Abstract

Recent research shows that tax compliance varies widely across advanced industrial

democracies, but exactly why this is the case remains unresolved. Specifically, scholars

often have di�culty disentangling the influence of institutional factors from broader

cultural values and norms in explaining citizens’ willingness to pay their taxes. To

address this problem, we conduct laboratory experiments in two countries (UK and

Italy) which exhibit significantly di↵erent rates of tax evasion. Our design allows us

to examine citizens’ willingness to contribute to public goods via taxes while holding

institutions constant. We report a surprising result: when faced with identical insti-

tutions, incentives and risks of punishment, Italian subjects were significantly more

likely to comply than Britons. Further, we find that country-level di↵erences are not

driven by individual-level characteristics such as gender, age, or risk attitudes. Our

results highlight the need to separate institutional from values-driven components in

the cross-national study of tax compliance.

Modern welfare states face a set of difficult challenges as they adapt to the demographic,

economic and political strains of the early 21st century.1 States must struggle to maintain ad-

equate support for social welfare and educational programs in the face of growing distrust of

bureaucratic institutions, intense pressures to cut taxes for politically powerful constituen-

cies, and fiscal burdens arising from an aging population. The ability of governments to

collect revenues in an efficient and cost-effective manner is of central importance to how

successfully they meet their policy goals. And to ensure a healthy fiscal foundation, states

must be able to control (or reduce) tax evasion on the part of their citizens.

Yet, while Western European states generally possess tax systems sharing many of the

same formal features (Alm and Torgler 2006), the actual compliance rate varies widely across

these societies (Alm, Martinez-Vazquez, and Schneider 2004; Edlund 1999; Schneider and

Enste 2013; Svallfors 1997; Taylor-Gooby 1995; Torgler and Schneider 2007). Moreover, eva-

sion rates also seem to follow a geographic pattern, with high levels of compliance in northern

Europe, and widespread under-reporting in the countries further south. For example, if we

compare Great Britain and Italy as “representatives" of these regional groupings,2 Slemrod

(2007) estimates the evasion rate in the UK to be around 8% or 9% of GDP (similar to that

of Sweden), while comparative figures for Italy can reach as high as 46% (Petrini 2005).

The literature has advanced two broad theories to account for this cross-national variation

in tax compliance. First, institutional explanations focus on the relationship between the

1The research leading to these results has received funding from [anonymized grant]. All of

our experimental materials - oral scripts, software, replication data, and Stata .do files used

to produce the tables and figures - will be posted online upon publication.2Using the size of the “shadow economy” as a proxy for tax evasion, Schneider and Enste

(2013) show that Britain is fairly representative of the “median ” advanced industrial econ-

omy in terms of compliance, while Italy falls near the absolute bottom of cross-country

rankings, near countries such as Spain, Portugal and Greece.

1

quality of government and citizens’ willingness to comply with fiscal demands.3 Specifically,

this literature argues that citizens are more likely to pay their taxes if they believe that the

government is spending their money honestly and efficiently (Cummings et al. 2009; Edlund

1999; Frey and Feld 2002; Frey and Torgler 2007; Levi 1989; Levi, Sacks, and Tyler 2009;

Pommerehne, Hart, and Frey 1994; Scholz and Lubell 1998; Smith 1992; Smith and Stalans

1991; Torgler and Schneider 2007). By contrast, when citizens perceive public institutions

as corrupt and wasteful, they are likely to reciprocate by being dishonest in turn (Bergman

2009; Rothstein 2011). Along these lines, public opinion data from the Quality of Governance

Institute show that (as is typical of countries in southern Europe) Italians give their public

institutions consistently low ratings in terms of quality, impartiality and honesty (see Table

1). By contrast, British institutions rank near the “median” for northern European countries

(with the notable exception of France). Thus, one explanation for cross-national differences

in levels of tax compliance is that, in some countries, people often interact with low quality

institutions for which they are (unsurprisingly) unwilling to pay.

Table 1 about here

A second set of theories links tax compliance to cultural variation in social norms and

values. One important axis of cultural variation concerns how different societies draw the

boundaries of moral behavior (Banfield 1967; Bergman 2009; Ekeh 1975; Hofstede 2001;

Platteau 2000; Tabellini 2010). Specifically, in societies characterized by “amoral familism”

or “limited morality,” ethical conduct is often confined to only a small circle of familial or

3The literature here is vast, and concerns not only the quality of institutions. For example,

other scholars have noted the importance of factors such as participation in direct democracy

or “fair treatment” at the hands of the tax authorities (Alm, Jackson, and McKee 1993;

Feld and Frey 2002, 2007; Feld and Kirchgässner 2000; Torgler 2005). However, we have no

evidence that these factors vary systematically between Italy and Great Britain.

2

personal relationships, while outside of this circumscribed network, selfish or opportunistic

behavior is norm. In the public sphere, individuals follow the rules not out of some internal-

ized sense of “right” and “wrong,” but only when they are coerced to do so. By contrast, in

societies emphasizing a more “generalized” morality, individuals are presumed to apply the

same ethical principals that prevail within familial relations to conduct in the civic realm. As

Putnam (1994; 88) notes, “Citizens in a civic community, though not selfless saints, regard

the public domain as more than a battleground for pursuing personal interest.”

