THREE ESSAYS ON INCOME AND WEALTH - Summitsummit.sfu.ca/system/files/iritems1/9265/etd4259.pdf · propose using a Receiver Operating Characteristic (ROC) curve to determine the optimum

THREE ESSAYS ON INCOME AND WEALTH

by

Chunling Fu

B.A., Renmin University of China, 1992

M.A., Simon Fraser University, 2000

A THESIS SUBMITTED IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

in the Department

of

Economics

© Chunling Fu 2008

SIMON FRASER UNIVERSITY

Fall 2008

All rights reserved. This work may not be

reproduced in whole or in part, by photocopy

or other means, without the permission of the author.

APPROVAL

Name:

Degree:

Title of Project:

Examining Committee:

Chair:

Chunling Fu

Doctor of Philosophy

Three Essays on Income and Wealth

Lawrence Boland, FRSCProfessor, Department of Economics

Krishna PendakurSenior SupervisorProfessor, Department of Economics

Geoffrey DunbarSupervisorAssistant Professor, Department of Economics

Simon WoodcockSupervisorAssistant Professor, Department of Economics

Brian KrauthInternal ExaminerAssociate Professor, Department of Economics

Kevin MilliganExternal ExaminerAssociate Professor, Department of EconomicsUniversity of British Columbia

Date Defended/Approved: December 2,2008

ii

SIMON FRASER UNIVERSITYLIBRARY

Declaration ofPartial Copyright LicenceThe author, whose copyright is declared on the title page of this work, has grantedto Simon Fraser University the right to lend this thesis, project or extended essayto users of the Simon Fraser University Library, and to make partial or singlecopies only for such users or in response to a request from the library of any otheruniversity, or other educational institution, on its own behalf or for one of its users.

The author has further granted permission to Simon Fraser University to keep ormake a digital copy for use in its circulating collection (currently available to thepublic at the "Institutional Repository" link of the SFU Library website<www.lib.sfu.ca> at: <http://ir.lib.sfu.ca/handle/1892/112>) and, without changingthe content, to translate the thesis/project or extended essays, if technicallypossible, to any medium or format for the purpose of preservation of the digitalwork.

The author has further agreed that permission for multiple copying of this work forscholarly purposes may be granted by either the author or the Dean of GraduateStudies.

It is understood that copying or publication of this work for financial gain shall notbe allowed without the author's written permission.

Permission for public performance, or limited permission for private scholarly use,of any multimedia materials forming part of this work, may have been granted bythe author. This information may be found on the separately cataloguedmultimedia material and in the signed Partial Copyright Licence.

While licensing SFU to permit the above uses, the author retains copyright in thethesis. project or extended essays, including the right to change the work forsubsequent purposes, including editing and publishing the work in whole or inpart, and licensing other parties, as the author may desire.

The original Partial Copyright Licence attesting to these terms, and signed by thisauthor, may be found in the original bound copy of this work, retained in theSimon Fraser University Archive.

Simon Fraser University LibraryBurnaby, BC, Canada

Revised: Fall 2007

Abstract

This thesis consists of three empirical essays that study two independent topics: income

under-reporting and immigrants' portfolio allocations.

The first essay forms Chapter 2 where we use data from the Survey of Financial Security

and the Survey of Household Spending to estimate the incidence and extent of income

under-reporting in Canada. We find that roughly 20% to 40% of households under-report

income by, on average, roughly $6,000 in 1999. In contrast to the existing literature, we

show that self-employment status is a poor indicator of income under-reporting. We find

that roughly 26% of non self-employed households under-report income, regardless of how

self-employment status for households is determined. We profile income under-reporters

and find that income under-reporting is pervasive.

We propose a simple ratio method of identifying income under-reporting households for

our second essay, Chapter 3. Our method is a straight-forward application of the Permanent

Income Hypothesis; that is, households make consumption decisions based on their expected

lifetime income not their reported lifetime income implying that consumption-to-income

ratios should be higher for under-reporting households. We argue for using housing costs

as the consumption measure in our approach. Our results confirm that households that

under-report their income have mortgage-to-income ratios (MIR) or rent-to-income ratios

(RIR) well in excess of those households that do not under-report. Using this finding, we

propose using a Receiver Operating Characteristic (ROC) curve to determine the optimum

cutoff threshold for MIR/RIR to detect under-reporters.

Our third essay, Chapter 4, uses data from the 1999 and 2005 Survey of Financial Secu

rity to investigate the differences in portfolio allocations and values between immigrants and

Canadian-born households. In general, we find that immigrants hold more real estate and

less pension assets relative to Canadian-born households. Limited cohort analysis suggests

III

that settled immigrants portfolio allocations are similar to that of Canadian-born house

holds in contrast to recent immigrants portfolios. We also find evidence that the length of

time living in Canada has a positive effect on ownership rate, share and value of both real

estate and pension assets.

Keywords: income under-reporting; tax evasions; immigrants; portfolio allocations

Subject terms: taxation; public economics; immigration; portfolio allocations

iv

v

To Sid

Acknowledgments

I would like to express my gratitude to my supervisors Geoffrey Dunbar, Krishna Pendakur

and Simon Woodcock, whose excellent mentoring and expertise guided me through finishing

this thesis. I thank all the faculty at the Department of Economics, especially David An

dolfatto, Don DeVoretz, Robert Jones, and Gordon Myers for their support and comments.

Special thanks to Ross Hickey and my fellow graduate students for useful discussions and

exchanges of knowledge. I would also like to thank the office staff for all their support and

assistance, especially Kathy Godson, Laura Nielson, Gwen Wild, and Dorothy Wong. Fi

nally, I would like to thank Sidney Fels for your deep insight, great inspiration and constant

support.

VI

Contents

Approval

Abstract

Dedication

Acknowledgments

Contents

List of Tables

List of Figures

1 Introduction

2 Income Illusion

2.1 Introduction.

2.2 The Data . .

2.3 Imputing Consumption from the SHS

2.4 Income Under-reporters and Tax Evasion.

2.4.1 Adjusting for Savings

2.4.2 Profiling Tax Evasions

2.4.3 The Underground Economy and Tax Loss

2.5 Conclusion .

VB

ii

iii

v

vi

vii

ix

xi

1

7

7

12

16

22

26

29

35

37

3 The Money Trail 51

3.1 Introduction. 51

3.2 Expenditure and True Income . 53

3.3 The Canadian Data 56

3.4 Income Under-reporting 59

3.4.1 Conditional MIR and RIR . 60

3.4.2 MIR and RIR as indicators 62

3.4.3 Adding demographic characters. 65

3.5 Conclusion .. 68

4 Planting Roots 13

4.1 Introduction. .. 73

4.2 Why Immigration Status Might Matter 75

4.3 Data and Summary Statistics 76

4.3.1 Descriptive analysis 77

4.3.2 Age and arrival cohort analysis 81

4.4 Regression Analysis .. 90

4.4.1 Do immigrants have different housing assets? 92

4.4.2 Do immigrants have adequate pensions? 94

4.4.3 Robustness 95

4.5 Conclusion 96

A Definitions and Measurements 101

A.l Non-Durable Consumption Measure . 107

A.2 Description of major retirement funds . 108

viii

List of Tables

2.1 Interest rate assumed for debt payment calculation 13

2.2 Self-reported income vs. spending 14

2.3 Incidence of Income Under-reporting by Self Employment Status,

using $8 per day consumption measure . . . . . . 16

2.4 Imputation Regression Fit 21

2.5 Income Under-reporting and Self Employment Status, 1999 23

2.6 Income Under-reporting and Self Employment Status, 2005 24

2.7 Income Under-reporting and Self Employment Status for Savers,

1999 28

2.8 Income Under-reporting and Self Employment Status for Savers,

2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 29

2.9 Income Under-reporting and Self Employment Status for Dis-savers,

1999 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.10 Income Under-reporting and Self Employment Status for Dis-savers,

2005 . . . . . . . . . . . . . . . . . . . . . . . . 31

2.11 Income Under-reporting by Region, 1999 32

2.12 Income Under-reporting by Occupation, 1999 39

2.13 Income Under-reporting by Education, 1999 . 40

2.14 Income Under-reporting by Reported Income Level, 1999 40

2.15 Interest Payments Comparison. . . . . . . . . . . 41

2.16 Sample Selection from the SFS 41

2.17 Demographic Comparison of the SFS and SHS 42

2.18 On-going Expenses Comparison of the SFS and SHS . 43

2.19 Imputed Consumption, 1998 . . . . . . . . . . . . . . . . 44

IX

2.20 Imputed Consumption, 2004 .

2.21 Income Under-reporting by Region, 2005

2.22 Income Under-reporting by Occupation, 2005

2.23 Income Under-reporting by Education, 2005 .

2.24 Income Under-reporting by Reported Income Level, 2005

45

46

47

48

48

3.1 Conditional MIR and RIR by Income Deciles 61

3.2 Thresholds of MIR and RIR 62

3.3 Confusion Matrix . . . . . . . 65

3.4 Selected Coefficient for Logistic Regression of income under-reporting 70

4.1 Demographic comparison of Canadian-born (CB) and Foreign-born

(FB) households, 1999 and 2005 . . . . . . . . . . . . . . . . . . . . . .. 78

4.2 Balance sheet summary for Canadian-born and Foreign-born, 1999 97

4.3 Balance sheet summary for Canadian-born and Foreign-born, 2005 98

4.4 Regression of Real Estate Assets Holdings 99

4.5 Regression of Pension Assets Holdings. . . . 100

4.6 Mean and Standard Error of Portfolio Shares, by age and immi-

gration cohort 101

4.7 Mean and Standard Error of Ownership rate, by age and immigra-

tion cohort . 102

4.8

4.9

4.10

Median and Standard Error of Real Estate and Pension asset, by

age and immigration cohort . . . . . . . . . . . . . . . . . . . . .

Supplementary Regressions of Real Estate Assets Holdings

Supplementary Regressions of Pension Assets Holdings . . .

x

. 102

. 103

.104

List of Figures

2.1 Imputation Errors and Under-reporting Incidence using Non-durable Con

sumption, 1999 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 25

2.2 Imputation Errors and Under-reporting Incidence using Non-durable Con-

sumption, 2005 . . . . . . . . . . . . . . . . . . . 26

2.3 Distribution of True and Reported Income, 1999 34

2.4 Distribution of True and Reported Income, 2005 35

3.1 Kernel density of MIR and RIR, by income reporting status, 1999. 59

3.2 Kernel density of MIR and RIR, by income reporting status, 2005. 60

3.3 MIR and RIR by True-to-reported-income Ratio, 1999 61

3.4 Thresholds of MIR and the proportion of TN, FP, FN and TP for home owners 63

3.5 Thresholds of RIR and the proportion of TN, FP, FN and TP for renters. 64

3.6 ROC for MIR Threshold, 1999; thresholds are indicated as labels on the ROC

curve. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 66

3.7 ROC for RIR Threshold, 1999; thresholds are indicated as labels on the ROC

curve. . . . 67

4.1 Portfolio shares for young (20-34) and immigration cohort, 1999 and 2005;

height indicates median value. . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

4.2 Portfolio shares for middle (35-49) and immigration cohort, 1999 and 2005;

height indicates median value. . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4.3 Portfolio shares for older (50-64) and immigration cohort, 1999 and 2005;

height indicates median value. . . . . . . . . . . . . . . . . . . . . 85

4.4 Real estate holdings, by age and immigration cohort, 1999-2005. 87

4.5 Pension holdings, by age and immigration cohort, 1999-2005. .. 89

Xl

Chapter 1

Introduction

This thesis is a collection of three empirical essays that study two independent topics. The

first topic investigates income under-reporting and uncovers potential indicators for this be

haviour. The second topic documents portfolio allocations of Canadian immigrants relative

to Canadian born households. Collectively, these essays contribute to our understanding of

both income under-reporting and immigrants' economic assimilation.

In the first essay, Chapter 2, we propose a direct method of detecting income under

reporting. Income statistics playa central role in both the design and the evaluation of

public policy in most industrialized countries. At the household level, most tax and transfer

mechanisms employed by governments use self-reported income data to determine the level

of tax and transfers. Despite enormous care and scrutiny, it is difficult for authorities to

accurately measure true income or even determine whether income is reported truthfully.

Existing studies of income under-reporting and tax evasion exploit consumption de

mand equations to estimate the true income of households which are suspected of income

under-reporting. One standard assumption in this literature, e.g. Schuetze (2002) and

Pissarides and Weber (1989), is that only self-employment income can be under-reported.

Thus, estimating demand equations for salaried households yields a function that can be

inverted to yield the income of self-employed (under-reporting) households. There are two

other approaches that are also used to identify income under-reporting. The first approach

uses monetary aggregates and/or national account data, e.g. Cagan (1958), Tanzi (1980),

and Mirus, Smith and Karoleff (1994). The second approach uses Taxpayer Compliance

Measurement Program (TCMP) conducted by the US Internal Revenue Service (IRS), e.g.

Andreoni et. al. (1998). Both approaches have drawbacks. Aggregate data does not allow

1

CHAPTER 1. INTRODUCTION 2

for distributional analysis and TCMP is only available in the US. Thus, our study investi

gates an alternative strategy using consumption levels and reported income discrepancies.

Using data from the 1999 and 2005 Survey of Financial Security (SFS), we construct

a household level income statement and check for inconsistency in reported income and

calculated consumption levels. Since SFS only collects a subset of consumption items, we

impute the consumption from the 1998 and 2004 Survey of Household Spending (SHS) into

SFS. One advantage of the SFS is that it specifically asks households whether their income is

greater than, equal to or less than their expenses. We first concentrate on those households

who report income equals to spending and compare the calculated consumption with their

reported income. If a household's imputed spending exceeds its reported income then it is

assumed to be under-reporting its income. We further imputed savings plus consumptions

for those households who self-identify as having income greater than spending, and dis

savings (i.e. asset sale or additional loan) for those who self-identify as income less than

spending. If the imputed savings/dis-savings plus consumption is more than their self

reported income then the households are labeled as under-reporters.

We make two contributions to the existing literature using this approach. First, we show

that income under-reporting is not confined to the self-employed. We find that roughly 30%

of non self-employed households under-report income, regardless of how self-employemnt

status for households is determined. Second, we profile income under-reporters and find that

income under-reporting is pervasive and our estimates are in-line with those of Andreoni et.

al. (1998) in the US and Schuetze (2002) in Canada.

In summary, our work described in Chapter 2 establishes a direct approach in detecting

income under-reporting, which sets the stage for Chapter 3. In Chapter 3, we propose a

simple ratio test for identifying income under-reporting households using our direct method

to establish the test's effectiveness. The intuition underlying our test follows from the

Permanent Income Hypothesis; that is, households spend according to their true permanent

income and not their reported income. Thus the ratio of particular consumption expenses

to reported income provides a gauge of whether a household is under-reporting or not. Our

method is intuitive and a test using Canadian data appears robust. For instance, in our

data, we find that most households that under-report their income have mortgage-to-income

ratios (MIR) or rent-to-income ratios {RIR) well in excess of households that do not under

report. In addition, we suggest using a Receiver Operating Characteristic (ROC) curve to

determine the optimum cutoff threshold for MIR/RIR to detect under-reporters.


Our results appear dual to the theoretical literature on tax evasion in that we suggest

a test of income under-reporting where the theoretical literature proposes a tax shift for

efficiency gains. For instance Boadway and Richter (2005) suggest that taxing an observable

good in the AllinghamjSandmo model improves the efficiency of taxation. Rather than

introduce a new tax, our empirical methodology provides an easy method to detect possible

tax evasion provided that governments collect consumption expenditures on shelter. Thus,

similar to Boadway and Richter, we base our approach on the notion that consumption

decisions depend on true income, not reported income. While our method is susceptible

to a change in the economy-wide level of spending on shelter, we do not feel that in the

short-run such changes are likely to occur. Hence, our test may be an effective approach for

encouraging tax compliance.

Our third essay, Chapter 4, investigates immigrants' wealth accumulation and allocation

relative to Canadian-born households to provide a deeper understanding of immigrants'

financial assimilation in terms of asset holdings. To date most of the studies on immmigrants'

economic well-being has concetrated on labour market performance such as employment and

earnings (Chiswick, 1978; Baker and Benjamin, 1994). Few researchers have studied the

financial assmilation of immigrants in terms of their portfolio selections. However, portfolio

mix matters for reasons of income risk and potential income or wealth gains which is an

integral part of the financial well-being of immigrants.

For this investigation, we again use the 1999 and 2005 Survey of Financial Security

(SFS) conducted by Statistics Canada to analyze data on the value and composition of

assets of immigrant households relative to Canadian-born households. The univariate de

scriptive analysis suggests that immigrants average assets are comparable to Canadian born

households. Using limited cohort analysis, we find the settled immigrants have portfolios

that are similar to Canadian-born households, but their median wealth is higher. The re

cently arrived immigrants, though, have a portfolio weighted towards durable goods that

does shift towards other parts of their portfolio such as real-estate the longer they stay in

Canada. However, their wealth accumulation lags Canadian-born and settled immigrants.

Our regression analysis confirmed that the length of time living in Canada has a positive

effect on ownership rate, share and value of both real estate and pension assets.

In summary, we are able to take advantage of the 1999 and 2005 SFS and the 1998 and

2004 SHS to make several contributions. The first two essays established a new approach for

estimating income under-reporting and a indicator based on consumption-to-income ratio.


The third essay studied immigrant wealth portfolios and investigated the progression of im

migrants' financial status compared to Canadian-born households. Our research establishes

a foundation for further investigation of tax evasion, policy creation and compliance. As

well, our research provides new insight into how immigrants fare financially as they create

a new life in Canada.

Bibliography

[1] Allingham, M., and Sandmo, A. 1972. "Income Tax Evasion: A Theoretical Anal

ysis," Journal of Public Economics, 1(3/4), 323-38.

[2] Andreoni, J., Erard, B. and Feinstein, J. 1998. "Tax Compliance," Journal of

Economic Literature, 36(2), 818-860.

[3] Baker, M. and Benjamin, D. 1994. "The Performance of Immigrants in the Cana

dian Labor Market," Journal of Labor Economics, 12(3), 369-405.

[4] Cagan, P. 1958. "The Demand for Currency Relative to the Total Money Supply,"

Journal of Political Economy, 66(4), 303-28.

[5] Chiswick, B. 1978. "The Effect of Americanization on the Earnings of Foreign-born

Men," Journal of Political Economy, 86(5), 897-921.

[6] Cobb-Clark, D., and Hildebrand, V. 2006. "The Wealth and Asset Holdings of

U.S.-born and Foreign-born Households: Evidence from SIPP Data," Review of Income

and Wealth, 52(1), 17-42.

[7] Egan, J.P. 1975. Signal Detection Theory and ROC Analysis, Academic Press, New

York, USA.

[8] Erard, B. 1997. "A Critical Review of the Empirical Research on Canadian Tax Com

pliance," Department of Finance Working Paper, 97-6, Canada.

[9] Milligan, Kevin 2005. "Lifecycle Asset Accumulation and Allocation in Canada,"

Canadian Journal of Economics, 38(3), 1057-1106.

[10] Mirus, R., Smith, R. and Karoleff, V. 1994. "Canada's Underground Economy

Revisted: Update and Critique," Canadian Public Policy, 20(3), 235-252.

5

BIBLIOGRAPHY 6

[11] Pissarides, C. and Weber, G. 1989. "An Expenditure-based Estimate of Britain's

Black Economy," Journal of Public Economics, 39, 17-32.

[12] Richter, W. and Boadway, R. 2005. "Trading Off Tax Distortion and Tax Evasion,"

Journal of Public Economic The077}, 7(3), 361-381.

[13] Schuetze, H. 2002. "Profiles of Tax Non-compliance Among the Self-Employed in

Canada: 1969 to 1992," Canadian Public Policy, University of Toronto Press, vol.

28(2), pages 219-237, June.

[14] Tanzi, V. 1980. "The Underground Economy in the United States: Estimates and

Implications," Banco Nazionale del Lavro, 135, 427-453.

[15] Yitzhaki, S. 1974. "A Note on Income Tax Evasion: A Theoretical Analysis," Journal

of Public Economics, 3(2), 201-02.

Chapter 2

Income Illusion:

Canadal

2.1 Introduction

Tax Evasion •In

Income statistics playa central role in both the design and the evaluation of public policy

in most industrialized countries. At the household level, most tax and transfer mechanisms

employed by governments use self-reported income data to determine the level of tax and

transfers. Despite enormous care and scrutiny, it is difficult for authorities to accurately

measure true income or even determine whether income is reported truthfully. In con

sequence, income under-reporting distorts the outcomes of tax and transfer schemes and

lowers the funds available to governments to finance public policy.

The motivation for households to under-report income is clear. By under-reporting

income, households lower the level of their income tax obligations and thus retain more

money for their personal consumption (or savings). In addition, households that under

report income may also become eligible for public transfers depending on the applicable tax

and transfer policies. While it is not clear, theoretically, that income tax evasion necessarily

constitutes a social welfare loss, all tax and transfer policy has, by construction, social

welfare implications. Thus, the reliance of most tax and transfer systems on income data

suggests that policy makers ought to, at least, be cognizant of the extent of tax evasion

when designing policy.

IThis chapter is based on a work co-authored with Geoffrey Dunbar.

7

CHAPTER 2. INCOME ILLUSION 8

There are three basic approaches to measuring income tax evasion that are exploited in

the literature. One approach uses monetary aggregates and/or national account data, e.g.

Cagan (1958), Tanzi (1980), Mirus and Smith (1981) and Mirus, Smith and Karoleff (1994),

to estimate the aggregate amount of underground (unreported) economic activity. There

is no distinction in this approach between income that earned illegally and income that

is earned legally but is unreported. Moreover, aggregate data does not allow for analysis

of under-reporting at a household level so the causes and consequences of under-reporting

are left unaddressed. A second approach is specific to the US. The Taxpayer Compliance

Measurement Program (TCMP) conducted by the US Internal Revenue Service (IRS) audits

household tax returns. Andreoni et. al. (1998) report that the estimates from the TCMP

suggest that roughly 40% of US households under-reported their income to the IRS in 1988.

The third approach exploits consumption demand equations or expenditure functions to

estimate the true income of households which are suspected of income under-reporting.

One standard assumption in this literature, e.g. Tedds (2007), Lyssiotou et. al. (2004),

Schuetze (2002) and Pissarides and Weber (1989), is that only self-employment income

can be under-reported. Thus, estimating demand equations for households which are not

suspected of income under-reporting yields a function that can be inverted to yield the

income of households which are suspect.

In this paper, we make two contributions to the existing literature. First, we show that

income under-reporting is not confined to the self-employed. This finding is quite natural

- there are many methods of earning income that need not be reported or identified as

self-employment. One clear example is that a home-owner may rent a suite in his or her

home without reporting such income to the government. Second, we show that income

under-reporting is common and our best estimates are in-line with those of Andreoni et. al.

in the US. Nor do there appear to be common socio-demographic profiles for tax evaders

income tax evasion is pervasive.

To identify income under-reporters we use household survey data to construct a house

hold's income statement. Let a household's estimated gross consumption be Gt , and its

reported income be fit, then when fit - Gt >= 0 we consider this a true reporter. When

fit - Gt < 0 we consider this an under-reporter. \Ve consider the three cases for calculating

gross consumption (Gt ) based on whether a household is a saver, balancer or dis-saver. Let

cdenote household's expenditure, s denote saving and bdenote borrowing, then:

CHAPTER 2. INCOME ILLUSION

(1) Ot = Ct if balancer

(2) Ot = Ct + St if saver

(3) Ot = Ct - bt if dis-saver

9

Whether a household is a saver, balancer or dis-saver in our study is based on the response

to a survey question (How is your income compared to spending?) which is discussed in

detail in section 2.2.

The data for our study come from the 1999 and 2005 Survey of Financial Security (SFS)

conducted by Statistics Canada. First, we concentrate on those households with balanced

budget and compare the expenditure with their reported income. The reported income

variable (fit) we use is household income reported in SFS which is identical to the income

reported to Revenue Canada2 . For the expenditure variable Ct, SFS only collects a subset

of consumption items (e.g. shelter costs, utility costs, child care payment, etc.) which is

insufficient to calculate the full income statement. We follow three strategies to estimate

households' expenditure levels as described in the next paragraphs.

First, we ignore other consumption entirely and calculate the fraction of households

under-reporting using the existing expenses surveyed in the SFS. Depending on one's per

spective, these households have either incorrectly answered survey questions or have not

considered that the collected data could be used to verify their answers. The former could

be considered as indicative of measurement error and the second could be considered evi

dence of respondent myopia. We find that, while non-zero, the fraction of households that

under-report by this measure is small.

Our second approach is to assume that all households have the same consumption func

tion, $8 per day times the square root of the number of household members. This assumption

is clearly false. These consumption levels are intended to represent a bare minimum of both

the incidence and level of under-reporting. As these estimates indicate, both self-employed

and not self-employed households under-report, regardless of how self-employment is defined.

Our third approach uses information from the Survey of Household Spending (SHS) to

impute the missing consumption to the SFS, exploiting the economic, demographic and

geographic information available in both datasets. The detailed description of SHS and our

2 At the time of the interview, the respondents have a choice of granting access to their tax record throughRevenue Canada to skip all the income questions. Out of all respondents, 85% in 1999 survey and 80%in 2005 survey granted record linkage. In this study, we exclude those households if any member of thehousehold did not permit the record linkage.


imputation methods can be found in section 2.3. Our imputation procedure yields aggregate

moments in the SFS that are quite similar to those in the SHS. One advantage of having

to impute consumption into the SFS is that households should have had no incentive to

'cheat' on their remaining expenses in the SFS as they should have little reason to believe

that their responses can be verified. Moreover, we are able to condition our imputation not

only on the socio-demographic profiles of households but also on the consumption levels

that are reported in both the SFS and SHS. Indeed, our estimated imputation equation

for consumption almost certainly suffers from endogeneity but this is an (perhaps rare)

instance when endogeneity is welcome. We have no interest in the coefficient estimates

of the consumption-imputation regression and the tendency of endogenous covariates to

'over-fit' is in fact advantageous.

Imputing consumption effectively standardizes consumption levels on observable vari

ables. In effect, all households that are observationally identical are assumed to be exactly

identical. We attempt to investigate the sensitivity of our results to this effect using a

Monte Carlo approach. We do 500 replications of our consumption imputation adding ran

dom draws from the error terms from our consumption-imputation regression. OLS, by

construction, renders the covariance between consumption and the residuals zero, condi

tional on the covariates. Thus, our approach of adding our errors is unbiased and consistent

and gives a sense of how sensitive our results are to imputing at the mean.

Imputing consumption is also not without precedent. Skinner (1987) imputes consump

tion from the Survey of Consumer Expenditure (CEX) into the Panel Study on Income

Dynamics (PSID) and considers a range of control variables and proposes two approaches,

a reduced form and an extended form. Palumbo (1999) extends Skinner's imputation ap

proach and also proposes a structural model of household expenditure. Blundell, Pistaferri

and Preston (2006) propose inverting a food demand equation estimated from the CEX to

yield total consumption in the PSID. They compare their approach to that of Skinner and

find that Skinner's approach, while under-estimating the level of consumption, does match

the variance of log consumption reasonably well. Unfortunately, the SFS does not collect

food expenditure and we are unable to exploit the micro-founded approach of Blundell,

Pistaferri and Preston. Fisher and Johnson (2004) impute consumption from the CEX to

the PSID using a broader range of control variables (mainly demographic) than Skinner and

compare their approach to Skinner and Blundell, Pistaferri and Preston. Fisher and Johnson

suggest that imputing using demographic information yields the most plausible estimates.


