Alari Paulus. Tax evasion and measurement error. An analysis of income survey data linked with tax...

Introduction Methodology Data Findings Summary

Tax evasion and measurement errorAn analysis of income survey data linked with tax records

Alari Paulus

Institute for Social & Economic Research (ISER)University of Essex

Eesti PankNov 19, 2013


Outline

1 Introduction

2 Methodology

3 Data

4 Findings

5 Summary


Motivation

Tax evasion undermines the role of taxation and affectsresource allocationWho evades? The extent of evasion?Empirical evidence on income tax evasion at micro-level:

(random) audits - only partial detectionsurveys - self-reported non-compliance, indirect methodsfocus on the scaleexperiments - difficult to link compliance decisions with realincome distribution and job characteristics; actual scalestill limited as data availability is a challenge!

Very low non-compliance wrt salaries/wages due tothird-party reporting

employees and employers can colludemainly based on audits


Research questions and contribution

Which individuals are more likely to evade taxes onemployment income in Estonia? How extensive?

Individual-level survey data linked to tax recordsOnly other example by Baldini et al. (2009) but ignoremeasurement errorStudies on income measurement error ignore tax evasion,e.g. Bound et al. (1994), Kapteyn & Ypma (2007)

A new econometric approach to model these jointlyExtend evidence for post-socialist countriesRich information on individual characteristicsLink between true income and tax evasionFind substantial underreporting of earnings (10-15%)


Existing literature: theory

The deterrence model (Allingham & Sandmo 1972,Srinivasan 1973, Yitzhaki 1974)

probability of detection and penalty ratemarginal tax rate (ambiguous)total income - evasion increases in absolute terms,ambiguous in relative termsrisk aversion

Endogenous income (Andersen 1977, Pencavel 1979)Interactions with tax authority (see Andreoni et al. 1998)Behavioural economics (see Hashimzade et al. 2012)Kleven et al. (2009): third-party reporting and firm size


Existing literature: empirics

AuditsMTR (mixed)income (mixed)married people ↑ and elderly ↓ (Clotfelter 1983, Feinstein1991, Martinez-Vazques and Rider 2005)

Experimentssupport for auditing and penaltiesMTR (mixed)income (mixed)males ↑ and elderly ↓ (Baldry 1987, Pudney et al. 2000)

Surveypeople in smaller firms, construction, non-Estonians, men,less education, young and elderly more likely to evade (Krizet al. 2008, Meriküll & Staehr 2010)60% of income underreported by hh-s with businessincome (Kukk & Staehr 2013)


Employee vs employer

Incentives for both sides (except for public sectoremployers) and requires co-operationEmployer in a stronger position if few alternativeemployment optionsBut employers more likely to be punished when found out→ smaller risk of exposure for smaller companiesDisincentives for employee: no health insurance, lowerpension entitlement, difficult to obtain bank loan/mortgagesEvidence of declaring part of earnings (close to theminimum wage)Assume final decision up to the individual (but controllingfor a few firm characteristics)


Model structure

Three dependent variables:1 An individual i has employment income yT

i (true earnings)2 Declares to the tax authority y r

i (reported earnings)none, some or all of yT

i (but not more)3 States in the survey ys

i (survey earnings)noisy measure of true earningscan understate or overstate (even ys

i > 0 while yTi = 0)

Highly non-linear system→ specify functional forms and errordistributions and estimate parameters with the MaximumLikelihood method


Part 1: True earnings yTi

Allow for the possibility that actual income is zeroAssume log-normal distribution (if positive){

ln yTi ∼ N(xiβ, σ

2ε ) with probability p

yTi = 0 with probability 1− p

where xi is a vector of personal characteristics


Part 2: Reported earnings y ri

A two-limit Tobit modelThe proportion of true earnings reported (multiplicative), afunction of true earnings and personal characteristics (zi ):

y ri =

0 if yT

i = 0 (no earnings)0 if yT

i > 0 and r∗i ≤ 0 (full evasion)r∗i · yT

i if yTi > 0 and 0 < r∗i < 1 (part evasion)

yTi if yT

i > 0 and r∗i ≥ 1 (no evasion)

where

r∗i =

{0 if yT

i = 0θyT

i + ziγ + ui if yTi > 0

and ui ∼ N(0, σ2u)


Part 2: Reported earnings y ri

A two-limit Tobit modelReported earnings in levels (additive), a function of trueearnings and personal characteristics (zi ):

y ri =

0 if yT

i = 0 (no earnings)0 if yT

i > 0 and y∗ri ≤ 0 (full evasion)

y∗ri if yT

i > 0 and 0 < y∗ri < yT

i (part evasion)yT

i if yTi > 0 and y∗r

i ≥ yTi (compliance)

where

y∗ri =

{0 if yT

i = 0θyT

i + ziγ + ui if yTi > 0

and ui ∼ N(0, σ2u)


