“How do older displaced workers get by: re-employment ......“How do older displaced workers get...

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“How do older displaced workers get by: re-employment, early retirement, or disability benefits?” Ross Finnie School of International and Public Affairs University of Ottawa 55 Laurier Ave. East Ottawa, ON K1N 6N5 [email protected] David Gray Department of economics University of Ottawa 55 Laurier Ave. East Ottawa, ON K1N 6N5 [email protected] December 2011 JEL codes: J63, J65, J68, J26 Key words: involuntary displacement, social insurance receipt, labour market transitions, retirement Abstract The central objective is to investigate the income sources and patterns of prime-age and older workers having a high degree of attachment who suffer a layoff. Using a unique data base that links the event of layoff with data on earnings, we track all of their sources of income over an interval that spans four years prior to the separation to five years after it. We conduct an econometric analysis of the incidence of several alternative destinations following the event of layoff, such as early retirement, re-employment, self-employment, or reliance on social insurance. The most common destination state for prime-age and older workers who have not yet reached normal retirement age are early retirement and continued labour market activity. It is rare for them to draw on social insurance benefits, and we find little evidence that disability benefits and workers’ compensation are functioning as disguised unemployment benefits to any great extent. We also estimate an econometric model of the probability of remaining in the labour force that includes a control group of similar workers who were laid off. We find that older laid-off workers are more likely than their non-displaced counterparts to withdraw from the labour force before reaching the normal retirement age. This effect becomes stronger with the passage of time since the year of layoff.

Transcript of “How do older displaced workers get by: re-employment ......“How do older displaced workers get...

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“How do older displaced workers get by: re-employment, early retirement, or

disability benefits?”

Ross Finnie School of International and Public Affairs

University of Ottawa 55 Laurier Ave. East

Ottawa, ON K1N 6N5

[email protected]

David Gray Department of economics

University of Ottawa 55 Laurier Ave. East

Ottawa, ON K1N 6N5 [email protected]

December 2011

JEL codes: J63, J65, J68, J26 Key words: involuntary displacement, social insurance receipt, labour market transitions, retirement

Abstract

The central objective is to investigate the income sources and patterns of prime-age and older workers having a high degree of attachment who suffer a layoff. Using a unique data base that links the event of layoff with data on earnings, we track all of their sources of income over an interval that spans four years prior to the separation to five years after it. We conduct an econometric analysis of the incidence of several alternative destinations following the event of layoff, such as early retirement, re-employment, self-employment, or reliance on social insurance. The most common destination state for prime-age and older workers who have not yet reached normal retirement age are early retirement and continued labour market activity. It is rare for them to draw on social insurance benefits, and we find little evidence that disability benefits and workers’ compensation are functioning as disguised unemployment benefits to any great extent. We also estimate an econometric model of the probability of remaining in the labour force that includes a control group of similar workers who were laid off. We find that older laid-off workers are more likely than their non-displaced counterparts to withdraw from the labour force before reaching the normal retirement age. This effect becomes stronger with the passage of time since the year of layoff.

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I. Introduction

Due to the demographic phenomenon of an aging labour force in Canada, a

greater share of displaced workers will be over 50 years old. As mentioned in Kuhn

(2003), such workers are likely to be less mobile and ‘…less likely to flow in response to

variety of shocks which affect the economy. Also, it is well-known that involuntary job

turnover becomes more costly with age...’ (p. 9) Some of the contributing factors appear

to be the accumulation of firm-specific, industry-specific, or occupation-specific skills,

the deterioration of alternative skills as the length of prior job tenure increases, and

possible discrimination on the part of would-be employers.

While older displaced workers typically face limited job opportunities relative to

their younger counterparts, they have a wider variety of options; the latter group normally

has no option other than to search for a new job. For the older displaced workers, in

contrast, there are several alternative destination states that involve some form of labour

force withdrawal. These transitions are detrimental to achieving greater labour market

participation among older workers, a policy objective that the OECD (2003,2006)

believes is both attainable and desirable.

The topic of this paper is the post-displacement labor market outcomes of prime-

age and older workers. For both genders we investigate their post-displacement profiles,

which can involve a number of alternative destinations and sequences of states. There is

a large literature that deals with post-displacement outcomes, but many of those studies

deal with outcomes occurring within a short-run time frame, such as the wage of the first

newly found job and the length of joblessness immediately following the layoff. There

are relatively few studies that track the post-displacement labour market profiles for

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extended time periods in order to measure the longer-term consequences. Those studies

that do involve a long-term time horizon tend to treat the outcomes of wage and earnings

losses. We seek to fill this void by measuring and analyzing the entry of older laid-off

workers into a number of alternative destination states, which can be categorized into

several types, namely i) the receipt of some form of social insurance, ii) early-retirement,

iii) self-employment, and iv) re-employment (at potentially lower earnings).