Since the SFS data do not allow us to follow Blundell, Pistaferri and Preston we impute

consumption from the SHS to the SFS similarly to Fisher and Johnson. Nevertheless, we

are comforted that the results of Blundell, Pistaferri and Preston suggest that our imputed

consumption is biased lower.

We use our results on income under-reporting to present a number of findings. We

estimate both the amount of unreported income (the underground economy) and the income

tax loss for Canada. We find that the total unreported income in 1998 tax year is at least

$7.0 billion, which translates into $2.5 billion of lost tax revenue (using 36% marginal tax

rate). The corresponding numbers for 2004 are $12.4 billion of unreported income and $4.5

billion of lost revenue3. We note that these amounts are sufficient to provide a number

of national public programs - for instance the cost of a national childcare program was

estimated at $5 billion dollars over 5 years in 2005.

In addition, we follow Schuetze (2002) and differentiate the incidence of tax evasion

by occupation. Our results are similar to his and suggest that the bulk of income evasion

is concentrated among the service sector. We also find that the incidence of income tax

evasion varies significantly by province which may indicate different incentives or social

costs between provinces. We also find that the incidence of income under-reporting varies

by reported income level. In particular, we find that 70 per cent of households that report

income of less than 20,000 dollars per year under-report by roughly a factor of 2 which

suggests that transfer policies based on reported income may transfer income from poorer

households to richer ones. We leave an exploration of these findings to future research.

One caveat with our study is that our expenses are based on imputed values. We have

provided means to mitigate imputation errors using conservative methods and extra robust

ness checks. However, there may remain imputation error causing some of our households

labeled as 'tax evaders' or 'under-reporters' to be wrong. However, we find that pattern

of our results is consist across the two survey years and our results are similar to existing

research, giving us confidence that the qualitative aspect of our results are reliable even if

there may be error in the precise quantitative values.

3In this paper we use current dollars (the dollar value reported at the year of the survey) to measure allincome and expense items. Unless otherwise noticed, the dollar values are not directly comparable acrossdifferent years.


2.2 The Data

12

The primary data sources for our study are the 1999 and 2005 Survey of Financial Security

(SFS) collected by Statistics Canada. We use this data set to compare the reported income

(ih) with the estimated gross consumption Ct. If fh - Ct >= 0 we consider this a true

reporter. If Yt - Ct < 0 then we consider this an under-reporter. SFS is a self-report survey

of the assets and debts of Canadian households at the time of the survey and the income and

expenses for the previous calendar year, 1998 and 2004 respectively. The SFS is comprised

of two sub-samples. The first subsample is drawn from the Labour Force Survey (LFS)

sampling frame and reports households across the ten provinces excluding those households

on Indian Reserves or located on federal institutions (such as military bases). The second

subsample is drawn from high-income neighbourhoods to account for the disproportionate

wealth held by these households. The sample size for the 1999 and 2005 SFS are 15,933 and

5,282 respectively. Survey weights are provided to balance the unequal selection probabilities

and response rate, so that the survey is representative of the Canadian population.

The SFS collects asset and liability information from each surveyed household and in

come and demographic information from each adult (15+) respondent for the household.

As pointed out in section 2.1, the income data we report are the same as the income data

reported to the Canadian Revenue Agency (the federal government department responsible

for taxation) and so are free from measurement error to the extent that reported income

is free of measurement error. Moreover, the data reported are both the household's gross

income for the year and the household's net after-tax income. Thus, the effect of tax shelters

or tax credits (such as the investment tax credit) on household income is captured in the

latter.

To estimate the household's consumption level, we divide the expenses into three parts

based on the structure of SFS data: Interest expenses, on-going expenses and other con

sumptions. Both the 1999 and 2005 SFS permit us to estimate interest expenses, however,

1999 requires using liability level data to estimate interest expenses while the 2005 data

provides direct reporting of interest expenses. Specifically, the SFS collects data on the

levels of household liabilities, such as mortgages, student loan debts, credit card debts, etc.

In 2005, the SFS also collects the aggregate (annual) interest expense for households and

the level data for liabilities are not especially relevant for constructing household income

statements. However, in 1999 the annual costs for only a subset of household liabilities


(mortgages) are directly reported. We estimate the annual interest costs for the remaining

debt instruments. One complication is that the liability level data are for the time of the

data collection (May to July 1999) and thus the annual interest cost is sensitive to when

the debt is incurred. An additional complication is that households may not face similar

interest rates. In an attempt to be conservative in our estimate of the total interest cost for

households, we choose to set interest rates that seem to be near the lower range of available

data (Table 2.1). We include amortization payments assuming a ten year amortization for

student loans. We note that student loan interest payments were not tax deductible during

the period of the 1999 SFS survey. We assume an interest rate of 9% for credit card debt on

the assumption that some households shift balances from high interest cards to low interest

cards. Other interest rates (in the -3 % to +3% range) are tested and our results do not

appear sensitive to the interest rate selected4 . This is perhaps not too surprising as the

levels of these debts are, in general, not very large in relation to other expense items.

Table 2.1: Interest rate assumed for debt payment calculationType of debt Interest rate assumedStudent loan 7%Credit card debt 9%Home equity loan 6%Line of credit 7%Other debt 8%

As noted, the payment on non-mortage debt for 2005 is directly provided in the data.

We use the debt payment information from the 2005 SFS to check the robustness of our

estimated interest expenses. We regress the non-mortgage debt payment on the remaining

debt levels and use the estimated coefficients to impute the corresponding debt payment in

1999, correcting for both CPI and interest rate differences. The predicted debt payments

using regressional method are compared to our estimates based on the interest rates in

Table 2.15. We also compare the actual 2005 payments after adjusting for CPI inflation

and interest rate differences to the 1999 payments. The estimated debt repayment using

our interest rate assumption is smaller across the entire distribution and this suggests our

estimates are biased downward in 1999.

4The results of other interest rates are available upon request.


The SFS data also includes a number of on-going expenses such as: housing costs (mort

gage or rent payment); utility payments for oil, gas, water and electricity; car insurance

expenses; childcare expenses, and; child and alimony support payments. The consumption

of non-durable goods, services and durables excluding housing is not reported in the SFS

data. The lack of full consumption data is both a concern and a benefit. One possible

advantage of incomplete consumption data is that households that under-report income are

less likely to be concerned about getting caught and are less likely to underestimate the

consumption items that they do report. However, it is clear that the lack of a large amount

of consumption data is also concerning.

We follow three strategies in assigning household consumption levels, with each strategy

intended to illuminate a possible area of concern. As noted in the introduction, our first

strategy is simply to ignore household consumption entirely. We sum the households' on

going expenses and debt payments collected in the SFS to get a measure of total household

spending for the reference year and compare this to the reported household after-tax income.

A negative balance does not necessarily imply that the household under-reports its income

since that household may be dis-saving. However a unique question asked in the SFS is: How

is your income compared to spending? The possible responses are (1) less than spending,

(2) equal to spending, or (3) more than spending. Based on the answer to this question,

we categories households into dis-saver, balancer and saver, respectively. The distribution

of responses to this question is presented in Table 2.2.

(1) Dis-saver:(2) Balancer:(3) Saver:

Income < spendingIncome = spendingIncome > spending

Table 2.2: Self-reported income vs. spending1999 2005% %

16.5 18.343.0 40.040.5 41.7

If a household indicates that its income is enough to cover its spending, response (2), but

it has a negative balance then the household is assumed to be under-reporting their income.

Perhaps surprisingly, we find that roughly 4 per cent of households in the survey appear

to under-report their income by this measure. Largely, this appears due to either the rent

payment or the mortgage payment. There are, at least, two possible conclusions one can

draw from this finding. First, one may conclude that households have either misunderstood


the question regarding their income and expenses or else that the responses have been

miscoded. Certainly, it would be surprising if the survey was entirely free from error.

However, a second conclusion is simply that these households are in fact reporting truthfully

(and either ignore or do not care that their responses are at odds with the data they provide).

We are unable to distinguish between either interpretation. Nevertheless, the fraction of

households that fall in this category are small and do not seem to affect the qualitative

conclusions we draw in this paper.

The accuracy of responses to question regarding income and expenses is also perhaps

questionable and so we condition using other survey questions regarding assets sales, gifts

and pawnbroking. The SFS asks households if they have needed to sell an asset or deposit

an item at a pawnbrokers in order to payoff a bill, whether the household is behind in a

debt repayment or whether the household has received any gift money. We use responses to

these questions to construct an indicator of households that may be spending beyond their

income. Similarly, we remove from our sample households whose major source of income

is from pension income. We are concerned that the definition of income for households

whose major source of income is from retirement savings and pensions may be difficult to

accurately assess. For instance, we are unsure about how a household may define dissavings

from wealth. Conditioning our measures of income under-reporting on these variables do

not qualitatively change our results.

Our second approach is to assume a constant level of consumption across similar house

holds. We assume that households spend $8 times the square root of the number of household

members per day. (The concave transformation is meant to reflect increasing returns-to

scale.) We choose a level of $8 per day to roughly equates to poverty-line consumption for

food, clothing and transportation. We add this consumption measure to the ongoing ex

penses and debt payments for households and again compare to the level of reported income.

Like the first approach, this approach is largely uninformative about the level of incidence

of income under-reporting. (It may represent the bare minimum level of under-reporting).

The approach highlights a second finding of our study - namely that income under-reporting

is not confined to the self-employed5 . Thus, measurements of income under-reporting that

assume that the non-self-employment report truthfully should be treated with caution. Nor

5In this paper, we consider four different definitions of self-employment: the household's major source ofincome is from self-employment; at least one household member owns a business; the main income earner(MIE) or the spouse of the MIE are self-employed and; the MIE is self-employed.


is this result surprising. There are a number of ways for salaried individuals to earn ex

tra income that they mayor may not choose to report. Examples include the firefighter

plumber, the tow-truck mechanic, the ebay entrepreneur, the basement suite landlord, the

cottage landlord, the graduate student tutor, and the list goes on.

Table 2.3: Incidence of Income Under-reporting by Self Employment Status,using $8 per day consumption measure

1999 2005% of % % of %

Sample Under- reporting Sample Under-reportingBy major source of incomeNon Self-employed 95.3 8.5 96.1 7.8Self-employed 4.7 21.9 3.9 20.8By business indicatorNon Business owner 81.4 8.3 84.1 7.7Business owner 18.6 12.9 15.9 11.6By household employment statusNon Self-employed 84.4 8.0 83.8 6.4Self-employed 15.6 14.9 16.2 18.2By Major Income Earner's employment statusNon Self-employed 90.4 8.0 89.9 6.7Self-employed 9.6 19.6 10.1 22.8Total 100.0 9.1 100.0 8.3

2.3 Imputing Consumption from the SHS

The increase in the incidence of income under-reporting from adding a small amount of

per-capita consumption highlights the sensitivity of our approach to estimates of household

consumption. Our third approach to estimating household consumption is to use information

from the Survey of Household Spending (SHS) to impute consumption for households in the

SPS. The SHS is a self-report annual survey of detailed spending and income of Canadian

households across all provinces and territories6 . The sample sizes for the 1998 and 2004

SHS are 15,457 and 14,154 respectively.

6The territories are only covered in selected years.


The SHS and SFS have many of the same demographic, geographic and expenditure

questions in common which aid our imputation approach. We assume that the two data

sets are random samples from the same underlying population since both SHS and the main

sample of SFS follow the sampling framework of the Labour Force Survey (LFS) and are

designed to be representative of Canadian population. One additional difference is that the

SFS over-samples high income neighbourhoods compared to SHS, but this can be corrected

by applying survey weights provided by these surveys.

To ensure that the samples from each survey are comparable, we remove part-year

households, multi-family households and households living in the territories from the SHS

data. We also removed elderly households (reference person and/or spouse more than 65

years old) and households with extremely low income (before tax income less than $5007).

Our working sample consists of 10,651 and 10,311 cases for the 1998 and 2004 SHS, and

11,835 and 3,672 cases for the 1999 and 2005 SFS, respectively (see Table 2.16 on page 41

for details).

We report the demographic characteristics of households in the SFS and SHS in Ta

ble 4.1 by comparing the weighted means and standard deviations of some of the household

characteristics from these two data sources by year, including demographic characteristics,

type of dwellings, size of the area of residence, home ownership status, and vehicle ownership

status. As the Table indicates, most of the characteristics of these two data sets are very

similar despite the inclusion of the 'high-wealth' sub-sample in the SFS. The only notable

difference is the age and family structure. The reference person, defined as the person with

most knowledge of the family's financial situation, in SHS is slightly older than the reference

person in SFS (by 1.2 years in 1999 and 1.5 years in 2005); there is also a higher percentage

of married households in the SHS. These differences are probably due to a larger fraction of

unattached individuals in the SFS (32% in 1999, 29% in 2005) than in SHS (24% in both

years). This is also likely be the reason that SHS has slightly larger family size, and higher

percentage of homeownership. We control for these demographic factors in the imputation

procedure.

In addition to the household demographic characteristics, we also condition our imputa

tion on the major source of household income, household income, and mortgage (or rent) to

7We noticed that there are quite a few similar households with identical low income level in SFS whichappears to be imputed in by survey technicians. There are no such observations in SHS. We decide to removethese income outliers from both SHS and SFS data since they will likely bias our imputation results.


income ratios (in logarithm form). The reason for the last variable is explained in greater

detail in Chapter 3 but we briefly explain the intuition here. In some sense, we do not

wish to use the level of income to help predict the levels of consumption because we sus

pect that at least some households under-report income. However, we also do not wish to

lose possible conditioning information. Ideally, we would like some method of controlling

for possibly under-reporting households. The permanent income hypothesis suggests that

households consume based on their (true) lifetime income. If true, the consumption ratios

for truthfully reporting households would be lower than for under-reporting households of

equal reported income levels. In our companion paper we show that this intuition is borne

out by the data. Thus, conditioning on the mortgage (or rent) to income ratio helps control

for income under-reporting households.

The SFS and SHS also report some identical consumption information for ongoing ex

penses such as housing service expenses, utility payments and support payments. Table 2.18

compares the mean and standard deviations of these ongoing expenses by year. Most of

the individual items and the total on-going expenses in the two data sets are remarkably

similar. The two exceptions are the mortgage and rent payment, households in SHS on av

erage pay $300-$400 more on mortgages, and less (about the same amount) on rent. This is

consistent with the fact that there are more families (and more home owners) in SHS. In one

of our imputation procedures we take into account this differences in sample composition of

SHS and SFS and estimated families and individuals, renters and owners seperately. The

differences in the average on-going expenses is about $144 in 1998 and $369 in 2004. We

use these consumption items to help control for household consumption preferences. For

instance, some households may prefer to spend relatively a larger fraction of their income on

housing by reducing their consumption of other items such as a vehicle. A classic example

is that one couple may prefer to live in an expensive, urban, condo and take public transit

while another household may prefer to spend live in a suburban house and drive a SUV.

We impute the households consumption expenses according to the equation:

c = 0' + pif3 + Xii + e, (2.1 )

where the dependent variable, C, is our measure of the household's gross consumption, P

are the consumption items reported in both the SFS and the SHS as listed in Table 2.18, X

are the socio-demographic and geographic characteristics of households and e are residuals.

More specifically, X includes a significant variety of socio-demographics including: age of the


Main Income Earner (MIE) and its square; age of the spouse; married MIE; male MIE; weeks

worked by MIE and spouse; major source of income; number of adults, youth, child and

income earners in the households (quadratic); home mortgage free; own vehicle; province

of residence; urban size; type of dwelling; rent-to-income ratio (logarithm); mortgage-to

income ratio (logarithm) and before-tax income of household. One difference between the

control variables across these two years is that we are only able to include education levels

in the 2004 imputation as they are not reported in the 1998 SHS.

In an attempt to provide some robustness, we consider two choices of C. Our first

choice is simply to calculate the total non-durable consumption for the household using the

data in the SHS (excluding items that are explicitly measured in the SFS). We label this

consumption measure CNDC. Appendix A lists all the items that are included in the non

durable consumption measure CNDC. This approach avoids a "frequency" bias in that we

exclude large purchases that are infrequent to all households in our sample. We note that

if a large purchase is financed by debt then we should already capture the flow cost of that

expense in our consumption measures. What is not included is durable consumption that is

full-paid at the time of purchases. We argue that this omission biases our results downward

and thus we are likely to underestimate the true amount of income under-reporting by this

method.

Our second approach is to sum all current consumption for the household as reported in

the SHS. We label this consumption measure CTOT. The main different between GTOT and

GN DC is the former include durable consumptions such as purchases of household furnishings

and equipments. We examine our results using both imputation methods (and then again

adding savings).

To ensure that our imputation is robust to different specifications, we impute the con

sumption using both linear and logarithm specifications. Under the logarithmic specifica

tion, we aggregated on-going expenses into housing expenses, vehicle expenses and childcare

expenses before taking the natural logarithm. In addition, as noted earlier, we are concerned

that different types of households (singles vs. families, renters vs. owners) may have dif

ferent consumption patterns and estimating one equation for the whole sample might be

too restrictive (we label this approach 'restricted'). Therefore, we divided households by

households type (singles vs. families) and housing tenure status (renter vs. owner) and

estimated each group separately (we label this approach 'unrestricted'). Our logarithmic

specification generates a narrower range of predicted values, especially for the upper tail,


than the linear imputation. After some experimentation, we conclude that the linear OL8

specification matches the data better8 . Therefore we use the linear specification for our

under-reporting results reported in this paper.

It is almost certain that our imputation regression, Equation (2.1), has endogenous co

variates. In particular, it is unlikely that the covariance of P and e is zero. The consequence

is that our coefficient estimates (3 are likely to be biased and inconsistent. Any inference

based on our imputation regression is fraught with peril. However, we are uninterested in

inference and are uninterested in OL8 as a regression method. What we seek is to evaluate

conditional means and to use these covariates to predict the conditional means. In other

words, we are interested in OL8 as a statistical techinque and do not require any of the as

sumptions of the typical OL8 regression9 . The tendency of endogenous covariates to overfit

the regression is actually helpful to us. To see why, consider the probability distribution of

the residuals in Equation (2.1) and ignoring X for notation simplicity:

Z' ( -1 'M) 2 I Z' ( -lpl p)-lP zmn---->oo n e pe = 170 - W P zmn---->oo n w (2.2)

where w = pZimn---->oo(n-1pie), 175 is the error variance of the true data generating process,

Mp = I - P(PI p)-l pi (I is the identity matrix) and Mp is the projection matrix on

the subspace spanned by P. Therefore if w is nonzero (as it would be with endogeneity)

then the probability limit of the squared residuals is less than the variance of the true

errors. Thus, while inference on the coefficients is infeasible, the fit of imputation regression

improves with endogeneity because some of the variation in C that is really due to variation

in e has been attributed to P. We therefore do not report the coefficient estimates from

our imputation regressions because they are, in all probability, meaningless. (The detailed

imputation results are available from the authors upon request). Table 2.4 reports the

number of observations and the R2 for each regression. We note that the R2 for the restricted

imputation is not directly comparable to that of the unrestricted imputations. The R 2

for our imputation regression, while not as high as we would like, nevertheless appears

reasonable.

We compare the distribution of non-durable consumption and current consumption that

Sane problem with OLS is the negative predicted consumption levels, While the occurrence is small,we set the minimum predicted value to be $1000. We also tested difference model specifications and ourimputation results appear to be robust,

9See for instance, Davidson and MacKinnon (1993) Chapter 1 and pages 209-210.


Table 2.4: Imputation Regression FitRestricted Unrestricted

Single Single Family FamilyRenters Owners Renters Owners

1998 SHSObservations 10,651 1,064 810 2156 6621R2

Non Durable ConsumptionLinear 0.638 0.600 0.485 0.645 0.532Logarithum 0.715 0.696 0.790 0.713 0.614

Total ConsumptionLinear 0.724 0.666 0.513 0.659 0.577Logarithum 0.780 0.754 0.681 0.716 0.659

2004 SHSObservations 10,311 1,121 901 1744 6545R2

Non Durable ConsumptionLinear 0.642 0.606 0.496 0.571 0.546Logarithum 0.711 0.668 0.586 0.650 0.635

Total ConsumptionLinear 0.722 0.654 0.550 0.595 0.563Logarithum 0.783 0.762 0.699 0.672 0.652

21

we impute into the SFS with the actual (and imputed non-durable and current) consumption

in the SHS in Table 2.19 and Table 2.20. Our imputed consumption levels in the SFS are

smaller than the actual consumption levels in SHS at most of the percentile, especially

the upper tail of consumption. This is again evidence that we may underestimate income

under- reporting.

However, on balance, the distribution of imputed non-durable consumption appears to

match the actual distribution quite well, especially the unrestricted linear model. Our

first percentile through fiftieth percentile non-durable consumption estimates appear well

within $500 of the non-durable consumption levels reported in the same percentile in the

SHS. Therefore, unless otherwise mentioned, we will report the results from the unrestricted

linear model. As an additional robustness check for our results, we will assume that our

income under-reporting measures are inaccurate for a range of $1000 and recalculate our

results for those with a balance of less than $1000 to ensure our results are not affected by


small imputation errors.

In addition, we note that the covariance between the explanatory variables and e is zero

by construction in OLS. This follows from an application of the Frisch-Waugh-Lovell The

orem (OLS splits C into orthogonal components - one conditional and one unconditional).

It follows that the best predicted value of an observation is the fitted value (conditional)

plus the expected error term (unconditional). Therefore the error term does not bias the

conditional expected value and thus the conditional expected value plus a random draw

from the error distribution is an unbiased estimate of the true value of an observation. This

observation motivates a second robustness exercise - 500 Monte Carlo replicate imputations

using random draws from the imputation residual distribution to evaluate the sensitivity of

our results to imprecision in our imputation procedure.

2.4 Income Under-reporters and Tax Evasion.

In Section 2.2 we outlined our strategy for identifying income under-reporters by recon

structing household income statements using measures of imputed household consumption.

In this section we report our estimates of the incidence of income under-reporting (the ex

tensive margin) and the implied under-reported income (the intensive margin) for the two

different methods of imputing consumption.

We first concentrate on households who report that their income is equal to spending.

Households' reported income is compared to their expenditure to identify those households

who are under-reporting their income (i.e. expenditure exceed income). As noted earlier,

we use two definitions of expenditures: The narrower definition consists of non-durable

consumption (imputed), interest expenses and ongoing expenses. We will refer to this as

non-durable consumption for notational simplicity. The broader definition of expenditures is

the imputed total current consumption plus interest expenses for households. This imputed

value is conditioned on a household's ongoing expenses in the SFS but does not include the

SFS ongoing consumption data.

Our initial results using only non-durable consumption suggest that 28.3% households

under-reported income in 1999 survey and 29.1% under-reported income in 2005 survey (see

Table 2.5 and Table 2.6). Our results using total consumption are considerably higher

46.7% and 48.3% respectively. The increase is not unexpected - non-durable consumption

is only a subset of most household spending and thus our measure of household expenses

CHAPTER 2. INCOME ILL USION 23

that only includes non-durable consumption likely understates expenses. We note that our

imputation results, Table 2.19, Table 2.20 and our analysis of ongoing expenses, Table 2.18,

suggest that adding non durable consumption to ongoing expenses understates household

expenses by roughly $8,000 (or roughly one quarter of total consumption). Nevertheless, our

results using total consumption may be too high as they assume that all households make

at least a fraction of durable goods purchases. This assumption is strong as it implies that

households that have tight household budgets and do not, in general, purchase durables are

imputed as if they did so.

Table 2.5: Income Under-reporting and Self Employment Status, 1999Restricted Unrestricted

balance<O balance<-1000 balance<O balanc<-1000Non Durable Consumption

By major source of incomeNon Self-employed 25.4 21.1 26.6 21.8Self-employed 63.4 63.3 63.4 61.0By business indicatorNon Business owner 24.9 20.4 26.2 21.4Business owner 37.2 34.9 37.3 33.3By household employment stat'usNon Self-employed 24.4 19.9 25.8 21.0Self-employed 42.5 40.3 42.0 37.8By Major Income Earner's employment statusNon Self-employed 25.0 20.6 26.1 21.5Self-employed 48.4 46.5 49.3 44.2Total 27.2 23.1 28.3 23.6

Total ConsumptionBy major source of incomeNon Self-employed 43.2 38.1 45.1 39.7Self-employed 77.1 74.9 78.9 74.7By business indicatorNon Business owner 41.8 36.3 44.5 38.8Business owner 58.0 55.0 56.3 52.7By household employment statusNon Self-employed 41.7 36.4 44.3 38.8Self-employed 61.9 58.1 59.5 55.3By Major Income Earner's employment statusNon Self-employed 42.8 37.6 45.0 39.5Self-employed 64.0 60.8 63.0 58.6Total 44.8 39.8 46.7 41.4

It is well accepted in the literature that self-employed individuals are more likely to

conceal part of their income and most of the existing tax compliance literatures concentrates


Table 2.6: Income Under-reporting and Self Employment Status, 2005Restricted Unrestricted

balance<O balance<-lOOO balance<O balanc<-lOOONon Durable Consumption

By major source of incomeNon Self-employed 30.8 26.9 27.9 25.7Self-employed 59.7 59.7 59.7 59.7By business indicatorNon Business owner 29.8 25.8 27.2 25.1Business owner 43.5 40.9 39.2 37.2By household employment statusNon Self-employed 28.9 24.8 25.6 23.3Self-employed 47.7 45.5 47.2 46.1By Major Income Earner's employment statusNon Self-employed 30.6 26.4 27.3 25.1Self-employed 43.7 43.7 45.4 43.7Total 32.0 28.2 29.1 27.0

Total ConsumptionBy major source of incomeNon Self-employed 50.5 48.1 47.7 42.9Self-employed 61.4 61.4 63.8 63.8By business indicatorNon Business owner 48.3 46.1 45.6 41.0Business owner 64.6 61.8 62.7 58.5By household employment statusNon Self-employed 47.9 45.6 44.8 40.4Self-employed 66.5 64.0 66.7 61.2By Major Income Earner's employment statusNon Self-employed 49.4 47.2 46.6 42.5Self-employed 64.3 60.3 63.8 55.0Total 50.9 48.6 48.3 43.7

24

on self-employed individuals and households (Erard 1997). This proposition is confirmed by

our study. Our results are presented in Table 2.5 and Table 2.6. We note that our results

suggest that income under-reporting is not confined only to the self-employed. Regardless

of the definition of being self-employed, the results all suggest the same finding - although

the self-employed are relatively more likely to under-report income, income under-reporting

is pervasive. Indeed, the incidence of income under-reporting for the non self-employed is

similar to the overall numbers reported above because the self-employed are a relatively

small fraction of the population. This finding appears to cast doubt on estimates of income

under-reporting derived by inverting demand equations for the non-self employed, e.g. Tedds

(2007) and Schuetze (2002) and Pissarides and Weber (1989). Nor, as we stress above, should


this finding be construed as unusual.

