3...... .33.. 23.33.43. . . 2.. .2............
hfiuwh 3. _ . . V bswufiflmwmvm'fiwvfl . . . w 22......5; .
I. p». KmMnP . . .
. . C

A
.1
r

t... .
.3
.t. ...

z» . . ....ur.

a an“.
.

2.3.42.4...
521...... .8. . .

 

.... ...¢.
.2 ..2..l.

 

 

T1 Fférc

Z LIBRARY
., 30' Michigan State
"‘ " University '

@22l9390

This is to certify that the
dissertation entitled

ESSAYS ON EMPLOYMENT INSURANCE,
INCOME MOBILITY, AND
FAMILY INCOME DISTRIBUTION

presented by

Wen-Hao Chen

has been accepted towards fulfillment
of the requirements for the

 

 

 

 

Doctoral degree in Economics
(jc/ 27 Q [
/ (
Major Professor’s Signature /
/ 2P [AAA/{4M 2 0 0 ‘7‘
U
Date

MSU is an Affirmative Action/Equal Opportunity Institution

— -.------0---c-u-o-o-o-0--

oo--o-~----.-.-.— -— 4

PLACE IN RETURN BOX to remove this checkout from your record.
TO AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE

DATE DUE

DATE DUE

 

NOV 2 0 2005

 

F p719'9&

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2/05 cJClRC/DateDueindd-pJS

ESSAYS ON EMPLOYMENT INSURANCE, INCOME MOBILITY,
AND FAMILY INCOME DISTRIBUTION

By

Wen-Hao Chen

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

" DOCTOR or PHILOSOPHY
Department of Economics

2004 '

ABSTRACT

ESSAYS ON EMPLOYMENT INSURANCE, INCOME MOBILITY.
AND FAMILY INCOME DISTRIBUTION

By
Wen-Hao Chen

The Canadian labour market has experienced several changes in the past two
decades. Government played a declining role in the labour market during the 19903 in
order to cope with huge deficits from the late 19803 and initiated a fundamental
restructuring of the unemployment insurance system in the mid—19903. However, equality
and opportunity become important policy issues as family structure changed, and there
was a rapid increase in the number of immigrants. The first chapter of this dissertation
studies the impact of the new employment insurance repayment provision. The second
chapter examines levels of income mobility, while the third chapter discusses changes in
the structure of family income distribution.

A stricter repayment policy was introduced in the Employment Insurance (E1) Act
enacted in 1996. The first chapter examines the impact of this new provision on the
probabilities of filing an El claim for eligible, laid-off workers. Due to a lack of
experimental data, this study adopts regression and propensity score matching methods to
evaluate policy effects. The results suggest that the new repayment policy has reduced the
probabilities of filing a claim among workers whose annual income is equal to or greater
than $48,750. The estimated decline in the claim rate ranges from 6 to 12 percentage
points, depending on the datasets and the methods of estimation.

The second chapter uses a recently available Canadian panel survey—the Survey

of Labour and Income Dynamics (SLID)—together with the Panel Survey of Income

Dynamics (PSID) from the US. to examine the levels of income mobility for the entire
population and particular subgroups. The results reveal that there is significant income
mobility in both Canada and the US. and that poverty or high income is a temporary
experience for most people. Mobility also varies across subgroups. Less-educated people
and minority groups tend to experience persistent poverty. Immigrants to Canada
experienced slightly longer periods of poverty, but they were less likely to fall back into
poverty once their income increased. In terms of cross-national comparisons, it was found
that Canada’s redistributive system significantly increases income stability. In addition,
there is evidence that low-income (or high-income) spell beginnings or endings in the
US. are mostly associated with events concurrent with changes in labour earnings, while
demographic events play a relatively important role in spell beginnings or endings in
Canada.

The distribution of family income in Canada became more unequal between 1980
and 1997. The third chapter employs a conditional re-weighting procedure developed by
DiNardo, Fortin, and Lemieux (1996) to assess the effects of changing family structure
and changing characteristics of immigrants on family income distribution. The results
show that the increasing trend toward single-adult families had a substantial impact on
increasing family income inequality. explaining one-fifth of the increase in the Gini
coefficient and one-third of the growth in the low-income rate between 1980 and 1997.
Changing characteristics of immigrants also affected income distribution, particularly in
the lower half of the distribution. It explained about one-third of the increase in the 50-10
and 90-10 ratios, and it was responsible for $462 of the decline in median income

between 1980 and 1997.

Copyright by
WEN-HAO CHEN
2004

FOR MY PARENT
Andrew Chen, and in the loving memory
of Judy C heng

And for Y u-Mei Hsu
The best part of my life

ACKNOWLEDGEMENTS

I am grateful to have had the advice and expertise of Professor Stephen A.
Woodbury. His knowledge, continuous support and encouragement have been a great
inspiration throughout the dissertation and his careful attention to questions in hand has
disciplined my thoughts. .

I thank Professor Jeff Biddle for sharing his many insights, providing valuable
suggestions and encouragement.

I owe a special debt of gratitude to Professor Steven Haider who was so generous
with his time in reviewing my drafts. His extensive and insightful comments clearly
pointed out errors I have stubbornly retained and offered many suggestions.

I would like to give special thanks to Miles Corak of Statistics Canada for his
support and encouragement during all phases of my dissertation work.

I am very grateful to my wife Yu-Mei Hsu for her endless support. I cannot fully

express my gratitude to her.

vi

TABLE OF CONTENTS

LIST OF TABLES ............................................................................ ix
LIST OF FIGURES ........................................................................... xi
INTRODUCTION ............................................................................. 1
CHAPTER 1

THE IMPACT OF THE EMPLOYMENT INSURANCE REPAYMENT POLICY:
NON-EXPERIMENTAL APPROACHES

1.1 Introduction ............................................................................. 6
1.2 Employment Insurance and High-Income Claimants ............................. 8
1.3 Identifying Causal Effects in Non-experimental Studies ........................ 11
1.3.1 Regression Adjusted Estimators ............................................. 12
1.3.2 Matching Estimators and Propensity Score Matching 13
1.4 Data Sources ........................................................................... 17
1.5 Results .................................................................................. 21
1.6 Economic Impacts of Repayment Policy .......................................... 26
1.7 Conclusion ............................................................................. 29
CHAPTER 2
HOUSEHOLD INCOME MOBILITY IN CANADA AND THE UNITED STATES
2.1 Introduction ............................................................................ 41
2.2 Literature Review ..................................................................... 44
2.3 Data Sources and Cross-sectional Statistics ....................................... 47
2.4 Income Mobility ....................................................................... 50
2.4.1 Persistence of Low Income and High Income .............................. 50
2.4.2 Government Transfers and Income Mobility ............................... 53
2.4.3 Short-term versus Long-term Mobility ...................................... 56
2.4.4 Cross-National Comparison ................................................... 58
2.5 Empirical Hazards of Low-Income and High-Income Transitions ............. 59
2.6 The Correlates of Income Spell Endings and Beginnings 62
2.7 Econometric Model of Income Dynamics ......................................... 65
2.8 Conclusions ............................................................................ 68

vii

CHAPTER 3

EVOLUTION IN THE STRUCTURE OF FAMILY INCOME IN CANADA, 1980-

1997: THE EFFECTS OF CHANGING FAMILY STRUCTURE AND CHANGING
CHARACTERISTICS OF IMMIGRANTS

3.1 Introduction ............................................................................ 94
3.2 Literature Review ..................................................................... 96
3.3 Data and Historical Trends ........................................................... 99
3.4 Methodology .......................................................................... 104
3.4.1 Estimation of Conditionally Re-weighting Functions 108
3.5 Results ................................................................................. 112
3. 5. 1 Reverse-order Decomposition. ... ..............116

3. 5.2 Decomposition Breakdown by Two Periods: w1980-1989 and
1989-1997 ...................................................................... 117
3.6 Conclusions ........................................................................... 119
CONCLUSION .............................................................................. 13 7
APPENDICES ............................................................................... 142
A. Additional Tables for Chapter 1 ................................................... 142
B. Sample Construction for CIE and SLID data ..................................... 144
C. Stata Output for Propensity Score Matching ..................................... 147
D. Technical Note for Reverse-order Decomposition ............................... 156
BIBLIOGRAPHY ........................................................................... 158

viii

LIST OF TABLES

1.1 Summary of Selected E1 Studies .......................................................... 3]
1.2 Summary (mean) Statistics, Survey of Changes in Employment (CIE) ............ 33
1.3 Distribution of the Treatment and Control Samples .................................... 34
1.4 Estimated Distribution of Propensity Score for CIE and SLID ...................... 35
1.5 Estimates of Treatment (Policy) Effects ................................................ 36
1.6 Economic Impacts of Repayment Policy ................................................ 37
2.1 Summary Statistics for the Household Equivalent Income ........................... 73
2.2 Incidence of Low and High Income, by Characteristics .............................. 74
2.3.1 Distribution of Income among Longitudinal Sample ............................... 76
2.3.2 Income Mobility by Characteristics ................................................... 77
2.4 Probabilities of Moving Out of the Low— and High-Income Group ................. 78
2.5 Five-Year Earnings Mobility for Wage/Salary Workers, Cross-National
Comparison .................................................................................. 79
2.6 Kaplan-Meier Estimates of Exiting Low Income 80
2.7 Kaplan-Meier Estimates of Exiting High Income 82

2.8 Low Income Spell Ending Types, by Person’s Household Type in the Last

Year of Low Income Spell ................................................................ 84
2.9 Low Income Spell Beginning Types, by Person’s Household Type in the

First Year of Low Income Spell .......................................................... 85
2.10 High Income Spell Ending Types, by Person‘s Household Type in the

Last Year of High Income Spell .......................................................... 86
2.11 High Income Spell Beginning Types, by Person’s Household Type in the

First Year of High Income Spell ........................................................ 87
2.12 Coefficients of Hazard Model, Canada ................................................ 88
2.13 Coefficients of Hazard Model, USA ................................................... 89
3.1 Weight Used in Density Decomposition ............................................... 123

3.2 Primary-order Decomposition of Changes in the Distribution of Family
Equivalent Income, 1980-1997 (1997 as Reference Year) .......................... 124
3.3 Primary-order Decomposition of Changes in the Distribution of Family
Equivalent Income, 1980-1997 (1980 as Reference Year) .......................... 125

3.4 Reverse-order Decomposition of Changes in the Distribution of Family

Equivalent Income, 1980-1997 .......................................................... 124
3.5 Primary-order Decomposition of Changes in the Distribution of Family

Equivalent Income, 1980-1989 .......................................................... 127
3.6 Primary-order Decomposition of Changes in the Distribution of Family

Equivalent Income, 1989-1997 .......................................................... 128
A.1 Major Changes of Legislation from UI to El .......................................... 142
A2 EI Payment: Repayment of Benefits at Income Tax Time .......................... 143
A3 Data Collection Period for CIE data ................................................... 143

LIST OF FIGURES

1.1 Net Income Schedule before and after the UI/EI Repayment Policy ............... 38
1.2 Histogram of Estimated Propensity Scores, CIE 39
1.3 Histogram of Estimated Propensity Scores, SLID .................................... 40
2.] Incidence ofLow Income 90
2.2 Persistence of Low Income by Demographic Groups ................................ 91

2.3 Probability of Moving out of Low Income by Length of Income Tracked and
Characteristics (pre-transfer income) ................................................... 92

2.4 Classification of Income and Demographic Events Associated with a Spell

Transition between Year t-1 and t ...................................................... 93
3.1 Trends of Inequality and Low Income ................................................ 129
3.2 Indexed Equivalent Income by Percentile, 1980-1997 ............................. 129
3.3a Kernel Density for Equivalent Income, 1980 and 1997 ........................... 130
3.3b Kernel Density for Equivalent Income, 1980 and 1989 130
3.3c Kernel Density for Equivalent Income, 1989 and 1997 131
3.4 Inequality Measures for Male Earnings (age 15-64) 131
3.5 Proportion of Working Wives by Husband’s Quartile Earnings Distribution,

Couple Families ......................................................................... 132
3.6 Family Composition ..................................................................... 132
3.7 Median Equivalent Market Income (age 15-64) ..................................... 133
3.8 Full-time Employment Rate (age 15-64) .............................................. 133
3.9 Proportion of Immigrants whose mother tongue is English or French .......... 134

3.10 1997 Equivalent Income with 1980 Immigrants’ Full-time Employment .....134
3.11 1997 Equivalent Income with 1980 Immigrants’ Full-time Employment

Mother Tongue and Duration of Residency Characteristics 135
3.12 1997 Equivalent Income with 1980 Immigration Factors and Family

Structure .................................................................................. 135
3.13 1997 Equivalent Income with 1980 Immigration Factors, Family

Structure, and Other Attributes 136
3.14 Residual ................................................................................... 136

xi

INTRODUCTION

During the past few decades, Canadians have experienced substantial social and
economic changes. These changes and concerns about government deficits prompted an
unprecedented scrutiny of Canada’s social programs during the 19805 and 19905.
Unemployment Insurance—one of the largest Canadian income support programs—has
been cut back significantly since the 19805. A reform of the unemployment insurance
program—the Employment Insurance Act, 1996 (E1 Act)—re5ulted in a fundamental
restructuring of the system. The EI Act changed El from a passive benefits program to a
more active labour market program.

Many people became concerned about income inequality and the growing poverty
rates in Canada in the 19805 and 19905. They thought that a situation in which the most
fortunate thrive and the poor are left behind could have a detrimental effect on Canadian
society. As a result,lthe extent of income mobility has become a central part of poverty
prevention policy discussions.

Canadian demographics also changed during the past few decades. The number of
typical “couple-children” families has declined, and there has been an increase in single-
parent families. Life for single-parent families tends to be difficult, and single-parent
families with women as the head of the household often live in poverty.

The structure of Canada’s population also changed substantially during the past
few decades. With a declining fertility rate and increasing life expectancy, Canada’s
population growth now relies largely on immigration. However, patterns of immigration

have changed. At one time, most immigrants to Canada had British and French ancestry.

Immigrants today are mostly visible minority groups, especially people with Asian
ancestry, whose mother tongue is neither English nor French.

The changes in Canada’s economic and social characteristics have affected the
Canadian labour market. Therefore, the main objective of this dissertation is to provide
empirical research—with particularly attention paid to employment insurance, income
mobility, and family income distribution—that documents the patterns, possible causes.
and consequences of these changes. This information is used to discuss policy
implications.

The first chapter examines the impact of the El Act. Similar to other evaluation
literature, this study was motivated by a quasi-experiment due to the changes in policy
parameters. Briefly, a claimant under the old UI system was required to repay 30 percent
of the benefits if his or her annual income reached 1.5 times the maximum insurance
earnings. With the new El system, a claimant is required to repay between 30 and 100
percent of the benefits if his or her annual income reaches 1.25 times the maximum
insurance earnings. This stricter repayment policy is designed, for equity purposes. to
reduce the benefits paid to repeat EI claimants who earn a relatively high income.

In 2000, Human Resource Development Canada (HRDC) investigated the
effectiveness of various parts of the new EI legislation using a series of evaluations.
However, HRDC did not examine the effect of the stricter repayment policy. Therefore,
the main purpose of this study is to fill a gap in the E1 evaluation literature by providing
an analysis of the impact of the new repayment policy. Specifically, this chapter attempts
to answer the following question: “To what extent does the change in the new repayment

provision affect an eligible, laid-off worker’s decision to file a El claim?” Estimates of

policy effects are based on two approaches: (1) regression methods and (2) propensity
score matching methods.

The second chapter examines the levels of income mobility and their patterns
across subgroups. The extent of income mobility has been considered an essential
ingredient to be placed with information about the income distribution at a particular
point in time. Inequality based on a snapshot of income distribution in a single year is
likely to be misleading if people are able to move easily through different levels of
income. A high level of inequality is not considered a problem when there is substantial
income mobility. As a result, understanding the level of income mobility becomes
relevant for policy formation, particularly for creating anti-poverty policies.

This study used data from a newly released Canadian longitudinal survey—the
Survey of Labour and Income Dynamics (SLID, 1993—1998). Prior to the SLID, mobility
analysis in Canada was very limited because it relied solely on tax-based files that are
usually not available to researchers. It was also limited because many important policy
variables (e. g., education and minority) were not included in the tax data. This study used
the new survey data to analyze the general pattern of income mobility in Canada. The
results of this analysis are compared to the tax data. This chapter also provides
information about income mobility among groups of interest to policy makers (e. g._. less-
educated people and immigrants). The information in this chapter complements the
existing Canadian literature and addresses questions that cannot be answered from the tax
data alone.

Using transition matrices from both pre-transfer and post-transfer income

measures and a decomposition analysis that classifies a transition into a mutually

exclusive concurrent event, the second chapter of this dissertation attempts to answer
three specific questions: (1) What is the extent of mobility and poverty (and high-income)
persistence, and how do these patterns vary across subgroups? (2) To what extent does a
nation’s redistributive policies affect changes in market-driven income mobility? (3) To
what extent does the occurrence of household events (e.g., marriage) as well as changes
in other income sources affect entries into and exits from low income and high income?

The final chapter in this dissertation discusses changes in the structure of family
income distribution and the factors that contributed to these changes in income
distribution. Previous studies have shown that income inequality in Canada has grown
rapidly since the mid-19705. At the same time, various aspects of Canadian society—
such as family structure, the labour market, and population—underwent substantial
changes. It was assumed that some of these changes had more effect on inequality or
poverty rates than other changes. Therefore, understanding the relationship between
income distribution in Canada and factors, such as family structure and population,
becomes relevant for developing social policies. which is the main purpose of this
research.

This chapter assesses the impact of demographic shifts on family income in
Canada. Of particular interest are the effects of changing family structure and the
characteristics of immigrants. Both of these factors have undergone substantial changes
in the past few decades, and economic outcomes vary greatly by family structure and by
cohorts of immigrants. The typical Canadian family is harder to find today than it was a
few decades ago. Similarly, immigrants have shifted from British and French ancestry to

visible minority groups, especially people with Asian ancestry. Therefore, to investigate

the impact of these changes on income distribution, this chapter attempted to answer the
following question: “What would the distribution of family income look like if family
structure and immigrants characteristics had not changed in the past 20 years?” The
counterfactual distributions are constructed through a relatively new re-weighting

technique developed by DiNardo. F ortin, and Lemieux (1996).

Chapter 1

The Impact of the Employment Insurance Repayment Policy:
Non-experimental Approaches

1.1 Introduction

The research discussed in this chapter examines the impact of the Employment
Insurance (El) system’s strengthened repayment policy (also known as clawback) on
eligible, laid-off, Canadian workers’ decisions to file a claim. The new Employment
Insurance Act in 1996 introduced a fundamental restructuring of unemployment
insurance in Canada and influenced the behaviour of many people in and out of the
labour market. A list of legislative changes is presented in Table A1 in Appendix A.
Some changes, such as a reduction in benefit level and entitlement, directly apply to all
claimants. Some changes affect individuals who are casual workers (e.g., increasing
entrance requirements for new entrants and re-entrants). Some changes target repeat users
(e.g., the intensity rule), and some changes penalize high-income claimants (e.g., the
repayment policy). Furthermore, the new system introduces more active labour market
programs to help unemployed people reenter the workforce. For instance, programs such
as work sharing offer monetary assistance to employers to avoid layoffs during temporary
work slowdowns, and wage subsidies provide incentives for employers to hire workers
who lack experience.

Various aspects of the new EI legislation have been intensively evaluated, with
most of these studies being conducted for Human Resource Development Canada
(HRDC). Table 1.1 contains a summary of selected studies that have evaluated the

changes made to El. For example, Phipps and MacPhail (2000) and Kapsalis (2000)

examined the impact of stricter entrance requirements on new entrants and re-entrants.
Green and Riddell (2000) assessed the effect of the switch from a weeks-based to an
hours-based system on the length of employment. Kuhn (2000) employed a cost-benefit
analysis to investigate the financial outcomes for part-time workers (those working less
than 15 hours per week) who were previously excluded from UI benefits but now are
covered under the new EI system. Fortin and Audenrode (2000) analyzed the impact of
the intensity rule (experience rating on repeated claimants) on unemployed workers’ job-
searching behaviours.

However, there are no studies that examine the effect of repayment on
unemployed workers’ job-searching behaviours. Under UI, a claimant was required to
repay 30 percent of the benefits if his or her annual income reached 1.5 times the
maximum insurance earnings. Under El, a claimant is required to repay between 30 and
100 percent of the benefits if his or her annual income reaches 1.25 times the maximum
insurance earnings (see Appendix A for a detailed schedule). These changes reduce the
net income schedule for El claimants and could affect their decision to file a claim.
Therefore, this study contributes to the existing evaluation literature by examining the
impact of the strengthened repayment provision.

When evaluating a program, it is difficult to identify the relationship between
outcomes and policy parameters. 15 it possible to be sure that the changes in an
individual’s behaviour are the result of a policy and not something else? Experimental
evaluation, known as random assignment, is generally viewed as the most reliable
method for evaluating a program’s effect on behaviour. However, the high operating

costs and problems (e. g., ethical issues or losing participants before they enter the

program) associated with experimental studies also concern researchers and policy
makers.

Due to the high cost and problems associated with experimental studies, the study
discussed in this chapter employed various non-experimental methods, particularly
propensity score matching, to evaluate the impact of the Employment Insurance (El)
system’s strengthened repayment policy on eligible, laid-off, Canadian workers’
decisions to file a claim. The various non-experimental methods are also compared to see
whether results are robust under different approaches.

Using data from two different surveys, Survey of Changes in Employment (CIE)
1995-1999 and Survey of Labour and Income Dynamics (SLID) 1994—1998, the results
of this study show that the new repayment policy has reduced the probabilities of filing a
claim among high-income individuals by about 4.2 to 6.2 percentage points for the CIE
sample, and about 9.7 to 12.7 percentage points for the SLID sample. Based on the
estimate of a 6.2 percentage-point decline in claim rate, this study suggests that the new
rules could have reduced the average monthly number of regular beneficiaries in post-
policy period by about 31,482, and reduced total annual regular benefits by about $629

million.‘

1.2 Employment Insurance and High-Income Claimants

Under the Canadian UI system, a claimant has to pay back some of the benefits if
his or her annual income is beyond a certain limit. This repayment policy was first
introduced in 1979. Claimants with an annual net income (including benefits) in excess of

1.5 times the annual maximum insurable earnings were required to repay 30 percent of

the benefits. Nevertheless, the threshold was fairly high, and only claimants with very
high incomes were affected. For instance, the threshold was $63,570 in 1995 (one year
before EI reform). while the average full time, full-year worker earned about $41,000. As
a result, most high-income people were not penalized for collecting UI benefits.

The repayment policy was revised in July 1996. It places more restrictions on
high-income recipients. Figure 1.1 illustrates the net income schedule before (solid line)
and after (dash lines) the changes for eligible claimants. Annual income limits dropped
23 percent (from $63570 to $48,750) for occasional claimants—those who had received
less than 20 weeks of benefits in the previous 5 years—and 39 percent (from $63,570 to
$39,000) for frequent claimants—those who had received more than 20 weeks of benefits
in the past 5 years. It is likely that the decline in annual income limits affects more people
under the revised repayment policy, including high-income claimants and relatively high-
income claimants. In addition, the repayment rates also increased, ranging from 30
percent (schedule B) to as much as 100 percent (schedule D). depending on worker’s
claim history.

What is the expected impact of the change? Intuitively, E1 is less attractive to
high-income individuals because the replacement rate is too low or because collecting El
benefits is a “stigma.” According to the 1998 Employment Insurance Monitoring and
Assessment Report by the Canadian Employment Insurance Commission (1998), the
number of people having to repay benefits increased from 19,000 (0.7% of people
receiving UI benefits) in 1995 to 82,000 (3% of people receiving EI benefits) in 1996.

Benefit repayments also rose substantially. from about $20 million in 1995 to about $70

 

1Currency in this chapter is in Canadian dollars.

million in 1996. Why would high-income people collect UI or E1? How do the reforms
affect people’s decisions to apply for benefits?

Normally, high-income individuals might apply for U1 or E1 when unexpected job
loss occurs and when there are few available jobs. A stricter repayment policy reduces
benefits for those claimants, but it does not necessarily stop them from filing a claim. A
person who would likely be affected by the new repayment scheme is someone with a
fiJll-time, part-year job who still earns an above-average income (e.g., seasonal jobs).
Due to a non-experience rated UI tax system in Canada, UI made working in seasonal
industries more attractive because it effectively subsidized wages. Seasonal firms or
industries received more subsidies through the U1 system, which helped them reduce
costs (in terms of wages. layoffs, and re-hiring) and therefore create more demand for
workers. Workers and employers in such firms or industries often had an “implicit
contract” that took advantage of UI’s subsidies (see Feldstein, 1976). When the new
repayment policy. was implemented, real income for seasonal workers declined, and the
incentive to stay in unstable industries decreased. This forced seasonal firms to increase
wages to keep quality workers, but it reduced demand for new workers due to the decline
in “wage subsidy.”

In addition, the old UI program encouraged people to stay in relatively high
unemployment areas, which helped to stabilize local economies. A stricter repayment
policy might force people to migrate from high unemployment regions to low
unemployment regions. Similarly, the occupational choices may have changed because

people find manual work such as fishing or construction less attractive under the new E1

program. It is possible that the new El program will result in more demand for higher
education, training,.or career changing services.

From a policy perspective, the change also creates questions about equity and
efficiency. Equity is about helping the least fortunate in society. Nakamura (1996) and
Nakamura and Diewert (2000) argue that the old system was not a fair insurance because
it allowed high-eaming people to repeatedly collect benefits, and these workers did not
even pay the El tax on earnings exceeding the maximum insurance earnings. The
experience-rated repayment provision introduced under EI promotes equity since the real
benefits are reduced significantly for all high-income EI repeaters. However, as
mentioned above, the new repayment policy also distorts an individual’s supply and a
firrn’s demand decision, which reduces efficiency. These issues need to be discussed

thoroughly when evaluating the true impact of the new repayment policy.

1.3 Identifying Policy Effects

Analysis in this study was conducted using a one-group design, before and after
analysis. One group refers to unemployed individuals who are eligible for El benefits and
whose potential net income is at least $48,750 Canadian dollars, the targets of the
repayment policy.2 People are assigned to the treatment group if they lost jobs in the post-
policy period and to the control group if they lost jobs in the pre-policy period. Without
randomized data, a simple practice of difference-in-mean comparison between a

treatment group and a control group might be misleading because these two groups are

 

2The sample does not include individuals whose potential income is less than $39,000 because they are
unaffected by the new repayment policy. Individuals whose potential income is between $39,000 and
$48,750 are also excluded because whether they are affected by the new policy depends on their claim
history, which is not available in either dataset.

ll

 

not selected at random. Two methods were used to evaluate the effects of the new EI

policy: (1) regression methods and (2) propensity score matching methods.

1.3.] Regression Adjusted Estimators

Consider that, for each unemployed worker, there are two potential outcomes, Y“.
denoting individual i's decision whether to file an El claim if he or she is exposed to the
policy change, and Y0,. denoting individual i ’s decision if he or she is not exposed to the
policy change. The effect of the policy change on an individual worker is Y],- — l’(),, but
this is never observed directly since only one potential outcome is ever observed for each
individual at any time. The standard way to isolate the effect of policy on outcomes
involves controlling for observable differences between treatment and control groups
using regression methods. As described in Angrist and Krueger (1999), we can write the
two potential outcomes as:

Y0,=X,',B+u,, YI,=Y0,+6 (1)
where Y 0, has been decomposed into a linear function of observed covariates. X {,8 . and a

residual u,. Using T ,~ to indicate whether an individual is exposed to the policy change,
this leads to the regression equation,

Y,=X,',B+T,§+u, (2)
where I"5 is the observed outcome. An individual is assigned to the treatment group (T~=I)
if he or she is exposed to the policy change. and to the control group (T =0) if not exposed
to the policy change. The coefficient for the policy indicator, 5 , is interpreted as the

treatment effect,

The identification assumption for this method is Y,,.Y0, _L T, l X, , which implies

that being assigned to the treatment group is random conditional on X,. That is, all
relevant differences between treatment and control groups are assumed to be captured by

their observables, X,.

1.3.2 Matching Estimators and Propensity Score Matching

More recently, the method of matching has received significant interest as a tool
for evaluation. The basic idea is to pair each treated unit with a control unit that shares
similar characteristics and interpret the difference in their outcomes as the effect of the
program. This can be done when conditioning variables are discrete and the data have
many observations so that each cell contains both treated and control units. However, this
becomes less practical when the numbers of variables increase or when continuous
variables are involved. To reduce the dimensionality problem, Rosebaum and Rubin
(1983) suggest a propensity score approach, which is simply the conditional probability

of treatment, given the characteristics X,:
P(X,)=pr(T,~=llX,)- (3)
Rosebaum and Rubin also show that if the exposure to treatment is random within
cells defined by X,- it is also random within cells defined by the one-dimensional
propensity score:
{K,,l’().iT,}lX.=—>{K,~KniT,}|17(X,) (4)
The average treatment effect on the treated (ATT) can then be estimated as follows:
r = E{E[Y., IT, =1.p(X,>l-E[Y., IT, =0.p<X,)lI T, =1} (5)

where the outer expectation is over the distribution of p(X,) in the treated units.

13

Like regression estimators, the method of matching also assumes that selection

can be explained purely in terms of observable characteristics, Y Yo, J. T, l X , . However,

,,,
the matching estimators are different from regression estimators in some ways. Smith
(2000) points out that matching avoids the functional form restrictions implicit in running
a linear regression with continuous covariates. Incorrect function form assumptions may
seriously affect estimate outcomes. Nevertheless, this difference fades when a sufficient
number of higher-order and interaction terms are included in the regression. Angrist and
Krueger (1999) argue that the essential difference between regression and matching is the
weighting scheme used to pool estimates at different values of the covariates: “The
matching estimator combines covariate-value-specific estimates to produce an estimate of
the effect of treatment on the treated; regression produces a variance-weighted average of
these effects” (p. 1317). Using a simple example where there is a single binary covariate,
x, and the probability of treatment is positive at both values of x, Angrist and Krueger
show that the weights underlying matching estimators are proportional to the probability
of treatment at each value of the covariates, while the weights underlying regression
estimators are proportional to the variance of treatment at each value of the covariates.
Thus, whether regression results will differ from matching results depends on how much
treatment effects vary with X. If, for example, the treatment group is much more likely to
include non-seasonal workers (x = 0), the regression estimators will put more weight on
the treatment effect for seasonal workers (x = 1) because the variance of treatment is

larger for that group.

14

Matching methods have two other advantages over regression. First, the use of a
propensity score ensures that 0 < P(X) < I and rules out any values of X such that P(X :—
I because we cannot estimate treatment effect by including people who were treated with
certainty, conditional on X. Second, a common support restriction can be easily
implemented in matching methods. That is, the samples are limited to the observations
whose propensity score belongs to the intersection of the supports of the propensity score
of treated and controls. Imposing the common support condition in the estimation of the
propensity score may improve the quality of the matches used to estimate the ATT.3

Of course, there are drawbacks to the matching methods. In addition to the
identification assumption described above, Heckman. Ichimura, Smith, and Todd (1998)
show that the estimates produced by matching can be very sensitive to the choice of
variables used to construct the propensity score, and there is no existing guide to help
researchers choose variables. In addition, Smith (2000) points out that the choice of
matching method can make a difference in small samples. Certain methods have
properties that make them a better choice in particular contexts.

In this study, three common matching methods—nearest-neighbor matching,
radius matching, and kernel matching—are used to estimate the treatment effect on the
treated.4

In nearest-neighbor matching, treated unit 1' is matched to the single control unitj

with the closest propensity score such that

 

3It should be noted that although the quality of the matches may be improved by imposing the common
support restriction. However, it is possible to lost high quality matches at the boundaries of the common
support. Besides, the sample may be considerably reduced, so imposing the common support restriction is
not necessarily better (Becker and [chino 2002).

4Propensity scores and matching estimators are estimated through a user-written Stata program designed by
Becker and Ichino (2002).

lp. -p, |= min){|p, -p,. i}- (6)

ke(l=0
This method is done with “replacement” because a control unit can be a best match for
more than one treated unit. Once each treated unit is matched, ATT can be obtained by
averaging the difference in means of the outcome between the treatment and control
groups. In nearest—neighbor matching. some of these matches are fairly poor because the
nearest neighbour may be very far from some treated units, which reduces the quality of
the match. This problem can be overcome by using radius matching. In radius matching,
the treated unit is matched to all the control units with estimated propensity scores falling

within a self-defined radius r from p, :

r>|p,-p,,|= min{|p,-p,.|}. (Z)

ke(l=0)
Let 1”,]. and Yf be the observed outcomes of the treated and control units, N 7' be the

number of treated individuals, and N f be the number of control units matched with

treated unit i. The ATT for radius matching can be expressed as

T=7372[Y.T — Zwyl’f] ,where w,j 2%. (fl)

tel” 16(II) I
Obviously, the quality of the matches would improve as the size of the radius becomes
smaller. However, it should be noted that when the radius is very small some treated units
are not matched because the neighbor does not contain comparable controls. If too many
treatments are not matched, the results no longer represent the treated population.
Both nearest-neighbor and radius matching use only a subset of the control group.
Efficiency may be compromised if some of the available information is not used. An

alternative method is kernel matching, described in Heckmen, Ichimura, and Todd

16

(1997a, 1997b, 1998). It constructs a match for each treated unit using a kernel-weighted
average over all individuals in the control group. The weights depend on the distance
between each control and treated unit. Control units that are very far away get a very low

weight rather than a zero weight. The ATT with kernel matching is

 

 

l (‘ pt ‘19, I
r- 1 Z< YT 1‘:ij k( h” )f (9)
-7..— . I — ,_ ,
167 zk(p p)
l jet hn .

 

 

where the second term in the bracket is a consistent estimator of the counterfactual

outcome Y0, .

