Corruption and Tax Structure in American States

Abstract

We examine the extent to which public corruption influences the tax structure of American states. After controlling for other tax structure influences, we find that states with greater measured public corruption have more complex tax systems, have higher tax burdens, rely more heavily on regressive indirect taxes, and have smaller shares of their tax burdens with initial impact on business. These are significant structural impacts on the tax systems.

Keywords

corruption fiscal illusion indirect tax tax regressivity tax share by business

Introduction

Kaufmann (2010) provocatively asks “whether corruption may adversely affect public finances in industrialized countries?” An abundant literature has focused on corruption impacts in developing and transition countries (Fjeldstad & Tungodden, 2003; Ghura, 1998; Grimes & Wängnerud, 2010; Moloney & Chu, 2016; Ohemeng & Owusu, 2015; Tanzi & Davoodi, 1997; Themudo, 2014) and a new literature is finding impacts of corruption on American state finances (Bayoumi, Goldstein, & Woglom, 1995; Butler, Fauver, & Mortal, 2009; Depken & LaFountain, 2006; Liu, Moldogaziev, & Mikesell, 2017; Maher, Deller, Stallmann, & Park, 2016; Moldogaziev, Liu, & Luby, 2017). This article extends this examination of corruption impacts by considering whether corruption in American states might impact the structures used to raise tax revenue.

Corruption means “misuse of public office for private gain” and, given the capacity of a tax system to distribute costs among private entities, it would not be surprising to find an impact of corruption on that system of distribution. Corrupt officials may be susceptible to illegal inducements from private entities interested in changing the tax structure to their advantage. Although a number of scholars have investigated the causes, consequences, and cures of corruption, corruption impacts on tax systems have not been investigated. Liu (2017) surveys the existing literature about the causes, consequences, and cures of corruption. Furthermore, the challenges of governance and corruption in the industrialized world have been less-examined than has its impact in developing countries (Kaufmann, 2010). If government finance systems can be distorted for private gain, there is ample reason to examine whether public corruption might impact the structures used to raise revenue for public programs. Moving the cost of government to others and hiding those economic impacts can be of considerable economic advantage to those with influence.

Developed countries likely have a higher level of tax compliance and tax morale than do developing ones. However, evidence of the impact of corruption on other elements of the fiscal system raises a suspicion that tax systems might be influenced as well. It is a question not previously examined. This article fills this gap by examining how corruption affects the level and composition of tax revenue in the U.S. state and local governments over the period 1997-2013. We examine how public corruption is associated with the level of tax burden, the extent of tax progressivity, and the degree of tax transparency across the states.

Literature Review and the Logic of Corruption Influence

Businesses and individuals may reduce their tax obligations by three general approaches. First, they may take illegal and intentional actions to reduce their tax obligations. They may evade “by underreporting incomes; by overstating deductions, exemptions, or credits; by failing to file appropriate tax returns; or even by engaging in barter to avoid taxes” (Alm, Martinez-Vazquez, & McClellan, 2016). Traditional tax evasion theory is often utilized to explain the corruption effects on the tax structure of the developing countries and the transition economies (Fjeldstad & Tungodden, 2003; Ghura, 1998; Tanzi & Davoodi, 1997).

Second, they may structure their operations to reduce their tax liabilities through legal means. That includes taking advantages of deductions, exemptions, or credits provided in the law; by timing transactions to reduce liabilities; by structuring transactions to take advantage of lower tax rates provided in the law; and so on. These avoidance actions reduce tax obligations but, in contrast to the evasion tactics, are legal within the existing law. Avoidance activities are recognized as acceptable in a Supreme Court case: “The legal right of a taxpayer to decrease the amount of what otherwise would be his [or her] taxes, or altogether avoid them, by means which the law permits, cannot be doubted” (Gregory v. Helvering, 293 U.S. 465 [1935]).

Third, businesses and individuals may reduce their tax obligations by changing the tax law so that liabilities are reduced within the scope of that law. That approach requires influence on lawmakers and tax administrators and that opens the door for use of corrupt practices. If corrupt entities can induce a tax structure favorable to their interests, they can reduce their own tax burden as long as the tax structure stays in place and they are freed from the need to aggressively practice avoidance or evasion. When there are corrupt public officials, this approach may be the most efficient for entities working to reduce tax burdens.

The largest impact of corruption on tax systems may, however, be in regard to burden transparency. Any manner of tax system manipulation for gain of public officials and their associates would be easier if the general public is unaware of tax structures and tax burden distribution. Hence, public corruption may be expected to have an impact on tax structures not only in regard to the burden patterns they produce but also, critically, in regard to the degrees to which the system itself operates in transparent fashion. A fiscal illusion, particularly in regard to taxpayer misperceptions about the level and structure of the tax system, would be an invaluable tool for public corruption. Indeed, fiscal illusion may facilitate all other manipulation of the tax system to restructure, redistribute, or change tax burdens within the law. Consideration of the impact of corruption on fiscal illusion is critical for the present analysis.

While there is a paucity of analysis of the impact of public corruption on tax structure, there are many studies on the association between corruption and tax evasion. These studies focus on public officials’ “self-seeking” behaviors from taxpayers who have the intent to avoid taxation. They follow the household income tax evasion model of Allingham and Sandmo (1976); corruption and high tax rates (Chander & Wilde, 1992); wage incentives system to curb corruption (Besley & McLaren, 1993); optimal design of tax collection schemes (Hindriks, Keen, & Muthoo, 1999); and size of bribe and tax evasion (Akdede, 2006). Others focus on evasion efforts by firms which evade their tax obligations by underreporting their income and sales, by overstating deductions, and by failing to file their tax returns (Rice, 1992; Wang & Conant, 1988); audit selection rules and firm compliance (Alm, Blackwell, & McKee, 2004; Murray, 1995); contractual relationship between shareholders and tax managers (Crocker & Slemrod, 2005); market distortion due to tax evasion by firms (Goerke & Runkel, 2006); corruption activities by firms (Goerke, 2008); corruption and tax compliance in the transition economies (Uslaner, 2010); and the association between the size of bribes and corporate income tax evasion (Alm et al., 2016; Wu, 2005).