As noted above, one robustness exercise we conduct is to examine the sensitivity of

our results to the imputation errors. The Frisch-Waugh-Lovell Theorem implies that an

unbiased estimate of a households imputed consumption is the fitted conditional mean,

0: +pi(3+X' 'Y, plus a random draw from the residuals, e, since these residuals are orthogonal

to the fitted value and have an expected value of zero. We perform 500 Monte Carlo

replications of our imputation exercise, adding a random draw from the residuals to imputed

consumption for each household. This procedure returns a distribution of possible values

of the incidence of income under-reporting. Figure 2.1 presents the histogram plot of the

frequency distribution of estimates of the incidence of under- reporting (x axis) for 1999 using

unrestricted imputation results. Recall that our estimate of the incidence of income under

reporting using only the fitted conditional mean was 28.3% for non-durable consumption

in 1999, and 29.1% in 2005 (see figure 2.2, respectively. The histogram indicates that

imputation error is unlikely to negate our findings.

Figure 2.1: Imputation Errors and Under-reporting Incidence using Non-durable Consumption, 1999

ov

oCV1

>,+J'enc:<lIOON

o

o.27 .28 .29 .3 .31 .32

Simulated Proportion (Non-durable Consumption, 1999)


Figure 2.2: Imputation Errors and Under-reporting Incidence using Non-durable Consumption, 2005

o(V')

oN

a'iiic:(l)

Cl

o

.26 .28 .3 .32Simulated Proportion (2005 NDC)

.34

2.4.1 Adjusting for Savings

One caveat with the above analysis is that income under-reporting households may also

report income greater than their expenses (i.e. savers) or income less than their expenses

(i. e. dis-savers). In the latter case, households are either pretending to incur debt or effec

tively laundering money through asset sales and cannot be distinguished in our approach.

In the former case, households that under-report income are financing savings. To test if our

results generalize to savers and dis-savers, we impute the households' gross consumptions

(i.e. consumption plus savings for savers, and consumption plus dis-savings for dis-savers)

for these two types of households.


There are two potential candidates in SHS can be used to measure households' savings/dis

savers: money flows and changes in RRSP. Money flows measure the net changes in house

holds' assets and liabilities during the survey year, including the contributions to and with

draws from the RRSplO. By definition, this variable is intended to measure household sav

ings. However, saving data is considered noisier than consumption data in typical household

surveys. As a robustness check, we also estimated results using net changes in RRSP as an

alternative measure of households' savings. The results are very similar to using the money

flows measurell .

We adjust our imputation regression to include imputed savings only for those households

who have positive money flows in the SHS and report income greater than expenses in SFS.

The incidence of income under-reporting for households that report income greater than

expenses is 19.2% using non-durable consumption and money flow, and 33% using total

consumption and money flow in 1999. The corresponding numbers for 2005 are 21.8% and

33.3%, respectively. It appears therefore that the incidence of under-reporting is lower for

the savers. Such an occurrence would not be unlikely in most inter-temporal utility models

when income is not too stochastic.

Similarly, we attempt to investigate whether households who report income less than

spending also under-report their income. We imputed the amount of dis-savings (i.e. asset

sell and/or addition to loan) from those households with negative money flow in SHS and

estimated the level of dissaving for those households who reported income less than spending

in SFS. The under-reporters in this category is slightly smaller than the savers. After

adjusting for dissaving, there are 17% (28.4%) of net borrowers under-report their income in

1999 survey, and 18.8% (27%) under-reported in 2005 survey, depending on the consumption

measure (numbers in parentheses are based on total consumption).

In summary, we find that income under-reporting is common among both self-employed

individuals and salaried workers, it is also common for both net savers and net lenders.

Since our results are based on imputed consumption, it is possible that the "tax evasion"

indicator is subject to some imputation errors. However, given that the qualitative results

I°Items included in money flows: net changes in bank balances; money on hand; money owed to thehousehold; money owed by the household; purchase and sale of stocks and bonds; personal property, andreal estate; expenditures on home additions, renovations and new installations; and contributions to andwithdrawals from registered retirement savings plans.

lIThe results of using RRSP savings are not reported in the paper but is available upon request from theauthors.


Table 2.7: Income Under-reporting and Self Employment Status for Savers, 1999% of Restricted Unrestricted

Sample balance<O balance<-1000 balance<O balance<-1000Non Durable Consumption

By major source of incomeNon Self-employed 93.8 17.8 14.9 18 15.1Self-employed 6.2 43.4 35.6 38 34.8By business indicatorNon Business owner 76 17.7 14.5 18.4 15.3Business owner 24 24.9 20.9 22 19.5By household employment statusNon Self-employed 81.1 16.7 13.9 17.1 14.2Self-employed 18.9 30.9 26 28.3 25.2By Major Income Earner's employment statusNon Self-employed 88 16.7 13.8 17.2 14.3Self-employed 12 39.0 33.2 34.4 31.0Total 100 19.4 15.1 19.2 16.3

Total ConsumptionBy major source of incomeNon Self-employed 93.8 32.9 27.8 31.2 25.7Self-employed 6.2 55.1 61.9 50.9 57.3By business indicatorNon Business owner 75 32.9 27.9 31.3 25.5Business owner 24 41.3 36.3 38.4 34.3By household employment statusNon Self-employed 81.1 32 27 30.4 25Self-employed 18.9 47.7 42.2 44.1 38.9By Major Income Earner's employment statusNon Self-employed 88 32.2 27 31 25.3Self-employed 12 54.9 50.9 47.4 44.6Total 100 34.9 29.9 33 27.6

of our estimation are not affected by a constant consumption measure (i. e. $8 per day),

and the Monte Carlo simulation generated a narrow band around our estimates, it is very

unlikely that our results are invalidated by imputation. In addition, we note that our results

are, in general, comparable in magnitude to those of Andreoni et. aZ. (1998) using TCMP

data. We also compare our estimates of tax evasion with a small-scale, Canadian survey that

directly asked the respondents questions related to income-reporting behaviour. A Financial

Post/Compas poll in 1995 of 820 Canadian adults reported that 20% of the respondents

admitting hiding income to avoid paying tax. Given the small size of the survey, it appears

reasonable to conclude that our estimates are comparable. In the following sections, we

will combine the three groups (i. e. reporting income equals to, larger than, and less than


Table 2.8: Income Under-reporting and Self Employment Status for Savers, 2005% of Restricted Unrestricted

Sample balance<O balance<-lOOO balance<O balance<-lOOONon Durable Consumption

By major source of incomeNon Self-employed 94.2 23.1 20.4 20.8 18.2Self-employed 5.8 33.6 29.5 38.1 35.2By business indicatorNon Business owner 78.5 24.5 21.3 21.6 18.8Business owner 21.5 20.7 19.4 22.9 20.4By household employment statusNon Self-employed 81.5 22.5 19.8 21 18.4Self-employed 18.5 29.2 25.7 25.5 22.4By Major Income Earner's employment statusNon Self-employed 88.4 21.8 19.2 20,4 17.8Self-employed 11.6 38.2 34.1 33.2 29.6Total 100 23.7 20.9 21.8 19.2

Total ConsumptionBy major source of incomeNon Self-employed 94.2 33.4 31 31.4 27.9Self-employed 5.8 65.3 63.5 64.7 62.8By business indicatorNon Business owner 78.5 35.6 33.1 33.3 29.5Business owner 21.5 34.1 32.1 33.4 31.4By household employment statusNon Self-employed 81.5 34 31.3 32.8 29.2Self-employed 18.5 40.6 39.8 35.8 32.9By Major Income Earner's employment statusNon Self-employed 88.4 33.4 30.8 32.2 28.5Self-employed 11.6 49.3 48.4 42 40.6Total 100 35.2 32.9 33.3 29.9

spending) and study the incidence and amount of income under-reporting for the entire

sample.

2.4.2 Profiling Tax Evasions

One question that may be of interest to policy makers is the demographic, geographic, oc

cupational and income profile of tax evaders. Geographic differences across provinces or

cities may point to the efficacy of different tax codes at detering income under-reporting.

Demographic, occupational and income profiles may assist policymakers in determining ap

propriate tax and transfer mechanisms (after all, income tax evasion is an implicit transfer).


Table 2.9: Income Under-reporting and Self Employment Status for Dis-savers,1999

% ofSample

Restricted Unrestrictedbalance<O balance<-1000 balance<O balance<-1000

Non Durable ConsumptionBy major source of incomeNon Self-employed 96.6 19.4 15.9 16.5 14.4Self-employed 3.4 35.5 24.2 31.8 20.5By business indicatorNon Business owner 84 19.4 15.9 16.3 14.1Business owner 16 22.8 17.5 20.9 17.7By household employment statusNon Self-employed 89.2 18.5 15.1 15.5 13.4Self-employed 10.8 31.7 25.4 29.6 24.9By Major Income Earner's employment statusNon Self-employed 93.3 18.4 14.7 15.2 13.2Self-employed 6.7 41.9 37.0 41.8 34.4Total 100 19.9 16.2 17.0 14.7

Total ConsumptionBy major source of incomeNon Self-employed 96.6 30 23.6 27.9 20.5Self-employed 3.4 45.4 43.1 41.8 29.6By business indicatorNon Business owner 84 29.1 22.5 28.7 20.6Business owner 16 37.7 33.7 26.8 22.2By household employment statusNon Self-employed 89.2 28 21.8 27.8 20.1Self-employed 10.8 50.8 44.8 33 27.1By Major Income Earner's employment statusNon Self-employed 93.3 28.5 22.2 27.9 20.2Self-employed 6.7 58.5 53.6 35.7 29.4Total 100 30.5 24.3 28.4 20.8

The SFS contains conditioning information to investigate whether the incidences of

income under-reporting are different across households with different characteristics. We

consider four main groups of characteristics. First, we examine income under-reporting by

province and also across the three major metropolitan areas in Canada: Toronto, Montreal

and Vancouver. Second, we examine income under-reporting by occupation. Third, we

examine income under-reporting by level of education. Fourth, we examine income under

reporting by reported income levels. The profiles of the 2005 survey are very similar to

the 1999 survey so are not discussed specifically. However, the corresponding tables are

provided on pages 46 to 48.


Table 2.10: Income Under-reporting and Self Employment Status for Dis-savers,2005

% ofSample

Restricted Unrestrictedbalance<O balance<-lOOO balance<O balance<-1000

Non Durable ConsumptionBy major source of incomeNon Self-employed 93.1 19.6 19.4 18.9 16.9Self-employed 6.9 26.5 26.5 17.9 3.3By business indicatorNon Business owner 84.3 18.8 18.6 18 15.8Business owner 15.7 26.8 26.8 23.1 16.7By household employment statusNon Self-employed 83.4 19.5 19.3 18.7 16.5Self-employed 16.6 22.7 22.7 19.1 13.1By Major Income Earner's employment statusNon Self-employed 90.6 20.0 19.8 19.3 16.1Self-employed 9.4 20.5 20.5 14.3 14.3Total 100 20.1 19.9 18.8 15.9

Total ConsumptionBy major source of incomeNon Self-employed 93.1 30.1 26 26.5 23.7Self-employed 6.9 35.3 15.4 33.4 31.2By business indicatorNon Business owner 84.3 30.1 26 26.4 24Business owner 15.7 32.7 21.1 30.1 25.3By household employment statusNon Self-employed 83.4 30.7 26.1 26.5 24Self-employed 16.6 29.2 21 29.4 24.9By Major Income Earner's employment statusNon Self-employed 90.6 31.5 26.2 26.5 24.2Self-employed 9.4 20.6 16.8 31.6 23.7Total 100 30.5 25.3 27 24.2

Table 2.11 presents our estimates of the incidence of income under-reporting by province

of residence12 . We find some evidences of heterogeneity in the proportion of under-reporters

by province of residence. For the 1999 survey, using the non-durable consumption measure,

the region with the highest proportion of income under-reporting households is British

Columbia (28.6%), followed by Prairies (24.1%), and Quebec (23.2%). We do not seek to

address the reasons for the geographic differences in income under-reporting in this paper

12We grouped Maritime provinces into the Atlantic region, and Manitoba and Saskatchewan into Prairiesfor two reasons. First, the number of observations in each of these provinces is, by itself, small - particularlyfor the 2005 survey. Second, there is virtually no difference between them in terms of the proportion ofunder-reporters and so grouping them in this way does not appear to change our results.


Table 2.11: Income Under-reporting by Region, 1999% of Restricted Unrestricted


By region:Atlantic 8.1 20.2 16.5 20.8 17.1Quebec 27.7 23.7 19.1 23.2 20.2Ontario 36 21.0 18.1 21.1 17.3Prairies 6.4 22.2 18.2 24.1 19.2Alberta 9.9 23.8 19.9 22.6 19.1British Columbia 11.9 27.7 24.2 28.6 24.9Total 100 22.8 19.1 22.9 19.3

Selected citiesVancouver 6.4 33.9 29.9 33.5 29.7Toronto 14.1 23.7 20.9 23 19.9Montreal 12.5 26.2 21.8 25.1 21.6

Total ConsumptionBy region:Atlantic 8.1 35.1 30.0 35.9 29.8Quebec 27.7 35.2 29.6 37.4 30.4Ontario 36 39.5 34.1 37.3 31.9Prairies 6.4 42.4 37.2 41.5 36.1Alberta 9.9 43.7 39.5 40.2 35.6British Columbia 11.9 40.7 36.1 42.3 38.6Total 100 38.7 33.5 38.4 32.8

Selected citiesVancouver 6.4 47.9 43 48.4 45.7Toronto 14.1 39.5 34.6 34.5 29.7Montreal 12.5 35.5 30.1 38.5 31.2

32

but conjecture that occupations and tax schedules are unlikely to be homogeneously dis

tributed across all provinces13 . We find more evidence of heterogeneity in the incidence of

income under-reporting across the three major metropolitan areas with Vancouver having

the highest incidence of income under-reporting, followed by Montreal.

In terms of the occupational distribution of income under-reporting, as shown in Ta

ble 2.12 on page 39, our results also point to substantial heterogeneity in the proportion

of under-reporters across occupational groups, with sales and service occupations (31.7% in

1999, 29.2% in 2005), occupations unique to primary industry (28.5% in 1999, and 22.2%

13This point was made also by Schuetze (2002).


in 2005) and occupations in art, culture, recreation and sport (20.4% in 1999, and 48.7% in

2005) being the three occupation groups with the highest percentage of under-reporters14 .

Table 2.13 summarizes the proportion of under-reporters by the Main Income Earner's

education levels. The incidences of tax evasion in general appear to decline with education

attainment. Using the non-durable consumption measures, 29.8% of the households with

less than high school education under-report their income. The corresponding number for

University graduates is only 19.3%.

In Table 2.14 we report the proportion of income under-reporters by reported income

levels and both the average true-income-to-reported-income ratio and the average amount

of income under-reporting. True income Yt is defined as the following:

Yt = fh if true-reporter

Yt = fit + (fit - Cd if under-reporter

We find the proportions, by and large, astonishing. In the lowest range of income ($0

$20,000 per year) approximately 70% of households under-report their income and that on

average their true income is roughly 2 times as large. The numbers in higher income ranges

are less staggering, in part because of lower base effects in the ratio (as is evidenced by the

levels of income unreported). Nevertheless, around 27% of households reporting income of

$20,000 - $40,000 per year under-report income and have true income that is approximately

20% higher than they report. It appears in general that if households under-report income

they tend to do so in large measure as the average amount of unreported income is above

$5,000 for all under-reporters15 .

We next reconstruct the distribution of income in both 1999 and 2005, using non-durable

consumption as consumption measure. We compare our estimate of the true income dis

tribution to the reported income distribution to determine how the income distribution is

affected by unreported income16 . We find that the true income profile is skewed to the right

and that the true incidence of income poverty at any absolute level is lower than reported

14The high percentage of under-reporters in 2005 for some groups may be due to the small sample size,which is in total 2,157.

E'We conjecture that this may reflect the penalty structure imposed by Canadian tax laws but do investigate this claim further.

16We estimate the kernel density of true income and reported income using an Epanechnikov estimatesand select the optimal bandwidth as the bandwidth that minimizes the mean intergrated squared error if thedata were Gaussian and a Gaussian kernel was used. Given the skewed nature of our data this bandwidthmay be too smooth in some sense.


(see Figures 2.3 and 2.4). We also find that the proportion of households in what could be

characterized as the middle income range is larger than reported income measures would

suggest.

II

III

I,IIIII

III

II

II

/o

~igure 2.3: Distribution of True and Reported Income, 1999

ooo~

a 20000 40000 60000 80000 100000trueincomeallfm

1-----True income --- Reported income I

Our finding suggest that tax noncompliance exists across all occupations, regions, edu

cation and income levels. We do not find these results surprising. In addition to marginal

costs and benefits associated with tax evasion that are specific to observable characteristics

such as occupational status (i.e. it is easier to hide gratuities than salary), individuals also

likely face marginal costs and benefits that differ across personal characteristics. Therefore,

it is not surprising that an occupational indicator, such as self-employment status, is not a

reliable instrument for income under-reporting.

As noted above, different rates of tax evasion across geographic locations may reflect

properties of the different income tax codes. For instance, the province of Ontario allows

renters to claim their annual rent payments for an income tax credit. The tax credit is an

incentive for renters to report their rental income and these payments can be compared with

the income tax returns of the landlords, making rental tax evasion difficult.


Y,igure 2.4: Distribution of True and Reported Income, 2005

10000080000

---

40000 60000trueincomeallfm

.-ooo~

.-00

~00 ,

>,' I...'iii Ic IQ)l.O I°0 I

c1l I0 I0~

,Lr) I

II

II

I0

0 20000

1-----True income --- Reported income I

2.4.3 The Underground Economy and Tax Loss

Our analysis thusfar has mainly focused on the extensive margin of income under-reporting.

As we argue in the previous section, the extensive margin is important for the design of

social policy. However, the intensive margin is also important for policy as it reflects the

amount of unreported income. The intensive margin matters for both tax revenues and the

size of government social transfers.

In this paper, we provide a rough estimate of the concealed income and the implied lost

tax revenue in Canada using our income under-reporting identifier. The reported proportion

of unreported income from previous research ranges widely from 0.3% to 28% of GDP (see

Erard 1997), with most of the estimates in the 5-15% range. We calculate the true income

of a household by adding the missing income to a household's reporting income for those

under-reporters.

Based only on our final sample, the estimated under-reported income in 1999 ranges

from $7.0 billion to $12.7 billion, depending on whether non-durable consumption or total

consumption is considered. In 2005, the estimated under-reported income using the two

measures are $12.4 billion and $23.3 billion, respectively, which translate into approximately


$10.7 billion and $20.3 billion in 1998 dollars. As a percentage of reported income, this is

between 2.6%-4.7% in 1999, and 3.2% to 6.0% in 2005 which are similar in magnitude to

previous studies.

However, we note that these estimates are based on our sample which represent only

about 40% of the population of households (see Table 2.16 on page 41 which details our

sample construction). If one assumes that the remaining population is similar to those in

our working sample, then we can obtain a rough estimate of the aggregate size of Canada's

underground economy. In 1999, approximately $18 - $32 billion income was concealed from

the government, using the non-durable and total consumption measures respectively. In

2005, the corresponding numbers are $30 billion and $57 billion, respectively. We note that

the size of the underground economy by this measure has increased between 66 - 74 %, far

outpacing nominal GDP growth of roughly 16 % over the same period. Thus, it appears

that the proportionate amount of under-reported income is rising at a sizeable pace (we

note that this is also true in just our sample).

Estimating the lost tax revenue requires information on the value of each income item

and eligible tax credit which is not readily available. Previous research typically applies a

uniform average or marginal rate to calculate the lost revenue from under-reported income.

Using this method, we apply a 36% marginal tax rate to the under-reported income for

our sample which implies missing tax revenue for 1999 survey (1998 tax year) in the range

of $2.5-$4.6 billion, and for 2005 survey (2004 tax year) of $4.5-$8.4 billion. If we again

consider our sample representative of the population as a whole, then we obtain missing tax

revenue in the range of $6.4- $11.7 billion and $10.9 - $20.7 billion in 1998 and 2004 tax

year, respectively.

For comparison, we also use a tax simulator developed by Milligan (2008) to calculate the

marginal tax rate conditional on observable household characteristics. Milligan provides a

tax simulator that allows us to specify the tax year, province of residence, income by source,

and a set of household characteristics. We do not have information on households' eligible

deductions. However, it would seem consistent to assume that households that under-report

income also report their deductions to minimize their tax liabilities. Under this assumption,

we can estimate the size of missing tax revenue by comparing a households' tax obligation

before and after the unreported income is added back to households' total income. This

method allow us to get an estimate of lost tax revenue without needing information on

eligible deductions. Based on our simulation, the lost tax revenue in 1998 tax year is between


$6.0 billion (using non-durable consumption) and $11.9 billion (using total consumption)

at the national level. In 2004 tax year, the corresponding numbers are $9.5 billion and

$20.2 billion, respectively. We note that these numbers are similar to those obtained using

a marginal tax rate of 36%. Putting these numbers in perspective, about 4.2-8.4% and

5.3-11.3% of tax revenue is missing from the 1998 and 2004 tax years, respectively. We

caution that the assumption that the households in our sample are identical to the rest of

the population is strong. In particular, the extent to which the elderly household under

report their income is unclear. Nevertheless, even if our national numbers are ignored, the

numbers from our sample alone appear sizeable.

We stress that our estimates of tax loss to the government can not be viewed as a

proxy for the social burden of tax evasion. Our estimates of the tax loss do not incorporate

social efficiency considerations resulting from transfers from poorer households to richer

households (in income terms) through a misapplied tax and transfer system. Our estimates

of the income distribution suggest that such transfers do occur.

2.5 Conclusion

Using data from the Survey of Financial Security and the Survey of Household Spending we

demonstrated that self-employment is a poor indicator of possible income under-reporting.

Indeed, we find that roughly one quarter to one third of non self-employed households under

report income, regardless of how self-employment status for households is determined. While

our results are in line with the existing literature, we also revealed that incidence of income

under-reporting is pervasive regardless of geographic location, occupation and education

levels. In addition, we illustrated that reported income as a poverty measure is misleading.

We find that about 70% of the households in the low income range (less than $20K) under

report their income. Thus, tax and transfer policies based on income may have unwelcome

social-efficiency costs.

We are understandably cautious about our results and attempt to be conservative in

our reported findings. We subject our measures to robustness exercises and find that our

results appear, by and large, to be robust. Our estimates of unreported income suggest

that approximately 5% of Canada's GDP is unreported. Our estimates of the missing tax

revenues suggest that governments under-collect about 5%-10% of income tax. We also find

that income evasion is growing at a rate far in excess of income which implies that income


unreporting is a growing phenomenon.

38


Table 2.12: Income Under-reporting by Occupation, 1999% of Restricted Unrestrieted


By occupation:Management Occupations 10.6 15.4 14.0 16.7 14.4Business, Finance and Ad- 12.7 18.5 15.8 19.1 16.8ministrationNatural and Applied Sci- 8.3 10.3 8.3 10.4 7.3encesHealth Occupations 4.4 11.2 8.1 11.0 9.1Occupations in Social Sci- 6.8 11.3 10.1 13.6 11.5ence, Education, Govern-ment ServiceOccupations in Art, Cul- 1.6 23.2 18.7 20.4 16.5ture, Recreation and SportSales and Service Occupa- 15.9 34.6 28.1 31.7 25.3tionsTrades, Transport and 14.6 17.9 14.8 19.3 16.7Equipment OperatorsOccupations Unique to 2.5 30.1 25.0 28.5 24.8Primary IndustryProcessing, Manufacturing 7.9 14.9 12.0 14.8 12.5and UtilitiesTotal 100.0 22.8 19.1 22.9 19.3

Total ConsumptionBy occupation:Management Occupations 10.6 32.6 29.0 31.5 26.6Business, Finance and Ad- 12.7 33.1 27.5 31.1 25.9ministrationNatural and Applied Sci- 8.3 28.0 22.0 28.3 23.0encesHealth Occupations 4.4 32.9 27.2 28.3 24.1Occupations in Social Sci- 6.8 23.3 21.9 25.6 20.2ence, Education, Govern-ment ServiceOccupations in Art, Cul- 1.6 43.8 34.9 36.2 26.9ture, Recreation and SportSales and Service Occupa- 15.9 49.7 44.2 45.8 39.9tionsTrades, Transport and 14.6 36.7 31.9 37.0 32.8Equipment OperatorsOccupations Unique to 2.5 48.2 44.1 49.8 44.3Primary IndustryProcessing, Manufacturing 7.9 32.3 26.1 30.8 25.5and UtilitiesTotal 100.0 38.7 33.5 38.4 32.8


Table 2.13: Income Under-reporting by Education, 1999% of Restricted Unrestricted


By education:< High school 20.3 29.4 25.0 29.8 25.2High school 23.6 24.9 20.1 24.4 20.7Post secondary 31.5 20.1 16.3 20.1 16.2University 24.7 19.0 16.9 19.3 17.0Total 100 22.8 19.1 22.9 19.3

Total ConsumptionBy ed-ucation:< High school 20.3 41.9 37.2 43.9 37.4High school 23.6 42.1 35.5 42.1 35.4Post secondary 31.5 38.5 33.7 37.2 32.2University 24.7 32.9 28.4 31.7 27.2Total 100 38.7 33.5 38.4 32.8

6,1155,4637,7829,65811,984

MeanUnreported

Income

21.21.171.151.09

1.941.181.171.161.11

67.629

10.64.42

%Under

reporters

Table 2.14: Income Under-reporting by Reported Income Level, 1999Restricted Unrestricted

Mean Mean % MeanTrue/report Unreported Under- True/reportIncome ratio Income reporters Income ratio

Non Durable Consumption6,019 67.74,815 26.68,039 12.110,766 5.414,687 2.6

0-20K20-40K40-60K60-80K80K+Total 22.8 1.6 6,164 22.9 1.64 6,392

Total Consumption0-20K 73.9 2.07 7,485 74.7 2.11 7,52620-40K 54.1 1.22 6,180 49.3 1.24 6,61040-60K 35.8 1.13 5,956 36.6 1.13 6,12860-80K 15.] 1.1 6,448 16.6 1.1 6,43780K+ 10.5 1.05 7,681 11.7 1.05 7,641Total 38.7 1.49 6,707 38.4 1.51 6,903


Table 2.15: Interest Payments Comparison

41

1999 Imputed 2005 Actual

NMeanSdSkewnessKurtosisP5PlOP25P50P75P90P95

Calculated UsingInterest Rate

79501,1321,5434.7146.97

3564214665

1,4942,6403,520

RredictedUsing Regression

79661,6082,3533.38

25.741942

163606

2,2584,5936,124

11865,4667,60811.39

199.26735

1,1552,3094,1996,29810,49712,974

Table 2.16: Sample Selection from the SFS

1999 2005Unweighted Weighted Unweighted Weighted

N N N NStarting observations 15,933 12,215,625 5,282 13,347,681remove: income<500, -4,098 -1,610age>65 and key variablesvalue missingWorking sample 11,835 9,419,993 3,672 9,724,174remove (listwise)(1) sold assets, pawned, etc -3,182 -778(2) major source of income -335 -105from pension(3) at least one person in -2,276 -632the household refused link-ing to CRASample used to calculate 6,042 4,788,021 2,157 5,427,460percentage of under-reporters

~ ~T

able

2.17

:D

emo

gra

ph

icC

om

pa

riso

no

fth

eS

FS

an

dS

HS

'i:l~ ~

I19

98

I20

04S

HS

IS

FS

SHS

IS

FS

~

Var

iabl

eM

ean

Std

.D

ev.

