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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
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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
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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.
CHAPTER 1. INTRODUCTION 3
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.
CHAPTER 1. INTRODUCTION 4
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.
CHAPTER 2. INCOME ILLUSION 10
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.
CHAPTER 2. INCOME ILLUSION 11
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.
CHAPTER 2. INCOME ILLUSION
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
CHAPTER 2. INCOME ILLUSION 13
(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.
CHAPTER 2. INCOME ILLUSION 14
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
CHAPTER 2. INCOME ILLUSION 15
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.
CHAPTER 2. INCOME ILLUSION 16
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.
CHAPTER 2. INCOME ILLUSION 17
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.
CHAPTER 2. INCOME ILLUSION 18
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
CHAPTER 2. INCOME ILLUSION 19
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,
CHAPTER 2. INCOME ILLUSION 20
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.
CHAPTER 2. INCOME ILLUSION
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
CHAPTER 2. INCOME ILLUSION 22
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
CHAPTER 2. INCOME ILLUSION
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
CHAPTER 2. INCOME ILLUSION 25
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)
CHAPTER 2. INCOME ILLUSION 26
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.
CHAPTER 2. INCOME ILLUSION 27
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.
CHAPTER 2. INCOME ILLUSION 28
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
CHAPTER 2. INCOME ILLUSION 29
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).
CHAPTER 2. INCOME ILLUSION 30
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.
CHAPTER 2. INCOME ILLUSION 31
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.
CHAPTER 2. INCOME ILLUSION
Table 2.11: Income Under-reporting by Region, 1999% of Restricted Unrestricted
Sample balance<O balance<-1000 balance<O balance<-1000Non Durable Consumption
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).
CHAPTER 2. INCOME ILLUSION 33
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.
CHAPTER 2. INCOME ILLUSION 34
(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.
CHAPTER 2. INCOME ILLUSION 35
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
CHAPTER 2. INCOME ILLUSION 36
$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
CHAPTER 2. INCOME ILLUSION 37
$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
CHAPTER 2. INCOME ILLUSION
unreporting is a growing phenomenon.
38
CHAPTER 2. INCOME ILLUSION 39
Table 2.12: Income Under-reporting by Occupation, 1999% of Restricted Unrestrieted
Sample balance<O balance<-1000 balance<O balance<-1000Non Durable Consumption
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
CHAPTER 2. INCOME ILLUSION 40
Table 2.13: Income Under-reporting by Education, 1999% of Restricted Unrestricted
Sample balance<O balance<-1000 balance<O balance<-1000Non Durable Consumption
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
CHAPTER 2. INCOME ILLUSION
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
CHAPTER 2. INCOME ILLUSION
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
CHAPTER 2. INCOME ILLUSION 47
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
CHAPTER 2. INCOME ILLUSION 48
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
[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] 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.
[6] Erard, B. 1997. "A Critical Review of the Empirical Research on Canadian Tax Com
pliance," Department of Finance Working Paper, 97-6, Canada.
[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.
[12] Mirus, R., Smith, R. and Karoleff, V. 1994. "Canada's Underground Economy
Revisted: Update and Critique," Canadian Public Policy, 20(3), 235-252.
[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.
[14] Pissarides, C. and Weber, G. 1989. "An Expenditure-based Estimate of Britain's
Black Economy," Journal of Public Economics, 39, 17-32.
[15J Richter, W. and Boadway, R. 2005. "Trading Off Tax Distortion and Tax Evasion,"
Journal of Public Economic Theory, 7(3), 361-381.
[16] 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.
[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
of Public Economics, 3(2), 201-02.
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
CHAPTER 3. THE MONEY TRAIL 53
(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.
CHAPTER 3. THE MONEY TRAIL 54
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.
CHAPTER 3. THE MONEY TRAIL 55
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).
CHAPTER 3. THE MONEY TRAIL 56
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.
CHAPTER 3. THE MONEY TRAIL 57
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.
CHAPTER 3. THE MONEY TRAIL 58
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.
CHAPTER 3. THE MONEY TRAIL 60
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.
CHAPTER 3. THE MONEY TRAIL 61
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.
CHAPTER 3. THE MONEY TRAIL 63
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
CHAPTER 3. THE MONEY TRAIL 64
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
CHAPTER 3. THE MONEY TRAIL
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.
CHAPTER 3. THE MONEY TRAIL
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).
CHAPTER 3. THE MONEY TRAIL 67
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.