Post-displacement transitions into the receipt of social insurance benefits, such as

subsequent UI claims, social assistance, and long-term disability have garnered attention

in the US literature. Autor and Duggan (2006) claim that the social security disability

regime has evolved from its original function of insuring workers against earnings loss

attributable to a medical condition to providing long-term income support for the quasi-

unemployable – a phenomenon that they label ‘non-employability insurance’. We search

for similar patterns for the incidence of receipt of the Canadian equivalent to the US

program, namely the Canadian Pension Plan Disability (CPPD) Regime and its Quebec

equivalent, the QPPD.

The empirical approach is based on following cohorts of workers who were laid

off in a given reference year. The outcome variable takes the form of the proportion of

workers that are observed in a given destination state during each of the first five post-

layoff years. In previous studies (Finnie and Gray (2009,2011)), we provide descriptive

statistical analyses based on a typology of post-displacement destination states and

configurations of income from various sources. In this paper we extend the scope by

estimating multinomial econometric models of their relative likelihoods of occurrence

conditional on experiencing a prior layoff. Following the methodological approach of

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Chan and Stevens (1999,2001) and Ichino et al. (2007), we also examine the impact of

layoff on the post-displacement probabilities of the outcome of strong labour force

attachment relative to the outcomes of control groups of workers who were not displaced.

We draw primarily on the Longitudinal Administrative Database (LAD), which is

a panel of annual data derived from tax declarations. It contains detailed, disaggregated,

and accurate information on the sources of income as well as the respective levels.

Another advantage of this file is that it is very encompassing and is quite representative

of the Canadian working-age population. Attrition via migration to other provinces is not

a problem with the LAD file due to its national scope. It allows one to observe and

follow people over long time periods, facilitating an accurate assessment of pre-

displacement earnings as well as the post-displacement labour market activity profile.

The fourth advantage of the LAD file is its tremendous size.

There are also a number of shortcomings of the LAD file. First, one cannot

observe the event of a job separation, and so it must be imputed. Second, as is common

with administrative data sets, there is no information pertaining to several variables that

are important for labour market outcomes, such as education and skill level, tenure at a

given job, the number of jobs held over an interval, the length of time worked over an

interval, or wage rates. A third disadvantage of the LAD file is that the frequency of the

data is annual. Given that the layoff can occur at any point in time over a calendar year,

this feature raises timing issues as far as the reporting of income and the point of

displacement is concerned.

II. Survey of the Literature

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The most comprehensive study on worker displacement in Canada is by Abe et al.

(2002), which is based on the Canadian Out-of-Employment Panel (COEP) survey from

the mid 1990s. This file consists of a representative sample of job separators, which are

followed over a fairly short time horizon of about 15 months. These authors estimate re-

employment hazards of jobless workers as well as wage changes for those who are re-

employed.

Morissette et al. (2007) deal with displaced workers in Canada over the interval

from 1988-1997. They focus on the earnings losses of displaced workers based on an

administrative data source called the Longitudinal Worker File. The data allow for the

identification of laid-off workers and for a comparison their post-layoff outcomes to

those observed for a control group of workers who have not been laid off – a strategy that

we adopt for part of our analysis. The results indicate that high-seniority workers who

are displaced from either mass layoffs or total plant closures suffer earnings losses that

are substantial and persistent. Unlike our analysis, they restrict their sample to workers

under 49 years of age at the time of layoff in order to omit any cases of early retirement.

The methodology mentioned in the paragraph above was developed by Jacobson

et al. (1993). That influential US study was one of the first to investigate earnings

outcomes of displaced workers over a time horizon spanning periods long before and

long after the point of displacement. Perhaps more importantly, they included in their

analysis a comparison group of workers who were not displaced, which essentially

amounts to a difference-in-difference approach to estimating wage losses that better

accounts for the counterfactual earnings of displaced workers. While the focus of our

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paper is not on wage losses, our data set has some of the same features, and we employ a

variation of their approach for analyzing displacement costs.

Another recent Canadian study is Neill and Schirle (2008), who estimate earnings

losses of displaced workers between the ages of 50 and 65 years based on longitudinal

data drawn from the Survey of Labour Income Dynamics (SLID) over the interval 1993-

2005. Unlike many of the studies in the literature, they account for the outcome of early

retirement. Their estimates suggest that male workers of all ages suffer substantial and

persistent earnings losses stemming from displacement, of which the length of tenure at

the prior job plays a more important role than does the worker’s age per se.