Mea

nS

td.

Dev

.M

ean

Std

.D

ev.

Mea

nS

td.

Dev

.~

Dem

ogra

phic

Cha

ract

eris

tics

C1A

geof

refe

renc

epe

rson

43.1

2(1

0.80

)41

.88

(11.

76)

44.1

2(1

1.04

)42

.64

(12.

28)

0

Age

ofsp

ouse

42.5

8(1

0.46

)41

.97

(10.

53)

43.7

6(1

0.66

)43

.23

(10.

73)

~M

arri

ed0.

70(0

.46)

0.61

(0.4

9)0.

68(0

.47)

0.59

(0.4

9)~

Num

ber

ofad

ult

(19+

year

sol

d)1.

98(0

.81

)1.

82(0

.79)

1.97

(0.8

4)1.

80(0

.81)

t-<

Num

ber

of

yout

h(1

5-19

year

sol

d)0.

23(0

.53)

0.20

(0.5

0)0.

22(0

.54)

0.18

(0.4

6)c:::

::V

1

Num

ber

of

chil

dren

(0-1

4ye

ars

old)

0.66

(1.0

1)

0.57

(0.9

4)0.

58(0

.96)

0.50

(0.9

0)0

Num

ber

ofea

rner

s1.

71(1

.03)

1.57

(0.9

5)1.

76(1

.03)

1.55

(0.9

6)2;

Wee

ksw

orke

dfu

llti

me

(ref

eren

cepe

rson

)32

.27

(23.

48)

34.4

0(2

3.08

)33

.88

(22.

92)

30.9

3(2

4.08

)W

eeks

wor

ked

full

tim

e(s

pous

e)31

.17

(23.

92)

30.2

6(2

4.53

)33

.66

(23.

11)

30.7

2(2

4.54

)M

ajor

sour

ceof

inco

me

-wag

e0.

74(0

.44)

0.76

(0.4

3)0.

76(0

.42)

0.74

(0.4

4)M

ajor

sour

ceof

inco

me

-sel

f-em

ploy

men

t0.

08(0

.27)

0.06

(0.2

3)0.

09(0

.29)

0.06

(0.2

4)Ty

peo

fdw

elli

ngs

Sin

gle

deta

ched

0.60

(0.4

9)0.

55(0

.50)

0.59

(0.4

9)0.

54(0

.50)

Dou

ble

0.05

(0.2

1)

0.05

(0.2

1)

0.05

(0.2

2)0.

05(0

.22)

Row

orte

rrac

e0.

06(0

.24)

0.06

(0.2

4)0.

06(0

.23)

0.08

(0.2

6)D

uple

x0.

05(0

.21)

0.06

(0.2

3)0.

05(0

.21)

0.04

(0.2

0)A

pt

low

rise

(0-5

stor

ey)

0.15

(0.3

6)0.

18(0

.38)

0.16

(0.3

7)0.

20(0

.40)

Apt

high

-ris

e(5

+st

orey

)0.

07(0

.26)

0.08

(0.2

7)0.

07(0

.26)

0.07

(0.2

6)A

rea

of

resi

denc

eL

arge

urb

anar

ea(>

=5

00

K)

0.50

(0.5

0)0.

46(0

.50)

0.52

(0.5

0)0.

49(0

.50)

Sm

all

urb

anar

ea«

50

0K

)0.

33(0

.47)

0.38

(0.4

8)0.

37(0

.48)

0.35

(0.4

8)H

ome

owne

rshi

pst

atus

Ow

nw

itho

utm

ortg

age

0.25

(0.4

3)0.

21(0

.41)

0.24

(0.4

3)0.

22(0

.41

)O

wn

wit

hm

ortg

age

0.43

(0.5

0)0.

38(0

.48)

0.42

(0.4

9)0.

36(0

.48)

Veh

icle

owne

rshi

pst

atus

Ow

nve

hicl

e0.

82(0

.38)

0.79

(0.4

1)0.

82(0

.38)

0.76

(0.4

3)

IN

=10

,651

IN

=11

,818

IN

=10

,311

IN

=3,

675

..,. I'-:>

@T

able

2.1

8:

On

-go

ing

Ex

pen

ses

Co

mp

aris

on

of

the

SF

San

dS

HS

> '"tJ ~

I19

98

I20

04~

SHS

IS

FS

SH

SI

SF

S~

Var

iabl

eM

ean

Std

.D

ev.

Mea

nS

td.

Dev

.M

ean

Std

.D

ev.

Mea

nS

td.

Dev

.~

Hou

sing

Cl

Mor

tgag

epa

ymen

t(P

R)

3,96

5.61

(605

9.18

)3,

653.

20(6

002.

65)

4,72

9.05

(651

0.69

)4,

272.

86(7

570.

51)

0

Ren

t2,

121.

47(3

502.

97)

2,45

4.29

(363

6.43

)2,

310.

90(3

769.

26)

2,73

3.96

(388

7.87

)~

Pro

per

tyta

x(P

R)

1,29

3.27

(131

3.64

)1,

202.

27(1

838.

16)

1,48

3.03

(150

3.05

)1,

415.

70(2

024.

96)

~In

sura

nce

expe

nses

(PR

)31

5.98

(372

.26)

347.

78(5

14.5

4)39

4.30

(442

.54)

437.

67(5

62.3

4)t-< ~

Con

dofe

es99

.13

(531

.05)

92.7

6(5

25.4

5)14

9.30

(741

.61)

122.

77(6

45.2

3)0

Uti

lity

~E

lect

rici

ty94

7.71

(727

.64)

952.

07(8

86.7

6)1,

116.

24(9

02.4

0)1,

093.

54(1

215.

85)

Oil

and

gas

paym

ent

455.

86(5

38.3

7)45

2.46

(687

.37)

773.

37(9

12.5

0)72

1.74

(923

.49)

Wat

erpa

ymen

t17

4.19

(324

.86)

123.

18(2

58.2

0)22

0.62

(453

.31)

148.

51(3

10.8

9)O

ther

Veh

icle

expe

nse

1,16

6.75

(990

.50)

1,12

7.91

(120

2.70

)1,

652.

84(1

485.

80)

1,53

7.40

(164

0.85

)C

hild

care

paym

ent

384.

33(1

475.

93)

375.

65(1

536.

46)

375.

21(1

409.

41)

456.

40(2

394.

89)

Sup

port

paym

ent

260.

25(2

012.

92)

259.

30(2

227.

04)

311.

91(3

333.

39)

206.

78(1

420.

72)

Tot

alon

goin

gex

pens

es11

,184

.53

(750

7.53

)11

,040

.86

(792

8.60

)13

,516

.77

(897

4.81

)13

,147

.33

(982

6.74

)

IN

=1O

,651

IN

=11

,818

IN

=10

,311

IN

=3,

675

~ <:.oJ

~T

ab

le2

.19

:Im

pu

ted

Co

nsu

mp

tio

n,

19

98

~ "1j ~

SH

S(N

=1

0,6

51

)S

FS

(N=

11

,86

3)

~Im

pu

ted

Imp

ute

d~

Lin

ear

IL

ogL

inea

rI

Log

~re

stri

cted

unre

stri

cted

rest

rict

edun

rest

rict

edre

stri

cted

unre

stri

cted

rest

rict

edun

rest

rict

edN

on

-du

rab

leco

nsu

mp

tio

n(j 0

Mea

n20

,119

20,1

1920

,119

19,2

4519

,264

18,7

0418

,766

17,3

2517

,459

~S

d11

,326

9,05

09,

156

8,83

18,

845

9,59

29,

525

8,83

38,

961

Ske

wne

ss1.

571.

051.

060.

850.

901.

871.

811.

121.

90t:1 t"-

'K

urto

sis

9.08

7.39

7.03

4.70

5.15

28.1

823

.18

6.97

28.6

9~

PI

3,34

72,

992

4,08

64,

394

3,87

41,

799

3,11

83,

500

3,35

9P

55,

790

6,79

16,

801

6,45

56,

579

4,84

15,

828

5,06

35,

011

0 ~P

107,

592

9,36

58,

910

8,42

78,

559

7,48

07,

715

6,72

36,

860

P25

12,4

2714

,056

13,8

1512

,852

13,0

6612

,245

12,0

1410

,779

11,0

46P

5018

,227

19,6

3219

,616

18,5

0518

,587

18,1

4117

,930

16,4

3816

,662

P75

25,8

1225

,049

25,1

5624

,238

24,2

3323

,972

24,0

8822

,448

22,5

14P

9034

,397

30,9

7231

,230

30,4

9330

,341

29,9

1730

,124

28,3

6728

,506

P95

40,6

2635

,267

35,4

1735

,224

35,0

0134

,160

34,5

3632

,728

32,6

46P

9957

,617

46,7

1246

,794

45,9

5045

,746

46,3

6846

,824

43,2

1643

,756

To

tal

cu

rren

tco

nsu

mp

tio

nM

ean

39,7

7439

,774

39,7

7438

,215

38,2

8837

,680

37,8

8034

,449

34,9

53Sd

23,2

1019

,631

19,7

0917

,834

18,1

5120

,899

20,6

5817

,499

17,8

48S

kew

ness

2.01

1.45

1.48

0.84

0.99

2.10

2.00

1.05

1.07

Kur

tosi

s12

.94

9.78

10.0

64.

695.

5323

.95

21.0

86.

626.

46P

I7,

299

5,20

07,

381

8,61

98,

077

3,56

66,

671

6,72

46,

825

P5

11,6

6912

,498

12,2

2312

,360

12,6

279,

344

11,0

8710

,209

10,1

35P

1015

,469

17,6

1116

,759

16,3

7616

,606

13,7

7814

,420

13,4

4813

,702

P25

23,8

0026

,477

25,8

3725

,121

25,0

3223

,713

23,2

1221

,430

21,6

70P

5035

,842

37,9

8138

,274

36,8

5836

,696

35,9

1835

,814

32,8

0233

,194

P75

50,6

5650

,277

50,3

6448

,845

48,7

4348

,363

48,8

8044

,865

45,2

89P

9067

,739

62,3

9562

,617

60,4

8760

,663

61,9

5662

,258

56,9

4057

,728

P95

80,8

6272

,802

72,6

8769

,386

69,8

7471

,277

71,7

4664

,796

66,3

31P

9911

3,12

198

,761

97,8

6490

,766

93,6

8010

1,24

810

0,62

784

,244

88,6

64*'" *'"

gT

ab

le2

.20

:Im

pu

ted

Co

nsu

mp

tio

n,

20

04

~ ~

SH

S(N

=lO

,31

11

SF

S(N

=3

,67

SY

--

~ ~Im

pu

ted

Imp

ute

d!'=>

Lin

ear

IL

ogL

inea

rI

Log

~re

stri

cted

unre

stri

cted

rest

rict

edun

rest

rict

edre

stri

cted

unre

stri

cted

rest

rict

edun

rest

rict

edN

on

-du

rab

leco

nsu

mp

tio

nC

l 0M

ean

25,1

4525

,145

25,1

4523

,975

24,0

1423

,144

23,1

2921

,262

21,4

31~

Sd

14,8

0211

,864

12,0

2211

,574

11,5

7212

,515

12,6

1911

,816

11,7

50S

kew

ness

1.69

1.82

1.57

1.38

1.29

1.70

1.59

1.23

1.12

~

Kur

tosi

s9.

0321

.90

15.7

09.

538.

3529

.60

21.1

17.

566.

41t-

'c:::

:P

I3,

723

3,74

64,

631

5,30

84,

702

373

2,98

73,

196

3,58

0~

P5

6,99

88,

373

8,48

27,

700

7,63

65,

681

6,26

35,

708

5,48

60 ~

P10

9,71

011

,584

11,1

6610

,476

10,5

458,

769

8,89

17,

652

7,79

4P

2514

,910

17,2

5716

,899

16,1

4016

,206

14,0

8313

,876

12,4

9312

,703

P5

022

,597

23,9

7923

,842

22,7

2222

,666

22,0

4721

,638

19,6

6319

,943

P75

32,0

6031

,496

31,7

4030

,230

30,0

6230

,186

30,3

4228

,145

28,4

98P

9043

,037

39,1

0639

,494

37,9

0938

,346

37,9

9738

,547

36,3

8836

,242

P95

52,2

0444

,839

45,4

2344

,049

44,2

7844

,547

45,1

3241

,561

42,2

20P

99

76,1

4259

,771

59,7

9259

,176

60,0

2260

,556

61,2

1257

,998

58,2

77T

ota

lcu

rren

tco

nsu

mp

tio

nM

ean

49,7

4849

,748

49,7

4847

,625

47,8

0747

,122

46,9

7942

,508

43,2

17S

d30

,811

25,9

7226

,142

23,7

0924

,451

28,6

2428

,909

24,0

7924

,783

Ske

wne

ss2.

112.

001.

801.

371.

492.

292.

221.

171.

31K

urto

sis

12.1

519

.21

15.1

89.

499.

9727

.61

23.0

37.

267.

78P

I8,

673

7,63

08,

170

9,86

29,

210

4,97

73,

934

6,13

86,

985

P5

13,5

0915

,733

14,8

8614

,486

14,5

6211

,098

12,6

5410

,834

10,7

17P

10

18,9

3221

,400

20,8

4719

,822

20,0

1915

,975

16,4

3114

,979

15,5

77P

25

28,8

5532

,341

32,0

1531

,096

30,7

6127

,313

26,6

8224

,322

24,8

67P

50

43,6

7546

,279

46,2

5745

,348

44,8

1842

,729

42,5

8438

,789

39,3

00P

7563

,082

62,8

9663

,016

60,7

0160

,702

61,7

4761

,773

57,2

1557

,514

P9

085

,766

80,6

4181

,025

76,2

9477

,375

80,7

4580

,662

72,6

3674

,310

P95

104,

409

93,5

1694

,291

87,9

6690

,657

95,9

3596

,409

84,4

1387

,401

P99

157,

453

128,

560

128,

115

118,

715

124,

040

137,

784

137,

095

114,

956

120,

749

..,. 01


Table 2.21: Income Under-reporting by Region, 2005% Restricted Unrestricted

Sample balance<O balance<-1000 balance<O balance<-1 000Non Durable Consumption

By region:Atlantic 6.8 21.4 19.3 22.4 19.2Quebec 27.5 30.5 28.1 27.6 25.6Ontario 37.2 26.3 22.3 24.5 22.0Prairies 7 13.9 10.7 14.4 10.8Alberta 11.8 24.1 22.9 21.3 19.4British Columbia 9.7 33.1 30.7 27.8 24.6Total 100 26.6 23.8 24.4 22.0

Selected citiesVancouver 4.9 33.8 30.4 32.2 27.3Toronto 14.2 27 21.7 24.6 21Montreal 13.7 33.9 32 30.9 29.4

'lbtal ConsumptionBy region:Atlantic 6.8 33.2 31.7 32.3 28.9Quebec 27.5 42.8 40.8 40.1 34.7Ontario 37.2 42.2 39.8 42.0 38.7Prairies 7 29.8 25.7 26.7 23.1Alberta 11.8 41.7 38.5 36.6 32.1British Columbia 9.7 44.7 39.5 37.7 36.3Total 100 41.1 38.4 38.7 34.9

Selected citiesVancouver 4.9 44.2 39.6 39.9 39.5Toronto 14.2 49.5 47.1 45 40.2Montreal 13.7 42 41.1 40.8 36.2

46


Table 2.22: Income Under-reporting by Occupation, 2005% of Restricted Unrestricted

Sample balance<O balance<-1000 balance<O balance< -1000Non Durable Consumption

Management Occupations 9.1 12.1 11.3 14.0 12.4Business, Finance and Ad- 13.6 22.0 17.4 22.1 19.8ministrationNatural and Applied Sciences 9.5 16.9 12.7 12.5 11.4Health Occupations 5.2 18.6 18.6 17.6 17.6Occupations in Social Science, 7.8 18.1 16.0 14.2 13.5Education, Government Ser-viceOccupations in Art, Culture, 2.8 57.8 50.9 48.7 48.7Recreation and SportSales and Service Occupations 13.4 32.6 29.5 29.2 25.9Trades, Transport and Equip- 13.9 13.7 12.9 16.0 14.1ment OperatorsOccupations Unique to Pri- 2.6 25.1 25.1 22.2 18.7mary IndustryProcessing, Manufacturing 7.0 22.9 19.5 16.3 16.3and UtilitiesTotal 100.0 26.6 23.8 24.4 22.0

Total ConsumptionManagement Occupations 9.1 32.5 30.8 34.0 29.4Business, Finance and Ad- 13.6 34.6 32.0 35.4 31.1ministrationNatural and Applied Sciences 9.5 32.0 31.9 31.7 28.6Health Occupations 5.2 34.7 30.9 26.9 24.5Occupations in Social Science, 7.8 34.5 32.4 29.1 24.5Education, Government Ser-viceOccupations in Art, Culture, 2.8 57.7 56.1 58.1 55.5Recreation and SportSales and Service Occupations 13.4 53.4 51.6 46.8 40.5Trades, Transport and Equip- 13.9 28.3 25.2 28.0 26.9ment OperatorsOccupations Unique to Pri- 2.6 37.4 26.8 32.0 28.5mary IndustryProcessing, Manufacturing 7.0 37.1 34.4 36.3 31.2and UtilitiesTotal 100.0 41.1 38.4 38.7 34.9


Table 2.23: Income Under-reporting by Education, 2005% of Restricted Unrestricted

Sample balance<O balance<-1000 balance<O balance<-1000

Less than high schoolGraduated high schoolPost secondaryUniversity or certificate

13.825.430.130.6

36.430.425.020.7

Non Durable Consumption34.6 34.527.3 30..520.6 20.919.1 18.4

31.327.118.716.8

Total 100 26.6 23.8 24.4 22.0

Less than high schoolGraduated high schoolPost secondaryUniversity or certificate

13.825.430.130.6

45.542.440.139.0

Total Consumption40.6 43.440.0 42.537.1 37.537.2 34.6

39.637.333.332.3

Total 100 41.1 38.4 38.7 34.9

10,5636,3368,91811,30013,994

MeanUnreported

Income

3.041.221.191.161.13

3.121.241.191.151.13

Income Under-reporting by Reported Income Level, 2005Restricted Unrestricted

Mean Mean % of MeanTrue/report Unreported Under- True/reportIncome ratio Income reporters Income ratio

Non Durable Consumption9,434 73.46,702 36.68,932 19.110,584 8.414,507 4.8

80.943.319.67.54.2

% ofUnder

reporters

Table 2.24:

0-20K20-40K40-60K60-80K80K+Total 26.6 2.07 8,771 24.4 2.02 9,312

Total Consumption0-20K 84.8 3.43 11,927 78.2 3.33 10,56320-40K 62.8 1.29 8,289 57.6 1.25 6,33640-60K 41.2 1.23 11,061 35.9 1.23 8,91860-80K 24.4 1.15 10,293 24.4 1.17 11,30080K+ 15.3 1.12 13,758 17 1.12 13,994Total 41.1 1.9 10,712 38.7 1.84 9,312

Bibliography





[3] Blundell, R., Pistaferri, L. and Preston, I. 2004. "Imputing consumption in the

PSID using food demand estimates from the CEX," IFS Working Papers, W04/27,

Institute for Fiscal Studies.

[4] Cagan, P. 1958. "The Demand for Currency Relative to the Total Money Supply,"

Journal of Political Economy, 66(4), 303-28.

[5] Davidson, R. and MacKinnon, J. 1993. Estimation and Inference in Econometrics,

Oxford University Press, New York.



[7] Fisher, J. and Johnson, D. 2006. "Consumption Mobility in the United States: Ev

idence from Two Panel Data Sets," Topics in Economic Analysis and Policy, Berkeley

Electronic Press, 6(1), Art. 16.

[8] Lemieux, T., Fortin, B. and Frechette, P. 1994. "The Effect of Taxes on Labor

Supply in the Underground Economy," The American Economic Review, 84(1), 231

252.

[9] Lyssiotou, P., Pashardes, P. and Stengos, T. 2004. "Estimates of the Black

Economy Based on Consumer Demand Approaches," Economic Journal, 114,622-639.

49

BIBLIOGRAPHY 50

[10] Milligan, Kevin 2008. Canadian Tax and Credit Simulator. Database, software and

documentation, Version 2008-1.

[:1.1] Mirus, R. and Smith, R. 1981. "Canada's Irregular Economy," Canadian Public

Policy, 7( 3), 444-453.



[13] Palumbo, M. 1999. "Uncertain Medical Expenses and Precautionary Saving Near the

End of the Life Cycle," Review of Economic Studies, 66(2), 395-421.



[15J Richter, W. and Boadway, R. 2005. "Trading Off Tax Distortion and Tax Evasion,"

Journal of Public Economic Theory, 7(3), 361-381.



28(2), pages 219-237, June.

[17] Skinner, J. 1987. "A superior measure of consumption from the Panel Study ofIncome

Dynamics," Economic Letters, 23, 213-216.

[18J Tanzi, V. 1980. "The Underground Economy in the United States: Estimates and

Implications," Banco Nazionale del Lavro, 135, 427-453.

[19] Tedds, L. 2007. "Estimating the Income Reporting Function for the Self-Employed,"

MPRA Working Paper, 4212

[20J Yitzhaki, S. 1974. "A Note on Income Tax Evasion: A Theoretical Analysis," Journal


Chapter 3

The Money Trail: Using the

Permanent Income Hypothesis to

Uncover Tax Evasion1

3.1 Introduction

Income statistics playa central role in both the design and the evaluation of public policy

in most industrialized countries. At the household level, most tax and transfer mechanisms

employed by governments use self-reported income data to determine the level of tax and

transfers. Despite enormous care and scrutiny, it is difficult for authorities to accurately

measure true income or even determine whether income is reported truthfully.

There are two margins of income under-reporting. The intensive margin is the fraction

of income that is unreported and estimates of its size directly relate to lost tax revenue. The

intensive margin operates at both the individual and at the aggregate level. The extensive

margin is the fraction of tax filers who under-report income. The extensive margin can be

thought of as a measure of confidence in the tax system's ability to catch under-reporters.

Both the intensive and extensive margin of income under-reporting pose a challenge for the

design of tax and transfer systems.

It is not clear theoretically that the social policy goal of a tax and transfer system

requires the truthful reporting of income. However, it is hard to imagine that the truthful

IThis chapter is based on a work co-authored with Geoffrey Dunbar.

51

CHAPTER 3. THE MONEY TRAIL 52

reporting of income is not assumed by most tax and transfer systems. First, governments

typically levy penalties, including incarceration, for under-reporting income which suggests

that they are trying to deter income under-reporting. Second, if truthful reporting is not

required to implement the policy goals of a tax and transfer system then it is unclear that any

redistribution would take place as long as the marginal utility from additional consumption

is positive. Indeed, casual theorizing would suggest that all agents would minimize the tax

and maximize the transfer by reporting identical incomes.

Income under-reporting is also a problem for microeconometric studies of household con

sumption behaviour and studies of income inequality. Under-reported income is sometimes

considered a type of measurement error that is uncorrelated with the residuals. In such a

case, most estimators remain consistent. A claim is also often made that poor households

have less incentive to under-report income since tax and transfers typically redistribute

income in their favour. The converse of this claim does not necessarily follow because

households that report low income may not in fact be poor. Under-reported income may

be correlated with the residuals and thus estimates of income inequality, consumption in

equality and Engel curves may be inconsistent.

Perhaps the most direct evidence that households under-report their income to tax au

thorities comes from the Taxpayer Compliance Measurement Program (TCMP) and the sub

sequent National Research Program conducted by the US Internal Revenue Service (IRS).

Andreoni et. al. (1998) suggest that roughly 40% of US households under-report their

income to the IRS according to the 1988 TCMP. Bloomquist (2003) examines the change

in the intensive margin of income under-reporting using tax evasion data by income source

from the 1988 TCMP. He concludes that total under-reported income increased from 3.6

to 5.6 per cent of national income from 1980 to 2000 and that the increases stems from

changes in the composition of income by source. Unfortunately, these results are difficult

to generalize internationally as the TCMP program appears unique to the US.

In this paper, we propose a ratio test for identifying income under-reporting housesh

olds. The intuition underlying our test follows from the Permanent Income Hypothesis

households spend according to their expected lifetime income and not their reported income.

Thus the ratio of particular consumption expenses to reported income provides a gauge of

whether a household is under-reporting or not. Our method is intuitive and a test using

Canadian data appears robust. For instance, in our data, we find that most households that

under-report their income have mortgage-to-income ratios (MIR) or rent-to-income ratios


(RIR) well in excess of households that do not under-report. A non-negligble fraction of

households in our data report positive savings and a MIR greater than 1.2

We use data from the Survey of Financial Security (SFS) conducted by Statistics Canada

111 1999 and 2005 and data from the Survey of Household Spending (SHS) to estimate the

fraction of under-reporting households in the data. 3 Our methodology follow Chapter 2

which suggest comparing a household's estimated gross consumption, Gt , and its reported

income, fit, to detect income under-reporting. If a household has Yt - Ct >= 0 then it is

considered a true reporter. If a household has Yt - Gt < 0 then it is considered an under

reporter. Consumption data are imputed into the SFS from the SHS since the SFS do

not collect full consumption information. SFS and SHS share a number of socia-economic

characteristics at the hosuehold level which are used to evaluate conditional means.

Our results appear dual to the theoretical literature on tax evasion in that we suggest

a test of income under-reporting where the theoretical literature proposes a tax shift for

efficiency gains. For instance Boadway and Richter (2005) suggest that taxing an observ

able good in the Allingham/Sandmo model improves the efficiency of taxation. Rather than

introduce a new tax, our empirical methodology provides an easy method to detect possible

tax evasion provided that governments collect consumption expenditures on shelter. Thus,

similar to Boadway and Richter, we base our approach on the notion that consumption

decisions depend on true income, not reported income. Nor is it unusual for governments to

collect consumption information. The US government collects mortgage payment informa

tion as part of the general tax file. The Ontario provincial government collects rent payment

information from renters. While our method is susceptible to a change in the economy-wide

level of spending on shelter, we do not feel that in the short-run such changes are likely to

occur.

3.2 Expenditure and True Income

In this section, we propose a simple test of income under-reporting. Our theoretical approach

borrows partly the intuition from the literature on tax compliance and tax evasion, reviewed

2A related question not addressed in this paper is exactly how such households qualify for mortgages fromfinancial institutions.

3Note that the income and expenses in the SFS 1999 and 2005 data are actually for 1998 and 2004,respectively. That is why we compare to SHS data from these dates.


in Andreoni et. at. (1998). While we are not directly interested in the design of an optimal

tax policy, we are interested in the effet of tax evasion on thc composition of the household's

consumption bundle. We propose a stylized model of the income reporting function for an

individual and analyze the effect of tax evasion on the composition of a ratio of individual

consumption to reported income. In effect, what we propose is a simple ratio-test to detect

income under-reporters in existing survey data.

