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Introduction

Methodology

Data

Findings

Tax evasion and measurement error
An analysis of income survey data linked with...
Introduction

Methodology

Outline

1

Introduction

2

Methodology

3

Data

4

Findings

5

Summary

Data

Findings

Sum...
Introduction

Methodology

Data

Findings

Summary

Motivation
Tax evasion undermines the role of taxation and affects
res...
Introduction

Methodology

Data

Findings

Research questions and contribution
Which individuals are more likely to evade ...
Introduction

Methodology

Data

Findings

Existing literature: theory

The deterrence model (Allingham & Sandmo 1972,
Sri...
Introduction

Methodology

Data

Findings

Summary

Existing literature: empirics
Audits
MTR (mixed)
income (mixed)
marrie...
Introduction

Methodology

Data

Findings

Summary

Employee vs employer
Incentives for both sides (except for public sect...
Introduction

Methodology

Data

Findings

Summary

Model structure

Three dependent variables:
1

An individual i has emp...
Introduction

Methodology

Data

Findings

Summary

Model structure

Three dependent variables:
1

An individual i has emp...
Introduction

Methodology

Data

Findings

Part 1: True earnings yiT

Allow for the possibility that actual income is zero...
Introduction

Methodology

Data

Findings

Summary

Part 2: Reported earnings yir
A two-limit Tobit model
The proportion o...
Introduction

Methodology

Data

Findings

Summary

Part 2: Reported earnings yir
A two-limit Tobit model
Reported earning...
Introduction

Methodology

Data

Findings

Part 3: Survey earnings yis

In log-terms, conditional on yis > 0
Assume that a...
Introduction

Methodology

Data

Findings

Maximum likelihood estimation

Probability density:
∞

f (yir , yis |xi , zi , ...
Introduction

Methodology

Data

Findings

Maximum likelihood estimation

Probability density:
∞

f (yir , yis |xi , zi , ...
Introduction

Methodology

Data

Findings

Identification

Two observed income measures and three equations
Key identifying...
Introduction

Methodology

Data

Findings

Identification

Two observed income measures and three equations
Key identifying...
Introduction

Methodology

Data

Findings

Summary

Data

1

Estonian Social Survey 2008
basis for the Estonian part of th...
Introduction

Methodology

Data

Findings

Summary

Evolution of the sample
Sample
Initial sample of ESU 2008
Linked with ...
Introduction

Methodology

Data

Findings

Summary

Mean earnings

Sample

Survey
Tax records
Difference
b
se
b
se
b
se
ES...
Introduction

Methodology

Data

Findings

Summary

Distribution of earnings
Survey

Tax records

.15

Density

.2

.15

D...
Introduction

Methodology

Data

Findings

Summary

Reported vs survey earnings by sector

unconstrained sector

constrain...
Introduction

Methodology

Data

Findings

Summary

Regression estimates (p < 0.05)
1

True income equation:
age (-), age ...
Introduction

Methodology

Data

Findings

Summary

Marginal effects (conditional on true earnings)
(ref: secondary)

0
-....
Introduction

Methodology

Data

Findings

Summary

Elasticity of expected value of declared earnings

at sample means/mod...
Introduction

Methodology

Data

Findings

Extent of non-compliance
Unconstrained sector
Whole sample
Additive
Multipl. Ad...
Introduction

Methodology

Data

Findings

Distribution by (gross) true earnings decile groups
Constrained sector
(observe...
Introduction

Methodology

Data

Findings

Sensitivity analysis
Alternative
samples
definitions for the constrained sector
...
Introduction