Along this dimension, Italy is often vilified - both in the press as well as in popular

opinion - as the quintessential “amoral” society in which people cannot be trusted to behave

ethically outside the network of personal relations. Nowhere is trait more evident than in

the usage of the word furbo - meaning “sly,” “devious,” or “cunning,” - which, in Italian,

can actually take on a positive connotation, indicating an ability to “play” the system.

Italians’ presumed skillfulness at circumventing official rules probably explains why they

are consistently regarded as the least trustworthy in Western Europe by other Europeans,

and also why Italians are the least likely to trust their own co-nationals (Mackie 2001).4

By contrast, Britain falls in the middle of the pack amongst non-Mediterranean countries in

terms of trustworthiness as judged by other Europeans, alongside other nations like Germany,

France and Ireland.

This distinction between limited and generalized morality has direct implications for the

level of tax compliance within a society. If breaking the rules imposes little moral cost,

then we can expect to observe low compliance, especially since audit rates and punishment

probabilities are insufficient to deter cheating in most cases (Alm, McClelland, and Schulze

1992; Andreoni, Erard, and Feinstein 1998; Frey and Feld 2002; Graetz and Wilde 1985;

Torgler 2002).

4Of course there is variation between regions of Italy (Putnam, Leonardi, and Nanetti 1994;

Tabellini 2010), but these public perceptions hold for the country as a whole.

3

However, there is another dimension to the moral argument worth considering: namely,

since fiscal receipts often underlie the provision of public services, the decision to evade taxes

represents not only a decision to “cheat the system,” but also a decision to cheat one’s fellow

citizens who may rely upon these services. Thus, tax compliance arguably reflects both

the moral costs from breaking the rules, as well as a “prosocial” concern for the welfare of

compatriots. In this vein, “limited morality” societies may be more prone to evasion since

citizens are presumed to care only about their own well-being (and that of their familial

network), with utter disregard for anyone else.

Yet, despite its intuitive appeal, we believe that the moralist explanation for tax com-

pliance rests upon some shaky assumptions. In particular, popular stereotypes often infer

morals from observed behavior, without recognizing that both may be a function of the insti-

tutional environment. For example, skillfulness at circumventing the rules (i.e. being furbo)

may only be an admirable trait in a society where the rules themselves are so byzantine,

and the institutions so corrupt, that the very legitimacy of these institutions can be brought

into question (Tyler 2006). Thus, we may believe that we are observing the influence of

different moral systems when, in fact, outcomes may simply reflect variation in the quality

of government. To test whether the morality of tax compliance does indeed differ across

cultures, it is therefore crucial to hold institutions constant.

Our research attempts to conduct exactly such a test through the use of behavioral exper-

iments. In these experiments, which involved over 500 participants across multiple locations

in Italy and the UK, individuals were presented with identical choices under a common set of

institutional rules. Our team spent over a year designing and re-designing our experimental

protocols to ensure that, as far as possible, we were holding the treatments constant across

languages and cultural differences. In the end, we decided to engage our participants in two

separate tasks to test the two components of the moralist argument. The first task consisted

of a tax compliance scenario in which participants earning real money were asked to report

4

their income under a variety of tax rates and redistributive scenarios. By comparing com-

pliance rates across countries, we are able to investigate whether, independently of quality

of fiscal institutions, Italians are indeed more opportunistic (less rule-abiding) than Britons

when faced with identical choices.

Second, we also wanted to know whether Italians are, in general, less concerned about

the welfare of their fellow citizens. To test this hypothesis, we implemented a series of mini-

dictator games designed by Murphy, Ackermann, and Handgraaf (2011) to measure other-

regarding preferences - what these authors term “Social Value Orientation" (SVO). In these

games, we elicit how much participants are willing to pay in order to increase the earnings

of an unknown partner. By comparing the decisions of Italian and British participants, we

are able to capture differences in the distribution of egoistic versus “prosocial” values.

Prevailing cultural stereotypes would lead us to predict that, even controlling for the

institutional environment, Italians would be more prone to engage in opportunistic or selfish

behavior, as manifest in both greater evasion in the tax compliance task, and less generous

allocations in the SVO task. However, to preview our results, we find no evidence to support

the contention that Italians are simply less moral than Britons. Instead, in the tax exper-

iment, we find that the compliance rate amongst Italian participants is significantly higher

than amongst Britons. In addition, in the SVO experiment, Britons and Italians displayed

an almost identical level of generosity towards other individuals. These results remain robust

to the inclusion of a host of demographic controls, and have been reproduced in multiple

experimental locations in the two countries.

In summary, our work makes two main contributions to the tax compliance literature.

First, at the theoretical level, we identify two dimensions (i.e. respect for the rules, and

other-regarding preferences) by which countries may differ with respect to the morality of

tax evasion. Second, although stereotypes about the “amorality" of Italians abound in the

popular consciousness, empirically we show that cultural values cannot explain the significant

5

cross-national variation in evasion rates that we observe in the real world. In the concluding

section, we discuss several implications of our findings for future work.

Overview of the Experiment

Studies involving experimental methods have become more and more widespread in political

science in recent years. By allowing scholars to carefully control for specific features of the

decision-making environment, experiments can help scholars to isolate cultural differences in

norms and values.5 Moreover, in research on illicit behavior (such as tax evasion or corrup-

tion) where observational data are scarce due to the illegality of these activities, experiments

have proved invaluable as tools for data generation.