Consider an individual with income Y, consumption, c, at (exogenous) prices P, savings s

which earns an (exogenous) after-tax rate ofreturn (l+r), a time discount factor 0 < (3 < 1,

and a utility function u(c) that satisfies the Inada conditions.4 Let subscript t indicate time

and assume that the individual faces a known end-of-life in period T. For simplicity, we

assume that the individual faces a voluntary tax rate Tt on all income she chooses to report

in period t. The problem facing the individual is:

T

max L f3t u (ct} S.t.Ct,Tt t=O

PtCt + St = Yt - TtYt + (1 + rt}St-l

(3.1 )

It is immediate that the solution to the individual's problem is characterized by Tt = O.

This result is trivial and simply serves to motivate that individuals typically prefer to pay

no tax and do not when it is costless to avoid.

Instead, suppose taxation is involuntary but households pay a utility cost to hide income

from taxation, I'(Yt), where we assume that I' is differentiable and 1"0 > O. We interpret

the utility cost as any or all of: the effort cost to the individual to hide the income, the

expected punishment if caught or the moral cost to the household of cheating. We choose

not to model the risk to the individual of being audited as is often analyzed in the tax evasion

literature (e.g. Allingham and Sandmo (1972), Yitzhaki (1974) and Boadway Richter (2005)

for instance) for two reasons. First, our arguments described in this section doubtlessly hold

in these models under typical expected utility and punishment assumptions - i.e. as long

as individual consumption rises when income is under-reported. Second, we do not seek

to explain why individuals under-report or what mechanisms a taxation authority could

feasibly design for social efficiency (or any other policy goal).

4We assume here that income from savings cannot be hidden but this assumption is simply for exposition.


Define fit = Yt - fh as the individual's reported income. The individual's problem thus

becomes:

T

max I:>8du(Ct) - 'Y(Yt)] s.t.Ct,Yt t=O

PtCt + St+1 = Yt - Tt{jJt) + (1 + rt}St

We re-write the problem as:

(3.2)

(3.3)

For simplicity, we focus on the periods t < T. Only the first-order conditions with respect

to Yt are relevant for our discussion:

_ '(') '() aCt Tt {3 aV(Yt+l, St+d OSt+1 (] ) - 0'Y Yt + U Ct ~ A + t ~ ~'- + rt+1 - ,

uYt Pt uSt+ 1 uYtwhich can be rewritten as:

(3.4)

(3.5)

(3.6)

'(' ) _ '( ) OCt Tt + (3 OV(Yt+1, St+1) OSt+l (1 + )'Y Yt - U Ct ~- t ~ ~, rt+1 .UYt Pt USt+1 UYt

In words, the household will choose to under-report income Y until the marginal cost of

under-reporting equals the expected marginal benefit from doing so. A second implication

of Equation (3.5) is that an individual's consumption, Ct, is increasing in Yt:

aCt _ [ '(') (3 aV(Yt+l' 8t+1) OSt+1 (1 + )] Pt 0aYt - 'Y Yt - t OSt+1 aih rt+l U'(Ct)Tt > ,

by our assumptions on utility, 'Y and that Equation (3.4) holds. Next, consider a consumption

to-income ratio, C I R, defined as:

CIR = Ct.fit

The C IRis increasing in unreported income since:

Cgg

d I R = dYt +~ > 0dYt fit (fit) 2

(3.7)

(3.8)

Thus, individuals that under-report income should have higher consumption-to-reported

income ratios than households that truthfully report at a given income leve1. 5 'Ve note that

5These findings mirror, in some respects, the intuition that governments can reduce income tax evasionby taxing a consumption good, e.g Boadway and Richter (2005).


the consumption measure, Ct, examined here is an on-going consumption expense. Ideally,

the on-going consumption expense should have no fixed costs, have no 'necessity' welfare

implications (such as subsistence food) or be an infrequent expense such as a refridgerator

purchase. In other words, an individual's choice of the level of on-going consumption should

be an unconstrained choice as much as possible. Fortunately, individuals typically have

one on-going expense that, we argue, does largely satisfy these conditions - shelter costs.

Mortgage payments and rent have no infrequency problem, there is typically an active

market in shelter and, at least in relative terms, the fixed costs of shelter can be small, e.g.

for renters. In the empirical work which follows, we show that mortgage-to-reported-income

(MIR) and rent-to--reported-income (RIR) ratios can be used to identify tax evasion.

3.3 The Canadian Data

The primary data sources for our study are the 1999 and 2005 Survey of Financial Security

(SFS) collected by Statistics Canada. SFS is a self-report survey of the assets and debts of

Canadian households at the time of the survey and the income and expenses for the previous

calendar year, 1998 and 2004 respectively. The SFS is comprised of two sub-samples. The

first subsample is drawn from the Labour Force Survey (LFS) sampling frame and reports

households across the ten provinces and excluding those households on Indian Reserves or

located on federal institutions (such as military bases). The second subsample is drawn

from high-income neighbourhoods to account for the disproportionate wealth held by these

households. The sample size for the 1999 and 2005 SFS are 15,933 and 5,282 respectively.

Survey weights are provided to balance the unequal selection probabilities and response

rate, so that the survey is representative of the Canadian population.

In order to identify households that under-report their income we construct household

income statement using detailed income and expenditure data for the survey year. If a

household has gross consumption greater than its reported income, then the household is

assumed to be under-reporting income. More specifically, the SFS collects income infor

mation from each adult (15+) respondent. Of these income records, 85% in 1999 survey

and 80% in 2005 survey are directly taken from Revenue Canada's tax record. 6 We only

include those households whose income information is directly taken from the tax record

6 At the time of the interview, the respondents have a choice of granting access to their tax record throughRevenue Canada to skip all the income questions.


thus the income data we report are the same as the income data reported to the Canadian

Revenue Agency (the federal government department responsible for taxation). The SFS

also provides a variety of on-going expenses, such as housing costs, utilities, car insurance

expenses and child support payments. However, the consumption of non-durable goods and

services is not reported in the SFS data. The lack of full consumption data is both a con

cern and a benefit. One advantage of incomplete consumption data is that households who

under-report income are less likely to be concerned about getting caught and consequently

less likely to underestimate the consumption items that they do report.

\Ve impute consumption for households surveyed in the SFS from the Survey of House

hold Spending (SHS). The SHS is a self-report annual survey of detailed spending and income

of Canadian households across all provinces and territories. 7 The sample sizes for the 1998

and 2004 SHS are 15,457 and 14,154 respectively. SHS does not include any wealth data.

We choose to impute non-durable consumption into the SFS rather than imputing wealth

into the SHS for two reasons. First, there is typically less variance in non-durable consump

tion data than wealth data. Second, the SFS questionnaire asks households whether they

are spending at, beyond or below their income for that year. This question helps to identify

income under-reporting households. In this study, we only consider those households who

self-identify as having income equal to spending.

In addition to the non-durable consumption, another expense item missing from the 1999

SFS is the payment on non-mortage debt. We approximate the on-going debt payments

using information on the type and amount of debt provided by the SFS. We estimate the

annual debt payment using an estimate of the average interest rate (plus any principle

repayment if applicable) of that particular kind of debt in that years. The payment on

non-mortage debt for 2005 is directly provided in the data.

We conduct two imputation exercises. Our first approach is to impute non-durable con

sumption from the SHS to the SFS and then to add this estimate to the ongoing expenses

collected in the SFS. Comparison of aggregate moments suggests that this approach under

states consumption at the mean by roughly $1,350 in 1999 and $2,000 in 2005. Nevertheless,

this approach avoids imputing infrequent expenditures to all households and therefore is a

somewhat conservative approach. Our second approach is to impute total consumption from

the SHS to the SFS and then to replace all consumption items in the SFS with this estimate.

7The territories are only covered in selected years.

8 see Chapter 2 section 2.2 for details.


The aggregate moments yield by this approach also understates consumption at the mean

by similar amount comparing to the first approach (i.e. $1,900 in 1999 and $2,770 in 2005).

Nevertheless, this approach may overstate the extensive margin of income under-reporting

as all households are effectively assumed to have made a durable goods purchases. At the

intensive margin, however, this approach is likely to provide a better measure of the size of

missing income.

The imputation approach used here is outlined in detail in Chapter 2, although we briefly

sketch here the empirical method. The imputation takes advantage of many demographic,

geographic and expenditure categories in common in both the SFS and the SHS which are

potential determinants of consumption levels. We treat the two data sets as random samples

from the same underlying population since both SHS and the main sample of SFS follow the

sampling framework of the Labour Force Survey (LFS) and are designed to be representative

of Canadian population. In order to ensure the samples are consistent, we removed part

year households and multi-family households from the SHS. After eliminating missing cases

for the key variables, our working sample consists of 13,576 and 12,924 cases for the 1998

and 2004 SHS, and 14,966 and 4,665 cases for the 1999 and 2005 SFS, respectively.

The characteristics of households in the SFS and SHS are very similar despite the inclu

sion of the 'high-wealth' sub-sample in the SFS. In addition to the household demographic

characteristics, we also condition our imputation on major source of household income,

household income, and mortgage (or rent) to income ratios. The consumption items matched

in the SHS to the SFS are ongoing expenses such as housing service expenses, utility pay

ments and support payments. There are a number of on-going expenses included in both

SHS and SFS. Each individual item and the total on-going expenses in the two data sets

are remarkably similar. The differences in the mean on-going expense is $144 in 1999 and

$369 in 2005.

One caveat with our study is that our expenses are based on imputed values. We have

provided means to mitigate imputation errors using conservative methods and extra robust

ness checks. However, there may remain imputation error causing some of our households

labeled as 'tax evaders' or 'under-reporters' to be wrong. However, we find that pattern

of our results is consist across the two survey years and our results are similar to existing

research, giving us confidence that the qualitative aspect of our results are reliable even if

there may be error in the precise quantitative values.

CHAPTER 3. THE MONEY TRAIL

3.4 Income Under-reporting

59

Once we have a direct measure of income under-reporting households we can compare the

relative mortgage-to-income (MIR) and rent-to-income (RIR) ratios of true-income and

income-under-reporting households. We first present a kernel density estimate of distribu

tion of MIR by income-reporting status for 1999 in Figure 3.1, selecting only those house

holds that have purchased their principal residence within the past 5 years and only consid

ering imputed consumption.9 The density estimates strongly suggest that the distribution

of the MIR is different across groups. This suggests that a MIR threshold can differentiate

between true income reporters and income under-reporters. lO The results for RIR, also

shown in Figure 3.1, show a similar pattern.

"I II I

I II II II II II I, II ,I II ,I

I II II II ,

-,o ~_.:::--.=.::;---:..:..;--:.=.;:--.:.::-- -=::--::=--::;=;---:::::-:::::::::::~

"t'lI ,I ,I ,I ,I ,I II II II II III II I1 II \I \, ," ~--""'"

o ~_-----.,~--=:---:.:.:--:.=.;:--.:..::.-- -~--:::::--:=::---:::::--::=:;:-:::;:...

o .5MIR

1.5 o .5RIR

1.5

1- ---- True-reporters -- Under-reporters I

Mortgage to income ratio (MIR)

1----~ True-reporters -- Under·reporters I

Rent to income ratio (RIR)

Figure 3.1: Kernel density of MIR and RIR, by income reporting status, 1999.

Since the debt payment data for 1999 is an estimate, we also compare kernel density

estimates of the distribution of the MIR and RIR ratios for true-income and income-under

reporting households in the 2005 SFS survey. Figures 3.2 demonstrates that distibution of

9I.e. we ignore the effects of imputed savings since we only include households who reported income equalto spending.

lOWe note that Canadian banks tend to grant a total mortgage of three times a family's gross income(or approximately 36 percent debt service ratio). Almost all the non-under-reporters' MIR falls below thispercentage suggesting that banks are applying this policy to these people. However, a significant proportionof under-reporters have a MIR well beyond this threshold and yet still are able to receive a mortgage. Wecan only speculate why they would be approved for a mortgage when their reported income is clearly outsideof the norm.


the MIR and the RIR are also different in 2005 and suggest that our findings for 1999 are,

in all likelihood, robust to the debt payment estimate in 1999.

1- ---- True-reporters -- Under-reportersI1.5

RJR.5

1----- True-reporters -- Under-repOrters Io

I'1\I 'I 'I I

I 'I \I \

I 'I I

I 'I \I \, \

I ": \

I \I \

/ "''''''',

o ~'~_~'-.::..::..:-- -~-- -~--=---:;:=:---::=::::--:=:::-- _==:;_-_=--.-.8.4 .6

MIR.2o

Mortgage to income ratio (MIR) Rent to income ratio (RIR)

Figure 3.2: Kernel density of MIR and RIR, by income reporting status, 2005.

3.4.1 Conditional MIR and RIR

Our raw findings suggest that the distribution of the MIR and RIR ratios differ across

households that truthfully report and those that under-report their income. However, our

theoretical model implies that the MIR and RIR ratios differ across households conditional

on their true income. In other words, while the unconditional distributions of the MIR and

RIR suggest a difference across income reporting types, a better test of our model is to

examine the MIR and RIR ratios conditional on income.

We construct deciles of true income11 , using our estimates of true income and then

compare the MIR and RIR within these deciles. Our results are presented in Table 3.1

and show that the average MIR and RIR are higher for Under-reporters throughout the

entire income distribution for both years. In essence, for the same true income level, under

reporters pay less tax and consequently have more disposable to finance consumption (and

savings), including consumption on housing.

In addition to identifying the incidences of under-reporting, the extent of income under

reporting should, in theory, be correlated with the relative magnitude of MIR and RIR.

llTrue income Yt is defined as the following: Yt = fit if true-reporter; Yt = fit + (fit - C\) if under-reporter.


Table 3.1: Conditional MIR and RIR by Income Deciles

1999 2005RIR

UnderReporter

0.671.390.890.410.470.440.3G0.230.190.57

True-Reporter

0.340.230.260.230.230.210.160.140.150.11

MIRTrue- Under-

Reporter Reporter0.17 0.440.15 0.300.18 0.450.14 0.230.13 0.370.16 0.650.10 0.210.11 0.170.09 0.090.08 0.12

RIRUnder

Reporter0.780.690.800.490.440.420.330.680.270.38

True-Reporter

0.360.340.280.260.190.210.190.140.150.10

MIRTrue- Under-

Reporter Reporter0.16 0.740.16 0.410.15 0.320.15 0.350.15 0.320.14 0.410.13 0.250.13 0.360.11 0.300.11 0.47

IncomeDeciles

123456789

10Total 0.14 0.48 0.19 0.61 0.13 0.34 0.19 0.73

Figures 3.3 plots the MIR and RIR against the true-income-to-reported-income ratio for

under-reporting households. 12 Both fitted lines are upward sloping and have narrow con

fidence bands, consistent with theory. For a MIR and RIR around 1, on average, the true

income is about 3.5 times the reported income. In part this result could be due to the 'base

effect'.

0.......- ,--__---.. .....,... .......Z True-to-report~d-income Ratio 4

IW6ZMIJ@ 95% CI -- Fitted valuesI234

True-to-reported-income Ratio

I_M.W" 95% CI -- Fitted values I

Figure 3.3: MIR and RIR by True-to-reported-income Ratio, 1999

12While we would like to be able to report scatterplot diagrams we are unable to do so for confidentialityreasons.

HAPTER 3. THE MONEY TRAIL

.4.2 MIR and RIR as indicators

62

One question is whether or not there is a MIR or RIR threshold at which one can infer that

a household is, in all likelihood, under-reporting income. We calculate the MIR and RIR

levels at which 90% and 95% of households are found to be under reporting income for both

our 1999 and 2005 survey years. The results are reported in Table 3.2. These numbers imply

that in the 1998 tax year, if a family ha.'3 a mortgage that is greater than 0.3 times their

reported gross income then there is a 90% chance that they are an income under-reporter.

For 2004 tax year, the corresponding MIR cutoff threshold is 0.27. For renters, we find that

with a RIR above 44% in the 1998 tax year, or 40% in the 2004 tax year, 90% of households

under-report their income. We note that these MIR and RIR levels are basically constant

across years.

Table 3.2: Thresholds of MIR and RIR

90 percent cutoff95 percent cutoff

1999MIR Rlfi0.30 0.440.32 0.48

2005MIR RIR0.27 0.400.33 0.42

The threshold to achieve 95% is slightly higher than the 90% cutoff rate. If we continue

to increase the MIR or RIR threshold, eventually we can approach a 100% hit rate; that

is eventually, the threshold will be so high that there will only be a few household left

in the category and they will most likely be under-reporters. However, there is only a few

households in this category, which means many under-reporters will be missed. To illustrate

this tradeoff when increasing the MIR/fiIR threshold, we examine four types of situations

as shown in table 3.3: (1) True positive (TP) or hit: the under-reporters with MIR/RIR

above the threshold. (2) False positive (FP) or false alarm, type I error: the true-reporters

with MIR/RIR above the threshold. (3) True negative (TN) or correct rejection: the true

reporters with MIR/RIR below the threshold. (4) False negative (FN) or miss, type II

error: the under-reporters with MIR/RIR below the threshold. We plot the percentage of

TP, FP, TN and FN for homeowners against the MIR threshold in Figure 3.4. As the figure

shows, when increasing the MIR threshold, the number of false alarm (type I error) drops.


However, the number of misses (type II error) increase. The graph for renters (Figure 3.5)

shows a similar pattern.

Figure 3.4: Thresholds of MIR and the proportion of TN, FP, FN and TP for home owners

80

10

80

40

30

20

10

\,~____ 6··········b.

_____,. .~ ._: ~.~•• ~.:.-" ... ~.:_~.~~:~.~.~.~.~:~••••••••A"-

-_. __ ..• -.6 •••• - ~ ----lI-_ -)l---- __x

-I-----__~---_--------------~----'----=-=.-==_4t__~__ _.10 20 25 3D

MIR thrHholdl40 4. .0

Based on our analysis, it is possible to evaluate the tradeoff between catching under

reporters correctly versus inadvertently causing true-reporters to be audited by adjusting

the threshold of MIR or RIR that categorizes a possible under-reporter and thus would

trigger an audit to determine correctly whether the household is an under-reporter. There

are four cases that are possible that are represented by a confusion matrix as shown in

table 3.3. Intuitively, a perfect MIR or RIR threshold would catch all the under-reporters

to be audited but not categorize any true-reporters to be audited. However, this is generally

not possible due to the distribution of the two. Thus, we can find a metric that determines

the tradeoff as we adjust the MIR or RIR threshold that then allows us to choose how many

under-reporters we are willing to miss in exchange for not triggering a true-reporter to be

suspected of under-reporting.

To make a more concise representation, we use a Receiver Operating Characteristic

(ROC) curve as an analytic tool to illustrate the tradeoffs when setting the MIR/RIR


Figure 3.5: Thresholds of RIR and the proportion of TN, FP, FN and TP for renters

70

40

"... - -- - -~ - -- ---30

- -M- __

----M~ ...

4540

• •• -6

~:- ~.- ~..: .~~ ~~-

25 30

RIR t"re8hold

1~ 20

~........, .'.6

.6. - - - _......> ....:~-- _• __ .6 ••• --_.- ----~--- _

.~

5010

I10 ~

!-+-TN ~p ··-A···FN - ~

cutoff threshold. ROC is widely used in the engineering, medicine and psychology lit

erature to select optimal cutoff values (Egan, 1975). It is the plot of true positive rate

(TPR=TP/(TP+FN), i.e. sensitivity) against the false positive rate (FPR=FP/(FP+TN),

i.e. I-specificity). In our context, TPR is the proportion of correctly identified under

reporters (i.e. with MIR/RIR above the thresholds) out of all under-reporters, whereas the

FPR is the proportion of true-reporters who are mislabeled as under-reporters (i.e. with

MIR/RIR above the thresholds). Of particular interest for our discussion is that a diago

nal line from the origin to the upper right corner at (1.0, 1.0) is the "random guess" line

or line of no discriminability that represents essentially flipping a coin to decide whether

someone should be audited or not. As well, the upper left corner of the ROC curve (0,1)

represents 100% hit rate and 100% correct rejection rate which is a perfect threshold with

the maximum discriminability. To find the optimal threshold, assuming costs and benefits

are equal for each category, you find the maximum perpendicular distance to your ROC

from the diagonal line.

For our data, we plot the ROC curve for MIR and RIR thresholds in Figure 3.6 and


under-reporteraudit Hit

(True Positive)

no audit Miss(False Negative;Type II error)

true- reporterFalse Alarm

(False Positive;Type 1 error)

Correct Rejection(True Negative)

65

Table 3.3: A typical confusion matrix with the actual situation shown on the top row and theleft column indicating whether the household was categorized to be audited or not. A "hit"means that you audited an under-reporter, a "miss" means you did not audit an underreporter, a "correct rejection" means you did not audit a true-reporter and a "false alarm"means you did audit a true-reporter. A perfect threshold would only have under-reporteraudited and no true-reporter audited but is usually not possible if the two distributionsoverlap. These terms are also referred to by other names as listed in the table.

Figure 3.7. The optimal cutoff point depends upon the relative costs of auditing a true

reporter (false alarm) versus how much tax revenue is lost for a missed under-reporter. If

these are equal, then the optimal threshold is where the maximum perpendicular distance

from the ROC to the diagonal line occurs. In this case, our optimal MIR is around 32% and

RIR is around 27%. The optimal points may be adjusted to reflect the economic benefit

of correct identification and cost of miss classification through weighting the proportions

accordingly. Factors that could be used for the weighting include lost tax revenue, hourly

cost of doing an audit, inconvenience, cost of appeal process and so on that end up compiled

into a payoff matrix. This analysis is left for future work as the derivation of the payoff

matrix is outside the scope of this study.

3.4.3 Adding demographic characters

Our framework also suggests that occupations, locations and other observables matter for

under-reporting for two reasons. First, the costs of hiding income ("y) are presumably

not uniform across observable individual characteristics. Second, the marginal tax gains

of hiding income are also unlikely to be uniform across individuals. We control for these

factors with shelter cost ratios to investigate the predictive power of the shelter cost ratios.

We ran a number of logistic regressions of being an under-reporter on RIR, MIR and sets

of control variables.


1.00 .

0.90

0.80

0.70

0.60

a:0.500.

~

0.4020%

0.3015%

0.20 10%

5%

0.10

0.00

0.00 0.20 0.40FPR

0.60

50%

0.80 1.00

66

Figure 3.6: ROC for MIR Threshold, 1999; thresholds are indicated as labels on the ROCcurve.

Table 3.4 presents the results of logistic regression for the two survey years. The table

shows the regression for 1999 and 2005 income under-reporting. For each year, we run three

sets of regressions including; MIR and RIR threshold dummy variables, MIR and RIR as

continuous variables, and finally, MIR and RIR with added demographics variables such as

age, education, province of residence, occupations, income and marginal tax rate (MTR13).

At the bottom of the table, we report the classification tables for each model. The positive

predicted value is defined as the probability of being under-reporters larger than 50%. The

results show that the MIR and RIR continue to be highly significant even when controlled for

other factors. Perhaps more importantly, controlling for the remaining factors only accounts

for a small gain in predictive power. The correctly classified percentage only increases by

13Marginal tax rate is estimated using the tax calculator provided by Milligan (2008).


1.00

1.00

45% .50°;'.40%0.90

0.80

0.60

It0.500.

l-

0.40

0.30

0.20

0.10

0.00

0.00 0.20 0.40 0.60 0.80

FPR

Figure 3.7: ROC for RIR Threshold, 1999; thresholds are indicated as labels on the ROCcurve.

less than 5% after additional personal and households characteristics are added.

These results support our conjecture that shelter costs are useful predictors of tax evasion

providing tax authorities and policy makers simple and reliable indicators for income under

reporting. Shelter costs are easy to observe and can be subject to third party reporting. For

example, policy makers can create incentives or legislation for banks and landlords to report

customers' mortgage and rent payment information. The Ontario provincial government col

lects rent payment information from renters, and the proportion of income under-reporters

in Ontario are among the lowest in the country (See Chapter 2).

It is worth noting that a household could have a high housing cost-to-income ratio due

to other reasons. For example, a temporary negative productivity shock, such as unemploy

ment, childbearing or illness would lead to a temporary reduction in household's income.


Similarly, if a household expect high future income growth, it would increase current hous

ing consumptioll. All these would lead to a high housing cost-to-income ratio. Given the

one-dimensional nature of our indicator, it would be surprising if our results are perfect

predictors. However, as a screening devise, MIR and RIR are simple to use and provide

reliable predicting results that doesn't depend on any ad hoc assumptions.

Another appeal about using shelter costs as indicators of income under-reporting is that

the economic pressure imposed on the under-reporter to hide income requires them to adjust

their housing options. However, we believe it is difficult to find a close substitute for shelter,

making its demand relatively inelastic. Hence, it is unlikely that an income under-reporter

would live in a substandard residence just to avoid getting caught cheating on taxes since

improved housing may be one of the motivations for avoiding taxes. For this reason, when

using MIR and RIR thresholds in tax audit rules, we believe that tax authorities should

make these rules public as it will be an effective deterrence mechanism. Thus, our results

support the notion that MIR and RIR provide a very useful set of indictors for including in

tax rules.

3.5 Conclusion

In this paper, we proposed a simple and intuitive indicator of income under-reporting. Our

method is a straight-forward application of the Permanent Income Hypothesis - households

make consumption decisions based on their true expected lifetime income, not reported in

come. Thus the relationship between reported income and spending must be systematically

different across income under-reporting households and the true-income reporting house

holds. In particular, we studied the housing costs to reported income ratio (i.e. rent to

income ratio (RIR) for renters, and mortgage payments to income ratio (MIR) for home

owners). We found that most households that under-report their income have mortgage

to-income ratios (MIR) or rent-to-income ratios (RIR) well in excess of households that do

not under-report providing an effective indicator.

MIR and RIR provide powerful, simple and reliable indicators of tax non-compliance

even though they are not perfectly accurate. To take advantage of these indicators, tax

authorities could collect information on housing costs, either directly or from third-parties,

and use MIR and RIR ratios as a tax audit trigger. We also suggest that policy using these

indicators provides an effective deterrent against under-reporting if made public though


using "lifestyle" feedback. In addition, in empirical work, we suggest using MIR and RIR

ratios to split populations to estimate empirical variables that reqUIre true-income, not just

reported income. In effect, by following the money trail of people's spending we have an

effective mechanism to uncover tax evasion.

Q ~T

ab

le3

.4:

Sele

cte

dC

oeff

icie

nt

for

Lo

gis

tic

Reg

ress

ion

of

inco

me

un

der-

rep

ort

ing

'"!j ~ ~

1999

20

05

SNT

hre

sho

ldO

nly

Rati

oO

nly

Ad

dD

emo

Th

resh

old

On

lyR

ati

oO

nly

Ad

dD

emo

coef

seco

efse

coef

seco

efse

coef

seco

efse

~M

IR>

0.30

3.48

3***

(0.4

05)

3.00

7***

(0.7

73)

tr.l

RIR

>0.

453.

594*

**(0

.264

)3.