1.4 Data sources

This study used data from two different sources: (1) the Survey of Changes in
Employment (CIE) cohorts 2 to 10, 13, and 17 (1995—1999), and (2) the Survey of
Labour and Income Dynamics (SLID) 1993—1998. The CIE is a quarterly survey funded
by Human Resources Development Canada (HRDC) used to evaluate and monitor
government policy. It samples individuals who experienced “job separations” and were
issued a Record of Employment (ROE) within a specified 3-month period, regardless of
the reason for job separation (see Appendix A).5 Survey respondents are asked about their
employment history in the 18 months prior to the interview. The survey also collects

information about respondents’ dates in and out of employment, reasons for interruptions,

l7

job characteristics, search activities, recall expectations, UI or E1 benefits, demographics,
household expenditures, and some income information. The advantage of CIE is that it
targets the right population for this study, while the disadvantage of CIE is that it contains
very limited information about personal incomes.

To compensate for the weakness of CIE data, a nationally representative dataset
(SLID) was also used in this study. Briefly, SLID is a panel survey that began in 1993 for
multi-dimensional purposes. The main advantage of this survey is its broad coverage of
labour market information. including almost every variable in the CIE, plus a large
amount of detailed income information. However. SLID is not designed for monitoring
the unemployed population. As a result, the identification of unemployed people and their
El eligibility in SLID is not straightforward.

The main challenge of this type of research is to construct samples that only
include people who experienced job: interruptions and are also eligible for U1 or E1
benefits. In Canada, the basic requirements for applying UI or E1 benefits are based on
two criteria: (1) work history and (2) region of residency. To be eligible, an individual
must work at least 12 to 20 weeks under UI (or 420—700 hours under EI), depending on
the regional unemployment rate at the time of being unemployed. Unfortunately, there is
no existing variable in either dataset that can directly identify such a sample. Certain
assumptions are, therefore, required. The procedure used to identify the Ul/El eligible

sample is described as follows:

 

5The first 10 cohorts were sampled during the period from July 1995 to December 1997. Due to
administrative reasons, only two samples were collected between January 1998 and June 2000: cohort l3
(July—September I998) and cohort 17(July—September 1999). Beginning in July 2000. the CIE restarted its
regular quarterly collection (cohort 21) with redesigned weighting methodology. The CIE is still active. and
the most recent available data is cohort 25. For consistency reasons. cohorts 21 and onward that used a
redesigned weighting scheme were not used in this study. Cohort 1 was also excluded because it was
merely a test, and its target population was different from other cohorts.

l8

1. Select people who experienced job interruptions.

IQ

Restrict to individuals who ended a job because of layoffs, business
slowdowns, or end of contracts.
3. Exclude people who are 65 years of age and over.
4. Exclude people who had been self-employed.
5. Restrict to those who had full-time jobs (30 or more hours worked per
week).
6. Exclude people who did not work enough weeks/hours to meet the
UI/EI entrance requirement.
Condition 2 excludes individuals whose job separations were associated with quitting.
injury, illness, parental leave. other family responsibilities, returning to school,
retirement, or dismissal by the employer. These people usually have quite a different
pattern of U1 or E1 behaviour. In many cases, they are not even eligible for benefits.
Conditions 3 and 4 delete people who are not covered by the UI or E1 system. Condition
5 removes all part-time workers because their labour market behaviours are quite
different, and usually, they do not have enough worked weeks/hours to qualify for
benefits. Individuals satisfying conditions 1 through 5 are not necessarily eligible for
benefits. In Canada. workers have to work at least 12 to 20 weeks (or 420—700 hours
under the new policy), depending on the local unemployment rates, in order to qualify for
benefits. As a result, condition 6 further excludes people who did not have enough

weeks/hours to qualify for UI/EI benefits.6 Because the repayment policy only affects

 

6Identifying this condition is a bit tedious. See Appendix B for details.

19

high-income individuals, the samples are limited to those who had a potential annual
personal income equal to or greater than $48,750.7

The outcome variable used in this study is UI/EI participation (or take-up).8 It is a
binary variable that equals ‘1 if unemployed people received benefits and 0 if they did not
receive benefits. Eligible people who were re-employed within 2 weeks (waiting period)
after the job separation are also included. The resulting samples consist of 2,985
individuals from the CIE data and 1,118 individuals from the SLID data.9’IO Distribution
of treatment and control groups from both surveys is shown in Table 1.3. Basically, in the
CIE sample, the treated and control units are not systematically different from each other
in most characteristics (except for the quarter of the year they were unemployed), while
the differences are more apparent in the SLID sample. For instance, compared to the
control units, the treated units in SLID consist of more blue-collar workers, more

childless couples, and fewer immigrants.

 

7Since most people do not know their actual annual income at the time of job separation, an individual’s
income from the previous year is used as a proxy for potential annual income. Unfortunately, income from
the previous year is not available in the CIE data; therefore, two methods are used to calculate an
individual’s potential annual income. See Appendix B for details.

8See Appendix B for details.

9For the CIE data, the original sample from cohort 2-10, 13, and 17 consists of 47,964 observations. Sixty-
six were dropped from the sample because they did not live in any of the 10 provinces, 613 were eliminated
because of age (2 65), and 868 were removed due to self-employment. Individuals whose reasons for
separation were other than “layoffs” or “contract ended” were excluded (15,706), and those who were not
eligible for UI/EI (as defined in Appendix B) were excluded (2,672). The sample included only full-time
workers (4,233 were removed), and those who had no information about their previous income and
education status were excluded (619). Finally, only high-income workers were selected from the sample
(an additional 20,202 were dropped), resulting in 2,985 observations selected from the CIE data.

10F or the SLID data, there were 57,729 job interruptions that occurred during 1993 and 1998 (33,543 were
permanently displaced, and 24,186 were temporarily laid off). Among them, 154 were dropped because of
age (_>_ 65), and 978 were removed due to self—employment. Individuals whose reasons for separation were
other than “layoffs” or “contract ended” were excluded (34.767), and those who were not eligible for Ul/EI
(as defined in Appendix B) were excluded (4,718). The sample included only full-time workers (2,373
were removed), and those who had no information about education and firm size were excluded (471).
Finally, only high-income workers were selected for the sample (an additional 13,150 were dropped),
resulting in 1,118 observations from the SLID data.

20

It should be noted that the outcomes of these two datasets are not directly
comparable because of survey designs and different systems for imputation of some
variables. For instance, the derivation of the potential personal income variable is not the
same in both datasets. In SLID, an individual’s income from the previous year is used as
a proxy for potential income. Due to limited information, wage earnings rather than
previous year income is used for the CIE sample, and this could result in an
overestimation of true income. Notice that yearly earnings were calculated based on the
respondent working the reported number of weeks per month and hours per week over a
12-month period. Obviously, it is overestimated because these people did not work all 12
months during the year. They all experienced a job interruption at some point. As a result,
many high-income people who were placed in the treatment group using this definition
were actually unaffected by the repayment policy, resulting in a lower estimate of
difference in mean participation rate. This can be seen explicitly from the first row of
Table 1.3. The change of mean participation rate was only -0.025 (insignificant at the 0.1
level) for the CIE data, while it was -0.08 (significant at the 0.05 level) for the SLID data.
Hence, the estimate of difference in participation rate for the CIE sample might be

considered the lower limit of the analysis.

1.5 Results
The treatment effect was analyzed using the regression and matching models
described above. The balancing test is used to estimate the propensity score, and the

common support restriction is used in matching estimators.” Lists of controls are

 

11The balancing hypothesis can be written as T _L X I p(X) (see Becker and Ichino, 2002). To satisfy

the balancing property, observations with the same propensity score must have the same distribution of
characteristics independent of treatment status. In other words, for each covariate, differences in mean

21

displayed in Table 1.3. Factors such as seasonal fluctuation (as measured by the quarter
of the year a worker was unemployed indicator) and local labour market conditions (as
measured by regional or provincial unemployment rates) are also included. These
variables are very important in this before-after study because they allow for policy
effects to be separated from temporal effects.

Table 1.4 displays the distribution of the estimated propensity scores for each
dataset. First of all, the balancing property is satisfied in both cases, showing that the
means of each characteristic do not differ between treated and control units within each
interval. Second, only one out of 920 control units in the CIE sample is excluded under
the common support restriction, suggesting the control units are very comparable to the
treated units. Therefore, the final size of the control sample is 919 for the purpose of
matching. In addition, Figure 1.2 and Figure 1.3 show that each interval contains enough
controls to compare with the treated units, suggesting the quality of matches will not
deteriorate, even when matching without replacement.

Turning to the estimates of the average treatment effect on the treated (ATT), row
2 of Table 1.5 displays results from the nearest—match method. It shows that all 2,065
treated units were matched with 624 of the 919 controls (with replacement) for the CIE
sample. Similarly, 282 of 464 control units were used to match all treatments for the
SLID sample. The ATTs for the single best match method are -0.062 and -0.1 19 for the
CIE and SLID samples, respectively, and both are significantly different from zero

(0.05). This suggests that the new repayment policy is responsible for a drop of 6.2 to

 

across treated and control units within each interval are not significantly different from zero. Notice that the
individuals are divided into intervals according to the estimated propensity score.

22

11.9 percent in the El participation rate among eligible, unemployed workers whose
potential annual income is $48,750 or more.

The next 5 rows show results from the radius matching that allows multiple
controls to be matched to a treated unit if they all fall within a tolerance range. When the
size of the neighborhood was very small (r = 0.0001), many of the treated units were
unable to be matched: Only 533 out of 2065 in the CIE (and 92 out of 654 in SLID)
samples were matched in this range. Estimated effects were somewhat larger (but with
larger standard errors) in both cases compared to the nearest-neighbor estimators.
However, this result may not represent the population of treated because only 26 percent
and 14 percent of treated units in the CIE and the SLID samples, respectively, were
matched. As the radius increased, more control units were used, and the probability of
finding a match for all treated units also increased. For a radius of 0.01, all control units
were used, and all treated units found matches in the CIE sample. Similarly, nearly all
(462 out of 464) control units were used to match 641 (out of 654) treated units in the
SLID sample. Estimates of the treatment effect are quite sensitive to the choice of radius.
The estimated drop in El participation rate ranges from 2.5 to 7.6 percentage points for
the CIE data and 9.7 to 18.4 percentage points for the SLID data.

The next 2 rows show results from kernel matching that used all control units
(through a kernel-weighted average of the outcomes) to match each treated unit. Two sets
of kernel matching are reported: one with a bandwidth of 0.001 and the other with a
bandwidth of 0.01. Unlike radius matching, kernel matching uses controls that lie outside
the “window,” but those controls that were far away have a very small weight. Generally.

results from kernel matching are similar to those from radius matching (when radius

equals bandwidth) in the CIE sample; however, there are minor differences (not
statistically significant) in the SLID sample.

Finally, a regression adjustment of the full sample is presented on the bottom
rows of Table 1.5. Compared with matching estimates, the estimate of the impact of E1
repayment from regression adjustment is slightly smaller and statistically insignificant for
the CIE sample, but it similar to the kernel estimate, with a bandwidth = 0.001, for the
SLID sample. As suggested by Wooldridge (2002, p. 617), a simple estimate from an
OLS regression on the estimated propensity score is also reported in the last column. This
is estimated in two steps. F irst. a probit of the treatment on X is estimated, and then an
OLS regression of outcomes on the treatment dummy and fitted propensity score is
estimated. The estimated propensity score should contain all the information in the
covariates that is relevant for estimating the treatment effect. The advantage of using the
propensity score as a regressor is that it rules out any values of X such that 1700 = 1.
However, it ignores the residual error from the first-stage probit estimation. In this study,
the estimated treatment effects are very close to those obtained from simple regression,
while the uncorrected standard errors seem to be larger.

Variation of the treatment effects across the estimators in Table 1.5 is due to the
use of different control samples and the weighting schemes. For instance, the weight on a
given control j when compared with treated i is equal to 1 in the nearest-neighbor
matching, while it is I/N‘ in the radius matching (where N‘ is the total control within the
radius of treated i). However, Table 1.5 also exhibits two noticeable patterns. First, the
estimated policy effects are much greater in SLID than in CIE across all estimators. The

different underlying survey designs and sampling schemes are possible reasons for these

differences. These differences might also be due to the type of variables used in CIE. As
described in section 1.4, the imputation of the income variable for the CIE sample may
overstate the true income value for an individual. Many unrelated unemployed workers—
those who were not really subject to repayment—were included in the at-risk population.
Since these people were unaffected by the repayment policy, the average claim rate is
expected to be higher by including these people. It might explain why the estimates of
difference in El claim rates are always lower for the CIE sample.

Second, in both datasets, policy effects show a monotonic decline as the radius
width increases. The effects were disproportionately large when the radius was very
small (r = 0.0001). The effects then became smaller at r = 0.0005, remained stable when
r = 0.001, and dropped monotonically as the width increased further. As mentioned
above, matching estimators based on a very small radius may not represent the treated
population and, therefore, should be excluded from discussion. For the rest of the radius
estimators, it is necessary to determine whether the use of more controls improves or
worsens the estimation. Dehejia and Wahba (1998) point out that using more controls
might increase the precision of the estimators, but it could also adversely affect the
quality of the match on the propensity score. Without an experimental benchmark, it is
very difficult to determine whether more controls improve or worsen the estimation
because the relationship between radius r and the resulting bias is unknown. The
interpretation used in this study is rather intuitive.

An increase in radius width may introduce additional “unrelated” controls in the
matching for the CIE sample, while an increase in radius width improves the

representation of the treated population in the SLID sample. Notice that in the CIE.

25

treated and control units have closer propensity scores than the SLID. About 93 percent
(1926 out of 2065) of the treated units in the CIE were able to find comparable matches
within a neighborhood of 0.001. By increasing the radius width, the possibility of
including unrelated controls to match a given treated unit also increased. As shown in
Table 1.5, the standard errors did not appreciably decline as the width of the radius
increased. The declining policy effects suggest that these unrelated controls were likely to
be non-El participants.

The difference between propensity scores of treated and control units is greater in
the SLID sample. For example, only 52 percent of the treated units were matched in a
radius of 0.0005. The treated population increased to 68 percent and 98 percent as the
radius expanded to 0.001 and 0.01, respectively. As a result, the changes in policy effects
(through radius widths) in the SLID sample are likely the result of changes in the

representation of the treated population.

1.6 Economic Impact of New Repayment Policy

As shown in the previous section, the strengthened repayment policy is
responsible for a 6.2 to 12.7 percentage-point decline (depending on dataset used) in E1
claim rates among hi gh-ineome workers. The extent to which the change in claim rates
affects economic outcomes is discussed in this section. It focuses on the impact of claim
rate changes on the number of beneficiaries and government benefit payments. Table 1.6
shows the average monthly number of new regular claims (column 1), average monthly
number of regular beneficiaries (column 3), and average weekly regular benefits

(columns 5 and 6)—the data are from the aggregate source Employment Insurance

Statistics (EIS). The estimated results from the previous section are used to fill in column
(2), then the results from columns (1) to (3) are used to extrapolate column (4), which
would be the average monthly number of regular beneficiaries if the old repayment rules
was still in effect. To estimate the budgetary impact, columns (7) and (8) are calculated
using the information from (3) to (6) and the difference is compared.

Unfortunately, column (2) cannot be obtained directly because the estimated
results of the previous section only apply to eligible workers whose annual income was
equal to or greater than $48,750. Therefore, a regression was employed to model the
relationship between the total number of new EI claims and the claim rate among high-
income workers:

RClaim, = a + ,BClaimrate _ highincome, + e, (10)
Equation (10) assumes that the number of new regular claims (RClaim) is a function of

the claim rate from high-income workers (C laimrate_highincome) . and subscript t is the

month from which the data are obtained. The monthly claim rates among high-income

workers (C laimrate_ highincome) are imputed from the microdata CIE, while the

number of monthly regular new claims (RClaim ) can be obtained either from an
aggregated source (EIS) or from the microdata CIE. In panel A. Table 1.6, RClaim was
taken from EIS, and C laimrate_highincome was derived from CIE. The estimated
equation is

RClaim, = 72276.7 + 226638.4Claimrate _ highincome, t = 1. 2,. . 24. R2 = .162.
(1:0.91) (t=2.06)

These estimated coefficients (column 2), together with results from the previous section,

answer the question “What would be the number of new claims in the post-policy period

if the old repayment rules were in effect?” For example, based on the nearest-neighbour
matching from CIE, the strengthened repayment rules will reduce EI claim rates for hi gh-
income workers by 6.2 percentage points. This estimate, applied to equation (10),
suggests that the potential average number of new claims would be 247,715
{72276.7+226638.4*(0.7120885+0.062)} if the repayment rules had not been changed. '2

Once the number of new claims was obtained, the potential beneficiaries under
the old rules (column 4) were calculated based on an extrapolation of the results from
columns (1) to (3). The result suggests that the average monthly number of beneficiaries
would have been higher by 31,482 (from 567,958 to a potential 599,440) if the old rules
were still in effect. Finally, annual benefits under the new rules and old rules
(counterfactual) are calculated in columns (7) and (8), respectively. Assuming that the
entire decline in El claim rates among high-income workers can be attributed to the new
repayment rules, government could have saved about $629 million in benefits paid out
annually. Applying a 95% of confidence interval to this underlying estimate (see footnote
1, Table 1.6), the average number of monthly beneficiaries would have been higher:
between 11,570 and 78,778. Correspondingly, the government could have saved $362
million to $1,264 million a year.

In panel B, Table 1.6, instead of taking values from an aggregate source, the

values are derived from CIE for RClaim . The estimated equation becomes

RClaim, =129220.9 + 1 32075.9Claimrate_highincome, t = 1. 2,. . 24 (months). R2 =

.06.
(t=1.65) (1:121)

 

l2Predicted number of new claims is evaluated at the mean value of Claimrate_highincome (= 0.7120885).

28

Based on a 6.2 percentage-point decline in El claim rate, it is shown that the average
number of monthly beneficiaries would have been higher: between 7,054 and 48,042.
Correspondingly, the annual saving for government could be $301 million to $851

million.

1.7 Conclusion

The study discussed in this chapter examines how the new El repayment policy
affects the probability that an eligible, laid-off worker will file an El claim. Various
propensity score matching approaches and regression-adjusted models are used to
estimate the policy impact. Both matching and regression methods rely on the crucial
assumption that the outcomes are independent of assignment to treatment and control,
conditional on X variables.

The results of this study show that the new repayment policy has reduced the
probabilities of filing a claim among high-income individuals by about 4.2 to 6.2
percentage points for the CIE sample, and about 9.7 to 12.7 percentage points for the
SLID sample. Based on the estimate of a 6.2 percentage-point decline in the claim rate,
this study suggests that the new rules could have reduced the average monthly number of
regular beneficiaries in the post-policy period by about 31,482 (or 5.3 percent), and
reduced total annual regular benefits by about $629 million (or 8.5 percent).

However, a few caveats must be borne in mind when interpreting the results.
First, results from the two samples are difficult to compare because of different
underlying survey designs and different controls in each survey. Second, the estimation

approaches—regression and propensity score matching—used in this study rely on

observables. It is possible that unobserved heterogeneity in the control and treatment
groups exists. If so, the results would be suspect. Third, the decline in the claim rate
could be due to employment effects. With the effective reduction in benefits imposed by
the repayment policy, many frequent claimants may no longer be willing to accept
seasonal jobs and, instead, seek employment in more stable industries. As a result, the
claim rate for the remaining at-risk population would be lower because many individuals
who were certain to file a claim no longer belonged to the at-risk population in the post-
policy period. Additional analysis of employment effects in response to the repayment
policy is needed in future research. Furthermore, it must be remembered that the data
used in this study only cover the 2 to 3 years after the legislation was passed, and
individuals usually take time to adjust to policy changes. The effects might be even larger

as people learn more about the new El regulations.

30

Table 1.1

Summary of Selected EI Studies

 

 

 

 

 

 

 

 

 

 

 

 

 

Study El parameters Outcome of Parameter Main findings
interest changes from ;
UI to El
i Phipps and l) The impact of the Benefit receipt From 20 weeks New requirement
MacPhail stricter entrance of new and re- to 26 weeks (or reduced access to
(2000) requirement on new and entrants 910 hours) 1 benefits for new/re-
re-entrants ? worked entrants, especially
i youth and returning
2) The effect of a U1: ineligible mothers
switch from a weeks- , if worked
based to an hours-based ' <15hrs The net reduction in
system E El: all hours access to benefits for ,
: count new/re-entrants resulting
fi'om the switch to E1 is "
small
Kapsalis The impact of the i Benefit receipt From 20 weeks New requirement ,
(2000) stricter entrance of new and re- to 26 weeks (or significantly reduced the
requirement for new entrants 910 hours) 2 number of beneficiaries
entrants and re-entrants worked and the amount of total
benefits paid in 1997
(7%)
Green and 1 The effects of the shift 1) Eligibility U1: ineligible Overall, the change has
Riddell to an hours-based i and entitlement if worked no impact on eligibility
(2000) entrance requirement <15hrs but has led to an increase
E1: all hours in entitlement
2) Job duration count 1
5 ‘ The change has no
impact on job duration,
except for the length of
seasonal jobs, which
tended to be shorter
_ . under El
Kuhn (2000) The extension of El j Financial U1: ineligible Part-time workers
coverage to part-time ' outcomes for if worked experienced a small net
workers part-time <15hrs ., gain under El (about $39
'3 workers El: all hours per worker per year)
........ f .. - count
Fortin and The impact of the A El payment El: Data from COEP show .
Audenrode intensity rule on and benefit replacement that the intensity rule
(2000) unemployed workers 3 duration rate is reduced alone could reduce El ,
3 by 1 to 5%. payments by 2.5 percent;
depending on also. a slight increase in ’
claim history :1 claimants leaving after ,
i 19 weeks to avoid future
; benefit reductions i'

 

 

 

 

 

Table 1.1 (Cont’d)

 

-..—...-.. ... .. ._,___,_-._.4..~......... “...—.-. . _....._-..

 

 

 

 

 

 

 

 

‘El
1
I
E
I
1

 

El: 65 to 80%

 

Study j El parameters Outcome of . Parameter Main findings
. ' interest . changes from i
i U1 to E1
’ Sweetman . The impact of E1 on Distribution of _ U1: ineligible if 1 Except for the Atlantic
(2000) those working less than the hours of worked <15h1‘s region, there has been a
15 hours per week jobs 1 El: all paid small decrease in jobs .
i part-time under 15 hours per week
workers are for men and an increase
; covered in the percentage of jobs
over 30 hours. No 5
change is observed for
women
Jones An assessment of E1 Unemployment 1 Various The quasi-experimental
(2000) legislation duration and 1 changes (see research suggests small
1 benefit receipt ' text) but significant effects on
. i ‘ unemployment duration
5 but no change in the
length of time a
i claimant is unemployed
Phipps, 1 Impact of switch from Family income i U]: 60% F8 is more targeted to
MacDonald, i dependency rate (DR) i replacement low-income families
and under U1 to the family , rate for than the DR; FS also
MacPhail . supplement (FS) under , claimants with provides more income to
(2000) 1 dependents claimants who have

dependents

 

(a)
1d

 

Table 1.2
Summary (mean) Statistics, Survey of Changes in Employment (CIE),a weighted

 

 

 

Control Treatment
(income < $39,000) (income > $48,750)
Pre-policy Post-policy Pre-poliey Post-policy
(l) (2) (3) (4)
El participation 82.0 75.8 77.6 74.4
Age 36.6 36.4 40.3 41.5
Male (%) 53.9 - 52.0 93.9 91.1
Education (%)
Less than HS 30.5 28.4 29.3 27.7
HS graduated 31.2 28.2 30.0 25.4
Some post-secondaiy 29.1 33.0 30.0 34.4
University 9.1 10.3 10.7 12.4
Household structure (%)
Single 37.9 38.9 19.5 22.5
Couple w/o kids 30.3 31.3 41.4 41.3
Couple w/ kids 29.0 25.4 37.0 33.4
Single parent 2.8 4.4 2.1 2.7
Region (%I
Atlantic 13.4 16.1 6.9 6.7
Quebec 34.5 34.4 26.3 27.0
Ontario 29.1 26.2 32.5 32.7
Prairie 13.9 12.8 14.0 13.2
BC. 9.0 10.5 20.3 20.3
Canadian born (%) 86.9 88.6 86.6 83.7
Seasonaljob (%) 38.0 39.5 31.5 31.0
Received advanced notice (%) 43.4 43.7 36.6 33.6
F inn size (%)
< 20 45.2 45.5 32.0 31.7
20—99 29.4 29.9 26.8 28.9
100—499 17.9 17.8 19.9 21.7
500 + 7.5 6.7 21.3 17.8
Blue collar worker (%) 52.5 49.4 75.3 74.8
Permanent employee (%) 49.6 43.0 51.4 47.0
Recall expectation with date (%) 27.6 26.7 26.0 23.0
Unemployed (%) for
Jan—April 32.6 11.1 39.2 12.3
May—August 18.6 36.5 14.0 39.6
Sept-Dec. 48.8 52.4 46.8 47.7
Unemployment rate“ 9.95 10.0 9.45 9.25
Observations 5,073 12,324 920 2,065

 

*Samples include cohorts 2—10. 13, and 17.
"Combing information from the Monthly Labour Force Survey (LFS). this variable refers to a provincial
unemployment rate at the time of an individual’s job interruption.

b)
b)

Table 1.3
Distribution of the Treatment and Control Samples, weighted

 

 

 

 

 

 

 

 

 

Survey of Changes in Survey of Labour and
Employment (CIE) Income Dynamics (SLID)
Control Treatment Difference Control Treatment Difference
Pre-policy Post- Pre—policy Post-
policy policy
( I) (2) (3) (4) (5) (6)
Outcome variable
El participation rate 77.6 74.4 -3.2 F 65.1 57.1 -8.0‘
Mean personal characteristics
Age 40.3 41.5 1.2‘ 41.0 41.8 0.8
Male (%) 93.9 91.1 -2.8 85.8 90.4 4.6
Education (%)
Less than HS 29.3 27.7 -1.6 24.0 27.2 3.2
HS graduated 30.0 25.4 —4.6 9.5 11.1 1.6
Some post-secondary 30.0 34.4 4.4 49.4 46.5 -2.9
University 10.7 12.4 1.7 17.1 15.1 -2.0
Household structure (%)
Single 19.5 22.5 3.0 29.2 26.0 -3.2
Couple w/o kids 41.4 41.3 -O.I 15.6 23.1 7.5”
Couple w/ kids 37.0 33.4 ~3.6 51.2 49.1 -2.1
Single parent 2.1 2.7 0.6 4.0 1.8 -2.2
Region (%)
Atlantic 6.9 6.7 -0.2 7.5 9.9 2.4
Quebec 26.3 27.0 1.0 20.5 17.8 -2.7
Ontario 32.5 32.7 0.2 32.3 35.5 3.2
Prairie 14.0 13.2 -0.7 15.2 14.4 -0.8
B.C. 20.3 20.3 0.0 24.4 22.3 -2.1
Canadian born (%1 86.6 83.7 -29 82.0 90.1 3.1"
Mean characteristics associated with ROE job
Seasonal job (%) 31.5 31.0 -O.5 31.7 -0.7
Rec‘d advanced notice (%) 36.6 33.6 -3.0 - - -
Firm size (%)
< 20 32.0 31.7 -0.3 33.3 29.3 -4.0
20—99 26.8 28.9 2.1 28.8 24.8 -4.()
100-499 19.9 21.7 1.8 21.3 25.8 4.5
500 + 21.3 17.8 -3.5 16.5 20.0 3.5
Blue collar worker (%) 75.3 74.8 -05 63.4 76.6 13.2“
Permanent employee (%) 51.4 47.0 -4.4 - - -
Recall expectation (%) 26.0 23.0 -3.0 - - -
Unemployed (%) from
Jan-April 39.2 12.3 -265" 33.5 25.9 -7.6‘
May- August 14.0 39.6 25.6" 46.4 35.1 -1 1.3“
Sept.- Dec. 46.8 47.7 0.9 20.0 39.0 19.0"
Unemployment rate” 9.45 9.25 -0.20’ 10.9 ms -04"
Observations 920 2,065 464 654

 

 

Data: CIE cohorts 2—10. 13. and 17; SL1D(1993-—1998)

*Difference is significant at the .1 level, two-tailed test.

"Difference is significant at the .05 level. two-tailed test.

"*Combining information fiom the Monthly Labour Force Survey (LFS). this variable refers to
provincial unemployment rates at the time of an individual's job interruption for the CIE data. while it
refers to a regional (Ul/El) unemployment rate at the time of an individual’s job interruption for the SLID
data.

Table 1.4
Estimated Distribution of Propensity Score for CIE and SLID, weighted

 

 

 

Propensity score Survey of Changes in Survey of Labour and
Employment (CIE)l Income Dmamics (SLID)2
Control Treatment Control Treatment
0.0—0.2 - - 59 40
0.2—0.4 124 63 I84 181
0.4—0.5 236 176 104 151
0.5—0.6 93 80 63 1 1 1
0.6—0.7 133 352 35 99
0.7—0.8 155 542 16 55
0.8—0.9 140 691 3 17
0.9-1.0 3 161 - -
Total 919’ 2.065 464 654
Region of common support [.2864, .9537] [.0466, .8842]
Balancing property Satisfied Satisfied

 

'The propensity score for the CIE data is estimated using a probit of treatment status on personal
characteristics (age. sex, education. household composition, regions. Canadian born), ROE job
characteristics (union. seasonal job, recall expectation with date, firm size. received advanced notice.
permanent employee, blue-collar worker), and macro conditions (quarter of unemployment, provincial
unemployment rate at the time of job separation).

2The propensity score for the SLID data is estimated using a probit of treatment status on personal
characteristics (age, sex, education, household composition, regions, Canadian born), interrupted job
characteristics (union, seasonal job. firm size, blue-collar worker), and macro conditions (quarter of
unemployment, Ul/El regional unemployment rate at the time of job separation).

3One observation was dropped because its estimated propensity score was not in the region of common
support.

Table 1.5

Estimates of Treatment (Policy) Effects, weighted

Outcome: Probability of participation in Ul/EI program

 

Survey of Changes in

Survey of Labour and

 

 

 

 

 

 

 

Employment (CIE) Income Dynamics (SLID)
Mean ATTr ATT‘
(observatiorg) (s.e.) Jobservations) (s.e.)
Control Treatment Control Treatment
Original sample 0.776 0.744 -0.032 0.651 0.571 -0.080'
(n=920) (n=2065) (0.029) (n=464) (n=654) (0.044)
Matchitg sample (with common support)
Nearest-neighbor 0.816 0.754 0062‘ 0.702 0.583 -0.1 19"
matching (n=624) (n=2065) (0.021) (n=282) (n=654) (0.041)
Radius matching
r=0.000l 0.809 0.733 -0076" 0.684 0.500 -0184‘
(n=4l 7) (n=533 (0.038) (n=85) (n=92) (0.102)
r=0.0005 0.802 0.750 -0052” 0.686 0.566 -0120”
(n=773) (n=l6l 1) (0.026) (n=292) (n=339) (0.055)
r=0.001 0.804 0.751 -0.053“ 0.697 0.570 -0.127”
(n=855) (n= 1 926) (0.019) (n=3 78) (n=446) (0.040)
r=0.01 0.797 0.754 0043“ 0.683 0.581 0103”
(n=9 1 9) (n=2065) (0.018) (n=462) (n=641 ) (0.028)
r=0.1 0.779 0.754 -0.025 0.680 0.583 -0097“
(n=9 1 9) (n=2065) (0.019) (n=464) (n=654) (0.028)
Kernel matching 0.807 0.753 -0054“ 0.688 0.582 0106”
Bwidth=0.001 (n=9l9) (n=2065) (0.021) (n=464) (n=654) (0.039)
Kernel matching 0.795 0.753 -0042” 0.700 0.582 -O.1 18"
Bwidth=0.01 (n=9l9) (n=2065) (0.016) (n=464) (n=654) (0.032)
Regression -0.044 -0107"
(0.027) (0.042)
(n=920) (n=2065) (n=464) (n=654)
Regression on -0.43 -0.106”
Pseore (0.029)’ (0.046)’

 

 

lThe average treatment effect on the treated (ATT). Standard errors (in parentheses) for matching estimates
are calculated using the bootstrapping method (50 replications).

*Significant at the .1 level.
“Significant at the .05 level.
+The standard error is not adjusted for the probit first-stage estimation.

Table 1.6
Economic Impacts of New Repayment Policy

 

Post-Policy Period

 

 

 

 

1f the new Average Average Average Annual
repayment monthly monthly number weekly regular benefits
policy reduces number of new of regular regular benefit (million)
claim rates of regular claims beneficiaries
high-income New Old New Old New Old New Old
workers by rules. rulesi rules. rules” rules. rules. rules2 rules2
(percentage
point)

(1) (2) (3) (4) (5) (6) (7) (8)
A.
6.2 233.663 247.715 567,958 599,440 251 258 7.413 8.042
2.1 233,663 238,423 567,958 579.528 251 258 7,413 7.775
(lower bound)l
14.3 233,663 266,073 567.958 646.736 251 258 7.413 8.677
(upper bound)l
B.
6.2 223,271 ..31.459 567,958 588,787 251 258 7.413 7.899
2.1 223,271 226.044 567,958 575,012 251 258 7.413 7,714
(lower bound)l
14.3 223,271 242,157 567.958 616.000 251 258 7.413 8.264
(upper bound)1

 

Note: In panel A. the values of monthly number of new claims came from an aggregate source
(Employment Insurance Statistics). In panel B, the values of monthly number of new claims were derived
from microdata CIE.

'Employment Insurance Statistics—Monthly, Statistics Canada

IPredicted value using equation (12) evaluated at mean value of Claimrate_highincome.

”Value from extrapolation of columns (I) to (3).

'The 95% confidence interval of the underlying estimate of 6.2 is obtained by[ ,6 5; l .96 -se ( fi )] . Since

se( ,6) =0.02l (see Table 5 or Appendix C). the confidence interval should be [0.062-1.96*(0.02|).