Another group of evasion studies focuses on the macroeconomic consequences of corruption on taxation, often connecting corrupt activities by public officials with the various aspects of their fiscal and tax policies. Allowing tax auditors to accept bribe can decrease the amount of revenue collected (Chander & Wilde, 1992). Corruption reduces the tax collection of governments when corruption contributes to tax evasion, improper tax exemptions, or poor tax administration (Alm, Bahl, & Murray, 1991; Friedman, Johnson, Kaufman, & Zoido-Lobaton, 2000; Gupta, 2007; Ivanyna, Moumouras, & Rangazas, 2016; Johnson, Kaufmann, & Zoido-Lobaton, 1999; Sanyal, Gang, & Goswami, 2000; Tanzi & Davoodi, 1997). In contrast, some studies argue that corruption can reduce tax evasion and increase tax revenue as a consequence. When expected benefit from corruption, for example, bribes, is high, a tax collector has incentives to monitor taxpayers more intensively. This increases the expected cost to taxpayers of evading taxes, which results in a lower level of tax evasion and a higher level of tax collection. This positive effect of corruption on tax revenue actually happened in the developing countries (Chand & Moene, 1999; Mookherjee, 1997), although Fjeldstad and Tungodden (2003) conclude that this is a short-term phenomenon, at best, and disappears in the long run.

Most of these macroeconomic analyses examine the association between corruption and the level of government tax revenue in developing and transition economies, not developed economies. The contexts of the developing and the transition economies differ from that of the developed countries. The average tax revenue to gross domestic product ratio in the developed world, approximately 35%, is much higher than the developing countries in which the ratios range from 12% to 15% (Cobham, 2005), possibly because of lower tax evasion in the developed societies. Often more than half of the taxes that should be collected cannot be traced by the government treasuries due to corruption and tax evasion (Fjeldstad & Tungodden, 2003). Tanzi (1996) notes that corruption may be more common at local than at the national level, although less severe in developed countries.

The links between corruption and tax evasion found in developing countries cannot be directly transferred to the United States. The tax morale of Americans, meaning “the intrinsic motivation to pay taxes,” is found to be higher than even that of Europeans, which is expected to result in high tax compliance rates in the United States (Alm & Torgler, 2006). The National Research Program (NRP), a program of research audits conducted by the Internal Revenue Service (IRS), estimated that the overall noncompliance rate of the U.S. federal individual income tax was around 18% in 2001 (IRS & U.S. Department of the Treasury, 2006), which is much lower than that of people residing in the developing countries. Even with lower tax evasion and higher tax morale, we believe that public corruption may have an impact on taxation through influence on public officials that works to shape the tax structure in advantageous ways. Thus, illegal evasion (or even legal avoidance) is not necessary if the legal framework for the tax has itself been attractively constructed.

Hypotheses

Corruption and Fiscal Illusion

We extend the fiscal illusion literature to hypothesize how corruption affects the tax structure of the U.S. states.¹ Fiscal illusion implies “systematic, persistent, recurring and consistent” misperception of key fiscal parameters by the citizenry due to the fact that most significant elements of the fiscal system become largely hidden to the citizenry. The idea focuses particularly on significant and regular underestimation of the costs of government programs by the citizenry. Public officials are presumed to be “self-seeking.” They will design and manipulate fiscal systems to create a fiscal illusion so that they may make taxpayers underestimate the actual fiscal burden and support large public revenue and outlay in the end and they will be receptive to efforts of private entities to shape the tax structure. Fiscal illusion results in a public sector of excessive size from this perspective. The literature identifies multiple hypotheses regarding fiscal illusion, particularly involving tax complexity, indirect taxes (Mill’s hypothesis), the income-elasticity hypothesis, the flypaper effect hypothesis, the renter illusion hypothesis, the debt illusion hypothesis, the inflation rate hypothesis, and the withholding hypothesis (Dell’Anno & Dollery, 2014; Oates, 1988).

Fiscal illusion creates misperceptions about tax structures and that misperception could be useful for corrupt public officials. The officials can pursue their interests (or interests of their “clients”) more easily if the public does not accurately perceive the tax structure, so illusion becomes the critical building block for all other manipulations of the tax system. The fiscal illusion literature concludes that illusion-inducing fiscal structures are “deliberate” choices by public officials seeking their own utility. A fiscal system creating a greater fiscal illusion is beneficial for their individual utility-maximization. Corrupt officials have a strong incentive to make the fiscal system more complex and less transparent. This helps them hide their corruption.

Corruption, Tax Complexity, and Tax Revenue

The complex tax illusion hypothesis predicts a potential corruption effect on the level of tax revenue. Buchanan (1967, p. 135) argues that “. . . to the extent that the total tax load on an individual can be fragmented so that he confronts numerous small levies rather than a few significant ones, illusionary effects may be created.” Thus, the more complicated a tax system, the more difficult it is for a taxpayer to determine the tax-price of public outputs, the more likely it is that he will underestimate the actual tax burden associated with public programs, and the larger will be the level of tax revenue ceteris paribus. Corrupt officials can pursue their utility by exploiting the complex tax illusion. We predict that a corrupt government has a more complex tax system than a less corrupt government, which helps her raise tax revenue in the end. Prior research finds that corruption increases U.S. state expenditure (Liu & Mikesell, 2014). Therefore, it is not unreasonable to expect that corruption will increase tax revenue. Higher tax revenue provides more spoils to be distributed.

Hypothesis 1: A state with a higher level of corruption is likely to have a more complex tax structure, all else being equal.

Hypothesis2: A state with a higher level of corruption is likely to have a higher level of tax revenue, all else being equal.

Hypothesis 3: A state with a higher level of corruption is likely to have a more complex tax structure, which results in a higher level of tax revenue, all else being equal.

Corruption, Indirect Taxes, and Tax Regressivity

The Mill’s fiscal illusion hypothesis maintains that “Taxpayers may systematically underestimate the tax burden from indirect taxes as compared to direct taxes because indirect taxes are incorporated into (and therefore ‘hidden’ in) the prices of goods” (Sausgruber & Tyran, 2005, p. 39). That illusion can be valuable to a corrupt official: The larger the portion of tax revenue from indirect taxes, that is, taxes on purchase or sale of goods and services, the more difficult it is for a taxpayer to determine the tax-price of public outputs and the more likely it is that he will underestimate the tax burden associated with public programs. Not only are these taxes generally invisible, they are also generally regressive. Because state and local governments rely heavily on these taxes, they serve to hide the cost of government and distribute that cost in a regressive fashion (Decoster, Loughrey, O’Donoghue, & Verwerft, 2010). The average share of the sales and gross receipts tax revenue amounts to about 36% of the total state tax revenue in the period 1997-2013. We hypothesize the association among corruption, a reliance on indirect taxes, and tax progressivity as follows.

Hypothesis 4: A state with a higher level of corruption is more likely to rely on sales and gross receipt taxes, which makes her tax system less direct, less progressive, and less visible as a consequence, all else being equal.