412*

**(0

.416

)~

RIR

8.69

9***

(0.5

12)

4.58

4***

(0.6

71)

8.86

4***

(0.8

78)

5.76

5***

(1.1

89)~

MIR

9.64

6***

(0.8

07)

11.2

52**

*(1

.116

)12

.343

***

(1.6

38)

12.6

59**

*(2

.001

)trl

Age

-0.0

24**

*(0

.007

)-0

.021

(0.0

13)

"<M

SI-

Wag

e-1

.661

**(0

.783

)-1

.434

(1.0

87)~

MS

I-S

elf

0.64

1(0

.870

)-0

.775

(1.3

12)~

MS

I-In

v1.

074

(1.0

56)

2.49

0(2

.504

)t:i

MS

I-G

ov-1

.584

**(0

.772

)-2

.282

**(1

.125

)H

ighs

choo

l-0

.029

(0.2

43)

0.27

6(0

.408

)P

ost

Sec.

0.47

9**

(0.2

34)

0.11

3(0

.424

)U

nive

rsit

y1.

005*

**(0

.315

)1.

002*

(0.5

41)

Log

(inc

ome)

-2.9

14**

*(0

.237

)-2

.642

***

(0.4

63)

MT

R2.

284*

*(1

.035

)8.

464*

**(2

.474

)C

onst

ant

-1.3

52**

*(0

.068

)-2

.997

***

(0.1

45)

28.2

11**

*(2

.392

)-1

.215

***

(0.1

19)

-3.1

04**

*(0

.269

)22

.385

***

(4.5

89)

Cla

ssif

icat

ion

Tab

leT

RU

EF

AL

SE

TR

UE

FAL

SET

RU

EF

AL

SE

TR

UE

FAL

SET

RU

EF

AL

SE

TR

UE

FA

LS

EP

redi

cttr

ue

13.9

%1.

5%17

.8%

3.2%

22.5

%5.

3%15

.4%

1.9%

21.2

%4.

1%23

.5%

6.0%

Pre

dict

Fal

se17

.4%

67.2

%13

.6%

65.5

%8.

8%63

.4%

18.9

%63

.8%

13.2

%61

.5%

10.9

%59

.7%

Co

rrec

tly

clas

sifi

ed81

.1%

83.3

%85

.9%

79.2

%82

.7%

83.1

%

***

p<0.

01,

**p<

0.05

,*

p<0.

1

Sam

ple

only

incl

udes

thos

ew

hore

port

edin

com

eeq

ual

tosp

endi

ng.

For

hom

eow

ners

only

thos

ew

hopu

rcha

sed

thei

rpr

oper

ties

wit

hin

the

last

5ye

ars

are

incl

uded

.T

here

are

1,57

1ob

serv

atio

nsin

1999

and

486

obse

rvat

ions

in20

05.

Reg

ress

ions

also

incl

ude

cont

rols

for

prov

ince

,oc

cupa

tion

.M

SIre

fers

tom

ajor

sour

ceof

inco

me.

MT

Rre

fers

tom

argi

nal

ta..x

rate

.S

tand

ard

erro

rso

fth

eco

effi

cien

tes

tim

ates

are

repo

rted

inpa

rent

hese

s.

d

Bibliography





[3] Bloomquist, K. 2003. "Trends as Changes in Variance: The Case of Tax Noncom

pliance" IRS Research Conference, June, us Internal Revenue Service, electronic pro

ceedings.

[4] Blundell, R., Pistaferri, L. and Preston, I. 2004. "Imputing consumption in the

PSID using food demand estimates from the CEX," IFS Working Papers, W04/27,

Institute for Fiscal Studies.

[5] Egan, J.P. 1975. Signal Detection Theory and ROC Analysis, Academic Press, New

York, USA.



[7] Fisher, J. and Johnson, D. 2006. "Consumption Mobility in the United States: Ev

idence from Two Panel Data Sets," Topics in Economic Analysis and Policy, Berkeley

Electronic Press, 6(1), Art. 16.

[8] Lemieux, T., Fortin, B. and Frechette, P. 1994. "The Effect of Taxes on Labor

Supply in the Underground Economy," The American Economic Review, 84(1), 231

252.

71

BIBLIOGRAPHY 72

[9] Lyssiotou, P., Pashardes, P. and Stengos, T. 2004. "Estimates of the Black

Economy Based on Consumer Demand Approaches," Economic Journal, 114,622-639.

[10] Milligan, Kevin 2008. Canadian Tax and Credit Simulator. Database, software and

documentation, Version 2008-1.

[ll] Mirus, R. and Smith, R. 1981. "Canada's Irregular Economy," Canadian Public

Policy, 7(3), 444-453.







[15] Richter, W. and Boadway, R. 2005. "Trading Off Tax Distortion and Tax Evasion,"

Journal of Public Economic Theory, 7(3), 361-381.



28(2), pages 219-237, June.

[17] Skinner, J. 1987. "A superior measure of consumption from the Panel Study of Income

Dynamics," Economic Letters, 23, 213-216.

[18] Tanzi, V. 1980. "The Underground Economy in the United States: Estimates and

Implications," Banca Nazionale del Lavoro, 135, 427-453.

[19] Tedds, L. 2007. "Estimating the Income Reporting Function for the Self-Employed,"

MPRA Working Paper', 4212

[20] Yitzhaki, S. 1974. "A Note on Income Tax Evasion: A Theoretical Analysis," Journal


Chapter 4

Planting Roots: The Asset

Allocation of Canadian Immigrants

4.1 Introduction

The wealth of a household is the cornerstone of its economic security. It provides the means

necessary to buy shelter, to secure loans, to insure against unforeseen financial hardships,

and the income and resources for current and future generations. Wealth is a portfolio of

assets and the portfolio mix matters for reasons of income risk and potential income or wealth

gains. Therefore, understanding the wealth accumulation and allocation of immigrants

relative to Canadian-born households is crucial in assessing the economic integration of

Canadian immigrants. Unfortunately, so far most of the research on immigrants' economic

well-being has concentrated on labour market performance such as employment and earnings

(Chiswick, 1978; Baker and Benjamin, 1994; Bloom et. ai., 1995). Of the research that

has studied the wealth position of immigrants, most only look at the total wealth gap

and its change over time (Shamsuddin and DeVoretz, 1998; Zhang, 2003) and few have

documented the portfolio choices explicitly 1. This paper intends to fill this gap by examining

whether there exist differences in portfolio selection between immigrants and Canadian-born

households, and whether they adjust their portfolio selection over time.

The wealth accumulation and portfolio allocations of immigrants may differ from those

of the Canadian-born population because of differences in preference, labour market income,

IThe exceptions is Cobb-Clark and Hildebrand (2006) and (2008).

73

CHAPTER 4. PLANTING ROOTS 74

family structure, pre-existing wealth and also access to financial services. It is well accepted

in the literature that immigrants face labour market disadvantages in terms of employment

and earnings. The disadvantage in labour market may further affect immigrants' financial

market outcome due to lower earnings and greater labour market risks. Immigrants may

also face barriers accessing financial services due to lack of knowledge and language ability.

In addition, given their diverse cultural backgrounds, immigrants might have different pref

erences relative to the Canadian-born households. Understanding these influences provides

a better picture of the immigrant financial landscape in Canada.

Out of the few literatures that studied immigrants wealth accumulation and allocations,

the results are mixed at best. Empirical evidence based on US data suggest that the

immigrants in the US are worse off in terms of asset accumulation and portfolio building

(Borjas, 2002; Cobb-Clark and Hildebrand, 2006). Using the Canadian data, Zhang (2003)

found that immigrants' wealth positions are bipolar in nature. At the higher percentiles,

immigrants fare better than the Canadian-born. Whereas, the reverse holds at a lower

percentile. However, Zhang did not look at the portfolio composition of immigrants as we

do here.

In this paper, using the 1999 and 2005 Survey of Financial Security (SFS) conducted by

Statistics Canada, we analyze data on the value and composition of wealth accumulation

of immigrant households relative to Canadian-born households. While we do find that on

average the amount of total assets of immigrants are comparable to those of Canadian-born

households, upon closer inspection we find two separable groups of immigrants: settled and

recently arrived. The settled immigrants have portfolios that are similar to Canadian-born,

but their median wealth is higher. The recently arrived immigrant, though, has a portfolio

weighted towards durable goods that does shift towards other parts of their portfolio such

as real-estate the longer they stay in Canada. However, their wealth accumulation lags

Canadian-born and settled immigrants. One possibility is that recently arrived immigrants

take time to save enough capital for a real-estate investment. Essentially, while the recent

immigrant is trying to catch up to Canadian-born and settled immigrants, they appear to

be starting at a significant disadvantage.

The outline for the remainder of the paper is as follows. Section 4.2 presents some

theoretical background as for why immigrants might differ. Section 4.3 describes the data

and presents some descriptive and cohort analysis. Regression results are presented in

Section 4.4 and Section 4.5 concludes.

CHAPTER 4. PLANTING ROOTS

4.2 Why Immigration Status Might Matter

75

Immigrants are doubly selected - they choose to participate in the immigration market first,

and then they are accepted by the receiving country based on some economic and/or demo

graphic criteria. Thus, there may be certain characteristics that distinguish them from the

random population of both the home country and the receiving country. Consequently, they

might have different human capital, innate ability, preferences, family structures, and size

of remittances than Canadian-born that impact their willingness and ability to accumulate

wealth and participate in the financial markets of their host country. We consider three

main aspects of immigrants that may affect their wealth positions.

First, Immigrants might have different earnings and income level comparing to the

Canadian-born of the same socia-economic status. Considerable amounts of research have

documented negative earning gaps at arrival and the lack of assimilation of immigrants

on the labour market comparing to the native born population (Borjas, 1993; Bloom. et.

ai., 1995). According to the 2006 census, the average wage gap between immigrants and

Canadian-born with similar characteristics is about 20-25%2. However, this disadvantage on

labour market mayor may not translate into the relative wealth position. On the one hand,

immigrants' performance on financial market could be worse than labour market due to the

lack of initial wealth, the cumulative effect of lower earnings, and the imbalanced portfolio

mix. On the other hand, if immigrants possess a large amount of assets upon arrival, which

generate a steady stream of income for them, the poor labour market performance could be

a choice for them after carefully weighting the potential gain and cost.

Second, immigrants might make different asset building and portfolio allocation choices

due to culture differences. Previous researches has shown that ethnic origin and cultures

affect financial decsions. For example, Fontes and Fan (2006) found that some Asian culture

put a great deal of emphasis on real estate buildings, whereas Yaa, Gutter and Hanna (2005)

documented different risk tolerances between Blacks, Hispanics and Whites. The family

structure might also be related to the portfolio choices. Using 2000 US census data, Van

Hook and Glick (2007) showed that immigrants are more likely to live with extended families

and consequently requires more housing and service generating assets such as durables.

These differences influence their choices for acquiring assets, potentially putting them at a

disadvantage (or advantage) relative to their Canadian-born counterpart.

2Eased on 2006 Census tabulations 97-563-X2006059.


Third, immigrants might face different constraints than Canadian-born households.

Some immigrants might have limited access to the financial services due to lack of knowl

edge of the Canadian financial market, limited official language skills, or discriminatory

treatment. Immigrants are also more likely to face borrowing constraints due to shorter

credit history and/or less social network. These factors potentially cause immigrants to

have difficulty accumulating high-yield assets, insure against financial hardship or purchase

durable consumption items leading to a spiral of poverty for immigrants.

This paper considers these factors and how they impact the longer-term wealth prospects

of immigrants. Of particular importance is to first establish if there is a difference in portfolio

allocation when an immigrant arrives in Canada, and if so, whether this difference converges

to be similar to the Canadian-born households. Immigrants will become more familiar with

host country's financial system and become more proficient in the official languages, as they

invest more on home country specific skills they are also more committed to home country.

We expect new immigrants to hold more liquid, low risk and low yield assets when they

arrive, and then gradually shift towards higher yielding assets such as real estates, as they

settle. Determining whether this occurs or not may help us understand the adaptations of

immigrants and design policies to facilitate such adaptations.

4.3 Data and Summary Statistics

The primary data for our study comes from the 1999 and 2005 Survey of Financial Security

(SFS) collected by Statistics Canada. These cross section surveys collect information on

all major income, expenses, assets, and debts of Canadian households. The survey covers

private households across the ten provinces, excluding those households living on reserves

and institutions. The main samples are drawn from the sampling frames of the Labour Force

Survey (LFS). The second source of the samples is drawn from high-income neighbourhoods

to account for the disproportionate wealth held by these households. Survey weights are

applied to balance the unequal selection probabilities, so the results are representative of

the Canadian population.

The SFS collects asset and liability information from each surveyed household and in

come and demographic information from each adult (ages 15+) respondent for the house

hold. We use the terms household and family interchangeably in this study, however, the

unit of data collection for SFS and for our reporting is family which includes economic


families of two or more and unattached individuals. In addition to the standard list of

demographic characteristics such as age, sex, marital status and education, SFS asks a rich

set of questions regarding the individual's immigration status including: whether the re

spondent is an immigrant, respondent's citizenship (by birth or by naturalization), year

landed, and first language. About 20.8% adult in 1999 and 21.2% in 2005 indicated that

they are immigrants, which is consistent with the national aggregate reported by Statistics

Canada. The rich information on detailed assets portfolio, immigration status and other

demographic information lend themselves well to examining the differences in assets alloca

tion between immigrants and Canadian-born households. An additional advantage of SFS

is that it collects information on offshore assets explicitly which provides relatively complete

balance sheets for both Canadian-born and foreign-born households.

Analyzing the relationship between wealth accumulation and personal characteristics

such as immigration status poses some difficulty given that SFS does not directly provide

information on assets and debts for each individual. In this study, Collecting family wealth

profiles is the standard approach of reporting wealth since a families' wealth is difficult

to separate at the level of the individual. For the purpose of the analysis in this paper,

households' demographic variables are defined using the Main Income Earner's (MIE) char

acteristics (such as age and immigration status).3 This implies that for mixed couples,

the immigration status of the household is determined by the MIE's status. However, for

regression analysis, we control the individual characteristics of both the MIE and the spouse.

The sample size for the 1999 and 2005 SFS are 15,933 and 5,282 respectively. We remove

foreign-born households who are only temporarily residing in Canada, such as students, as

they are not immigrants for the purpose of our study. In addition, we remove the observa

tions with negative total &isets or negative total income. Our working sample consists of

15,702 and 5,110 observations for 1999 and 2005, respectively.

4.3.1 Descriptive analysis

This sections describes the demographic differences between Canadian-born (CB) and Foreign

born (FB) in both years and their wealth portfolios in 1999. These provide a basis for our

regression analysis in section 4.4.

3\Vhile there may be differences between portfolio allocation decisions among pure immigrant couples(both MIE and spouse are immigrants), mixed couples (only one is immigrants) and pure Canadian-borncouples, this is beyond the scope of this paper.


Demographic comparison

78

We begin by providing summary statistics for Canadian-born and Foreign-born households

for the two survey years. Table 4.1 compares socio-economic characteristics including: age,

gender, marital status, education and labour market activity for the main income earner

(MIE), and the size and income for the households. As the table indicates, the heads of

the immigrant families are on average older, better educated and more likely to be married

compared to the heads of Canadian-born families. Immigrant families are also larger and

more likely to reside in a larger urban area. Between the two survey years, there is some

evidence that immigrants' financial condition is deteriorating relative to Canadian-born

households. In 1999, the average immigrants family's income is 1.07 times of Canadian

families, however, this number decreased to 0.99 times in 2005.

Table 4.1: Demographic comparison of Canadian-born (CB) and Foreign-born (FB)households, 1999 and 2005

Number of observationsHousehold incomeAge of MIEMale MIEMarriedFamily sizeLarge urban areaSmall urban areaWeeks worked full timeHigh SchoolPost secondaryBachlorAge at landing

Wealth Porfolio

1999 SFSCB FB

13,011 2,69149,348 52,61246046 49.960.63 0.630.56 0.652.35 2.810.39 0.720043 0.2131.39 31.240.24 0.220.29 0.270.20 0.28

25.52

2005 SFSCB FB

4,213 89760,130 59,34147.00 50.770.61 0.610.55 0.632.24 2.760040 0.750040 0.1830.27 29.320.27 0.230.29 0.250.22 0.36

27.27

Table 4.2 reports the average households' balance sheets for the Canadian-born and Foreign

born households in 1999. More specifically, we present five sets of descriptive statistics

for CB and FB: asset ownership (the proportion of households with positive holdings of


each assets), share of each asset (at the mean), the unconditional means of their holdings,

the conditional mean and Standard Deviation of their holdings (conditioned on holding

the asset), and the conditional median. Assets are decomposed into four categories and

sub-categories: financial assets (deposits, investment and other financial assets), pensions

(self-directed pension, employer pension plan (EPP) and other pension funds), non-financial

assets (real estate and durables), and business equity. Debts are also separated into four

categories: mortgage debt, student loans, credit card debt, and other debt. Net worth is

defined as the difference between assets and debts and is reported at the bottom of the table.

Only the 1999 wealth portfolio is discussed in detail in the body of the paper to focus the

descriptive analysis. However, the 2005 portfolio is provided in Table 4.3 and noteworthy

differences between 1999 and 2005 are reported where appropriate.

In terms of total assets and net worth, immigrants as a whole fare better than the

Canadian-born households. The mean total assets of foreign-born households in the 1999

SFS is $319,567, which is about $40,000 more than the Canadian-born households ($279,147).

The median total assets holdings are about 60% of the mean for both Canadian-born and

foreign-born households, indicating that the distribution of assets are skewed to the right.

A similar pattern holds for net worth, with immigrants' mean net a.'3set holdings ($274,372)

exceeding Canadian-born ($243,813) by roughly $30,000. This is consistent with Zhang

(2003) 's findings for the average Canadian-born compared to foreign-born. Though, Sham

suddin and DeVoretz (1998) and Zhang (2003) find that recent immigrants are at a wealth

disadvantage relative to Canadian-born populations. They also find evidence that this gap

shrinks over time as do we as discussed in sections 4.3.2 and 4.4.

The first group of assets is financial assets, which include bank deposits, financial in

vestment (bonds, mutual funds and stocks), and other financial assets. Overall, 90% of

Canadian-born and 92.5% of immigrant households possess some kind of financial assets

with most of it being in bank deposits (about 90% have this asset) and fewer with financial

investment (about 30%). Canadian-born households show a slight preference to hold riskier

investment assets (by about 3.6%)4. The unconditional mean value of total financial assets

for immigrants in 1999 is roughly $36,360, which is slightly higher than the Canadian-born

41999 SFS does not allow a detailed break-down of risky vs. non-risky financial assets. For example, itdoes not distinguish whether a mutual fund is an equity fund or an income fund, or whether bonds are riskierlong-term corporate bonds or relatively risk free short-term government bonds. For lack of proper term, werefer to bonds, mutual funds and stocks as risky assets.


households ($35,000). This pattern also holds for the conditional mean and median. In

addition, the conditional median is well below the mean, signifying a highly skewed distri

bution.

We divide pension funds into self-directed private pension savings, Employer pension

plans (EPP) and other pension funds. The self-directed private pension savings including

Registered Retirement Saving Plan (RRSP), Registered Retirement Income Funds (RRIF)

and Locked-in retirement accounts (LIRA)5. EPP include all current, deferred and in pay

pension from current and previous employers. Some less common pension funds, such as

Deferred Profit Sharing Plans (DPSP), annuity and foreign pensions are categorized as other

pensions. All pensions are valued on termination basis. On average, relative to Canadian

born, immigrants are less likely to hold a pension (68.7% comparing to 72%), and pension

contribute a smaller share to their total portfolios (25.2% comparing to 30.6%). However,

conditional on holding the asset, the mean and median of immigrants' self-directed pension

savings, EPP and other pension are higher, signaling a more uneven distribution of pension

assets among immigrants.

All families possess some kind of non-financial assets. For families with real-estate, this

asset dominates their portfolio value. SFS includes the value of all household contents, col

lectibles and other valuables as 'other' durables which is seen by 100% of households having

this asset. Nearly the same proportion of immigrants (65%) and Canadian-born (64%)

households own real estate. However, the most noticeable difference between immigrants

and Canadian-born families is the large value of real estate immigrants hold compared to

Canadian-born households. For example, conditioned on holding real-estate, immigrants

hold $57,000 more real estate at the median, and $74,500 more at the mean than their

Canadian counter part. When considered in relation to their total mean assets, real es

tate constitutes a larger share of an immigrant's total assets (46.8% of $319,567) than for

Canadian-born (35.7% of $279,147).

The last asset category is business equity. Compared to Canadian-born households,

immigrants are more likely to have a family business (19.2% compared to 18.3%). However,

conditional on owning a business, the equity of immigrants business are lower on average6 .

°RRSP is converted to RRIF after the owner turns 69. Appendix A has a short description of eachprogram.

6There are 29% of Canadian-born and 38% of foreign born business owners with equity value equal toone, which means the book value of asset for these businesses are zero.


Debt holdings by foreign birth status are also summarized. Not surprisingly, accompany

ing the larger amount of real estate assets, immigrants incur larger mortgage debt. However,

the net equity in real estate is still higher for immigrants. All other debt amounts (student

loans, credit card debt and other debt) are relatively small and similar across immigration

groups.

Table 4.3 shows the balance sheets of Canadian-born and foreign-born households in

2005. From 1999 to 2005, the Canadian economy experienced strong growth in real estate

market and stock market7 . This is marked by a 20% increase in the median assets and

18% increase in the median net worth, after adjusted for inflation (not shown in the table).

This overall growth is enjoyed by both immigrants and Canadian-born households. The

pattern in 2005 is very similar to 1999, except for a sign of deteriorating pension position

of immigrants. In contrast to 1999, the value of self-directed, employer and other pension

are all lower for immigrants in 2005. This is especially noticeable for employer provided

pension; the unconditional average employer pension for immigrants is about $22,600 below

their Canadian-born counter-part.

In summary, Table 4.2 and 4.3 identifies that the differences between immigrant and

Canadian-born households lies mostly in real estate and pension assets. Part of this may

be due to differences in demographic characteristics. As table 4.1 shows that the heads of

the immigrant families are on average older, better educated and more likely to be married

compared to the heads of Canadian-born families and they typically reside in larger urban

areas. We investigate this further in subsequent sections.

4.3.2 Age and arrival cohort analysis

The previous section summarized some simple descriptive statistics of households' portfolio

holdings by birth status. At first glance, there does not seem to be a significant difference

between immigrants and Canadian-born households' asset holdings. However, as immigrants

are a more heterogeneous group, univariate analysis may mask the bipolar pattern of their

asset allocations. For example, a very important determinant of wealth position is the life

cycle stage of the households, and for immigrants, the length of their staying in Canada as

well. In this section, we conduct some limited cohort analysis by comparing the portfolio

composition of Canadian-born and immigrants who are in a similar life-cycle phase. We

7Stock market plunged in 2002 but recovered by 2005.


also present the changes in ownership rate and asset value between the two survey years of

households.

We choose three cohorts based on their age in 1999. The cohorts are young (20-34 in

1999 and 26-40 in 2005); middle age (35-49 in 1999 and 41-55 in 2005) and older (50-64 in

1999 and 56-70 in 2005). This is to roughly capture those: just starting asset building (20

34), at the peak of earning (35-49) and in their pre-retirement years (50-64). In addition,

for immigrants, we concentrate on recent immigrants who landed within 5 years in 1999

(i.e. landed between 1994 and 1999) and those settled immigrants who are landed for at

least 15 years (i. e. landed before 1985).

Young Cohort

The age and cohort specific portfolios for the young cohort are compared in the pie charts in

figure 4.1. Each row displays households of the same age group, with Canadian-Born, settled

immigrant, and Recent Immigrant graphed in columns 1, 2 and 3, respectively. The second

row shows the same cohort 6 years later (in 2005), The portfolio composition is depicted in

the slice proportions while the height of the pie chart indicates the relative median value of

the portfolio within a cohort8 . Thus, as can be seen, when comparing immigration cohorts

for the young, settled immigrants hold the largest value of asset whereas recent immigrants

hold the smallest. Note that this holds true in general for all cohorts as can be seen in the

subsequent charts9 .

In 1999, the largest item in a young Canadian-born households' portfolio is other durables,

which contribute to 47% of the total asset holdings. The remaining parts of the portfolio

consists of 12% of financial assets, 13% of pensions, 26% of real estate, and 2% of busi

ness equity. Six years later, this group's housing share grew by 10% to 36% and the share

of pension grew by 4%. The young settled immigrant's portfolio more or less mimics a

Canadian-born youth in 1999, with higher pension and real estate holdings (by 5% and 7%,

respectively) and less durables (by 9%). By 2005, about half of the young settled immi

grants' assets are in the form of real estate (49%), while the share of pension and durables

drop by 3% and 14%, respectively. Note the increase in the share of real estate and drop

in the share of pension and durables are not a result of substitution, but rather an increase

in the absolute amount of real-estate holding from newly acquired wealth. Young recent

8The standard error of these portfolio shares are reported in Table 4.6.

9The recent older immigrants' median asset is higher than Canadian-born and settled immigrants in 2005.However, it is very un-precisely estimated due to low sample size (see the standard error in Table 4.6.


Figure 4.1: Portfolio shares for young (20-34) and immigration cohort, 1999 and 2005;height indicates median value.

Canadian-born, Young, 1999 Settled Immigrant, Young 1999 Recent Immigrants, Young, 1999

IIIIl FIN

C2%

.OUR • PEN47% • 13%

REIt!! BUS 260'

2% '0

III! FIN

CO%

.OUR • PEN37% ~ 18%

IIIIlBUS RE2% 33%

.OUR55%

1111 FIN23%

.PEN5%

RE16%

Canadian-born, Young, 2005

III! FIN

C%

• OUR .• PEN36% 17%

It!! BUS RE3% 36%

Settled Immigrant, Young 2005

• OUR l1li FIN

23%~12"PENIIIIBUS~ 15%1%-'-

RE49%

Recent Immigrants, Young, 2005

.OUR l1li FIN

240

QVO 19;opEN

10%IIIIlBUS

4%

RE43%

immigrants, however, paint a very different picture in 1999. Young recent immigrants hold

most of their a..<;sets in durables (55%). They also hold significant financial assets (23%)

compared to the Canadian-born and their more settled counterpart. This is not surprising

since most of the items in other durables are due to consumption generating assets such as

vehicles, furniture and household contents. Given the small amount of assets held by recent

immigrants in 1999, it is likely that it must be in the form of a consumption generating

asset since investment in real-estate, for example, would require a relatively large minimum

capital investment to get started. Six years later, the young recent immigrants started to

accumulate more real estate assets (43%) and the size of their total assets also grow. While

it is not surprising that young recent immigrants need time to get started with real-estate

given their relatively low assets, this analysis does not reveal whether other explanations

are at play, such as preference for assets given their new immigration status.


Middle-age Cohort

84

Figure 4.2: Portfolio shares for middle (35-49) and immigration cohort, 1999 and 2005;height indicates median value.