0.062+l .96*(0.021)] = (0.021, 0.143).
2Column (7) = column (3) * (5) * 52; and column (8) = column (4) * (6) * 52.

Figure 1.1
Net Income Schedule Before and After the UI/EI Repayment for El Claimants

Net income after

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

repayment
A. Schedule before new
clawback law I
./'__
B. Had received < 20 C. Had received 21—40
weeks El since 07/96 ‘ ’3 ‘ ’ weeks El since 07/96
u‘y.'
/"
‘7'. ‘
D. Had received 120 +
‘ ’ weeks El since 07/96
E3 E3 E4 E,
E, $39000 $63570
$48750 Net income before repayment

Figure 1.2
Histogram of Estimated Propensity Score, CIE

Number of treatment and control

 

 

 

 

 

 

 

 

691
542

 

 

 

 

176

‘ 152 161
63 80

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

38

_/

 

 

 

L'ITreatment
(post-policy)

lControl
(pre—policy)

 

 

 

am

0.2-0.4 0.4-0.5 0.5—0.6 0.6-0.7 0.7-0.8 0.8-0.9 0.9-1.0

Estimated propensity score

39

 

Figure 1.3
Histogram of Estimated Propensity Score, SLID

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

'3 181

b 151

I:

O

U

'8

cu DTreatment
ӎ (post-policy)
g IControI
ea re- olie
b (p p y)
'd-I

o

h

o

.9

E

:3

Z

 

 

 

 

0.0—0.2 0.2-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9

Estimated propensity score

40

Chapter 2

Household Income Mobility in Canada and the United States

2.1 Introduction

Levels of poverty and pre-transfer family income inequality have grown rapidly
in Canada since the 19805. Statistics show that the Gini coefficient with respect to pre-
transfer family income rose from .37 in 1980 to .43 in 2001 for economic families and
from 0.44 to 0.50 for all family units.l3 Increases in income inequality have created
intense public policy debate about the effectiveness of anti-poverty policies and raised
concerns about social exclusion. While there is extensive Canadian literature
documenting trends and causes of rising inequality.l4 there is relatively little research that
examines income mobility.

Understanding the levels of income mobility in Canada is of interest for two
reasons. First, this information is essential for understanding the long-term consequences
of inequality and poverty and for formulating policies that are appropriate. Inequality of
annual income may overstate the problem of low income if people are able to eventually
move into a higher income bracket. Pe0ple will tolerate periods with high levels of
income inequality or poverty if there are opportunities for them to improve their incomes.
Therefore, it is important to understand the extent of income mobility and poverty
persistence in order to develop appropriate social policies, particularly anti-poverty

measures.

 

'3 Source: Statistics Canada, CANSIM Table 202-0705.

1“See Morissette (1995). Morissette, Myles, and Picot (1995), and Zyblock ( 1996a) for a discussion about
the changes in earnings inequality: and Wolfson (1986), Zyblock (1996b), Beach and Slotsve (1996), and
Frenette, Green, and Picot (2003) for a discussion about the changes in family income inequality.

41

Second, income mobility can supply information, although in potentially
conflicting ways, about the degree of opportunity in and economic stability of a society.
The degree of opportunity shows how the ease or difficultly of upward mobility is related
to a person’s skill or effort. A high level of market-driven income mobility provides
incentives for individuals to work hard, and this productivity makes investments in
human capital more rewarding. The latter, however, indicates income movements related
to exogenous shocks. Thus, a high level of mobility may also be viewed as a synonym for
economic insecurity. Given the integration of the US. and Canadian economies, it is
important to know whether Canada’s upward mobility is similar to the upward mobility
in the United States. In addition, given Canada’s more progressive government transfer
system, Canadian policy makers should know to what extent Canada’s redistributive
policies contribute to changes in market-driven mobility and to what extent these changes
compare to market-driven mobility in the United States.

In this study, short- and long-term mobility and how these types of mobility vary
across educational and minority groups are examined using longitudinal data collected
over a 6-year period. Both pre-transfer and post-transfer income measures are used to
examine how a nation’s redistributive system affects the levels of inequality, mobility,
and the incidence of persistent poverty (or high income). Also, a decomposition method
pioneered by Bane and Ellwood (1986) is used to describe the relation between each
income movement and its concurrent event. A parametric hazard model is then employed
to examine the role of events and various characteristics in estimating the risks for low-
and high-income exits and reentries. To place these estimates in a cross-national context,

comparable estimates are computed for the United States.

The results of this study show that significant household income mobility
occurred in both Canada and the United States. Although 40 percent of the population in
both countries was in the low-income bracket at least once during the survey years, only
10 to 13 percent of the population was in the low-income bracket every year. The results
indicate that Canada’s redistributive system significantly increased income stability.
From any given year (1-1 to t). the proportion of people who remained at the two
extremes of income distribution decreased from 43.5 percent to 23 percent when post-
transfer measures were used, while the proportion who remained in the same middle
income groups increased correspondingly (from 21% to 39%). There was considerable
income immobility in both countries as well. About 65 percent (70%) of Canadians
(Americans) who were living in poverty at the beginning of the survey were still living in
poverty after 6 years. Rates of mobility varied greatly across socio-demographic groups.
Less-educated and non-white people in both countries tended to experience persistent
poverty. Furthermore, there is little evidence in this study that Canadian immigrants
whose mother tongue was not one of the two official languages experienced higher risks
of continued poverty. A low-income (or high-income) spell beginning or ending in the
US. was mostly associated with events concurrent with changes in labour earnings, while
demographic events, such as family formation or separation, played an equally important

role in spell change in Canada.

43

2.2 Literature Review

The collection of longitudinal data in many countries has produced a vast body of
literature on earnings dynamics.” Analyses of household income mobility are also well
documented (e.g., Gardiner and Hills, 1999; Gittleman and Joyce, 1999; Gottschalk and
Danziger, 1997; Jarvis and Jenkins, 1998; McMurrer and Sawhill, 1998), and many of
these analyses focus on poverty dynamics (e.g., Band and Ellwood, 1986; Stevens, 1999).

Income mobility has received relatively little attention in Canada. In part, this is
due to a lack of longitudinal data. Until recently, Canadian research on income mobility
relied on tax-based longitudinal files, which are generally not available to researchers.
Among these scarce studies, most of them focus on earnings mobility. F innie (1997) uses
a longitudinal administrative databank (LAD) to conduct a descriptive analysis of earning
mobility of Canadians from 1982 to 1992 based on quintile transition matrices. Baker and
Solon (1999, 2003) use tax records from 1976 to 1992 to examine changes in earning
mobility. They decompose the growth in earnings inequality into transitory (e.g., earnings
instability) and permanent (e.g., increase returns in education) components. They found
that both components play important roles in explaining the grth in earnings
inequality. Beach and Finnie (2001) use LAD from 1982 to 1996 to examine the cyclical
pattern of changes in earnings mobility. They measure the short-term mobility (year-to-
year) of workers’ earnings over the business cycle and find that higher levels of
unemployment decrease net earning mobility significantly. Beach and Finnie (2004)

further employ a cohort analysis of age-eamings profiles using longitudinal data. Their

44

results echo Beaudry and Green’s (2000) findings and show that the earnings of recent
cohorts (between 1990 and 1994) of young workers have slipped considerably. However,
they also found that returns to experience have increased for these groups because of
steepened profiles.

With respect to income mobility, Laroche (1997) uses LAD from 1982 to 1993 to
estimate the persistence of low income in Canada. He employs a hazard model that
accounts for multiple spells and finds that about 40 percent of the non-senior, Canadian,
low-income population is characterized by relatively low exit rates and high reentry rates.
F innie (2000) uses LAD from 1992 to 1996 to explore the dynamics of low income in
Canada. His analyses focus on a variety of issues, including total time spent in the low-
income bracket, and he estimates exit and reentry rates. His findings show that while a
substantial percentage (40%) of the population experienced low incomes at some point
during 1992 to 1996 only 6 percent were poor every year. In addition, his results also
show a strong duration dependence: The longer an individual remains in poverty, the less
likely that person will be to escape poverty.

The use of administrative data has many advantages in mobility analysis. It is
more accurate on income measures. It has longer and generally more consistent series
across time, and its enormous sample size enables researchers to explore more flexible
models compared to conventional survey data (Baker and Solon, 1999, 2003). However.
it is also limited—there is a shortage of personal information—and therefore, it offers

fewer opportunities to develop specific policy initiatives. For example, many US. studies

 

lsSee, for example. Atkinson, Bourguignon. and Morrison (1992), Gottschalk and Moffitt (1994),
Gittleman and Joyce (1996). Gottschalk (1997). Buchinsky and Hunt (1999). Solon (1999b), Haider (2001),
Corcoran (2001), and Moffitt and Gottschalk (2002). See also OECD (1996) and Burkhauser, Holtz-Eakin,

45

(such as Gittleman and Joyce, 1999) consider race to be one of the determining factors of
income mobility. Similarly, in Canada, ethnic backgrounds or education level would
likely be an important factor influencing income mobility. Nevertheless. these types of
questions cannot possibly be answered from tax data alone.

The primary contribution of the present study to the existing Canadian income
mobility literature is that it uses the first wave of longitudinal survey data, which became
available recently, to examine household income mobility in Canada during the 19905.
The results not only provide a benchmark that can be compared to the results from the tax
data. but also further provide information about income mobility among subgroups,
particularly in the areas of education and minority status, which are not available in the
tax data. This fills a gap in the literature and assesses the influence of education and
ethnic backgrounds on an individual’s income mobility. Both pre-transfer and post-
transfer income measures were used to conduct the mobility analysis. This creates a
picture of how a nation’s redistributive policies contribute to changes in market-driven
mobility. In addition to the conventional focus on changes in the labour market, this study
addressed the importance of household decisions and the roles of secondary family
members on income mobility. A decomposition method was used to describe the relation
between the beginning or ending of a low-income (or high-income) period and its
concurrent event (such as family formation). Finally, to see whether Canadian income
mobility is different from other countries. US. data were also analyzed for cross-national

comparison.

 

and Rhody (1997) for cross-national comparison; and Solon (1999a. 2002) for a survey of cross-national
comparison on intergenerational earnings mobility.

46

2.3 Data Sources and Cross-Sectional Statistics

The analysis uses data from the Cross-National Equivalent Files (CNEF) version
of the Panel Study of Income Dynamics (PSID, 1990-1997) for the US. sample'6 and the
Survey of Labour and Income Dynamics (SLID. 1993—1999) for the Canadian sample.
Variables have been defined in a similar manner across the surveys in order to encourage
cross-national research.'7 The CNEF data has a key advantage. It provides imputed
aggregate income variables and defines them consistently across different datasets. Such
variables include pre- and post-transfer household income, estimates of annual labour
income, assets. imputed rent, private and public transfers, and taxes paid at the household
level. The extensive information available for each household income creates a complete
picture of the relative roles of market, family, and state in determining income level.

The analyses of the CNEF data are conducted for both pre- and post-transfer
income levels. Pre-transfer income is defined as the sum of household labour income,
asset income, imputed rent, and private transfers.I8 Post-transfer income is the amount of
pre-transfers plus public transfers, social security pensions, and tax components.‘9 The

definition of household includes people living in the same dwelling unit. whether related

 

16The CNEF version of the PSID does not distinguish between the Survey Research Center (SRC) sample
and the Survey of Economic Opportunity (SEO) sample. The former is a nationally representative sample,
and the latter is a national sample of low-income families. The PSID core sample combines the SRC and
SEO samples. Many U.S. mobility studies use only the SRC sample and exclude SEO from the PSID to
avoid overrepresentation of poor individuals (e.g., Haider, 2001). As a result. careful interpretation is
needed when comparing results from this study to other US. studies. However, it should be noted that
although the CNEF version of PSID does not exclude SEO from PSID weights are used to keep the sample
nationally representative.

l7See Burkhauser et a1. (2000) for a detailed description of the CNEF data.

l8Private transfers include private pensions. annuities, child support, alimony, and income from non-
household members.

47

by blood or marriage or unrelated. To properly measure the well being of each family
member, total family incomes are standardized through an equivalence scale in order to
take into consideration both monetary and demographic components. The equivalence
scale used here is simply a square root of family size. The adjusted income, hereafter
called equivalent income, can be regarded as an estimate of potential disposable income
for each household member under the assumption of equal sharing.

There is no consensus between Canada and the United States with respect to the
definition of poverty or high income. For example, the US. has an official “poverty” line,
while many cutoffs are used to define low income in Canada.20 In this study, measures of
low income and high income are based on the income distribution in each country in a
given year. Individuals are considered low income if their equivalent incomes fall below
50 percent of the national median in the first year of the analysis. Similarly, they are
considered high income if their equivalent incomes are 150 percent of the national
median. In this chapter, the terms “low income” and “poverty” are used interchangeably,
although they are not technically the same thing.

The population samples are meant to be representative of all individuals. Table
2.1 offers an overview of cross-sectional statistics for both countries in the 19905; both

pre-transfer and post-transfer income measures are displayed. The data are adjusted for

 

19Public transfers in Canada include child tax benefit, employment insurance, workers compensation. social
assistance, provincial income supplements. old age security. guaranteed income supplement and spouse
allowance, GST credits, and provincial tax credits. Public transfers in the US. are the sum of AFDC
payments, Supplemental Security Income (SSI). unemployment insurance. worker‘s compensation. other
welfare income. and the face value of food stamps. Tax components in SLID include federal and provincial
income taxes, while in PSID, the tax components cover income taxes as well as payroll taxes.

20Instead of a single official poverty line. three different low-income cutoffs are commonly used in Canada.
First is the Low Income Cutoffs (LICO). which is calculated according to seven family sizes and five sizes
of area of residence to measure low income. Second is the Low Income Measure (LIM), which is defined
as half of the median income. The third measure is called market basket measure (MSM). developed by
Human Resource Development Canada (HRDC) in 2000. It costs out a basket of necessary goods and
services, including food, shelter, clothing and transportation. and a multiplier to cover other essentials.

48

inflation and expressed in 1997 dollars. To help with interpretation, purchasing power
parities (consumption based) are used to convert Canadian dollars to the US. equivalent
over the sample period.

At first glance, the degree of inequality, as measured by the Gini coefficient and
the percentile ratios, was generally higher in the United States. Compared to the
Canadian data, the Gini coefficients with respect to pre- and post-transfer income were
about 3 and 8 percent higher in the US, respectively. Pre-transfer income dispersion was
quite substantial in both countries: the 10 to 50 ratios were about one-tenth and one-
seventh for Canada and the US. respectively. The low-income rates were surprisingly
high: Approximately 1 in 4 people in both countries would be low income if there was no
tax-transfer system.

The story changed dramatically when transfers were considered. In general, the
degree of inequality was reduced significantly in both countries, as indicated by the
smaller Gini coefficients and smaller gaps between percentile ratios. Effects of
government transfers were relatively significant in Canada: The 10 to 50 ratio increased
to 0.47 from 0.1 when transfer incomes were added. The comparable increase for the
US. data was from 0.15 to 0.35. In terms of low-income rates. the tax-transfer system
reduced low-income rates by about 14 percentage points in Canada and about 7
percentage points in the United States. The transfer system in Canada also had a stronger
impact on the upper portion of the income distribution: The more progressive tax systems
in Canada reduced the high-income rate by an average of 7 percentage points (compared

to a 3 to 4 points reduction in the US).

49

As shown in Table 2.1, pie-transfer income distributions do not differ drastically
between Canada and the United States. However, when tax-transfer systems are taken
into account, Canada redistributes more income from the two ends of the distribution, and
therefore reduces the incidence of poverty. This is consistent with the fact that Canada’s
social insurance systems—particularly“social assistance and public pension benefits—are
generally more progressive and more generous for low-income people compared to

systems in the US. (e.g., Kennedy and Gonzalez, 2003; Turner. 2001).

2.4 Income Mobility
2.4.1 Persistence of Low Income and High Income

Table 2.1 shows the number of people in the low-income bracket in a given year
and how government transfers reduced the rate of poverty. However, it does not tell how
many people were in poverty on a long-term basis, nor does it provide information about
how government transfers reduced the persistence of poverty. Using longitudinal data.
Table 2.2 illustrates the incidence of low income and high income. The sample included
individuals present in all years during the survey period.

With respect to pre-transfer income, each country had approximately 40 percent
of the population in the low-income bracket at least once during the survey period.
However, only about 10 and 13 percent of the population in the US. and Canada,
respectively, experienced low incomes in all survey years. This indicates that low income
is likely a temporary experience for most people. Similarly, about 56 percent of
Canadians and 48 percent of Americans achieved high-income status at least once during

this period, while only 14.4 and 16.3 in Canada and the US, respectively, were able to

50

stay in the high-income bracket on a long-term basis. However, such estimates may be
understated because this approach ignores the left- and right-censoring periods at the
boundaries. Accordingly, an alternative measure, which is based on average income. is
also reported in Table 2.2.2' This measure resulted in higher incidences as shown in
columns (3) and (6) of Table 2.2. For example, about 21 percent, rather than 11 to 13
percent, of the population in both countries experienced persistent poverty under this
measure.

When subgroups are analyzed, patterns of income persistence show differences
between countries. Generally, well-educated people (college or more) in the US. were
less likely to experience a persistence of poverty and tended to stay longer in the high-
income bracket: The incidence of persistent poverty and high income among this group
was about 2.5 and 29.5 percent, respectively, in the US. compared to 6.7 and 19.5
percent in Canada (see Table 2.2, columns [2] and [5]). The stronger high-income
stability among well-educated people in the U .S. is likely to increase long-run inequality,
which usually is ascribed to a large increase in the returns to education. Consistent with
previous findings, this effect is not significant in Canada (Bar-Or et al., 1993).

Minority is another story. No discussion of social policy in Canada or the US.
would be complete without mentioning minority groups. In the US, racial characteristics
are seen as barriers to equal opportunity, while ethnic backgrounds and language profiles
appear to be a major factor influencing social exclusion in Canada.22 Table 2.2 shows that

non-whites in both countries tended to experience poverty and persistent poverty and

 

21That is, an individual is considered always poor if his or her sum of equivalent incomes across survey
years is less (greater) than the sum of the low (high) income thresholds across survey years.

51

were less likely to attain high-income status. Compared to the average, the chance of
experiencing poverty was about 4 and 25.6 percentage points higher for non-whites in
Canada and the US, respectively, and about 12.7 and 25.2 percentage points lower for
experiencing high income. In Canada. Francophone Canadians showed a higher incidence
of poverty and a lower chance of high income. However this might be due to the
province-specific effect because most Francophone Canadians live in the province of
Quebec, and the average family incomes were relatively lower in this province compared
to the national average.23

Immigrants in Canada exhibited two distinct patterns in low- and high-income
incidences. Immigrants whose mother tongue was not one of the two official languages
experienced about a 5 percentage point higher rate of poverty and experienced about a 15
percentage point lower rate of high income than the national average. On the other hand,
immigrants who were proficient in either of Canada’s official languages did even better
than the average person in Canada: They had fewer risks of being in the low-income
bracket and better chances of staying in the high—income bracket. This finding is not
surprising because Canada‘s immigration process involves a points system that favours
those who possess talents, including language skills. Immigrants who are proficient in
either of Canada’s two official languages usually are able to transfer foreign skills

successfully.

 

22The original CN EF version of SLID does not contain information about mother tongue and immigration
status. This information was obtained by linking the SL1D_CNEF files to Statistics Canada’s SLID Master
records.

23 The average pre-transfer family income was $48,591 in the province of Quebec (compared to the national
average $55,619) over the period of 1993 and 1998 (Statistics Canada. 2003).

52

2.4.2 Government Transfers and Income Mobility

To look at how redistributive systems affect individuals’ mobility through the
income distribution, post-transfer income measures are reported in the second and third
sections of Table 2.2. The second section defines low income and high income based on
relative cutoffs (i.e., below 50% and above 150% of post-transfer median), while the
third section applies absolute cutoffs—the same cutoffs used in the pre-transfer income
measure—to define low income and high income. By comparing these two measures, it
was possible to get a sense of how much change in the incidence of low income or high
income is due to transfers or cutoff changes.

Basically, transfer systems in both countries reduced the variations of bad and
good income years for an individual and. therefore, increased income stability,
particularly in Canada. Compared to the pre-transfer measure, the persistence of low
income in Canada dropped from 13 to 2.9 percent. The decline in the low-income rate is
mostly due to transfers and not to differences in the cutoff. Using the same poverty cutoff
line as a pre-transfer measure would slightly change the low-income rate to 3.8 percent.
This suggests that a large proportion of low-income individuals in Canada improved their
income level through government transfer systems.

The effect of US. transfer systems on low-income incidences was smaller. More
interesting, a significant portion of the decline in incidence was caused by a change in the
cutoff line, not transfers. For instance, the persistence of poverty in the US. dropped 5
percentage points (from 10.7% to 5.7%) with the inclusion of government transfers.
However, the decline in persistent poverty shrank to 2.8 points if the same pre-transfer

cutoff was used. This suggests that US. transfer systems distributed little money to

people living in poverty. Some of these people might rise above the poverty line because
of the decline in overall median. but they were still below the average standard of living
if a pre-transfer cutoff was used.

The Canadian transfer system also exhibited great impacts on income stability
among subgroups. The rates of persistent poverty among less-educated people dropped
from 22.4 percent to 4.7 or 6.3 percent and from 18 percent to 2.7 or 4.2 percent for
immigrants who did not speak one of the two official languages. On the other hand, the
cutoff effect is more salient among subgroups in the US, especially for non-whites:
About two-thirds of the decline in persistent poverty for this group was caused by a
change in the cutoff line.

To further illustrate how the redistributive system affects income mobility, the top
section of Table 2.3.1 shows the distribution of individuals across income intervals, and
the second section shows the short-term (1 -year) mobility rates using both pre-transfer
and post-transfer income measures. An absolute cutoff based on pre-transfer income is
used throughout this Table. Individuals are placed into income groups according to the
size of their incomes relative to the absolute median income. Generally, the population at
the two extremes of the income distribution reduced significantly when transfers were
considered, suggesting an increase in overall income stability. Prior to transfers, more
than 50 percent of individuals in each country had their income located in the two ends of
the income distribution. With transfers, the proportion of low-income people dropped
from 24 percent to 13 percent in Canada, while it only dropped from 24 percent to 21

percent in the US.

54

The second section of Table 2.3.1 shows l-year transition rates. With respect to
the pre-transfer income, movement at the tails of the distributions was rather rigid in both
countries. Roughly four-fifths of individuals in the lowest and the highest income groups
were likely to remain in the same group the next year. Of those who moved out of the
lowest (or the highest) income group, more than half moved into neighbouring groups.
Mobility among individuals not in the two extremes was larger. About 41 to 47 percent
(3 7%—43%) of people in the middle-income groups in Canada (the US.) remained in the
same income group the next year, while about 12 to 18 percent (12%—24%) of
individuals among these groups in Canada (the US.) moved up or down more than one
income group.

With transfers, the distribution of individuals was more concentrated in the
middle—income levels (as shown in the top section), and more people tended to stay in the
same income level over time, indicating an increase in income stability. In Canada, for
instance, the proportion of individuals located in “0.5—0.75” income category in any
given year increased from 11 to 20 percent when transfers were added; the proportion of
people staying at this income level over time also increased significantly from 47 to 63
percent. The increase in income stability was more apparent in the lower half of the
distribution.

Table 2.3.2 reiterates the impact ofa redistributive system on mobility. Instead of
the transition rates shown in Table 2.3.1, each entry in Table 2.3.2 represents the percent
of individuals from year t-I to year I. There were two main effects of transfers on
mobility. First, the percent of individuals who stayed at the two extremes was reduced.

while the percent of people who stayed at the same income level other than the two

55

extremes increased correspondingly. Second, long-range mobility (move of more than
two income groups) decreased. If they moved, people were most likely to move into
neighbouring groups.

Overall, the redistributive systems in the US. play a relatively smaller role in
income stability compared to Canadian systems. With transfers, for example, the percent
of people who stay at the same income level (not at the two ends) increased by 9.5
percentage points in the US. (versus 17.4 in Canada). The cross-national differences are
more obvious among subgroups. For individuals with high school or less education,
Canadian transfers reduced the risk of staying in the low or high end of the income scale
for consecutive years by about 24.8 percentage points (from 44.7% to 19.9%) and
increased the chance of staying at the same income level among people in the middle-
income range by about 22.2 percentage points (from 21.7% to 43.9%). The comparable
numbers for the US. are 10.9 and 8.8 percentage points, respectively.

Tables 2.2, 2.3.1, and 2.3.2 provide an analysis of US. and Canadian
redistributive policies on market-driven income mobility. These Tables show that a
redistributive system significantly reduced the incidence of low income and high income
and increased income stability for people in the middle of the income spectrum
(particularly the lower half). The patterns were more evident in Canada than in the United

States.

2.4.3 Short-term versus Long-term Mobility

Using longitudinal data collected over 6 years, it was possible to break down

mobility by length of income tracked. Table 2.4 shows mobility rates out of the lowest

56

and the highest income groups by length of time (1 year, 3 years. and 6 years) and also by
characteristics. Only pre—transfer income measures were used. Overall, short-terrn
mobility is slightly higher in the US. than in Canada. On average, 22.5 percent of
Americans who were observed in low income in the first year of survey left low income
the next year (versus 18% for Canadian). However, long-term mobility (6 years) shows
an opposite trend: 36 percent of Canadians who were observed in low income in the first
year of survey were able to escape low income in 6 years (versus 30% in the US).
Upward mobility was even salient in subgroups. Among low-income Canadians who
possessed a college education in 1993, 24 percent rose above the poverty line the next
year, and only 62.4 (51.7%) percent of the original group remained below the poverty
line after 3 (6) years. Compared to the Canadian experience, low-income Americans who
possessed a college education tended to rise above the poverty line quickly: About 35
percent of them escaped low income in 1 year. However, the upward mobility rate did not
increase proportionately as the length of time increased: About 55.4 percent of the
original groups remained in low income after 3 or 6 years. It seems that accumulation of
market experience in Canada is a much more important factor than an individual’s
demographic or ethic background in the process of income mobility.

This pattern also applied to non-whites and immigrants who did not speak either
of Canada’s two official languages. For instance, among low-income immigrants in 1993,
their upward mobility was modest: about 14.8 percent lefi low income in next year.
However, their upward mobility increased significantly as they gained more experience
living in Canada: Nearly 30 percent left low income in 6 years. This has policy

implications because this type of immigrant is not considered a benefit to the labour

57

market because of the language barrier and problem of credential recognition. Some
people are afraid that the immobility of income among this type of immigrant may result
in welfare dependency and, therefore. create financial and societal problems. The
longitudinal data suggest that this problem might be mitigated as the immigrant becomes
assimilated into Canadian society.

In the US, long-term mobility also increased for minority groups, but the
improvement was rather limited. Among low-income non-whites, about 14 percent left
low income the next year. while about 82 percent of the original group remained in
poverty after 6 years.

Turning to high income mobility. the probability that college-educated people
would drop out of the high-income bracket after 1 year was about the same (15%) for
Canada and the US, but nearly 80 percent of the original group in the US. was able to

remain in the high-income bracket after 6 years (compared to 72% in Canada).

2.4.4 Cross-National Comparison

15 Canada an outlier in income mobility compared to other OECD countries?
Unfortunately, there is no study that can be compared directly with the study discussed in
this chapter. Rather than income itself, a few international studies examine earnings
mobility. For instance, OECD (1996) examined 5-year earnings mobility for all wage and
salary workers for eight OECD countries (Canada not included) between 1986 and 1991,
and Burkhauser, Holtz-Eakin, and Rhody (1997) compared earnings mobility in the
United States and Germany during the growth years of the 19805. In order to compare the

results of these two studies to the results of the present study, a quantity (based on

58

household earnings) was calculated for Canada and the US. using definitions similar to
those used in the two international studies (see Table 2.5). The focus is on 5-year
earnings mobility for wage or salary workers. It is, however, still difficult to compare
results across countries due to differences in the specific time period analyzed.
Fortunately, the United States appears in all these studies. and it can, therefore, be used as
a benchmark. Generally, the results from this present study show that earnings mobility
in the US. and Canada are very similar. Mobility in the US. does not display significant
differences from other countries in other two studies; therefore, Canada might also have
similar patterns of earnings mobility compared to other countries. Nevertheless, cross-

national comparisons of household income mobility remain an issue for future research.

2.5 Empirical Hazards of Low-Income and High-Income Transitions

In the previous section, income mobility was measured as the probability of
making income transitions between one year and the next. However, nothing is known
about how the length of time being poor affects the probability of leaving poverty. Do the
chances of making a transition grow or decline as the time spent in the current income
state increases? What are the risks of reentering after escape? This section addresses
these questions by offering empirical hazard rates of the associated transitions.

To avoid problems due to left censoring, only individuals who were observed
starting a new income level during the survey period are included.24 In the case of the
low-income exiting rate, for example, an individual’s entry into low income had to be

observed over the survey period in order to be included in the calculation. Moreover,

59

following Jenkins (2000), small income changes around income cutoffs (e.g., earned $1
dollar above poverty threshold) are not considered a transition.25 Pre-transfer income
measure was used in this analysis.

Tables 2.6 and 2.7 show Kaplan-Meier estimates of low- and high-income
transition rates, respectively. People entering a new income level were assumed to remain
at that level for the rest of the year because only annual data were observed; therefore,
there is no hazard rate for the first year of transition. Overall, both Canada and the US.
displayed very similar patterns in low-income exit rates. About one-third of the poor were
able to escape poverty after 1 year. The exit rates then fell as time spent in low income
increased, reaching about 0.14 in the fifth year of low income. One exception is Canadian
immigrants whose mother tongue is one of the two official languages. Their exit rates
from low income remained relatively high even after 5 years. As for poverty reentry,
people with a high school diploma or less tended to have higher risks of returning to
poverty, particularly in the United States: About 62 percent of this group in the US. fell
back into poverty in the next 5 years, compared to 48 percent in Canada. The results for
Canadian immigrants who do not speak either of the two official languages are somewhat
counterintuitive: Their risks of reentry were relatively smaller when compared to other
groups.

Information about poverty exit and reentry shows that less-educated people in
both countries and non-whites in the US. were more likely to experience a persistence of

poverty. They had lower chances of leaving poverty, while they faced relatively higher

 

24The exclusion of left-censored samples may introduce a selection bias (Ham and LaLonde, 1996: Stevens,
1999). For example, individuals who are poor throughout the observed period are excluded from the
sample. This may result in an over-estimation of exit rates from poverty.

60

risks of returning to poverty. lnforrnation about immigrants in Canada reveals another
story. Immigrants who did not speak either of the two official languages as a mother
tongue tended to remain longer in poverty, but they were less likely to fall back once they
reached another income level. The possible reason is that skilled immigrants are more
likely to be admitted to Canada because of the points system. Immigrants who do not
know Canada’s official languages but are still admitted usually possess certain skills that
are needed in Canada.26 These immigrants may start very slowly, but they are likely to
improve their economic status as soon as their skills are transferred and utilized.

High income seems a more permanent fixture than poverty, especially in Canada.
Those people who remained in the high-income bracket after 5 years were 49 percent and
40 percent for Canada and the US, respectively. Probabilities of exiting high income
were roughly the same for college-educated people in both countries. However, high-
income reentry rates were relatively greater in the US. among this group: About 52
percent of college graduates who left high income were able to re-attain high-income
levels in the next 5 years versus only 39 percent in Canada. Although it is not certain
whether highly-educated people in the US. were better at reentering high income or
whether there was simply more demand for skilled workers in the US, the results do
reveal a relatively better opportunity for well-educated people in the US. This finding

could have implications for a future “brain-drain” from Canada.

 

25 Following Jenkins (2000), post-transition earnings that rise (fall) by not more than 10 percent above
(below) the low-income line are not considered an exit (reentry). A similar definition is used for high-
income transitions.

61

2.6 The Correlates of Income Spell Endings and Beginnings

Often, an individual’s well being changed as he or she moved into or departed
from a household or as a result of an increase (or decline) in income sources from
earnings of secondary family members or benefits from welfare programs. In order to
examine the importance of household events (e.g., separation) as well as changes in other
income sources in relation to entries into and exits from low income and high income for
an individual, a decomposition approach was applied to link the occurrence of an income
movement and concurrent event. The aim was to assess the roles of market conditions,
family, and welfare programs on low-income (or high-income) spell beginnings or
endings.

Similar to Bane and Ellwood (1986), and Jenkins (2000). we relate each spell
ending or beginning to 1 of 15 mutually exclusive groups (see Figure 2.4). For each entry
or exit into a particular state, it was first determined if there was a concurrent change in
the head and size of the household”. If there was no change in head and family size, then
this was referred to as an “income event.” If there was no change in head but a change in
family size, two possibilities were allowed. The transition was considered an income
event if change in head’s earnings accounted for 50% or more of the change in household
income. Otherwise, it was considered a demographic event. Similarly, if there was a

change in household head. two possibilities were allowed. The transition was considered

 

2"Under Canada’s points system, proficiency in official languages accounts for 15 points in the selection for
skilled worker; education and occupation-specific factors account for another 34 points. The initial pass
mark is 70 points. Note that the selection criteria were revised in 2002, with a shift away from a focus on
occupational shortages and towards the human capital indicator of long-term earnings potential (see
McHale, 2003).

62

a demographic event if the change in household income was largely resulted from the
variation in earnings due to head changes. That is, a transition was referred to as a
demographic event if the difference in earnings between the new head and the old head
accounted for 50% or more of the change in household income. Otherwise. it was
considered an income event.