Corruption, Corporate Income Tax, and Tax Share by Business

An issue of contention in all state tax policy discussions is the balance between taxes on businesses and taxes on individuals. This is an artificial distinction because businesses act as a conduit of tax burden to individuals, either through higher prices for products sold, lower payments by the business for resources purchased from individuals, or reduced return to individual owners of the business. However, it has traction in tax structure discussions. There are three political reasons for this. First, the burden of tax with initial impact on business gets hidden as it is transmitted to individual taxpayers. That violation of transparency is attractive to many politicians. Second, a tax with initial impact on business appears to avoid placing tax burden on individual voters. That is also attractive to many politicians. Third, the chance that a tax with initial impact on business will be exported to individuals residing in other states is high. That is likely if the tax gets embedded in prices charged by the firm or if the tax reduces the return to out-of-state owners of the business. In either case, the result is attractive to politicians.

Higher impact on business is not attractive to businesses and neither is heavier use of corporate income taxes. Therefore, it is reasonable to expect that businesses, organized and individually, would be interested in reducing the business share of state and local taxes and the share of tax from corporate income, even though such structures might be attractive to the population. In an environment of corrupt public officials, one approach could be the use of illegal inducements to structure the state tax system to the benefit of businesses. That approach to burden reduction could be an attractive option in comparison to the ordinary devices of evasion and avoidance. In return for bribes and lobby from entrepreneurs, corrupt officials are more likely to design tax preferences to the businesses (Belitski, Chowdhury, & Desai, 2016). That is the influence to be tested here. Following these arguments, we hypothesize on the association between corruption and business tax structure as follows.

Hypothesis 5: The share of the corporate income tax revenue in total tax revenue is likely to become smaller in a more corrupt state, all else being equal.

Hypothesis 6: The share of taxes levied by businesses in total tax revenue is likely to become smaller in a more corrupt state, all else being equal.

Model, Methodology, and Data

Model and Methodology

Our econometric approach to examining the effect of corruption on the tax structure of the U.S. state governments (defining state tax structure as the combination of state and local taxes, in light of the extent to which state governments have the power to define tax options available to its localities) is a dynamic panel regression model, controlling for both state- and year-fixed effects with robust errors. Our data accommodate the period 1997-2013. Our choice of the data period is not arbitrary. A consistent database of gross state product (GSP) and its subcategories across the states is just available from 1997 since the U.S. Bureau of Economic Analysis (BEA) changed her industry classification system from the Standard Industrial Classification (SIC) to the North American Industrial Classification System (NAICS) in 1997. The 2013 U.S. state tax revenue data across subcategories from the Census are the most recent datasets publicly available at the point of our analysis. Thus, we decided to answer our research questions with the data over the period 1997-2013. The model controls for a multiple sets of covariates including corruption (the key test variable), state economic variables (Mahdavi, 2013), state demographic variables, state political variables (Ho, 2003; Merrifield, 2000; Sauser, 1993), and state fiscal institutional variables (Gade & Adkins, 1990; Giertz & Giertz, 2004; Joyce & Mullins, 1991), which is as follows²:

TS = f (corruption; TS in the previous year; real per capita gross state product (GSP); percent of GSP produced in agriculture, forestry, fishing, and hunting; percent of GSP produced in education services, health care, and social assistance; percent of GSP produced in manufacturing; percent of GSP produced in government; percent of GSP produced in accommodation; natural log of state total population; state population growth rate; share of state population of age 18-64; natural log of state population residing in urban areas; dummy of gubernatorial election years; dummy of governor’s party affiliation; extent of party competition in state legislature; dummy of the existence of governor’s veto power; index of state TELs stringency; index of local TELs stringency; year dummies; errors), where TS = a measure of tax structure.

The U.S. State Corruption and Its Measurement

To measure official corruption across the U.S. state governments, we use the U.S. Department of Justice (DOJ) Report to Congress on the Activities and Operations of Public Integrity Section (https://www.justice.gov/criminal/pin). The DOJ publishes the annual numbers of federal, state, and local officials who are convicted of violations of federal corruption-related laws within and across the states. The report understands corruption as “crimes involving abuses of the public trust by government officials,” which is consistent with the definition of corruption in the literature. The report provides a comprehensive record of corruption conducted by public officials in the executive, legislative, and judicial branches.³ The U.S. state-level data in this study are measured and collected in homogeneous and consistent ways, so they provide us with a panel database which is long and large enough to make our econometric models identified.

No available corruption index captures the extent of corruption completely and perfectly but convictions, representing a general sample of total corrupt activity in a jurisdiction, provide a reliable, relevant, and valid criterion. It is noted that that the limitations of the measurement should also be acknowledged, including poor documentation and data-generating process, internal inconsistency, uneven distribution of officials across government levels and ranks. The public integrity section (PIS) data just count the number of public employees who violated corruption laws without differentiating the seriousness of corruption between high-level officials and low-level employees. Compared with state and local public officials, the percent of federal officials convicted is dominant in the data (Cordis & Milyo, 2016; Zhang & Kim, 2017). Cordis and Milyo (2016) favor the administrative records on federal corruption prosecutions available from the Transactional Records Access Clearinghouse (TRAC) which are accessible with a license. Some studies also doubt the validity of the conviction measures because the measures might capture the extent of anticorruption activities and efforts, not the extent of corruption at the state level (Alt & Lassen, 2014; Archambeault & Elmore, 1983; Maass, 1987).

But many studies use the number of convictions published by DOJ to measure the extent of corruption at the state level (Butler et al., 2009; Depken & LaFountain, 2006; Glaeser & Saks, 2006; Goel & Nelson, 2011; Meier & Holbrook, 1992). The DOJ is the most reliable and complete source of conviction data for U.S. public officials and it is generally accepted that the numbers of convictions are highly correlated with the extent of corruption across the states. According to Meier and Holbrook (1992) and Glaeser and Saks (2006), state corruption rankings based on the conviction measures match the perception of general Americans and professional reporters working in state legislatures.

Regarding validity, we ran a number of regressions of the conviction measures on caseload, pending rate, U.S. attorney’s working hours, the number of judges, and state judiciary expenditure. All are generalized by the number of public employees and the state population before running regressions. The two corruption measures are the number of convictions per 10,000 public employees and the number of convictions per 100,000 persons of population. Compared with the second corruption index, the first one works better because we focus on public officials’ corruption. Thus, we use the first index for our benchmark analyses. Finding that none of these variables are significant determinants of our conviction measures over the period 1997-2013, we conclude that our convictions measures do not reflect prosecutorial efforts, law enforcement/slackness, or courts’ resources but do capture the extent of corruption across the states.