Canadian-born, Middle-age,1999

III FIN• DUR 7%

27

0t.C· PEN24%

_BUS4%

RE38%

Canadian-born, Middle-age,2005

Settled Immigrant, Middle-age,1999

III BUS4%

RE47%

Settled Immigrant, Middle-age,2005

Recent Immigrants, Middle-age,1999

III FIN18%

• DUR.... • PEN41%~ 11%

I!lIBUS RE2% 28%

Recent Immigrants, Middle-age,2005

• DUR IiIlFIN

23;;1%7% .PEN29%

mBUS4%

RE37%

.DUR17% I

III BUS1%

RE48%

• PEN29% Iii BUS

3%

RE54%

lIII FIN15%

• PEN13%

Looking at middle age, Canadian-born households shows that the share of pension (24%)

and real estate (38%) expand, and financial assets become less important in their portfolio

(7%) from the young Canadian-born (using 1999 as a reference date). Their portfolio

allocation stays more or less the same six years on, in 2005, except for an expected increase

(5%) in their pension holdings. Likewise, the middle aged settled immigrants at this age

have a very similar portfolio trend but with different percentages: 47% in 1999 and 48% in

2005 for real-estate, 9% in 1999 to 5% in 2005 in financial assets and a 10% increase in their

pension share. In contrast, middle-aged recent immigrants still have durables as the largest

asset in their portfolio (41 %), although the proportion of real estate also increased by 12%

from their young counterparts (16% for young in 1999 and 28% for middle-age in 1999).


Six years on, the middle-aged recent immigrant has increased their real-estate share by 26%

(28% to 54%) and their pension share by 2% that is reflected in their other durables share

reduction by 26% (41% to 15%) and their other holdings remain about the same. Thus,

it appears that middle-age recent immigrants are shifting their portfolios to reflect home

ownership and some growth in their pension share.

Older Cohort

Figure 4.3: Portfolio shares for older (50~64) and immigration cohort, 1999 and 2005; heightindicates median value.

Canadian-born, Older, 1999

.OUR III! FIN21

0VO 9%

mBUS4%

• PEN34%

RE32%

Canadian-born, Older, 2005

Settled Immigrant, Older, 1999

.OUR

1Qi%II!~;~Ii BUS

3% • PEN29%

RE44%

Settled Immigrant, Older, 2005

Recent Immigrants, Older, 1999

11II FIN

.OUReS%

3.2% .PEN4%

Iii BUS RE9% 40%

Recent Immigrants, Older, 2005

• OUR III! FIN

1

3.% 10%

IIJBUS3%

• PEN

RE 39%

30%

mBUS2%

• PEN32% • PEN

19%

Finally, at the pre-retirement age, pension becomes the dominant asset (34% in 1999

and 39% in 2005) for Canadian-born households. Financial asset shows a small rebound

compared to the middle age group (by 2%). For the more settled immigrants, however, real

estate (44% in 1999 and 43% in 2005) continues to be the largest asset in their portfolios.

Older recent immigrants are relatively rare and so the number of households in 1999 and


2005 do not provide reliable statistics and arc not discussed here.

Milligan (2005) documented life-cycle wealth accumulation patterns using the 1999 SFS

and the 1977 and 1984 Survey of Consumer Finance (SCF) data for Canadians. Our findings

mirror his. The trends seen for Canadian-born and settled immigrants show a familiar

pattern of a typical life-cycle of increasing wealth associated with the main changes in

portfolio being the growth of pension and real-estate shares and a corresponding shrinkage

of durables, at least until retirement. Recent immigrants are more difficult to determine how

their wealth portfolio changes from these statistics and thus, we consider further analysis

in section 4.4. However, the data does suggest that with enough time, they do normalize to

typical life-cycle patterns of Canadians as they become settled. Before then though, there

are several factors that may be at play to determine how their portfolio evolves. First, recent

immigrants have less total resources as signified by the thinner pie than other households in

the same age group. Thus, their ability to accumulate high-yielding assets is hindered by the

need to acquire consumption-yielding assets such as household durable goods, preventing

their share in real-estate from increasing for example. Second, recent immigrants may have

a higher probability to move back to their home country or elsewhere, and thus prefer not

to invest in assets that will tie them down such as a house. Third, as noted in the literature,

they also may be disadvantaged in the labour market negatively impacting their ability

to invest beyond other durables and have a pension plan. Fourth, as also noted in the

literature, they may have language difficulties and not be knowledgeable with the Canadian

investment landscape making it difficult to adjust their portfolios. Understanding these

relationships may assist with helping recent immigrants develop their portfolios with more

sensitivities to the factors they are facing in their new home.

Asset ownerships and values

We now turn to the changes in ownership rates and values between the two survey years.

We concentrate on real estate and pension asset. The two graphs in Figure 4.4 display the

proportion of households with positive holdings of real estate asset and the unconditioned

median value of real estate asset by age and immigration cohort, respectively. Each estimate

and its standard error is also tabulated in Table 4.7 and 4.8. The connected lines show

the change from 1999 to 2005 for the same cohort lO . For Canadian-born households, the

10As mentioned earlier, the estimates for older recent immigrants are not reliable due to low sample size,as evidenced by the large standard errors. The graph for this cohort is shown in dashed line and a hollow


home ownership rate increases sharply early in the life cycle (from 41 % to 56%), peaking

around mid-40s at 77%, then holds steady till retirement age, much as Milligan (2005)

would predict. Settled immigrants follow a similar pattern, except the ownership rate is

higher than Canadian-born at early ages (by about 10%), and the homeownership rate

keeps increasing until pre-retirement age, peaking at around 83%. However, we observe a

different pattern for recent immigrants. Through the entire life-cycle, recently immigrants'

homeownership rate is well below the Canadian-born and settled immigrants for the same

age groups suggesting that they do not have either the means or preference for owning a

home. However, there is a sharp increase (around 20%) from 1999 to 2005 for each recent

immigrant cohort indicating that very quickly after arrival, many do buy a home no matter

what age they are when they arrive.

-- g~

~:::1•..._-,_¥..~ ~

/ ..~

1I e

//

I .figw'" I :~~. "'N

1'l5

'l5~

jc..c ~8l!!~<t

CO

~~0 :>:

"! 0

30 40Age Co~~rt 60 70 30 40

Age Co~rt 60 70

---4-- Canadian~n --4-- Settled immigrant ...--.- Recent Immigran

Proportion with positive holdings

~ Canaliao-bom ~ Settled imlTllgral'lt --4-- Recent mmigran

Median value (in 1999 dollars)

Figure 4.4: Real estate holdings, by age and immigration cohort, 1999-2005.

The median value of real estate asset follows a hump shape over the life-cycle for

Canadian-born and settled immigrant groups. For all three immigration groups, the median

20-34 years old households do not own any property (i.e. zero median values). The disad

vantage faced by recent immigrants (within 5 years oflanding) in home ownership rates also

shows in the lower value of their unconditioned housing asset compared to others in their

age groups. Although, middle-aged recent immigrants enjoy an increase in median value of

circle to indicate this unreliability.


more than $150,000 between the two survey years which is comparable or better than their

Canadian-born counterparts. Young recent immigrants, however, do not show the sign of

catching up. Interesting, however, is the high real estate value held by settled immigrants

and its sharp increase between the two survey years. The median value of real estate assets

for the settled immigrants are between 1.5 and 2.3 times higher than their Canadian-born

counterpart. In addition to the higher home ownership rate, a few other factors might con

tribute to this differences. For example, many immigrants live in large urban centres that

can have a higher return on real-estate holdings than Canadian-born who are more evenly

distributed. Alternatively, immigrants have significantly larger families on average requiring

a larger investment in real estate.

Based on the limited cohort comparison, there appears to be some evidence that immi

grants catch up and eventually surpass their Canadian peers in terms of real estate asset

holdings. Between the two survey years, we see sharp increases in both ownership rate and

median values for the recent immigrants. In addition, when comparing immigrant's arrival

cohort, the settled immigrants fare better than the recent immigrants. However, our results

do not necessarily imply that recent immigrants will have the same assets as the settled

immigrants after they stay in Canada for the same length. As Borjas (1994) points out, this

could be caused by different cohort quality and thus, requires panel data to determine how

the number of years in Canada impacts their wealth holdings for a given cohort.

In addition to the analysis of real estate asset, we conduct the same cohort analysis

on pension asset. Pension coverage and median values are displayed in figure 4.5 and the

standard errors of these estimates are displayed in Table 4.7 and 4.8. Between 1999 and

2005, pension coverage for young Canadian-born increased from 62% to 72%. The pension

coverage for middle-age and older Canadian-born remain relatively stable at around 80%.

Similar to the real estate holdings, the pension holding of settled immigrants follow those

of Canadian-born closely. Only 23% of the young recent immigrants arriving between 1994

and 1999 have private pension asset in 1999, however, this group saw a large increase in

pension coverage rate from 1999 to 2005 (to 61 %). The middle-aged recent immigrants,

however, remain at low coverage rate (below 56%) between the two survey years. We do

not have sufficient statistics for older recent immigrants to estimate reliably their pension

holding proportions. We expect that there will be some that retire in Canada and bring their

pension with them and others who may not be able to, making this analysis complicated.


----A -..--O~ ..- .....~_.-<lIII:o-----t!

-I

/"-0<0 ,"-":J:' /~

~ I~": /

/I

/"!

,30 40

Age Co~%rt 60 70

Proportion with positive holdings

89

8g ,I

g )g

I

/~I

0 I~~ // I

II

o~I

...-----.. J30 40

Age co~rt60 70

- Canadian..oom --...- Settled imrmgrant -+-- Recent tmmigran

Median value (in 1999 dollars)

Figure 4.5: Pension holdings, by age and immigration cohort, 1999-2005.

The median value of pension asset for Canadian-born and settled immigrants follow

an accelerated growth path until retirement age. For the young cohort, whether you are

Canadian-born or an immigrant the median pension is less than $11,000 in 1999, and there

is hardly any growth between 1999 and 2005. For the middle age group, the median pension

value for Canadian-born and settled immigrants grew by around $30,000. Finally, at old

age, both Canadian-born and settled immigrants enjoyed more than a $40,000 increase in

pension assets between the two survey years. The steep increase in pension wealth near

retirement age is also documented by Milligan (2005). The most striking result, however,

is the low pension holdings and lack of growth of middle-age recent immigrant group. The

median pension value for this group is close to zero. This is because almost half of the

households in this group do not have any private pension asset.

In summary, the cohort analysis seems to suggest that whereas the new immigrants

face disadvantages in real estate they do appear to catch up. However, the disadvantage

that the new immigrants face in retirement fund accumulation appear to hold later in life

since neither young nor middle aged pensions gain significant value for them from 1999 to

2005. The complication with pension funds is that a middle aged recent immigrant may

have pension funds accumulated coming from their home country that are not realized

until after retirement and may not be reported in these statistics. Settled immigrants

fare relatively well comparing to the Canadian-born peers. While these statistics provide


insight into the types of financial choices immigrants are making related to their Canadian

born counterpart, they are descriptive in nature. Other factors, such as education, income,

family structures, and preferences, may account for some of the differeces. Stronger tests

are required to determine the financial position of immigrant families given similar socio

economic characteristics. We turn to this topic in the next section.

4.4 Regression Analysis

Descriptive analysis of our data thus far points to a possibly important difference in wealth

level and portfolio holdings between Foreign-born and Canadian-born households. In this

section, regression methods are employed to investigate whether the differences still exist

after controlling for some observable household and individual characteristics. We explore

extensive margin (assets ownership) and intensive margin (share and value of assets) to

establish if there's a meaningful difference and the magnitude of it. We restrict our regression

to estimating whether immigrants have different holdings of real estate and retirement funds

relative to their Canadian-born counterpart. Further, we pool the 1999 and 2005 SFS

togetherll and report the regression results based on the pooled data. We eliminate those

households whose Main Income Earner or Spouse are more than 65 years old. All values are

in 1999 dollars.

For our analysis, we estimate wealth (W) by:

W = Cl' + 1',8 + X 'r + E (4.1 )

where the dependent variable, ~V, is either the ownership, share or the value of the as

set. I are the characteristics associated with immigrants, X are the socio-demographic and

geographic characteristics of households and E are residuals. SFS provides a rich set of

immigrant characteristics for I. In addition to a flag for immigrants, SFS reports the cit

izenship, year landed, and the first language the respondent learned in childhood and still

understands. From this information, we construct indicators in connection to the immi

grant's characteristics that might affect the wealth allocation outcome. We use the dummy

variable (IMMI) to indicate whether the respondent is an immigrant, and define year since

migration (YSM) to capture the effect of immigrants' length of time living in Canada.

llThe same sets of analyses were also conducted on the two years separately and the results are verysimilar to the pooled data and is available upon request.


We want to control for access to financial services, however, there is no direct measure in

SFS. Therefore, we use a proxy for financial service access by using the prevalence of ethnic

enclaves in the population. We believe this is a good choice since people with common

ethnic origin support each other, creating a critical mass for service providers to tailor to

their specific needs. Similarly, ethnic community networks may provide more channels to

transfer information and knowledge to use existing services. Unfortunately, SFS does not

directly report ethnicity. Instead, it reports the first language that a respondent can still

speak similar to the mother tongue definition in the Census. Thus, we use the 20% Sample

2001 Census data to compute the proportion of the population in the Census Metropolitan

Area (CMA) who have the same mother tongue as our measure of ethnic enclave prevalence.

In addition to the immigration related variables, we also condition our regression on some

household and individual demographic characteristics, including age of the Main Income

Earner (MIE) and the spouse (quadratic), the highest levels of education for the MIE and

the spouse, household income (in logarithm), survey year, marital status, gender of the

MIE, number of earners and number of children in the households. To make the results

easier to interpret, the age variables are recoded so that the mean ages are all around z;ero

(i.e. age = actual age - 42). We also include interactions between immigration status and

household income, survey year and the age and education of the MIE to allow for different

marginal effects for immigrants12 .

\Ve report our main results of the Probit model for the ownership regression, and Tobit

model for the share and value regression in table 4.4 and 4.5 and discuss them in the next

subsections. We use Probit and Tobit models to accommodate the structure of the data.

We check the sensitivity of our model by using a few different estimation methods to check

whether the results are sensitive to the estimators we chose. For this, we also estimated the

ownership results using a linear probability model (OLS) and logistic model. For share and

value estimation we ran additional OLS and quantile regressions. The results using these

estimators are very similar to our main results and are reported in Table 4.9 and Table

4.1013. Thus, we will discuss the results from the Probit and Tobit models for housing and

pension regressions for the remaining analysis.

12We included interaction terms between immigration status and all demographic covariates initially, and<;ubsequently dropped insignificant ones.

13We also tried the inclusion (and exclusion) of different set of explanatory variables, the results are notsensitive to different specifications.


4.4.1 Do immigrants have different housing assets?

92

We investigate whether the immigrants' advantage in real estate asset holdings seen in the

descriptive analysis still holds after we control for the differences in socia-economic charac

teristics. To do this, we regress the ownership, share and value (in logarithm form 14 ) of the

real estate asset on the conditioning variables described earlier. The estimated coefficients

on selected variables are displayed in table 4.4. Column 1 shows the marginal effect on home

ownership from the Probit model. Column 2 and 3 are the estimated coefficient from the

Tobit model on share and value of real estate asset, respectively.

The coefficients on age and its square (without interaction terms) are associated with

Canadian-born households. In the ownership regression, the reported coefficients are the

marginal effect for each continuous variable and discrete change (from 0 to 1) for each

dummy variable, while keeping other covariates at mean value. Based on the estimation,

one additional year of age increases the probability of becoming a homeowner by roughly

1.3% at mean age (42) for Canadian-borns15 . We also evaluated this effect for younger (30

years of age) and older (60 years of age) households; the predicted change in homeownership

is 1.5% and 0.9%, respectively. The share and value of real estate assets of a Canadian-born

households both increase with age, but at a decreasing rate, reaching the peak at around

50-60 years of age. This is consistent with the life-cycle real estate accumulation pattern

documented by Milligan (2005). However, as Borjas (1994) pointed out, the impact of age

from cross-sectional data reflect both age effect and cohort effect. Care needs to be taken

when interpreting these results since to truly identify the life-cycle wealth accumulation

pattern we require multi-period panel data following the same individual for a long period

of time. This kind of data is rarely available for assets and wealth. In addition to age, higher

income and education, ill general, are also associated with higher probability of holding real

estate and higher value of real estate for Canadian-born households.

The entry effect of being an immigrant on the real estate ownership, share and value can

not be precisely estimated using our data, as indicated by the large standard errors of the

coefficients on the immigrant dummy (IMMI). The interaction term between age and immi

gration status are negative and significant in the ownership and value regression, suggesting

14 All 0 values are coded back to 0 after the transformation.

15To be more precise, we estimated the marginal effect of age for Canadian born by setting all the variablesassociated with immigrants to zero. The results are almost identical to the marginal effect at mean.


that immigrants' age profile are flatter than that of the Canadian-born. The coefficient

estimates on the the year since migration (YSM) variables are traditionally interpreted as

assimilation effect, which reflects the return to the length of time the immigrant has spent

in Canada. The estimated coefficients on YSM from the ownership, share and value re

gressions are all positive and significant at 10%, 5% and 1% level respectively, providing

some support for positive assimilation effect. However, just as the age variable, YSM also

includes cohort effects.

The effects of the presence of ethnic enclave on real estate ownership, share and value are

all negative and significant at the 1% level. This suggests that immigrant households living

in a neighbourhood with a high concentration of a similar ethnic population are less likely

to own real estate. The share and value of their real estate are also lower than their peers.

There are multiple factors that may be influencing immigrant's financial decisions within the

enclave. As noted earlier, if knowledge and language skills are affecting immigrants' access

to financial market, then a higher concentration of immigrants with similar background will

help to reduce this effect. For example, a financial institution may be more likely to provide

services in the immigrants' language making it easier to get advice about investing options in

the Canadian market. Similarly, an ethnic community network may provide more channels

to transfer information and knowledge to each other. This may prompt the immigrants

to choose more balanced portfolios and behave more like Canadian-born households which

mayor may not direct portfolio decisions toward home ownership. As well, higher ethnic

concentration may be associated with a poorer neighbourhood, and poorer immigrants may

self-select into living in such neighbourhood which in turn reduces the ownership and value

of their real-estate. Further research into the causes for this effect is needed to determine

what role different factors play.

In summary, similar to the cohort analysis, our regression results also indicate that

immigrants catch up in accumulating housing asset. Our results are in general opposite to

the findings using U.S. data. For example, Borjas (2002) reported lower home ownership

rates for immigrants using US censuses and Current Population Surveys (CPS). He also

found that the gap widened between 1980 and 2000. One possible explanation for this

difference is that most of Canada's point system for immigrants selection are based on the

labour market and economic factors.


4.4.2 Do immigrants have adequate pensions?

94

In this section, we attempt to answer the question of whether immigrants have comparable

levels of retirement funds as Canadian-born. Immigrants face challenges in terms of pension

accumulation making this an important aspect to study. First, immigrants, especially those

who arrive later in life, have less time to accumulate a Canadian pension16 and utilize

tax-deferred financial instruments (i.e. RRSP). Second, some public pensions are residency

tested, which further decreases the available funds for late arrivals.1 7 Thirdly, it is known

that immigrants' labour market performance is worse than Canadian-born's, thus, they are

more likely to have a lower paying job with minimal pension benefits. Finally, as mentioned

in the previous section, immigrants might face barriers in the financial market so that they

might not fully utilize tax-sheltered private pension savings, such as RRSP. Note too, that

having less time to accumulate and less knowledge about RrrSp impacts immigrants' ability

to purchase real-estate using the RRSP Home Buyer plan which may also be a factor in the

recent immigrant's portfolio as described in the previous subsection.

To answer this question, we regress the ownership, share, and value of pension on the

same sets of explanatory variables as before. The pension funds include both self-directed

savings such as RRSP and employer pension plans. Similar to the real estate accumulation,

the pension accumulation of Canadian-born households also grow with age (all significant

at 1 percent). Education and income both show strong and positive impacts on pension

holdings in terms of ownership, share and value.

Holding other factors constant, being an immigrant seem to have negative impact on

pension holdings, although these effects are not significant at conventional levels. Similar

to the real estate accumulation, we also find evidence of a cross-sectional assimilation effect

on pension accumulation: ownership, share and value of pension all grow with year since

migration (all significant at 1 per cent). When a 42 year old, university educated immigrant

family first arrives in Canada, the probability of acquiring some kind of private pension

increases by roughly 2.3% per year (estimated by setting YSM=O, not shown in the table). In

10 years, the probability of getting private pension grew by roughly 8% (change in predicted

probability between YSM=O and YSM=lO). In terms of the ethnic enclave effect, our results

16For immigrants that can transfer pension from their home country with a comparable pension systemto Canada, this will not pose a problem. However, for some immigrants this may not be the case.

17For example, the Old Age Security (GAS) program requires at least 10 years of residency in Canada.


point to a positive correlation between the concentration of similar ethnic population and the

share (p<O.Ol) of the pension holdings. The effect of ethnic enclave on pension ownership

and value are not statistically significant.

The coefficient on the interaction between being an immigrant and age suggest that

immigrants' share and value of pension grows slower with age than that of Canadian-born.

In addition, the interaction terms between immigration status and education are all negative

and statistically significant, offsetting largely the advantage in pension holdings enjoyed by

Canadian-born households with higher education.

In summary, we find that the increase in pension coverage, share and value associated

with age and education are weaker for immigrants than for Canadian-born population. We

also find immigrants pension holdings improve with time spent in Canada. We compare

our estimates of pension coverage with Morissette (2002), who find the assimilation effect of

recent immigrants in EPP coverage between 1988 and 1998. However, as mentioned earlier,

the time spent in Canada in cross-sectional data captures both assimilation effect and cohort

effect.

4.4.3 Robustness

We investigate different samples to account for the possibility that our analysis is biased

due to attrition. More specifically, we only observe those immigrants who choose to stay

in Canada in our sample. If the immigrants facing financial difficulties are more likely to

leave Canada, then our results will paint an over-optmistic picture on immigrants' financial

situation. In an attempt to partially account for this potential self-selection bias, we run

the analysis again only on those immigrants who became a Canadian citizen. Acquiring

citizenship shows a greater commitment to the host country from the immigrant's side. It

also requires at least three years of residency in Canada. Therefore naturalized immigrants

are more likely to stay. In addition, we estimated our model using only a subsample to check

the sensitivity of our results to sample selection. First, we excluded those who arrived in

Canada before 15 years of age. These individuals almost certainly are educated in Canada

and their behaviour in theory should be more in line with the Canadian-born. Second,

we ran our regression on married households only to ensure the results are not driven by

the different family structure of Canadian-born and immigrants households. Finally, we

estimated our results separately for those with at least a bachelor degree and high-school

drop-outs. Our results are not sensitive to any of these different samples.


4.5 Conclusion

96

Using data from the Survey of Financial Security we compare the portfolio allocation of

Canadian-born and immigrant households. We emphasize the accumulation of real estate

and pension asset which have important long-term implications on households' financial po

sitions. While we found that immigrants fare relatively well in their overall financial position,

there exist some portfolio differences between immigrants and Canadian-born households.

Immigrants allocate more resources in housing assets and less on retirement pension relative

to their Canadian-born counterpart. Either differences in preferences or constraints could

lead to these observed disparities. While it is beyond the scope of this paper to identify

the causes of such difference, we speculate that this is due to a combination of both. First,

immigrants have larger families which require additional housing. Second, immigrants may

have a stronger preference toward housing due to cultural differences, as the need to acquire

status conveying asset to compensate for setbacks in their social status when they move to

a new country.

In contrast to the above, if immigrants have the same preferences as Canadian-born

households, then there exist imbalances in their portfolio allocations since Canadians are

assumed to allocate their resources across different types of assets to equalize their risk

adjusted returns. Consequently their differential risk exposure leads to differential wealth

accumulation; for example, at the time of housing market turmoil, immigrants will be

disproportionately exposed to housing price shocks. This imbalance may be mitigated by

ethnic enclave. Our findings reveal ethnic enclaves reduce immigrants' real estate holdings

suggesting that more access to financial market information and service associated with

enclaves does lead to less housing asset holdings for immigrants in line with Canadian-born

behaviour.

Comparing the wealth position and portfolio mIX over time between immigrants and

Canadian-born households provides a measure of how well immigrants are assimilating into

a host country's financial markets and their ability to overcome financial hardships that they

may endure. Our results support the conjecture that immigrants are assimilating into the

host's financial market. The one caveat is that recent immigrants typically face imbalances

in financial resources, such as a poorly diversified portfolio or an over accumulation of

certain assets, thus, there exists a role for government to intervene to help recent immigrants

assimilate financially.

Cl ~ '"cJ

Tab

le4

.2:

Bala

nce

sheet

sum

mary

for

Can

ad

ian

-bo

rnan

dF

ore

ign

-bo

rn,

19

99

t;5 ~

Pro

po

rtio

nS

har

eU

ncon

diti

onal

Con

diti

onal

~

hold

ing

the

asse

to

fto

tal

asse

tM

ean

Mea

n(S

d)M

edia

n>-c

JC

BF

BC

BF

BC

BF

BC

BF

BC

BF

Bt-< ~ ~

Dep

osit

s0.

874

0.90

00.

045

0.04

712

,663

15,1

5714

,492

(51,

722)

16,8

45(4

6,33

9)2,

500

3,30

0""3 ~

Inv

estm

ent

0.31

50.

279

0.07

00.

056

19,6

2017

,764

62,2

33(3

21,7

21)

63,7

66(2

06,5

96)

6,90

011

,500

GO

ther

0.12

40.

134

0.01

00.

011

2,72

43,

437

21,9

81(7

5,05

0)25

,576

(120

,583

)4,

000

5,00

0~

Fin

anci

alT

ota

l0.

897

0.92

50.

125

0.11

435

,007

36,3

5839

,029

(214

,970

)39

,317

(147

,885

)4,

400

6,00

00 0

55,0

49(1

08,2

62)

""3S

elf-

dire

cted

0.60

30.

588

0.11

90.

109

33,1

9134

,784

59,1

14(1

08,0

90)

20,0

0022

,000

(J)

EP

P0.

489

0.42

50.

183

0.14

051

,191

44,6

5710

4,74

7(1

45,4

60)

105,

092

(146

,069

)49

,258

49,9

53O

ther

pens

ion

0.03

80.

037

0.00

30.

004

942

1,15

024

,756

(53,

368)

31,1

56(5

5,33

9)7,

500

12,0

00P

ensi

on

To

tal

0.72

00.

687

0.30

60.

252

85,3

2480

,591

118,

554

(178

,199

)11

7,35

0(1

80,0

07)

50,9

1949

,200

Rea

les

tate

0.63

70.

648

0.35

70.

468

99,6

6714

9,58

115

6,44

2(1

92,5

47)

230,

980

(267

,226

)12

3,00

018

0,00

0O

ther

dura

bles

1.00

01.

000

0.10

50.

087

29,3

2627

,773

29,3

26(9

0,11

9)27

,773

(51,

517)

19,0

0015

,600

No

n-f

inca

nci

al1.

000

1.00

00.

462

0.55

512

8,99

317

7,35

312

8,99

3(2

14,2

49)

177,

353

(263

,075

)99

,000

140,

000

To

tal

Bu

sin

ess

Eq

uit

y0.

183

0.19

20.

107

0.07

929

,824

25,2

6516

3,17

5(7

30,8

32)

133,

181

(880

,624

)10

,000

4,00

0

Ass

etT

ota

l1.

000

1.00

01.

000

1.00

027

9,14

731

9,56

727

9,14

7(6

04,2

48)

319,

567

(632

,701

)15

9,77

320

0,20

0

Mor

tgag

ed

ebt

0.35

20.

