxes in Belgium and Uganda, respectively. To the best of our knowledge,
no evidence is yet available from firm-level data in a cross-country dimension.
The lack of empirical investigation into corporate tax evasion in a cross-country
dimension is regrettable, particularly in light of recent criticisms raised against cross-border
corporations that fail to fulfil their ‘fair share’ of taxes (Elbra and Mikler 2017; Campbell
and Helleloid 2016). This issue has contributed to the increasing societal dissatisfaction
with income inequality as tax systems are often perceived to favour global companies at the
expense of domestic firms (Otusanya 2011; Tørsløv et al. 2023). Moreover, as underscored
by Tan and Sawyer (2003) and Torgler (2011), such a literature gap marks a permanent
need for comprehensive international inquiries into the realm of tax evasion, particularly
when examined through the lens of corporate entities. Hence, this paper aims to fill the
void by attempting to address the question of whether a complex tax system increases the
propensity of firms across countries to engage in tax evasion.
We explored the question using the World Bank’s Enterprise Survey (WBES) and
the World Bank’s Doing Business (WBDB) databases. This study operationalised firm tax
evasion through two distinct measures: a ratio and a binary dummy variable, both values
of which were obtained from respondents’ responses to the tax compliance-related question
asked in the WBES. Tax complexity was gauged by two alternative variables provided by
the WBDB: tax time and tax payment. We linked these main variables to an array of control
variables (including macroeconomic, tax burden, demographic, and institutional factors),
which resulted in a unique and expansive dataset spanning 83 countries and encompassing
data from over 46,000 firms.
By harnessing the extensive dataset at our disposal, this study makes substantial and
multifaceted contributions to the existing body of literature. First, our research synthesised
two distinct yet interconnected units of analysis within the area of tax evasion, namely, the
tax evasion practises of firms and the prevalence of tax evasion across different nations
worldwide. This dual analytical approach allowed us to paint a comprehensive picture of
tax evasion that spanned from the individual firm to the global stage.
Second, the study stands as a pioneering effort to address a crucial lacuna within
academic discourse. It caters to the growing exigency within the scholarly community for a
comprehensive investigation into the impact of tax-related regulatory factors, such as tax
time and tax payment, on the phenomenon of tax evasion. Antecedent studies by Beck et al.
(2014) and Ahamed (2016) with similar databases have only included these variables within
the vector of control variables. At the same time, it is vital to have a deeper comprehension
of the relationship between the intricacy of tax rules and the occurrence of tax evasion since
this has exhibited its efficacy in mitigating tax evasion tendencies over an extended period.
Third, it considers various sources of endogeneity bias that might potentially under-
mine the validity of our empirical findings, such as omitted variables and simultaneity.
Economies 2024, 12, 97 3 of 35

Researchers often overlook these issues while conducting empirical analyses on the link
between tax complexity and tax evasion. Hence, as a distinction from previous studies, we
applied regression models under the instrumental variable (IV) approaches to help mitigate
concerns stemming from those potential sources of endogeneity bias and demonstrate
the robustness of our empirical findings. We also executed several additional analyses by
exploring the country’s income level and industry differences and considering potential
spatial dependencies to strengthen our empirical results.
The subsequent sections of this work are meticulously structured to provide a coherent
narrative. Section 2 offers a concise yet comprehensive overview of the existing body of
research that orbits the nexus between tax complexity and firm tax evasion. This literature
review serves as the foundational backdrop against which the empirical analysis is framed.
Moving forward, Section 3 meticulously details the data employed in the estimation
process, elucidating the sampling strategies meticulously deployed. Section 4 unveils the
econometric framework designed to yield empirical results. In Section 5, the study engages
in an in-depth analysis and discussion, dissecting the impact of tax complexity on tax
evasion. Last, Section 6 synthesises the findings and presents cogent policy implications,
drawing the curtains on this comprehensive academic endeavour.

2. Literature Review
A tide of academic research on tax evasion has vastly grown, perhaps first triggered by
the emergence of the seminal and pioneering deterrence model of tax evasion introduced
by Allingham and Sandmo (1972). This model drew inspiration from Becker’s (1968)
economics-of-crime model. In this conceptual framework, taxpayers characterised by risk
aversion are confronted with the intricate task of determining the extent to which they
should engage in tax evasion. Algebraically, the model delineates the evasion decision as a
function of tax rates and enforcement intensity, including the extent of auditing and the
probability that evasion will be subject to penalties.
Allingham and Sandmo’s (1972) model has been used in nearly all studies exploring
various policy-related aspects of tax evasion, with a particular emphasis on assessing the
impact of audit frequency and penalty rates on evasion behaviour. The results from these
studies generally confirm that improvements in these enforcement variables deter evasion
decisions, although the magnitude of the effects is uncertain (e.g., Kleven et al. 2011; Alm
2012; Choo et al. 2016; Carrillo et al. 2017; DeBacker et al. 2018; Li et al. 2019; Alm et al.
2020; Kogler et al. 2022; Bergolo et al. 2023).
As Slemrod (2019) noted, the success of the primary enforcement mechanisms pro-
posed in the deterrence model of tax evasion (i.e., audit and penalty) may be closely related
to the simplified tax regime. Unintelligible and ambiguous tax regulations generate arbi-
trary enforcement that violates the constitutional principles of the regulations themselves
(Demin 2020; Calford and DeAngelo 2023). Some non-compliance may go undetected or
unreported, while some reports deemed to be compliant may be disallowed and subject
to penalties (Andreoni et al. 1998). This argument is somewhat supported by an actual
experiment, as mentioned in Tanzi (2018), wherein inquiries seeking clarification or guid-
ance on the handling of specific tax matters directed to distinct tax offices within the same
tax administration elicited markedly disparate responses. This problem of haphazard
enforcement again means that tax authorities can never escape the risk of potential revenue
losses (Neck et al. 2012; Ivanyna et al. 2016).
In addition, as laid out in Kirchler et al.’s (2008) ‘slippery slope’ framework, tax
evasion decisions are much more complicated than simply the taxpayers’ calculation of
the specific costs to be borne from it but also their trust in the authorities’ competence in
managing the problems. In this case, taxpayers frequently exhibit a proclivity to assume
the legitimacy of the existing tax system, often without contemplation of the prospect of
evading taxes (Dickson et al. 2022; Berkel et al. 2022). However, the complexity of the tax
regime is believed to undermine trust among taxpayers in the competence and integrity of
tax authorities (Kirchler et al. 2008).
Economies 2024, 12, 97 4 of 35

As mentioned earlier, in dealing with tax regulations, taxpayers often seek help from
tax advisors (Frecknall-Hughes et al. 2023). These tax advisors, possessing specialised
knowledge and expertise in tax law, play a dual role as both ‘exploiters’ and ‘enforcers’ of
tax regulations (Murphy 2004). Klepper et al. (1991) and Hasseldine et al. (2011) suggest
that in situations where taxes are less complex, tax advisors are more inclined to ensure
compliance by surmounting the numerous informational and computational barriers that
typically impede tax compliance.
Conversely, when tax practitioners contend with a highly complex tax system, where
tax code ambiguities are prevalent, they often guide their clients in exploiting opportuni-
ties for tax non-compliance. Taxpayers usually receive this offer mainly from aggressive
tax advisors who have a good network and know what problems are being targeted by
the tax authorities at that time (Sakurai and Braithwaite 2001). Aggressive tax advisors
generally collaborate with tax officials to make their jobs easier. Tax officers who ‘facili-
tate’ such a taxation process seek private gain by imposing ‘fees’, which thus raises tax
corruption (Awasthi and Bayraktar 2015). Increased opportunities for non-compliance
provided by aggressive tax advisors and acts of corruption supplied by tax officials can
have important consequences for perceived tax unfairness, which would undermine trust
and consequently degrade compliance behaviour (Baldry 1987; Cowell 1992; Joulfaian 2009;
Alon and Hageman 2013).
The theoretical predictions above provide confidence that a complex tax system in-
creases tax evasion in two important channels: weakening enforcement and ruining public
trust (Krause 2000). As in the ‘slippery slope framework’, the former channel will re-
duce enforced tax compliance, and the latter one will decrease voluntary tax compliance.
Recent empirical studies largely corroborate these theoretical predictions. For example,
Bellemare et al. (2019) present evidence from a laboratory experiment in Germany that
in complex tax arrangements, individuals often assign unintentional filing errors as an
excuse for non-compliance. Furthermore, a survey experiment conducted by Blesse (2023)
in Germany supports these findings by concluding that unintentional individual tax non-
compliance due to complex tax regulations gains support from society. These findings
are also in line with the omission bias theory, where individuals perceive errors due to
negligence as being judged less harshly than errors in carrying out actions in situations
that have equivalent outcomes (Baron and Ritov 2004). Other evidence comes from Kaulu
(2022), who uses data on individual taxpayers in Zambia to conclude that individual egoism
strengthens the relationship between tax complexity and tax evasion.
In this study, we examine the effect of tax complexity on tax evasion activities by firms.
Although the theoretical framework summarised above applies naturally to tax decisions
made by individuals (Slemrod 2019), we assume that the behaviour of businesses is similar
to that of individuals. This assumption is in line with what was pointed out by Slemrod
(2007, p. 36): the corporate tax evasion literature “adapts the theory of tax evasion, which
for the most part concerns individual decision makers, to the tax compliance decisions
made by business”. Therefore, the causal mechanisms underlying the relationship between
tax complexity and tax evasion discussed earlier are likely also applicable at the firm level.
The simple logic is that decisions regarding evasion, or compliance, are made by individual
managers or entrepreneurs who are essentially acting as individuals (Arias 2005). It is
especially true in this study where business owners who make compliance decisions both as
individuals and as top-level managers were the primary respondents in the WBES survey.
Meanwhile, their answers to questions related to tax compliance asked in the WBES were
the basis for measuring corporate tax evasion in this study.
As such, it is reasonable to expect that tax complexity also increases the prevalence of
tax evasion at the firm level. Moreover, recent empirical studies have proven this hypothesis.
For example, Owusu et al. (2023) draw on data from micro and small businesses in Ghana to
find that businesses’ perceptions of tax complexity are negatively related to tax compliance
intentions. Last, Hoppe et al. (2023) recently introduced an index of tax complexity faced
by multinational companies in a country and found that a complex tax system reduces
Economies 2024, 12, 97 5 of 35

the tendency of companies to comply with tax rules because administering them is costly
and protracted.