Our conviction measures have a couple of comparative advantages compared with most corruption-related indexes. Most existing corruption indicators are measured based on the perception of corruption which should be subjective and inconsistent across individuals and societies. The DOJ applies the federal corruption laws, neither state nor local laws, and provides objective numbers of convictions which are consistent across states and years. A recent study also argues that the PIS data should be the most accurate and reliable data for American corruption because the prosecutors know more of corruption convictions than any other parties (Zhang & Kim, 2017). Table 1 describes the rankings of the U.S. states based on multiple criteria in the period 1997-2013, on average. According to the corruption index, the 10 least corrupt states during the period are New Hampshire, Oregon, Nebraska, Minnesota, Iowa, Kansas, Utah, Washington, Colorado, and South Carolina. The 10 most corrupt state governments, from the most corrupt one, are Louisiana, Kentucky, South Dakota, Mississippi, Montana, Alaska, Virginia, Florida, Alabama, and Pennsylvania. The corruption rankings of state governments based on the second corruption index are not remarkably different from those from the first index, which will be found in Table 1.

Table 1.

The Rankings of the U.S. State Governments (1997-2013, on Average).

Rank	Corruption (per public employee)	Corruption (per population)	Tax revenue	Tax complexity	Sales/indirect/regressive tax	Coporate Income Tax share	Business share
1	Louisiana	Louisiana	Maine	New Hampshire	Washington	Alaska	Alaska
2	Kentucky	Alaska	New York	Alaska	Nevada	New Hampshire	Wyoming
3	South Dakota	South Dakota	Vermont	Washington	Tennessee	Delaware	North Dakota
4	Mississippi	North Dakota	Alaska	Texas	Louisiana	New York	Texas
5	Montana	Mississippi	West Virginia	South Dakota	South Dakota	West Virginia	South Dakota
6	Alaska	Kentucky	Hawaii	Oregon	Hawaii	Massachusetts	Louisiana
7	Virginia	Montana	Rhode Island	Tennessee	Arkansas	California	New Mexico
8	Florida	Alabama	New Jersey	Wyoming	Florida	Tennessee	Delaware
9	Alabama	Virginia	North Dakota	New Jersey	New Mexico	Michigan	Washington
10	Pennsylvania	Missouri	Wisconsin	Florida	Alabama	Illinois	New Hampshire
11	Missouri	New Jersey	Connecticut	Rhode Island	Mississippi	New Jersey	West Virginia
12	North Dakota	Delaware	Mississippi	Massachusetts	Texas	Kentucky	Tennessee
13	New Jersey	Ohio	Minnesota	Vermont	Arizona	Indiana	Montana
14	Delaware	Florida	Montana	Arizona	Oklahoma	Pennsylvania	Arizona
15	Illinois	Illinois	Maryland	Nevada	Utah	North Dakota	Nevada
16	Ohio	Pennsylvania	Pennsylvania	Connecticut	West Virginia	North Carolina	Oklahoma
17	Maryland	Oklahoma	Wyoming	Maine	Missouri	Montana	Florida
18	Tennessee	Maryland	Kansas	Montana	Georgia	Minnesota	Mississippi
19	West Virginia	Tennessee	Michigan	Hawaii	Kentucky	Mississippi	Vermont
20	Oklahoma	West Virginia	Arkansas	Maryland	Kansas	Arkansas	Nebraska
21	Arizona	Wyoming	Ohio	Georgia	Colorado	New Mexico	Kansas
22	Rhode Island	New York	Florida	Wisconsin	North Dakota	Arizona	Maine
23	Texas	Texas	California	Arkansas	South Carolina	Idaho	Colorado
24	Massachusetts	Massachusetts	Kentucky	Colorado	North Carolina	Oregon	Alabama
25	New York	Hawaii	Idaho	Louisiana	Indiana	Wisconsin	Iowa
26	Georgia	Rhode Island	New Mexico	Kansas	Idaho	Kansas	Illinois
27	Hawaii	Arkansas	Nebraska	Michigan	Wyoming	Utah	South Carolina
28	Arkansas	Arizona	Illinois	Illinois	Michigan	Alabama	Rhode Island
29	Connecticut	Georgia	Massachusetts	Delaware	Iowa	Florida	Kentucky
30	Nevada	Vermont	Iowa	Indiana	Nebraska	Maine	California
31	Indiana	Connecticut	Oklahoma	New York	California	Connecticut	New York
32	Michigan	Idaho	Indiana	Nebraska	Minnesota	Nebraska	Georgia
33	Vermont	Indiana	Arizona	Ohio	Illinois	Vermont	Idaho
34	Idaho	Maine	South Carolina	Virginia	Ohio	Oklahoma	Indiana
35	California	Michigan	Nevada	Iowa	Pennsylvania	Georgia	Pennsylvania
36	Wyoming	New Mexico	Washington	Utah	Rhode Island	Maryland	Missouri
37	Maine	California	Utah	Mississippi	Wisconsin	Louisiana	Minnesota
38	Wisconsin	Wisconsin	North Carolina	South Carolina	Vermont	Rhode Island	Ohio
39	New Mexico	North Carolina	New Hampshire	Missouri	Maine	Iowa	Hawaii
40	North Carolina	Nevada	Louisiana	New Mexico	Connecticut	Virginia	Michigan
41	South Carolina	Kansas	Virginia	California	Virginia	Colorado	Arkansas
42	Colorado	South Carolina	Missouri	North Carolina	New York	South Dakota	Utah
43	Washington	Colorado	Alabama	Idaho	Maryland	South Carolina	Wisconsin
44	Utah	Utah	Georgia	Minnesota	New Jersey	Missouri	Massachusetts
45	Kansas	Washington	Colorado	Kentucky	Massachusetts	Ohio	New Jersey
46	Iowa	Iowa	Oregon	North Dakota	New Hampshire	Hawaii	Virginia
47	Minnesota	Minnesota	Tennessee	Alabama	Montana	Wyoming	Oregon
48	Nebraska	Nebraska	Texas	Pennsylvania	Delaware	Texas	North Carolina
49	Oregon	New Hampshire	South Dakota	Oklahoma	Alaska	Nevada	Maryland
50	New Hampshire	Oregon	Delaware	West Virginia	Oregon	Washington	Connecticut

Note. All columns rank states from the highest to the lowest, based on each index. The details of the indexes are explained in Table 2.