Tables 2.8 and 2.9 display events associated with low-income phenomena. Post-
transfer income measures were used in this section. Overall, the majority of low-income
episodes ended or began because of an income event, particularly due to a change in
household head’s earnings. An increase in income from a social security pension had a
substantial impact on raising senior citizens in Canadians out of poverty: About 37
percent of senior households ended their poverty spells because of an increase in social
security pensions. Assets and private transfers. on the other hand. were relatively
important in helping senior citizens escape poverty in the US. It is noteworthy that public
benefits in both countries provided little support in helping single-parent households
move out of poverty. Rather, about 40 percent and 30 percent of single-parent households
in the US. and Canada, respectively, relied on their own efforts to escape poverty.
However, the effect of public transfers might be understated because some of the benefits
were actually embedded in the form of earnings (e.g., U.S. earned income tax credit,
EITC, and the Canadian National Child Benefit Supplement”) because these benefits are

only available to people with earned income.

 

27In both surveys the male partner was always identified as the household head. Notice that in the general
SLID database, the household head is defined as the person with the greatest individual income (total from
all sources before taxes) for the year. However. a modification to this concept has been made for the
purposes of the equivalence file. In CNEF, if the major income earner is a female living with a spouse, the
male partner is identified as the household head.

28The Canadian National Child Tax Benefit provides a refundable tax credit to assist low-income workers.

63

Demographic events were more frequently observed in Canada, particularly
among subgroups. In Canada, about 29 percent of single-parent households ended their
poverty spells because of events associated with marriage or partnership. and about 40
percent of single-parent households began poverty spells with events related to family
separation. The corresponding figures were about 18 and 31 percent in the US. It is also
noteworthy that, in Canada. only 27 percent of low-income spell endings among single-
unattached families were associated with a rise of income from own labour earnings,
while about 47 percent of the spell endings among this family type were associated with a
demographic event. If young adults comprised most of the population in this family type,
it may suggest a great hardship faced by young Canadians who became independent.
Thus how to provide immediate social assistance or how to establish an access to labour
market or education may be very important.

Tables 2.10 and 2.11 show the events coinciding with high-income transitions.
About 83 percent (87 percent) of high-income spell endings (beginnings) in the US. were
associated with an income event, compared to 63 percent (78 percent) in Canada.
Basically, changes in earnings were the main reason for achieving or forfeiting high-
ineome status. However, the US. and Canada do exhibit quite different patterns,
particularly among traditional households (couple with children). In Canada, 33 percent
of high-income spell beginnings in this group were associated with a rise in secondary
earnings compared to only 17 percent in the US. Instead, in the US, 53 percent of
beginnings among couple/children households were the result of an increase in the

household head’s earnings. This suggests that double earners in a household are an

64

important factor in attaining high-income in Canada, while it is high-paying jobs, rather

than double earners. that propel U.S. families into wealth.

2.7 Econometric Model of Income Dynamics

The above section highlights the importance of household events on the transition
probabilities. However, such arithmetic is not modeling, and it does not provide a means
for predicting future risks of income transitions. In this section, a discrete-time hazard
model is used to incorporate these events as regressors alongside other personal and
household characteristics. Estimated coefficients are also used to predict the transition
probabilities.

Event variables are defined in a hierarchical fashion (see section 2.6). Each event
is summarized by a binary variable that is equal to 1 in the interval the event occurred
and 0 for all other intervals.29 The other covariates include duration, calendar year
indicators, female-head indicator, dummies for age groups, family compositions, regions,
minority status, and level of education for household head (defined as the primary wage
earner).30 Mother tongue and immigrants status are also included as regressors in the
Canadian data.

Analyses cover individuals who had fresh spells (non-left censored), and those
individuals had a nonzero cross-sectional weight.3 ' Income transitions were based on the
definition of pre-transfer incomes throughout this section. Pre-transfer income was used

to provide a picture of income mobility prior to interventions.

 

298y doing this, it is assumed that the effects of events are entirely contemporaneous. The impact of an
event that persists over time is ignored.

30In case of equal earnings within a household, the characteristics of the oldest male in the household are
used.

65

Tables 2.12 and 2.13 show the estimated parameters for four transitions: (1)
leaving low income, (2) reentering low income, (3) leaving high income, and (4)
reentering high income. The predicted transition probabilities for the reference person are
also provided at the bottom of the Tables. In SLID, a reference person was defined as an
18- to 34-year-old adult (in 1997), living in an Ontario household containing a couple
with children who had not experienced any demographic events within a year and whose
household head is a native-born white male with 12 years of education who speaks
English as a first language. In PSID, a reference person was defined as an 18- to 34-year-
old adult (in 1996), living in a northeastern U.S. household containing a couple with
children who had not experienced any demographic events within a year and whose
household head is a male with 12 years of education and does not belong to a minority
group.

Basically, the econometric results echo descriptive findings from the previous
sections. With respect to the role of events, marriage and household merge (e.g., two
families move into one house) were found to have a strong effect on leaving poverty in
both countries, while events such as divorce or newly established family (e.g., child
becomes head or spouse) significantly affect the probabilities of poverty reentry. For
example, compared to the reference person, the predicted poverty exit rates for Canadians
(Americans) whose household underwent a marriage event were about 31 (24) percentage
points higher. Events of addition or subtraction to household (other than head and spouse)
exhibited mixed results. Child-birth events tended to reduce the chance of leaving

poverty and increased the risks of poverty reentry in Canada. while the effects were not

 

3 lAlso see footnote 24 for possible bias by exclusion of left-censored samples.

66

significant in the US. data. Addition of adults to household increased the probabilities of
leaving poverty in the Canadian data, while it showed an opposite effect in the US. data.

For high-income transitions. demographic events—despite being observed less
frequently in a hierarchical fashion of spell ending and beginning (see Tables 2.10 and
2.11)—had a strong impact on the probabilities of ending or beginning a high-income
spell. Generally, events such as newly established family, divorce. and household merge
led to the end of a high-income spell, while marriage, household merge, and subtraction
to family (needs fall) events resulted in the start of a high-income spell.

As for the role of other characteristics, both countries exhibit strong duration
dependence in low income transition: The longer an individual stays in low income (non-
low income), the more difficult it is for him or her to escape (reenter). Parameter
estimates show that seniors, single-parent families, those less educated. and individuals in
households headed by a minority or a female tended to stay longer in poverty and
experienced higher risks of reentry. One exception is non-white people in Canada. They
tended to stay longer in poverty; however, they were less likely to reenter poverty once
they escaped it. After controlling for regions and other characteristics, the poverty exit
rates for Canadians who speak French as a mother tongue was no different from their
Anglophone counterparts. However, their poverty reentry rates were significantly higher.
The other interesting finding is that poverty exit or reentry rates show no difference
between immigrants who had little knowledge of Canada’s two official language and
native-bom Canadians once all other observed factors were controlled.

The effect of education on an individual’s transition into and out of poverty was

substantial. In Canada. the predicted poverty exit rate (reentry rate) for less-educated

67

people was about 8.0 (5.0) percentage points lower (higher), compared to the reference
person. The effect seems stronger in the United States: The predicted poverty exit and
reentry rate for less-educated group was about 12.0 points lower and 7.9 points higher.
respectively, compared to the reference person.

In the case of high-income transitions, the role of the covariates shows some
different patterns between Canada and the US. First, duration dependence becomes less
apparent in Canada but still shows a strong effect in the US. data. In the US, for
example, the high-income exit rate was fairly high for the reference person (about 0.40).
and the rate declined significantly to about 0.18 in the fourth year. Second, it is
noteworthy that obtaining a college or above education is especially helpful in remaining
at the high-income level, particularly in the US. Compared to the reference person.
people with a college education in the US. were estimated to have about 38 percent (or
15 percentage points) lower high-income exit rates, and about 37 percent (or 7.5
percentage points) higher reentry rates. The comparable figures are about 33 and 16
percent, respectively, in Canada. This finding shows that returns to skills is more

rewarding in the US.

2.8 Conclusion

This study used longitudinal survey data from the 19905 to look at patterns of
household income mobility in Canada. The approach involved examining levels of
inequality and mobility from both pre-transfer and post-transfer income measures and
short-term and long-term income mobility through transition matrices and conducting a

decomposition analysis to examine the relationship between income transitions and

68

concurrent events. F urthermore. using longitudinal survey data also revealed income

mobility across different demographic groups, particularly in the areas of education and

minority status. This type of information is not available in the tax data that was used in

previous studies. To assess how the Canadian experience differs from other countries.

similar US. and Canadian data were compared. Based on the 19905’ longitudinal data

from Canada and the United States. six conclusions can be drawn-from the results of the

study discussed in this chapter:

1.

There is evidence that significant household income mobility
occurred in both Canada and the US. Although 40 percent of the
population in both countries experienced low income at least once
during the survey years, only 10 to 13 percent of them were in low
income every year (see Figure 2.1). Mobility rates also vary across
subgroups. Based on one-year transition matrices, 24 (35) percent
of the Canadian (American) college graduates who were observed
in poverty in year t rose above poverty line the next year. The
comparable figures for people with high school or less education
were about 14 and 18 for Canada and the US. respectively (see
Figure 2.3).

Canada’s redistributive system significantly increased income
stability. Using both pre-transfer and post-transfer income
measures, Canada’s redistributive system significantly reduced the
variations of bad or good years for an individual and greatly

increased the probability of staying in the middle-income groups.

69

From any given year (t-I tot), the chance of staying both years in
either of the two extremes of income distribution was reduced from
43.5 to 23 percent when post-transfer measures were used, while
the chance of staying in the same middle income group increased
correspondingly (from 2 1 %—3 9%). The redistributive system in the
US. also contributes to income stability. but the effect was

relatively smaller.

. The Canadian transfer system also exhibited great impacts on

income stability among subgroups. The rates of persistent poverty
among less-educated people dropped from 22.4 percent to 4.7 or
6.3 percent and from 18 percent to 2.7 or 4.2 percent for
immigrants who did not speak one of the two official languages
(see Figure 2.2).

. There is considerable immobility in both countries. About 65
percent (70%) of Canadians (Americans) who were in poverty at
the beginning of the survey year were still in poverty after 6 years.
Rates of upward mobility out of poverty were lower for less-
educated (high school or less) people and for visible minorities:
About 75 percent of less-educated people in both countries and 82
percent of non-whites in the US. who were in poverty in the first
year of the survey were still in poverty 6 years later (see Figure

2.3).

70

5. There is little evidence that Canadian immigrants are more likely
to experience higher risks of poverty persistence if they spoke non-
official languages as a first language. The empirical hazard model
suggests that these immigrants tended to stay slightly longer in
poverty, but they were less likely to fall back once they reached
higher levels of income. The parametric estimates from the
econometric model show that these immigrants and native-bom
Canadians have the same probability of poverty leaving or reentry,
given that other things were equal.

6. Household events are more relevant to the spell beginnings or
endings in Canada, while income transitions in the US. were
mostly associated with events concurrent with changes in earnings.
About 43 percent of low-income spell endings among single-parent
households in Canada were related to partnership or household
merges, the comparable figure is about 28 percent in the US.

This present study complements Canadian studies that were based on tax data.
Basically, evidence from both tax and survey data suggest that although many individuals
experienced poverty in any given year, poverty is a temporary state.32 Given the
advantages of the data, this study uncovered income dynamics across finer demographic
groups, which was not possible in previous studies. This new information provides

implications for developing different types of policy initiatives.

71

Furthermore, using comparable longitudinal data from the US, it was found that
Canada does not appear to be an outlier in terms of income mobility in general. There are
common aspects as well as differences in income dynamics between Canada and the US.
Market source income mobility displays similar patterns across both countries,
suggesting a more integrated economy between Canada and the United States. The
differences, on the other hand, are likely to reflect the heterogeneity of the population as

well as differences in welfare programs.

 

32Finnie (2000) shows that more than one-quarter of the population experienced poverty between 1992 and
1996, but only 6 percent were poor in every year. The present study found that about 39 percent of the
population experienced poverty between 1993 and 1998. while persistent poverty was about 3 percent. This
study’s estimates are relatively smaller. However. the use of equivalent income rather than family income
and the inclusion of longer period (6 instead of 5 years) in this study are likely to result in a smaller
estimate in poverty persistence.

1211

Sun

 

(111’.
190
19111
NW

lW,‘
1W5
law
399‘

\

‘Dai’d s
e‘lUlldil
dfilngd
.‘\Car
it'i'tll‘l 10
575313.01
51101131,»,

Table 2.1

Summary Statistics for the Household Equivalent Incomes in the 19905*

(CPI and PPP adjusted Currencies are expressed in I 997 U. S. dollar equivalent)

 

 

 

 

 

%‘ %‘ Unwgt.

90-10 90-50 10-50 Low High sample
Year“ Median Mean Gini. Ratio Ratio Ratio income income size
Pre-transfer Income
Canada
1993 20.591 23.286 0.42 20.4 2.18 0.11 25.6 27.1 38.811
1994 20.918 23.493 0.42 23.3 2.15 0.09 26.2 27.3 38.549
1995 21.001 23.784 0.42 -3.7 2.17 0.09 25.6 27.7 37.856
1996 20.751 23.744 0.43 22.0 2.20 0.10 27.0 27.2 79.208
1997 21.114 24.331 0.43 21.9 2.21 0.10 26.2 28.1 79.218
1998 22.695 26.067 0.44 22.5 2.20 0.10 25.0 30.2 79.636
1999 22.928 26.364 0.43 18.3 2.19 0.12 24.1 31.2 78.891
United States
1990 25.068 31.358 0.45 15.6 2.46 0.16 24.7 27.8 15.199
1991 24.149 30.175 0.45 15.2 2.45 0.16 25.3 26.9 15,173
1992 24.017 30.398 0.46 16.9 2.48 0.15 25.6 27.1 15.289
1993 24.359 30.990 0.46 18.6 2 54 0.14 25.4 29.0 14.924
1994 23.600 31.055 0.49 21.6 2 52 0.12 27.1 27.3 14.977
1995 23.653 30.876 0.47 19.4 2 56 0.13 26.8 26.5 15.548
1996 24.121 31.501 0.47 17.3 2.59 0.15 26.1 28.0 15.087
1997 23.606 30.743 0.50 62.9 2.69 0.04 29.1 28.3 1 1.675
Post-transfer Income
Canada
1993 19.436 21.616 0.29 3.86 1.84 0.48 l 1.3 20.2 38.811
1994 19.533 21.695 0.30 4.01 1.83 0.46 12.3 20.1 38.549
1995 19.703 21.748 0.29 3.95 1.83 0.46 12.0 20.2 37.856
1996 19.801 21.890 0.30 4.07 1.83 0.45 12.9 20.3 79.208
1997 20.012 22.288 0.30 4.17 1.88 0.45 12.4 21.3 79.218
1998 21.122 23.637 0.31 4.13 1.88 0.45 11.6 23.2 79.636
1999 21.417 23.926 0.30 4.1 l 1.87 0.46 10.7 24.8 78.891
United States
1990 22.116 26.394 0.37 5.83 2.12 0.36 17.9 24.3 15.199
1991 21.479 25.442 0.36 5.60 2.07 0.37 18.5 23.0 15.173
1992 21.617 25.654 0.37 5.84 2.10 0.36 18.2 23.1 15.289
1993 21.900 25.957 0.37 5.98 2.1 l 0.35 18.2 24.8 14.924
1994 20.904 25.413 0.40 6.74 2.15 0.32 20.4 22.1 14.977
1995 21.045 25.504 0.38 6.09 2.16 0.35 19.7 22.3 15.548
1996 21.445 26.140 0.38 5.96 2.21 0.37 18.6 23.3 15.087
1997 21.825 26.057 0.42 10.64 2.23 0.21 22.0 25.5 11.779

 

1"Data source: CNEF. Incomes are total household equivalent incomes. standardized through an
equivalence scale to adjust household demographic components. In this context, equivalence scale is

defined as the “square root of household size.”

"Year in Canada refers to reference year, while year in the US. refers to survey year.

IIndividuals are defined as being in low income if their equivalent incomes fall below half of the national
median in the first year of the analysis. Similarly. individuals are classified as being in high income if their
equivalent incomes are above 1.5 times the national median in the first year of the analysis.

Table 2.2

Incidence of Low and High income, by characteristic,”

 

 

 

 

Poverty Poverty Poverty High High High Unwgt.
at least in in all income income income Number
once all years years by at least in in of obs.
%) (%) average. once all years all years (n)
incomel (%) %) by
(%) average
incomel
(%)
(l) (2) (3) (4) (5) (6) (7)
I’m-transfer Income
Canada 39.1 13.2 21.1 55.8 14.4 28.1 29,772
High school or less 52.0 22.4 32.7 30.4 7.3 16.4 13,876
College or more 29.4 6.7 12.8 56.8 19.5 36.8 14,768
Non-White 43.0 19.3 27.4 43.1 10.6 24.3 1.062
Native-bom Canadians 35.5 10.5 17.6 50.2 16.6 31.2 18.141
Anglophone
Native-bom Canadians 43.6 16.5 25.8 35.7 10.5 21.0 6.459
Francophone
lmmigrants— 34.6 1 1.6 18.0 58.2 23.8 36.4 1.131
Official languages
Immigrants— 44.4 18.2 26.8 41.2 1 1.0 25.6 1.782
Nonofficial languages
United States 40.3 10.7 20.5 47.7 16.3 30.1 12,150
High school or less 54.7 17.5 31.2 30.5 5.81 14.3 6,819
College or more 22.4 2.53 7.14 67.7 29.5 49.6 4.709
Non-White 65.9 26.9 46.0 22.2 6.07 1 1.4 4.558
Post-transfer Income
Canada 24.1 2.88 8.2 37.5 9.40 20.7 29,772
High school or less 28.1 4.75 l 1.3 24.3 4.62 12.1 13,876
College or more 20.6 1.46 5.92 47.1 12.8 27.0 14,768
Non-White 34.3 4.92 15.0 37.0 10.5 19.5 1.062
Native-bom Canadians 22.7 1.94 6.6 41.6 1 1.4 23.6 18.141
Anglophone
Native-bom Canadians 25.7 5.10 10.6 24.6 4.5 12.7 6,459
Francophone
lmmigrants— 20.2 1.96 5.31 48.4 15.9 29.6 1.31 1
Official languages
Immigrants— 24.6 2.74 10.8 38.6 9.40 19.3 1.782

Nonofficial languages

 

74

Table 2.2 (Cont’d)

 

 

 

 

Poverty Poverty Poverty High High High Unwgt.
at least in in all income income income Number
once all years years by at least in in of obs.
(%) (%) average once all years all years (n)

incomeI (%) (%) by
(%) average
incomeI
(%)
(l) (2) (3) (4) (5) (6) (7)

United States 32.8 5.73 13.6 43.1 13.6 25.3 12,150
High school or less 43.6 9.37 20.7 27.8 4.31 1 1.8 6,819
College or more 17.8 1.17 4.25 61.5 22.5 42.1 4.709
Non-White 60.9 18.8 36.6 19.1 4.39 8.49 4.558

Post-transfer Income

(using the same cutoffs as pre-transfer income)

Canada 26.44 3.78 9.75 32.57 7.44 17.15 29,772
High school or less 31.06 6.33 13.36 19.83 3.13 9.82 13.876
College or more 22.31 1.88 7.07 41.84 10.39 22.54 14,768
Non-White 35.62 6.88 18.76 31.74 8.80 16.25 1,062
Native-bom Canadians 24.86 2.92 7.71 36.66 8.77 19.91 18.141

Anglophone
Native-bom Canadians 28.23 5.61 12.30 20.54 3.39 10.1 1 6.459
Francophone
Immigrants— 22.37 2.97 6.50 43.06 15.14 24.47 1,131
Official languages
lmmigrants— 25.97 4.19 13.66 33.74 7.35 16.26 1,782
Nonofficial languages

United States 38.09 7.89 17.54 34.90 8.24 18.64 12,150
High school or less 50.03 12.60 26.15 20.13 2.14 8.13 6,819
College or more 21.75 2.07 6.31 52.69 15.93 31.83 4.709
Non-White 66.22 23.81 43.36 14.60 2.68 6.25 4,558

 

*CNEF subsample for individuals presented in all 6 years period (1993—1998 for Canada. and 1991-1996
for US). The last year of longitudinal weights are used to take into account the problem of panel attrition.
Subgroups are divided by characteristics of household head.

1Based on average incomes, people are considered always in poverty (wealth) if their sum of equivalent
incomes across all years is less (greater) than the sum of the low (high) income threshold across all years.

75

Table 2.3.1

Distribution of Income among Longitudinal Sample (Annual AvergeV
% in each income category relative to fixed median cutoff

 

 

 

 

 

< 0.5 0.5 ~ 0.75 ~ 1.0 ~ 1.25 ~ > 1.5 Total
0.75 1.0 1.25 1.5
Canada
Pre-transfer income 24.42 1 1.42 12.65 12.65 10.63 28.24 100
Post-transfer income 12.85 19.93 20 73 17.43 I 1.85 17.20 100
USA
Pre-transfer income 23.93 1 1.91 13.1 1 1 1.35 9.30 30.38 100
Post-transfer income 20.73 17.96 17.72 14.08 10.07 19.44 100
One-Year Transition Rates (Annual Average)”
Outflow rates (%) from year t-l to year t
< 0.5 0.5 ~ 0.75 0.75 ~ 1.0 1.0 ~ 1.25 1.25 ~ 1.5 > 1.5
I’m-transfer Income
Canada
< 0.5 82.0 10.7 3.6 1.7 1.0 1.1
0.5 ~ 0.75 19.7 46.6 21.8 7.3 2.3 2.3
0.75 ~ 1.00 6.9 14.8 45.9 21.6 6.2 4.7
1.00 ~ 1.25 3.8 4.8 15.3 45.0 22.0 9.1
1.25 ~ 1.50 2.6 3.3 6.0 16.9 41.0 30.2
> 1.50 1.4 1.4 2.2 3.8 7.6 83.5
USA
< 0.5 77.5 1...7 4.8 1.9 1.0 2.1
0.5 ~ 0.75 24.6 2.3 21.0 6.1 2.8 3.1
0.75 ~ 1.00 9.5 l .1 42.7 19.6 6.1 5.9
1.00 ~ 1.25 5.7 7.5 19.6 38.0 18.3 11.0
1.25 ~ 1.50 3.9 3.4 6.3 19.3 36.7 30.5
> 1.50 2.3 1.5 2.8 4.2 8.0 81.4
Post-transfer Income
(using the same cutoffs as prc-transfer income)
Canada
< 0.5 69.55 20.57 5.42 2.21 1.18 1.07
0.5 ~ 0.75 12.00 63.28 18.87 3.79 1.12 0.94
0.75 ~ 1.00 3.88 14.62 56.02 20.09 3.59 1.80
1.00 ~ 1.25 1.95 3.99 17.08 52.77 19.24 4.97
1.25 ~ 1.50 1.30 2.90 5.49 19.11 46.37 24.84
> 1.50 1.34 1.32 2.54 4.46 11.50 78.83
USA
< 0.5 73.49 17.59 5.17 1.76 0.85 1.14
0.5 ~ 0.75 19.39 52.45 20.37 4.54 1.84 1.41
0.75 ~ 1.00 6.73 19.02 47.89 18.16 4.56 3.64
1.00 ~ 1.25 3.51 6.46 19.95 45.04 16.90 8.14
1.25 ~ 1.50 2.59 3.15 8.73 19.91 38.48 27.14
> 1.50 1.66 1.50 3.45 5.86 12.53 74.99

 

*Data source: CNEF subsample for individuals presented in all 6 years period (1993—1998 for Canada and
1991-1996 for USA). There are 29.772 longitudinal samples from SLID and 12,150 from PSID. People are
classified into income groups according to the size of their income relative to fixed real income cut-offs
equal to 0.5, 0.75. 1.0, 1.25. and 1.5 times median year 1 income.

76

Table 2.3.2 .
Income Mobility by Characteristics (Annual Average)

 

From year t-I to year 1.
percent (%) of individuals

 

 

Spent both Stayed in the moved into moved over Total
years in same income neighboring 2 + income
poverty or group (not income groups
high income the two group
ends)
Pre-transfer Income
Canada 43.49 21.26 23.82 1 1.42 100
HS or less 44.75 21.69 23.41 10.13 100
College or more 42.74 21.06 24.04 12.16 100
Non-White 45.68 19.88 22.76 1 1.68 100
Native-bom Canadians
Anglophone 43.33 21.25 23.56 1 1.85 100
Francophone 41.53 23.81 24.33 10.34 100
lmmigrants——OLl 50.02 20.21 19.77 10.00 100
—NOL 45.83 17.72 25.48 10.97 100
United States 42.92 18.70 24.50 13.89 100
HS or less 39.16 20.78 26.26 13.81 100
College or more 47.56 16.86 22.56 13.02 100
Non-White 50.07 16.50 22.20 1 1.25 100

Post-transfer Income
(using the same cutoffs as pre-transfer income)

Canada 22.79 38.69 29.44 9.08 100
HS or less 19.92 43.94 28.43 7.72 100
College or more 24.78 35.21 30.19 9.82 100
Non-White 30.49 30.86 30.07 8.59 100
Native-bom Canadians

Anglophone 23.40 37.36 29.38 9.87 100
Francophone 18.68 45.21 28.82 7.30 100
Immigrants—0U 28.39 37.54 25.45 8.63 100
—NOL 25.09 35.10 31.88 7.95 100

United States 26.67 28.20 29.80 12.33 100
HS or less 28.24 29.55 30.34 11.88 100
College or more 31.17 27.33 29.17 12.32 100
Non-White 42.08 21.73 26.58 9.59 100

 

Data source: CNEF subsample for individuals presented in all 6 years period (1993—1998 for Canada and
1991—1996 for USA). There are 29,772 longitudinal samples from SLID and 12,150 from PSID. People are
classified into income groups according to the size of their income relative to fixed real income cut-offs
equal to 0.5, 0.75, 1.0. 1.25. and 1.5 times median year 1 income.

*Characteristics of household head (defined as major income earner).

1OL: Speak official languages as mother tongue: NOL: Speak nonofficial languages as mother tongue.

77

Table 2.4 *
Probability of Moving Out of the Low- and High-Income Group , by
Characteristics

 

 

 

l-year mobility 3-year mobility 6-year mobility
Moving Moving Moving Moving Moving Moving
up from down up from down up from down
poverty from high poverty from high poverty from high
income income income
I’m-transfer Income
Canada 18.0 16.5 27.6 26.0 35.8 29.8
HS or less 14.0 20.0 20.0 31.6 24.4 35.9
College or more 24.0 15.1 37.6 23.8 48.3 27.6
Non-White 14.2 19.4 22.7 33.9 32.6 38.6
Native-bom Canadians
Anglophone 20.5 16.1 31.6 25.0 39.8 28.3
Francophone 15.5 16.8 24.0 27.1 26.8 33.0
lmmigrants—OL' 21.8 13.2 30.8 21.1 34.9 24.2
—NOL 14.8 16.5 22.4 26.2 29.6 33.6
United States 22.5 18.6 27.7 26.3 29.7 27.3
HS or less 18.2 29.0 22.2 41.5 24.5 44.2
College or more 35.2 14.3 44.6 19.8 44.5 20.7
Non-White 13.8 23.0 16.3 33.6 17.9 36.0

 

Data source: CNEF subsample for individuals presented in all 6 years period (1993-1998 for Canada and
1991—1996 for USA). There are 29.772 longitudinal samples from SLID and 12.150 from PSlD.

*An individual is classified in the low- (high-) income group if his or her equivalent income was less
(greater) than 0.5 (1.5) of the median.

“Characteristics of household head (defined as major income earner).

IOL: Speak official languages as mother tongue; NOL: Speak non-official languages as mother tongue.

78

Table 2.5
Five-year Earnings Mobility for Wage/Salary Workers, Cross-National Comparison

(Sample: WageAS‘a/arj’ workers in both survetgt’ears)

 

Transitions among quintiles

 

 

 

Year Average Stayed in Move Move
quintile the same one 2 or more
move quintile quintile quintiles
(%) (%) (%)
(l) (2) (3) (4) (51
OECD (l996)’
Denmark 1986-1991 0.812 46.2 34.8 19.0
Finland 1985- 1 990 0.891 44.1 34.4 21.5
France 1986-1991 0.683 53.0 32.4 14.6
Germany 1986-1991 0.647 52.3 35.3 12.4
Italy 1986-1991 0.685 50.3 35.4 8.5
Sweden 1986-1991 0.684 50.5 36.1 13 .4
United Kingdom 1986-1991 0.660 51.4 35.4 13.2
United States 1986-1991 0.758 47.2 36.4 16.4
Burkhauser et al. (I 99 7):
United States 1982-1988 - 55.2 43.3 21.9
Germany 1983-1988 - 56.1 42.2 22.1
Results in this study
United States 1991-1995 0.626 55.4 31.8 12.
United States 1992-1996 0.645 54.2 32.3 13.
Canada 1993-1994 0.637 ‘ 54.4 32.5 13.1
32.9 13.3

Canada 1994- l 998 0.650 53.8

 

'Source: Table 3.6 (OECD. 1996)
2Source: Table 2 (Burkhauser, Holtz-Eakin. and Rhody. 1997). Notice that columns (3) to (5) do not sum to
100 (see their text for explanation).

79

Table 2.6

Kaplan-Meier Estimates of Low Income Transitions

Exiting from Low Income1

 

 

 

 

 

 

 

 

 

 

Number of Cumulative percentage remaining in low income (%)
years since (Annual exit rate from low income)
start of low- Canada
income . . .
spell All ngh College Non- Native-bom Immigrants
school or more Whites Canadians
or less
Anglo Franco Otlicial Non-
phone phone lang. official
lang.
1 100 100 100 100 100 100 100 100
2 66.5 70.9 61.3 75.4 65.5 67.1 61.4 71.4
(.335) (.291) (.387) (.246) (.345) (.328) (.386) (.286)
3 50. 7 58.0 42.3 46.2 49.9 51.2 44.8 51.5
(.237) (.182) (.310) (.387) (.238) (.237) (.270) (.279)
4 40.3 49.1 29.8 33.4 39.1 42.3 31.7 40.1
(.206) (.153) (.296) (.277) (.217) (.175) (.293) (.222)
5 34.5 41.7 26.1 31.7 33... 36.9 21.1 36.2
(.141) (.152) (.123) (.050) (.150) (.128) ( 333) (.095)
Spells 19,943 10.887 8.095 777 1 1.799 4.121 724 1.315
Unweighted 9,069 4.831 3.770 343 5.323 1.852 323 590
individuals
Number of Cumulative percentage remaining in low income (%)
years since (Annual exit rate from low income)
start of low- United States
income
5P6“ All High school College Non-
or less or more Whites
1 I00 100 100 100
2 65.4 70.2 55.9 72.2
(.345) (.298) (.441) (.278)
3 50.3 56.1 40.2 57.8
(.232) (.201) (.282) (.200)
4 40.3 45.9 29.7 48.4
(.199) (.182) (.260) (.162)
5 35.4 40.8 25.5 42.6
(.145) (.110) (.143) (.121)
6 30.3 34.0 22.3 38.5
(.120) ‘ (.167) (.100) (.095)
Spells 9,993 6.815 2.532 4.893
Unweighted 3, 769 2.508 1,015 1,766

individuals

 

lKaplan-Meier estimates are based on all non-left censored low-income spells. All people starting a low-
income spell are assumed to last at least 1 year. Post-transition earnings that rose to not more than 10%

above low-income line are not considered an exit.

80

Table 2.6 (Cont’d)

Reenteringinto Low Income1

 

 

 

 

 

 

 

 

 

 

Number of Cumulative percentage remaining in non-low income (%)
years since (Annual reentry rate to low income)
start of non- Canada
low-income , , ,
spell All High College Non- Native-bom Immigrants
school or more Whites Canadians
or less
Anglo Franco Official Non-
phone phone lang. official
lang.
1 I00 100 100 100 100 100 100 100
2 81.5 77.6 85.0 92.6 81.6 80.3 84.2 86.1
(.185) (.224) (.150) (.074) (.184) (.197) (.158) (.139)
3 72.0 66.4 77.2 80.1 72.7 70.5 71.5 76.7
(.117) (.144) (.092) (.127) (.108) (.121) (.150) (.110)
4 63.5 56.6 70.2 70.8 64.4 62.2 60.2 71.5
(.118) (.148) (.091) (.125) (.114) (.118) (.158) (.068)
5 59. 7 52.0 67.5 - 60.1 59.0 - -
(.060) (.081) (.039) - (.068) (.053) - -
Spells 22,623 1 1.295 10.019 1.062 13.620 4.812 801 1.479
Unweighted 10,090 4,958 4.437 518 6,012 2,091 356 686
individuals
Number of Cumulative percentage remaining in non-low income (%)
years since (Annual reentry rate to low income)
start of non- United States
low-income
SP9" All High school College Non-
or less or more Whites
1 I00 100 100 100
2 70.7 67.1 79.1 63.8
(.293) (.329) (.209) (.367)
3 57.4 52.8 67.2 47.3
(.188) (.216) (.150) (.259)
4 47.9 43.9 55.5 38.0
(.166) (.169) (.174) (.196)
5 41.0 38.0 46.0 31.5
(.143) (.134) (.171) (.172)
6 37.2 34.3 42.6 27.0
(.094) (.098) (.073) (.143)
Spells 9,183 5.700 2.738 4.193
Unweighted 3,494 2.1 73 963 1.653
individuals

 

IKaplan-Meier estimates are based on all non-left censored low-income spells. All people starting a non-
low-income spell are assumed to last at least 1. year. Post-transition earnings that fell to not more than 10%

below low-income line are not considered a reentry.