3. Data and Sample

3.1. Measuring Tax Evasion
The concept of tax evasion lacks a universally accepted definition (Christians 2014).
Nonetheless, tax evasion can be broadly characterised as illicit activities undertaken by tax-
payers to deviate from legal norms that stipulate that taxes should be paid (Elffers et al. 1987).
To evade taxes, taxpayers usually underreport their received income, inflating deductions,
exemptions, or credits, neglecting to file required tax returns, or even participating in barter
transactions (Alm 2012). While these actions are commonly associated with individuals,
these types of actions can be applied to other levels of taxpayers, such as firms (Rice 1992). It
is important to note that this paper exclusively examines instances of tax evasion observed
during tax inspections conducted on firms.
To measure firm tax evasion, a few studies have analysed several applicable ap-
proaches (e.g., Elffers et al. 1987; Alm 2012; Korndörfer et al. 2014). Broadly speaking, Alm
(2012) classified these approaches into traditional and modern. Traditional approaches
can be based on audit samples of firm income tax returns (e.g., Joulfaian 2000) or survey
evidence, where firm owners are directly questioned about their evasion behaviour (e.g.,
Barth and Ognedal 2018; Onu et al. 2019). Traditional approaches can also be based on
measures of informal business activity (shadow economy), which reflect the gap between
the estimated economic activity level and official national accounts (e.g., Schneider et al.
2010; Elgin and Oztunali 2014).
Modern approaches encompass a diverse range of innovative techniques, including
controlled field experiments (e.g., Slemrod et al. 2001; Iyer et al. 2010; Kleven et al. 2011),
consumption-based measures (e.g., Pissarides and Weber 1989), or even the utilisation
of luminosity data derived from satellite observations to estimate real economic activity,
which is then compared with official economic levels to obtain a proxy for tax evasion (e.g.,
Henderson et al. 2016). Interestingly, Alm (2012) emphasised that there is no universally su-
perior method for quantifying tax evasion, as each approach possesses inherent limitations
and constraints on its validity.
Amid many measurements, the question arises, which method is most appropriate,
especially the one allowing the current study to compare firm tax evasion internationally?
The only viable option is to employ the confidential firm-level survey responses database
provided by the WBES. The survey results from 2002 to 2023 were used as our specific
reference. Overall, the database contains more than 252,000 firms from 164 countries around
the globe. Every country has a varying number of firms, ranging from 65 to 24,000, that
serve as representatives throughout 27 different industries. None of them are fully owned
by local and national governments.
The WBES team (surveyor) anonymously surveys these firms’ business owners and
top-level managers (respondents) to obtain information on a country’s business environ-
ment, how individual firms perceive and experience it, how it evolves, and the constraints
affecting performance and growth. To engender mutual trust and understanding, respon-
dents in the survey were assured of complete and stringent confidentiality for both them
and their respective firms (Beck et al. 2014). Importantly, the survey process is conducted
entirely independently of government officials and financial institutions, with no access
to raw data or any information that could potentially reveal the identity of individual
respondents’ responses (Williams 2016). This commitment to anonymity and data security
is crucial in eliciting honest and unbiased responses from the respondents.
The surveyor employed a uniform sampling method (i.e., randomly stratified sam-
pling) and standardised survey instruments to minimise measurement errors and produce
data that can be compared between countries. Beck et al. (2014) and Williams (2016)
stated that respondents’ responses to the survey are accurate and reliable. Several em-
pirical studies using the survey database corroborate the statements by showing that the
Economies 2024, 12, 97 6 of 35

respondents’ responses are directly related to measurable results in tax evasion, corruption,
infrastructure, crime, competition, employment, obstacles to growth, and performance
(e.g., Johnson et al. 2000; Djankov et al. 2003; Acemoglu and Johnson 2005; Beck et al.
2005; Ayyagari et al. 2008; Barth et al. 2009). Hence, it is natural that such a database in
cross-country analysis has become increasingly popular in recent years compared with
the use of aggregate country-level data (e.g., Beck et al. 2014; Awasthi and Bayraktar 2015;
Ahamed 2016; Abdixhiku et al. 2018; Mason et al. 2020; Xiao et al. 2022; Khan 2023).
In this study, the tax evasion variable was constructed using respondents’ responses to
the following survey question: “Recognising the difficulties many enterprises face in fully
complying with taxes and regulations, what percentage of total sales would you estimate
the typical establishment in your area of activity reports for tax purposes?” In the WBES,
this question identification code is given as c241.
Based on those responses, we can construct two variables related to tax evasion at
the firm level in the cross-country dimension: tax evasion ratio and tax evasion dummy.
The tax evasion ratio is calculated as one minus the numerical answer to the c241 question.
Hence, the tax evasion ratio equals zero if the respondents answer that 100% of sales are
reported for tax purposes. The tax evasion dummy is assigned as one if the tax evasion
ratio exceeds zero. The former variable measures the extent of tax evasion, while the latter
measures the incidence of tax evasion.
Following well-established survey techniques, the c241 question was phrased indi-
rectly to elicit more honest responses. A direct question can be considered very sensitive
because, after all, tax evasion is an illegal act, and respondents are expected to conceal their
fraud by giving responses that are in accordance with public norms (Tourangeau and Yan
2007; Krumpal 2013).
One might argue that the indirect nature of the c241 question will produce measure-
ment error as responses may reflect industry average perceptions and not firm behaviour.
However, for several reasons, we believe this issue will not bias our results. First, the large
variations found in tax evasion responses across industries within each country indicated
that respondents’ responses to the c241 question were based on their firms’ behaviour
instead of the industry’s behaviour in general. Second, as mentioned previously, govern-
ment officials and financial institutions were not involved in the survey—respondents
were therefore highly likely to simply respond based on their experiences without con-
sidering their potential relationships with government officials and financial institutions
(Johnson et al. 2000). Third, we found a significant positive correlation (p-value < 1%) be-
tween the shadow economy measures compiled by Schneider et al. (2010) and Elgin and
Oztunali (2014) and the tax evasion ratio. These results at least minimised our concerns
about the validity issue as Elffers et al. (1987) reported—there was no evidence of corre-
spondence between self-reports from the survey and actual behaviour from official records
in tax evasion data.

3.2. Measuring Tax Complexity

Diverse perspectives exist regarding the definition of tax complexity (Tran-Nam and
Evans 2014). For instance, according to tax accountants, tax complexity pertains to the time
required for preparing income tax reports, encompassing tax planning and the provision of
tax advice and consultation. Tax attorneys perceive the complexity of the tax system as the
difficulties of comprehending, interpreting, and translating tax laws to ensure adherence to
them. Taxpayers evaluate the intricacy of taxes by considering the time required and costs
incurred to comply with the relevant tax statutes. In addition, it is worth noting that tax
complexity manifests in various dimensions, such as computational complexity, procedural
complexity, tax return complexity, and a lack of clarity in the regulations (Pau et al. 2007;
Saw and Sawyer 2010; Borrego et al. 2015). The bottom line is that the intricate nature of
tax regulations poses challenges for anyone in meeting their tax responsibilities.
Given numerous definitions and forms of tax complexity, how can we obtain cross-
country comparison measures? The WBDB report is the only source worth considering
Economies 2024, 12, 97 7 of 35

in obtaining tax complexity measures that would allow international comparison. The
WBDB report measured the ease of doing business for domestic, small, and medium firms
in 190 economies. The report covered ten crucial topics: starting a business, dealing with
construction permits, obtaining electricity, registering property, accessing credit, protecting
minority investors, paying taxes, facilitating international trade, enforcing contracts, and
resolving insolvency. The paying taxes topic specifically provides two indicators that can be
used as proxies for tax complexity: time to comply (tax time, hereafter) and the number of
payments (tax payment, hereafter). The underlying assumption is that as the time required
to adhere to the tax system increases and the number of payments grows, companies face
more challenges in meeting their tax responsibilities.
Specifically, the tax time is an indicator that quantifies the number of hours a business
must dedicate each year to prepare, file, and fulfil its tax obligations, encompassing three
primary types of taxes: corporate income tax, value-added tax (VAT) or sales tax, and labour
tax. The preparation time encompasses the duration required to gather the necessary
information and data for the purpose of calculating the tax obligation and completing
the declaration forms. The tax regime’s intricate laws, which demand the disclosure of
information that may not be readily accessible to a corporation during its regular operations
or in its typical financial accounting, result in increased time to comply. Additionally, the
duration required to fill out declaration documents and the duration needed to process
payments are both accounted for. If the declaration forms are intricate, lengthy, and
monotonous, this will lead to an increased duration for compliance. If the payment
processes are not easy and efficient, the time required to comply with them will grow.
All these increase compliance costs for taxpayers. They provide incentives for businesses
to pursue non-compliance options, which highlight the link between tax complexity and
tax evasion.
Similarly, tax payment serves as a good indicator of the intricate tax payment processes.
It reflects the total amount of taxes paid, the method of payment, payment frequency, and
filing frequency within a specific annual period. This metric also includes taxes withheld
by the firm, such as sales tax, VAT, and labour taxes paid by employees. In inefficient tax
administration, taxpayers may encounter burdensome payment processes, have restricted
choices for payment locations, and be required to endure lengthy queues while submitting
their tax payments. The WBDB methodology encompasses all of these aspects, and it
takes into account the advantages of electronic filing and payments. In the methodology,
electronic filing and payment systems receive substantial weight, especially when they are
both allowed and commonly utilised by firms. In such cases, taxes are calculated and paid
once a year, even if the process of filing and payments occurs more frequently throughout
the year. Therefore, electronic taxation systems are given enormous weight, which in turn
is expected to help reduce opportunities for evasion. Based on these considerations, we see
tax payment as a useful tax complexity indicator for this paper.
The collection of data related to tax time and tax payment involved a meticulous sur-
vey. The questionnaire used during the survey was based on a fictitious and standardised
firm ostensibly operating in each sample economy, called ‘TaxpayerCo’. TaxpayerCo’s
illustrative profit and loss statements were designed based on straightforward multi-
ples of the respective country’s income per capita, denominated in the local currency
(Djankov et al. 2010). These illustrative financial statements were submitted to accountants
and tax lawyers from some local firms in each sample economy (mainly including Pricewa-
terhouseCoopers) who computed taxes and statutory contributions for TaxpayerCo in their
respective countries and responded to several survey questions.
According to the conceptual definition and data collection methodology, it seems
that for this study, tax time and tax payment were the most appropriate measures of tax
complexity. These two indicators are also used to measure complexity in several cross-
country studies. For example, Lawless (2013) examined the relationship between tax
complexity and foreign direct investment, Liu and Feng (2015) investigated the correlation
Economies 2024, 12, 97 8 of 35

between tax complexity and corruption, and Awasthi and Bayraktar (2015) quantified the
impact of tax system simplification on tax corruption.
In addition, an external panel constituted by the president of the World Bank reviewed
the WBDB report and recognised that the report is a unique source of comparable global
data; is relevant to researchers, businesses, and policymakers; and has the potential to have
great value for informing decision-making by governments and firms (Alfaro et al. 2021).
Therefore, the report we used to measure tax complexity is considered a ‘flagship’ product
of the World Bank (Basu 2018).
Nevertheless, the panel levelled several criticisms regarding the methodology and
presentation of data collected for all ten topics covered in the WBDB report. Hence, to
ensure the continuity of the report, the panel recommended a major overhaul of the project,
including significant modifications to the methodology behind the data. Unfortunately,
the report publication has been discontinued since September 2021, especially after data
irregularities were discovered in the 2018 and 2020 WBDB reports (Machen et al. 2021).
Therefore, from now on, the World Bank will seek to implement new approaches to provide
similar information. However, the point is that the data listed in the report compose the
only dataset available that can provide an objective worldwide comparison of indicators of
tax regime complexity.

3.3. Control Variables

Our set of control variables began with macroeconomic factors that influenced a
firm’s operations but were beyond the firm’s ability to control. Drawing from relevant
empirical studies (e.g., Yamen et al. 2018; Abdixhiku et al. 2018; Islam et al. 2020), four
proxies capturing national economic performance were used in the study, including real per
capita gross domestic product (GDP), inflation rate, unemployment rate, and agricultural
contribution as a percentage of GDP. All of them were from the World Bank’s World
Development Indicators (WDI) database.
Real per capita GDP is a widely recognised metric for assessing a country’s economic
development level (Boyd and Jalal 2012). Existing empirical studies offer diverse insights
into the relationship between this macroeconomic factor and firm tax evasion. Some authors
concluded that higher levels of economic development correlate with a reduced incidence
and extent of tax evasion (Beck et al. 2014), aligning with the notion that more prosperous
economies may exhibit lower tax evasion rates. Conversely, other studies propose that
as overall prosperity increases, businesses may engage in higher levels of tax evasion
(Abdixhiku et al. 2018; Mason et al. 2020). While both perspectives are represented in the
literature, expecting a negative influence of real per capita GDP on firm tax evasion would
be more rational.
The inflation rate is a common indicator that reflects the country’s price stability
(Kuncoro 2023). Some authors, such as Islam et al. (2020), consider the inflation rate a
proxy for a country’s monetary freedom. This paper quantifies the inflation rate as the
nominal and real GDP ratio (GDP implicit deflator). The impact of inflation on tax evasion
is a subject that elicits divergent perspectives, which may be broadly categorised into
two contrasting categories. According to Fishburn (1981) and several other viewpoints,
a positive correlation exists between inflation and tax evasion since taxpayers may be
motivated to evade taxes to regain lost purchasing power. In contrast, Tanzi (1980) posits
that taxpayers choose to defer their tax payments during elevated inflation, resulting in a
net inverse association between inflation and tax evasion.
A higher unemployment rate is typically associated with a decline in national income,
which increases the tax evasion level (Boame 2009; Torgler and Schneider 2009). Agriculture
is frequently considered a ‘hard to tax’ sector (Mawejje 2019; Jensen 2022; Khujamkulov
and Abizadeh 2023), which is sometimes due to its subsistence nature (Liu 2022). Therefore,
it is rational to say that the tax evasion level rises as the dominant share of agriculture in
GDP expands.
Economies 2024, 12, 97 9 of 35

We also included a conventional determinant of tax evasion called a tax burden.