Methodology

Data

Findings

Summary

Concluding points

First study analysing tax evasion and measurement e...
Introduction

Methodology

Data

Findings

Thank you!
apaulus@essex.ac.uk

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

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

Open seminar at Eesti Pank, 19.11.2013

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

  1. 1. Introduction Methodology Data Findings Tax evasion and measurement error An analysis of income survey data linked with tax records Alari Paulus Institute for Social & Economic Research (ISER) University of Essex Eesti Pank Nov 19, 2013 Summary
  2. 2. Introduction Methodology Outline 1 Introduction 2 Methodology 3 Data 4 Findings 5 Summary Data Findings Summary
  3. 3. Introduction Methodology Data Findings Summary Motivation Tax evasion undermines the role of taxation and affects resource allocation Who evades? The extent of evasion? Empirical evidence on income tax evasion at micro-level: (random) audits - only partial detection surveys - self-reported non-compliance, indirect methods focus on the scale experiments - difficult to link compliance decisions with real income distribution and job characteristics; actual scale still limited as data availability is a challenge! Very low non-compliance wrt salaries/wages due to third-party reporting employees and employers can collude mainly based on audits
  4. 4. Introduction Methodology Data Findings Research questions and contribution Which individuals are more likely to evade taxes on employment income in Estonia? How extensive? Individual-level survey data linked to tax records Only other example by Baldini et al. (2009) but ignore measurement error Studies on income measurement error ignore tax evasion, e.g. Bound et al. (1994), Kapteyn & Ypma (2007) A new econometric approach to model these jointly Extend evidence for post-socialist countries Rich information on individual characteristics Link between true income and tax evasion Find substantial underreporting of earnings (10-15%) Summary
  5. 5. Introduction Methodology Data Findings Existing literature: theory The deterrence model (Allingham & Sandmo 1972, Srinivasan 1973, Yitzhaki 1974) probability of detection and penalty rate marginal tax rate (ambiguous) total income - evasion increases in absolute terms, ambiguous in relative terms risk 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 Summary
  6. 6. Introduction Methodology Data Findings Summary Existing literature: empirics Audits MTR (mixed) income (mixed) married people ↑ and elderly ↓ (Clotfelter 1983, Feinstein 1991, Martinez-Vazques and Rider 2005) Experiments support for auditing and penalties MTR (mixed) income (mixed) males ↑ and elderly ↓ (Baldry 1987, Pudney et al. 2000) Survey people in smaller firms, construction, non-Estonians, men, less education, young and elderly more likely to evade (Kriz et al. 2008, Meriküll & Staehr 2010) 60% of income underreported by hh-s with business income (Kukk & Staehr 2013)
  7. 7. Introduction Methodology Data Findings Summary Employee vs employer Incentives for both sides (except for public sector employers) and requires co-operation Employer in a stronger position if few alternative employment options But employers more likely to be punished when found out → smaller risk of exposure for smaller companies Disincentives for employee: no health insurance, lower pension entitlement, difficult to obtain bank loan/mortgages Evidence of declaring part of earnings (close to the minimum wage) Assume final decision up to the individual (but controlling for a few firm characteristics)
  8. 8. Introduction Methodology Data Findings Summary Model structure Three dependent variables: 1 An individual i has employment income yiT (true earnings) 2 Declares to the tax authority yir (reported earnings) none, some or all of yiT (but not more) 3 States in the survey yis (survey earnings) noisy measure of true earnings can understate or overstate (even yis > 0 while yiT = 0) Highly non-linear system → specify functional forms and error distributions and estimate parameters with the Maximum Likelihood method
  9. 9. Introduction Methodology Data Findings Summary Model structure Three dependent variables: 1 An individual i has employment income yiT (true earnings) 2 Declares to the tax authority yir (reported earnings) none, some or all of yiT (but not more) 3 States in the survey yis (survey earnings) noisy measure of true earnings can understate or overstate (even yis > 0 while yiT = 0) Highly non-linear system → specify functional forms and error distributions and estimate parameters with the Maximum Likelihood method
  10. 10. Introduction Methodology Data Findings Part 1: True earnings yiT Allow for the possibility that actual income is zero Assume log-normal distribution (if positive) ln yiT ∼ N(xi β, σ 2 ) yiT = 0 with probability p with probability 1 − p where xi is a vector of personal characteristics Summary
