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Scrutiny of behavior and anonymity of participants
Laboratory experiments involve a different relationship between participants and experi-menter, compared to the field. Participants know that their behaviors will be under great scrutiny in the lab, and they also know that their anonymity is not totally insured.
To illustrate the latter, List (2006) has presented the very well-known baseball cards traders experiment. In a lab experiment, baseball cards traders rewarded, as in a gift exchange game, higher prices with higher quality cards. However in the field, when the baseball card sellers were not scrutinized, this relationship between quality and prices did not exist anymore. Pro-social behaviors would be exaggerated in the lab compared to the field. It would translate in producing less evasion in TEG, compared to the field. To answer this concern, Bloomquist (2009) first of all underlined that it is not sure “that subjects perceive a heightened sense of scrutiny in the lab versus outside the lab” (p. 118). It is especially true when the rather high compliance rates in the field are compared to the rather low lab compliance rates. Moreover in real life, unless cash transactions off the desk, taxpayers know that tax administration fights against tax evasion and audits some taxpayers each years. Real taxpayers, as participants from TEG, are already scrutinized, even though not as much.
The latter is illustrated using List, Berrens, Bohara, and Kerkvliet (2004) article: the less a pub-lic good game is anonymous (using a randomized response technique), the more participants contribute. Knowing that someone observes you should make you less willing to cheat in a TEG. However first of all, “double blind” procedures–procedures that ensure full anonymity of participants–can be used to limit this effect. Secondly, Bloomquist (2009) also highlights that all TEG do not automatically possess a public good. Third, once again, no one remains anonymous towards tax administration.
To sum up, the problem of scrutiny of behavior is disputable when it comes to the TEG, es-pecially because taxpayers are considered to be already scrutinized. Anonymity of taxpayers is also not ensured in real life. This problem could also be easily countered by using “double blind” procedures.
Nature of income: self-employed vs salaried job
After deciding if subjects of a TEG earned or were endowed with an income, experimenters can also propose to subjects to choose between an income where they have the opportunity to cheat (self-employed) and one in which they cannot (salaried), so as to reveal participants’ preferences. In Gërxhani and Schram (2006), participants chose first between unregistered (self-employed) and registered (salaried) income. They then draw randomly within one of these sets, an income. Registered income have a high average and a low standard deviation and unregistered income have a lower average and a higher standard deviation. The registered income was audited for sure. The unregistered income was audited with the probabilities 0%, 16.67% or 50%. The results show that participants who chose a registered income declared truthfully their income. Participants chose more often an unregistered income when tax evasion was possible. However all participants who chose an unregistered income did not cheat. In Alm, Deskins, and McKee (2009), participants earned an income and this income was divided in a “matched” income and “non-matched” income. The probability of detecting matched income was 100%. The probability of detecting non-matched income varied across treatments between 25% to 75%. Thus, non-matched income came from self-employment. Overall subjects did not declare all of their non-matched income. No connection could be made between percent of income received as non-matched income and compliance. There was a slight downward trend but compliance was at the highest when participants received half of their income as non-matched income. Elaborating on the example of Gërxhani and Schram (2006), Lefebvre, Pestieau, Riedl, and Villeval (2015) decided to make participants chose between a registered and an unregistered income. A lottery drew the amount of gross income effectively perceived by the participant, across a set of possible incomes. Unregistered income had the highest standard deviation and registered income, the lowest. The registered income was automatically taxed. People with unregistered income had first to choose between reporting or not, and then decided of the amount to report. The results show that 60.64% of participants chose an unregistered income and among them, 40.65% chose to evade a portion of their income.
When income comes from a salaried job and has 100% chances of getting audited, participants declare their income more truthfully, in comparison to when they have an unregistered income. Unregistered income is rather successful: participants choose more often an unregistered in- come when it is available. As unregistered income is expected to be lower when fully taxed, it reveals some intentions to cheat. However it does not lead automatically to more evasion. Therefore people like to keep an opportunity to cheat.
The decision task in a TEG is a valid measure of tax behavior
Another important criticism towards tax evasion games (as there are against experimental eco-nomics in general) is that one can wonder if the experiments are really revealing of behaviors in the field. In an experiment, choice set is reduced. To illustrate this, in Lazear, Malmendier, and Weber (2012), participants could opt out from a dictator game and it led to significant different sharing rates.