Chen and Morissette (2010) is the most recent study treating displaced workers in

Canada. Their estimating sample covers the period between 1979 and 2004 and is

restricted to workers between 50 and 54 years old. Their objective is to determine the

extent to which post-displacement employment patterns – including employment rates

and average earnings losses - of older displaced workers evolved over that period. This

study does not involve any analysis of counterfactual outcomes.

To our knowledge, a series of articles by Chan and Stevens (1999,2001,2004) are

the mostly closely related studies from the US literature. Based on a specialized survey

drawn from the Health and Retirement Study, with three waves dating from 1992, 1994,

and 1996, these authors track the post-displacement employment probabilities of older

displaced workers over extended time horizons. This framework allows them to take

account of subsequent events such as job instability and early retirement. They estimate

hazard models for exiting from the spell of joblessness following displacement as well as

exiting from a new, post-displacement job. Their equations control for age and the

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number of months already spent in the respective initial states. They discern sharp

differences in these post-displacement profiles between those who were laid off at a

given point of time and a control group of counterparts who were not. For example,

experiencing job loss after age 50 roughly doubles the annual probability of retirement in

subsequent time periods. There are substantial and lasting gaps in employment rates

between the two groups, reflecting both the initial period of non-employment after job

loss and the subsequent instability of post-displacement jobs. They also conclude that

while displaced older workers face a reduced incentive to work and a greater incentive to

retire following job loss (through the channel of pensions), the barriers to re-employment

(through the channel of unattractive labour market opportunities) are the primary

explanatory factor for relatively low re-employment rates.

Based on Austrian administrative data, Ichino et al. (2007) model the post-

displacement employment rates of workers, comparing their outcomes with those of a

control group of matched non-displaced workers. They determine that both displaced

workers of prime-age and of older ages have significantly lower employment rates

relative to their respective control groups. We estimate a somewhat similar equation in

the second component of our econometric analysis.

Drawing on data from four countries contained within the European Community

Household Panel, Tatsarimos (2010) estimates hazard models (based on inflow samples

of jobless older workers) in which the effect of job displacement on transitions into

retirement and into the state of re-employment within a competing hazards framework. .

He distinguishes between the short and the long-run effects of displacement, and he pays

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particular interest to the role of institutions such as government-financed early retirement

regimes.

III. Data Issues and Methodology

III.1 Sample Selection

The population consists of prime-age and older workers having a relatively high

degree of labour market attachment. This group’s employment pattern has been stable

and uninterrupted over a relatively long time period; we label it a ‘clean’ employment

record prior to the event of a layoff. Given the breadth of the LAD’s coverage, there are

many subjects whom we omit from the working sample, particularly those with a low

degree of labour market attachment as well as those working in fragmented, seasonal,

and/or part-year jobs. More specifically, we only include person-year observations for

which the subject is between the ages of 45 and 64 years for all of the years in the

sampling window. We omit all observations for which the subject is a full-time student,

which can be readily identified.1 Because our focus is on displaced workers, we omit

those subjects who appear to be primarily self-employed by deleting all those

observations that involve a non-trivial amount of self-employment income, defined as

that exceeding $1,000 expressed in constant 2005 dollars. The observations for non-filers

are also deleted.

In order to identify workers with a ‘clean’ employment record, we have to

observe their activity over an extended period of time. To this end, we construct four-

year windows of data. The LAD file became very representative of the labour force

starting in 1992, before which many low-income individuals did not have a strong 1 Given the age-related criteria, this case should not cause many observations to be deleted.

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incentive to file a tax return, and thus were not necessarily sampled during every year.

With the implementation of the GST tax credit in 1991, however, almost all working-age

adults have an incentive to file a tax return. Given this seam in the LAD file, we

commence the sample selection process in 1992.2 We subsequently follow the cohort of

all subjects that are observed in the LAD file in 1992 (and met the age-related selection

criteria) for four consecutive years up to and including 1995. Once these years of data

have been observed for an individual, that four-element vector for the first cohort is

retained. We repeat this procedure for 1993, for which we will sample new entrants in

addition to all of those subjects from the 1992 cohort that are observed in 1993. This

window of observation closes in 1996, and we thus form the second cohort of subjects.

These two cohorts share most of their subjects. This procedure is then repeated for each

year from 1992 until 1998, which yields a total of 7 cohorts, the last of which has an

employment record from 1998 to 2001. While we continue to follow subjects over the

years 1998-2005, no new entrants are added to the working sample after that cohort.