Dependent Variables

Our dependent variable of the model examining Hypothesis 1 is measured by the generalized Herfindahl–Hirschman Index (HHI) with seven subcategories of tax revenues (property taxes, general sales and gross receipts taxes, total selective sales taxes, individual income taxes, corporation net income taxes, total license taxes, and other taxes), which is the most often used index of tax complexity across the states (Chapman & Gorina, 2012). Use of this variable to capture complexity (and illusion) began with Wagner’s (1976) seminal article and, as Oates (1988) has observed, “every subsequent study of the revenue-complexity hypothesis has used this index as the measure of the illusion variable.” Although this article also considers an illusion variable based on the direct tax/indirect tax balance (as discussed later), the HHI is a fundamental measure. Our analysis is of tax systems and the HHI captures system effects better than any variable based on a particular tax. The 10 states with the least complex tax structure during the period are West Virginia, Oklahoma, Pennsylvania, Alabama, North Dakota, Kentucky, Minnesota, Idaho, North Carolina, and California. The 10 states with the most complex tax structure over the period, from the highest, are New Hampshire, Alaska, Washington, Texas, South Dakota, Oregon, Tennessee, Wyoming, New Jersey, and Florida (see column Tax Complexity in Table 1). To reduce possible confusion about the direction of impacts, the model uses the reciprocal of HHI, so that a higher value means greater complexity.

There are possible stability and efficiency advantages from having a diverse revenue structure, so one might argue that a high HHI, signaling more diversity, is a tool of positive financial management. However, it remains the case that a diverse structure makes it more difficult to identify tax burden on an individual or a business and the cost of government becomes less clear. This opacity is to the advantage of a corrupt official, making diversity/complexity attractive, even if it might also have some other fiscal advantages. It is this attractiveness to corruption, regardless of other effects, that the model seeks to identify. And hidden taxes are surely inconsistent with democratic governance.

The dependent variable of the model testing Hypotheses 2 and 3 is measured by the ratio of total tax revenue to GSP, multiplied by 100. The 10 states with the smallest tax revenue during the period are Delaware, South Dakota, Texas, Tennessee, Oregon, Colorado, Georgia, Alabama, Missouri, and Virginia. The 10 states with the largest tax revenue over the period, from the largest, are Maine, New York, Vermont, Alaska, West Virginia, Hawaii, Rhode Island, New Jersey, North Dakota, and Wisconsin (see column Tax Revenue in Table 1).

Our dependent variable of the models examining Hypothesis 4 equals the share of sales and gross receipt taxes in total tax (the indirect tax ratio), an indication of tax invisibility and regressivity of the state and local system. For the U.S. states and localities, the indirect taxes are included in classification C107 Sales and Gross Receipts Taxes by the Governments Division, U.S. Bureau of Census. The U.S. Census Bureau (2010) defines the category as

Taxes, including “licenses” at more than nominal rates, based on volume or value of transfers of goods or services; upon gross receipts, or upon gross income; and related taxes based upon use, storage, production (other than severance of natural resources), importation, or consumption of goods.

The total amounts paid by an entity during a year are easily observable (more transparent) for direct taxes (income and property), while the totals paid in sales and excise taxes are not. Hence, the indirect tax ratio—an element of the tax system that is subject to decisions made by lawmakers—provides a useful measure of transparency/opacity for our investigation of the impact of corruption on the tax system. The 10 states with the most visible (and least regressive) tax structures during the period are Oregon, Alaska, Delaware, Montana, New Hampshire, Massachusetts, New Jersey, Maryland, New York, and Virginia. The 10 states with the least visible (and most regressive) tax structures over the period, from the highest, are Washington, Nevada, Tennessee, Louisiana, South Dakota, Hawaii, Arkansas, Florida, New Mexico, and Alabama (See column Sales/indirect/regressive Tax in Table 1).

Two dependent variables capture the tax burden initially levied on firms which are used to examine Hypotheses 5 and 6. One measures the share of corporation net income taxes in total tax, while the other measures the share of state and local taxes paid by businesses in state and local total taxes.⁴ Businesses in states with high values for these variables have been less successful in shifting the balance of tax impact from business taxes to individual taxes. Those states offer fewer tax preferences to firms or levy structures affording higher impact rates on businesses. According to the Ernst & Young measure of the share of total tax revenue with initial impact on business used elsewhere in this article, the 10 states with highest business shares are Alaska, Wyoming, North Dakota, Texas, South Dakota, Louisiana, New Mexico, Delaware, Washington, and New Hampshire. The 10 states with lowest business shares are Connecticut, Maryland, North Carolina, Oregon, Virginia, New Jersey, Massachusetts, Wisconsin, Utah, and Arkansas (see column Business Share in Table 1). The column CIT Share in Table 1 shows the state ranking measured by the corporate income tax share index.

Explanatory Covariates and Controls

Table 2 provides comprehensive information on the dependent and independent variables: how to measure them and where to collect them, including descriptive statistics of them. Note our regression models include the lagged value of their dependent variables as their independent variable. This is to control for one of the most characteristic features of government finance, that is, incrementalism. Most taxes remain in place unless changed explicitly by legislative action, making past decisions critical for current law. This variable makes our models dynamic panel regressions.

Table 2.

Descriptive Statistics.

Variable	Label	Observations	M	SD	Minimum	Maximum	Source
Dependent variables
Tax burden	Ratio of total tax revenue to GSP, multiplied by 100	750	8.70	1.27	5.92	18.35	U.S. Census
Tax complexity	Inverse of the generalized Herfindahl–Hirschman Index (HHI). The larger the index, the more complex system.	750	1.15	0.08	0.99	1.79
Sales/indirect/regressive tax	Share of sales and gross receipt tax (%) in total tax revenue. Proxy for the extent of indirect/regressive taxes; the higher index, the less progressive system	750	35.68	12.35	5.62	64.83
Corporate Income Tax share	Share of corporate income tax (%) in total tax revenue	750	3.66	2.55	0.00	22.42
Business share	Share of tax revenue collected from businesses (%) in total tax revenue	500	47.33	10.13	28.90	100	Ernst & Young LLP
Independent variables
Corruption (employee)	Number of convictions per 10,000 public employees	847	0.50	0.40	0.00	2.73	U.S. Department of Justice
Corruption (pop)	Number of convictions per 100,000 people of the state population	847	0.34	0.30	0.00	2.55	U.S. Department of Justice
GSP^a	Real per capita GSP, divided by 10,000	850	0.04	0.01	0.02	0.08	U.S. Bureau of Economic Analysis
Agriculture (%)^a	Percent of GSP in agriculture, forestry, fishing, and hunting (NAICS)	850	1.68	1.91	0.12	12.99
Education (%)^a	Percent of GSP in education services, health care, and social assistance (NAICS)	850	7.75	1.83	3.21	13.54
Manufacture (%)^a	Percent of GSP in manufacturing (NAICS)	850	13.38	5.87	1.80	30.59
Government (%)^a	Percent of GSP in government (NAICS)	850	13.85	3.11	9.17	25.27
Accommodation (%)^a	Percent of GSP in accommodation (NAICS)	850	1.11	1.78	0.25	14.81
Natural log of population	Natural log of state total population	850	15.11	1.01	13.10	17.46	Book of the States, U.S. Census
Pop growth	State population growth rate	850	0.92	0.78	−5.99	5.87
Age1864	Share of state population of age 18-64	850	0.62	0.01	0.58	0.66
Urbanization	Natural log of state population residing in urban areas	850	14.74	1.14	12.17	17.41
Election	Dummy of years of gubernatorial election	850	0.25	0.43	0.00	1.00
Party	Dummy of governor’s party affiliation (1 = Democrats)	850	0.43	0.50	0.00	1.00
Competition	Extent of party competition in state legislature, 1 minus the average of proportions of Democrats in House and Senate	850	0.50	0.17	0.09	1.00
Veto	Dummy of governor’s veto power (1=yes, 0=no)	850	0.87	0.33	0.00	1.00
State TEL	Index of the stringency of state TELs	839	9.26	8.40	0.00	30.00	Amiel, Deller, and Stallman (2009)
Local TEL	Index of the stringency of local TELs	842	15.78	10.62	0.00	38.00	Amiel, Deller, and Stallman (2009)