This question is addressed in an analysis done by Bloomquist (2009) and republished in Alm, Bloomquist, and McKee (2015). The aim of the study was to compare reporting behavior from a group of US taxpayers and participants from different tax evasion experiments. Again, tax-payers were Schedule C filers subjected to IRS random audits. The results show similarities in behaviors. In the field, the mean compliance rate was about 31.30% for an audit probability of 1.72%, compared to 28.80% (or 40.40%) compliance when the audit was 0% (or 5%) in the lab. The results show that the distributions of compliance were also similar: both adopted a bi-modal distribution, with the first mode being 0% of compliance and the second one being 100% compliance. Authors also noted that they observed approximately equal shares of fully compliant individuals in both settings. These similarities were being observed when scrutiny, anonymity, context, size of stakes, pool of participants, individual characteristics, time were significantly different.
To conclude on the criticisms of TEG, when compared to the appropriate data, compliance rates obtained in the lab are globally equivalent to those observed in the field, whatever the framing, size of stake and pool of subjects. Therefore Levitt and List (2007) criticisms are not fully righteous, when applied to the external validity of TEG.
The impact of traditional deterrent variables on lab tax compli-
It has been shown previously the elements to take into account to produce a valid tax evasion game, and what do these elements produce in terms of compliance. We have also seen pre-viously that the Allingham and Sandmo model predicted that: audit probability, size of fine should increase tax compliance and tax rate should have an ambiguous impact on tax compli-ance. Yitzhaki’s addition predicted that tax rate should have a negative impact on tax evasion. This Section puts theory to the test of experimental practice, it describes below the impact of the traditional deterrent variables–tax rate, audit probability, size of fine–in incentivized TEG.
From traditional deterrent variables to non-monetary incentives to comply
To conclude on the impact of the traditional economic deterrent variables on lab tax compli-ance, it concludes in favor of Allingham and Sandmo (1972) findings. They predicted that audit and fine had a positive impact on compliance while tax rate had an expected uncertain impact. It is exactly what is demonstrated when studying tax lab compliance. Traditional deterrent variables have exactly the expected impact foreseen by the classical Expected Utility model.
However even though these parameters globally explain well lab tax compliance, they rather fail to explain real life tax compliance. Indeed, these parameters have been found to be so low that they could not explain the full amount of compliance observed in real life. The research question changed in the early 90s from “Why do people evade?” to “Why do people in fact comply so much?”. For example, Alm, McClelland, and Schulze (1992) wrote about this tax evasion puzzle: “Although it is clear that detection and punishment affect compliance to a degree, it is equally clear that these factors cannot explain all, or even most, tax compliance behavior. The percentage of individual income tax returns that are subject to a thorough tax audit is quite small in the United States, less than 1 percent in recent years. In addition, the penalty on fraudulent evasion in the United States is only 75 percent of unpaid taxes, and the penalties on non-fraudulent evasion are even less. A purely economic analysis of the evasion gamble implies that most individuals would evade if they are “rational”, because it is unlikely that cheaters will be caught and penalized. Yet compliance with the individual income tax remains relatively high; that is, individuals pay far more in taxes than suggested by the standard expected utility theory of compliance. It seems implausible that the low penalties and the low probability of detection that prevail in the United States, indeed in most countries, can by themselves act as an effective deterrent to evasion, unless individuals’ aversions to risk far exceed conventional assumptions. In fact, the Internal Revenue Service (1978) has found that there are numerous factors other than detection and punishment that affect the decision to pay taxes” (p. 21-22).
Foundations of tax morale from moral psychology
Moral psychology is a growing field within psychology, whose aim is to understand why peo-ple behave well and badly (Doris, 2010). Two different ways are explored to measure partici-pants’ morality: through moral emotions in the first experiment and through moral judgment in the second one. As in Calvet and Alm (2014), we choose to measure morality through moral emotions as a recent trend of papers shows its importance as a determinant of moral judgments (Haidt, 2001, 2008; Jourdheuil and Petit, 2015). Behaving morally requires to be able to make moral judgment, i.e. “judgment that something has moral significance. In expressing moral judgments we use terms such as right and wrong, good and bad, just and unjust, virtuous and base” (Prinz and Nichols, 2010, p. 113). Moral judgment drives what one ought to do. The second experiment focuses then on moral judgment directly, without focusing particularly on any of its different components.