81

Table 2.7

Kaplan-Meier Estimates of High-Income Transitions

Exiting from High-Incomel

 

 

 

 

 

 

 

 

 

 

Number of Cumulative percentage remaining in high income (%)
years since (Annual exit rate from high income)
start of Canada
high- . . .
income All High College Non- Native-bom 1mmigrants
spell school or more Whites Canadians
or less
Anglo Franco Official Non-
phone phone lang. ofiicial
lang.
1 I00 100 100 100 100 100 100 100
2 77. I 73.2 79.4 85.6 77.1 80.0 81.8 80.9
(.229) (.268) (.206) (. 144) ( .229) (.203) (.182) (.191)
3 63. 7 57.4 66.6 62.8 63.4 66.5 69.6 65.0
(.175) (.216) (.161) (.266) (.177) (.166) (.149) (.197)
4 55.6 47.0 60.1 60.1 55.5 58.5 67.5 60.3
(.127) (.180) (.091) (.031) (.124) (.119) (.029) (.071)
5 48.6 37.5 54.9 50.7 50.0 50.1 60.0 34.5
(.125) (.204) (.094) (.167) (.100) (.133) (.111) (.429)
Spells 16,420 5.317 9.975 683 10.600 2.692 664 1.020
Unweighted 7,868 2.528 4,726 330 4.946 1,308 351 507
individuals
Number of Cumulative percentage remaining in high income (%)
years since (Annual exit rate from high income)
start of United States
high-
income All High school College Non-
spell or less or more Whites
1 I00 100 100 100
2 65.2 51.8 74.6 57.5
(.349) (.482) (.254) (.425)
3 53.4 36.4 65.3 47.0
(.180) (.297) (.125) (.183)
4 45.8 27.8 58.0 38.8
(.143) (.237) (.112) (.174)
5 39.7 22.0 51.9 35.1
(.133) (.208) (.105) (.096)
6 34.6 18.7 45.9 33.1
(.128) (.150) (.1 15) (.056)
Spells 6,01 7 2.073 3,653 1.128
Unweighted 2,310 866 l .287 473
individuals

 

'Kaplan-Meier estimates are based on all non-left censored high-income spells. All people starting a high-
income spell are assumed to last at least 1 year. Post—transition earnings that fell to not more than 10%
below high-income line are not considered an exit.

Table 2.7 (Cont’d)

Reentering into High-Incomel

 

 

 

 

 

 

 

 

 

 

Number of Cumulative percentage remaining in non-high income (%)
years since (Annual reentry rate to high income)
start of non Canada
mime All High College Non- Native-bom lmmigrants
spell school or more Whites Canadians
or less
Anglo Franco Official Non-
phone phone lang. official
long.
1 100 100 100 100 100 100 100 100
2 85.0 88.0 83.8 83.7 85.2 86.8 75.0 84.2
(.185) (. 120) (.162) (.163) (. 148) (.162) (.250) (.158)
3 74.9 77.7 73.8 78.2 74.8 78.5 62.4 76.1
(.117) (.117) (.120) (.066) (.122) (.162) (.168) (.096)
4 69.1 72.6 68.1 74.6 69.2 74.6 - 67.5
(.118) (.066) (.077) (.045) (.075) (.162) - (.1 16)
5 62.9 67.4 61.0 59.4 62.5 70.8 56.5 55.9
(.06) (.071) (.105) (.204) (.097) (.162) (.094) (.171)
Spells 16,298 5.679 9.975 690 10.596 2.466 641 1.072
Unweighted 6,984 2.467 4.726 268 4.466 1.088 288 438
individuals
Number of Cumulative percentage remaining in non-high income (%)
years since (Annual reentry rate to high income)
start of non United States
high- .
income All High school College Non-
spell or less or more Whites
1 I00 100 100 100
2 82.8 87.8 78.5 89.7
(.172) (.122) (.215) (.103)
3 70.9 78.3 64.9 81.1
(. 144) (.108) (.174) (.096)
4 63.1 70.0 57.8 71.0
(.110) (.106) (.109) (.124)
5 55.0 64.3 48.1 62.8
(.129) (.081) (.168) (.115)
6 53.2 63.1 45.4 61.9
(.032) (.019) (.057) (.014)
Spells 6,682 3.086 3.300 1.549
Unweighted 2,316 1.042 1.170 498
individuals

 

lKaplan-Meier estimates are based on all non-lefi censored high-income spells. All people starting a non-
high income spell are assumed to last at least 1 year. Post-transition earnings that rose to not more than

10% above high-income line are not considered a reentry.

83

.o~_m Bop—82o; E 5:025. a 2 26 m_ 5
6.9.8.5: 858 8 96:. 2.5.6365 omsoaméwocéoc .8 6:8: 328 o. 828 omzogmémo; :33 25.2: 83:55..

6.9.3.5: to..:% 8 3.3363; 0533 220 2a 8.95me

3:252? 3.2.8.: 3302? van 62:36.? .052me 5:33 owe 2o 08 .65 .99... E .250 can ago 23 03m E 322% 3:38 38mm

.2. 0858.. 8? 83 8:85 woo; he 2:? 8m. 2: .28 .0885

8333 55o 03 .5 ..mm .EuEba on“? E856 83mg... 6:33 dim E .3680 28 can .90 .m<O .<m .03 .5 .532. 28 220 332: 03m E 98:5: 23:3
.8382: 23323-5: Eat 0:52: 98 €6.53 2:? 5.22:.“ .2233 EoEEoSwéo: 6.5.2: 80%—8: 29:...

.mczucu cm 3522.8 8: 2c. 0:: 0585-32 26% $2 52: 89: 8: S um: 85 @5520 285285.83 .wctomcoo

to. Co 3.22%“: “520 E cutomno $.65 :09. 0885-30. 5?» 0302. :a :o 323 $3.22 ”302 .cozatomno oZ - ”mccgtumso cm 5:: mm». 5 comes mofiEcmma

 

 

 

 

 

w _ 2 Ram a _ m 8” one 83 as... w _ 3“ E Scan. 8 _ ._ no}. 8:68 =8. .8 82.52
- .36 - - - .86 - - . _ .o - - .86 22.5
.2 9m .2. .3 2 am 2 A... .3 .3 3 n. ...... 882
3 - .2 S. .2 S M: - .wd o.” .2 mm 6.20-85
- qm ...... - .3. 3 - n. .3 - .3 3 E22 2:52 2 8:63.
3: ow .3 9m 3 Q N: E .8 SN 3: ad .88.: 228.8:
2.: .. .. 3 - 3 SN - - 3 - 3 825282282...
.22 .3 .3 - .3 2o .22 .3 .3. - . _ .o .3 882382288
on .2 - no . _ .m 3 _.~ 9. - 3. am am 3:52 88528 2.482
a .3 a .aQ .88.. asstmasaq
- . _ .o - ...o .3 . _ .o .2. no 8.. . _ .o .3 MS ...... 85¢
3 .3 .mm 3 0.: 3. .2 3 .....m 3 3m ”x .888 £88. :88
.2 2 .mm .2 LA. 3 a... 3. 2: ca 3 a... .2282. 23....
ms 2” .3 3. 4.2 3 an 3. 8.... we 2: 3. 22252 2.2:.
.5 mm .4... .3 _.~_ 3 .3 gm .2 2 S 3 2:85 .22
ma 3 - .3 a... ”a 3 3. . _ ._ 3 3 mm 2.85 .582 850
3. «.3. 0.8. S: 3 an. . _ .N of ”.2 mm 3. do 2:85 .88. $258..
2:. can a? new 0.2 3... com 92 «.2. EN 2: 3m 2:82 SB... m.28:
Q .3» 2 Mt ..Ee..\m58=.~\o 28%
883 88:3 83 o: Evan an}? v.23. o:
o_w=_m 2950 2950 o_w:_m + 0303 2w£m 2980 2980 o_w=_m + 0303
8 v a? 8 8... __< 8 v 8< 8 8< __< 2.88 =8...
moamum @0323 $5ch :33 6035035 Eo>u Em:

 

3878a: 82% 82.5 as. 3838:
«@230 £25 2:85-26..— wo 23> awe..— uf 5 mafia. Boson—5: yucca—ma— .3 .8th wcmvcm =on @585 So.—

wfi mink.

84

.02: 29.88.. :. :o...::: ..: 2 0:: m. ..:

...—9.89... 8...: 2 0>o.: m.::2>.::. 88:282-:8 .0 2:9. 2.2:: 9 522 8:98:82. ..:2. 08.02 803.85..

29.88.. ....o....:: 8 8:98:82. 08:00.. :._..0 0.: 807.88.”...

08:32.: 82.0...9: 83:2... :9: 00832.: 2.58.: 5:88 0w: 2: 2: x0... .05.. :. .....O :5: ....0 2: 9.5 ... 8:280: 2...:08 .:.0om.

.3. 29:9... 8.: 08. 85:8 :9... .... 2...... 00:. 0... 2.: 2:8...

2:...0... 8...: .03 ... . ..mm ..:098: 0D...< 22:8 20.82. 0.3:: Em: ... 82:20 ..:. :8 m5 .m<O .<m .03 ..: 4:08.. ..:. 2E0 0:202 0.4m ... 8.0.8:: 0.3:...
8.2.9.0.: :.9.8:9.-:c: ...o... 2:82 :9. 29.98 220 285:: 8:280: .:0.:E0>:w-:o: 0:22.. 20.89... 2:35.

.w:.::.w0.. : :20288 .9. 2: 0:: 2:82-32 30.0.. 2.... ..:... 2o... ...: 2 ..:. ..:... 82:80 828278.... $2.880

c0. .... 80:280. ..m. 20 :_ 888.... 8.8.0.0.. =08 2:82-32 ....3 2.8.0.. ..: :o :82. m.m...::< ”2:2 .:o..:>.8..o cZ - 8:25.888 cm 8... 80. :o :88 82:88....

:2... .8. 8m :8 .8 8:: :2... ..:.N E :2: ...: SN... m2....88........;....:..E..z

 

 

 

 

- - - - - - - - . . ... - - . _ .... 22.5
...m m... .... ....m ....N N: ...: ...: .2. ...... .... ... .3... .802
.... .m. ....m ...: :.: ...N ....m .... no ...: .... ...: .....8. o. 88:88
.... . ...: ...... :... ...: ...: 8.. . ...: .2. ...: ...: .... .88... 29.0.8:
. . ... ...... ....N - ...... 2. .2. ...... ...... - .m... .2. 0::...:E\.._..8=8.
m. . m - - :... ...... a... S... - - :... .m... :8 882.82.82.88
m: ..m ...: ...... .2. m... ..: ...N 0:. ...: a. ...... ......8. 8.2.8.8 ..82
.: :m. : .... Q ...88 93:82.53
. . ... - - ..:... . . ... . . ... .2. .... .:.. ...... ...... m. 0.... 8......
...: .:.. .... ...m 8. m... .... .2. ...... .... m.:. :.m .888 8:8. .28:
...m m: ...: N... .... ...: :.... ...... ...: :.... .... :.~. .828: o..:...
...... ..m ...: a... ...N. w... ...: 8 ..: .... ...: m... .888: 28....
...... ... ...: .2 N... ...: ...... ...: ...... ...... :2 m: 2:8... .8...
.... ...: .2. ...: 2.. .... .:.. ..m ...... .... o... ..m 058... .88. .050
m... :.:_ 2.: .2. ...: ...: ....m M... ...... :... N. ..: 058... .88. 88......
2.: :8 ..:: ...... a... ...... n... no: .8 ..:. :2 :8 :58... .88. ..:8:
«:2. a N... “588285.... ..:...

25.—mm waxy—>5 m6; 0: 25th £033» mEv— O: ,

0.w:.m 0.950 0.980 0_w:_m + 0.9.0.. 0.2.5 0...:00 0...:o0 0.w:_w + 0.80..

..: v 8.. ..: 8< ..< ..: v 8.. ..: 8;. ..< 2.8.8.. :8...
82:5 :22: :::::0 ....3 8.288: .:0>0 :.:.>.

 

387:8: 8:8 8...... :5. 88.4.8: 8.80
2.2—m 08853.25 a: 80> 8...: 0... ... 09:. 29.88: 8.880.— 3 .893. w:_::_w0m :25 058..— 3:1—
QN 033—.

85

.88 2228: 5 8528 :u o. 08 2 .—c
22.28; :28 2 26:: 2:83:05 28:25:05-8: 8 .28: .83: 2 E22 28:282. :8: 8.85 2:53?

6.2.28: to-5_:m 8 88:282. 2:82. 250 2: 2:53?

88.55.: 2285 83:53 :8 68:88:: 25:82: 5:88 own 2: 2: :85 .05: 5 .::O 8: ::0 2: 03m 5 8580: 9:82 222%M

.3 2:58: :2: 82 85:8 88 :o 28> 8:: 2: 8: 2:85

25.83 :28 03 .5 ..mm 82:8: 0D:< 58:8 20:28: 23:: Em: 5 ”3:88 x8 :8 £0 .95 .<m .03 .5 4:88: 8 250 2285 03m 5 228:: 2.8%
.23an 28285-8: 52: 2:85 :8 58:8 250 $825.: .8580: “85:82:28: 2285 20.58.: 2:55.

.w5:8 c: 88588 8: 2: 0:: 2:85.52: 320: $2 85 2:5 8: 2 =5 65 @558 858828: .8888

:2 ..o 22:58.. :220 5 8288 8:08 :o:m 2:85-53: :53 2:8: =: :o 88: 2mba:< x82 dauntomno oz - ”856528 cm :2: m8. 8 8%: 8852mm...

 

3 _ mum; 2.: SN ewm mmqm mm. 3:5. ovm; cow 3% owmd $508 =on .3 83:52
- - - - - - - - - - - - 82:0
...cd Wm _.N_ . _ .o ix. wé * _ ._ N: N41 5: 3.. 5w cont £082

 

 

 

 

1.: NM ...o.m ii . _ .m _.m 1.: 9m w.m w: :6 Wm 2:52 8 888:8
3.: 9m ...md ...: 1: 9m 0.2 No. .m._ 9.: m.m 5w $905 2052.82
:3 - - .2 - .. _ .o *8 - - a: - _. _ .o 0285:2225:
8.: _.m ...o.m - ...md S *9. 0.: 5: - 1.. Wm 885383888
...m.m_ m.m ...wd 3.. ...: QM VNN ..: .w._ m.mm m.m Wm. 3:55 852388 2:82
«m N N : S x Q :88 8328:30st
- ..:.o .30 .o._ - Lao - o.m ...: *2 m6 ed 2: 8me
..:: Lao ...m._ ..:.o ..:.m :2 - - - - .. _ .o - M838: 5:88 .28m
..:.v *md .56 ...: ...m._ ..:.o Lam m._ *NN *2 ..:.m w; mafimcg 23::
.m.m_ :6 ..:.c .. . s ..:. 9.x 3.2 Wm ad. 9m mdm 3: .2288 28::
1.: 0.: gm: * _ s mdm :6. ..:}V w.m m.m ...m._ No 0.». 2:85 83<
..:.mm m.m ...:d :72 of 9w 0: 5m 3.. ...: 0.: Wm 2:85 582 850
- of No. - m6 w; _ - of of - Nd 9N. 2:85 :88. 28:8:
*2.— fiev :3 m.mm 9mm m. .v 8.3 Nmm m.mm 0:8 m. _ m v.2” 9585 .582 2:8:
:0 .8 s .3: “583828 :3:
82:: 222? 222 o: 82:: 833 8:. o:

285 2:80 2:80 2w5w + 2:8: 2w5m 2:80 2:80 2w5m + 2:8:
8 v a? 8 a? __< 8 v ow< 8 ow< __< 2.65 =2:
8.8m 85:: 88:0 :53 8:28me .88 522

 

Gala: :38 3:5 :5 333%: 2.2.5
.=o:m 2:855”:— u: 28> .84 2: a. 83‘ 2888: 2:88.— 3 $2.3. 25.2—m— =2_m 2:8:— :3:

cfiu 93.;

86

.8? 22.82:. E .5526. a o. 26 m. ....

6.9.83; .55.. o. 26.... 2.2636... umzoam\wmo;-:o: 8 625.. E23 9 ESP. umzoamfiau; :3... 25.0... $383”...

.2332... .co-.__% 8 umzoamhéu; 2:32. 2:? 8m 83:5...9

.8532... $2.58 33023 v.8 .8532... @5236 2:593. own 2o 08 35 d5; ... ..EO ES .50 we Dim ... £55ch btsuom 328mm

.3. 29.80.. 02.. now. 8:33 woo. .0 2:9 3.... o... E... 8:52..

2...:3. 55.. 03 .5 .mm 45:55.. be“? 58:8 88mg: 33.... 9mm E ”3:55 x... can m5 .m<O .<m 03 .5 4:25.. .8. 220 2.22.. aim E 80.55.... 33....“
.8382: 2053355: :5... 2:8... 23 225.5 220 5.55.... 8:222. .5552...ch 3822.. 80.35... 8.3:...

.chEwon a 3.25:3 8: 2a 2.: oEoucTcwE .86.... $2 5.... 08:. .o: o. 3.. .9: mean :o_._m:§.-.mo.. .wctcmcuu

...2 ..o 3.22%... {.520 E tutor... chEwun =on oEOoEAME 53. 29.2. :a co 3%... 5.292. .302 62.9.3.5 oZ - 85:333.. cm :9: 3.2 :o 3%... 3853mm...

 

 

 

 

 

8. 8.... ...: o8 8.. E...“ R... .... ..V .2. S . .. ..8 m . 2 8589.. ......m ..o 8....52
- - - - - - - . . ... a... - - .. . ... 9.2.5
......N Z 3 . . .o. .3 3 .. ..m .3 E .... .... o... ...... 882

- .2. ....N .....m ..:. .... .3 ..m... o8 S. .8... mm 22.8-82.

- L... - - - ......o - mm - - ...m. .... 6:5. 3...... o. 8:63
...... o... ...m... .3 ...: 3 ..mm 3 3. 0.8 .3 ...... .88... 2888..
...... Z 3 - ...m. ...N - .... N8 - - ..N Dwage....35....

- - - ...... ...... .. . ... ...... - .. . .o .. . .o - .. . ... 8533:2588
.0. .2. . . .m ...... ......o m. ...... ...... ....m on ...... f .....E... 85.328 :52

R .2 . Q gm. ...:msm uEQEMsEmQ

- - - ...... ...... ..N... ...... Qm .. . .. .. . .N ......N on .... 8...;
......N - .. . ._ - 3. .... - - ...”... ...m... ......m *2. .8322. 8:83 ....8w
.3. ...m... ..:. .9... a... .2. ...m. E ..:. ...m. .3 0.. .83.... 0....E.
......N ..o .s ...... _ we... .1 . .3. o... 9m 2. gm ..o 38.2.... 2.3....
*3. 3. oh .3 EN 0... .3 mm mm ...... ...: m... 2:8... .32
......N. ...m .2. *3 ......m 3 9mm E. .. . .. ..: 2. Wm. 0E8... .88. 850

- ...: m. . ... - .3 mm. - E. 0.8 - no of 058... .88. MES...
98 a? «.2 >3 ... . N 2.. mom .8 E... ...R ...: ...N... 058... 52.... ......8:

.3 .... «3.... ..se.§585\e 8;.
2.23 833. mu} 2. .228 was?» 32 0:
03:5 2..on 29.00 BwEm + 038.. o_w:_m 2980 29.00 BwEm + 0302.
8 v ow< 8 am... ..< 8 v 8A.. 8 8.... ..< 8.8.8.. =8.
83% 32.5 «9...ch 5.3 38.08%. Eo>o £32

 

38782. 82.5 8...... ...... .8248: 8.35
£on «Ecofiéwi a: 53> 3.5— 2: E «9.3. 6.2—8.5: ...—:3.qu .3 £235. Miafiwom =on 2:8:— :fl:

:.N 035,—.

Table 2.12
Coefficients of Hazard Model, Canada (1993—1998)

 

Probability of Probability of

 

 

 

low income high income
Leaving Reentry Leaving Reentry
(I) (2) (3) (4)
Duration (year: I )
2 -.151’ -.316‘ .021 -.092'
3 -.388‘ - 370‘ -.080 -.185‘
4 -.482‘ .383‘ -.l 16 -.033
Year (base= l 997)
Age group (18-34)
<6 -.O4l .202‘ .161’ .119
6-l 7 -.O6l .188’ .037 .003
35-64 -. l 34‘ .l 14‘ -.001 .050
65 + -.576‘ .401’ .312' -.154‘
Family Structure (Couple w/kids)
Single -.286‘ .150’ .264‘ -.393‘
Single-parent -.544‘ .309' .622' -.562‘
Couple w/o kids -.I 10’ .084" .127' -.251‘
Regions (Ontario)
Atlantic -.l33‘ .142’ .444‘ -.063
Quebec -.1 16" -.009 .096 .041
Prairie .088‘ .096‘ .068“ .052
B.C. .030 .076 .043 .135’
Female—head -.280’ .231‘ .450' -.357‘
Non-white-head -.272‘ -.245' -.332' -.108
Mother tongue (English)
French .092 .141 ' -.078 -.017
Others -.020 .249‘ .01 l .017
Ethnic Origin-head (Native)
Immigrant-official languages .261. -.O65 —.22l' .281.
lmmigrant-non-official lang. .066 -.| 12 80 -.060
Education-head (High School)
Less than HS -.208’ .250‘ .037 .004
More than HS .109' -.159‘ -.196’ .l 19’
Binary Event variables
Newly established family .070 .985' 1.53' .020
Separation/Divorce .033 1.07' 1.61’ -.l78
Partnership/Marriage .838' -.445 -.633 .670‘
Household merge 1.17‘ .124 .769‘ .955'
Addition to family (Child) -.920‘ .420‘ .408‘ -1.1 l‘
(Non-child) .464' -.009 .550‘ .318‘
Subtraction to family .255’ .188‘ .156‘ .364‘
Constant -. 164‘ -1.343* -1 439* -.847*
Log Likelihood -5204.51 4454.14 4167.66 -3697.79
Predicted transition rate
jar Reference person 0.435 0.090 0. 075 0. I 98

 

* Indicates significance at the 5 percent level, ** at the IO percent level.

88

Table 2.13
Coefficients of Hazard Model, USA (1990-1996)

 

 

 

 

Probability of Probability of
low income high income
Leaving Reentry Leaving Reentry
(I) (2) (3) (4)
Duration (year= l)
2 -.3l2‘ -.283‘ -.494‘ -.184‘
3 -.415‘ -.335' -.709‘ -.275'
4 -.548‘ -.394‘ -.726‘ -.263’
5 2815‘ -.513‘ -.667‘ -.588‘
Year (base= l 996)
Age group (18-34)
< 6 -.210‘ .130“ -.180“ .085
6-l7 .036 -.013 -.228‘ .223‘
35-64 -080” .084" -.O69 .103“
65 + -.507‘ .752‘ .777‘ -.400‘
Family Structure (Couple w/kids)
Single -.184‘ .084 -.049 -. 101
Single-parent -.456‘ .335‘ .193“ -.786‘
Couple w/o kids -.O32 -.l62‘ -.|65. .297.
Regions (Northeast)
Midwest -.008 .062 .335‘ -.01 1
South ' -.023 -.012 .140‘ -.048
West -.02I .054 . 159‘ .008
Female-head -.162‘ .219‘ .302‘ -.197‘
Non-white—head -. l 99‘ .302‘ .107“ -.306‘
Education-head (High School)
Less than HS -.302‘ .278‘ .259‘ -.123
More than HS .239. -.249' -.426' .243'
Binary Event variables
Newly established family .351. .62 I ' 1.93. -.365
Separation/Divorce -.383“ .865‘ .831' .145
Partnership/Marriage .727‘ -.151 -.l78 .986‘
Household merge .652” .426 1.30. .632.
Addition to family (Child) .207 -.200 -0.99 -.l97
(Non-child) -.399‘ .395‘ .541 ‘ -. 188
Subtraction to family .181‘ -.070 .038 .385‘
Constant .200‘ -.968‘ -.262 -.833‘
Log Likelihood -3593.86 -3078.77 -205 l .30 -l933.80
Predicted transition rate
for Reference person 0.579 0. I6 7 0.397 0.202

 

* Indicates significance at the 5 percent level. ** at the 10 percent level.

89

Figure 2.1
Incidence of Low Income

 

5d»

39.]

40.3

 

 

40—

 

 

$

 

Percent (%)

N
‘P

 

    
    

3.78

‘.7!\1 7. ‘
. ”‘9:
1 ,3 .

Poor at least 1 Poor in all
year years

 

 

 

Canada

year

38.09

 

 

 

I Pre-tlansfer
I Post-transfer]

D Post-transfer2

 

 

   
 
   

 

 

Poor at least 1 Poor in all

10.7

 

   

7.89

 

a\ ,
\r. ' ‘
. ‘.\_ '7: .:
\‘ri '.‘.
> . -‘
.. -. .
‘_ .

years

USA

Data Source: Subsample for individuals present in all 6 years periods (l993—l998 for Canada

and 1991—1996 for US).

Post-Transfer]: Poverty cutoffs are calculated based on post-transfer income.
Post-TransferZ: Poverty cutoffs are calculated based on pre-transfer income.

90

 

Figure 2.2

Persistence of Low Income by Demographic Groups

 

30-

 

25‘

22.4

 

20-

Percent (%)

 

 

 

17.5

 

 

 

 

633

 

 

    

 

    

 

_4.l9

High School College or lmmigrants“
or less

Data Source: Subsample for individuals present in all 6 years periods (l993—l998 for Canada

more

Canada

and l99l—l996 for US.)
Post-TransferZ: Poverty cutoffs are calculated based on pre-transfer income.
*Immigrants who speak nonofficial languages as mother tongue

91

 

 

 

   

-
.l t

26.9

23.8l

 

 

High School College or Non-Whites

or less

more

USA

 

 

I Pre-transfer

El Post-transferz

 

 

 

Figure 2.3
Probability of Moving out of Low Income by Length of Income Tracked and
Characteristics (pre-transfer income)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Percent (%)

 

 

lO-l . g
r ‘8". .

5—1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

'9“ .-’o- gt»

 

0- _ . ..., g .
All High College lmmigrants“ All High College Non-Whites
School or more School or more
or less or less
Canada USA

 

 

I l-year l 3-year D 6-year

 

 

Data Source: Subsample for individuals present in all 6 years periods (1993-1 998 for Canada
and 1991—1996 for US).
*lmmigrants who speak nonofficial languages as mother tongue.

Figure 2.4
Classification of Income and Demographic Events Associated with a Spell
Transition Between Year t-I and t

 

Each spell ending

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

or beginning
J
Same head, same size Same head. Different head
between H andt different size between H andt
between H andt
l 1
Income event If change in head's lf difference in earnings
earnings accounts between the new head and
" for 50% or more of the the old head accounts for
change in HH income 50% or more of the
change in HH income
! l
l J
Which income source h - .
[ Increase d the most? J [ 0t ervvlse ]_, [ Demeogfiphic ] OtherWIse
] I
8 typles of . . |
Which dem ra hlc "“0"“
[ income events event is asggcia‘led ”9‘“
with spell change?
7 types of
demographic events

 

Chapter 3

Evolution in the Structure of Family Income in Canada, 1980 to 1997:
The Effects of Changing Family Structure and
Changing Characteristics of Immigrants

3.1 Introduction

Previous Canadian studies have Shown that pre-transfer income and earnings
inequality have increased Since the mid-19705, particularly during the two recessions—
early 19805 and early 19905, respectively.33 The widening gap in family income was
observed between and within virtually all age groups and family types (see, for example.
Zyblock, 1996b). Studies that examine the underlying causes of increasing inequality
have received much attention over the past decade. Issues such as technological change
or changing trade structures are often discussed as important forces contributing to
increasing earnings disparities. While growing earnings disparities have undoubtedly
contributed to the trend in family income inequality, previous research Shows that
demographic shifts also influence changes in income distribution. The study discussed in
this chapter employs a relatively new decomposition technique to assess the importance
of demographic shifts on the evolution of family income in Canada. Of particular interest
are the effects of changing family structures and the characteristics of immigrants.

Changes in family composition and immigrant characteristics and their
relationship to income distribution are of interest because both factors have undergone

substantial changes in the past few decades, and economic outcomes vary greatly by

 

3 3 See Morissette (1995), Morissette, Myles, and Picot (1995). and Zyblock (1996a) for discussions about
the changes in earnings inequality. See also Wolfson (1986). Zyblock (1996b), Beach and Slotsve (1996).
and Frenette. Green, and Picot (2003) for discussions about changes in family income inequality.

94

family structure and by cohorts of immigrants. In 2000, about 7.7 percent of two-parents
families and 35.8 percent of Single-mother families were in the low-income bracket, and
about 18.5 percent of all immigrants and 34 percent of recent immigrants (living in
Canada 10 years or less) fell below the low-income line in 2000.34 As a result,
information about this relationship is essential for implementing policies, such as anti-
poverty measures, or initiating active labour-market programs. Such issues are especially
relevant because government transfers have been declining since the 19903. If an increase
in non-traditional families and new immigrants is associated with low income, then it
may be imperative to find ways to reduce the hardship suffered by single parents and help
newly-arrived immigrants use their foreign skills. In addition, the impact of immigrants
on overall income inequality has not received much attention. The number of immigrants
increased significantly over the past decade, and immigrant characteristics changed
dramatically. Country of origin, for instance, changed greatly: from Western European
countries in the 19705 and 19803 to Asia and developing countries in the 1990s. The
extent to which changes in immigrant characteristics affected earnings or overall income
distributions is discussed in this chapter.

The decomposition approach (DiNardo, Fortin, and Lemieux, 1996) used in this
study has two advantages compared to the often-used standard regression-based
decomposition (Daly and Valletta, 2000). First, it enables estimation of the entire density
of income rather than just the mean or quintiles uncovered by the standard regression

model. Second, it provides more behavioral content because the counterfactual

 

3’4 Source: Statistics Canada (2003) and Picot and Hou (2002).

95

distributions constructed by this approach allow a conditional relationship between
explanatory factors and a set of other attributes.

The results of this decomposition Show that the increase in non-traditional
families had a substantial impact on family income inequality, explaining one-fifth of the
increase in the Gini coefficient and one-third of the growth in the low-income rate
between 1980 and 1997. Changing characteristics of immigrants also affected income
distributions, particularly in the lower half of the income distribution. It explained about
one-third of the increase in the 50-10 and 90-10 ratios, and it was responsible for $462 of
the decline in median income during this period. Nevertheless, a substantial proportion of
changes in income distribution remained unexplained, indicating the important influence

of other factors (e. g., technological change, trade structure etc.) on income inequality.

3.2 Literature Review

The growth in income or earnings inequality has been investigated extensively
over the past decade. In the United States, the increasing polarization of family-income
distribution often is associated with a dramatic increase in male earning disparities that
are driven by changes in the demand for Skilled workers. These changes in the labour
market have been caused by technological changes (Bound and Johnson, 1992),
fluctuations in the demand rate for Skilled workers (Katz and Murphy, 1992),35 shifts in
industrial composition that are largely associated with trade or economic integration
(Murphy and Welch, 1991), and changes in institutional settings (DiNardo, Fortin, and
Lemieux, 1996; Katz and Murphy, 1992; Podgursky, 1983). Other studies have found

that demographic changes have as much impact as earning disparities on the growing

96

family income inequality. For instance, Karoly and Burtless (1995), Burtless (1999), and
Daly and Valletta (2000) Show that the increase in Single-head families is responsible for
a larger proportion of the spread in overall income inequality in the United States. There
is also literature that discusses the increasing role of wives’ earnings in family income
growth. Shorrocks (1983), Lerman and Yitzhaki (1985), and Karoly and Butless (1995)
decompose the change in inequality indices (e.g., Gini) by family income components
and find that wives’ earnings magnify family income inequality. In contrast, Cancian,
Danziger and Gottschalk (1993) Show that wives’ earnings equalize the distribution of
family income. Although such mixed results are due in large part to differences in
sample, time period considered, or definition of income and inequality indices, Cancian
and Reed (1998) argue that the impact of wives” earnings on inequality can be assessed
meaningfully only by comparing the observed distribution of income with a reference
distribution.

Increasing income inequality has also been well documented in Canada. Gera, Gu.
and Lin (2001) Show that, at the aggregate level. the demand for Skilled workers did not
increase significantly from 1981 to 1994—a period of rapid technological change.
Although they also Show that the variations are very substantial across industrial sectors,
Skill upgrading at the national level is leSS evident. Their results implicitly suggest a
smaller role for technological change in increasing income inequality. With respect to
earning disparities, MoriSsett'e (1995) shows that the increase in earnings inequality in the
19803 not only occurred with changes in income, but also in conjunction with changes in

the distribution of hours worked.

 

35Technological change and rising wage inequality is discussed in Card and DiNardo (2002).

97

Picot (1998) reviewed the earnings inequality trends in the 19903 and found that
real-wage rates had declined among young Canadians, while overall income inequality
changed little from 1980 to the mid-19903. An early study by Henderson and Rowley
(1977) suggests that the decline in family size is one of the major reasons for the increase
in income inequality because income variation is much stronger in smaller families.
Wolfson (1986) examines the impact of changing family structure on income inequality.
His results suggest that the increasing incidence of Single-parent families, reflected by
lower fertility rates or rising separation rates, enhanced income inequality from 1965 to
1983.

Studies by Beach (1988) and McWatterS and Beach (1990) discuss changes in
family income distribution in relation to the “vanishing” middle class from the 19603 to
19803. They examine many possible factors from both Sides of supply and demand and
conclude that the declining middle class is largely associated with a drop in the labour
market participation rate of males and a rise in the labour market participation rate of
females. Effects from other possible factors, such as business cycles or industrial shifts,
are relatively small.

Zyblock (1996b) analyzes the effect of both demographic and non-demographic
factors on the change in family incomes from 1981 to 1993. His results Show that, in
either age group or family type, the within-group contribution dominates the between-
group contribution to overall family income inequality. He also points out that the shift
toward one-parent families accounted for. nearly 40 percent of the increase in inequality

over this period.

98

A recent Study by Picot and Hou (2002) discusses the impact of immigrants on
family income distribution. They Show that aggregate low-income rates in Canada
increased 2.3 percent between 1989 and 1999, and two-thirds of this increase was the
result of low-income rates among immigrants. The effect was even stronger in regions

with high immigrant populations, such as Toronto and Vancouver.