Our experiments were conducted at six universities across the United Kingdom and Italy

at various points during the academic year 2013-2014: [locations anonymized]. Each of the

six universities maintains an electronic database of individuals who had expressed interest

in participating in behavioral experiments.6 These participant pools are composed mainly

of undergraduate students, but also include a number of non-students and people who had

already graduated. Several days prior to the actual session, individuals in the database

received an email informing them of the opportunity to take part in an upcoming research

study, and inviting them to subscribe to an experimental session at a particular date and

time. The email also included information on the estimated length of the session, as well as

the expected earnings per participant.

On the day of the experiment, upon arrival in the laboratory, participants were given a

randomly-drawn, anonymized ID number and assigned to a corresponding personal computer

5Several previous papers have employed this strategy in the tax compliance context (Alm,

Sanchez, and De Juan 1995; Cummings et al. 2009; Torgler and Schaltegger 2005).6For more details on the online recruitment system (ORSEE), see Greiner (2004).

6

terminal. Participants undertook all experimental tasks via computer, and the terminals were

partitioned to ensure that individuals could not communicate during the session, nor observe

what other participants were doing. Also, to ensure anonymity, we announced that decisions

and payments would be linked only to participants’ ID-numbers, and not to individual names.

At this point, participants were asked to sign a consent form specifying these details, and

also informing them of their right to discontinue participation at any time.7

Once all participants had been seated at their individual terminals, we began the session

by reading a short introductory script. Participants were informed that they would be asked

to complete a number of tasks (which we would gradually describe to them) and make a num-

ber of choices. Based on their choices and the choices of the other participants, they would

earn experimental currency units (ECUs), which would be converted into real money at the

end of the session.8 As mentioned above, the experiment itself consisted of a tax compliance

task and a SVO task, which we describe in more detail below.9 Finally, after making all of

their decisions and viewing the results, participants completed a post-experimental survey

before receiving payment. In all, the experiment lasted about 75 minutes, and participants

earned an average of 16 euros / 14.5 pounds for their time.

7Participants who wished to leave the experiment early could elect to receive a 5 euro /

pound show-up fee. In practice, all participants remained until the end of the session.8These units were converted into local currencies (pounds and euros) so that at the end of

the experiment, the average participant would receive an income of approximately twice the

average hourly wage for student employment in the local context.9The tasks are programmed using a free open-source software program called zTree, which

is commonly employed for economic experiments of this type (Fischbacher 2007).

7

Participants

The data we present in this paper are drawn from 31 different experimental sessions involving

a total of 671 participants. Because we are interested in comparing specifically British and

Italian attitudes towards tax compliance, we retain the data for only native students, whom

we define as those individuals born in Britain (Italy) to British (Italian) parents.10 The

result leaves us with a subset of 531 participants, of which 281 (52.3%) are from Italy and

250 (47.1%) are from the UK.11

Table 2 about here

In Table 2, we present some demographic information on our participant pool, and also

break down the means by country. Column (8) in Table 2 reports how the two populations

differ. Overall, 56% of our participants were male, with an average age of 23.8 years (s.d. =

7.7 years). Also, a good number (>80%) of our participants had previous experience with

behavioral experiments, such that they were already familiar with the general features of

experimental studies (e.g. interaction over computer, anonymity, etc.). However, only a

minority (35%) of participants reported studying economics as a major or minor field.

Turning to cross-country differences, we see that British participants were significantly

more likely to be employed and to report a higher willingness to take risks, while Italians

were more likely to study economics. On the other hand, we detect no significant differences

across the two populations in terms of gender, age, or previous participation in experiments.

We control for these demographic characteristics in our analyses below.

10Because of a misunderstanding at the recruitment stage, one session in Oxford enrolled

many immigrants. While we excluded the immigrants’ data, we were also concerned that

interacting with a disproportionate number of foreigners may have skewed the behavior of

even native-born Britons. We therefore drop this session entirely from the analysis.11Including the entire sample of 671 participants does not substantively change our results.

8

Tax Compliance Experiment

Participants begin the tax compliance experiment by completing a clerical task. In this

task, participants must copy rows of information from a sheet of paper into the computer.

For each correctly copied row, participants earn 10 ECUs. Next, participants are asked

to declare this income for taxation purposes under three different scenarios (this income

reporting screen is shown in Figure 1).12 In the terminology of the experiment, each scenario

constitutes a “round.” Participants are informed that they are free to declare any amount

of their income - from 0% to 100% - in each round, with the knowledge that they would

only pay taxes on the reported portion of their incomes (unless they were audited, in which

case they were assessed the full amount due in taxes, plus an additional penalty). Once

participants have made three separate declarations, this entire stage game (i.e. the clerical

task plus three reporting rounds) is repeated two more times, such that by the end of the

experiment, participants will have undertaken three clerical tasks and nine rounds of tax

reporting.

Figure 1 about here

In each round, we specify slightly different rules for the taxation and redistribution of

declared income. In Rounds 1 through 3, we hold tax rates constant (at a flat 30% rate),

12In order to encourage participants to treat the decision problem within the experiment as

a tax compliance problem, we intentionally incorporated tax language in our protocols,

using words such as “income,” “taxes,” and “audit” (Calvet and Alm 2014; Cummings et al.

2009). Of course, the issue of framing effects in tax experiments is far from settled (Alm,

McClelland, and Schulze 1992; Wartick, Madeo, and Vines 1999). However, we believe this

design choice offers an improvement over the use of neutrally-framed compliance games in

terms of the ability to stimulate taxpayer motivations.