Part 3: Survey earnings ysi

In log-terms, conditional on ysi > 0

Assume that a function of log-true earnings and personalcharacteristics (wi )A shift parameter (δ0) if true earnings are zero

ln ysi = ρ ln yT

i · 1(yTi > 0) + δ0 · 1(yT

i = 0) + wiδ + vi

where 1(·) is an indicator function and vi ∼ N(0, σ2v )


Maximum likelihood estimation

Probability density:

f (y ri , y

si |xi , zi ,wi) =

∫ ∞

y ri

f (y ri , y

si |xi , zi ,wi , yT )f (yT |xi) dyT

=

∫ ∞

y ri

f (y ri |xi , zi ,wi , yT )f (ys

i |xi , zi ,wi , yT )f (yT |xi) dyT

assuming independence of random terms

Semi-infinite integrals:solved numerically using Gauss-Hermite quadraturenodes and weights as calculated in Steen et al. (1969)15 quadrature points


Identification

Two observed income measures and three equationsKey identifying assumptions:

People working in the public sector cannot evade taxesNo differences between public and private sector wrtprocesses underlying true income and survey income

Intuition:Constrained sample (largely) identifies parameters in thetrue earnings and measurement error equationUnconstrained sample identifies the compliance equationSimultaneous estimation

consistent estimatesshift parameters for the (un)constrained sector


Data

1 Estonian Social Survey 2008basis for the Estonian part of the EU-SILC2007 incomes by source (earnings either gross or net)

2 Individual tax reports for 2007personal declaration if submitted, otherwise companydeclarations for employeesgross incomes by source and taxes (withheld, final)

3 Linkageby Stat. Estonia, no consent required from the respondentsbased on unique PIN (available for sampled persons, askedfor other household members, matched for the rest)very few non-matches, possibly some incorrect matches


Evolution of the sample

Sample Number of persons Omitted atTotal A00 A0s Ar0 Ars each step

Initial sample of ESU 2008 15,123 - - - - -Linked with tax records 15,048 - - - - 75Aged 16 or oldera 12,861 - - - - 2,187Responded in ESUb 10,951 - - - - 1,910Earnings reported in ESU 10,237 3,672 341 846 5,378 714Employedc 6,570 5 341 846 5,378 3,667Employment duration reported 5,496 5 280 7 5,204 1,074Worked full time for whole year 4,171 1 139 - 4,031 1,325- constrained sectord 927 - 12 - 915 -- unconstrained sector 3,244 1 127 - 3,116 -

Notes: (a) that is being subject to a personal interview; (b) for newsample members the number of non-respondents includes only

sampled persons without other household members; (c) employmentreported as the main activity at least for one month in the income

reference period and/or having positive earnings in either datasource; (d) constrained sector sub-sample includes public sector

employees, except those who changed jobs or have a second job.


Mean earnings

Sample Survey Tax records Difference Nb se b se b se

ESU non-respondents with positive earnings in the tax recordsUnit non-response - - 8.48 0.64 - - 1,114Item non-reponse - - 8.76 0.43 - - 563

ESU respondents with positive earnings in either sourcePositive earnings in ESU (A0s) 6.78 0.44 0.00 - 6.78 0.44 341Pos... in the tax records (Ar0) 0.00 - 1.41 0.12 -1.41 0.12 846Pos... in both sources (Ars) 7.46 0.11 7.32 0.13 0.13 0.08 5,378- constrained sector 7.37 0.20 8.24 0.27 -0.88 0.15 1,048- unconstrained sector 7.48 0.12 7.05 0.14 0.43 0.10 4,330

Notes: annual (net) earnings in thousand EEK divided by 12;estimations take into account design weights and clustering at thehousehold level; constrained sector sub-sample includes public sectoremployees, except those who changed jobs or have a second job.


Distribution of earnings

0

.05

.1

.15

.2

Den

sity

0 10 20 30 40

Survey

0

.05

.1

.15

.2

Den

sity

0 10 20 30 40

Tax records

Annual (net) earnings divided by 12, in thousand EEKNotes: final estimation sample (i.e. worked full time for whole year) excluding those with zero earnings or monthly earnings above 40 thousand EEK, N = 4,012; bandwith 0.5; vertical line shows monthly minimum net wage (3,175 EEK).


Reported vs survey earnings by sector

0

10

20

30

40

0 10 20 30 40 0 10 20 30 40

unconstrained sector constrained sector

observed 45-degree line linear fit locally weighted regr.