3.3 Data and Historical Trends

The study discussed in this chapter used data from the Survey of Consumer
Finance (SCF) for 1980 to 1997. The sample includes individuals aged 15 years and older
in families (unattached individuals also included), but it excludes families whose major
income recipients are aged 65 years and older because market-based (pre-transfer)
incomes usually account for a small proportion of household income among elderly
families. In this study, family is defined as the “census family,” which consists of a
married couple or common-law couple with or without unmarried children or a Single
parent with an unmarried child or children. Compared to economic family, definition of
census family is much narrower because it excludes relatives living in the same unit.36
However, it is also more plausible when equivalent income (see below) is discussed
because the pooling of family income resources is more likely to occur among members
of a census family.

Family income is the sum of earnings, investment, and private transfers from all
family members. Sources of income are measured in annual terms rather than at the time

of an interview, which avoids temporary income fluctuation and is more likely to reflect

 

3“’Economic family people refer to household members who are related to each other by blood. marriage.
common-law, or adoption.

99

the true well being of a family. To properly measure the well being of an individual, the
total family income is standardized through an equivalence scale in order to adjust for
demographic components. In this context, the equivalence scale is defined as the square
root of family Size. The use of an equivalence scale reduces part of the inequality caused
by big-Sized families. In this setting. individuals rather than families are considered the
basic unit of analysis.

The definition of low income is quite controversial in Canada.37 In this study, an
individual is considered in low-income if his or her equivalent income falls below one-
half of the median equivalent income. For the decomposition section, data from two peak
years, 1980 and 1989, and one near peak year, 1997, are used. These three particular
years are used to reduce any variations that may result from business cycle effects.38 All
income measures are expressed in 2000 constant Canadian dollars.

Figure 3.1 illustrates trends of income inequality (as indicated by the 75-25
percentile ratio) as well as the low-income rate. The cyclical effects were very obvious as
Shown by the two spiked areas that represent the 1983-1984 and 1992—1993 recessions,
respectively. Income inequality increased during the peak or near-peak years of 1980,
1989, and 1997, while the low-income rate increased only during the 19903. While both
indices increased substantially during the early 19903, the economic trends failed to
return to the previous levels, despite an upswing in economic grth in the late 19903.

Figure 3.2 shows that increasing income inequality was mainly associated with a

poor economic performance by people from the bottom end of the income distribution.

 

37Three low-income cutoffs are commonly used and discussed in the Canadian literature: low income cutoff
(LICO), low income measure (HM), and the recently developed market basket measure (MEM). See, for
example, Statistics Canada (2003) for detailed definitions.

100

For example. the real income for people at the 25‘h percentile in 1997 was about 15
percent lower compared to the level in 1980. The situation was even worse for those who
were extremely poor: Between 1980 and 1997, real income dropped by as much as 40
percent for people at the 10‘h percentile. Yet people at the top of the distribution scale
experienced very little change in terms of real income during this period. The increase in
real income between 1980 and 1997 for persons at the 75‘h and 90‘h percentile were only
4 and 7 percent, respectively. These patterns are very different from those found in US,
where polarization was obvious (see. for example, Daly and Valletta, 2000).

In order to display changes in the entire income distribution scenario, estimates of
kernel density for the equivalent income are Shown in Figures 3.3a, 3.3b, and 3.3c.39
Between 1980 and 1997, the distribution widened, with a hollowing of the middle and a
density Shift at both ends of the scale. However, the Shift of density was more
concentrated to the left of the income distribution, suggesting the deterioration of well
being among individuals located at the lower half of the scale. The evaluation was
Significantly influenced when the two periods were individually assessed. Change in
income distribution in the 19803 was mostly concentrated in the movement from the
middle portion to the upper portion, and it showed very little change in the lower portion.
Also, much of the density mass shifted to the left in the 19903, while changes in the upper
tail were relatively small.

Figure 3.4 displays inequality measures for male earnings between 1980 and

1997. The sample, which includes people with zero earnings, shows that earning disparity

 

38In terms of business cycles, 1999 or 2000 rather than 1997 should be considered a peak year.
Unfortunately, SCF was terminated in 1997 and replaced by the Survey of Labour and Income Dynamics
(SLID). For consistency, SCF is used throughout the analysis, and 1997 is the last available year from SCF.

lOl

was relatively stable in the 19803, but it increased notably in the 19903. Moreover, the
increasing earning disparity was likely a result of declining earnings among individuals in
the lower portion of the distribution (as indicated by a declining median) and was less
likely to represent individuals’ increased earnings in the upper portion. This pattern
contrasts with findings from the US, where earnings polarization was apparent and
where technological changes and trade were thought to have a substantial impact on
earning disparity.

Figure 3.5 depicts the correlation between husband‘s earnings and wife’s labour
market participation. It Shows that, in general, female participation rates had increased
over time. However, women whose husband had relatively high earnings Showed the
highest participation rates. For example, among husbands whose earnings were right on
the top quartile of the distribution scale, the proportion of working wives increased
significantly, from .55 in 1980 to nearly .8 in 1997. On the other hand, the proportion of
working wives remained relatively stable over time among husbands whose earnings
were on the bottom quartile of the distribution. The increasing correlation between a
husband’s earnings and a wife’s labour market participation is likely to be an important
source of increasing inequality in family income.

Figure 3.6 illustrates a historical trend of family composition. Generally, family
structure in Canada has been changing. Traditional "husband-wife-children” families
have declined (6.2% in 1980 versus 55% in 1997), while the three other family types have
increased. Single-adult families (Single unattached and single parents) increased about 5.1

percent from 1980 to 1997. The movement toward Single-adult families is believed to

 

39The kernel density is estimated using the Gaussian functional form and optimal bandwidth (see
Silverrnan, 1986 for more details).

increase the proportion of low-income families and. therefore. result in increasing income
inequality.

Immigrants could possibly affect overall income inequality. During the past 2
decades, the number of recent immigrants increased substantially, and immigrant
characteristics also changed.40 According to SCF, recent immigrants accounted for about
25 percent of the total immigrants throughout the 19803, but the share increased to 33
percent in 1997. Figure 3.7 illustrates that new immigrants who arrived in the 19903 were
more likely to experience economic difficulties compared to their counterparts in the
19803. In the 1983 recession,~for example, median equivalent income for immigrants was
about $4,000 lower than the national average; the gap increased to $15,000 in the 1993
recession. Part of the decline can be attributed to the weaker labour market performance
among immigrants during the recession, particularly full-time employment probabilities.
A historical trend of full-time employment is provided in Figure 3.8. It Shows that full-
time employment rates did not differ Significantly between recent immigrants and the
national average during the 19803, but these rates plunged quickly in the 19903 and never
returned to original levels.“

Recession in the early 19903 obviously had a negative impact on immigrants. AS
pointed out by many studies (for example, McDonald and Worswick, 1998), the rate of
earnings assimilation by immigrants was quite sensitive to macroeconomic conditions.

However, Figure 3.8 suggests a cohort effect because recent immigrants from the 19903

 

40In this chapter, an immigrant family is defined as a family headed by an immigrant. The person with the
highest earnings is identified as the household head. In case of a tie in earnings, the oldest male is
considered the head. Recent immigrants are defined as those who have been living in Canada 10 years or
less.

“Frenette and Morissette (2003) show that real earnings of immigrants have dropped over the past 2
decades, even among those employed full year full time. They suggest that changes in the wage structure
are likely to play a significant role in explaining the decline in recent immigrants‘ median income.

103

experienced more difficulty finding full-time jobs during recessions compared to the
earlier cohorts from the 19803. It would be interesting to see how Skill distribution
changed among immigrants. Figure 3.9 Shows the share of immigrants who speak either
of Canada’s two official languages as their mother tongue. Ability to speak official
languages is considered one of the most important Skills for immigrants in host countries.
Immigrants who are proficient in either of Canada’s official languages were more
attractive to Canadian employers and able to transfer their foreign skill effectively.
Surprisingly, Figure 3.9 shows that about 45 percent of immigrants or recent immigrants
spoke either English or French as mother tongue in 1981, while in 1997, the figures
declined to 28 percent and 16 percent among immigrants and recent immigrants,
respectively. The changes in immigrant characteristics Shown in Figures 3.8 and 3.9

suggest a potential impact on the change in family income inequality.

3.4 Methodology

The technique used to decompose changes in the density of equivalent income is
based on the “conditional re-weighting procedure” developed by DiNardo, Fortin, and
Lemieux (l996)—hereafter called DF L. This technique has been used in many recent
studies (see Daly and Valletta, 2000; Chiquiar and Hanson, 2002). Basically, it is Similar
to the commonly used “Oaxaca-Blinder decomposition” (Oaxaca, 1973). The main
advantage of this procedure is that it allows the entire conditional distribution to be
projected, while the Oaxaca-Blinder decomposition only focuses on the means of
distribution. The estimated conditional weights can be combined with sampling survey

weights to produce a counterfactual distribution.

104

Notice that a standard Oaxaca-Blinder decomposition follows from an income

equation:
a. m. =1}... NY... 47.01”"... *(fla 43’s.). (I)

The left-hand Side is the change in average equivalent income (i) between 1980 and
1997. It is equal to the sum of two components: one, which is due to differences in the
mean characteristics ()7- ), and two, which is due to differences in the returns to these
characteristics (,0 ). The first term on the right-hand Side (1) represents a counterfactual
“what would the average equivalent income have been in year 1997 for individuals with
the mean characteristics of the 1980 levels.” As mentioned, this decomposition only
estimates the means of distribution and ignores changes, for example, at the tails.
Distribution changes other than changes in means (such as quintiles or deciles) cannot be
investigated. Fortunately, DF L proposed an approach that allows analysts to construct
counterfactual densities that work with the entire density of incomes through kernel

density estimation:
c. 1 ” «9 y — Y
= _ _' K __I 2
./<y)n;h(h) ()
Equation (2) is an estimate of kernel density based on a random sample (Y ,...,Y,, ) , with

sampling weights (6I ,...,6,, ) , h , which is the smoothing function referred to as the

bandwidth, and K , the kernel function.42 The choice of h and K may be sensitive to the
distribution and has been subject to many discussions in the literature. In this context, the

“optimal bandwidth” (Silverman, 1986) and Gaussian kernel function are used.

 

42See Silvennan (1986) for a detailed discussion about kernel density estimation.

105

To provide an exposition of this technique. consider a simple binary variable—
mother tongue—that equals 1 if an immigrant communicates in either of the official
languages as a mother tongue and zero if neither language is spoken as a mother tongue.
Without controlling other characteristics, the proportion of immigrants who speak either
of the official languages as a mother tongue has declined significantly over time (see
Figure 3.9). Therefore, the Simplest way to impose the earlier distribution of languages on
current income distribution is to “up-weight” immigrants who communicate in official
languages as a mother tongue by a number that is equal to the percentage decrease in the
Share of this group over time. Similarly, immigrants who do not speak either English or
French as a mother tongue will be “down-weighted” because the possibility of being part
of this group has increased over time. Every immigrant is, therefore, assigned a new

weight (it/WW WW ), with adjusted mother-tongue distribution.

In the real world, the decline in English- or French-speaking immigrants may be
more concentrated in certain groups—the young age group, for example. Therefore, it is
more plausible to construct a counterfactual distribution that held constant mother-tongue

distribution at an earlier year‘s level and also was conditional on age (1,1/mm WW, 1. M, ). In

other words, each immigrant would be re-weighted according to the proportional change
in mother-tongue distribution within each age group. Daly and Valletta (2000) refer to
this technique as “greater behavioral content” because the counterfactual distributions
constructed in this way allow conditional relationships between explanatory factors and a
set of other variables.

To begin with, consider the distribution of equivalent income, Y, in 1997. which

consists of four explanatory factors: (1) a binary variable, I. that equals 1 if an immigrant

106

is employed full time and 0 if not employed full time; (2) a discrete variable, L, that
indicates the four combinations of mother tongue and duration of residency
characteristics for an immigrant;43 (3) a discrete variable, S, that indicates the four types
of family structure;44 and (4)‘a vector X of other attributes, including age, sex. education.
province dummies. Size of area dummies. and 16 census metropolitan area dummies. The
distribution of income can be shown as the product of joint densities I. L, S. and X:

fr(Y) = .f(Y|[1' = 97~’I:/..s,.l' : 97~’/.s..i' = 97~tsx = 979’s = 97)

= I [ll/(Y 11.L.S.X.t,. = 97 )dF(1 I L,S.X,t, ,H,.,. : 97)dF(L | s.X.r,,_,,. = 97)
-dF(S | X.t,.,,. = 97)dF(X 11,. z 97)

(3)

The challenge here is to construct counterfactual densities that satisfy the following:
“What would the density of equivalent income have been in 1997 if immigration factors.
family structure, and other attributes—but nothing else———had remained the same as the
1980 levels?”

f,’ (Y) = f(Y | 1,. = 97:905-): = 80.1,,“ = 80.15. A. = 80.1,. = 80)

= j iii/(Y I 1.3. X,:, = 97)dF(1 | L,S, X.t,.,,,. = 80)dF(L | S. X.r,,v,. = 80)
dF(S | X,:, .1- = 80) - dF(X | 1,. = 80)

(4)

With proper arrangement. the counterfactual density in (4) can be expressed as

fi(Y):.f(Yltl' =97’tll...\‘..\' :SOJLSJ' =80~tsx :8O‘t.\' =80)

dF(I 1L.S.X.r,l,,,. = 80)
dF(1 | L,S,X,r,;,,,,. = 97)

 

= l “Iffl’ I 1, L,S,X,t,. = 97)dF(I I L.S.X.t,,,,,,,. = 97)

 

‘3 The four combinations are (1) official languages speaking. non-recent immigrant. (2) official languages
speaking, recent immigrant. (3) non-official languages speaking, non-recent immigrant, and (4) non-official
languages speaking, recent immigrant.

“The four family types refer to (1) single unattached. (2) couple without children. (3) couple with children.
and (4) Single-parent family.

107

dF(L | 3.x.r,,_,. = 80)

-a’F(L | 3.2m, ,. ,. = 97)
“ -- dF(L I S,X.r,,_,,. = 97)

 

dF s X,:..,.=80 .=
(I ).dF(X1r,.=97)dF(X"-‘ 80)

-dF(S I X4” = 97)
dF(S I X,z,,,. = 97) dF(X | r, = 97)

 

 

= j jjjf(r|1.L.s,X.r,. = 97)dF(I I L.S, X,t,,,q_,.v\. = 97)dF(L 13./ram = 97)
~dF(S I X,:,” = 97)dF(X 1r, = 97) (5)
-11,],W,_\.(1.L,S.X)-1,1,.V.‘,.(L,S.X)~xis»1.1-(S.X)~/1\.(X)

which is equal to the exact density of equivalent income in 1997 times four re-weighting

functions: xi, 7.5.x (I.L.S,X) . 1,151,. (L,S.X) , xix,- (S,X) , and A". (X) . The new weights

(xi ) can then be incorporated into the estimation of the kernel density:

. l " 9% y—Y
=__ I K t 6
fly) "2 h ( h ) _ ()

 

3.4.1 Estimation of Conditionally Re-weighting Functions

As seen in the previous section, labour market performance among immigrants
has been deteriorating since 1990. To investigate impacts of immigrants on the change in
overall income distribution, the following question was asked: “What would the family
income distribution look like if the labour market performance (in terms of full-time
employment) of immigrants. conditional on other observable characteristics (e.g., age,
education, family structure, geography, mother tongue. and duration of residency), had
remained at their 1980 levels?”

In terms of notation, let I be a binary variable with value 1 if an immigrant is

employed full time and zero if not employed full time:

108

_ dFU l L959X”!|l.,.\‘..l' = 80)
_ dF(1| L,S,X,t, W. = 97)

 

371..s..r(1.L.S.X)

Pr(1 =11 L,S.X,t,,u,.fl,. = 80) Pr(I = 0| L.S,X,t, ,M = 80)

= 1 +(1— 1) (7)
Pr(I =1| L,S,X,r,?,q,.u,. = 97) Pr(1 = 0| L.S,X,t,,,qsfl\. = 97)

 

 

AM“. (1, LS. X) in (7) represents changes in the probability that immigrants would

work full time between 1980 and 1997, conditional on the characteristics (L, S, and X).

Every immigrant in the distribution will be either up-weighted (if 2,”, >1) or down-
weighted (if A)”, <1 ) in order to adjust full-time employment status to its 1980 level.

An estimate of the conditionally re-weighting function in”, (I, L.S,X ) can be derived

by assessing the conditional probabilities in (7) through a probit model. For non-
immigrants, the original sample weights are used without implementing an adjustment. In
other words, the re-weighting function for non-immigrants is set to 1.

In addition to the change in full-time employment probabilities. the characteristics
of immigrants—in terms of mother tongue and duration of residency—also changed
Significantly. Thus,the second decomposition sequence is to hold constant the interaction
effect of mother tongue and duration of residency to 1980 levels. In a Simple framework,
the population of immigrants can be divided into four mutually exclusive groups in a
given year according to mother tongue (= 1 if speaks official languages, = 0 if speaks
another language) and duration of residency (= 1 if in Canada more than 10 years, = 0 if
in Canada less than 10 years). The re-weighting function for this factor is then expressed
as

dF(L | s.X,r,,,.,. = 80)

Al.1.\'_,\'(L.S,X) =
dF(L I S,X’tl.-.S,X : 97)

 

109

_ Pr(L=]|S,Xst]m\‘w\' =80)+ Pr(L =2|S’X"II..\'..\' =80)
' Pr(L =11s.X.1,,,,,. = 97) 2 Pr(L = 21S,X.1,,,.,,. = 97)

 

 

 

 

L Pr(L = 31s.X.r,,,,,. = 80) + L Pr(L = 41S.X,r,,,_,. = 80)
3 Pr(L = 31S.X.t,,,._,. = 97) 4 Pr(L = 41S,X,t,,_,,. = 97)

_ :L Pr(L = cl 8X71... = 80‘)
.. m = c 1 S.X.t,,.,,1. —- 97) '

L

 

(3)

Since the dependent variable in equation (8) is now a categorical variable rather than a
binary variable, the estimate of (8) can be obtained through a “multi-nominal logit”
model. Again, the re-weighting function is set to l for all non-immigrants.

Similarly. an estimate of the conditional re-weighting function for the family

structure x1“ (S ,X) can be derived in a same fashion as equation ('9):

_ dF(S 1 X.z,.,,. = 80 Pr(S = c 1 X.t,.,. = 80)

) 4
2,, (s. X) _ = ZS,
dF(S 1 X,:,” = 97) .,. Pr(S = c 1 X,:, X = 97)

 

 

(9)

Finally, applying Bayes’ rule, the conditional re-weighting function for “other attributes”
,1“. (X) is written as

dF(X 1 r, = 80) : Pro, = 801 X) . Pr(r_\. = 97)

71,. (X) = , .
dF(X 11,. = 97) Pro, = 97 1 X ) Pro, = 80)

 

(10)

It is equal to the relative probability of observing an individual with the characteristics X
in the 1980 sample versus the 1997 sample times the unconditional probabilities of being
in either sample. The conditional probabilities are obtained through a probit model, while
the unconditional probabilities are the population ratio.

Changes in the density of equivalent income between 1980 and 1997 are.

therefore, model-based on the following decomposition:

110

f9? (Y) — 11800;) : f97(Y;t11/..s..l' = 97*’I.:.\'..\' = 97’t.\'l.\' = 979(31’ = 97)

i
‘f97(Y;’//..s..\' = 80~t1.1.s..\' = 97’[.\'.l' = 9791-1' = 97) ( )
+ f97(Y;’/.I..s..l' = 80*’1.1.s..i' = 97‘IXX : 97a” = 97) (n)
' 11
‘fc)7()8;’1,1..s..\' = 80"].l.\‘..\' = 809t.\‘1.l’ : 97~’.\' = 97)
+.fi)7(Y;’/1L.s..l' = 80~’/.1s..i' : 809ISIX = 9731.1' z 97) (...)
III
— .fg-j (Yul/”53X = 80,0132}. 2 80.6%". 2 80,1}. = 97)
+ .797(Y;’/11.,s..\' = 80~’I.,S.X = 809131.1' = 80’t.\’ = 97) (. )
‘ 1V
_ f97(Y;’/11..s..\' = 809’1.1s,_1' : 80,183,. = 802” = 80)
+fi)7(Y;t/1I..s..\' = 80~’I.s..i' = 809’31X = 80"“ = 80) ( )
V

_ f8(1(Y;tl1l...\‘..\’ = 80~t1.1.\'..\' : goatslx = 8091.1: = 80)

The five components in the above equation represent the effects of the changing full-time
employment rate of immigrants, changing characteristics of language/duration of
residency among immigrants, changing family structure, changing other attributes. and
residual factors, respectively.4S

Criticism of this approach is often related to its inability to distinguish
overlapping effects between factors. The possibility of a general equilibrium or an
endogenous relationship between factors would confound the true contribution of each
factor. A Simple alternative is to perform the same decomposition but in different order
sequences and see whether the results are as sensitive under an alternative arrangement.

In this study, reverse-order decomposition was also employed.

lll

3.5 Results

In this section. the impact of factors from the decomposition procedure is
discussed, and Table 3.1 Shows the weights used in the density decomposition. In the
primary-order decomposition (panel A), immigration factors are placed in the first two
sequences. followed by family structure, other attributes, and residuals. Variables in the
“other attributes, X” include age, sex, education,46 dummies for provinces, size of areas.
and census metropolitan areas. Thus, effects such as an aging population, a Shift in
educational attainment, or geographic location are captured in this category. F inally,
some important but unexplained factors, such as “skill-biased technological shocks,”
“international trade.” and “correlation between husband and wife earnings,” were placed
into the last category.

Figures 3.10 to 3.13 display changes in the density of equivalent income between
1980 and 1997 in primary-order decomposition sequences. Each graph adjusts an
additional modeled factor to its 1980 level. The solid line in Figure 3.10 represents the
original density of equivalent income for 1997, while the dash line represents the
counterfactual density that has been adjusted for the full-time employment rate of
immigrants. It shows that the lower portion of the income distribution would have been
slightly narrower if a full-time employment rate among immigrants held constant at 1980
levels. While the adjusted distribution moved density from the left tail to the middle, it
made virtually no impact on the right tail. Figure 3.11 further adjusts distribution by
holding constant the mother tongue/duration of residency characteristics of immigrants at

the 1980 level. Changing these two immigrant characteristics reduced the density in the

 

45 Notice that changes in the wage structure that affect the earnings of recent immigrants employed full year
full time are considered as effect in residual factors.

112

upper middle, with a corresponding increase ofmass in the lower middle of the
distribution. The visual effect became more significant when the effect of changing the
family structure was estimated. Figure 3.12 shows that changes in family structure
between 1980 and 1997 resulted in a uniform shift of income distribution to the left, but
the effects were more concentrated in the lower tail of the distribution. AS for the impact
of changing other attributes, Figure 3.13 shows an opposite movement. The whole
density Shifted toward the left when other attributes were held constant to 1980 levels.
suggesting some changes in this factor—likely education or age structure—contributed a
notable gain in median income between 1980 and 1997. Finally, residual effect is
illustrated in Figure 3.14. Compared with the original densities in Figure 3.3, it Shows
that the counterfactual distribution explained some changes in the lower portion of the
income distribution, while most of the change in the upper half of the distribution
remained unexplained.

The quantitative comributions of graphic analysis are Shown in Table 3.2.
Statistics include changes in a series of distribution measures, low-income rates, and
median income between 1980 and 1997. Generally, inequality indices and the low-
income rate—based on equivalent income—increased during this period. Overall
inequality, as measured by Gini coefficients, increased about 4.2 percent. However, the
increasing inequality was principally concentrated in the lower half of the income
distribution. Despite a declining median income, changes in the 90-50 ratio were less
noticeable, but increases in the 50-10 ratio were quite substantial, suggesting that people

in the lower end suffered major income losses during this period.

 

4‘’11 includes 3 educational categories: less than high school, high school graduated. and college or more.

113

Turning to the contribution of each factor, column 1 Shows the effects of a
changing full-time employment rate among immigrants. Overall. it explained 24 and 26
percent of the increase in the 90—10 and 50-10 ratio, respectively, 12 percent of the
increase in the Gini coefficient. and about 17 percent (or 0.7 percentage points) of the
increase in the low-income rate during this period. The stronger effects on the 90-10 and
50-10 ratios indicate that a proportionate increase of non-full-time jobs was likely
associated with low incomes.

Column (2) displays the effects that were attributed to the changes in mother
tongue and duration of residency among immi grants. By holding these two characteristics
at 1980 levels. column (2) indicates that these factors contributed to an increase of about
7 percent in the Gini coefficient, a 0.5 percentage point grth in the low-income rate,
and about $228 of the decline in median income. The magnitudes were not large in terms
of overall changes. When the effects from (1) and (2) are combined, changes in
immigration factors accounted for a total of 19 percent of the increase in the Gini
coefficient, 33 percent of the increase in the 50-10 ratio, a 1.2 percentage point growth in
the low-income rate, and $462 of the decline in median.

Among explanatory factors, the change in family structure had the most influence
on income distribution (column 3). It accounted for about 20 percent of the increase in
the Gini coefficient and 32 percent (equivalent to 1.4 percentage points) of the growth in
the low-income rate. The impacts were more apparent in the lower half of the income
distribution, explaining 33 and 36 percent, respectively, of the increase in the 90-10 and
50-10 ratios. However, it contributed relatively little to the change in the upper half of the

income distribution, explaining only 13 percent of the increase in the 90-50 ratio. The

114

counterfactual suggests that the growing number of Single-adult families (single
unattached or Single parents) was more likely associated with low income. It is also
noteworthy that changes in the family structure resulted in a decline of median income by
about $755 during this period.

AS for the contributions of other attributes, column (4) Shows that such effects
were fairly small across different measures. except for movement in median income.
Actually, factors in this category contributed to inequality in an opposite way: The low-
income rate dropped by 0.7 percentage points, and the median income grew by $803
between 1980 and 1997. Embedded factors, such as increasing proportion of prime-age
population, growing educational attainment, as well as urbanization. seem to increase the
national median and had some equalizing effects on inequality.

Finally, the contributions of residual factors were fairly large (see column 5).
More than half of the increase in the Gini coefficient and low-income rate remained
unexplained. The residual effects were even stronger in the upper half of the income
distribution. For instance, more than 70 percent of the increase in the 90-50 and 75-50
ratios cannot be explained by any of the four explanatory factors. The relatively large
residuals raise concerns about the creditability of the estimated effects of factor. Are large
residuals really a result of changes from unknown or unexplained factors? Could they
also come from a poor estimation during the decomposition process? In the latter case,
the previous estimates on factor effects will not have much creditability. To validate these
effects, an alternative decomposition based on a different reference (base) year was

employed (see Table 3.3). That is, 1980 was used as a reference year rather than 1997

115

and factors were held constant to 1997 levels. If factors were estimated consistently in
both years. Similar results would be expected, regardless of reference year.

Basically, with few exception, results from these two alternative counterfactuals
exhibited similar patterns. Most of the changes in the inequality measures remained
unexplained even if the counterfactual was evaluated at a different reference year. This
suggests that most of the residuals can be attributed to factors that were not included in
the model, which requires further investigation in future studies. It is also noteworthy that
compared to the data in Table 3.2 the change in family structure had a relatively strong
impact on income distributions. One of the reasons is that Single-adult households were
not very common 20 years ago, and the labour market participation rate among this type
of family (e. g., single mother) was relatively low at that time. As a result, using 1980 as
the reference year and holding constant family structure to the 1997 level would

introduce more low-income families than it would have with reference year values at

1997 levels.

3.5.1 Reverse-Order Decomposition

As mentioned, the effect of a factor might change with respect to its order in the
decomposition if there are overlapping effects between factors. To examine the extent of
sensitivity. a reverse-order decomposition was conducted (see Table 3.4). The weights
used in the reverse-order are shown in panel B, Table 3.1, and the derivation of re-
weighting functions for reverse-order is described in Appendix D. Generally, all
measures display similar patterns as those shown in the primary-order decomposition in
Table 3.2. The contributions of other attributes with respect to income disparities

remained insignificant (except for distribution in the lower tail), even if they were

116

considered first in the decomposition process. Interestingly, the contribution of the 50-10
ratio associated with “other attributes” increased noticeably, at the expense of
immigration factors. One possibility is that immigrants, particularly low-skilled
immigrants, were likely to be younger and were more concentrated in the metropolitan
areas. Therefore, part of the immigration effect is captured by the “other attributes” when
the decomposition sequence is reversed.

The effects of changes in the family structure increased slightly in the reverse-
order decomposition, notably in the upper half of the distribution. For instance, changes
in the family structure accounted for 20 percent of the increase in the 90-50 ratio in the
reverse-order decomposition, while the share was only 13 percent in the primary-order
decomposition in which the immigration factors were conditioned. It is likely that this
effect overlapped with some immigrant effects because changes in family structure,
especially the growth in Single-unattached households, are linked to the increase in young
professional immigrants under the skill-based “independent” visa category. Finally.
effects due to changes in immigrant-related factors were slightly lower when they were
considered last in the decomposition. Nevertheless. immigration factors were still
responsible for 0.8 percentage points of the increase in the low-income rate. with a

notable $376 reduction in median income during this period.

3.5.2 Decomposition Breakdown during Two Periods: 1980—1989 and 1989—1997
Tables 3.5 and 3.6 broke down the previous analysis into two periods, namely
1989—1989 and 1989—1997, representing a peak-to-peak (or near peak) year in the 19803

and 19903, respectively. Peak-to-peak years were selected to avoid possible variations

117

due to business cycles. Generally. the results displayed very distinct patterns between
these two periods. Levels of inequality remained unchanged, and the median income
increased substantially ($1,441), suggesting an overall improvement in the economy
between 1980 and 1989. Nevertheless. the Significant increase in median income in this
period was largely associated with residual factors, and part of the growth was actually
offset by immigration factors and family structure.

On the other hand, the increase in overall inequality was principally concentrated
in the second period. More than two-thirds of the increase in inequality and nearly 100
percent of the increase in the low-income rate between 1980 and 1997 took place in the
19903. Changes in the family structure continued to influence income distribution,
particularly in the lower half of the income dispersions. explaining 25 percent of the
increase in the 50-10 ratio and about a 0.6 percentage point increase in the low-income
rate during this period. Also. in the 19903, immigration factors became noticeable in the
decomposition. Immigrant factors (columns 1 and 2 of Table 3.6) accounted for about 20
percent of the increase in the Gini coefficient and about a 1 percentage point increase in
the low-income rate. Again, residuals Or unexplained factors dominated other aspects in
explaining changes in inequality, accounting for more than three-quarters of the increase
in most of the measures during this period. The residual factors alone reduced the median
income by about $1,604 and accounted for 74 percent (or 3.1 percentage points) of the
increase in the low-income rate between 1989 and 1997. This raises concerns about the
years of selection. Perhaps the 1997 economy was not good enough to compare to the

1989 or 1980 economy.

118

3.6 Conclusion

Income inequality, based on pre-transfer family income, in Canada increased
between 1980 and 1997, mainly in the 19903. A number of previous studies have shown
the importance of demand-Side factors (e.g., technological change or changing trade
structures) as well as supply-Side factors (e.g., Shifts in demographic compositions) on
increasing family income inequality. In this study, a relatively new re-weighting
technique—developed by DiNardo, Fortin, and Lemieux (1996)——was used to examine
the underlying causes of increasing inequality, with special attention paid to the
demographic Shifts. Of particular interest were the effects of changing family structures
and the characteristics of immigrants.

The results from this chapter echo Zyblock’s (1996b) finding that changing
family structure contributes to rising family income inequality. Despite differences in
time period considered and methodology, this present study confirms Zyblock’s finding
that the shift toward more Single-parent families accounted for a considerable proportion
of rising inequality, and the effects were more concentrated in the lower half of the
distribution.47 With respect to immigrant effects, Picot and Hou (2002) use a regression-
based decomposition approach and suggest that two-thirds (1.5 percentage points) of the
increase in the low-income rate between 1989 and 1999 was the result of increasing low-
income rates among immigrants. The decomposition results in the present study suggest
that the low-income rate would have been reduced by 28 percent (or 1.2 percentage

points) between 1980 and 1997 if the full-time employment rate and the

119

language/duration of residency characteristics of immigrants had remained at their 1980
levels.

The impact of changing family structure on family income distribution is similar
in the US. and Canada. Daly and Vallettea (2000) use the same DF L technique with
1969 and 1989 data from the US. Current Population Survey (CPS) and conclude that
changes in family structure substantially increased inequality over this period. They
found that its effects were concentrated in the lower half of the distribution of family
income, and changes in family structure explained about 50 percent of the increase in the
50-10 ratio and about 9 percent of the increase in the 90-50 ratio (compared to 36% and
13%, respectively, in this study). Cancian and Reed (2001) use the CPS from 1969 and
1998 to investigate the impact of changes in family structure on the poverty rate and
construct a counterfactual level of poverty using the 1998 shares of persons by family
type and 1969 poverty rates. They found that the total change resulting from family
structure and female employment would have produced an increase of 1.6 percentage
points in the poverty rate if all else had remained as it was in 1969. By comparison. this
present study shows that changes in family structure (conditional on the distribution of
other attributes) led to a 1.4 percentage points increase in the Canadian poverty rate
between 1980 and 1997.

The major findings of this study can be summarized as follows. First, a growth
toward more non-traditional families contributed the largest share in rising income

disparities among the four explained factors. Conditional on other attributes, changes in

 

47Zyblock’s finding, which was based on the decomposition of population subgroups, shows that the shift
toward single-parent families accounted for nearly 40 percent of the increase in the mean log deviation. To
compare. the same inequality measure (mean log deviation) is applied to the counterfactual distributions.
This reveal that. conditional on other attributes. changing family structure accounted for about 30 percent

120

the family structure were responsible for 20 percent of the increase in the Gini coefficient
and a 1.4 percentage point increase in the low-income rate between 1980 and 1997. The
impact was especially salient on the lower-half income distribution, suggesting that the
increase in single-adult families was more likely associated with low income.