Following Bittencourt et al. (2014) and Abdixhiku et al. (2018), we proxied the tax burden
using the fiscal freedom index from the Heritage Foundation, which combines several key
indicators: the top tax rates on individual and firm income and the overall tax revenue
as a percentage of GDP. The index is presented as a percentage, with a higher percentage
indicating a heavier tax burden and vice versa.
When analysing the impact of the tax burden on tax evasion, we expect an ambiguous
effect considering the benefit and opportunity cost of tax evasion whenever the tax burden
rises (Allingham and Sandmo 1972). On one hand, as taxes rise, the potential benefits of tax
evasion increase, incentivising taxpayers to engage in evasion to maximise their after-tax
income. On the other hand, a higher tax burden may result in better provision of public
goods and services by the government, lowering opportunity costs for not engaging in
evasion, as the benefits of tax-financed public goods become progressively substantial. In
line with the theory, empirical evidence regarding the impact of the tax burden on tax
evasion is quite controversial, considering that two contradictory results were found even
though the same data source (i.e., the United States Tax Compliance Measurement Program)
was used in their analysis. While Clotfelter (1983) reported a positive and significant effect
of marginal tax burden on evasion, Feinstein (1991) found a negative relationship between
marginal tax burden and evasion. Recent studies, such as Bittencourt et al. (2014) and
Abdixhiku et al. (2018), who treated tax burden as a control variable when studying
cross-border tax evasion, also concluded contradictory results.
To better understand the subject under study (i.e., firm tax evasion), Cuccia (1994)
and Cummings et al. (2005) strongly recommend a combination of economic and non-
economic tax evasion perspectives. We respected that point of view and, therefore, involved
non-economic factors in an array of our control variables, such as demographic and institu-
tional factors.
Following Richardson (2006), we used age and gender to capture demographic differ-
ences between countries. Age is measured by the percentage aged 15–24 years who provide
labour for the firms’ production activities, and gender is calculated as the percentage of
female labour. These two variables are from the World Bank’s Population Estimates and
Projections and Gender Statistics database. We expect that age has a positive effect on tax
evasion since multiple studies have shown that older taxpayers tend to exhibit higher com-
pliance levels than their younger counterparts (Ritsema et al. 2003; Torgler and Valev 2006;
Annan et al. 2014; Hokamp 2014). One possible explanation for this phenomenon might
be attributed to the fact that younger taxpayers tend to exhibit a greater propensity for
risk-taking behaviour and less sensitivity to punitive measures and manifest distinct social
and psychological characteristics that are associated with the era in which they were reared.
Previous studies identified that gender plays a crucial role in determining tax evasion. For
instance, Barber and Odean (2001), Croson and Gneezy (2009), and Hofmann et al. (2017)
have shown that female taxpayers exhibit greater compliance than their male counterparts.
Therefore, we suspect that gender negatively impacts tax evasion.
Following Yamen et al. (2018) and Saptono and Mahmud (2022), we defined the institu-
tional quality of a country using six indicators developed by Kaufmann et al. (2011): voice
and accountability, political stability, government effectiveness, regulatory quality, rule of
law, and control of corruption. All of them are from the World Bank’s Worldwide Gover-
nance Indicators (WGI) database. These indicators provide a comprehensive determinant of
tax evasion, considering the existence of a study that supports the notion that institutional
quality is closely linked to the strength of the enforcement regimes (Treisman 2007).
In addition, Besley and Persson (2014) and Moore and Prichard (2020) suggested
that these institutional factors play a crucial role in determining tax performance. For
example, one said that a flawed institutional environment is considered an efficiency-
enhancing force in tax administration by motivating tax authorities to work harder and
lowering the tax evasion level. Nevertheless, Fjeldstad and Tungodden (2003) showed
that such a proposition does not justify policies to downgrade the institutional quality.
Economies 2024, 12, 97 10 of 35

Anywhere, a strong institutional environment will always be associated with a rise in tax
compliance. Well-established evidence from a series of cross-country studies sheds light
on this matter (e.g., Imam and Jacobs 2014; Epaphra and Massawe 2017; Arif and Rawat
2018; Allam et al. 2023). Accordingly, we expect that the higher the level of institutional
quality, the lower the level of tax evasion. Table A1 provides comprehensive definitions
and sources of all variables.

3.4. Sample Selection

Our sample started with respondents’ responses to the c241 question in the WBES.
Respondents who did not respond were considered missing information and, therefore,
should be excluded from the sample. The remaining sample was further selected by
considering Elffers et al.’s (1987) conclusion—tax evasion measures obtained from a survey
tend to be sensitive to sample selection bias due to the subjectivity of the respondents’
responses. Therefore, our tax evasion ratio should be compared with another country-level
tax evasion database, such as the shadow economy. In this case, we revealed a significant
correlation between these two distinct tax evasion measures (see a discussion in Section 3.1).
To further mitigate the potential risk of sample selection bias, a comparative analysis
was conducted between two rankings of countries. The first ranking was based on the
tax evasion ratio derived from the WBES database, while the second ranking was derived
from an average of informal business activity databases developed by Schneider et al.
(2010) and Elgin and Oztunali (2014). Given that the suite of the WBES database has
subjective components, it is advantageous to conduct a comparison of country rankings
using two variables related to tax evasion to identify countries with ‘unexpected data’. In
line with the preceding definition, the tax evasion ratio ranges from 0 to 100, with larger
values indicating a greater level of tax evasion. Informal business activity, as estimated
using the multiple indicators multiple causes (MIMIC) and dynamic general equilibrium
(DGE) techniques, is expressed as a percentage of the GDP, where lower values indicate
greater levels of tax evasion. Following Awasthi and Bayraktar (2015), the study applied a
criterion for excluding countries from the sample. Specifically, countries were excluded if
the absolute difference between their rankings in the two variables exceeded a predefined
threshold of 70.
In addition, it is worth noting that the structure of the WBES procedure involved
deliberate targeting of firms in most countries within our sample for re-survey during
subsequent rounds of data collection. Therefore, we only used one survey sample period
for each country to avoid the possibility of overlapping information. We related the selected
sample to our tax complexity measures and control variables. We included observations
that contained complete information for all variables and excluded any observations
with missing data. This meticulous data-cleaning process resulted in a final sample of
46,046 firms drawn from a diverse set of 83 countries. Table 1 summarises our sample
selection procedure.

Table 1. Sample selection procedure.

Number of Firms Number of Countries

Confidential WBES database for the 2002 to 2023 survey period 252,834 164
Missing data on tax evasion ratio (180,019) (58)
Countries with inconsistent rankings for two measures of tax
evasion (the absolute value of the discrepancy between the two (458) (3)
ranks exceeds 70)
Only include one sample survey period of each country (12,941)
Missing data on tax complexity (9684) (11)
Missing data on control variables (3686) (9)
The final number of observations 46,046 83
Economies 2024, 12, 97 11 of 35

Table 2 provides a comprehensive overview of the mean values for the tax evasion ratio
and two tax complexity indicators across each country included in our sample. Notably,
there was substantial variation in the tax evasion ratio, showcasing that different countries
exhibited varying levels of tax evasion behaviour. Estonia boasted a lower tax evasion
ratio at a mere 3.1%, while Senegal had the highest at 79.8%. Regarding the tax complexity
measures, Ukraine stood out with the highest average number of tax payments required
annually, a staggering 147 times. Estonia, Kazakhstan, and Portugal had the same minimum
tax payment threshold, equivalent to seven times. Ukraine exhibited the most average
annual tax time, amounting to 2085 h, while Benin had the lowest tax time, totalling 52 h.
These findings underscored the fact that tax evasion and perceptions of tax complexity
varied significantly from one country to another.

Table 2. Sample description: tax evasion and tax complexity by country.

Country Number of Firms Tax Evasion Ratio Tax Time Tax Payment
1 Albania 198 0.230 364 45
2 Algeria 188 0.276 451 39
3 Angola 118 0.794 284 31
4 Argentina 1894 0.175 453 62
5 Azerbaijan 349 0.139 756 37
6 Belarus 320 0.072 986.5 125
7 Benin 176 0.144 270 52
8 Bolivia 1104 0.202 1080 42
9 Bosnia and Herzegovina 195 0.118 368 55
10 Botswana 209 0.131 140 19
11 Bulgaria 290 0.135 598 29
12 Burkina Faso 131 0.219 270 45
13 Burundi 540 0.157 140 33
14 Cambodia 423 0.520 137 39
15 Cameroon 168 0.121 654 45
16 Chile 1862 0.132 316 8
17 Colombia 1840 0.171 456 70
18 Costa Rica 287 0.284 402 43
19 Croatia 211 0.075 232 40
20 Czech Republic 333 0.131 866 27
21 Dominican Republic 139 0.439 232 75
22 Ecuador 1212 0.264 600 8
23 El Salvador 1108 0.192 320 41
24 Estonia 170 0.031 81 7
25 Eswatini 584 0.579 116 33
26 Georgia 164 0.109 448 46
27 Germany 1192 0.057 196 12
28 Ghana 292 0.313 304 37
29 Greece 502 0.110 264 19
30 Guatemala 986 0.270 344 38
31 Guinea 426 0.643 416 57
32 Guinea-Bissau 41 0.280 218 46
33 Guyana 156 0.262 288 35
34 Honduras 726 0.157 424 58
35 Hungary 592 0.113 340 13
36 India 3869 0.269 252.88 60
37 Indonesia 713 0.269 266 51
38 Ireland 490 0.038 75 9
39 Jamaica 72 0.110 324 72
40 Jordan 415 0.126 136 26
41 Kazakhstan 568 0.066 261 7
Economies 2024, 12, 97 12 of 35

Table 2. Cont.

Country Number of Firms Tax Evasion Ratio Tax Time Tax Payment
42 Kenya 395 0.183 444 52
43 Kyrgyz Republic 197 0.146 222 76
44 Lao PDR 242 0.038 672 34
45 Latvia 194 0.071 280 29
46 Lebanon 292 0.344 180 20
47 Lesotho 48 0.171 564 33
48 Lithuania 172 0.103 166 11
49 Madagascar 286 0.065 400 27
50 Malawi 132 0.303 376 23
51 Mali 97 0.766 270 58
52 Mauritania 456 0.470 696 37
53 Mauritius 156 0.122 161 8
54 Moldova 321 0.105 234 53
55 Mongolia 160 0.364 204 41
56 Morocco 834 0.039 358 28
57 Namibia 644 0.254 339 37
58 Nicaragua 834 0.409 240 66
59 Niger 111 0.127 270 41
60 North Macedonia 182 0.235 192 43
61 Oman 284 0.287 52 15
62 Panama 1096 0.371 560 52
63 Paraguay 926 0.192 328 35
64 Peru 1242 0.106 424 9
65 Philippines 598 0.218 195 48
66 Poland 969 0.100 420 41
67 Portugal 502 0.082 328 7
68 Romania 577 0.066 192 108
69 Rwanda 420 0.189 168 25
70 Senegal 190 0.798 696 59
71 Slovak Republic 191 0.045 325 32
72 Slovenia 205 0.072 248 22
73 South Africa 562 0.091 346 12
74 Spain 600 0.037 298 8
75 Sri Lanka 355 0.076 262 58
76 Tajikistan 200 0.090 296 69
77 Tanzania 834 0.469 172 47
78 Turkey 1120 0.495 254 10
79 Uganda 1094 0.464 237 32
80 Ukraine 573 0.107 2085 147
81 Uruguay 736 0.147 304 55
82 Vietnam 1609 0.094 1050 32
83 Zambia 157 0.158 183 38

Table 3 offers valuable insights into the average tax evasion ratio categorised by
industry. It is evident from the data that there were substantial variations in tax evasion
levels among different industries. The industry with the highest tax evasion ratio was
the ‘other transport equipment’ sector, where tax evasion stood at a significant 53.8%. In
contrast, the telecommunications industry exhibited the lowest tax evasion rate at just
5.6%. While these variations were noteworthy, they were generally smaller in magnitude
compared with the variations observed between different countries, as previously presented
in Table 2.
Economies 2024, 12, 97 13 of 35

Table 3. Sample description: tax evasion and tax complexity by industry.