Note. All government finance variables are measured by adding state and local values in total. GSP = gross state product; NAICS = North American Industrial Classification System; TEL = tax and expenditure limit.

We follow the industry classification system (NAICS) of the U.S. Census.

We identify real per capita GSP, divided by 10,000, as a proxy for the major tax base of the U.S. state and local governments.⁵ We expect that an expansion of tax base of a government will increase tax capacity and tax revenue, all else being equal. Separate from the aggregate tax base effect, we also add the shares of several subcategorical products in total GSP and examine the impact of economic structure on tax revenue. The value-added tax literature, for instance, Aizenman and Jinjarak (2008), makes clear that not all sorts of economic activity are equally likely to generate revenue from particular taxes and it is reasonable that similar impacts would exist across the American states. The GSP subcategories examined here include agriculture, education, manufacturing, government, and accommodation (following NAICS). We suspect that it is harder to tax the agricultural sector than other sectors including manufacturing because of profitability and compliance problems, but retail is easier. Value added in education and the government is mostly exempt from taxation. Values produced from accommodation may capture governments’ ability to export tax burden through tourists.

Our regression models include multiple demographic variables of the states. The natural log of the state population and the growth of population capture the extent of people’s demand for government services, which implies fiscal burden on the governments. However, it is also understood as a proxy for economies of scale in publicly provided services. The variable named Age 1864 measures the share of the population aged 18 to 64. Young (younger than 18) and elderly (older than 64) residents demand more public provided services such as public education and health care, which implies a higher demand for government services. The natural log of the number of people residing in urban areas is a proxy for the extent of urbanization, which requires for a higher fiscal burden on the governments. It is noteworthy that the literature provides much conflicting evidence of the effect of demographic variables on government finance and summarizes that it is not a normative but empirical issue, which may depend on data and cases.

The set of political and institutional variables includes a dummy of gubernatorial election years, a dummy of governor’s party affiliation (1 = Democrats, 0 = the others), the extent of political competition in the state legislatures, a dummy of the existence of gubernatorial line-item veto, the stringency of state tax and expenditure limits (TELs), and the stringency of local TELs. Many empirical studies argue that the existence of TELs is not sufficient to exert significant influence on government finance. Thus, instead, we use the measures of the strength of state and local TELs, updated by Amiel, Deller, and Stallman (2009). Politicians prefer expansionary fiscal policies when elections approach. Democrats are generally understood to be more generous to government expenditures. Political checks and balances make increasing taxes more difficult when there is greater political competition. It will be easier for a governor with veto power to reduce government spending as she is allowed to eliminate specific expenditures or tax proposals. A higher stringency of state and local TELs is expected to result in a more restrictive fiscal administration. As noted in Table 3, we also control for state-fixed effect and year effect.

Table 3.

Regression Results and the Tests of Fitness Corruption and the Level of State and Local Tax Revenues (1997–2013).

Variable	Model 1		Model 2		Model 3		Model 4
	Tax complexity		Tax revenue		Tax revenue		Tax revenue
	b	t value	b	t value	b	t value	b	t value
Main
Dependent variable at (t – 1)^a	0.31***	9.98	0.26***	8.79	0.47***	13.32	0.46***	13.24
Corruption (employee)	0.01***	4.59			0.26***	3.97	0.27***	4.16
Tax complexity			10.62***	8.79
Corrupt × Complexity^b					0.11*	1.98
GSP variables
GSP	−0.02***	−4.50	10.06***	19.23	33.72**	2.74	36.11**	2.95
Agriculture^c	0.01	0.79	−0.08**	−3.07	−0.12**	−3.44	−0.12**	−3.42
Education^c	0.06*	2.28	0.005	0.08	0.09	1.11	0.09	1.15
Manufacture^c	0.01*	2.53	−0.06***	−5.10	−0.08***	−5.35	−0.08***	−5.42
Government^c	−0.09***	−6.34	−0.01	−0.26	−0.13**	−2.73	−0.13**	−2.64
Accommodation^c	0.07*	2.02	−0.32***	−3.73	−0.29*	−2.57	−0.28*	−2.49
Demographic
Natural log of population	−0.03	−0.64	−1.14	−1.38	−0.92	−0.86	−0.87	−0.82
Pop growth	−0.004	−1.95	0.02	0.49	−0.05	−1.11	−0.06	−1.24
Age 1864	0.28	1.09	−4.34	−0.98	−0.2	−0.04	−0.88	−0.16
Urbanization	0.003	0.12	0.38	0.72	0.28	0.42	0.17	0.26
Political & institutional
Election	−0.002	−0.91	−0.002	−0.05	−0.03	−0.58	−0.03	−0.47
Party	−0.005*	−2.05	0.03	0.69	−0.06	−1.23	−0.06	−1.28
Competition	0.02	1.38	−1.00**	−3.39	−1.31**	−3.47	−1.27**	−3.36
Veto	0.0002	0.03	−0.04	−0.23	−0.05	−0.23	−0.03	−0.14
State TEL	−0.001	−1.40	0.02	1.39	0.02	0.77	0.02	0.9
Local TEL	0.001	0.47	0.01	0.36	0.02	0.46	0.02	0.45
Constant	1.26	2.09	9.77	0.91	16.56	1.19	17.55	1.26
Fixed effect	controlled		controlled		controlled		controlled
Year effect	controlled		controlled		controlled		controlled
Observations	637		589		585		587
R²(within)	.58		.70		.50		.50
F statistic	25.86***		40.51***		17.10***		17.49***

Note. The ratio of each categorical gross state products to total state and local tax revenues in Model 1. GSP = gross state product; TEL = tax and expenditure limit.

Lagged values of the dependent variables of each model. The ratio of GSP to total state and local tax revenues in Model 2.