Second, changes in immigrant characteristics influenced growing income
disparities. Holding constant full-time employment rates of immigrants at an earlier year
level would have reduced the increase in the 50—1 0 ratio by 26 percent and reduced the
low-income rate by 0.7 percentage points. When the Share of recent immigrants and
mother-tongue characteristics were held constant at the 1980 level, it further explained
the nearly 0.5 percentage point growth in the low-income rate and about 7 percent of the
increase in the Gini coefficient. The above-mentioned immigration factors account for
about 17 percent (or 1.2 percentage points) of the increase in the low-income rate and a
$462 decline in median between 1980 and 1997. When separate decompositions were
conducted for two different periods (1980 to 1989 and 1989 to 1997), it was found that
nearly all the immigration effects took place in the 19903—the period with the highest
intake of immigrants and the largest proportion of non-European-bom immigrants since
the beginning of the century.48

Third, the effects of other attributes (e. g., age, education) were relatively small
and actually showed an equalizing effect on the distribution of equivalent income. Its

counterfactual effect reduced the low-income rate by 0.7 percentage points and pushed

 

of the increase in the mean log deviation between 1980 and 1997 (i.e., from 0.881 to 0.903 out of 0.074,
where 0.074 is the total increase in the mean log deviation over this period).

48Statistics Canada (2003) reports that between 1991 and 2000, 2.2 million immigrants were admitted to
Canada. the highest number for any decade in the past century. Among immigrants who arrived during the
19903, 58% were born in the Asian countries, and 19% were born in Africa, the Caribbean. Central or
South America. These figures were up from 12.1% and l 1% respectively of those who came during the
19603. and were from these regions.

the median to more than $800. The growing prime-age population, urbanization, as well
as the growing educational attainments of Canadian workers during this period all
worked together to lift the median income. These aspects might explain the equalizing
effect in this category.

Finally, a substantial proportion of the increase in inequality and the low-income
rate remained unexplained during this period. The residual effects were even stronger in
the upper half of the income distribution and accounted for more than 70 percent of the
increase in both the 90-50 and 75-50 ratios. The residual effects remained substantial,
even if the counterfactual was evaluated at a different reference year. It is possible that
large residuals may be the result of unexplained factors such as technological change,
changing trade structures, or a correlation between husband-and-wife earnings. Future

studies are needed to properly distinguish the influence of these factors.

Table 3.1

Weights Used in Density Decomposition

A. Primary-Order Decomposition

 

 

 

 

 

Order Counterfactual distribution of family Equivalent Re-weighting

equivalent income in I997 held constant family
factors to 1980 in the following order income
Original Distribution Yin W97

1 Full-time employment rate of immigrants Yo7 Wo7*R1. 1-3.x

L Mother tongue/duration of residency Yo7 W97*R., L.S.X*RL, 5.x
characteristics of immigrants

S Family structure Y9? W97‘R11L.s.x*RL's.x*Rs1x

X Other attributes (age. sex. education. Yo7 W97*R1; Ls.x*R1.= $.x*Rss x‘Rx
province, size of area. CMA)

B. Reverse-Order Decomposition
Order Counterfactual distribution of family Equivalent Re-weighting

equivalent income in I997 held constant family
factors to 1980 in the following order income
Original Distribution Y97 W97

X Other attributes (age, sex, education, Y97 Wo7*Rxl 5.1“]
province. size of area. CMA)

S Family SU'UCIUI'C Yo7 W97*Rx1 S.L.I*RS L.l

L Mother tongue/duration of residency Yo7 Wo7*Rx. 5.1,.1*R5| L,1*R1, 1
characteristics of immigrants

1 Full-time employment rate of immigrants Yo7 W97*Rxf S_L_1*Rsr L1*RL1 1*R1

 

Note: Yo7 refers to equivalent family income in 1997. W97 is survey sampling weight, and Rs are estimated
conditioning weightings. The subscripts for the adjusted variables—l. L, S, and X—refer to full-time
employment rate among immigrants. mother tongue/duration of residency characteristics of immigrants,
family structure, and other attributes, respectively.

Table 3.2
Primary-Order Decomposition of Changes in the Distribution of Family Equivalent
Income, 1980—1997 (1997 as Reference Distribution)

 

 

 

Statistics Total Effect of
change Immigrant Immigrants Family Other Residual
FT ratel MT/DR2 Structure attributes3 factors
(1) (2) (3) (4) (5)
Gini 0.042 0.005 0.003 0.008 0.001 0.024
(0.12) (0.07) (0.20) (0.03) (0.57)
C .V. 0.095 0.009 0.006 0.008 0.020 0.052
(0.09) (0.07) (0.09) (0.21) (0.55)
90-10 5.384 1.278 0.379 1.789 -0.153 2.091
(0.24) (0.07) (0.33) (-0.03) (0.39)
90-50 0.169 0.009 0.01 1 0.022 0.009 0.1 18
(0.06) (0.06) (0.13) (0.05) (0.70)
75-25 0.526 0.079 0.027 0.104 -0.034 0.350
(0.15) (0.05) (0.20) (-0.07) (0.67)
75-50 0.088 0.010 0.005 0.014 -0.008 0.068
(0.1 1) (0.06) (0.15) (-0.09) (0.77)
50-25 0.252 0.040 0.012 0.053 -0.014 0.161
(0.16) (0.05) (0.21) (-0.05) (0.64)
50-10 2 263 0.586 0.154 0.814 -0.095 0.804
(0.26) (0.07) (0.36) (-0.04) (0.36)
Low-income 0.043 0.007 0.005 0.014 -0.007 0.024
rate (0.17) (0.1 1) (0.32) (-O.15) (0.55)
Median -457.17 —233.65 -228.13 -754.97 802.71 ~43. 13
(0.51) (0.50) (1.65) (-1.76) (0.09)

 

Note: Percent of total variation explained in parenthesis.

'Effect of immigrants held proportion of full-time working immigrants constant at earlier year levels.
2Effect of immigrants held mother tongue (MT) and duration of residency (DR) constant at earlier year

levels.

3 Other attributes are age. sex. education. province indicators. CMA, and sizes of area.

124

Table 3.3
Primary-Order Decomposition of Changes in the Distribution of Family Equivalent
Income, 1980—1997 (1980 as Reference Distribution)

 

 

 

Statistics Total Effect of
change Immigrant Immigrants Family Other Residual
FT ratel MT/DR2 structure attributes3 factors
(1) (2) (3) (4) (5)
Gini -0.042 -0.006 -0.002 -0.01 1 -0.001 -0.021
(0.14) (0.06) (0.27) (0.03) (0.50)
C .V. -0.095 -0.010 -0.001 -0.019 -0.009 -0.056
(0.11) (0.01) (0.20) (0.10) (0.59)
90-10 -5.384 -0.565 -O.391 -2.574 -0.13 l -l.722
(0.11) (0.07) (0.48) (0.02) (0.32)
90-50 -0.169 -0.016 -0.01 1 -0.022 -0.004 -0.1 17
(0.09) (0.06) (0.13) (0.02) (0.69)
75-25 -0.526 -0.051 -0.020 -0.161 0.046 -0.340
. (0.10) (0.04) (0.31) (-0.09) (0.65)
75-50 -0.088 —0.009 -0.007 -0.0 l 3 0.000 -0.060
(0.10) (0.08) (0.14) (0.00) (0.68)
50-25 -0.252 -0.025 -0.005 -0.096 0.031 -0. 157
(0.10) (0.02) (0.38) (-0.12) (0.62)
50-10 -2 263 -0.260 -0.l79 -l .256 -0.056 -0.512
(0.12) (0.08) (0.56) (0.02) (0.23)
Low-income -0.043 -0.009 -0.005 -0.018 0.006 -0.017
rate (0.21) (0.1 l) (0.44) (-0. l 6) (0.40)
Median 457.17 353.66 319.97 677.79 -790.43 -103.83
(0.77) (0.70) (1.48) (-1.73) (-0.23)

 

Note: Percent of total variation explained in parenthesis.

1Effect of immigrants held proportion of full-time working immigrants constant at earlier year levels.
2Effect of immigrants held mother tongue (MT) and duration of residency (DR) constant at earlier year
levels.

3 Other attributes are age. sex, education. province indicators. CMA. and sizes of area.

Table 3.4
Reverse-Order Decomposition of Changes in the Distribution of Family Equivalent
Income, 1980—1997

 

 

 

Statistics Total Effect of
change Other Family lmmigrants lmmi grants Residual
attributesl structure MT/DR2 FT rate3 factors
(1) (2) (3) (4) (5)
Gini 0.042 0.003 0.010 0.003 0.002 0.024
(0.07) (0.24) (0.07) (0.05) (0.57)
C.V. 0.095 0.017 0.016 0.005 0.004 0.052
(0.18) (0.17) (0.05) (0.04) (0.55)
90-10 5.384 0.710 1.961 0.433 0.189 2.091
(0.13) (0.36) (0.08) (0.04) (0.39)
90-50 0.169 -0.001 0.034 0.010 0.007 0.1 18
(0.00) (0.20) (0.06) (0.04) (0.70)
75-25 0.526 0.01 1 0.118 0.034 0.012 0.350
(0.02) (0.23) (0.06) (0.02) (0.67)
75-50 0.088 -0.006 0.019 0.003 0.004 0.068
(-0.06) (0.22) (0.03) (0.05) (0.77)
50-25 0.252 0.014 0.055 0.019 0.003 0.161
( 0.06) (0.22) (0.08) (0.01) (0.64)
50-10 2.263 0.341 0.857 0.186 0.076 0.804
(0.15) (0.38) (0.08) (0.03) (0.36)
Low-income 0.043 -0.002 0.014 0.004 0.004 0.024
rate (-0.06) (0.32) (0.10) (0.08) (0.55)
Median -457.17 441.45 -479.56 -153.34 -222.59 -43.13
(-0.97) (1.05) (0.34) (0.49) (0.09)

 

Note: Percent of total variation explained in parenthesis.

1Other attributes are age. sex. education. province indicators, CMA. and sizes of area.

2Effect of immigrants held mother tongue (MT) and duration of residency (DR) constant at earlier year

levels.

3Effect of immigrants held proportion of full-time working immigrants constant at earlier year levels.

126

Table 3.5
Primary-Order Decomposition of Changes in the Distribution of Family Equivalent
Income, 1980—1989

 

 

 

Statistics Total Effect of
change Immigrant lmmigrants Family Other Residual
FT ratel MT/DR2 structure attributes3 factors
(I) (2) (3) (4) (5)
Gini 0.013 0.001 0.001 0.004 0.001 0.006
(0.06) (0.06) (0.35) (0.09) (0.45)
C.V. 0.050 0.000 0.001 0.010 -0.002 0.041
(0.00) (0.03) (0.20) (-0.04) (0.81)
90-10 0.820 0.047 0.102 0.525 0.021 0.125
(0.06) (0.12) (0.64) (0.03) (0.15)
90-50 0.048 0.001 0.003 0.003 0.002 0.040
(0.02) (0.06) (0.05) (0.04) (0.83)
75-25 0.145 0.007 0.010 0.038 0.013 0.077
(0.05) (0.07) (0.26) (0.09) - (0.53)
75-50 0.018 0.001 0.000 0.002 0.002 0.013
(0.05) (0.01) (0.13) (0.10) (0.72)
5025 0.080 0.004 0.007 0.024 0.007 0.039
(0.05) (0.09) (0.29) (0.09) (0.48)
50-10 0.323 0.022 0.046 0.262 0.007 -0.013
(0.07) (0.14) (0.81) (0.02) (-0.04)
Low-income 0.002 0.001 0.003 0.008 -0.003 -0.008
rate (0.77) (1.70) (5.10) (-1.64) (-4.94)
Median 1440.59 -44.81 -84.30 -333.82 4 I 7.78 1485.74
(-0.03) (-0.06) (-0.23) (0.29) (1.03)

 

Note: Percent of total variation explained in parenthesis.

lEffect of immigrants held proportion of full-time working immigrants constant at earlier year levels.
2Effect of immigrants held mother tongue (MT) and duration of residency (DR) constant at earlier year

levels.

1 . . . . . .
'Other attrlbutes are age. sex, educatlon. provmce indlcators. CMA. and Sizes of area.

Table 3.6
Primary-Order Decomposition of Changes in the Distribution of Family Equivalent
Income, 1989—1997

 

 

 

Statistics Total Effect of
change Immigrant Immigrants Family Other Residual
FT ratel MT/DR2 structure attributes3 factors
(I) (3) (3) (4) (5)
Gini 0.029 0.004 0.002 0.004 0.000 0.019
(0.14) (0.06) (0.14) (0.01) (0.65)
C.V. 0.044 0.007 0.004 0.004 0.010 0.020
(0.16) (0.08) (0.08) (0.23) (0.44)
90-10 4.564 1.014 0.192 1.023 -0.094 2.429
(0.22) (0.04) (0.22) (-0.02) (0.53)
90-50 0.121 0.006 0.016 0.007 0.004 0.089
(0.05) (0.13) (0.06) (0.03) (0.74)
75-25 0.381 0.062 0.032 0.033 -0.021 0.276
(0.16) (0.08) (0.09) (-0.06) (0.72)
75-50 0.071 0.006 0.010 0.002 ~0.005 0.057
(0.09) (0.15) (0.03) (0.07) (0.80)
50-25 0.172 0.033 0.008 0.019 -0.008 0.120
(0.19) (0.05) (0.1 I) (-0.05) (0.70)
50-10 1.940 0.469 0.050 0.477 -0.055 0.999
(0.24) (0.03) (0.25) (-0.03) (0.52)
Low-income 0.042 0.006 0.003 0.006 -0.005 0.03 I
rate (0.14) (0.07) (0.15) (-0.1 I) (0.74)
Median -1 897.76 -163.49 -243.60 -255. I 8 368.45 -1603.95
(0.09) (0.13) (0.13) (-0.1 I) (0.85)

 

Note: Percent of total variation explained in parenthesis.

lEffect of immigrants held proportion of full-time working immigrants constant at earlier year levels.
2Effect of immigrants held mother tongue (MT) and duration of residency (DR) constant at earlier year

levels.

"Other attributes are age. sex. education, province indicators. CMA. and sizes of area.

Figure 3.1
Trends of Inequality and Low-income, Equivalent Income

 

 

 

————— Low-income rate —-o—-- 75/25 percentile ratio
1.4 -
1.3 r
1.2 —
a"
13 v- 1.1 _
-_ a
to co
.8 92 1 _
.9 -
.8 4
1 f F I I I I I
1980 1982 1984 1986 1988 1990 1992 1994 1996
year
Figure 3.2

Indexed Equivalent Income by Percentile, 1980—1997

 

90th percentile —er— 10th percentile
————— 75th percentile —+— 25th percentile
1 .4 4
1 .2 r
if). I -—— \\____\ - __‘ _____-
E 1 — 4‘ _____ I I ”” \ I
Q)
E
8
,g '11- .8 r
— O
<5 00
die! -
.6 —
.4 '-

 

 

1980 1982 1984 1986 1988 1990 1992 1994 1996

year

Figure 3.3a
Kernel Density for Equivalent Income, 1980 and 1997

Year 1980 ————— Year 1997

.00004 J

.00003 “

.00002 a

.00001 -

 

 

 

20000 40000 60000 80000

0.4

Equivalent Income

Figure 3.3b
Kernel Density for Equivalent Income, 1980 and 1989

—Year1980 . -—4—-Year1989

1

.00004

1

.00003

1

.00002

I

.00001

 

 

 

0 20000 40000 60000 80000

Equivalent Income

Figure 3.3c
Kernel Density for Equivalent Income, 1989 and 1997

 

 

 

 

 

-——————-Year1989 ————— Year1997
.00004 ‘
.00003 ‘
.00002 ‘
.00001 *
0 -—1
I
0 20000 40000 60000 80000
Equivalent Income
Figure 3.4
Inequality Measures for Male Earnings (age 15—64)
————— Gini coefficient —a—— Median
———4~———CLV.
16 ‘
l4 ‘
12 n
x‘
8
s g 1 ‘
“in
812 92
.8 r
.6 -
.4 —

 

 

r I I ”3* 1 I l I r
1980 1982 1984 1986 1988 1990 1992 1994 1996

year

Figure 3.5

Proportion of Working Wives by Husband’s Quartile Earnings Distribution, Couple

Families
.85
.8
E”
53 .75
O
a
O
c 7
.9
'8 m
31% 65
a. 3
.6
.55
Figure 3.6

 

Bottom quartile
—»— — -- rd quartile
I 1 l

—a.—— 2nd quartile
o- — Top quartile
I 1 l

 

 

.1

.—

 

 

 

 

1900 1982 1904 1906

Family Composition

.65
g, .55
(U
:0
:8 .45
.2
'O
.E
.8 .35
C
.9
'8 .25
Q
9.8
0. w-
.15
.05

—°—— Single
-—‘—8— Couple with children

F
1 988

1900 1902 1904

year

1906

——a— Couple without children

————— Lonel parent 1
1 I

I

 

—

‘

 

-..—-.. _—-
——~_ I. ..——'——’ ~_—' .
———————'— — ‘——-__—-—'

— .55

" .19
r" .16

” .09

 

 

I T I I
1980 1982 1984 1986

T I I I
1988 1990 1992 1994

year

1906

Figure 3.7
Median Equivalent Market Income (age 15-64)

————— Population ' —-°— lmmigrants
—a—— Recent Immigrants

 

 

 

 

35000 4
30000 2
'8
To 25000 d
.2
3
O'
0
S ‘” _
5 $20000
d.) U
2 .9
15000 -‘
10000 '7
1900 1902 1904 1906 1908 1900 1902 1904 1906
year
Figure 3.8
Full-time Employment Rate (age 15—64)
————— All —o— Immigrants
—a——— Recent immigrants
.75 -
.7 -
E .65 r
G)
E
8‘ _
a .6
E
LIJ
q, _.
g .55
=§.9
LL 9 .5 —
.45 —
.4 —

 

 

1900 1902 1904 1906 1908 1900 1902 1904 1906

year

Figure 3.9

Proportion of Immigrants Whose Mother Tongue is English or French

.5
.4
B (D
C w
8 -3
8%”
E .5
.2
.1
Figure 3.10

 

 

 

 

 

— — a» — — lmmigrants —.-s— Recent immigrants
l l 1 l I L l I
4
I I I fi I I 1 I I
1980 1982 1984 1986 1988 1990 1992 1994 1996
year

1997 Equivalent Incomes with 1980 Immigrants’ F uIl-time Employment

.00004

.00003

1997 Equivalent Family
Income

 

————— Adjusted for full-time employment
rate for immigrants

 

0 20000

40000 60000 80000

Equivalent Income

I34

Figure 3.11

1997 Equivalent Incomes with 1980 Immigrants’ Full-time Employment, Mother
Tongue, and Duration of Residency Characteristics

1997 income with 1980

 

 

----- Adjusted for immigrants’
0004 _ immigrants’ full-time mother tongue and
'0 employment rate duration of residency
characteristics
.00003 —
is: 2 .00002 '1
ID 0)
x D
.00001 —
0 —-1
fl 7 I I I
0 20000 40000 60000 80000
Equivalent Income
Figure 3.12

1997 Equivalent Incomes with 1980 Immigration Factors and Family Structure

1997 income with 1980
.00004 — i""“lgl'ation factors

————— Adjusted for family structure

.00003 ‘

Kernel

3300002 “
C
o
D

.00001 “

 

 

 

20000 40000 60000 80000
Equivalent Income

135

Figure 3.13

1997 Equivalent Incomes with 1980 Immigration Factors, Family Structure, and
Other Attributes

1997 income with 1980

————— Adjusted for other attributes
immigration factors and
.00004 7 family structure

.00003 -‘

 

 

 

 

 

 

0 20000 40000 60000 80000
Equivalent Income
Figure 3.14
Residual
1997 income with 1980 ————— 1980 Income
factors in figures 10-13
.00004 ‘
.00003 ‘
6 9500002 -
E c
0 cu
x a
.00001 ‘
O —-1
I I I
0 20000 40000 60000 80000
Equivalent Income

CONCLUSION

The chapters in this dissertation cover empirical research into three specific areas:
(1) employment insurance, (2) income mobility, and (3) family income distribution. The

conclusions are summarized as follows.

Chapter 1
The new EI repayment provision has reduced the probabilities of filing a claim

The switch to the El system led to a substantial reduction in the El participation
rate among high-income workers. Applying propensity score matching and regression
methods with data from two different data sources—the Survey of Changes in
Employment (CIE) and the Survey of Labour and Income Dynamics (SLID)—this study
shows that the new repayment policy has reduced the probabilities of filing a claim
among high-income individuals by about 4.2 to 6.2 percentage points for the CIE sample
and about 9.7 to 12.7 percentage points for the SLID sample. Based on the estimate of a
6.2 percentage point decline in claim rate, this study suggests that the new rules could
have reduced the average monthly number of regular beneficiaries in the post-policy
period by about 31.482 and reduced the total annual regular benefits by about $629
million.

However, it should be noted that both matching and regression estimates relied on
observables. thus the existence of unobserved heterogeneity could seriously bias the
parameter estimates. In addition, the decline in claim rates could be due to employment
effects. With the effective reduction in benefits imposed by the repayment policy, many

frequent claimants will leave for stable industries. The claim rates for the remaining at-

137

risk population are. therefore, smaller. Future research on repayment should take

employment effects into account.

Chapter 2
There is substantial income mobility in Canada and the United States

Using the longitudinal survey data from Canada (the Survey of Labour and
Income Dynamics, SLID) and the United States (Panel Survey of Income Dynamics,
PSID). this chapter shows that there is significant household income mobility in both the
US. and Canada. For most individuals, poverty is a temporary experience. For example,
although 40 percent of the population in both countries experienced poverty at least once
during the survey years, only 10 to 13 percent of them were in poverty every year. It was
also found that mobility rates vary across subgrOupS. Based on l-year transition matrices.
24 (3 5) percent of the Canadian (American) college graduates who were observed in
poverty in year t rose above the poverty line the next year. The comparable figures for
people with high school or less education were about 14 percent and 18 percent for

Canada and the US, respectively.

Canada ’s redistributive system contributes to income stability

Applying both pre-transfer and post-transfer income measures, this study
discovered that Canada’s redistributive system Significantly reduced the variations of bad
or good years for an individual and greatly increased stability. From any given year (H
to t), the chance of staying both years in either of the two extremes of income distribution

was reduced from 43.5 to 23 percent when post-transfer measures were used, while the

138

chance of staying in the same middle income group increased correspondingly (from
21% to 39%). The effect of the transfer system on income stability was also evident
among subgroups. The rates of persistent poverty among less-educated people dropped
from 22.4 percent to 4.7 or 6.3 percent and from 18 percent to 2.7 or 4.2 percent for
immigrants whose mother tongue was neither English nor French. The redistributive
system in the US. also contributed to income stability. but the effect was relatively

smaller.

Household events are more relevant to spell beginnings or endings in Canada

Using the Bane-Ellwood hierarchical classification, it was found that the
beginning or ending of an income spell was more likely to be associated with changes in
household income sources (mostly earnings) in both the US. and Canada. However.
household events were more relevant to the spell beginnings or endings in Canada.
especially among single-adult households. About 43 percent of low-income Spell endings
among single-parent households in Canada were related to partnership or household
merges. The comparable figure is about 28 percent in the United States. Similarly, as
many as 38 percent of low-income spell beginnings among Single unattached households
in Canada were associated with newly established family. compared to 15 percent in the

United States.

139

Chapter 3

Changing family structure and the characteristics of immigrants contribute to an
increase in income inequality and the poverty rate

What would be the inequality or poverty rates if family structure and the
characteristics of immigrants had remained as they were 20 years ago? This study
addressed this question by constructing counterfactual distributions through a newly
developed re-weighting procedure. This study Shows that, conditional on other attributes,
changes in family structure were responsible for 20 percent of the increase in the Gini
coefficient and a 1.4 percentage point increase in the low-income rate between 1980 and
1997. The impact was especially salient on the lower half of the income distribution,
suggesting that the increase in single-adult families was more likely associated with low
income. Similarly, in this study, a counterfactual distribution was constructed by holding
constant three immigrants’ characteristics—firll-time employment rate, duration of
residency, and mother tongue—to 20 years ago. The changes in these three characteristics
accounted for about 33 percent of the increase in the 50-10 ratio, 17 percent (or 1.2
percentage points) of the increase in the low-income rates, and a $462 decline in median

income over this period.

A large proportion of the changes in inequality remain unexplained

The results of this study Show substantial residual effects, particularly in the upper
half of the income distribution. These residual effects accounted for more than 70 percent
of the increase in both the 90-50 and 75-50 ratios. The residual effects remained
significant, even if the counterfactual was evaluated for a different reference year. The

large residuals may be due to the fact that many important changes (e.g., changes in

140

earnings disparities or changes in the correlation of wives’ earnings with other income
sources) were not factored into the decompositions. Future studies are needed to properly

distinguish the influence of other factors.

141

Table A1

APPENDICES

APPENDIX A
Additional Tables for Chapter 1

 

Major Changes of Legislation from U] to El

Program parameter

Unemployment
Insurance (Ul)

Employment
lasagna (El)

Remark

 

 

Entrance requirements

12—20 insurable weeks E

420—700 hours

 

Change from weeks-
based to hours-based
system

 

Entrance requirements

Minimum 20 weeks

Minimum 26 weeks (or

Re-entrants refer to

 

insurability

for new or re-entrants 910 hours) those absent from work
for 2 years
Minimum hours for 15 hours/week No All hours are counted

for eligibility: part-time
workers are covered

 

Entitlement schedule

Maximum 50 weeks

Maximum 45 weeks

 

 

 

 

 

 

 

 

received if net income
> 1.5 times the annual
MIE

 

benefit received if net
income > 1.25 times
the annual MIE,
depending on claim
history

 

Maximum insurable $845/week $750/week
earnings (MIE)
Replacement rate 55% 55% The average weekly
(the divisor rule) (No) (Yes) benefits are calculated
by summing earnings
Replacement rate for 60% 65%—80% over the last 26 weeks
claimants with by the greater of ( l) the
dependents (Family number of weeks
income supplement) worked or (2) the
Maximum benefit $465/week $413/week minimum divisor
amount number (see Divisor
Table below)
Intensity Rule No Replacement rate will Replacement rate is
drop 1—5°/o. depending reduced by 1 percent
on claim history (up to 5%) for every 20
weeks of regular
benefits claimed in the
past 5 years
Repayment (Clawback) Repay 30% of benefit Repay 30—100% of See Table A2 in

Appendix A for
detailed schedule for
repayment

 

Divisor Table
Regional

l0

divisor

 

142

Minimum divisor

17

l

 

Table A2
EI Repayment: Repayment of Benefits at Income Tax Time

 

 

 

 

 

 

 

 

 

Number of El benefits paid Qualifying for Percentage
since June 30, 1996 (over a S- repayment repayment
jear claim history)
0-20 weeks Net income > 1.25* Maximum 30%
Insurable Earnings ($48,750)
21—40 weeks 50%
41—60 weeks Net income > Maximum 60%
61—80 weeks Insurable Earnings ($39,000) 70%
81-100 weeks 80%
100—120 weeks 90%
Over 120 weeks 100%
*Source: Human Rescores Development Canada
Table A3
Data Collection Period for CIE data
Cohort Job 1033 date Reference period Interview date Obs.
Full sample
Pre-poliqv sanqile
2 1995 Q4 04/95—09/96 09/96 4.016
3 1996 01 07/95—1 1/96 1 1/96 4,578
4 I996 QZ 10/95—02/97 02/97 4.909
Post-policy sample
5 1996 Q3 01/96—05/97 05/97 5.128
6 1996 Q4 04/96—09/97 09/97 4.043
7 1997 Q] 07/96—1 1/97 1 1/97 3.664
8 1997 02 10/96—02/98 02/98 4.049
9 1997 Q3 01/97-05/98 05/98 4.433
'0 1997 Q4 04/97—09/98 09/98 4.486
13 1998 Q3 01/98—05/99 05/99 3,731
17 1999 Q3 01/99—05/00 05/00 4.927
Total 47,964

 

143

APPENDIX B
Sample Construction for CIE and SLID data

B.l Identifying UI or E1 Eligibility Condition in CIE and SLID

In Canada, the eligibility conditions for U1 or E1 benefits are based on two
criteria: (1) work history and (2) region of residency. To be eligible, an individual must
work at least 12 to 20 weeks under UI (or 420—700 hours under El), depending on the
regional unemployment rate at the time of being unemployed. For new entrants and re-
entrants. 20 weeks (or 910 hours under E1) of work is required, regardless of the
unemployment rate. It Should be noted that neither survey provides all the information
needed to calculate a person’s eligibility. As a result, certain assumptions are required for
each survey, and any comparison of results from these two datasets should be interpreted
carefully.

For the CIE data. work history can be obtained directly, while information about
residency is only available at provincial levels and not regional levels. Work history (pre-
displacement tenure) is taken from a variable labeled “eldjrdur,” which records the
duration of employment (in weeks) within the reference period. Using unemployment
rates at the provincial level might be misleading because variations could be very large
between regions within the same province. It would include some people who are not
supposed to qualify for benefits in the sample and vice versa. An alternative way is to
include everybody who had at least 12 weeks of pre-displacement tenure in the sample.
AS expected, some of the ineligible people were also included, and the UI or E1
participation rate was understated. F ortunately, this bias can be reduced by using another
variable in the survey, “plqdlb.” This variable is determined by asking the respondent

who had been unemployed for at least 4 weeks why they have not applied for benefits.

144

Those who answered “not eligible/did not work long enough” were excluded from the
sample.

For the SLID data, work history is not identified directly, while information about
region of residency is available. To select an unemployed sample, two variables in SLID
were used. First, unemployed people were identified with the variable “enddatl ” if they
were permanently displaced or with the variable “strdat8” if they were absent from work
(e.g., temporary layoffs). Second, pre-displacement tenure for each unemployed person
was calculated from the weekly labour force vector “wylfst28” if eligibility was based on
“weeks,” or it was calculated from usual hours per month “hwjmvl” if eligibility was
based on “hours.” With region of residency noted with the variable “eir25,” the regional
unemployment rate at the month of job separation for each individual can be calculated
by combining unemployment rates from the monthly labour force survey. Each

individual’s eligibility can then be obtained from the derived variables.

B.2 Identifying U] or EI Participation Rate (take-up) in CIE and SLID

In CIE, survey variable “p1 qdl ” is used to identify an individual’s UI or E1
participation rate. This variable is determined by asking respondents a yes/no question
about whether he or she received any El income since the ROE date. Those who
answered “Don’t know” (only about 0.4%) were not included.

Identifying UI or E1 participation in SLID is not straightforward. SLID does ask
respondents whether they received UI or E1 benefits during the reference year; however.
it might be misleading because individuals who made no U1 or E1 claim for the observed

job interruption could have received benefits in the early months of the year. Similarly,

145

individuals who lost their jobs at the end of the year might actually claim their benefits in
the next calendar year. To make sure benefits have been received after the observed
interruption, the longitudinal monthly compensation vector “mthrcvl4” and the
compensation type variable “comptype” were used. An individual was defined as
receiving benefits if U1 or E1 activities were observed following his or her job

interruption.

B.3 Calculation of Potential Annual Income for CIE

Computing potential individual annual income from the CIE data requires
information from various variables in the survey and some assumptions. First, CIE asks
respondents about total personal income (pqul 8) and household income (pqul 7) in the
past 4 weeks at the time of the survey, whether household income has changed compared
to the month before the ROE date (p1 qfl 7a), and by how much per month (p1 qfl 7b). If
an individual is the only income source in the household (pqul 8 = p1qf17), his or her
pre-displacement income can be derived using the above variables. However. if there are
other members who contributed to the household income, it is not possible to separate an
individual’s earnings from household income using the above variables. As a result,
calculations of potential income for this type of person (accounts for 61% of final CIE
sample) are based on the yearly wage/salary variable (eldgppy). The drawback to using
this measure is that it likely overestimated the true income because these people did not
actually work throughout the year due to job interruption. Therefore, it might

underestimate the policy effect.