Industry Number of Firms Tax Evasion Ratio

1 Accounting and finance 3 0.510
2 Advertising and marketing 501 0.086
3 Agroindustry 346 0.375
4 Auto and auto components 248 0.207
5 Beverages 830 0.121
6 Chemicals and pharmaceutics 2558 0.167
7 Construction 2685 0.210
8 Electronics 482 0.135
9 Food 5312 0.225
10 Garments 4369 0.227
11 Hotels and restaurants 1595 0.299
12 IT services 1039 0.153
13 Leather 305 0.147
14 Metals and machinery 3420 0.186
15 Mining and quarrying 143 0.080
16 Non-metallic and plastic materials 1518 0.181
17 Other manufacturing 3546 0.216
18 Other services 2582 0.212
19 Other transport equipment 85 0.538
20 Other unclassified 269 0.283
21 Paper 650 0.146
22 Real estate and rental services 489 0.074
23 Retail and wholesale trade 8228 0.256
24 Telecommunications 106 0.056
25 Textiles 2340 0.205
26 Transport 739 0.111
27 Wood and furniture 1658 0.267

4. Econometric Model
Based on the existing literature, our baseline model posits that firm tax evasion is
determined by tax complexity measures and different control variables, as represented in
the following equation:

TEVAijk = α0 + α1 COMPLEXITY k + α2 CONTROLk + I NDUSTRY j + YEARt + ε ijk (1)

TEVA is the tax evasion ratio or dummy reported by the firm i representing industry j
in country k. COMPLEXITY stands for two tax complexity measures in country k: tax time
and tax payment. Since COMPLEXITY variables are in levels while the other variables
are quoted in percent or index numbers, COMPLEXITY variables are expressed in log
terms in Equation (1). CONTROL is a vector of country-level control variables included as
covariates to ameliorate omitted variable bias (i.e., economic, tax burden, demographic,
and institutional variables). I NDUSTRY denotes a vector of 27 industry dummies that are
involved in accounting for unobservable time-invariant differences between industries,
which may affect firms’ tax evasion decisions. We also included a year dummy, YEAR,
in the year in which the survey was conducted to control for global business cycles. It
is reasonable wisdom since we observed random differences in the survey periods for
all countries sampled. Last, ε is an error term containing firm- and industry-fixed effects
and, of course, a random disturbance term. The descriptive statistics, including the mean,
standard deviation, minimum, and maximum, of the variables used in the regression
analysis are summarised in Table 4.
To explore equation (1), we used the Tobit model (Tobin 1958) when the tax evasion
ratio was employed as the dependent variable because this variable is bounded between
zero and one, a situation referred to by Wooldridge (2010) as ‘corner solution outcome’.
Such an outcome is treated similarly to a ‘censored response’ dependent variable, where
Economies 2024, 12, 97 14 of 35

using a Tobit model is most appropriate (Wooldridge 2010). The Tobit model is commonly
used in accordance with studies for corner solution outcomes (e.g., percentage impairment,
as conducted by Stein 2019). This model performs better than other alternatives, such as
censored quantile regression models, in the case of highly censored data (Karlsson and
Laitila 2014). In addition, this model is also used by several empirical studies that focus on
cross-country corporate tax evasion, such as Beck et al. (2014) and Mason et al. (2020).

Table 4. Descriptive statistics.

Variables Observations Mean Standard Deviation Minimum Maximum

Dependent Variables
Tax evasion ratio 46,046 0.214 0.317 0 1
Tax evasion dummy 46,046 0.456 0.498 0 1
Independent Variables
Log of tax time 46,046 3.897 0.576 1.311 4.601
Log of tax payment 46,046 3.450 0.762 1.946 4.990
Control Variables
Log of GDP 46,046 8.159 1.129 5.667 10.807
Inflation 46,046 8.663 10.356 −2.273 100.608
Unemployment 46,046 8.078 6.081 0.774 37.25
Agriculture 46,046 11.597 8.182 0.727 42.260
Tax burden 46,046 75.713 8.905 52.4 98.5
Age 46,046 7.337 4.590 1.953 18.868
Gender 46,046 50.552 1.320 44.135 54.152
Voice and accountability 46,046 0.076 0.752 −1.767 1.624
Political stability 46,046 −0.331 0.827 −2.095 1.300
Government effectiveness 46,046 −0.115 0.674 −1.296 1.736
Regulatory quality 46,046 −0.030 0.728 −1.454 1.516
Rule of law 46,046 −0.167 0.746 −1.405 1.664
Control of corruption 46,046 −0.158 0.745 −1.326 1.885

When the tax evasion dummy was treated as the outcome, we used the Probit model
suggested by Bliss (1934a, 1934b). This model is most suitable for regression analysis with
a dichotomous dependent variable coded as zero and one (more precisely, as zero and
non-zero) (Aldrich and Nelson 1984; Cameron and Trivedi 2010). We reported marginal
effects instead of coefficient estimates to measure the statistical and economic significance
of our regression results. Furthermore, we used White’s (1980) variance–covariance matrix
of the estimators (VCE) to overcome the heteroskedasticity problem normally arising in
cross-country data. Referring to the literature review section, α1 is expected to be positive
and significant, indicating that a more complex tax system is associated with a higher
incidence of firm tax evasion and a higher firm tax evasion ratio.

5. Results
5.1. Baseline Results
Table 5 shows the baseline estimation results—the impact of tax complexity on the
extent and incidence of tax evasion across a sample of 46,046 firms and 83 countries. We
reported the estimation results from the Tobit (Probit) models when the dependent variable
was the tax evasion ratio (dummy). We had three specifications for each model. The first
specification involved only two key regressors, namely, tax time (log-transformed) and
tax payment (log-transformed), and some country-level control variables consisting of tax
burden and macroeconomic indicators. We then gradually subsumed country-level control
variables to the model, starting with demographic profiles before finally including variables
that might explain cross-country variation in the institutional environment. Across all
specifications, we incorporated unreported industry and year dummies and the standard
errors that were robust to the disturbances being heteroskedastic

(White 1980). For the
estimation results, we outlined the marginal effects dy /d x rather than coefficient estimates
Economies 2024, 12, 97 15 of 35

to demonstrate statistically and economically significant relationships between covariates

and firm tax evasion variables.

Table 5. Baseline results: tax complexity and tax evasion.

Dependent Variables Tax Evasion Ratio Tax Evasion Dummy

Models Tobit Probit
Specifications (1) (2) (3) (4) (5) (6)
Log of tax time 0.067 *** 0.061 *** 0.046 *** 0.070 *** 0.064 *** 0.053 ***
(0.006) (0.006) (0.007) (0.004) (0.004) (0.005)
Log of tax payment 0.042 *** 0.077 *** 0.080 *** 0.052 *** 0.063 *** 0.059 ***
(0.005) (0.005) (0.006) (0.004) (0.004) (0.004)
Log of GDP −0.026 *** −0.102 *** −0.197 *** −0.046 *** −0.070 *** −0.127 ***
(0.006) (0.007) (0.009) (0.005) (0.006) (0.007)
Inflation 0.004 *** 0.004 *** 0.004 *** 0.003 *** 0.003 *** 0.004 ***
(0.0002) (0.0003) (0.0003) (0.0002) (0.0002) (0.0002)
Unemployment 0.0017 *** 0.0001 −0.0006 −0.001 *** −0.001 ** −0.001 **
(0.0006) (0.0006) (0.0007) (0.0005) (0.0005) (0.0005)
Agriculture 0.012 *** 0.011 *** 0.014 *** 0.010 *** 0.010 *** 0.012
(0.0008) (0.0008) (0.001) (0.001) (0.001) (0.0008)
Tax burden −0.0002 −0.002 *** −0.002 *** −0.001 ** −0.001 *** −0.001 ***
(0.0004) (0.0004) (0.0005) (0.0003) (0.0003) (0.0004)
Age 0.032 *** 0.041 *** 0.009 *** 0.015 ***
(0.001) (0.002) (0.001) (0.001)
Gender 0.006 ** 0.046 *** −0.007 *** −0.021 ***
(0.003) (0.004) (0.002) (0.003)
Voice and accountability 0.079 *** 0.061 ***
(0.008) (0.006)
Political stability −0.031 *** −0.020 ***
(0.006) (0.005)
Government effectiveness −0.383 *** −0.206 ***
(0.018) (0.014)
Regulatory quality 0.003 −0.008
(0.014) (0.011)
Rule of law 0.440 *** 0.282 ***
(0.193) (0.015)
Control of corruption −0.196 *** −0.157 ***
(0.014) (0.011)
Observations 46,046 46,046 46,046 46,046 46,046 46,046
Countries 83 83 83 83 83 83
Pseudo-R2 0.047 0.055 0.067 0.035 0.037 0.047
Industry and year dummies Yes Yes Yes Yes Yes Yes
Note: The estimation is
based on cross-sectional data. The pooled sample period is from 2002 to 2010. The
marginal effects dy /d x of the regressions are presented in all specifications. The marginal effect of a dummy
variable is measured as the discrete change in the predicted value of the outcome variable as the value of the
dummy changes from zero to one. Heteroskedasticity-robust standard errors are reported in parentheses. ** and
*** represent statistical significance at the 5% and 1% levels, respectively.

We found that a larger number of hours required to pay taxes and a larger total amount
of taxes paid were associated with a higher extent and incidence of tax evasion by firms.
Tax time and tax payment entered positively and significantly in all specifications and were
economically noteworthy. On average, a one standard deviation increase in tax time (0.576)
and tax payment (0.762) would lead to a significant increase in tax evasion by about 2.62%
and 6.11%, respectively. On account that the sample average of the extent of tax evasion
was 21.4%, these effects were tremendously substantial, which were 12.2% (2.62/21.407)
and 28.5% (6.11/21.407).
Existing works also support these baseline results. For example, Beck et al. (2014)
discovered that many aspects of the tax system had a strong and positive correlation
with the occurrence of corporate tax evasion in 102 countries. These aspects include the
Economies 2024, 12, 97 16 of 35

overall tax rate, the time required to prepare and pay taxes, and the total number of tax
payments. Awasthi and Bayraktar (2015) focused on the relationship between tax regime
simplification and tax corruption using firm-level data from 104 different countries. They
found that a convoluted tax system with ambiguous tax rules results in arbitrary and
partly ad hoc tax collection procedures. It also gives taxpayers and tax inspectors extensive
discretionary power to interpret tax laws, hence promoting corruption and escalating tax
evasion. Furthermore, they propose a straightforward solution: if a government wants to
decrease corruption and tax evasion associated with tax management, it should streamline
the tax regime.
Turning to the control variables, we found that real per capita GDP, agriculture, age,
political stability, government effectiveness, and control of corruption consistently appeared
as expected and were always significant at the 1% level (except agriculture in specification
6). Although expected to produce ambiguous effects, we generally observed inflation and
tax burden with significant positive and negative signs across all specifications (except
tax burden in specification 1). In the case of inflation, firms may attempt to compensate
for lost purchasing power in input markets as production costs rise (e.g., wages and raw
materials prices) by engaging in more evasion practices. Regarding tax burden, firms avoid
decisions containing absolute risk when their profits fall as tax rates rise. Moreover, the
penalties imposed on the evaded tax will degrade the benefits of the evasion practices
(Yitzhaki 1974).
Surprisingly, voice and accountability and the rule of law consistently emerged with
significant unexpected signs. There are some possible explanations for these findings. For
example, countries with a strong rule of law often strive to meet international standards
and commitments. This may result in the adoption of intricate tax legislation to conform
with global norms and treaties (Avi-Yonah 2004), prompting companies to take shortcuts
or seek ways to minimise their tax liabilities. With respect to voice and accountability,
although it reflects sound institutional environment quality, it may also have negative
consequences for taxation. As presented in Table A1, this variable captures a country’s
media freedom, a situation in which journalists are most likely to scrutinise and disclose
instances of tax evasion, corruption, or misappropriation of tax revenues by government
officials. Extensive media coverage of incidents like these might strengthen perceptions of
widespread non-compliance and corrupt trust in the tax system (Lambsdorff 2007).
In addition, the unemployment rate and gender had mixed coefficient signs. During
times of high unemployment, firms may face reduced profitability, causing them to seek
ways to minimise the tax burden (Clausing 2009). On the other hand, high unemployment
rates, especially during a crisis, can lead to increased government supervision and en-
forcement of tax regulations (Gordon 2000), potentially deterring companies from carrying
out these practices due to the increased risk of detection and fines. Increased women’s
participation in the labour market can increase corporate transparency and compliance
with tax laws due to diversity in decision-making and leadership roles (Cronqvist and Yu
2017). However, if women’s participation in the labour market is primarily concentrated in
lower-level positions with limited decision-making authority, its impact on corporate tax
evasion may be negligible (Matsa and Miller 2013). Last, regulatory quality was the only
variable that was persistently insignificant.