Morality and moral emotions
The psychology of moral emotions has emerged from the idea that moral emotions are devel-oped through evolution, to help people choose the best strategy in human interactions. This gives rise to a strong relationship between emotions and morality, emotions being seen as either serving reason (Frank, 1988), or complementary to it (Damasio, 1994). Prinz and Nichols (2010) distinguishes three types of moral emotions: pro-social emotions that promote “morally good behavior” (empathy, sympathy, concern and compassion), self-blamed emotions that evoke negative self-directed feelings (guilt and shame) and other-blamed emotions, i.e. negative feel-ings directed towards others (contempt, anger, disgust). We choose to include only the first two, pro-social and self-blamed emotions, in our analysis as the third type seems harder to relate to tax evasion. Regarding the effect of pro-social emotions on tax compliance, the empathy-altruism hypoth-esis (Batson, Dyck, Brandt, Batson, Powell, McMaster, and Griffitt, 1988) predicts that em-pathetic people are more altruistic and fair towards others. Calvet and Alm (2014) test this hypothesis in the framework of a laboratory tax evasion game combined with psychometric measures of empathy and sympathy. Only sympathy appears positively correlated with tax compliance. The components of self-blamed emotions, shame and guilt, also exhibit contrasted correlations with tax evasion. As regards shame, Coricelli, Joffily, Montmarquette, and Villeval (2010) show an increase in emotional arousal when evaders are informed that their pictures will be shown to other participants. Coricelli, Rusconi, and Villeval (2014) moreover find that the shame proneness scale from the TOSCA-3 test is negatively correlated with the intensity of the fraud after being caught. Experimental evidence on Guilt, the other self blamed emotion, is rather mixed. Thurman, John, and Riggs (1984) observe a significant impact of anticipated guilt on tax evasion decisions, but also show that evaders can resort to neutralization strategies to avoid this feeling. This might explain the discrepancies observed in the literature, as Coricelli, Rusconi, and Villeval (2014) for instance fail to find any correlation of tax evasion with the guilt proneness sub-scale from the TOSCA-3. This is confirmed by Dunn, Farrar, and Hausserman (2016), who substantiate an effect of guilt on tax evasion but also show that the effect varies according to guilt cognition.
Morality and moral judgment
Recognizing a situation as morally problematic requires first the ability to formulate moral judgment. We collect moral judgments on critical themes: ethics principles, integrity and mor-alization of everyday life. These dimensions are in particular part of the Measuring Morality project, a “nationally-representative survey of adults in the United States aimed at understanding the interrelations among moral constructs, and at exploring moral differences in the U.S. population”.1
The tax literature on these themes is too scarce to be conclusive. Tax ethics and tax morale are often seen as overlapping notions (e.g. Wenzel, 2005; McGee, 2011; Maciejovsky, Schwarzen-berger, and Kirchler, 2012; Noll, Schnell, and Zdravkovic, 2016), only a few experimental stud-ies investigate tax ethics as a driving force of compliance. Henderson and Kaplan (2005) mea-sure tax ethics using the Multidimensional Ethics Scale, and find a positive correlation with the likelihood of complying in hypothetical scenarios–a result that confirms the one obtained by Reckers, Sanders, and Roark (1994) on participants judging tax evasion as “ethically wrong”. Ghosh and Crain (1995) rely on a measure of Machiavellianism to control for tax ethics; they confirm a positive association with compliance. We complement this literature by adding two dimensions to ethics principles, which to the best of our knowledge have never been linked empirically to tax evasion. Integrity is defined as the attachment to ethical principles and is expected to foster the effect of one’s ethics on compliance. Ethics is a deep and abstract dimen-sion of personality. The third dimension we consider aims to take into account ethics in daily behavior, based on a measure of moralization of everyday life.
Using Principal Component Analysis to combine sub-scales
Once again, we use a PCA to synthesize the information obtained in our questionnaires. There are overall 9 variables measuring different traits.27 The PCA leads to identify four principal components that explain 80.29% of the overall original questions variance (Rho=0.8029). The KMO is equal to 0.6132 and is judged as acceptable. Table 1.9 presents the eigenvector after orthogonal rotations of the four retained components.28 Their content can be interpreted ac-cording to their degree of correlation with the original psychological questionnaires.