9

and vary how tax revenues are redistributed to all the participants, thus simulating behavior

under different levels of “efficiency" in providing public goods. In Rounds 4 through 6, we

hold redistribution constant, and vary the tax rate (from a flat 10% to 50%). In Rounds 7

and 8, we introduce two different progressive taxation schemes. In the first scheme, the top

10% of declared incomes pays a 50% tax rate, the bottom 10% of declared incomes pays a

10% tax rate, and everyone else pays a 30% rate.13 In the second scheme, all income over

100 ECU is taxed at a 50% rate, income between 50 and 100 ECU is taxed at a 30% rate,

and all income below 50 ECU is taxed at a 10% rate. Finally, in Round 9, we donate all tax

revenues to a real world charity.14 In all rounds, participants face a 5% probability of being

audited, in which case those who have under-reported their income must pay a fine equal to

twice the amount of uncollected taxes. The order of experimental scenarios, as well as the

rules in each round, are summarized in Online Appendix Table OA.1.

Importantly, participants were not informed of other participants’ decisions or perfor-

mance during the experiment. Additionally, participants had no knowledge of whether they

had been audited in the past, or whether their fellow participants were honestly declaring

their own incomes. Thus, we can be fairly certain that behavior in the game is not the

product of reciprocity, conditional cooperation, reputation or wealth effects.

By comparing how income is reported across nine identical taxation and redistribution

scenarios, we are able to investigate differences in tax compliance across a range of parame-

ters. Furthermore, because other researchers have also employed similar experimental designs

to test inter alia the effect of raising tax rates or increasing the efficiency of redistribution

(Alm, Jackson, and McKee 1992; Bosco and Mittone 1997; Torgler 2002), we are able to use

previous studies as an external check on the validity of our results.

13However, participants do not know exactly where they themselves fall in the overall distri-

bution of declared incomes.14We selected Oxfam for UK participants, and the UNICEF for Italian participants.

10

Results

Figure 2 about here

Figure 2 displays the average percentage of earned income that is reported in each of

the nine rounds, broken down between British and Italian participants. The vertical axis

displays the average tax compliance rate, defined as the percentage of total earned income

that is truthfully declared in each round. Several points stand out from the graph. First,

comparing Rounds 1 through 3, we see that compliance responds positively to the efficiency

of redistribution: individuals are more willing to declare a larger percentage of their income

when they know that tax revenues produce more public goods. Secondly, individuals respond

to higher tax rates by evading their fiscal obligations: compliance falls as we move from

Rounds 4 through 6. These results are in line with previous studies (Alm, Jackson, and

McKee 1992; Andreoni 1990; Bosco and Mittone 1997; Torgler 2002), and provide us with

some assurance about the validity of our experimental design.

Figure 3 about here

We also document a cross-national difference in compliance rates: on average, British

participants reported a smaller share of their total income in every round as compared to

Italians. As shown in Figure 3, this finding also is fairly consistent across all six experimental

locations. Thus, we have prima facia evidence that cultural stereotypes are wrong: when

faced with the exact same institutions, Italians are actually more willing to pay than Britons!

We can further evaluate the British-Italian gap by investigating the components of the

average compliance rate. In fact, if we consider each individual compliance decision sepa-

rately, we see that a statistic like the average compliance rate can be misleading because it

aggregates three different outcomes:

1. Complete Compliance: In over 40% of all decisions, participants honestly declared

100% of their earned income.

11

2. Partial Compliance: In around 30% of all decisions, participated under-reported

their income to some degree, with the mode at 50%.

3. Complete Evasion: In slightly under 30% of all decisions, participants reported that

they earned 0 income.

Table 3 examines how each of these three components differs across the UK and Italy.

Columns (1) through (3) estimate the probability that a participant will engage in complete

evasion. We see that in almost all rounds (except 50% Tax Rate and Charity), a significantly

greater percentage of Britons declare 0 income. The UK-Italy gaps are substantively large,

ranging from about 8% in Round 7 to almost 18% in Round 2.

Table 3 about here

In columns (4) through (6), we see the corresponding totals for the proportion of in-

dividuals who engage in complete compliance in each round. Here, the data tell a similar

story: in the majority of rounds, significantly more Italians reported their entire income. The

cross-country gaps range from 9% to 18%. Finally, we consider the partial compliance deci-

sions: given that people cheat (but not to the maximum extent possible), by how much do

they under-report? Interestingly, here we detect almost no statistically significant differences

between Italian and British participants (with the exception of the very first round).

In summary, it appears that the Italy-UK compliance gap is almost entirely driven by

differences in complete compliance and complete evasion between the two countries: Britons

are more likely to cheat (under-reporting their income by an average of 3.13 decisions out

of 9, compared to 4.45 for Italians), and when they cheat, they are more likely to cheat by

declaring zero income (3.45 decisions out of 9, compared to 2.03 for Italians).

12

Individual-Level Models

To what extent are these results explained by differences in individual-level characteristics

between the two participant populations? For example, British participants may be less

compliant because they are more risk-taking, and are therefore less likely to be deterred

by the threat of audits. We address this question by estimating individual-level models

for complete compliance and complete evasion with a host of participant characteristics as

regressors.

We begin with the complete compliance decision. Because our dependent variables con-

sists of count data, we employ a negative binomial model:

Log(Count_complyi

) = �1 + �2Italyi +⇥Xi

+ ✏i

(1)

where Count_comply represents the number of decisions (out of 9) characterized by complete

compliance made by participant i, Italy is a dummy variable for Italian participants, X

represents a vector of individual characteristics, and ✏ represents an individual-specific error

term clustered within experimental sessions.

