Behavioural and Social Explanations of Tax Evasion Nigar Hashimzade University of Reading Gareth D....
-
Upload
bruno-gordon -
Category
Documents
-
view
215 -
download
0
Transcript of Behavioural and Social Explanations of Tax Evasion Nigar Hashimzade University of Reading Gareth D....
Behavioural and Social Explanations of Tax Evasion
Nigar Hashimzade University of ReadingGareth D. Myles University of ExeterFrank Page Indiana UniversityMatthew Rablen Brunel University
Financial support from the ESRC/HMRC/HMT is gratefully acknowledged
Introduction
An understanding of the individual tax compliance decision is important for revenue services
Their aim is to design policy instruments to reduce the tax gap
Tax evasion is an area where orthodox analysis has been challenged by behavioural economics Non-expected utility theory Social interaction
Standard Model
The probability of being detected is p Taxpayer chooses declaration X to maximize
expected utility
max{X} E[U(X)] = [1 – p]U(Yn) + pU(Yc) Where
Yn = Y – tX = [1 – t]Y + tE Yc = [1 – t]Y – Ft[Y – X] = [1 – t]Y – FtE
The sufficient condition for evasion to take place (X < Y) is
p < 1/[1 + F]
Standard Model
The evasion decision can be written as
max{E} E[U(E)] = [1 – p]U([1 – t]Y + tE)
+ pU([1 – t]Y – FtE ) So it follows that E = [1/t]( . ) The result that compliance rises as t increases
runs counter to "intuition" and has mixed empirical support Problem of separating aggregate and individual
effects Weakness of experimental evidence
Behavioural Approach
Behavioural economics can be seen as a loosening of modelling restrictions
Two different directions can be taken:
(i) Use an alternative to expected utility theory
(ii) Reconsider the context in which decisions are taken
The consequences of making such changes are now considered
Non-Expected Utility
There are several non-expected utility models These have the general form
V(X) = w1(p, 1 – p)v(Yc) + w2(p, 1 – p)v(Ync)
w1(p, 1 – p) and w2(p, 1 – p) are translations of p and 1 – p (probability weighting functions)
v( . ) is some translation of U( . ) Different representations are special cases of
this general form
Prospect Theory
Prospect theory does three things (i) Translates the probabilities
(ii) Assumes payoff is convex in losses and concave in gains
(iii) Payoffs are measured relative to a reference point, R
xvpyvpV 21
0 if 0'' ,0 if 0'' ,0' zzvzzvzv
Prospect Theory
Yaniv (1999) studies the consequence of paying a tax advance With a tax advance of D
Use Y – D as the reference point D – tX is the gain if evasion is successful is the loss if evasion is
unsuccessful
tXDDYY n XYFttXDDYY c
XYFttXD
Prospect Theory
Observe that D – tX is achieved for sure So write objective as
Recall that prospect theory has v convex for losses and concave for gains
Yaniv analyzes the comparative statics of the necessary condition
XYFtpvtXDvV Y
0'' XYFtpFtvtXDtv
Prospect Theory
Consider the power function
First assume that D > tY The next slides illustrates VY for the parameter
values
Y = 1, t = 0.2, p = 0.1, F = 2, D = 0.3
0 ,
0 ,
zz
zzzv
Prospect Theory
25.2,88.0 4,4.0
Prospect Theory
For the power function we can prove:
"If there is an interior solution to the first-order condition it must be a minimum"
The same comments (and result) apply to other functional forms
The assumptions of prospect theory combine to create analytical problems
Prospect Theory
Two figures for D < tY
β = 0.5, γ = 4p = 0.25, F = 4 p = 0.25, F = 20
Prospect Theory
al-Nowaihi and Dhami (2007) argue that (i) The reference point should be R = (1 – t)Y (ii) Standard prospect theory should be used
For this objective it can be shown
A different reference point might change the result
XYFtvpwXYtvpwV KT 21 1
0
t
XY
dt
dX
Positive Results
One way to make progress is to assume the probability of detection depends on declared income
Within the prospect theory framework
VPT = w (1–⁺ p(X))v(t(Y – X)) + w (⁻ p(X))v(–Ft(Y – X)) An appropriate form of p(X) can make the