CHAPTER 3. THE MONEY TRAIL 68
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
CHAPTER 3. THE MONEY TRAIL 69
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
[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] 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.
[6] Erard, B. 1997. "A Critical Review of the Empirical Research on Canadian Tax Com
pliance," Department of Finance Working Paper, 97-6, Canada.
[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.
[12] Mirus, R., Smith, R. and Karoleff, V. 1994. "Canada's Underground Economy
Revisted: Update and Critique," Canadian Public Policy, 20(3), 235-252.
[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.
[14] Pissarides, C. and Weber, G. 1989. "An Expenditure-based Estimate of Britain's
Black Economy," Journal of Public Economics, 39, 17-32.
[15] Richter, W. and Boadway, R. 2005. "Trading Off Tax Distortion and Tax Evasion,"
Journal of Public Economic Theory, 7(3), 361-381.
[16] 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.
[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
of Public Economics, 3(2), 201-02.
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.
CHAPTER 4. PLANTING ROOTS 76
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
CHAPTER 4. PLANTING ROOTS 77
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.
CHAPTER 4. PLANTING ROOTS
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
CHAPTER 4. PLANTING ROOTS 79
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.
CHAPTER 4. PLANTING ROOTS 80
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.
CHAPTER 4. PLANTING ROOTS 81
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.
CHAPTER 4. PLANTING ROOTS 82
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.
CHAPTER 4. PLANTING ROOTS 83
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.
CHAPTER 4. PLANTING ROOTS
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).
CHAPTER 4. PLANTING ROOTS 85
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
CHAPTER 4. PLANTING ROOTS 86
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
CHAPTER 4. PLANTING ROOTS 87
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.
CHAPTER 4. PLANTING ROOTS 88
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.
CHAPTER 4. PLANTING ROOTS
----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
CHAPTER 4. PLANTING ROOTS 90
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.
CHAPTER 4. PLANTING ROOTS 91
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.
CHAPTER 4. PLANTING ROOTS
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.
CHAPTER 4. PLANTING ROOTS 93
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.
CHAPTER 4. PLANTING ROOTS
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.
CHAPTER 4. PLANTING ROOTS 95
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.
CHAPTER 4. PLANTING ROOTS
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
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(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
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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
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)12
3,00
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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
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(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
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319,
567
(632
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)15
9,77
320
0,20
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20.
351
0.75
90.
821
26,8
1537
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86,0
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tud
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s0.
118
0.12
10.
034
0.02
81,
213
1,27
910
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(10,
612)
10,6
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)7,
200
7,50
0O
ther
deb
t0.
449
0.32
00.
174
0.12
46,
148
5,60
113
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(22,
459)
17,4
89(3
1,84
5)8,
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10,0
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red
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rd0.
396
0.34
10.
033
0.02
71,
159
1,20
72,
926
(3,8
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3,53
9(4
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)1,
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2,00
0D
eb
tT
ota
l0.
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0.63
41.
000
1.00
035
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45,1
9650
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(62,
591)
71,3
04(9
4,60
6)27
,300
40,0
00
Net
Wo
rth
--
243,
813
274,
372
259,
816
(610
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)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
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ondi
tion
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hold
ing
the
asse
tof
tota
las
set
Mea
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ean
(Sd)
Med
ian
~C
BF
BC
BF
BC
BF
BC
BF
BC
BF
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Dep
osit
s0.
853
0.90
60.
042
0.04
716
,994
22,0
5919
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(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
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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
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)29
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(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
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)14
7,44
3(2
24,9
97)
74,0
9752
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Rea
les
tate
0.64
20.
677
0.38
70.
524
158,
394
243,
838
246,
735
(441
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)36
0,39
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99,6
22)
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000
280,
000
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erdu
rabl
es1.
000
1.00
00.
087
0.06
235
,757
28,6
3035
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(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
CHAPTER 4. PLANTING ROOTS 99
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.
CHAPTER 4. PLANTING ROOTS 100
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.
CHAPTER 4. PLANTING ROOTS 101
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)
CHAPTER 4. PLANTING ROOTS 102
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)
CHAPTER 4. PLANTING ROOTS 103
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.
CHAPTER 4. PLANTING ROOTS 104
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.
[14] Palumbo, M. 1999. "Uncertain Medical Expenses and Precautionary Saving Near the
End of the Life Cycle," Review of Economic Studies, 66(2), 395-421.
[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.