The first column of Table 4 confirms the significant positive association between Italian

nationality and complete compliance. The coefficient implies that Italian nationality is

associated with an increase of 25.6 log points, or approximately 29% percent, in the count

of decisions characterized by complete compliance.

Table 4 about here

Column (2) adds controls for gender, age, and employment status, but the estimated

effect of the Italian dummy remains robust. In Column (3), we include a control for risk

attitudes, as measured by a survey item which asks participants to rank themselves on a

10-point scale, with 1 signifying a person who “normally tries to avoid taking risks" and 10

13

signifying someone who is “completely willing to take risks.”15 For ease of interpretation,

we have standardized respondents’ answers to have mean = 0 and s.d. = 1. We see that

a higher willingness to take risks is significantly correlated with lower compliance (perhaps

because risk-taking individuals are less deterred by the fear of being audited), and also help

to explain the Italy-UK gap. In particular, British participants tend to be more accepting

of risks, so controlling for such attitudes decreases the expected Italian complete compliance

count from 129% to about 118% of the British count.

Column (4) looks at beliefs about whether other participants were likely to cheat in the

tax experiment. Previous studies have shown that individuals are willing to evade their

fiscal obligations if they believe that their fellow citizens will do the same (Alm, McClel-

land, and Schulze 1999; Alm and Torgler 2011; Blumenthal et al. 2001; Frey and Torgler

2007; Levi 1989; Lewis 1982; Scholz and Lubell 1998). Accordingly, we include variables

measuring whether a participant thought his counterparts in the experiment reported (a)

their entire earned incomes, (b) less than their entire earned incomes, or (c) much less than

their entire earned incomes. With (a) as the residual category, we see that both (b) and

(c) are statistically significant and correctly signed: whereas people who expected others to

be honest fully complied in 6.3 decisions, this number falls to 4.3 for people who believed

that others reported less than their full incomes, and falls again to 2.9 for the “much less”

category. Further, because British participants tend to hold relatively more optimistic be-

liefs about their fellow participants, the inclusion of these variables slightly increases the size

of the compliance gap: that is, Italians are more likely to comply, despite holding negative

stereotypes about their compatriots.

15The question is very similar to one asked on the German Socio-Economic Panel (SOEP):

“How do you see yourself? Are you generally a person who is fully willing to take risks or

do you try to avoid taking risks?" The SOEP also asks participants to indicate their risk

preference on a 10-point scale running from “risk averse” to “fully prepared to take risks.”

14

In Column (5) we control for the effects of economics training and past participation in

experiments. Previous research has suggested that economists are more likely to cheat in

tax compliance experiments, in part because they have been trained to look for the profit-

maximizing choice (Cullis, Jones, and Lewis 2006; Lewis et al. 2009). Further, this behavior

may also have been learned by participants with extensive previous experience in behavioral

economics experiments. We find both variables to be statistically significant, with signs in

the expected direction.16 Moreover, because a greater number of Italian participants were

economics majors, the inclusion of this variable also increases the size of the Italy dummy.

Finally, in Column (6) we report a full model in which we simultaneously enter all of the

above regressors. We see that, with the exception of age, all variables retain statistical

significance, and the size of the estimated UK-Italy compliance gap is virtually identical to

that reported in Column (1). In summary, it appears that even after controlling for a host of

individual-level characteristics, Italians were more likely to report their full incomes in the

tax experiment.

In Table 5, we conduct a similar set of robustness checks for our complete evasion result.

We employ a similar count model as before:

Log(Count_evadei

) = �1 + �2Italyi + �3Count_cheati

+⇥Xi

+ ✏i

(2)

only now we estimate the number of times that participant i declares absolutely zero income

(Count_evade), conditional on the number of times that she under-reports by some amount

(Count_cheat).

Table 5 about here

16Rerunning the model with a control for the number of times an individual has participated,

in lieu of the participation dummy, does not substantively change our results.

15

As shown in Column (1) of Table 5, �3 is positive and highly statistically significant, which

tells us that (unsurprisingly) individuals who under-report in a greater number of decisions

are also likely to report zero income a greater number of times. Further, as indicated by

the negative sign on �2, Italian nationality is associated with a 14.7% decrease in the count

of decisions characterized by complete evasion. In other words, even controlling for the fact

that Italians are less prone to cheat, Italians who do cheat are more likely to “fudge” (i.e. to

declare some amount between 0% and 100%), whereas Britons tend to declare zero income.

In addition, we find that only some of the covariates from Table 4 are significant in Table

5. In particular, men are more likely to declare zero income, as are younger participants. The

most interesting result, however, concerns the willingness to take risks. As shown in Models

(3) and (6), once we control for risk attitudes, the coefficient on the Italian dummy is reduced

in size, and ceases to be statistically significant. This suggests that Britons’ propensity to

declare zero income is explained by the fact that they are less risk-averse, and are hence less

deterred by the prospect of paying a larger fine if audited.

To summarize the tax compliance results, we find that when presented with identical

institutional environments, British participants are significantly more likely than Italians to

under-report their true income in this public goods experiment (although the evidence is less

clear about whether Britons are more likely to engage in complete evasion, once we control

for risk aversion). This result is consistent across experimental locations and institutional

scenarios, and is also robust to the inclusion of a host of individual-level controls.17 We take

these findings as strong evidence against cultural stereotypes about Italians’ lack of morality

regarding tax compliance decisions.