objective strictly concave Consider the power function of v( ) and
p(X) = αp₀X/Y
Positive Results
Now combine the Yaniv model with linear probability
pL(X) = α[1 – (1-p₀)(X/Y)]
Advance payment below the true tax liability (D < tY)
t = 0.2, X/Y = 0.74, p = 0.236
t = 0.3, X/Y = 0.50, p= 0.45
Solid: t = 0.2Dashed: t = 0.3
Social Interaction
Social interaction allows information to be transmitted through a network
This information affects evasion behaviour by changing beliefs
The network is determined endogenously through choices that are made
The choices are: Occupation (employed or self-employed) Level of evasion if self-employed
Occupational Choice
Assume that a choice is made between employment and self-employment
Employment is safe (wage is fixed) but tax cannot be evaded (UK is PAYE)
Self-employment is risky (outcome random) but permits provides opportunity to evade
Selection into self-employment is dependent on personal characteristics
Occupational Choice
A project is a pair {vb, vg} with vb < vg
An individual is described by a triple {w, q} Evasion level is chosen after outcome of
project is known So in state i, i = b, g, Ei solves
max EUi = pU((1–t)vi – FtEi) + (1–p)U((1–t) vi+tEi)
The payoff from self-employment is
EUs = (1–q) EUb (Eb*) + qEUg (Eg*)
Occupational Choice
Occupational choice compares payoffs from the alternatives
Self-employment is chosen if
EUs(q, vb, vg) > Ue(w)
What is the outcome in this setting? (i) Assume CRRA utility
U = Y(1 – )/(1 – ) (ii) Assume a uniform distribution for (, q, w)
Occupational Choice
Employment above the locus
Self-employment below the locus
The less risk-averse choose self-employment
But these people will also evade more
Separation of populationp = 0.5, t = 0.25, F = 0.75,
vb = 0.5, vg = 2, q = 0.5
Employed
Self-employed
Occupational Choice
The aggregate level of evasion can be increasing in the tax rate
This is the consequence of intensive/extensive margins
The result extends to borrowing to invest
E
t
E
t
Aggregate evasion
With borrowing
Social Interaction
A network is a symmetric matrix A of 0s and 1s (bi-directional links)
The network shown is described by
0100
1010
0101
0010
A
1
2
3
4
Social Interaction
Each period an action is chosen The network is revised as a consequence of
chosen actions A random selection of meetings occur (a
matrix C of 0s, 1s) Set of permissible meetings is determined by
the network (M = A.*C) At a meeting information is exchanged Beliefs are updated
Tax Evasion Network
There are n individuals Individual characteristics
{, w, p, q, vb, vg}
are randomly drawn at the outset A choice is made between e and s If s is chosen outcome b or g is randomly
realised Given the outcome evasion decision is made Those in s are then randomly audited
Tax Evasion Network
If audited pi goes to 1 other pi decays
pi = pi, ≤ 1
Type s only meet type s Links in network evolve as a consequence of
choice Meetings occur randomly between linked
individuals Information on p is exchanged
pi = pi + (1 – ) pj
Results
The model has been run for CRRA utility
n = 1000, = 100 uniform on [0, 10], True audit probability
a = 0.05 = 0.95, = 0.75 t = 0.25, F = 1.5
0 10 20 30 40 50 60 70 80 90 1000.51
0.52
0.53
0.54
0.55
0.56
0.57
0.58
0.59
0.6
0.61
p
Mean audit probability (belief)
Results
0 10 20 30 40 50 60 70 80 90 1001.75
1.8
1.85
1.9
1.95
2
2.05
2.1
2.15
0 10 20 30 40 50 60 70 80 90 1003
3.05
3.1
3.15
3.2
3.25
3.3
3.35
3.4
EmployedMean risk aversion
Self-employedMean risk aversion
0 10 20 30 40 50 60 70 80 90 1000.34
0.36
0.38
0.4
0.42
0.44
0.46
Results
The outcome is little changed if decay is increased
Figure uses = 0.25 The average belief
about audit probability remains high
p
Conclusions
Non-expected utility delivers nothing that is not given by adopting subjective probabilities in the EU model
It requires variable probability to reverse the tax result
Occupational choice selects those who will evade into situations where evasion is possible
Social interaction can lead subjective probability to differ from objective probability