146

APPENDIX C
Stata Output for Propensity Score Matching

CIE Sample

*~b*********i'iririrfir*‘Ilr'kir-k*****§*~k*******i**************

Algorithm to estimate the propensity score

*ivi'i***************\lr**.\lr************i'i’i‘ki‘k‘ki‘kiri'iri'i'i‘k‘k

The treatment is treat_48

treat_48 | Freq Percent Cum

____________ +_.._____________.._______..___________
0 1 920 30.82 30.82
1 | 2,065 69.18 100.00

____________ +_-_--______.__._._____._._________-__—_-

Total I 2,985 100.00

Estimation of the propensity score

(sum of wgt is 7.8222e+05)

Iteration 0: log pseudo-likelihood = -l790.03l3

Iteration 1. log pseudo-likelihood = —l605.2942

Iteration 2: log pseudo-likelihood = ~1603.56

Iteration 3 log pseudo—likelihood = -l603.5585

Number of obs =
Wald Chi2(24) =
Prob > Chi2 =

Probit estimates

147

Log pseudo-likelihood = —l603.5585 Pseudo R2 =
| Robust
treat_48 I Coef. Std. Err. z P>|z| [95% Conf.
_____________ +____-_-____-________-__________________________________-

age I .0074447 .0056042 1.33 0.184 -.0035393
sex I -.l462884 .2152577 —0.68 0.497 -.5681858
edu2 1 .0104486 .1212429 0.09 0.931 —.227l83l
edu3 1 .1431281 .1089022 1.31 0.189 —.0703162
edu4 1 .1429511 .208648 0.69 0.493 -.26599l4
hhcompZ | -.l391189 .1255095 —l.11 0.268 —.385113
hhcomp3 I -.1125886 .1142304 -0.99 0.324 —.336476
hhcomp4 1 .0380538 .2870789 0.13 0.895 —.5246105
_Iprov_2 1 .0294675 .1417924 0.21 0.835 -.2484404
_Iprov_3 1 -.0725862 .2021522 —0.36 0.720 —.468797l
_Iprov_4 I -.2596783 .2110278 -l.23 0.218 —.6732851
_Iprov_5 1 -.1037929 .1692809 —0.61 0.540 —.4355773
ur_jend 1 —.0304951 .0228681 —l.33 0.182 —.0753157
qtrer I 1.271901 .1197398 10.62 0.000 1.037215
qtroe3 I .6959259 .1080812 6.44 0.000 .4840907
blue_wk | .1702511 .1458186 1.17 0.243 —.1155481
fmsize2 1 -.0296974 .116347 —0.26 0.799 -.2577333
fmsize3 I .010592 .1178537 0.09 0.928 —.220397
fmsize4 1 -.1513779 .1440676 -l.05 0.293 -.4337451
anotice 1 -.0437274 .0961549 -0.45 0.649 —.2321876
peremp 1 —.0498306 .096191 -0.52 0.604 —.2383615
can I -.l3106l7 .1294141 —l.01 0.311 —.3847086
season 1 .0459809 .0962916 0.48 0.633 -.l427472
recall I -.0593268 .1058514 -0.56 0.575 —.2667918
_Cons 1 .1518435 .4626813 0.33 0.743 —.7549952

Interval]

.0184287

.275609

.2480803
.3565725
.5518936
.1068751
.1112988
.6007181
.3073754
.3236248
.1539285
.2279915
.0143254
1.506587
.9077612
.4560503
.1983385
.2415811
.1309894
.1447328
.1387002
.1225852

.234709

.1481381
1.058682

Note:

The region of common support is

the common support option has been selected
[.28642705,

Description of the estimated propensity score
in region of common support

Estimated propensity score

.9536743]

1%

10%

251‘;

75%
90%
951':
99%

****************+***f***************************+*****

Step 1:

Percentiles
.3304967
.3887933
.4307575
.5825589

.7257523

.8533551
.8905292
.9056701
.9267302

Smallest
.2864271
.2875965
.2910511
.2915601

Largest

.9468658
.9485399
.9515638
.9536743

The final number of blocks is 7

Obs
Sum of Wgt.

Mean
Std. Dev.

Variance
Skewness
Kurtosis

Identification of the optimal number of blocks

Use option detail if you want more detailed output
*iri'i'irir‘k‘kiririirir‘ki**********w***+~k***********************

2984
2984

.6985926
.1694598

.0287166
-.5865272
2.187079

This number of blocks ensures that the mean propensity score
is not different for treated and controls in each blocks

**************************+*********+*********************

Step 2:

Test of balancing property of the propensity score
Use option detail if you want more detailed output

*~k***ri*****+************+******************+*************

The balancing property is satisfied

This table shows the inferior bound,

treat_48

the number of treated
and the number of controls for each block

Inferior I
of block I
of pscore I
+

.2 l

.4 l

.6 l

.65 I

.7 I

.8 |

+

Total I

Note:

the common support option has been selected

*********+*****+***+*****+***********+*****

End of the algorithm to estimate the pscore
******~k*****‘kir'kir*irt-kirir‘k‘k‘k-k‘k-kiiki'iriir‘kiir-k'kirfirst

I48

*ir‘k‘kiri-iv'b'k‘ki'k‘k*‘k'ki'ir‘ki'i’*+****+***+******************+*************

Estimation of the ATT with the nearest neighbor matching method
Random draw version

****i**i+*iir-k*irfi'i'k******i**tiv‘k‘ki'i‘k‘kii‘ki'i'i'iriir'kir‘ki*************~k*

ATT estimation with Nearest Neighbor Matching method
(random draw version)
Bootstrapped standard errors

—-——-—---—..-———-——-—--—-—————---—-——————-———————-----———.

Note: the numbers of treated and controls refer to actual
nearest neighbour matches

******~k*****+*~k*******‘k****ir'kit*****~b****************i

Estimation of the ATT with the radius matching method

**********************~k+**i+***********+**+*******~k**

The radius is .0001

ATT estimation with the Radius Matching method
Bootstrapped standard errors

“—.—.-_—_—————_-_.————_———_-———_—.—_.—-—————————-——--———--———-—-

Note: the numbers of treated and controls refer to actual
matches within radius

***********‘k‘kir‘k‘k‘k‘kir********i'******************#******

Estimation of the ATT with the radius matching method

**********~k***~k*******+******iririk‘ki'k‘k'k‘k'k'kir‘k‘kiri*+***+**

The radius is .0005

ATT estimation with the Radius Matching method
Bootstrapped standard errors

Note: the numbers of treated and controls refer to actual
matches within radius

149

*-Ek******ik‘kiriri'i'*‘lr‘kirik*irilr‘k'k***************************

Estimation of the ATT with the radius matching method

******Vk*‘ki'irii*+**************************************

The radius is .001

TT estimation with the Radius Matching method
Bootstrapped standard errors

Note: the numbers of treated and controls refer to actual
matches within radius

***********************************~k*****************

Estimation of the ATT with the radius matching method

iari-irir'k*********************************i'i'i'ir‘kii'ii'i‘kiri'i

The radius is .01

ATT estimation with the Radius Matching method
Bootstrapped standard errors

Note: the numbers of treated and controls refer to actual
matches within radius

******************‘k************************+i'********

Estimation of the ATT with the radius matching method
*****‘k***********************************************

The radius is .1

ATT estimation with the Radius Matching method
Bootstrapped standard errors

Note: the numbers of treated and controls refer to actual
matches within radius

150

********************************f*********************

Estimation of the ATT with the kernel matching method

****‘k********************+***t*+********************‘k*

ATT estimation with the Kernel Matching method
Bootstrapped standard errors

Bandwidth=0.001

********+**************iv**ir‘k‘k*****‘ki'irf'kiir'ki'i'iiriirir‘k‘kiririr

Estimation of the ATT with the kernel matching method

*iviv*i-irir‘kirir‘k-k‘k‘k‘k'k-ki************+**********+************

ATT estimation WiCh the Kernel Matching method
Bootstrapped standard errors

Bandwidth=0.01

ATT estimation with the Kernel Matching method
Bootstrapped standard errors

15]

-‘ — ‘3‘ . 1.x;—

 

p ~'~. -

SLID Sample

*********************+***i*Vk‘k'i‘kiri'*iriiririviririkirakirir‘kiiriri'i'

Algorithm to estimate the propensity score

{vi-*iri'ir‘ki'i'ir'kiririr‘ki'irt*********i'i’irivir‘k*************‘k*****

The treatment is treat

Interval]

.0209727
.3215198
.2541758
.5198425
.369005
.2704792
.1988802
.5829669
-.l732571

-.0101705
—.l956138
-.2244843

.955143
.3222651
.3210429
.9045332
.2897991
.6491628
.5240847

-.0265589
.9219128
.5089177

treat I Freq Percent Cum
____________ +__________________________--_______
0 | 464 41.50 41.50
1 | 654 58.50 100.00
____________ +___-_______________________-__-____
Total I 1118 100.00
Estimation of the propensity score
(sum of wgt is 5.0229e+05)
Iteration 0: log likelihood = -764.43966
Iteration 1. log likelihood = -695.96295
Iteration 2: log likelihood = -695.3938
Iteration 3 log likelihood = -695.39355
Probit estimates Number of obs
Wald chi2(21)
Prob > chi2
Log likelihood = —695.39355 Pseudo R2
I Robust
treat I Coef. Std. Err. z P>IzI [95% Conf.
————————————— +____.——_—_______.___________——__..____—_———._______________-_.____._.....
age I .0094762 .0058657 1.62 0.106 -.0020204
sex I .0619113 .1956317 —0.32 0.752 —.4453424
fcompl I .0269368 .1434274 —0.19 0.851 -.3080494
fcomp2 I .2110264 .1575621 1.34 0.180 -.0977897
fcomp4 I .2209591 .3010076 ~0.73 0.463 —.8109232
edul I .1236396 .2010847 —0.61 0.539 -.5177584
edu3 I .1712465 .1888436 -0.91 0.365 -.5413732
edu4 I .0972827 .2478026 0.39 0.695 —.3884016
prov2 I .5682807 .2015464 —2.82 0.005 -.9633043
prov3 I .4010883 .1994515 —2.01 0.044 —.792006
prov4 I .6147034 .2138251 -2.87 0.004 —1.033793
prov5 l .5926422 .1878391 -3.16 0.002 -.9608001
can I .5688163 .1971091 2.89 0.004 .1824896
season I .0668904 .1302956 0.51 0.608 -.1884842
qt2 I .0441148 .1412924 0.31 0.755 -.2328133
qt3 I .6080709 .1512591 4.02 0.000 .3116085
fsize2 I .0073482 .1441102 0.05 0.959 -.2751027
fsize3 l .3480987 .1536069 2.27 0.023 .0470346
fsize4 I .1563223 .1876373 0.83 0.405 -.2114401
ur_jend I .0587674 .0164332 -3.58 0.000 -.0909759
blue_wk I .606134 .1611146 3.76 0.000 .2903552
_cons | -.575251 .5531575 -1.04 0.298 -1.65942
Note: the common support option has been selected
The region of common support is [.04658343, .8841608]

Description of the estimated propensity score
in region of common support

Estimated propensity score

———————--—_—-——-—-—_-————_____—__——_——————-——————————————————

17.;
L

10?
25%

501

7571':
90";1
95%
9933

Percentiles

.116041

.1587395
.2082163
.3217682
.4367883

.5694174

.682898

.7396715
.8272249

Smallest
.0465834
.0561395
.0603328
.0826598

Largest

.8625614
.8645329
.8775688
.8841608

The final number of blocks is 7
The balancing property is satisfied

This table shows the inferior bound,

Inferior
of block
of pscore

Obs

Sum of Wgt.

Mean

Std. Dev.

Variance
Skewness
Kurtosis

1118
1118

.4439448

.1715576

.029432
.1425513

2.438804

the number of treated
and the number of controls for each block

Note:

the common support option has been selected

************i'iit**+*******if*ir'ki'iir'ki'ir‘k'kti'ir‘k**********************

Estimation of the ATT with the nearest neighbor matching method

Random draw verSion
*ir‘k‘fi********i’i’************iv‘k'k*i‘***********f*********************

ATT estimation with Nearest Neighbor Matching method (random draw version)
Bootstrapped standard errors

—¢———m————_——u————.—_...._._.__..__...-_._._._..............._______fl__._______._-——__
———-————-—————-——————————_———___—_—__——_—_——-———_—-———-—_—-—--

Note: the numbers of treated and controls refer to actual nearest neighbour
matches

1k*‘ki'ki'i'ir‘ki-********************k******+**~k************

Estimation of the ATT with the radius matching method

*-*****************i*‘k‘k‘k*********i**fl*****************

The radius is .0001

ATT estimation with the Radius Matching method
Bootstrapped standard errors

 

Note: the numbers of treated and controls refer to actual
matches within radius

+***+ir*iri'irf‘kf***********************+*iriri'ir‘ki'iriffiir‘ki'ir'k

Estimation of the ATT with the radius matching method

*irf‘k‘k‘lr‘ki'ir‘k‘k'kiri'*‘k‘kir‘ki'ki'iirirat'k**************************

The radius is .0005

ATT estimation with the Radius Matching method
Bootstrapped standard errors

Note: the numbers of treated and controls refer to actual
matches within radius

*+**********iri'iri'*****************~k*+***~k****+*fir'ki'ik‘k'k

Estimation of the ATT with the radius matching method

*********it******************************************

The radius is .001

ATT estimation with the Radius Matching method
Bootstrapped standard errors

 

__..___-_—__.-——_———_———___——_________.—___———__-——_.____._——--—_

Note: the numbers of treated and controls refer to actual
matches within radius

154

*************************1'*******1’*******************

Estimation of the ATT with the radius matching method

+***+iii*ii*+*******+***********************i********

The radius is .01

ATT estimation with the Radius Matching method
Bootstrapped standard errors

__—._—_——___—_—.________—______—___—____—_—————_——__-——_———_
——————-—_—————__—_——————_-————————-_——_————————————————_—

Note: the numbers of treated and controls refer to actual
matches within radius

****++******i*i'iri'**+*************+*******************

Estimation of the ATT with the radius matching method

*+**+*‘k**‘kiriri'i'fi*i'*i'ir‘k‘lriri'*****t***+**+***+**‘ki'irfi'ir'ki'fi'ir-k

The radius is .1

ATT estimation with the Radius Matching method
Bootstrapped standard errors

Note: the numbers of treated and controls refer to actual
matches within radius

*iv‘kir‘f*‘ki‘k‘ki'i’iri‘k‘kirf'kaki‘kiri'i'iri******iririririr‘k‘k'ki'irir‘k‘firi'fii'i'i‘k‘kir

Estimation of the ATT with the kernel matching method

****i'iri'i'ki*i'i'irii'i'ir‘k************~k§+***iririri'k'k‘k‘kiV‘ki’irfir‘k‘k

Bandwidth=0.001

ATT estimation with the Kernel Matching method
Bootstrapped standard errors

*iriri'i’irfi'iriir1k*i‘*‘k'k***iri'iri'iririri‘i'fiririri'********************

Estimation of the ATT with the kernel matching method
*i-ir‘lrf'kiririr‘kiri'i'ukir'k‘ki'dri-i'************f*******************

Bandwidth=0.01

ATT estimation with the Kernel Matching method
Bootstrapped standard errors

-—___——————————-————————~——-—~—~—_.-_——_-—.——————-———o———————_—

155

 

 

APPENDIX D
Technical Note for Reverse-Order Decomposition

Family equivalent income distribution in reverse-order conditioning sequence can
be expressed as
f, (Y) = f(Y | t). = 97~’.\'.<.L./ = 97.13. 1.,1 = 97.1,“ = 97.1, = 97) (D1)
= I I I I f(Y I X.S. L. 1,1,. = 97 )dF(X I s. L.I.L‘I.I...,u, = 97)dF(S I L,1,z.;,u, = 97 )
~dF(L I Lt”, = 97)dF(I.I, = 97)

The counterfactual density of family income in 1997, holding constant “other

attributes X.” “family structure S,” and “immigration factors L, I” at their 1980 levels, is
f,(Y) = f(Y I t}. = 97,t‘\.;...‘,u, = 80!“, = 80,1“I = 80,t, = 80)
= I I I I f(Y I X.S, L.1,t,. = 97)dF(X I S,L.1.z‘...,._, = 80)dF(S I L, 14“”, = 80)
dF(L | 1.1” = 80)dF(1|t, = 80)

dF(X I S,L.I,t‘\w = 80)
dF(X I S,L,I.t‘I.I..‘, = 97)

 

= III If (Y I X .S. L, l.t,. = 97)dF(X I S,L,1,II.ISJ = 97)

dF(S I L.I,t..!,fi, = 80) dF(L I 1.1“, = 80)

~dF(SIL,I,t., , =97) -dF(L|I,t,I, =97)
‘ " dF(S I L, 1.1““, = 97) ' dF(L I Mu = 97)

 

 

dF(I It, = 80)
dF(I | t, = 97)

 

-dF(I II, = 97)

= I I I I f(Y I X,S. L.I,t,. = 97)dF(X I S,L.I.t‘\,_..‘,u, = 97)dF(S I L.I,t‘..,q, = 97)
odF(L I 1.2,‘4, = 97)dF(1|t, = 97 )

. AM, (X.S. L. 1) . L. ,q, (5. LI) - A“, (L. I) . L, (1) (D2)

l56

Equation (D3) shows that the re-weighting function for the immigration factor is
equal to the probability of observing an immigrant employed full time in the 1980 sample
versus the 1997 sample, multiple by the population ratio:

dF(1 I t, = 80) _ Pr(t, = 80 I I) . Pr(t, = 97)

21(1): . -
dF(IIt, =97) Pr(t, =97II) Pr(t, =80)

 

(D3)

Similarly, the re-weighting fiinction for mother tongue and duration of residency
characteristics of immigrants. conditional on a full-time employment rate. is

Pr(L = CI I.t,‘., = 80)

xi. L.I '
"A ) C Pr(L =CI1~ILII =97)

L

 

 

dF L 1,! =80 4
( I III ) (D 4)
.=,

= dF(L I 1.1“, = 97) =

t

The re-weighting function for family structure. conditional on immigration

factors. is

dF(S I L,1.z..,fl, = 80) 4 Pr(S = c I L,I,txi,", = 80)

L. S.L.1 = = S- '
. /..I( ) dF(S|L,Iatxz1fl/ :97) g ‘ PI‘(S =CI [4919,50,] :97)

 

 

(D5)

Both re-weighting functions in (D4) and (D5) can be estimated through
multinomial logit estimation. Finally, using a simple property. the re-weighting function

for other attributes Am. ,q , (X .S . L. I ) can be expressed as

L(X.S.L.1) = L(X I S,L,I)./1(S I L,1)-L(L I 1pm)

(D6)
= 2(1 I L,S.X)-/I(L I S,X)-).(S|X)-/I(X)

Rearranging equation (D6) and the AH‘ ,q , ( X ,S . L.I) can be obtained by the product of

the complete set of primary-order weights, divided by the product of three reverse
conditional weights (D5), (D4), and (D3):

1......I-(IIL.S.X>-L,.....-<LIs,X>-A.I..-<SIX>-2..<X>

XXX/”l (X l 59 [“1) :
Ami/(SI L91)'11,I1(Ll1)' 11(1)

 

(D7)

157

BIBLIOGRAPHY

Angrist, Joshua D.. and Alan B. Krueger (1999). Empirical Strategies in Labor
Economics. In Handbook of Labor Economics. Vol. 3. Eds. O. Ashenfelter and D.
Card. Elservier.

Atkinson. A. B., F. Bourguignon. and C. Morrison (1992). Empirical Studies of Earnings
Mobility. Switzerland: Harwood Academic Publishers.

Baker. Michael. and Gray Solon (2003). Earnings Dynamics and Inequality among
Canadian Men, 1976-1992: Evidence from Longitudinal Income Tax Records.
Journal of Labor Economics, 21(2), 289—321.

Bane. Mary Jo, and David Ellwood (1986). Slipping Into and Out of Poverty: The
Dynamics of Spells. Journal of Human Resources, 21(1), 1—23.

Bar-Or, Yuval. John Burbidge, Lonnie Magee. and A. Leslie Robb (1993). Canadian
Experience-Eamings Profiles and the Return to Education in Canada: 1971—1990.
Department of Economics Working Papers, 93-04. Hamilton, ON: McMaster
University. 5

Beach, Charles M. (1988). The ‘Vanishing‘ Middle Class? Evidence and Explanations.
Kingston: Industrial Relations, Queen’s University.

Beach. Charles M., and Ross Finnie (2001). Cyclical Changes in Short-Run Earnings
Mobility in Canada, 1982 to 1996. Unpublished manuscript.

Beach, Charles M., and Ross F innie (2004). Earnings Change in Canada. Canada Journal
of Economics, 37(1), 219—240.

Beach, Charles M., and George A. Slotsve (1996). Are We Becoming Two Societies?
Income Polarization and the Myth of the Declining Middle Class in Canada.
Toronto: C.D. Howe Institute.

Beaudry, Paul, and David A. Green (2000). Cohort Patterns in Canadian Earnings:
Assessing the Role of Skill Premia in Inequality Trends. Canada Journal of
Economics, 33(3), 907—936.

Becker, Sascha O., and Andrea [chino (2002). Estimation of Average Treatment Effects
based on Propensity Scores. The Stata Journal. 2(4). 358—377.

Bound. J ., and G. Johnson (1992). Changes in the Structure of Wage in the 19805: An
Evaluation of Alternative Explanations. American Economic Review. 82, 371—3 92.

158

Burtless, Gary (1999). Effects of Growing Wage Disparities and Changing Family
Composition on the US. Income Distribution. European Economic Review, 43.
853—865.

Buchinsky. Moshe. and Jennifer Hunt (1999). Wage Mobility in the United States. The
Review of Economics and Statistics, 81(3), 351—368.

Burkhauser, R.V., Douglas Holtz-Eakin, and Stephen E. Rhody (1997). Labour Earnings
Mobility and Inequality in the United States and Germany during the Growth Years
of the 19805. NBER Working Paper 5988.

Burkhauser et a1 (2000). The C ross-National Equivalent Files: A product of cross-
national research. Department of Policy Analysis and Management, Cornell
University.

The Canadian Employment Insurance Commission (1998). 1998 Employment Insurance
Monitoring and Assessment Report. Ottawa: Author.

Cancain, Maria, Sheldon Danziger, and Peter Gottschalk (1993). Working Wives and the
Distribution of Family Income. In Rising Tides: Rising Inequality in America. Ed.
Sheldon Danziger and Peter Gottschalk. New York: Russell Sage Foundation.

Cancain, Maria, and Deborah Reed (1998). Assessing the Effects of Wives’ Earnings on
Family Income Inequality. The Review of Economics and Statistics, 80(1), 73—79.

Cancain, Maria, and Deborah Reed (2001). Changes in Family Structure: Implications for
Poverty and Related Policy. In Understanding Poverty. Ed. Sheldon H. Danziger
and Robert H. Haveman. New York: Russell Sage Foundation.

Card, D., and J. E. DiNardo (2002). Skill-Biased Technological Change and Rising Wage
Inequality: Some Problems and Puzzles. Journal of Labor Economics, 20(4), 733—
785.

Chiquiar, Daniel, and Gordon H. Hanson (2002). International Migration, Self-Selection,
and the Distribution of Wages: Evidence from Mexico and the United States.
Working Paper 9242. Cambridge: National Bureau of Economic Research.

Cochran, W. G. (1965). The Planning of Observational Studies of Human Populations."
Journal of the Royal Statistical Society Series A, 128, 234—266.

Corcoran, Mary (2001). Mobility, Persistence, and the Consequences of Poverty for
Children: Child and Adult Outcomes. In Understanding Poverty. Ed. Sheldon H.
Danziger and RObert H. Haveman. New York: Russell Sage Foundation.

Daly, Mary C., and Robert G. Valletta (2000). Inequality and Poverty in the United
States: The Effects of Changing Family Behavior and Rising Wage Dispersion. San

159

Francisco: Economic Research Department, Federal Reserve Bank of San
Francisco.

Dehejia, Rejeev H., and Sadek Wahba (I 998). Propensity Score Matching Methods for
Non-Experimental Causal Studies. National Bureau of Economic Research
Working Paper 6829. Cambridge.

Dehejia, Rejeev H., and Sadek Wahba (2002). Propensity Score Matching Methods for
Non-Experimental Causal Studies. The Review of Economics and Statistics, 84,
151—161.

DiNardo, John, Nicole M. Fortin, and Thomas Lemieux (1996). Labor Market
Institutions and the Distribution of Wages, 1973-1992: A Semiparametric
Approach. Econometrica, 64, 1001—1044.

Feldstein, Martin (1976). Temporary Layoffs in the Theory of Unemployment. Journal of
Political Economy 84(5), 937—957.

F innie, Ross (1997). Earnings Dynamics in Canada: The Earnings Mobility of Canadians
1982-1992. Ottawa: Applied Research Branch Strategy Policy, Human Resources
Development Canada.

F innie, Ross (2000). The Dynamics of Poverty in Canada: What We Know, What We
Can Do. Toronto: C.D. Howe Institute, 2000.

Fortin, Pierre, and Marc Van Audenrode (2000). The Impact of Worker’s Experience
Rating on Unemployed Workers. Ottawa: Human Resources Development Canada,
2000.

Frenette Marc, David Green, and Garnett Picot (2003). Changing Income Inequality
Amid the Economic Recovery of the 19905. Unpublished manuscript.

Gardiner, Karen, and John Hills (1999). Policy Implications of New Data on Income
Mobility. The Economic Journal. 109, F9I—F111.

Gera, Surendra, W. Gu, and Z. Lin (2001). Technology and the Demand for Skills in
Canada: an Industry-level Analysis. Canadian Journal of Economics, 34, 132—148.

Gittleman, Maury. and Mary Joyce (1996). Earnings Mobility and Long-Run Inequality:
An Analysis Using Matched CPS Data. Industry Relations, 35, 180—196.

Gittleman, Maury, and Mary Joyce (1999). Have Family Income Mobility Changed?
Demography, 36(3 ), 299—314.

Gottschalk, Peter, and Moffitt Robert (1994). The Growth of Earnings Instability in the
US. Labor Market. Brooking Papers on Economic Activity, 2, 217—272.

160

Gottschalk, Peter (1997). Inequality, Income Growth, and Mobility: The Basic Facts.
Journal of Economic Perspectives, 11(2), 21—50.

Gottschalk, Peter, and Sheldon Danziger (1997). Family Income Mobility—How Much
Is There, and Has It Changed? In The Inequality Paradox: Growth of Income
Disparity. Ed. James A. Auerbach and Richard S. Belous. Washington, DC:
National Policy Association.

Green. David, and W. Craig Riddell (2000). The Effects of the Shift to an Hours-Based
Entrance Requirement. Ottawa: Human Resources Development Canada.

Haider, Steven J (2001). Earnings Instability and Earnings Inequality of Males in the
United Status: 1967-1991. Journal of Labor Economics, 19(4), 799—836.

Ham, John C, and Robert J. LaLonde (1996). The Effect of Sample Selection and Initial
Conditions in Duration Models: Evidence from Experimental Data on Training.
Econometrica, 64(1 ), 175—205.

Heckman, James. H. Ichimura, and P. Todd (1997a). Matching as an Econometric
Evaluation Estimator: Evidence from Evaluating a Job Training Program. Review of
Economic Studies, 64(4), 605—654.

Heckman, James, H. Ichimura, and P. Todd (1997b). Matching as an Econometric
Evaluation Estimator. Review of Economic Studies, 65(2), 261-294.

Heckman, James, H. Ichimura, J. Smith, and P. Todd (1998). Characterizing Selection
Bias Using Experimental Data. Econometrica, 66(5), 1017-1098.

Henderson, D. W., and J. C. R. Rowley (1977). The Distribution and Evolution of
Canadian Family Income, 1965—1973. Economic Council of Canada Discussion
Paper 91. Ottawa: Economic Council of Canada.

Jarvis, Sarah, and Stephen P. Jenkins (1998). How Much Income Mobility is there in
Britain. The Economic Journal, 108, 428-443.

Jenkins, Stephen (2000). Modelling Household Income Dynamics. Journal of Population
Economics, 13(4), 529—567.

Jones, Stephen (2000). E1 Impacts on Unemployment Durations and Benefit Receipt.
Ottawa: Human Resources Development Canada.

Juhn, Chinhui, and Kevin M. Murphy (1997). Wage Inequality and Family Labor Supply.
Journal of Labor Economics, 15, 72—97.

161

Kapsalis. Constantine (2000). The Impact of Bill C-12 on New Entrants and Re-Entrants.
Ottawa: Human Resources Development Canada.

Karoly, Lynn A., and Gary Burtless (1995). Demographic Change. Rising Earnings
Inequality. and the Distribution of Personal Well-Being, 1959—1989. Demography,
32(3), 379—406.

Katz, L., and K. Murphy (1992). Changes in Relative Wages, 1963—1987: Supply and
Demand Factors. Quarterly Journal of Economics, 107, 35—78.

Kennedy, Suzanne, and Steven Gonzalez (2003). Government Spending in Canada and
the United States. Working Paper 03-05. Ottawa: Economic and Fiscal Policy
Branch, Department of Finance.

Kuhn, Peter (2000). The Net Fiscal Incidence of the Employment Insurance Act on Full-
versus Part-time Workers. Ottawa: Human Resources Development Canada.

LaLonde, Robert J (1986). Evaluating the Econometric Evaluations of Training Programs
with Experimental Data. American Economic Review, 76(4), 604—620.

Laroche. Mireille (1997). The Persistence of Low Income Spells in Canada, 1982—1993.
Working Paper 98-02. Ottawa: Economic Studies and Policy Analysis Division,
Department of Finance.

Lerman, Robert, and Shlomo Yitzhaki (1985). Income Inequality Effects by Income
Source: A New Approach and Application to the United States. The Review of
Economics and Statistics, 67(1), 151-156.

McDonald, James T., and C. Worswick (1998). The Earnings of Immigrant men in
Canada: Job tenure, cohort, and macroeconomic conditions. Industrial and Labour
Relations Review, 51, 465—482.

McHale, John (2003). Canadian Immigration Policy in Comparative Perspective. In
Canadian Immigration Policy for the 21“" Century. Ed. Charles M. Beach, Alan G.
Green, and Jeffery G. Reitz. Kingston, ON: John Deutsch Institute for the Study of
Economic Policy.

McMurrer, Daniel P., and Isabel V. Sawhill (1998). Getting Ahead: Economic and Social
Mobility in America. Washington, DC: Urban Institute.

McWatters, C. G., and C. M. Beach (1990). Factors Behind the Changes in Canada’s
Family Income Distribution and the Share of the Middle Class. Industrial Relations.
45, 134-144;

Meyer, Bruce D., W. Kip Viscusi, and David L. Durbin (1995). Workers’ Compensation
and Injury Duration: Evidence from a Natural Experiment. American Economic
Review. 85(3), 322—340.

Moffitt, Robert A., and Peter Gottschalk (2002). Trends in the Transitory Variance of
Earnings in the United States. The Economic Journal, I 12(478), C68-C73.

Morissette. Rene (1995). Why Has Inequality in Weekly Earnings Increased in Canada?
Statistics Canada, Analytical Studies Branch Research Paper Series, No 80.

Morissette, Rene, John Myles, and Garnett Picot (1995). Earnings Polarization in Canada,
1969-1991. Ed. Banting and Beach, op. cit, pp.23-50.

Murphy, K., and F. Welch (1991). The Role of International Trade in Wage Differentials.
In Workers and Their Wages: Changing Patterns in the United States, Ed. M.
Kosters. Washington, DC: American Enterprise Institute.

Nakamura Alice (1996). Employment Insurance: A Framework for Real Reform.
Toronto: C.D. Howe Institute.

Nakamura Alice 0.. and W. Erwin Diewert (2000). Insurance for the Unemployed:
Canadian Reforms and Their Relevance for the United States. In Long-Term
Unemployment and Reemployment Policies. Eds. Laurie J. Bassi, and Stephen A.
Woodbury. Connecticut: JAI Press.

Oaxaca, R (1973). Male-Female Wage Differentials in Urban Labor Markets.
International Economic Review, 14, 693—709.

OECD (1996). Employment Outlook. Paris: OECD.

Phipps. Shelley, Martha MacDonald. and Fiona MacPhail (2000). Impact of the Family
Supplement. Ottawa: Human Resources Development Canada.

Phipps, Shelley. and Fiona MacPhail (2000). The Impact of Employment Insurance on
New-entrants and Re-entrant Workers. Ottawa: Human Resources Development
Canada.

Picot, Garnett (1998). What is Happening to Earnings Inequality and Youth Wages in the
19905? Statistics Canada, Analytical Studies Branch Research Paper Series, No
116.

Picot, Garnett, and Feng Hou (2002).. The Rise in Low-Income Among Recent
Immi grants in Canada. Statistics Canada, Analytical Studies Branch Research Paper
Series, No 198;

163

 

Podgursky, Michael (1983). Unions and Family Income Inequality. The Journal of
Human Resources, 38(4), 574-591.

Rosenbaum, Paul R.. and Donald B. Rubin (1983). The Central Role of the Propensity
Score in Observational Studies for Causal Effects. Biometrika, 70(1), 41—55.

Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. London:
Chapman and Hall, 1986.

Shorrocks, Anthony F. (1983). The Impact of Income Components on the Distribution of
Family Incomes. Quarterly Journal of Economics, 98, 31 1—33 1.

Smith, J. (2000). Evaluating Active Labor Market Policies: Lessons from North America.
Unpublished Manuscript, University of Western Ontario.

Smith, J ., and Todd, P. (2000). Does Matching Overcome LaLonde’s Critique of
Nonexperimental Estimators? Unpublished Manuscript, University of Western
Ontario.

Solon, Gary ( 1999a). Intergenerational Mobility in the Labor Market. In Handbook of
Labor Economics (3[A]). Ed. Orley Ashenfelter and Davis Card. Amsterdam:
North-Holland.

Solon, Gary (1999b). Mobility Within and Between Generations. Economics Department,
University of Michigan, Ann Arbor. Unpublished Paper.

Solon. Gary (2002). Cross-Country Differences in Intergenerational Earnings Mobility.
Journal of Economic Perspectives, 16(3), 59—66.

Statistics Canada (2003). Canada’s Ethnocultural Portrait: The Changing Mosaic. 2001
Census Series. Catalogue no. 96F0030XIE2001008. Ottawa.

Statistics Canada (2003). Income in Canada, 2001. Catalogue no. 72-202-XIE. Ottawa.

Stevens, Ann Huff (1999). Climbing Out of Poverty, Falling Back In: Measuring the
Persistence of Poverty Over Multiple Spells. Journal of Human Resources, 34(3),
557—588.

Sweetman, Arthur (2000). The Impact of E1 on Those Working Less than 15 Hours Per
Week. Ottawa: Human Resources Development Canada.

Turner. John A. (2001). Risk Sharing through Social Security Retirement Income

Systems: A Comparison of Canada and the United States. Kalamazoo, MI: W.E.
Upjoin Institute for Employment Research.

164

Wolfson. Michael C. (1986). Stasis Amid Change: Income Inequality in Canada 1965-
1983. The Review of Income and Wealth. 32(4), 337—369.

Wooldridge, Jeffrey M. (2002). Econometric Analysis of Cross Section and Panel Data.
Cambridge. MA: The MIT Press. '

Zyblock. Myles (1996a). Individual Earnings Inequality and polarization: An Exploration
into Sub-Population trends in Canada, 1981—1993. Working Paper W—96-8. Ottawa:
Applied Research Branch, Human Resources Development Canada.

Zyblock. Myles (1996b). Why is Family Market Income Inequality Increasing in
Canada? Examining the Effects of Aging. Family Formation, Globalization and
Technology. Working Paper W-96-1 1. Ottawa: Applied Research Branch, Human
Resources Development Canada.

165

   

 

IIIIIIIIIIIIIIIIIIIIII