17We also ran a series of tobit regressions, taking the average compliance across all nine

rounds as our main dependent variable. Substantive results remain unchanged.

16

Measuring Social Value Orientation

To test the hypothesis that Italians cared less about the welfare of their compatriots, we

implemented a second experimental task to measure other-regarding preferences, or Social

Value Orientation (SVO). As explained by Murphy, Ackermann, and Handgraaf (2011), SVO

can be represented within a simple utility framework:

U(⇡self

, ⇡other

) = ⇡self

+ ↵⇡other

(3)

where ⇡self

denotes the decision maker’s payoff, ⇡other

denotes the payoffs to another person,

and ↵ measures the weight the decision-maker places on the other’s earnings. In the case

where ↵ = 0, we can consider a decision-maker to be narrowly self-interested (i.e. indi-

vidualistic), while positive values of ↵ indicate increasing concern for others’ welfare (i.e.

prosociality).18

The SVO is operationalized in a series of dictator games with varying marginal rates of

substitution (see Figure 4). In particular, each participant is randomly matched to another

participant in the session, and makes a series of allocation decisions between herself and this

“other” (see Figure 4). For example, in Item 6 in Figure 4, the participant can forego up to

15 units of income in order to increase her counterpart’s income by a maximum 35 units.

Figure 4 about here

The results from each of these allocation decisions can be arrayed on a coordinate plane,

with the x-axis measuring the average allocation to self, and the y-axis measuring the average

allocation to the other (see Figure 5, and the six blue lines corresponding to the six items in

Figure 4). We can “average” the six allocations into a single SVO angle given by:

18↵ can also be negative, indicating an anti-social or competitive temperament: I will pay to

make you pay!

17

SV O � = arctan(↵) = arctan(⇡̄other

� 50

⇡̄self

� 50) (4)

where ⇡̄self

is the mean payoff allocated to the self and ⇡̄other

is the mean payoff allocated to

the other.

Figure 5 about here

From this arrangement, we can distinguish the following “ideal types” of individual mo-

tivations, corresponding to the following sets of SVO angles:

• Individualists prefer to maximize their own income across the six items (�7.82 �

SV O � 7.82 �)19

• Prosocial people prefer to maximize joint income between themselves and the other

(37.09 � SV O � 52.91 �)20

• Altruists prefer to maximize the other’s income (SV O � = 61.39 �)

• Competitive people prefer to maximize the relative distance between the their own

income and the other’s (SV O � = �16.26 �)

Of course, in reality, individuals’ motivations lie somewhere between these ideal types,

so in the analysis below, we consider both the type classification, as well as the actual value

of the angle itself.

19There is a range because the decision maker should be completely indifferent with respect

to the vertical line represented by Item 5.20Again, the range results from the fact that the decision maker should be indifferent with

respect to the constant-sum line with -1 slope, representing Item 1.

18

Results

In Figure 6, we display the distribution of SV O � by country. Higher values along the x-axis

indicate a greater concern for the other’s welfare. We have also expanded each of the ideal-

type ranges listed above by “splitting the difference” between adjacent groups. For example,

the maximum angle for a perfectly-consistent individualist is 7.82, while the minimum angle

for a perfectly consistent prosocial person is 37.09. We have therefore drawn the border

between the two groups at (7.82 + 37.09)⇥ 1/2 = 22.45.

Figure 6 about here

We see that the two distributions look very similar. Indeed, a Wilcoxon Ranksum test

comparing the two populations yields a p-value of 0.463, suggesting that the two distribu-

tions are not significantly different. Furthermore, if we consider typologies, we also do not

find much cross-national variation: 62.1% of participants in Italy can be classified as individ-

ualists, as compared to 64.4% in the UK (p-vlaue = 0.576), while prosocials make up 35.8%

of Italians, versus 34.8% of Britons (p-value = 0.807).21

In Table 6, we consider indiviudal-level models with demographic controls. Since our

dependent variable SV O � is continuous, we employ OLS regression. Our main concern is

control for the effects of studying economics and previous experimental participation, since

both of these experiences may be associated with more individual-maximizing behavior.

However, even though a greater percentage of Italian participants were economists, we still

find no national-level differences in other-regarding preferences after including these controls.

Table 6 about here

21We can classify only 7 participants as competitive, and only 1 as altruistic.

19

Linking Tax Compliance and SVO

Our results indicate that cultural stereotypes painting Italians as unconcerned (relative to

Britons) about the welfare of other individuals are simply wrong. In fact, Italians are just as

other-regarding as Britons in the SVO task. Putting the two parts of the experiment together,

we also find that prosocial inclinations are significantly related to higher tax compliance:

indeed the bivariate correlation between SV O � and the average compliance rate in our

experiment is 0.35 (p-value <0.001, N = 532). Yet, the SVO results (which do not vary

across countries) cannot explain the variation in compliance rates between Italian and British

participants.

How do we square these two findings? Another way of reading the data is that, taking

two individuals with the same measured SV O �, the Italian participant will be more likely

to report 100% of his income.22 This suggests that cross-national differences in the tax

compliance task (which, after all, has a public goods component) reflect a differential concern

with following the rules. This interpretation directly contradicts both popular perceptions

and journalistic accounts of Italians as culturally “amoral” and innately dishonest.