Interaction term between corruption and tax complexity.

Added values of each categorical gross state product (%) in Models 2, 3, and 4.

**,***: significant at 5%, 1%, and 0.1%, respectively.

Empirical Results

Corruption Versus Tax Burden

Model 1 in Table 3 describes how corruption affects the extent of the tax complexity in the U.S. state governments in the period 1997-2013. Model 2 in Table 3 shows how tax complexity is associated with the tax revenue of the U.S. states over the same period. Model 3 in Table 3 tests the third hypothesis of indirect impact of corruption on tax revenue through tax complexity by including an interaction term of corruption and tax complexity in the model of tax revenue. Model 4 in Table 3 is our benchmark model examining the effect of corruption on state and local total tax burden over the period. To the potential reverse causality and simultaniety issues, we use the lagged value of the corruption through our regressions. Our benchmark model (Model 4) does not include the tax complexity variable and the interaction term of corruption and tax complexity, because we assume that corruption effects the level of tax revenue through tax complexity.

Model 1 shows a positive association between corruption and the generalized HHI tax complexity index. The association is significant at the 0.1% level and means that a state government with a higher level of corruption is likely to have a more complex tax, thus supporting Hypothesis 1. Model 2 shows a positive association between tax complexity and total tax revenue. The impact is significant at the 0.1% level and implies that a state with a more complex tax system is likely to collect more tax revenue. Model 3 also shows a positive association between the interaction term (of tax complexity and corruption) and total tax revenue. The impact is significant at the 5% level. The results from Models 1 to 3 prove an indirect impact of corruption on tax revenue through tax complexity. A U.S. state can succeed in raising a larger amount of tax revenue by making its tax system more complex, supporting Hypothesis 3. Model 4 shows that there is a significantly positive association between corruption and tax revenue, which is also significant at the 0.1% level. This provides significant evidence in support of Hypothesis 2.

The regression results of the Models 1 through 4 are consistent with the fiscal illusion theory which argues that self-interested officials are motivated to make the fiscal system more complex to create fiscal illusion and make taxpayers underestimate their actual tax burden, which results in a larger amount of tax revenue in the end. A government with more corrupt officials is expected to make more efforts to create a fiscal illusion, for example, by making its tax system more complex. A state with greater corruption is likely to have a more complex tax system and the fiscal illusion that results allows a government to collect a larger tax revenue. This implies that U.S. citizens residing in a state whose public officials are more corrupt should shoulder heavier tax burden due to public officials’ corruption.

The regression results of the covariates in our benchmark model, Model 4, correspond to expectations from the literature. Other than the corruption variable, it appears that the significant determinants of tax revenue are the first lag of the dependent variable, GSP, the shares of products from agriculture, manufacturing, government, and accommodation, and the extent of political competition. We interpret the results one by one as follows. First, a higher level of tax revenue in a previous year is likely to have a positive impact on the level of tax revenue in the following year, which makes sense given the incremental nature of tax structures. Tax laws remain in place year after year, unless legislative action is taken to change them, and that is a relatively infrequent occurance. Second, it is natural that a bigger potential tax base, measured by real per capita GSP (divided by 10,000), should produce more tax revenue. Third, the subcategories of GSP, that is, agriculture, manufacturing, government, and accommodation, show a significantly negative association with tax revenue. The results for manufacturing and accommodation are puzzling and may have more to do with political influence than with expectations about the extent to which particular activities are likely to throw off taxable economic base (or, more properly, base that the tax authorities are capable of taxing). Most demographic, political, and institutional variables other than the extent of political competitiveness in the state legislatures do not exert a significant influence on tax collection during the study period. The check and balance function of competitive state legislatures seems to restrain the state governments from increasing tax burden on their residents.

Robustness Checks of the Results

We used several strategies to assess the robustness of our models and address the possible endogeneity of the empirical results in the benchmark model. We start from a simple dynamic panel regression model of tax-to-GSP ratios on corruption by controlling for the state-fixed and year effects. We extend the model to accommodate GSP and the GSP relevant variables. We further added the sets of covariates, that is, demographic, political, and institutionalfactors, set by set. The positive association between corruption and the level of tax revenue remains substantively and statistically significant across all nested and nonnested respecifications. Furthermore, instead of the number of convictions per 10,000 public employees, we used the number of convictions per 100,000 people in the population as a proxy for the state corruption. The significantly positive association between corruption and tax revenue remains. We also ran a number of generalized method of moments (GMM) regressions to control for the potential endogeneity problem of the corruption variable,⁶ both a two-step first difference GMM model and a two-step system GMM model. Both models address the small sample bias problem. In sum, the significantly positive association between corruption and tax burden remains across a number of variations. The regression results of the other factors of the state tax burden also correspond with those of Model 4 in Table 3. We conclude that the regression results of our benchmark model are consistent and robust.

Corruption Versus Tax Composition

The corruption effect will not be the same across the different types of taxes. The Mill’s hypothesis maintains that self-interested officials prefer indirect taxes to direct taxes because it is more difficult for taxpayers to assess their actual tax burden from those than these. Likewise, corrupt officials are more likely to create a fiscal illusion by designing an indirect-tax-oriented tax system and fool taxpayers to underestimate their actual tax burden. We use the share of tax revenue collected from Sales and Gross Receipt Taxes (C107, Census code) in total taxes as a proxy for the extent of indirect taxes across the states. Model 5 in Table 4 shows a significantly positive association between corruption and the share of sales and general receipt taxes in the state and local total taxes, which is significant at the 1% level. A state with a higher extent of corruption is more likely to collect her tax revenue from indirect taxes, which is in support of Hypothesis 4.

Table 4.

Regression Results and the Tests of Fitness Corruption and the Composition of State and Local Tax Revenues.