While we could form later cohorts, it is not possible to observe the post-displacement

outcomes for adequately long periods.

At this stage of the sample selection process, the working sample consists of four-

year vectors of observations for most subjects appearing in the LAD file.3 The next step

is to inspect each four-year vector of observations that we have for a worker in order to

select those with ‘clean’ employment records, who by construction have a stable

2 There is another reason related to reporting issues for which we do not deal with data before 1992. The details for that choice are given in the appendix. 3 For any subject having four or more consecutive years of observations within our interval of 1992-1998, there will be potentially several vectors, some of which will overlap. He/she will be sampled multiple times, but each case involves a different window of observation. It is also possible for an individual to have several spells of employment activity separated by an episode of labour force withdrawal. In such a case, the subject will be sampled more than once, and the windows drawn from the two periods will be disjoint.

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employment history over the window of observation. To this end we scan the four

consecutive annual observations that we have for him/her for certain outcomes that lead

to deletion. An annual observation is flagged if the earnings fall below a level of $15,000

(2005 dollars). That threshold was chosen because it represents a quasi lower bound on

labour income that would be earned by a full-time, full-year worker. We also flag any

annual observation if income from any of the following sources is reported: social

assistance benefits, employment insurance (EI) benefits, Canada Pension Plan (CPP)

benefits (both pension and disability income), or workers’ compensation benefits. We

then delete any four-year vector in which any flag appears. This procedure should cause

repeat users of EI to be dropped.

After the cohorts of individuals with ‘clean’ four-year employment records have

been formed, the unit of observation thus becomes an individual having that

characteristic. Those cohorts comprise the risk set for the event of experiencing a layoff

in the following year. We label the first year immediately following the ‘clean’ spell of

employment year T, which becomes the reference year for observing and recording the

event of displacement. If a layoff occurs during that year, we then follow the individual

over the subsequent five-year interval. It is over that ex post window of observation that

the post-displacement outcomes are observed and the analysis of their income sources is

carried out. The structure for the data set is summarized in text Table 1.

Text Table 1 – Structure of the cohorts of workers with uninterrupted employment records

Cohort number Window of

observation for ‘clean’ four-year employment record

Year of potential layoff

Years of post-layoff observations – five-year intervals

1 1992-1995 1996 1997-2001

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2 3 4 5 6 7

1993-1996 1994-1997 1995-1998 1996-1999 1997-2000 1998-2001

1997 1998 1999 2000 2001 2002

1998-2002 1999-2003 2000-2004 2001-2005 2002-2005 2003-2005

More details on the construction of the sample are provided in appendix Table 1,

the columns of which correspond to each calendar year, while each row indicates the

number of remaining observations after each cut is made. For each year, we commence

with over 4 million person-year data points in the full LAD file. We then retain the

approximately 61 % who filed a return. Next we delete all observations for which the

age of the subject falls outside of the 45 to 64 year range. A few observations are

deleted because either the subject died or left Canada. The exclusion of the self-

employed observations results in the deletion of 3 to 4 % of the observations from the

original sample. The earnings restriction that we apply in order to form the ‘clean

employment’ record nearly cuts the remaining sample in half. Approximately one-third

of these workers that meet the earnings criterion are dropped because there was some

receipt of social insurance income. A few additional observations are dropped because

the subject was a student or it turned out that he/she received special (i.e. sickness,

maternity, or parental) EI benefits. The estimating samples are then selected as a 10 %

draw, leaving estimating samples of between 31,000 and 43,000 observations for each

year.

III.2 Identification of a Separation

This task requires that our sample selected from the LAD file be linked to an

administrative data set generated by Human Resources and Skills Development Canada

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called the Status Vector and Record of Employment file (STVC/ROE). The flag for the

event of a layoff is the observation of the act of collecting income from the Employment

Insurance (EI) system, as reported in the LAD file. It is necessary, however, to

distinguish between the various types of EI benefits that might be reported in the LAD

file. If an individual declares receiving EI benefits on his/her tax return, it could reflect

‘regular’ benefits stemming from an unemployment spell experienced after a layoff, or it

could reflect ‘special’ benefits, such as maternity, parental, and sickness benefits that are

unrelated to a layoff. In order to verify that the EI income stems from a layoff, we use

the merged file. There is a line in the STVC/ROE record that identifies the ‘type’ of

benefit in the EI claim, and thus makes the distinction between ‘regular’ and ‘special’

benefits. We identify the subject as displaced if he/she declares EI/UI income during that

year, and if cross-verification with the STVC/ROE file indicates that these were ‘regular’