Sur

vey

Tax records

Notes: Annual (net) earnings divided by 12, in thousand EEK; final estimation sample (i.e. worked full time for whole year) excluding those with zero earnings or monthly earnings above 40 thousand EEK, N = 4,012; constrained sector sub-sample includes public sector employees, except those who changed jobs or have a second job.


Regression estimates (p < 0.05)

1 True income equation:age (-), age sq (-), male (+), Estonian nationality (+),education (+), region, industry, occupation (+), firm size (+),hours in main job (+), health status (+)Non-significant: marital status, rural area, studying, 2nd job, hours in 2ndjob, constrained sector

2 Tax compliance equation:age (+), male (-), Est. nationality (+), education (+), married(+), north-east region (-), industry, occupation, firm size (+),lease loan (+), studying (+)Non-significant: age sq, rural area, hours in main job, 2nd job, hours in 2ndjob, mortgage

3 Survey income equation:age (-), age sq (-), male (+), Est. nationality (+), education(+), # of waves (-), interview month (+), interview rating (+),how respondedNon-significant: persons present at interview, constrained sector


Marginal effects (conditional on true earnings)

-.3

-.2

-.1

0.1

0 5 10 15 20 25

(D = 10 years)age

-.3

-.2

-.1

0.1

0 5 10 15 20 25

(ref: male)female

-.3

-.2

-.1

0.1

0 5 10 15 20 25

(ref: secondary)tertiary education

-.3

-.2

-.1

0.1

0 5 10 15 20 25

(ref: north)north-east region

-.3

-.2

-.1

0.1

0 5 10 15 20 25

(ref: manufacturing)construction

-.3

-.2

-.1

0.1

0 5 10 15 20 25

(ref: 50+)1 to 10 employees

mar

gina

l effe

ct o

n th

e pr

edic

ted

prob

abili

ty o

f com

plia

nce

true earnings (thousand EEK per month)

additive multiplicative


Elasticity of expected value of declared earnings

.5

.6

.7

.8

.9

1

0 5 10 15 20 25

at sample means/modes

.5

.6

.7

.8

.9

1

0 5 10 15 20 25

aged 25

.5

.6

.7

.8

.9

1

0 5 10 15 20 25

aged 55

.5

.6

.7

.8

.9

1

0 5 10 15 20 25

female

.5

.6

.7

.8

.9

1

0 5 10 15 20 25

tertiary education

.5

.6

.7

.8

.9

1

0 5 10 15 20 25

north-east region

.5

.6

.7

.8

.9

1

0 5 10 15 20 25

construction

.5

.6

.7

.8

.9

1

0 5 10 15 20 25

1 to 10 employees

elas

ticity

of r

epor

ted

earn

ings

true earnings (thousand EEK per month)

additive multiplicative

Notes: The first plot is assessed at the sample means/modes. In other plots,one characteristic is adjusted in comparison with the first plot. Vertical linesshow predicted true (net) earnings (conditional on being positive).


Extent of non-compliance

Unconstrained sector Whole sampleAdditive Multipl. Additive Multipl.

Proportion of sample, %no income 0.2 0.5 0.4 0.7full evaders 3.5 3.1 2.7 2.4part evaders 23.3 28.8 18.0 22.2compliant 73.0 67.6 78.9 74.7Undeclared earnings as a share of total gross earnings, %All 16.3 15.7 12.9 12.4Decile 1 16.7 22.1 12.8 17.4Decile 2 12.3 12.7 10.0 9.6... ... ... ... ...Decile 9 17.5 16.8 13.1 13.2Decile 10 22.4 20.1 19.4 17.4

N = 3,130 N = 4,049


Distribution by (gross) true earnings decile groups

-.4

-.3

-.2

-.1

0

.1

.2

.3

.4

.5

1 2 3 4 5 6 7 8 9 10

Constrained sector(observed)

-.4

-.3

-.2

-.1

0

.1

.2

.3

.4

.5

1 2 3 4 5 6 7 8 9 10

Unconstrained sector(additive)

-.4

-.3

-.2

-.1

0

.1

.2

.3

.4

.5

1 2 3 4 5 6 7 8 9 10

Unconstrained sector(multiplicative)

shar

e of

tota

l (gr

oss)

true

ear

ning

s

Decile groups of true earnings

tax evasion measurement error


Sensitivity analysis

Alternativesamplesdefinitions for the constrained sectorset of co-variatesparameter constraints (i.e. differences between theconstrained and the unconstrained sector)survey design (weights, clustering)

Results appear robustparameter estimatesproportion of compliantproportion of earnings not declared


Concluding points

First study analysing tax evasion and measurement errorME instrumental for income differencesBroad set of characteristics associated with tax evasion (inline with previous studies)Scale and distribution of undeclared earnings

part vs full evasionhigh income earners

Substantial underreporting of wages/salaries despitethird-party reporting


Thank you!

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