351

0.75

90.

821

26,8

1537

,109

76,0

87(6

0,33

2)10

5,73

,)(9

6,64

1)65

,000

86,0

00S

tud

ent

loan

s0.

118

0.12

10.

034

0.02

81,

213

1,27

910

,279

(10,

612)

10,6

01(9

,895

)7,

200

7,50

0O

ther

deb

t0.

449

0.32

00.

174

0.12

46,

148

5,60

113

,702

(22,

459)

17,4

89(3

1,84

5)8,

000

10,0

00C

red

itca

rd0.

396

0.34

10.

033

0.02

71,

159

1,20

72,

926

(3,8

70)

3,53

9(4

,707

)1,

700

2,00

0D

eb

tT

ota

l0.

693

0.63

41.

000

1.00

035

,334

45,1

9650

,966

(62,

591)

71,3

04(9

4,60

6)27

,300

40,0

00

Net

Wo

rth

--

243,

813

274,

372

259,

816

(610

,655

)29

2,36

8(6

34,6

48)

120,

000

153,

600

CD ---1

Q ;.:.: "Tj

Tab

le4

.3:

Bala

nce

sheet

sum

mary

for

Can

ad

ian

-bo

rnan

dF

ore

ign

-bo

rn,

20

05

~ ~P

ropo

rtio

nS

hare

Unc

ondi

tion

alC

ondi

tion

al~

hold

ing

the

asse

tof

tota

las

set

Mea

nM

ean

(Sd)

Med

ian

~C

BF

BC

BF

BC

BF

BC

BF

BC

BF

B~ ~

Dep

osit

s0.

853

0.90

60.

042

0.04

716

,994

22,0

5919

,930

(69,

088)

24,3

55(7

9,76

5)3,

500

5,00

0~

Inve

stm

ent

0.26

00.

242

0.05

10.

052

20,9

8724

,166

80,7

12(4

05,4

21)

99,8

56(2

81,4

39)

11.0

0020

,000

~ 0O

ther

0.16

40.

191

0.01

10.

007

4,58

33,

365

28,0

03(1

22,3

39)

17,6

17(2

10,8

21)

6,00

07,

000

~F

inan

cial

To

tal

0.88

10.

929

0.10

40.

107

42,5

6449

,590

48,3

18(2

57,1

99)

53,4

04(2

09,1

13)

6,00

08,

000

a aS

elf-

dire

cted

0.58

80.

548

0.11

00.

080

44,9

1137

,128

76,3

52(1

40,2

32)

67,7

22(1

28,5

15)

30,0

0025

,000

~E

PP

0.50

.50.

430

0.20

30.

130

82,9

4160

,330

164,

232

(220

,216

)14

0,42

9(1

77,5

05)

70,9

2260

,586

Oth

erpe

nsio

n0.

033

0.03

30.

005

0.00

21,

858

973

.56,

372

(236

,568

)29

,162

(39,

351)

10,0

0010

,000

Pen

sio

nT

ota

l0.

720

0.66

80.

317

0.21

112

9,71

098

,430

180,

143

(257

,292

)14

7,44

3(2

24,9

97)

74,0

9752

,077

Rea

les

tate

0.64

20.

677

0.38

70.

524

158,

394

243,

838

246,

735

(441

.486

)36

0,39

5(3

99,6

22)

175,

000

280,

000

Oth

erdu

rabl

es1.

000

1.00

00.

087

0.06

235

,757

28,6

3035

,757

(58,

485)

28,6

30(3

8,86

2)22

,000

18,0

00N

on

-fin

can

cial

1.00

01.

000

0.47

40.

585

194,

151

272,

468

194,

151

(390

,601

)27

2,46

8(3

88,5

57)

131,

500

210,

180

To

tal

Bu

sin

ess

Eq

uit

y0.

157

0.19

50.

105

0.09

742

,787

45,0

2727

2,06

4(1

,043

,190

)23

1,03

0(7

,838

,844

)20

,000

5,00

0

Ass

etT

ota

l1.

000

1.00

01.

000

1.00

040

9,21

246

5,51

540

9,21

2(8

29,5

06)

465,

515

(3,6

14,7

22)

218,

872

282,

183

Mor

tgag

ede

bt0.

363

0.39

00.

728

0.81

639

,039

57,2

4510

7,43

7(1

13,7

35)

146,

787

(123

,685

)85

,000

132,

000

Stu

dent

loan

s0.

114

0.12

40.

026

0.02

51,

417

1,76

412

,397

(13,

426)

14,1

88(1

4,42

3)9,

000

9,20

0O

ther

debt

0.50

00.

383

0.21

10.

128

11,3

069,

003

22,6

25(4

0,45

3)23

,492

(35,

861)

12,0

0012

,000

Cre

dit

card

0.41

00.

322

0.03

.50.

031

1,89

22,

177

4,61

9(7

,044

)6,

752

(9,6

99)

2,20

03,

200

Deb

tT

ota

l0.

704

0.65

21.

000

1.00

053

,654

70,1

8976

,248

(109

,649

)10

7,72

0(1

26,6

47)

41,5

8866

,100

Net

Wo

rth

--

--

355,

558

395,

326

381,

554

(824

,848

)42

4,27

0(3

,733

,374

)16

9,31

520

3,97

8

CD 00


Table 4.4: Regression of Real Estate Assets Holdings

Ownership Share Value(dProbit) (Tobit) (Tobit)

coef/se coef/se coef/seAge 0.013*** 0.009*** 0.22***

(0.001) (0.001 ) (0.01)Age squared/100 -0.027*** -0.054*** -0.65***

(0.006) (0.005) (0.08)Highschool 0.049* 0.017 0.88**

(0.026) (0.025) (0.39)Postsecondary 0.091*** 0.046** 1.40***

(0.021 ) (0.021) (0.34)Bachelor 0.097*** -0.002 1.23***

(0.026) (0.026) (0.39)Log(income) 0.175*** 0.123*** 3.09***

(0.027) (0.024) (0.44)Year Dummy -0.014 0.006 -0.29

(0.040) (0.026) (0.51 )IMMI 0.107 -0.127 -0.65

(0.264) (0.311 ) (5.11)YSM 0.008* 0.010** 0.18***

(0.(l04) (0.005) (0.06)YSM squared/IOO -0 .. 001 -0.015 -0.16

(0.010) (0.010) (0.12)Ethnic Concentration -0.169*** -0.168*** -2.52***

(0.044) (0.040) (0.59)IMMI*Age -O.OOti*** 0.000 -0.08***

(0.001) (0.001 ) (0.02)IMMI*Age squred 0.049*** 0.043*** 0.73***

(0.015) (0.012) (0.19)IMMI*Highschool -0.004 0.035 0.07

(0.039) (0.035) (0.53)IMMI*Postsecondary -0.096** -0.057 -1.31 **

(0.051) (0.048) (0.64)IMMI*Bachelor -0.020 0.028 0.05

(0.051) (0.044) (0.64)IMMI*Income -0.028 0.005 -0.20

(0.026) (0.028) (0.45)Constant -5.465*** -1.198*** -29.86***

(0 .. 688) (0.283) (5.03)Adjusted R2 0.338 0.195 0.100

*** p<O.Ol, ** p<O,05, * p<O.lSample includes households with main income earner and spouse younger than 65 years old. Each

regression is based on 16,068 observations.Regressions also include controls for number of earners, number of children, main income earner's

marital status, sex, and spouse's immigration status, age, and education. IMMI refers to immigrationstatus. YSM refers to years since migration. Standard errors of the coefficient estimates are reported inparentheses.

The coefficients reported for the dProbit model are the marginal effects evaluated at mean, or thediscrete change from 0 to 1 for dummy variables.


Table 4.5: Regression of Pension Assets Holdings

Ownership Share Value(dProbit) (Tobit) (Tobit)

coef/se coef/se coef/seAge 0.006*** 0.009*** 0.14***

(0.001) (0.001 ) (0.01)Age squared/100 -0.002 0.002 -0.10

(0.004) (0.005) (0.07)Highschool 0.125*** 0.120*** 2.34***

(0.015) (0.021 ) (0.28)Postsecondary 0.144*** 0.120*** 2.61***

(0.018) (0.020) (0.33)Bachelor 0.223*** 0.176*** 3.54***

(0.015) (0.017) (0.30)Log(income) 0.224*** 0.142*** 3.53***

(0.016) (0.011 ) (0.16)Year Dummy -0.072*** -0.043*** -0.94***

(0.011) (0.009) (0.10)IMMI -0.032 -0.096 -4.23*

(0.217) (0.111) (2.42)YSM 0.016*** 0.009*** 0.27***

(0.004) (0.003) (0.04)YSM squared/lOO -0.020** -0.010** -0.32***

(0.008) (0.005) (0.08)Ethnic Concentration 0,067 0.136*** 0.81

(0.047) (0.024) (0.54)IMMI*Age -0.003 -0.005*** -0.06**

(0.002) (0.001 ) (0.03)IMMI*Age squred -0.008 -0.012 -0.05

(0.013) (0.009) (0.17)IMMI*Highschool -0.122*** -0.074*** -1.58***

(0.041) (0.021 ) (0.46)IMMI*Postsecondary -0.067* -0.045* -1.21**

(0.042) (0.024) (0.55)IMMI*Bachelor -0.205*** -0.101*** -1.83***

(0.055) (0.033) (0.62)IMMI*lncome -0.017 -0.006 0.05

(0.021) (0.010) (0.23)Constant -7.857*** -1.363*** -32.26***

(0,.573) (0.112) (1.52)Adjusted R2 0.348 0·419 0.122

*** p<O.Ol, ** p<0.05, * p<O.lSample includes households with main income earner and spouse younger than 65 years old. Each

regression is based on 16,068 observations.Regressions also include controls for number of earners, number of children, main income earner's

marital status, sex, and spouse's immigration status, age, and education. IMMI refers to immigrationstatus. YSM refers to years since migration. Standard errors of the coefficient estimates are reported inparentheses.

The coefficients reported for the dProbit model are the marginal effects evaluated at mean, or thediscrete change from 0 to 1 for dummy variables.


Table 4.6: Mean and Standard Error of Portfolio Shares, by age and immigrationcohort

Canadian-born Settled Immigrants Recent ImmigrantsMean SE Mean SE Mean SE

1999Young

Financial 0.123 (0.004) 0.100 (0.013) 0.228 (0.024)Pension 0.129 (0.004) 0.176 (0.018) 0.052 (0.014)Real Estate 0.259 (0.006) 0.328 (0.029) 0.157 (0.030)Business 0.021 (0.002) 0.019 (0.006) 0.013 (0.006)Durables 0.468 (0.006) 0.377 (0.028) 0.551 (0.032)

Middle-ageFinancial 0.072 (0.002) 0.088 (0.007) 0.179 (0.021 )Pension 0.237 (0.004) 0.192 (0.010) 0.111 (0.016)Real Estate 0.382 (0.004) 0,469 (0.015) 0.282 (0.032)Business 0.042 (0.002) 0.037 (0.006) 0.017 (0.009)Durables 0.268 (0.004) 0.213 (0.012) 0,411 (0.030)

OlderFinancial 0.091 (0.003) 0.090 (0.006) 0.154 (0.039)Pension 0.347 (0.005) 0.292 (0.010) 0.039 (0.025)Real Estate 0.316 (0.005) 0,439 (0.011) 0.402 (0.071 )Business 0.038 (0.002) 0.025 (0.004) 0.086 (0.040)Durables 0.208 (0.005) 0.153 (0.009) 0.319 (0.065)2005

YoungFinancial 0.083 (0.005) 0.118 (0.033) 0.188 (0.044)Pension 0.166 (0.007) 0.147 (0.030) 0.102 (0.033)Real Estate 0.362 (0.011) 0,492 (0.056) 0.420 (0.064)Business 0.026 (0.003) 0.010 (0.006) 0.045 (0.028)Durables 0.362 (0.010) 0.234 (0.041) 0.245 (0.046)

Middle-ageFinancial 0.065 (0.003) 0.054 (0.007) 0.150 (0.035)Pension 0.287 (0.007) 0.286 (0.023) 0.126 (0.036)Real Estate 0.378 (0.008) 0.483 (0.028) 0.536 (0.063)Business 0.040 (0.004) 0.008 (0.003) 0.035 (0.016)Durables 0.230 (0.008) 0.169 (0.022) 0.153 (0.036)

OlderFinancial 0.097 (0.005) 0.090 (0.011 ) 0.189 (0.095 )Pension 0.385 (0.010) 0.320 (0.019) 0.190 (0.071 )Real Estate 0.303 (0.009) 0,430 (0.020) 0.503 (0.125)Business 0.030 (0.004) 0.021 (0.006) 0.011 (0.011 )Durables 0.184 (0.009) 0.139 (0.015) 0.107 (0.056)


Table 4.7: Mean and Standard Error of Ownership rate, by age and immigrationcohort

Real EstateYoung Middle-age Older

Mean SE Mean SE Mean SE1999Canadian-born 0.406 (0.012) 0.721 (0.009) 0.760 (0.011)Settled Immigrant 0.497 (0.052) 0.731 (0.025) 0.833 (0.018)Recent Immigrant 0.240 (0.045) 0.408 (0.050) 0.576 (0.107)2005Canadian-born 0.559 (0.022) 0.773 (0.016) 0.769 (0.020)Settled Immigrant 0.672 (0.086) 0.748 (0.050) 0.828 (0.038)Recent Immigrant 0.557 (0.083) 0.672 (0.082) 0.767 (0.148)

Pension1999Canadian-born 0.618 (0.013) 0.793 (0.008) 0.798 (0.010)Settled Immigrant 0.732 (0.048) 0.814 (0.022) 0.836 (0.018)Recent Immigrant 0.231 (0.043) 0.521 (0.051) 0.194 (0.093)2005Canadian-born 0.723 (0.022) 0.790 (0.017) 0.799 (0.019)Settled Immigrant 0.738 (0.081) 0.852 (0.037) 0.842 (0.036)Recent Immigrant 0.607 (0.082) 0.557 (0.088) 0.597 (0.227)

Table 4.8: Median and Standard Error of Real Estate and Pension asset, by ageand immigration cohort

Real EstateYoung Middle-age Older

Median SE Median SE Median SE1999Canadian-born ° ° 92,308 (2,083) 100,000 (2,422)Settled Immigrant ° ° 140,000 (7,814) 180,000 (7,582)Recent Immigrant ° ° ° ° 80,000 (85,219)2005Canadian-born 63,607 (6,043) 130,234 (3,477) 112,869 (4,640)Settled Immigrant 145,862 (70,655) 191,009 (36,818) 205,509 (17,691)Recent Immigrant 20,837 (58,649) 160,622 (58,002) 314,731 (191,703)

Pension1999Canadian-born 2,000 (194) 30,231 (1,589) 85,352 (5,474)Settled Immigrant 10,354 (2,205) 30,000 (3,715) 84,824 (10,600)Recent Immigrant ° ° 200 (354) ° 02005Canadian-born 8,101 (736) 55,763 (7,362) 127,507 (11,912)Settled Immigrant 9,550 (14,210) 63,814 (16,058) 133,179 (23,592)Recent Immigrant 1,216 (1,434) 2,084 (3,014 ) 151,119 (70,617)


Table 4.9: Supplementary Regressions of Real Estate Assets Holdings

Ownership Share ValueOLS Logistic OLS Median OLS Median

coef/se coef/se coef/se coef/se coef/se coef/seAge 0.010*** 0.062*** 0.003*** 0.002*** 0.12*** 0.05***

(0.001) (0.006) (0.001) (0.000) (0.01) (0.00)Age squared/100 -0.025*** -0.117*** -0.031*** -0.007*** -0.27*** -0.04**

(0.005) (0.027) (0.003) (0.002) (0.05) (0.02)Highschool 0.042** 0.212 0.005 0.017*** 0.63*** 0.31***

(0.020) (0.130) (0.014) (0.006) (0.22) (0.06)Postsecondary 0.071*** 0.418*** 0.019* 0.013** 0.94*** 0.31***

(0.017) (0.105) (0.012) (0.006) (0.19) (0.06)Bachelor 0.064*** 0.415*** -0.013 -0.007 1.01*** 0.36***

(0.021 ) (0.125) (0.015) (0.007) (0.22) (0.07)Log(income) 0.114*** 0.925*** 0.043*** 0.009*** 1.46*** 0.66***

(0.012) (0.117) (0.008) (0.003) (0.15) (0.03)Year Dummy -0.015 -0.084 O.OUi 0.003 -0.06 0.04

(0.028) (0.193) (0.013) (0.003) (0.28) (0.03)IMMI -0.188 0.723 -0.199 -0.399*** -2.37 -2.27***

(0.178) (1.500) (0.146) (0.049) (2.26) (0.48)YSM 0.009*** 0.030 0.007** 0.011*** 0.13*** 0.07***

(0.003) (0.020) (0.003) (0.001 ) (0.04) (0.01)YSM squared/lOO -0.008 0.013 -0.012* -0.019*** -0.11 -0.07***

(0.006) (0.046) (0.007) (0.002) (0.08) (0.02)Ethnic Concentration -0.137*** -0.872*** -0.115*** -0.152*** -1.75*** -0.68***

(0.033) (0.210) (0.029) (0.013) (0.41 ) (0.13)IMMI*Age -0.004*** -0.028*** 0.002** 0.002*** -0.05*** -0.02***

(0.001) (0.008) (0.001) (0.000) (0.01 ) (0.00)IMMI*Age squred 0.035*** 0.216*** 0.024*** 0.005 0.40*** 0.08**

(0.010) (0.072) (0.008) (0.003) (0.13) (0.03)IMMI*Highschool 0.002 -0.023 0.024 0.029** -0.13 -0.03

(0.028) (0.189) (0.022) (0.014) (0.33) (0.14)IMMI*Postsecondary -0.063* -0.428* -0.030 -0.029** -0.91 ** -0.42***

(0.035) (0.240) (0.033) (0.013) (0.42) (0.13)IMMI*Bachelor 0.004 -0.054 0.Q18 0.016 -0.19 -0.19

(0.033) (0.243) (0.029) (0.014) (0.40) (0.13)IMMI*Incorne 0.002 -0.142 0.016 0.036*** 0.05 0.15***

(0.015) (0.132) (0.014) (0.005) (0.20) (0.05)Constant -0.773*** -10.280*** -0.198** -0.083*** -10.58*** -5.46***

(0.136) (1.272) (0.096) (0.029) (1.69) (0.29)R2 0.371 0.1.91 0.3.96

*** p<O.Ol, ** p<0.05, * p<O.l

Sample includes households with main income earner and spouse younger than 65 years old. Eachregression is based on 16,068 observations.

Regressions also include controls for number of earners, number of children, main income earner'smarital status, sex, and spouse's immigration status, age, and education. IMMI refers to immigrationstatus. YSM refers to years since migration. Standard errors of the coefficient estimates are reported inparentheses.


Table 4.10: Supplementary Regressions of Pension Assets Holdings

Ownership Share ValueOLS Logistic OLS Median OLS Median

coef/se coef/se coef/se coef/se cocf/se coef/seAge 0.006*** 0.037*** 0.007*** 0.007*** 0.11 *** 0.13***

(0.001) (0.005) (0.001) (0.000) (0.01 ) (0.00)Age squared/lOO -0.009** 0.009 0.008** 0.026*** -0.03 -0.10***

(0.005) (0.028) (0.003) (0.002) (0.05) (0.03)Highschool 0.141 *** 0.786*** 0.074*** 0.044*** 1.70*** 2.27***

(0.018) (0.112) (0.014) (0.006) (0.17) (0.12)Postsecondary 0.165*** 0.876*** 0.068*** 0.048*** 1.90*** 2.47***

(0.021 ) (0.124) (0.012) (0.006) (0.22) (0.12)Bachelor 0.221*** 1.610*** 0.119*** 0.108*** 2.84*** 2.86***

(0.020) (0.156) (0.012) (0.007) (0.20) (0.13)Log(income) 0.177*** 1.525*** 0.069*** 0.068*** 2.13*** 2.30***

(0.009) (0.086) (0.005) (0.003) (0.12) (0.06)Year Dummy -0.058*** -0.461 *** -0.022*** -0.030*** -0.59*** -0.49***

(0.008) (0.067) (0.008) (0.003) (0.09) (0.07)IMMI -0.220 -0.200 0.040 0.277*** -2.02 -6.38***

(0.161) (1.217) (0.056) (0.048) (1.60) (0.97)YSM 0.017*** 0.096*** 0.004* 0.002 0.20*** 0.21 ***

(0.003) (0.026) (0.002) (0.001 ) (0.03) (0.02)YSM squared/100 -0.021 *** -0.114** -0.002 0.005** -0.21*** -0.26***

(0.006) (0.051 ) (0.004) (0.002) (0.06) (0.04)Ethnic Concentration 0.054 0.373 0.108*** 0.059*** 0.56 0.48*

(0.036) (0.285) (0.020) (0.013) (0.41 ) (0.27)IMMI*Age -0.003 -0.020 ..0.005*** -0.006*** -0.06*** -0.05***

(0.002) (0.013) (0.001 ) (0.000) (0.02) (0.01 )IMMI*Age squred 0.004 -0.050 -0.011 -0.023*** 0.03 0.02

(0.013) (0.084) (0.007) (0.003) (0.13) (0.07)IMMI*Highschool -0.090*** -0.570*** -0.044*** -0.029** -1.14*** -1.04***

(0.030) (0.192) (0.014) (0.014) (0.27) (0.28)IMMI*Postsecondary -0.058 -0.306 -0.028** -0.031** -0.90*** -0.99***

(0.036) (0.194) (0.013) (0.013) (0.34) (0.27)IMMI*Bachelor -0.105*** -1.081*** -0.076*** -0.080*** -1.51*** -1.19***

(0.036) (0.216) (0.015) (0.013) (0.35) (0.27)IMMI*Income -0.001 -0.100 -0.012** -0.033*** -0.08 0.30***

(0.013) (0.117) (0.006) (0.005) (0.13) (0.09)Constant -1.284*** -15.404*** -0.519*** -0.604*** -16.66*** -17.31***

(0.088) (0.837) (0.056) (0.02D) (1.08) (0.59)R2 0.356 0.257 0·480

*** p<O.Ol, ** p<0.05, * p<O.lSample includes households with main income earner and spouse younger than 65 years old. Each

regression is based on 16,068 observations.

Regressions also include controls for number of earners, number of children. main income earner'smarital statlls, sex, and spouse's immigration status, age, and education. IMMI refers to immigrationstatus. YSM refers to years since migration. Standard errors of the coefficient estimates are reported inparentheses.

Bibliography

[1J Altonji, J. and Doraszelski, U. 2005. "The Role of Permanent Income and Demo

graphics in Bland/White Differences in Wealth," Journal of Human Resources, 40(1),

1-30.

[2J Baker, M. and Benjamin, D. 1994. "The Performance of Immigrants in the Cana

dian Labor Market," Journal of Labor Economics, 12(3), 369-405.

[3J Bloom, D., Grenier, G. and Gunderson, M. 1995. "The Changing Labor Market

Position of Canadian Immigrants," Canadian Journal of Economics, 28(4b), 987-1005.

[4J Borjas, G. 1994. "The Economics of Immigration," Journal of Economic Literature,

32(4),1667-1717.

[5J Borjas, G. 2002. "Homeownership in the Immigrant Population," Journal of Urban

Economics, 52(3), 448-76.

[6J Carroll, C., Rhee, B-K. and Rhee, C. 1994. "Are There Cultural Effects on Saving?

Some Cross-sectional Evidence," Quarterly Journal of Economics, 109(3), 685-99.

[7J Chiswick, B. 1978. "The Effect of Americanization on the Earnings of Foreign-born

Men," Journal of Political Economy, 86(5), 897-921.

[8] Cobb-Clark, D., and Hildebrand, V. 2006. "The Wealth and Asset Holdings of

U.S.-born and Foreign-born Households: Evidence from SIPP Data," Review of Income

and Wealth, 52(1), 17-42.

[9] Cobb-Clark, D., and Hildebrand, V. 2008. "The Asset Portfolios of Native-born

and Foreign-born Households," IZA Working Papers, No.3304, Institute for the Study

of Labor.

105

BIBLIOGRAPHY 106

[10] Fontes, A., and Fan, J. 2006. "The Effects of Ethnic Identity on Household Budget

Allocation to Status Conveying Goods," Journal of Family and Economic Issues" 27(4),

643-63.

[11] Milligan, Kevin 2005. "Lifecycle Asset Accumulation and Allocation in Canada,"

Canadian Journal of Economics, 38(3), 10,57-1106.

[12] Morisette, Rene 2002. "Pensions: Immigrants and visible minorities" Perspectives

on Labour and Income, Autumn, 36-41.

[13] Myers, D. and Lee, S.W. 1998. "Immigrant Trajectories into Homeownership: A

Temporal Analysis of Residential Assimilation," International migration Review, 32(3),

593-625.



[15] Shamsuddin, A. and DeVoretz, D. 1998. "Wealth Accumulation of Canadian and

Foreign-Born Households in Canada," Review of Income and Wealth, 44(4), 515-33.

[16] Toussaint-Comeau, M. and Rhine, S.2004. "Tenure Choice with Location Se

lection: The Case of Hispanic Neighborhoods in Chicago," Contemporary Economic

Policy, 22(1), 95-110.

[17] Van Hook, J. and Glick, J. 2007. "Immigration and Living Arrangements: Moving

beyond Economic Need versus Acculturation," Demography, 44(2), 225-49.

[1.8] Yao, R., Gutter, M. and Hanna, S. 2005. "The Financial Risk Tolerance of Blacks,

Hispanics and Whites," Financial Counseling and Planning, 16(1), 51-62.

[19] Zhang, X 2003. "The Wealth Position of Immigrant Families in Canada," Statistics

Canada Research Paper Series, No. 197, Analytical Studies Branch.

Appendix A

Definitions and Measurements

A.I Non-Durable Consumption Measure

FOOl food at home and at restaurants

HOOl household operation

less: childcare payment

less: domestic and other custodial services

JOOI clothing

KOO8 rented and leased auto

K019 operation of auto

less: auto insurance

less: auto registration fees

K03l public transportation

LlOl health care expenses

L201 personal care expenses

MlOl recreation

less: purchase of recreational equipment

M201 reading materials

M30l education

less: tuition fee

NlOl alcohol and tobacoo

107

APPENDIX A. DEFINITIONS AND MEASUREAIENTS

A.2 Description of major retirement funds

108

Registered retirement saving plans (RRSP): An RRSP is an private pension plan

that provide tax benefits for savings for retirement. The RRSP could be held in deposits,

mutual funds, stocks or bonds.

Registered retirement income funds (RRIFs): FUnds in RRSPs must be transferred

to RRIF after the owner turns 69, and a minimum amount must be withdrawn annually.

Employer pension plans (EPPs): An EPP is an employer-sponsored plan registered

with Canada Customs and Revenue Agency.

Locked-in Retirement Account (LIRA): An RRSP in which the money is locked-in

until the person reaches a specified age.

Documents

THREE ESSAYS ON INCOME AND WEALTH - Summitsummit.sfu.ca/system/files/iritems1/9265/etd4259.pdf · propose using a Receiver Operating Characteristic (ROC) curve to determine the optimum