Conclusion

In this paper, we have evaluated institutionalist and moralist arguments for why tax compli-

ance differs across societies. Our research makes both a theoretical and empirical contribu-

tion to the existing literature. First, we have identified two plausible accounts for how the

morality of tax compliance may differ between between countries. Secondly, we have tested

these hypotheses through a controlled laboratory experiment in the UK and Italy, and we

find no support for the moralist argument. By contrast, we show that not only are Italians

22Results are not reported, but are available from the authors upon request.

20

just as other-regarding as Britons, they are also more compliant when presented with the

same set of fiscal institutions and material incentives in an experimental tax setting.

Table 7 about here

Our results suggest that other factors besides cultural values must account for the (undis-

puted) fact that tax evasion is appreciably higher in Italy, as compared to Britain. We specu-

late that part of the cross-country variation may be explained by differences in the structure

of employment. Whereas public-sector employees and workers in large firms often have their

taxes deducted at the source, the same is not true for self-employed individuals who, more-

over, can often evade taxes by dealing in cash. As shown in Table 7, self-employment plays

a very small role in the Scandinavian countries - which we typically think of as the “clean-

est” in terms of tax compliance - whereas the percentage of self-employed workers is much

higher in Southern European countries such as Greece, Italy, Portugal and Spain. In this

list, the UK is again situated fairly closely to the “median” country. Thus, one reason that

Italy is characterized by higher evasion rates than Britain may simply be that Italians have

more opportunities to cheat on their taxes (although they are not necessarily more morally

predisposed to do so).

A second possible explanation throws us back onto the institutionalist argument. As

other scholars have noted, Italians are notorious for their antagonism towards their own

fiscal system (Lewis et al. 2009). For example, Berti and Kirchler (2001) find that Italian

respondents frequently associated taxation with injustice and a loss of personal freedom. But

if Italians are not more willing to cheat in general, why might they be more willing to cheat

their own government? Could it be that Italians regard their public officials as dishonest,

and are therefore more likely to be dishonest themselves? Or are Italians simply not willing

to pay for what they regard as low quality public services? A third explanation may be

that Italians believe the authorities to be less competent at identifying and prosecuting tax

21

evaders. We believe these are some important issues for explaining the observed variation in

tax compliance between Italy and the UK, and we hope to address them in future work.

22

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Tables

Table 1: Quality of Government: 14 Western European Countries

Country Quality Impartiality Corruption AverageFinland 1.657 1.296 1.266 1.406Netherlands 0.956 1.445 0.912 1.104Denmark 0.723 1.004 1.560 1.096Ireland 0.705 1.046 1.252 1.001United Kingdom 0.507 0.797 0.871 0.725

Sweden -0.030 1.128 0.897 0.665Belgium 1.440 -0.229 0.054 0.422Germany 0.265 0.322 0.651 0.413Austria 0.320 0.133 0.359 0.270Italy 0.187 0.187 -0.634 -0.087

Spain 0.083 -0.229 -0.115 -0.087France 0.210 -0.758 0.074 -0.158Portugal -0.259 -0.848 -0.745 -0.617Greece -1.287 -0.655 -1.304 -1.082Data are drawn from nationally-representative public opinion surveysconducted by the Quality of Government Institute about perceptions oflocal education, health and law enforcement institutions. Participantswere asked to rate each of the three institutions on quality, impartiality,and corruption (Charron, Dijkstra, and Lapuente 2013, 2014).

27

Table 2: Summary of Participant Characteristics: UK and Italy

Italy UK Diff. inObs. Mean Std. Dev. Min. Max. Mean Mean Means(1) (2) (3) (4) (5) (6) (7) (8)

Male 527 0.556 0.497 0 1 0.534 0.581 -0.047Age 527 23.780 7.675 18 73 23.875 23.671 0.205Employed 526 0.270 0.444 0 1 0.214 0.333 -0.119*Participated 525 0.821 0.384 0 1 0.817 0.825 -0.008Economics 527 0.349 0.477 0 1 0.399 0.293 0.106*Willingness to Take Risks 517 0 1 -2.070 1.886 -0.194 0.215 -0.410*We tested for differences in population means using Schlag’s Z-test (for dummy variables) and t-tests (for continuous variables). An asterisk (*) indicates whether differences between countries arestatistically significant at the 5% level.

Table 3: Compliance Gap: Italy and UK

Compliance Rate |Pr(Compliance = 0) Pr(Compliance = 1) 0 <Compliance <1(1) (2) (3) (4) (5) (6) (7) (8) (9)

Italy UK Diff. Italy UK Diff. Italy UK Diff.R1: No Redistribuiton 0.330 0.504 -0.174* 0.333 0.192 0.141* 0.586 0.474 0.112*R2: Redistribution 0.259 0.440 -0.181* 0.479 0.308 0.171* 0.520 0.484 0.036R3: Redistribution x 2 0.131 0.304 -0.173* 0.660 0.480 0.180* 0.507 0.546 -0.039R4: 10% Tax Rate 0.156 0.324 -0.168* 0.642 0.476 0.166* 0.599 0.488 0.111R5: 30% Tax Rate 0.230 0.344 -0.114* 0.468 0.368 0.100* 0.545 0.488 0.056R6: 50% Tax Rate 0.309 0.380 -0.071 0.390 0.348 0.042 0.512 0.483 0.029R7: Progressive 1 0.270 0.348 -0.078* 0.394 0.352 0.042 0.497 0.472 0.024R8: Progressive 2 0.198 0.320 -0.122* 0.385 0.292 0.093* 0.501 0.492 0.009R9: Charity 0.135 0.168 -0.033 0.695 0.632 0.063 0.552 0.531 0.021We employed Schlag’s Z-test to test for country-level differences in columns (3) and (6), and t-teststo check for differences in column (9). An asterisk (*) indicates whether differences between countriesare statistically significant at the 5% level.