5.2. Endogeneity Concerns

An important threat to our baseline model’s validity relates to the potential endogene-
ity of the tax complexity, which arises from two main sources: omitted variable bias and
simultaneity bias (Wooldridge 2010). As presented in Table 5, we included relevant control
variables, called by Antonakis et al. (2010), as a best practice to avoid omitted variable bias.
Nonetheless, we are overshadowed by a key point in Clarke (2009, p. 349): “It is impossible
to include all the relevant variables in a regression equation”. Thus, certain omitted vari-
ables may affect variance in tax complexity measures. If so, our tax complexity variables
Economies 2024, 12, 97 17 of 35

(primary regressors) violate the exogeneity assumption as their potential relationship with
particular omitted variables (e.g., Wooldridge 2008).
Concerning the simultaneity bias, we adhered to the subtle argument put forward by
Borrego et al. (2016). They argue that tax authorities in some countries normally formulate
more complex ‘anti-abuse regulations’ to prevent attempts at tax fraud, tax evasion, and
certain ways to reduce or completely avoid taxpayer commitments to pay taxes. However,
these efforts ultimately often become a springboard for taxpayers to realise various other tax
non-compliance schemes by exploiting the ambiguities and loopholes that tax complexity
provides. It is a ‘vicious circle’ where tax non-compliance and tax complexity nowadays
frequently appear as causes and effects of one another, confirming the existence of reverse
causality between our key variables.
To cope with the endogeneity issues, we apply an IV approach, which is widely used
in the literature (Angrist and Krueger 2001; Cameron and Trivedi 2005; Wooldridge 2008;
Antonakis et al. 2010; Kennedy et al. 2019; Wilms et al. 2021). Some cross-country studies
dealing with the endogeneity of tax systems incorporate instruments related to the home
countries (e.g., Nagac 2015; Acosta-Ormaechea et al. 2019). Although the instruments ap-
plied are usually relevant, they might violate the exogeneity constraint given their potential
impact on the outcome variable in ways other than through the tax systems. Therefore,
as a precaution against dwelling with ‘poor’ instruments, we draw upon instruments
related to the neighbourhood of the home countries. However, the main challenge would
be properly determining the neighbouring countries for each home country. Knowing their
locations are insufficient, we linked our dataset to the shapefile of national administrative
borders provided by IPUMS International. By doing this, we can map the neighbourhood
by constructing the contiguity and inverse-distance matrices.
Adapting Liu and Feng’s (2015) approach, our instruments were the weighted average
of the tax complexity measures (tax time and tax payment) in neighbouring countries,
weighted by constructed spatial weighting matrices (contiguity and inverse-distance ma-
trices). Hence, we had four instruments for the tax complexity variables. By adopting
theoretical reasoning from the conventional tax competition and tax mimicking literature,
we assume that the design of a tax system in the neighbouring countries will correlate
with the design of a tax system in the home countries because countries compete or imitate
their taxes to attract mobile tax bases. Therefore, in terms of the complexity level of the tax
system, there is pressure on each country to race to the bottom. However, the complex tax
system in neighbouring countries certainly does not directly affect the level of tax evasion
in the home countries without first affecting the complexity of the tax system in the home
countries. Such a theoretical justification serves as an optimal building block for saying that
the weighted average tax complexity measures in the neighbouring countries are relevant
and exogenous instruments for tax complexity variables in the home countries.
Since our outcome variables consisted of both censored (tax evasion ratio) and dichoto-
mous (tax evasion dummy) variables, as suggested by Chesher et al. (2023) and Guan et al.
(2019), we employed the IV approach for the Tobit (IV Tobit) and the Probit (IV Probit)
models. The second-stage regression results are presented in Table 6. The results were like
those in Table 5—the extent and incidence of tax evasion by firms increased as a country’s
tax system was increasingly complex. However, in all specifications, except specification 3
for the tax payment variable, the magnitudes of the effects were now much larger compared
with the baseline results, roughly five to ten times (for the tax time variable) and two times
(for the tax payment variable) as large as the corresponding Tobit results and approximately
eight to fourteen times (for the tax time variable) and three times (for the tax payment
variable) as large as the corresponding Probit results.
Economies 2024, 12, 97 18 of 35

Table 6. IV second-stage regression results.

Dependent Variables Tax Evasion Ratio Tax Evasion Dummy

Models IV Tobit IV Probit
Specifications (1) (2) (3) (4) (5) (6)
Log of tax time 0.442 *** 0.588 *** 0.211 *** 0.676 *** 0.882 *** 0.413 ***
(0.025) (0.024) (0.024) (0.044) (0.037) (0.045)
Log of tax payment 0.092 *** 0.142 *** 0.010 0.206 *** 0.252 *** 0.094 ***
(0.013) (0.015) (0.013) (0.026) (0.029) (0.027)
Log of GDP −0.074 *** −0.017 −0.095 *** −0.038 −0.060 * −0.225 **
(0.012) (0.016) (0.015) (0.024) (0.031) (0.031)
Inflation 0.005 *** 0.006 *** 0.003 *** 0.013 *** 0.015 *** 0.008 ***
(0.0003) (0.0003) (0.0003) (0.001) (0.001) (0.001)
Unemployment −0.010 *** −0.016 *** −0.007 *** −0.020 *** −0.025 *** −0.016 ***
(0.001) (0.001) (0.001) (0.002) (0.002) (0.002)
Agriculture −0.007 *** −0.013 *** −0.003 0.004 −0.007 ** 0.003
(0.002) (0.002) (0.002) (0.003) (0.003) (0.004)
Tax burden −0.0007 −0.0008 −0.004 *** −0.0002 −0.001 −0.005 ***
(0.0005) (0.0005) (0.001) (0.001) (0.001) (0.001)
Age 0.027 *** 0.030 *** 0.025 *** 0.034 ***
(0.002) (0.003) (0.005) (0.006)
Gender −0.023 *** −0.052 *** −0.058 *** −0.054 ***
(0.005) (0.005) (0.010) (0.011)
Voice and accountability 0.062 *** 0.085 ***
(0.012) (0.026)
Political stability −0.036 *** −0.070 ***
(0.007) (0.015)
Government effectiveness −0.314 *** −0.478 ***
(0.022) (0.045)
Regulatory quality −0.204 *** −0.380 ***
(0.024) (0.046)
Rule of law 0.511 *** 0.905 ***
(0.030) (0.060)
Control of corruption −0.214 *** −0.366 ***
(0.018) (0.039)
Observations 37,893 37,893 37,893 37,893 37,893 37,893
Countries 68 68 68 68 68 68
Industry and year dummies Yes Yes Yes Yes Yes Yes
Wald χ2 test: exogeneity 271.88 *** 602.85 *** 161.26 *** 130.40 *** 311.47 *** 67.24 ***
Note: The estimation is
based on cross-sectional data. The pooled sample period is from 2002 to 2010. The
marginal effects dy /d x of the regressions are presented in all specifications. The marginal effect of a dummy
variable is measured as the discrete change in the predicted value of the outcome variable as the value of the
dummy changes from zero to one. Heteroskedasticity-robust standard errors are reported in parentheses. *, **,
and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

In Table 6, we also observe more control variables with the expected coefficient signs
compared with the baseline results in Table 6. For example, gender and regulatory qual-
ity for the current regression (and in all specifications) aligned with our expectations—
negatively and significantly impacting the extent and incidence of firm tax evasion. As
discussed, gender in this study was expressed as the female participation rate in the labour
market, representing the gender structure of business in an economy (Petrongolo and
Ronchi 2020). The negative effect of gender thus indicated that the presence of female
workers in the business, especially for strategic positions, tended to encourage firms to be
more compliant with tax regulations (Damayanti and Supramono 2019) because of their
perception of greater tax fines and tax investigation risks when they evaded taxes (Olsen
and Cox 2001; Charness and Gneezy 2012). In addition, the negative effect of regulatory
quality implied that hostile and antagonistic interactions with the government that can
lead to higher levels of tax compliance would be reduced when the government could
Economies 2024, 12, 97 19 of 35

create a healthy environment for the firms to thrive through higher regulatory quality
(Hofmann et al. 2014).
On the other hand, the unemployment rate consistently had the opposite effect to that
expected. This phenomenon could be attributed to the government policy agenda, which
makes job creation the main priority when dealing with stubbornly high unemployment in
their countries. Of the various policy instruments directly affecting business, one option is
to cut corporate taxes to stimulate job creation. The enactment of a tax rate cut increases the
firm’s ability to pay within the same period, reducing the prevailing tax evasion practices
(Madzharova 2013). However, the job creation impact of such a policy is not observed
immediately, given that the business needs time to adjust to the new tax rate (Shuai and
Chmura 2013).
The first-stage regression results of the IV Tobit and IV Probit regression models are
presented in Table 7. The results demonstrated a significant relationship between the
endogenous regressors and the corresponding instrumental variables. These findings
aligned with our expectations, where tax time and tax payment in neighbouring countries
played a crucial role in determining the complexity of home countries’ tax systems. As
discussed earlier, the adjacent countries compete to reform their tax administration system
as simply as possible to create a more appealing tax world for mobile tax bases. However,
once a country within the neighbourhood downgrades (intentionally or unintentionally) its
tax system, the other countries will attempt to circumvent similar mistakes. This argument
may be a logical reason why mixed coefficient signs of instrumental variables are observed.

Table 7. IV first-stage regression results.

Dependent Variables Log of Tax Time Log of Tax Payment

Specifications (1) (2) (3) (1) (2) (3)
IV Tobit Model in Table 8
Log of tax time × contiguity matrix −0.126 *** −0.157 *** −0.351 ***
(0.009) (0.009) (0.013)
Log of tax time ×
0.620 *** 1.054 *** 1.885 ***
inverse-distance matrix
(0.034) (0.033) (0.043)
Log of tax payment ×
0.637 *** 0.556 *** 0.663 ***
contiguity matrix
(0.008) (0.007) (0.006)
Log of tax payment ×
−0.652 *** −0.075 ** −0.318 ***
inverse-distance matrix
(0.039) (0.037) (0.037)
IV Probit Model in Table 8
Log of tax time × contiguity matrix −0.126 *** −0.157 *** −0.351 ***
(0.009) (0.009) (0.013)
Log of tax time ×
0.620 *** 1.054 *** 1.885 ***
inverse-distance matrix
(0.034) (0.033) (0.043)
Log of tax payment ×
0.637 *** 0.556 *** 0.663 ***
contiguity matrix
(0.008) (0.007) (0.006)
Log of tax payment ×
−0.652 *** −0.075 ** −0.318 ***
inverse-distance matrix
(0.039) (0.037) (0.037)
Note: All control variables are included, but the estimation results are omitted for simplicity. ** and *** represent
statistical significance at the 5% and 1% levels, respectively.
Economies 2024, 12, 97 20 of 35

Table 8. Robustness test results.