  11. 11. Introduction Methodology Data Findings Summary Part 2: Reported earnings yir A two-limit Tobit model The proportion of true earnings reported (multiplicative), a function of true earnings and personal characteristics (zi ):  0 if yiT = 0 (no earnings)    0 if yiT > 0 and ri∗ ≤ 0 (full evasion) yir = ∗ · yT T > 0 and 0 < r ∗ < 1 if yi (part evasion)  ri  i i  yiT if yiT > 0 and ri∗ ≥ 1 (no evasion) where ri∗ = 2 and ui ∼ N(0, σu ) 0 T +zγ+u θyi i i if yiT = 0 if yiT > 0
  12. 12. Introduction Methodology Data Findings Summary Part 2: Reported earnings yir A two-limit Tobit model Reported earnings in levels (additive), a function of true earnings and personal characteristics (zi ):  if yiT = 0 (no earnings)  0   0 if yiT > 0 and yi∗r ≤ 0 (full evasion) yir = ∗r T > 0 and 0 < y ∗r < y T if yi (part evasion)  yi  i i  T yi if yiT > 0 and yi∗r ≥ yiT (compliance) where yi∗r = 2 and ui ∼ N(0, σu ) 0 T +zγ+u θyi i i if yiT = 0 if yiT > 0
  13. 13. Introduction Methodology Data Findings Part 3: Survey earnings yis In log-terms, conditional on yis > 0 Assume that a function of log-true earnings and personal characteristics (wi ) A shift parameter (δ0 ) if true earnings are zero ln yis = ρ ln yiT · 1(yiT > 0) + δ0 · 1(yiT = 0) + wi δ + vi 2 where 1(·) is an indicator function and vi ∼ N(0, σv ) Summary
  14. 14. Introduction Methodology Data Findings Maximum likelihood estimation Probability density: ∞ f (yir , yis |xi , zi , wi ) = yir f (yir , yis |xi , zi , wi , y T )f (y T |xi ) dy T ∞ = yir f (yir |xi , zi , wi , y T )f (yis |xi , zi , wi , y T )f (y T |xi ) dy T assuming independence of random terms Semi-infinite integrals: solved numerically using Gauss-Hermite quadrature nodes and weights as calculated in Steen et al. (1969) 15 quadrature points Summary
  15. 15. Introduction Methodology Data Findings Maximum likelihood estimation Probability density: ∞ f (yir , yis |xi , zi , wi ) = yir f (yir , yis |xi , zi , wi , y T )f (y T |xi ) dy T ∞ = yir f (yir |xi , zi , wi , y T )f (yis |xi , zi , wi , y T )f (y T |xi ) dy T assuming independence of random terms Semi-infinite integrals: solved numerically using Gauss-Hermite quadrature nodes and weights as calculated in Steen et al. (1969) 15 quadrature points Summary
  16. 16. Introduction Methodology Data Findings Identification Two observed income measures and three equations Key identifying assumptions: People working in the public sector cannot evade taxes No differences between public and private sector wrt processes underlying true income and survey income Intuition: Constrained sample (largely) identifies parameters in the true earnings and measurement error equation Unconstrained sample identifies the compliance equation Simultaneous estimation consistent estimates shift parameters for the (un)constrained sector Summary
  17. 17. Introduction Methodology Data Findings Identification Two observed income measures and three equations Key identifying assumptions: People working in the public sector cannot evade taxes No differences between public and private sector wrt processes underlying true income and survey income Intuition: Constrained sample (largely) identifies parameters in the true earnings and measurement error equation Unconstrained sample identifies the compliance equation Simultaneous estimation consistent estimates shift parameters for the (un)constrained sector Summary
  18. 18. Introduction Methodology Data Findings Summary Data 1 Estonian Social Survey 2008 basis for the Estonian part of the EU-SILC 2007 incomes by source (earnings either gross or net) 2 Individual tax reports for 2007 personal declaration if submitted, otherwise company declarations for employees gross incomes by source and taxes (withheld, final) 3 Linkage by Stat. Estonia, no consent required from the respondents based on unique PIN (available for sampled persons, asked for other household members, matched for the rest) very few non-matches, possibly some incorrect matches
  19. 19. Introduction Methodology Data Findings Summary Evolution of the sample Sample Initial sample of ESU 2008 Linked with tax records Aged 16 or oldera Responded in ESUb Earnings reported in ESU Employedc Employment duration reported Worked full time for whole year - constrained sectord - unconstrained sector Total 15,123 15,048 12,861 10,951 10,237 6,570 5,496 4,171 927 3,244 Number of persons A00 A0s Ar 0 3,672 341 846 5 341 846 5 280 7 1 139 12 1 127 - Ars 5,378 5,378 5,204 4,031 915 3,116 Omitted at each step 75 2,187 1,910 714 3,667 1,074 1,325 - Notes: (a) that is being subject to a personal interview; (b) for new sample members the number of non-respondents includes only sampled persons without other household members; (c) employment reported as the main activity at least for one month in the income reference period and/or having positive earnings in either data source; (d) constrained sector sub-sample includes public sector employees, except those who changed jobs or have a second job.