Variable	Model 5		Model 6		Model 7
	Sales and general receipt tax^a (Indirect/regressive taxes)		Corporate income tax^a (% of corporate income tax)		Business share^b (Taxes levied by businesses)
	b	t value	b	t value	b	t value
Main
Dependent variable at (t – 1)^c	0.70***	24.82	0.64***	19.75	0.33***	6.47
Corruption (employee)	0.43**	2.84	−0.23*	–2.39	–0.90**	−2.79
GSP variables
GSP^d	0.47**	2.63	0.17	1.52	–0.13	−0.29
Agriculture^d	−0.55	−0.89	−0.3	–0.76	–2.19	−1.61
Education^d	1.56	1.25	−1.01	–1.28	–6.86*	−1.96
Manufacture^d	−0.59*	−2.17	−0.18	–1	0.79	0.98
Government^d	−0.12	−0.17	1.36**	2.92	–1.23	−0.59
Accommodation^d	0.68	0.4	−1.54	–1.41	–5.52	−0.86
Demographic
Natural log of population	−2.29	−1.00	0.42	0.29	27.70***	3.61
Pop growth	−0.02	−0.20	0.05	0.66	–0.26	−1.13
Age1864	−5.85	−0.46	8.8	1.08	69.71	1.17
Urbanization	1.82	1.27	−0.41	–0.45
Political & institutional
Election	−0.03	−0.23	0.01	0.07	–0.24	−0.82
Party	−0.07	0.58	0.05	0.05	0.01	0.05
Competition	−2.20**	−2.60	0.39	0.72	–0.01***	0
Veto	0.05	0.11	0.14	0.47	–4.55***	−4.35
State TEL	0.16	3.44	−0.03	–1.02
Local TEL	0.18	1.81	−0.02	–0.29
Constant	12.22	0.40	−6.92	–0.36	–414.90**	−3.2
Fixed effect	controlled		controlled		controlled
Year effect	controlled		controlled		controlled
Observations	638		638		439
R² (within)	0.68		0.60		0.55
F statistic	40.21***		27.23***		19.24***

Note. The values are measured by Earnst & Young LLP and available from 2004 to 2013. Model 7 drops the variables of urbanization, state TEL, and local TEL automatically, due to collinearity. GSP = gross state product; TEL = tax and expenditure limit.

The share (%) of each tax revenue to total state and local tax revenues.

The dependent variable captures the ratio of taxes collected from businesses to total state and local tax revenues.

Lagged values of the dependent variables of each model.

The ratio of each categorical gross state products to total state and local tax revenues.

**,***: significant at 5%, 1%, and 0.1%, respectively.

Many tax studies use the share of sales and gross receipts taxes in total taxes as a proxy for the extent of tax regressivity. A tax system which relies heavily on these taxes is presumed to be regressive, or less progressive. Consumption spending is higher as a share of household income for lower income families than it is for higher income families. This is true not just for total consumption but also for most categories of expenditure. The effective tax rates of these taxes are higher for low-income households than that for higher income households. Thus, the distribution of the tax burden is regressive, which creates equity problem for the taxes (Mikesell, 2014, p. 447). In this regard, the regression result of Model 5 implies that public officials’ corruption is associated with state tax regressivity; thus, the actual tax burden of lower income households residing in a state whose government is more corrupt tends to become heavier than that of higher income households.

We use the share of corporate income tax in the total tax and the share of taxes levied by businesses in the total tax as two proxies⁷ for the tax revenue collected from businesses. Both Models 6 and 7 show that there exists a significantly negative association between corruption and the tax revenue levied by businesses, which are significant at the 5% and the 1% levels, respectively. Businesses operating in a state whose government is more corrupt are likely to face a smaller share of total tax revenue, compared with businesses operating in a state whose government is less corrupt. We interpret this that businesses operating in the states whose governments are more corrupt are more likely to find ways to evade (or avoid) their tax obligations and/or succeed in reducing their tax liabilities. The results support both Hypotheses 5 and 6.

Conclusion and Policy Implication

We extended the fiscal illusion theory to explain how corruption affects the tax structure of a developed country and examined empirically the effects through the case of the U.S. state and local governments. Most existent studies investigating the corruption effects on tax structure have focused on the experiences of the developing countries and the transition economies, so they adopted the tax evasion theory to explain the phenomena. There is room for an analysis of the corruption effects on taxation in developed economies, not through tax evasion but rather through manipulation of the legal tax structure in advantageous ways, thus reducing the attractiveness of evasion or avoidance.

The United States is one of the most developed economies, and it is found that the tax compliance and tax morale of Americans are higher than those of people in the other countries. Different from the existing tax evasion literature, we found that a U.S. state with a higher level of corruption is likely to collect higher taxes. Consistent with the arguments of the fiscal illusion theory, a state whose officials are more corrupt is likely to have a more complex tax structure and collect more taxes from its citizens thanks to the illusory tax system. Moreover, a state whose officials are more corrupt is more likely to rely on an indirect tax system than on a direct one, which makes its tax system more regressive or less progressive. The share of tax revenues levied by businesses tends to decrease in a more corrupt state. In sum, the corruption of the U.S. state and local governments results in a heavier tax burden on the general public, a more regressive and less transparent tax structure, and a smaller share of tax burden initially collected from business share of tax revenue levied by businesses, at least in the period 1997-2013.

The results raise serious governance issues. Although the United States is one of the most developed societies, corruption impacts its tax structure. Corruption effects result in heavier tax burden imposed to the general public. It is terrible that people should bear extra tax burden due to public corruption. Corruption also makes tax system more complex and less transparent, which increases taxpayer compliance cost. The more opaque tax system, moreover, the more unaware are taxpayers of their actual tax burden. Citizens in a democracy must know all information relevant to taxation, including the government service costs, taxpayers’ actual tax burden, the procedures of tax adoption and administration, and so forth. However, corruption undermines tax transparency.

A tax structure distributes government costs among private entities. The fundamental assumption relevant to taxation is that nobody is pleased to pay taxes, which is well expressed by the quote, “Don’t tax you. Don’t tax me. Tax the guy behind the tree” (Long, 1973). Moving the cost of government to others is advantageous. Thus, businesses and individuals are motivated to reduce their tax obligations within and/or outside the scope of the tax law. This approach requires influence on lawmakers and tax administrators, which often results in corrupt practices. We see that business tax share tends to decrease in states with a more corrupt government. Considering that the total tax burden increases in a society with a more corrupt government while business tax share decreases in the society, the tax burden of individuals will become heavier due to public officials’ corruption, as a consequence. Corruption undermines tax progressivity, which implies that lower income households shoulder relatively heavier tax burden compared with high-income households. Corruption destroys equity.

Corruption is significantly associated with the extent of the tax burden, complexity, transparency, and equity of a tax system. All of them are major issues relevant to tax policy of governments. A government should consider the influence of corruption on the tax strcutre when it designs and reforms its tax structure.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Cheol Liu is Assistant Professor of the KDI School of Public Policy and Management (Republic of Korea). His research focuses on public finance, budgeting, and revenue administration. Especially, he is interested in corruption impacts on various public finance variables. He holds a BA from Yonsei University, MSc in economics from London School of Economics (LSE), and PhD in public affairs from Indiana University-Bloomington.

John L. Mikesell is Chancellor’s Professor Emeritus of Public and Environmental Affairs at Indiana University. His research focuses on finances of subnational governments, budget systems and processes, and sales and property taxation. His textbook Fiscal Administration, Analysis and Applications for the Public Sector is widely used in graduate public administration programs. He holds a BA from Wabash College and MA and PhD in economics from the University of Illinois-Urbana.

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