Dependent Variables Tax Evasion Ratio Tax Evasion Dummy

Using Tax Using Tax
Adding a Including Adding a Including
Complexity Complexity
Modifications Control Country-Fixed Control Country-Fixed
Alternative Alternative
Variable Effects Variable Effects
Measurements Measurements
Specifications (1) (2) (3) (4) (5) (6) (7) (8)
Log of tax time 0.088 *** 0.020 * 0.065 *** 0.012 ***
(0.013) (0.011) (0.010) (0.002)
Log of tax payment 0.086 *** 0.054 *** 0.081 *** 0.138 ***
(0.010) (0.006) (0.008) (0.012)
TCI 0.214 *** 0.078 ***
(0.073) (0.015)
Tax code complexity 0.318 *** 0.478 ***
(0.052) (0.108)
Tax framework complexity 0.547 *** 1.611 ***
(0.111) (0.248)
Log of GDP −0.210 *** −0.211 *** −0.246 *** −0.239 *** −0.404 *** −0.405 *** −0.166 *** −0.475 ***
(0.010) (0.010) (0.019) (0.011) (0.023) (0.023) (0.016) (0.025)
Inflation 0.015 *** 0.016 *** 0.014 *** 0.018 *** 0.032 *** 0.034 *** 0.013 *** 0.034 ***
(0.001) (0.001) (0.001) (0.001) (0.002) (0.002) (0.001) (0.002)
Unemployment −0.007 *** −0.006 *** −0.004 *** 0.005 *** −0.014 *** −0.012 *** −0.003 *** 0.009 ***
(0.001) (0.001) (0.001) (0.001) (0.002) (0.002) (0.001) (0.002)
Agriculture 0.032 *** 0.030 *** 0.025 *** 0.031 *** 0.071 *** 0.067 *** 0.018 *** 0.070 ***
(0.001) (0.001) (0.003) (0.001) (0.003) (0.003) (0.002) (0.003)
Tax burden −0.001 −0.001 −0.010 *** −0.001 −0.0002 −0.0002 −0.006 −0.001
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
Age 0.029 *** 0.032 *** 0.087 *** 0.032 *** 0.021 *** 0.027 *** 0.050 *** 0.034 ***
(0.002) (0.002) (0.004) (0.002) (0.004) (0.004) (0.003) (0.004)
Gender −0.025 *** −0.028 *** 0.065 *** 0.028 *** −0.025 *** −0.033 *** −0.029 *** 0.030 ***
(0.005) (0.005) (0.006) (0.004) (0.009) (0.010) (0.005) (0.009)
Voice and accountability 0.037 *** 0.057 *** 0.292 *** 0.013 0.043 * 0.093 *** 0.189 *** 0.014
(0.011) (0.011) (0.016) (0.011) (0.024) (0.025) (0.011) (0.023)
Political stability −0.009 −0.012 −0.075 *** −0.001 −0.017 −0.011 −0.007 −0.011
(0.009) (0.009) (0.010) (0.008) (0.018) (0.018) (0.008) (0.016)
Government effectiveness −0.229 *** −0.220 *** −0.109 *** −0.283 *** −0.243 *** −0.215 *** −0.100 *** −0.354 ***
(0.023) (0.023) (0.031) (0.024) (0.049) (0.050) (0.025) (0.050)
Regulatory quality −0.081 *** −0.150 *** −0.199 *** −0.070 *** −0.119 *** −0.285 *** −0.095 *** −0.067 *
(0.017) (0.019) (0.025) (0.018) (0.035) (0.042) (0.019) (0.037)
Rule of law 0.369 *** 0.427 *** 0.196 *** 0.421 *** 0.721 *** 0.848 *** 0.131 *** 0.807 ***
(0.025) (0.026) (0.031) (0.025) (0.053) (0.056) (0.025) (0.053)
Control of corruption −0.216 *** −0.205 −0.133 *** −0.210 *** −0.602 *** −0.571 *** −0.159 *** −0.572 ***
(0.017) (0.017) (0.024) (0.018) (0.040) (0.041) (0.020) (0.042)
Tax morale −0.018 *** −0.019 ***
(0.003) (0.003)
Observations 46,046 46,046 29,712 46,046 46,046 46,046 29,712 46,046
Countries 83 83 39 83 83 83 39 83
Pseudo-R2 0.018 0.018 0.103 0.018 0.018 0.018 0.082 0.018
Industry and year dummies Yes Yes Yes Yes Yes Yes Yes Yes


Note: The estimation is based on cross-sectional data. The marginal effects dy /d x of the regressions are
presented in all specifications. The marginal effect of a dummy variable is measured as the discrete change in the
predicted value of the outcome variable as the value of the dummy changes from zero to one. Heteroskedasticity-
robust standard errors are reported in parentheses. * and *** represent statistical significance at the 10% and 1%
levels, respectively.

5.3. Robustness Test

To ensure the stability of baseline results, the replacement of explanatory variables
is a common approach in robustness testing (e.g., Yang et al. 2024). In this study, the tax
complexity index (TCI) developed by Hoppe et al. (2023), which measures the complexity
of the corporate income tax system in a country, was used as an alternative variable for
tax complexity resulting from baseline regressions measured using the WBDB database
(tax time and tax payment). This index covers the complexity of tax regulations (tax code
complexity) and the complexity of administrative and legislative features and processes in
the tax system (tax framework complexity). The index is expressed as zero (not complicated
at all) to one (very complicated). We took the average index from the years for which data
were available, namely, 2016, 2018, 2020, and 2020 (see http://www.taxcomplexity.org,
accessed on 12 March 2024). In Table 8, specifications 1, 2, 5, and 6 display the robustness
test results after replacing the main explanatory variables with TCI, tax code complexity,
and tax framework complexity, using the same Tobit and Probit models as Table 5.
Regression results from these two specifications showed that even after the explanatory
variables were replaced, tax complexity still showed a positive and significant impact on
Economies 2024, 12, 97 21 of 35

the extent and incidence of corporate tax evasion. Therefore, the robustness test was
successfully passed.
Furthermore, there is a consensus in the field of econometrics that it is important to in-
clude an extra control variable when examining the validity of the initial findings (Kennedy
2008; Angrist and Pischke 2008). In this regard, as suggested by Torgler and Schneider
(2007) and Alm and McClellan (2012), we used tax morale to isolate the specific impact
of tax complexity on corporate tax evasion. The tax morale variable data were obtained
from the World Values Survey (WVS) for the years 1999–2004, 2005–2009, and 2010–2014
(see https://www.worldvaluessurvey.org/WVSContents.jsp, accessed on 17 March 2024).
These three survey waves were chosen because they overlapped with the pooled sample
period used in this study (2002 to 2010). The WVS is the main data source used in studies
on tax morale (Doerrenberg and Peichl 2013). The survey is conducted globally and gathers
comparative data on various values and attitudes. It utilises standardised questionnaires
and ensures that each country’s sample size consists of at least 1000 respondents. The
specific question about tax morale is as follows: “Please tell me for each of the following
statements whether you think it can always be justified, never be justified, or something in
between (. . .) cheating on tax if you have the chance”.
The WVS used a ten-point scale index to assess the question, with one represent-
ing “never justified” and ten representing “always justified” at the opposite ends of
the scale. However, in line with a previous empirical study on tax morale, such as
Ciziceno and Pizzuto (2022), we modified the variable by reversing the values to facili-
tate the interpretation of the results. Thus, in our study, the response “never justified” for
cheating on taxes was given a numerical value of ten to signify a greater level of inherent
motivation to pay taxes. Given that each country sample of this study had only one sample
year, we calculated the average of these values from the three survey waves.
In Table 8, specifications 3 and 7 show estimation results involving tax morale as
an additional control variable. We find that tax morale had a significant negative effect
on tax evasion in both specifications. It indicated that high public tax enthusiasm would
reduce the incidence and extent of tax evasion by companies. These results were credible,
particularly in regard to market access and business relationships, where high public tax
morale might impact stakeholders’ views and decisions surrounding their interactions
with enterprises. For example, customers may prefer to do business with tax-compliant
companies, suppliers may impose stricter contractual terms on tax-evading companies,
and investors may be more hesitant to invest in companies that have a reputation for tax
evasion (Cowell and Gordon 1988; Pommerehne and Weck-Hannemann 1996). Another
primary point related to the findings is that our baseline results remained intact with the
introduction of tax morale into the estimation model.
Moreover, although the cross-country nature of the study seemed to be lost with
country-fixed effects, Table 2 underlines the fact that corporate tax evasion and their percep-
tion of tax complexity varied greatly from one country to another. Therefore, we introduced
country-fixed effects in our robustness analysis, thereby allowing us to control for inherent
variation between countries. In Table 8, specifications 4 and 8 present the regression results
after including country-fixed effects. A comparison with the baseline regression in Table 5
shows that the data analysis results, even after controlling for cross-country variation,
continued to show a significant positive relationship between tax complexity and the two
measures of corporate tax evasion. Thus, the analysis results obtained from the baseline
regression were robust.

5.4. Additional Analysis

5.4.1. Country Heterogeneity Analysis
As discussed in the previous section, there are variations in the calculation of the
tax evasion ratio in various countries around the globe. Law enforcement and public
trust in government performance are generally higher in high- and upper-middle-income
countries (Tanzi and Shome 1993). It indicates that the income level of countries can play
 were robust.

5.4. Additional Analysis

Economies 2024, 12, 97 5.4.1. Country Heterogeneity Analysis
22 of 35
As discussed in the previous section, there are variations in the calculation of the tax eva-
sion ratio in various countries around the globe. Law enforcement and public trust in govern-
ament
role performance
in determining are the
generally higherof
perceptions in existing
high- and taxupper-middle-income
evasion. In addition,countries differences(Tanzi
in
and Shome 1993). It indicates that the income level of countries can
factors such as technological developments, levels of access to information, and economic play a role in determining
the perceptionsbetween
developments of existing tax evasion.
countries can In addition,
cause differences
variations in thein factors such
complexity ofas technological
the tax system
developments,
at the global level.levels of accessthere
Therefore, to information, and economic
is value in conducting developments
additional analysis between coun-
by exploring
triesdimensions
the can cause variations
of income in theto
level complexity of the tax system
gain an understanding atdynamics
of the the globaloflevel. Therefore,
tax complexity
theretax
and is value in conducting
evasion in a country. additional analysis by exploring the dimensions of income level
to gain an understanding
To test whether the positive of the dynamics of tax complexity
and significant impact ofand taxtax evasion
system in a country.
complexity on
To testtax
corporate whether
evasion thewas
positive and significant
consistent in various impact of tax system
countries, complexity
the countries on corporate
included in the
tax evasion
dataset werewas consistent
divided intointwo various countries,
groups. The firstthegroup
countries included
included in the dataset
high-income andwere di-
upper-
middle-income
vided into two (HUP) groups.countries.
The firstThe second
group grouphigh-income
included consisted of and lower-middle-income
upper-middle-income and
low-income
(HUP) countries.(LLOW) Thecountries.
second groupWe followed
consistedthe of
World Bank classification,and
lower-middle-income which is based
low-income
on per capita
(LLOW) grossWe
countries. national
followed income (GNI)Bank
the World calculated with the
classification, Atlasismethod,
which based ontoper identify
capita
the country’s income groups. According to the classification, 46 countries
gross national income (GNI) calculated with the Atlas method, to identify the country’s in- from the dataset
were
come included in the first
groups. According to and 37 countries 46
the classification, in countries
the second group.
from The average
the dataset values of
were included in
the first
tax evasion ratio andintwo
and 37 countries thetax complexity
second group. Theindicators
averageinvalues
the two groups
of the of countries
tax evasion are
ratio and
presented in Figure indicators
two tax complexity 1. in the two groups of countries are presented in Figure 1.
500
400
300
200
100
0

HUP LLOW
Tax Evasion Ratio Tax Time Tax Payment

Figure1.1.Tax
Figure Taxevasion
evasion ratio,
ratio, taxtax time,
time, andand tax payment
tax payment by income
by income group.group.