28

Table 4: Negative Binomial Regressions: Complete Compliance

Dependent Variable: Number of Times Declared 100% of Income(1) (2) (3) (4) (5) (6)

Italy 0.256* 0.238* 0.168* 0.303* 0.324* 0.234*(0.0564) (0.0496) (0.0482) (0.0498) (0.0530) (0.0413)

Age 0.014* 0.006(0.004) (0.005)

Male -0.395* -0.273*(0.055) (0.044)

Employed -0.193* -0.142*(0.094) (0.081)

Risk -0.224* -0.145*(0.034) (0.032)

Others Report: -0.432* -0.368*Less (0.078) (0.082)

Others Report: -0.842* -0.694*Much Less (0.081) (0.086)

Econ major -0.395* -0.283*(0.063) (0.063)

Previous -0.222* -0.178*participation (0.080) (0.080)

Constant 1.238* 1.168* 1.250* 1.716* 1.497* 1.913*(0.050) (0.120) (0.044) (0.071) (0.072) (0.169)

N 532 526 517 524 525 513Log-Likelihood -1299 -1261 -1241 -1253 -1265 -1188Standard errors (in parentheses) clustered at the session level. * p<0.05

29

Table 5: Negative Binomial Regressions: Complete Evasion

Dependent Variable: Number of Times Declared 0% of Income(1) (2) (3) (4) (5) (6)

Italy -0.159* -0.146 -0.108 -0.178* -0.191* -0.136(0.080) (0.086) (0.082) (0.086) (0.081) (0.090)

Count_cheat 0.344* 0.317* 0.335* 0.331* 0.336* 0.301*(0.012) (0.013) (0.013) (0.011) (0.013) (0.014)

Age -0.012* -0.010*(0.004) (0.004)

Male 0.659* 0.591*(0.086) (0.098)

Employed -0.002 -0.053(0.116) (0.102)

Risk 0.165* 0.092*(0.038) (0.044)

Others Report: -0.132 -0.025Less (0.189) (0.180)

Others Report: 0.226 0.218Much Less (0.199) (0.189)

Econ major 0.134 0.039(0.079) (0.082)

Previous 0.137 0.145participation (0.111) (0.093)

Constant -1.142* -1.148* -1.140* -1.089* -1.241* -1.285*(0.0912) (0.166) (0.0961) (0.189) (0.138) (0.225)

N 532 526 517 524 525 513Log-Likelihood -966.5 -934.7 -939.6 -945.8 -955.6 -906.7Standard errors (in parentheses) clustered at the session level. * p<0.05

30

Table 6: OLS Regressions: Social Value Orientation

Dependent Variable: SVO Angle(1) (2) (3) (4)

Italy -0.815 -1.368 -0.094 -0.821(0.871) (0.931) (0.882) (0.927)

Age 0.247* 0.216*(0.090) (0.0982)

Male -3.578* -3.093*(1.372) (1.364)

Employed -3.648* -3.082*(1.527) (1.378)

Econ major -5.100* -3.931*(1.004) (1.095)

Previous participation -5.273* -5.370*(1.805) (1.747)

Constant 16.31* 13.62* 22.02* 19.48*(0.740) (2.515) (1.570) (3.571)

N 532 526 525 524Adj. R-squared -0.001 0.034 0.037 0.062Standard errors (in parentheses) clustered at the session level. * p<0.05

Table 7: Percentage of Self-Employed in Total Employment:16 Western European Countries

Year 3-yearCountry 2000 2010 2011 2012 AverageNorway 7.4 7.7 7.0 6.9 7.2Denmark 9.1 9.1 9.1 9.1 9.1France 9.3 9.3 9.5 - 9.4Sweden 10.3 11.0 10.4 10.5 10.6Switzerland 13.2 10.6 10.7 10.7 10.6Germany 11.0 11.6 11.7 11.6 11.7Finland 13.7 13.5 13.4 13.6 13.5Austria 13.1 13.8 13.8 13.3 13.6United Kingdom 12.8 13.9 14.0 14.6 14.1

Belgium 15.8 14.4 14.3 14.3 14.3Netherlands 11.2 15.0 15.0 15.3 15.1Ireland 18.8 17.1 16.6 16.7 16.8Spain 20.2 16.9 16.6 17.6 17.0Portugal 26.0 22.9 21.3 21.9 22.0Italy 28.5 25.5 25.2 25.1 25.3

Greece 42.0 35.5 36.3 36.8 36.2Source: OECD Factbook 2014

31

Figures

Figure 1: Example Screenshot: Reporting Your Earnings

Figure 2: British-Italian Compliance Gap

32

Figure 3: Compliance Rate Across All Locations

33

Figure 4: SVO Mini-Dictator Games

From Murphy, Ackermann, and Handgraaf 2011, Figure 1, p.772.

34

Figure 5: Constructing the SVO Angle

From Murphy, Ackermann, and Handgraaf 2011, Figure 2, p.773.

Figure 6: Distribution of SVO Angles, Italy and the UK

35