Component 1 and Component 2 are based on the MELS and have one variable in common (F6-Disgust). More precisely, Component 1 is based on F1-Deception, F2-Norm violation, F4-Failure and F6-Disgust. Component 2 is based on F3-Laziness, F5-Body violations and F6-Disgust. Component 1 is constituted of variables involving mainly others while the second is rather involving the one who commits these behaviors. Thus, Component 1 captures morally reprehensible behaviors committed against others and the second, those committed against oneself. They are respectively renamed “Morality towards Others” and “Morality towards Self”. Component 3 is made of the idealism subscale from the EPQ and the unique integrity scale. Integrity measures the attachment to the sense of ethics that one feels. Idealism is the optimistic belief that ethical behavior will provide the best outcome possible. This third com-ponent represents an idealistic integrity. It is simply renamed “Idealism”.
Table of contents :
Introduction
0.1 The traditional economic analysis of tax evasion
0.1.1 Economic analysis of crime
0.1.2 Economic analysis of tax evasion
0.1.3 An addition of Yitzhaki
0.2 Methodological approach of conceiving a TEG
0.2.1 Scrutiny of behavior and anonymity of participants
0.2.2 Context of the experiment
0.2.2.1 Neutral vs loaded frame
0.2.2.2 On the way to ask for compliance
0.2.2.3 Origin of income: earned vs windfall income
0.2.2.4 Nature of income: self-employed vs salaried job
0.2.2.5 Redistribution to participants
0.2.2.6 Public good fund
0.2.3 Size of stake
0.2.4 Students are a valid pool of subjects
0.2.5 Temporal limitation
0.2.6 The decision task in a TEG is a valid measure of tax behavior
0.3 The impact of traditional deterrent variables on lab tax compliance
0.3.1 Tax rate
0.3.2 Audit probability
0.3.3 Fine size
0.3.4 From traditional deterrent variables to non-monetary incentives to comply
0.4 Alternative sources of tax compliance
0.4.1 Personality traits
0.4.1.1 Definitions and examples
0.4.1.2 When does personality vary?
0.4.1.3 Does (stable) personality traits really exist?
0.4.2 Context
0.4.2.1 Framing
0.4.2.2 Priming
0.4.2.3 Commitment
0.5 How are context and personality traits integrated into the analysis of tax evasion?
1 Does tax morale really exist? A psychometric investigation
1.1 Introduction
1.2 Foundations of tax morale from moral psychology
1.2.1 Morality and moral emotions
1.2.2 Morality and moral judgment
1.3 Experiment 1
1.3.1 Design of the experiment
1.3.1.1 Psychometric measures of moral emotions
1.3.1.2 Experimental procedure
1.3.2 Results
1.3.2.1 Compliance behavior and morality
1.3.2.2 Multivariate analysis
1.3.2.3 Using Principal Component Analysis to combine sub-scales
1.4 Experiment 2
1.4.1 Design of the experiment
1.4.1.1 Psychometric measures of moral judgments
1.4.1.2 Experimental procedure
1.4.2 Results
1.4.2.1 Compliance behavior and morality
1.4.2.2 Multivariate analysis
1.4.2.3 Using Principal Component Analysis to combine sub-scales
1.5 Conclusion
2 Tax evasion under Oath
2.1 Introduction
2.2 Fighting dishonesty with commitment
2.3 Experiment 1
2.3.1 Design of the experiment
2.3.2 Experimental treatment
2.3.3 Experimental procedure
2.4 Results
2.4.1 Descriptive statistics
2.4.2 Income declaration: the impact of individual variables
2.4.3 Income declaration: the oath impact
2.5 Experiment 2
2.5.1 Design of the experiment
2.5.2 Experimental procedure
2.5.3 Descriptive statistics
2.5.4 Income declaration: the oath impact
2.5.5 Compliance under oath: light on the polarization effect
2.6 Conclusion
3 Disentangling commitment from social effect in a voting experiment on tax funds
3.1 Introduction
3.2 Why should voting increase compliance?
3.3 Design of the experiment
3.3.1 Experimental protocol
3.3.2 Avoiding selection effect
3.3.3 Experimental treatments
3.3.4 Experimental procedure
3.4 Comparison of treatments and participants
3.4.1 Participants are globally comparable between treatment
3.4.2 Participants make the same decisions in each treatment
3.5 Results
3.5.1 Descriptive statistics
3.5.2 Direct democracy effect disappears when taking into account the selection
3.5.3 A commitment effect is found but no social effect
3.5.3.1 In the full sample
3.5.3.2 In the truncated sample: keeping people who vary their declarations
3.5.4 Are other variables influencing compliance?
3.5.4.1 Questionnaires’ answers are rather different
3.5.4.2 Perceived legitimacy, fairness and importance of the selection: their impact on compliance
3.6 Conclusion
Conclusion