The regression results of the two groups are performed in Table 9. Generally, we find
strong and consistent evidence that tax complexity exacerbated firm tax evasion in HUP
(specifications 1 and 3) and LLOW countries (specifications 2 and 4). However, significant
differences were observed between the two income groups. Tax evasion by firms was more
responsive to the level of tax complexity in LLOW countries. The estimated tax time and tax
payment coefficients were preponderant in magnitude for this country group. These results
may be associated with the fact that the tax systems in LLOW countries are more complex
(Figure 1). This group of countries also tends to have higher levels of tax corruption than
HUP countries (e.g., Awasthi and Bayraktar 2015). It indicates that tax administration
problems in LLOW countries are much more comprehensive than in HUP countries. In
other words, investment in improving tax administration is much more necessary in LLOW
countries to reduce cases of tax evasion by companies.
In addition, like the regression results obtained from the baseline regression models
(Table 5), in Table 9, tax payment presents a larger coefficient size than tax time for both
income groups. Given that reducing the number of tax payments is relatively easier than
cutting the time required to prepare, file, and pay taxes (Djankov et al. 2010), these results
present encouraging news for policymakers in the LLOW group, where the problem of tax
evasion tends to be more severe.
Economies 2024, 12, 97 23 of 35

Table 9. Country heterogeneity analysis results.

Dependent Variables Tax Evasion Ratio Tax Evasion Dummy

Country Groups HUP Countries LLOW Countries HUP Countries LLOW Countries
Specifications (1) (2) (3) (4)
Log of tax time 0.046 *** 0.080 *** 0.113 *** 0.058 **
(0.006) (0.014) (0.011) (0.022)
Log of tax payment 0.104 *** 0.564 *** 0.168 *** 0.561 ***
(0.011) (0.033) (0.022) (0.053)
Log of GDP −0.314 *** −0.190 *** −0 712 *** −0.180 ***
(0.024) (0.029) (0.046) (0.046)
Inflation 0.018 *** 0.007 *** 0.026 *** 0.011 ***
(0.001) (0.0004) (0.003) (0.001)
Unemployment 0.0004 −0.001 0.006 *** 0.001
(0.001) (0.002) (0.002) (0.003)
Agriculture 0.042 *** 0.012 *** 0.072 *** 0.012 ***
(0.002) (0.002) (0.004) (0.004)
Tax burden −0.002 *** −0.019 *** −0.006 *** 0.024 ***
(0.001) (0.001) (0.001) (0.002)
Age 0.035 *** 0.126 *** 0.039 *** 0.135 ***
(0.002) (0.006) (0.004) (0.009)
Gender 0.041 *** −0.138 0.076 *** 0.104 ***
(0.007) (0.008) (0.013) (0.013)
Voice and accountability −0.053 *** −0.136 *** −0.024 −0.116 ***
(0.017) (0.021) (0.032) (0.034)
Political stability −0.064 *** 0.061 *** −0.144 *** 0.157 ***
(0.011) (0.014) (0.020) (0.022)
Government effectiveness −0.190 *** −0.354 *** −0.478 *** −0.460 ***
(0.034) (0.042) (0.067) (0.070)
Regulatory quality −0.082 *** −0.068 * −0.029 −0.017
(0.022) (0.037) (0.041) (0.058)
Rule of law 0.524 *** 0.864 *** 0.798 *** 1.055 ***
(0.033) (0.035) (0.063) (0.057)
Control of corruption −0.247 *** 0.052 −0.393 *** −0.131 **
(0.020) (0.034) (0.039) (0.055)
Observations 25,839 20,207 25,839 20,207
Countries 46 37 46 37
Pseudo-R2 0.063 0.142 0.034 0.070
Industry and year dummies Yes Yes Yes Yes
Note: The estimation is
based on cross-sectional data. The pooled sample period is from 2002 to 2010. The
marginal effects dy /d x of the regressions are presented in all specifications. The marginal effect of a dummy
variable is measured as the discrete change in the predicted value of the outcome variable as the value of the
dummy changes from zero to one. Heteroskedasticity-robust standard errors are reported in parentheses. *, **,
and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

5.4.2. Industry Heterogeneity Analysis

After analysing the entire sample, additional sample sorting was conducted by cate-
gorising 27 industries, which included over 46,000 companies, into three main industries:
raw material extraction (primary), manufacturing (secondary), and the service industry,
which supports the transportation, distribution, and sale of goods produced in the sec-
ondary industry (tertiary). To perform this categorisation, we relied on the International
Standard Industrial Classification of All Economic Activities (ISIC), Rev.4 (United Nations
2008). Consequently, two industries were designated as main industries, including a total of
5455 firms. Additionally, 14 industries were classified as secondary industries, with a total
of 24,955 companies. Furthermore, 11 industries were categorised as tertiary industries,
encompassing a total of 15,636 organisations. Using the tax evasion indicators for the three
industries as the basis, Tobit and Probit regressions were employed to analyse whether
the impact of tax complexity on the extent and incidence of corporate tax evasion was
Economies 2024, 12, 97 24 of 35

consistent across the various industries. The findings of the investigation on industry
heterogeneity are shown in Table 10.

Table 10. Industry heterogeneity analysis results.

Dependent Variables Tax Evasion Ratio Tax Evasion Dummy

Industry Tertiary Secondary Primary Tertiary Secondary Primary
Specifications (1) (2) (3) (4) (5) (6)
Log of tax time 0.021 *** 0.053 *** 0.109 *** 0.079 *** 0.049 *** 0.081 ***
(0.003) (0.009) (0.027) (0.022) (0.007) (0.018)
Log of tax payment 0.013 *** 0.094 *** 0.089 *** 0.050 *** 0.065 *** 0.068 ***
(0.001) (0.007) (0.018) (0.018) (0.006) (0.013)
Log of GDP −0.003 −0.241 *** −0.382 *** −0.039 −0.146 *** −0.231 ***
(0.017) (0.011) (0.028) (0.028) (0.009) (0.020)
Inflation 0.004 *** 0.004 *** 0.005 *** 0.006 *** 0.004 *** 0.004 ***
(0.0005) (0.0004) (0.001) (0.001) (0.0003) (0.001)
Unemployment 0.009 *** −0.004 *** −0.007 *** 0.008 *** −0.003 *** −0.004 **
(0.001) (0.001) (0.003) (0.002) (0.001) (0.002)
Agriculture 0.008 *** 0.012 *** 0.021 *** 0.020 *** 0.009 *** 0.014 ***
(0.002) (0.001) (0.003) (0.003) (0.001) (0.002)
Tax burden −0.001 * −0.004 *** 0.003 −0.004 *** −0.003 *** 0.002
(0.001) (0.001) (0.002) (0.001) (0.001) (0.001)
Age 0.007 ** −0.057 *** −0.071 *** 0.023 *** −0.026 *** −0.032 ***
(0.003) (0.002) (0.007) (0.006) (0.002) (0.005)
Gender −0.003 0.073 *** 0.103 *** −0.015 0.034 *** 0.054 ***
(0.006) (0.006) (0.014) (0.010) (0.004) (0.010)
Voice and accountability −0.073 *** 0.119 *** 0.135 *** −0.110 *** 0.099 *** 0.107 ***
(0.016) (0.012) (0.029) (0.026) (0.008) (0.020)
Political stability −0.037 *** −0.035 *** −0.087 *** −0.065 *** −0.022 *** −0.062 ***
(0.011) (0.009) (0.019) (0.019) (0.007) (0.014)
Government effectiveness −0.106 *** −0.474 *** −0.398 *** −0.119 ** −0.251 *** −0.208 ***
(0.032) (0.025) (0.058) (0.057) (0.020) (0.045)
Regulatory quality −0.084 *** −0.037 ** −0.169 *** −0.096 ** −0.046 *** −0.117 ***
(0.025) (0.019) (0.042) (0.042) (0.015) (0.031)
Rule of law 0.409 *** 0.447 *** 0.460 *** 0.604 *** 0.290 *** 0.259 ***
(0.035) (0.026) (0.054) (0.057) (0.020) (0.041)
Control of corruption −0.396 *** −0.116 *** −0.099 ** −0.607 *** −0.114 *** −0.080 ***
(0.023) (0.021) (0.050) (0.041) (0.016) (0.036)
Observations 15,636 24,955 5455 15,636 24,955 5455
Countries 70 83 75 70 83 75
Pseudo-R2 0.108 0.065 0.052 0.049 0.046 0.045
Industry and year dummies Yes Yes Yes Yes Yes Yes
Note: The estimation is
based on cross-sectional data. The pooled sample period is from 2002 to 2010. The
marginal effects dy /d x of the regressions are presented in all specifications. The marginal effect of a dummy
variable is measured as the discrete change in the predicted value of the outcome variable as the value of the
dummy changes from zero to one. Heteroskedasticity-robust standard errors are reported in parentheses. *, **,
and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

The findings obtained from the analysis in Table 10 indicated that variations in the
key independent variable, namely, tax complexity, had a substantial and positive influence
on the occurrence of corporate tax evasion across all three industries. This suggested
that the findings of the analysis on industry heterogeneity aligned with the overall study
conclusions. Consequently, the baseline regression findings of this research indicated that
there were no significant differences seen across industries. Nevertheless, it is important
to recognise the disparity in the magnitudes of the coefficients, and we intend to provide
a logical explanation for this phenomenon. Tax complexity had a greater impact on the
primary industry compared with the other two industries. The reason for this might be
because primary industries, such as agriculture, mining, and forestry, often have simpler
production processes and fewer intermediary stages compared with those of secondary and
Economies 2024, 12, 97 25 of 35

tertiary industries. The straightforwardness of operations in the primary industry might

result in a heightened need for tax planning tactics to negotiate intricate tax legislation and
reduce tax obligations, thereby raising the probability of tax evasion when confronted with
high tax complexity.

5.4.3. Spatial Analysis

Almost all cross-country studies of firm tax evasion practices ignore important threats
to the internal validity of the estimation strategy. Their analysis assumes statistical indepen-
dence between countries. However, as previously noted, the adjacent countries compete
to produce an efficient tax system to attract mobile tax bases. Meanwhile, the tax evasion
ratio in a country is a consequence of such a ‘yardstick competition’ (e.g., Foucault et al.
Economies 2024, 12, x FOR PEER REVIEW 26 of 36
2008). More clearly, when governments in neighbouring countries endeavour to streamline
their tax systems and expect increased levels of tax compliance, the governments in home
countries should do the same as a normal response. This circumstance reflects clear spatial
effects in the
the lowest tax evasion
quantiles. ratio, which,
To strengthen this ifvisual
not accounted
argument, for we in the model,
report will of
the results bias our
spatial
parameter estimates
autocorrelation tests (Anselin
using the1988).
global Moran’s I and Geary’s C. According to Table 11, the
Interestingly,
global Figure
Moran’s I value of 2the
shows that firm
average spatialtaxpatterns
evasion in firmwas
ratio taxgreater
evasionthan
ratios areand
zero easysig-
to
identify. For example, the countries in Western Africa, including Guinea, Guinea-Bissau,
nificant at the 1% level, while the global Geary’s C value of the average firm tax evasion ratio
Senegal, Mali,one
was less than andand
Mauritania,
significantwhich
at theshare common
1% level. Theseborders, have the highest
results confirmed quantiles
a significant of
spatial
firm tax evasion ratio. The two Baltic states, Estonia and Latvia, and countries
autocorrelation effect on the tax evasion ratio in all countries involved in this study. with close
geographical distances, such as Belarus, Poland, and Germany, have tax evasion ratios in
the lowest
Table quantiles.
11. Global Moran’sToI and
strengthen
Geary’s C this visual
of tax argument,
evasion ratio. we report the results of spatial
autocorrelation tests using the global Moran’s I and Geary’s C. According to Table 11, the
Moran’sI I value of Ithe averageE(I)
globalMoran’s sd(I) ratio wasZgreater than
firm tax evasion p-Value
zero and
0.112 −0.012 0.026 5.112
significant at the 1% level, while the global Geary’s C value of the average firm tax 0.000
evasion
ratio was less than one andCsignificant E(C) at the 1% level.sd(C)
These resultsZconfirmed ap-Value
significant
Geary’s C
spatial autocorrelation effect
0.853on the tax1.000
evasion ratio0.094
in all countries involved in 0.058
−1.570 this study.

Figure 2.
Figure 2. Quantile plot of average tax evasion ratio across
across sample
sample countries.
countries.