  20. 20. Introduction Methodology Data Findings Summary Mean earnings Sample Survey Tax records Difference b se b se b se ESU non-respondents with positive earnings in the tax records Unit non-response 8.48 0.64 Item non-reponse 8.76 0.43 ESU respondents with positive earnings in either source Positive earnings in ESU (A0s ) 6.78 0.44 0.00 6.78 0.44 Pos... in the tax records (Ar 0 ) 0.00 1.41 0.12 -1.41 0.12 Pos... in both sources (Ars ) 7.46 0.11 7.32 0.13 0.13 0.08 - constrained sector 7.37 0.20 8.24 0.27 -0.88 0.15 - unconstrained sector 7.48 0.12 7.05 0.14 0.43 0.10 N 1,114 563 341 846 5,378 1,048 4,330 Notes: annual (net) earnings in thousand EEK divided by 12; estimations take into account design weights and clustering at the household level; constrained sector sub-sample includes public sector employees, except those who changed jobs or have a second job.
  21. 21. Introduction Methodology Data Findings Summary Distribution of earnings Survey Tax records .15 Density .2 .15 Density .2 .1 .05 .1 .05 0 0 0 10 20 30 40 0 10 20 30 Annual (net) earnings divided by 12, in thousand EEK Notes: 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). 40
  22. 22. Introduction Methodology Data Findings Summary Reported vs survey earnings by sector unconstrained sector constrained sector 40 Survey 30 20 10 0 0 10 20 30 40 0 10 20 30 40 Tax records observed 45-degree line linear fit locally weighted regr.
  23. 23. Introduction Methodology Data Findings Summary 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 2nd job, 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 2nd job, mortgage 3 Survey income equation: age (-), age sq (-), male (+), Est. nationality (+), education (+), # of waves (-), interview month (+), interview rating (+), how responded Non-significant: persons present at interview, constrained sector
  24. 24. Introduction Methodology Data Findings Summary Marginal effects (conditional on true earnings) (ref: secondary) 0 -.2 -.3 -.3 -.3 -.2 -.2 -.1 -.1 -.1 0 0 .1 .1 tertiary education .1 female (ref: male) 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 1 to 10 employees (ref: manufacturing) (ref: 50+) .1 -.1 0 0 -.3 -.3 -.2 -.2 -.1 0 -.1 -.2 0 25 .1 construction (ref: north) .1 north-east region -.3 marginal effect on the predicted probability of compliance age (D = 10 years) 5 10 15 20 25 0 5 10 15 20 25 0 true earnings (thousand EEK per month) additive multiplicative 5 10 15 20 25
  25. 25. Introduction Methodology Data Findings Summary Elasticity of expected value of declared earnings at sample means/modes aged 25 aged 55 female 1 1 1 .9 .9 .9 .8 elasticity of reported earnings 1 .9 .8 .8 .8 .7 .7 .7 .7 .6 .6 .6 .6 .5 .5 .5 0 5 10 15 20 25 0 5 tertiary education 10 15 20 25 .5 0 5 north-east region 10 15 20 25 0 construction 5 10 15 20 25 1 to 10 employees 1 1 1 1 .9 .9 .9 .9 .8 .8 .8 .8 .7 .7 .7 .7 .6 .6 .6 .6 .5 .5 .5 0 5 10 15 20 25 0 5 10 15 20 25 .5 0 5 10 15 20 25 0 5 10 15 20 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 lines show predicted true (net) earnings (conditional on being positive). 25
  26. 26. Introduction Methodology Data Findings Extent of non-compliance Unconstrained sector Whole sample Additive Multipl. Additive Multipl. Proportion of sample, % no income 0.2 0.5 0.4 0.7 full evaders 3.5 3.1 2.7 2.4 part evaders 23.3 28.8 18.0 22.2 compliant 73.0 67.6 78.9 74.7 Undeclared earnings as a share of total gross earnings, % All 16.3 15.7 12.9 12.4 Decile 1 16.7 22.1 12.8 17.4 Decile 2 12.3 12.7 10.0 9.6 ... ... ... ... ... Decile 9 17.5 16.8 13.1 13.2 Decile 10 22.4 20.1 19.4 17.4 N = 3,130 N = 4,049 Summary
  27. 27. Introduction Methodology Data Findings Distribution by (gross) true earnings decile groups Constrained sector (observed) Unconstrained sector (additive) Unconstrained sector (multiplicative) .5 .5 .4 share of total (gross) true earnings .5 .4 .4 .3 .3 .3 .2 .2 .2 .1 .1 .1 0 0 0 -.1 -.1 -.1 -.2 -.2 -.2 -.3 -.3 -.3 -.4 -.4 1 2 3 4 5 6 7 8 9 10 -.4 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Decile groups of true earnings tax evasion measurement error Summary
  28. 28. Introduction Methodology Data Findings Sensitivity analysis Alternative samples definitions for the constrained sector set of co-variates parameter constraints (i.e. differences between the constrained and the unconstrained sector) survey design (weights, clustering) Results appear robust parameter estimates proportion of compliant proportion of earnings not declared Summary
  29. 29. Introduction Methodology Data Findings Summary Concluding points First study analysing tax evasion and measurement error ME instrumental for income differences Broad set of characteristics associated with tax evasion (in line with previous studies) Scale and distribution of undeclared earnings part vs full evasion high income earners Substantial underreporting of wages/salaries despite third-party reporting
  30. 30. Introduction Methodology Data Findings Thank you! apaulus@essex.ac.uk Summary

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