TableTo
11.consider the influence
Global Moran’s of the C
I and Geary’s interdependence effects between different countries on
of tax evasion ratio.
the link between tax complexity and firm tax evasion, we conducted further analysis using
I
the spatial autoregressive (SAR) modelE(I) for cross-sectional
sd(I) Z
data developed p-Value and
by Kelejian
Moran’s I
Prucha (1999), which0.112
considers the−spatial
0.012 autocorrelation
0.026 effects in the dependent
5.112 variable
0.000
by introducing a spatialC lag term (the tax
E(C) evasion ratio
sd(C)in neighbouringZ countries
p-Value the
affects
evasionCratio in the home countries). Both contiguity and inverse-distance matrices were
taxGeary’s
0.853 1.000 0.094 −1.570 0.058
constructed to run the models. The results are shown in Table 12. First, the Wald test results
indicated that the spatial econometric models used in the current analysis were appropri-
ate for estimating the spatial interaction effect on the relationship between tax complexity
and firm tax evasion. Second, after tolerating the presence of such effects, our two tax
complexity variables still positively affected the firm tax evasion ratio.

Table 12. Spatial analysis results.

Economies 2024, 12, 97 26 of 35

To consider the influence of the interdependence effects between different countries

on the link between tax complexity and firm tax evasion, we conducted further analysis
using the spatial autoregressive (SAR) model for cross-sectional data developed by Kelejian
and Prucha (1999), which considers the spatial autocorrelation effects in the dependent
variable by introducing a spatial lag term (the tax evasion ratio in neighbouring countries
affects the tax evasion ratio in the home countries). Both contiguity and inverse-distance
matrices were constructed to run the models. The results are shown in Table 12. First, the
Wald test results indicated that the spatial econometric models used in the current analysis
were appropriate for estimating the spatial interaction effect on the relationship between
tax complexity and firm tax evasion. Second, after tolerating the presence of such effects,
our two tax complexity variables still positively affected the firm tax evasion ratio.

Table 12. Spatial analysis results.

Dependent Variable Tax Evasion Ratio

Weighting Matrix Contiguity Matrix Inverse-Distance Matrix
Specifications (1) (2)
Log of tax time 0.112 * 0.087 *
(0.059) (0.052)
Log of tax payment 0.122 ** 0.113 **

(0.061) (0.048)
Marginal Effect dy /d x
Direct Effect
Log of tax time 0.116 * 0.091 *
(0.061) (0.055)
Log of tax payment 0.126 ** 0.119 **
(0.063) (0.050)
Indirect Effect
Log of tax time 0.045 0.041
(0.036) (0.035)
Log of tax payment 0.049 0.053
(0.033) (0.033)
Total Effect
Log of tax time 0.162 * 0.131
(0.090) (0.086)
Log of tax payment 0.175 ** 0.172 **
(0.087) (0.073)
Observations 83 83
Pseudo-R2 0.065 0.068
2
Wald χ test: spatial terms 14.02 *** 12.78 ***
Note: The estimation is based on cross-sectional data. The pooled sample period is from 2002 to 2010. Standard
errors are reported in parentheses. All control variables are included, but the estimation results are omitted for
simplicity. *, **, and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

It should be noted that the coefficients in the spatial econometric models discussed
above (pivot coefficients) did not directly serve as the marginal effects of tax complexity on
the firm tax evasion ratio. Therefore, direct, indirect, and total effects reflecting changes
in the tax complexity measures of a particular country are also reported in Table 12. The
direct effect represents the impact of the changes in tax complexity measures on the firm tax
evasion ratio in the home countries. The indirect effect is the impact caused by changes in
the tax complexity measures of neighbouring countries on the tax evasion ratio in the home
countries. The total effect is simply the combination of the direct and indirect effects. We can
observe the difference between the direct effect coefficients and the pivot coefficients—also
called feedback effects—representing the impact passing through neighbouring countries
and back to the countries themselves. These are caused by the unreported coefficients
of the spatially lagged tax evasion ratio variable and those of the spatially lagged tax
complexity variables.
Economies 2024, 12, 97 27 of 35

Generally, we found that the coefficients of the direct effects of tax complexity variables
had a similar distribution pattern in significance level to that of the pivot coefficients. Tax
time and tax payment are consistently significant at the 10% and 5% levels, respectively,
and these results held for both spatial weighting matrices (contiguity and inverse-distance
matrices). However, the coefficient magnitudes of the direct effects were broadly larger than
the corresponding pivot coefficients, indicating that tax time and tax payment were slightly
more responsive to an increase in the home countries’ tax evasion than the pivot coefficients
shown in all spatial econometric models. For instance, in the contiguity matrix-based
regression, the direct effects of tax time and tax payment were 0.116 and 0.126, respectively,
and the pivot coefficients were 0.112 and 0.122, respectively. The difference between the
direct effects and the pivot coefficients produced feedback effects of 0.0043 and 0.0046 for
tax time and tax payment, respectively. The positive feedback effects indicate that over
time, the firm tax evasion ratio in the home countries will decrease due to increases in the
tax complexity. Gradually, tax authorities will be motivated to manage the improvement
of their tax administration efficiency in response to rampant tax evasion; hence, they
can compete with neighbouring countries. These results also strengthen our argument
regarding the simultaneity bias between tax complexity and tax evasion.
In addition, the coefficients of the indirect effects found in all models were insignificant,
even at the 10% level. These results show that tax complexity in neighbouring countries
does not play a substantial role in determining tax evasion in home countries, corroborating
the exogeneity assumption of our instruments. However, it is worth noting that the
positive indirect effects take time to materialise, especially if geographically close countries
harmonise and consolidate their tax systems—which may currently be fragmented—to
significantly reduce compliance and administrative costs and thus lessen tax evasion
opportunities of cross-border operations by firms within the neighbourhood (Mintz 2004;
Barrios et al. 2020). These findings corroborate a large body of evidence from Nicodème
(2009), Riedel (2018), and van’t Riet and Lejour (2018), showing that global firms exploit
cross-country differences in corporate income tax rules. Such firms take advantage of
existing inconsistencies and loopholes within the international tax network through transfer
pricing, debt shifting, and the strategic allocation of intangible assets across tax jurisdictions.

6. Conclusions
This cross-country investigation delved into the intricate dynamics of firm tax evasion
and its association with tax complexity, shedding light on a phenomenon that transcends
borders and economic strata. While previous research has largely scrutinised tax evasion at
the individual level, this study attempted to explore the uncharted territory of corporate
entities and unveiled the interconnectedness of tax-related regulatory factors across diverse
nations. In doing so, it sought to bridge the gap in the existing literature, offering a holistic
view of how tax complexity influences the propensity of firms to engage in tax evasion on
a global scale. Drawing from an extensive dataset from over 46,000 firms in 83 countries,
our investigation provided compelling evidence that a complex tax system indeed begets
increased firm tax evasion. Robustness checks, including endogeneity tests, verified the
robustness of the baseline regression results. Furthermore, this study investigated the
heterogeneity of these impacts across country income levels and industries. Last, this study
also elaborated an analysis that considered spatial dependence in corporate tax evasion
ratios. Our main conclusions remained intact with such analyses.
The study’s findings yield several policy implications that can guide tax policy and
enforcement strategies. First, policymakers should prioritise tax simplification efforts to
alleviate the burden of tax complexity. Streamlining tax codes and regulations can enhance
compliance and reduce the attractiveness of tax evasion. Second, governments should en-
hance institutional quality, including governance, rule of law, and regulatory effectiveness,
which is pivotal in fostering a climate of tax compliance and regulatory trustworthiness.
Effective institutions play a crucial role in deterring tax evasion. Third, in countries with
lower per capita GNI, policymakers should adopt tailored strategies to address tax evasion
Economies 2024, 12, 97 28 of 35

unique to these settings. Recognising the interplay between tax complexity and the hard-
to-tax sectors (e.g., agriculture and informal activities) is imperative. Fourth, given the
observed spatial interdependence effects, countries should engage in collaborative initia-
tives to harmonise tax systems and close cross-border tax evasion loopholes. International
cooperation can curtail opportunities for tax evasion. Last, this study underscores the
need for responsive tax policies that acknowledge the deleterious impact of tax complexity
on firm tax evasion. By simplifying tax systems, fortifying institutions, and embracing
international collaboration, nations can embark on a path toward fairer and more effec-
tive tax regimes, enhancing fiscal sustainability and revenue collection in an increasingly
interconnected global landscape.
While this cross-country investigation provides valuable insights into the association
between tax complexity and firm tax evasion, several limitations merit consideration. First,
the study relied on data from the WBES and the WBDB databases, which, while robust,
may still be subject to measurement errors and limitations inherent in large-scale surveys.
Additionally, data availability for some countries may be limited or outdated. Second, the
study acknowledges the potential endogeneity of tax complexity as complex tax systems
may evolve in response to high levels of tax evasion. Although instrumental variables
are employed to mitigate this concern, the challenge of selecting suitable instruments
remains. Last, the study encompasses a diverse array of countries, but its findings may
not be universally applicable. Cultural, legal, and economic nuances across countries can
influence tax evasion behaviours, necessitating caution in generalising results.

Author Contributions: Conceptualization, G.M. and P.B.S.; methodology, G.M.; software, G.M.;
validation, G.M., P.B.S. and F.S.; formal analysis, G.M., P.B.S. and I.P.; investigation, G.M.; resources,
G.M.; data curation, G.M.; writing—original draft preparation, G.M., P.B.S., F.S., I.P., D.P. and I.K.;
writing—review and editing, G.M. and F.S.; visualization, G.M.; supervision, G.M., P.B.S. and F.S.;
project administration, I.P.; funding acquisition, P.B.S. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was funded by Universitas Indonesia, NKB-615/UN2.RST/HKP.05.00/2023.
Informed Consent Statement: Not applicable.
Data Availability Statement: The corresponding author will provide the data associated with this
work upon reasonable request.
Conflicts of Interest: The authors declare no conflicts of interest.

Appendix A

Table A1. Variable details.

Variable Definition Database

Tax evasion ratio Unreported sales tax. WBES, the World Bank
The value is equal to one if the ratio of tax evasion is
Tax evasion dummy WBES, the World Bank
more than zero; otherwise, it is equal to zero.
Log of the duration required for tax preparation and
Log of tax time WBDB, the World Bank
payment.
Log of the aggregate quantity of taxes paid by firms,
Log of tax payment WBDB, the World Bank
including the use of electronic filing methods.
Log of real GDP per capita, which is measured in
Log of GDP WDI, the World Bank
terms of 2015 USD.
The yearly growth rate of the ratio between the GDP
Inflation measured in current local currency and the GDP WDI, the World Bank
measured in constant local currency.
Economies 2024, 12, 97 29 of 35

Table A1. Cont.

Variable Definition Database

The proportion of the labour force that is actively
Unemployment WDI, the World Bank
seeking employment but currently without a job.
The contribution of agriculture, forestry, and
Agriculture WDI, the World Bank
fisheries to the GDP in terms of value added.
The tax liability imposed by the governing
Index of Economic Freedom, the Heritage
Tax burden authorities. A higher value (0–1) implies a heavier
Foundation
tax burden and vice versa.
The percentage of the population in the age range of Population Estimates and Projections, the
Age
15 to 24 who are engaged in economic activities. World Bank
Gender The percentage of female labour. Gender Statistics, the World Bank
The perceptions of the public participation level in
Voice and accountability government selection. A higher value (−2.5–2.5) WGI, the World Bank
implies a better institutional quality.
The perceptions on the probability of encountering
political instability and politically motivated
Political stability WGI, the World Bank
violence. A higher value (−2.5–2.5) implies a better
institutional quality.
The perceptions pertain to several aspects of public
Government effectiveness services. A higher value (−2.5–2.5) implies a better WGI, the World Bank
institutional quality.
The perceptions on the government’s capacity to
effectively devise and execute well-founded policies
Regulatory quality and regulations that facilitate and advance the WGI, the World Bank
growth of the private sector. A higher value
(−2.5–2.5) implies a better institutional quality.
The perceptions of the degree to which individuals
exhibit trust and compliance with societal norms. A
Rule of law WGI, the World Bank
higher value (−2.5–2.5) implies a better institutional
quality.
The perceptions on the degree to which public
Control of corruption authority is used for personal benefit. A higher WGI, the World Bank
value (−2.5–2.5) implies a better institutional quality.

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