EI/UI benefits.4

We note that this procedure for identifying the event of displacement will capture

only a subset of all displaced workers. For instance, there are some displaced workers

who either suffer no unemployment at all, and others who experience only brief spells of

it. They therefore will not file an EI/UI claim, and the layoff will not be observed. In

order for an individual to be identified as displaced in our sample, the jobless spell

following displacement had to have lasted at least three weeks (due to the universal

4 If the EI benefit period spans two contiguous calendar years, then some EI income will be declared for both years. In this instance, the separation must have occurred in the prior year. This ambiguity does not affect our measurement of a layoff, however, because during the earlier year of this pattern, the subject would not have fulfilled the requirement of a ‘clean employment’ record. Our sample selection procedure thus ensures that the separation occurs during the same year that we flag the record due to the receipt of EI income. We could certainly observe EI income during the subsequent year.

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application of a two-week waiting period for EI benefits).5 Note that the problem of

missing very short spells of joblessness also exists in the articles in the literature on

displaced workers that are based on retrospective surveys such as the US Displaced

Worker Survey.

There is also the case of displaced workers who do experience a non-trivial spell

of unemployment but who do not qualify for EI benefits. In that case the layoff will not

be observed. Most of the displaced workers literature is, however, oriented around

workers with a high degree of attachment to the labour force, and most such workers

would qualify for EI benefits. This conventional practice applies to our sample in light of

the selection criteria that we apply, and therefore we observe most workers of this type

who experience a layoff.6 In the case of seasonal workers, who typically do file claims

for UI/EI benefits, most of them are excluded from the working sample because they did

not satisfy the sampling criterion of a ‘clean employment’ record.

Another pertinent case is an individual who is temporarily laid off from his/her

position. Unless the jobless spell is quite short, this worker has a strong incentive to file

a claim for EI/UI benefits, and given our sampling criteria, is likely to qualify. The

sampling criteria should ensure that he/she is an individual with a stable employment

history for whom a layoff is an infrequent event. In the event that he/she is recalled to

the former employer, we will observe employment income in subsequent time periods.

As our data set contains no information on the employer, we cannot distinguish between

5 Most of the displaced workers who meet that criterion and who qualify for EI benefits do have a strong incentive to file a claim. For instance, consider the case of a worker electing to accept a ‘buyout’ or an early retirement package from his/her employer that qualifies for EI benefits. Both parties in this arrangement have an incentive for a third-party – namely the Federal government – to contribute. 6 Any laid-off worker in Canada qualifies for UI/EI benefits if they have 20 or more weeks of employment activity over the 52-week period preceding the layoff. In high-unemployment areas, one qualifies with even shorter employment spells. Virtually all individuals that are selected into our risk set easily meet that criterion

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the cases of recalls and new hires. Temporary layoffs are therefore treated in the same

way as permanent layoffs. The estimated proportion of all person-year observations (the

risk set for layoff) during which a layoff occurred in 1.92 %.

We note that like much of the existing literature on displaced workers, we take

layoffs to be an exogenous event.7 We do not take account of any selection issues that

would reflect unobservable attributes of workers, with presumably the less able ones

being targeted by firms for layoff. On the other hand, in the interests of reducing the

social and economic costs of mass layoffs, on some occasions workers selected for layoff

are those that have the most generous early retirement benefits or severance pay. They

could also be those with the most attractive outside opportunities in the form of

alternative employment in the labor market. We are thus uncertain of the impact of

possibly endogenous layoffs on our estimates.

To the extent to which there are time patterns in the unobserved attributes of our

sample, the compositional changes will be partially captured in year-specific effects, but

these estimated coefficients could also reflect changes in global labour market conditions,

and thus it is not possible to empirically isolate either pattern.

IV. Empirical Strategy

Our empirical approach uses a typology framework. The first step is to enumerate

the post-layoff patterns and profiles of income sources. We observe all of the income and

its numerous potential sources that are reported during that 5-year period commencing in

year T + 1, which is the first year after the layoff. Based on a tabulation of the flows of

older displaced workers transiting from layoff to the state of receiving income from a 7 One notable exception is Tatsarimos (2010).

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particular source or from a combination of sources, we develop a typology of income

profiles and configurations that have economic significance and policy relevance. For

each cohort, which is identified by the year of separation, the number of cases for receipt

of each of the types of income (described below) is tabulated for each of the five

subsequent calendar years.8 All of the person-year observations are pooled together

regardless of the year of layoff. At this stage the outcome variable is the raw frequency

of the number of laid off workers that received income from this particular source. This

figure is subsequently divided by the size of cohort in order to generate proportions that

are interpreted as incidence rates. Since many subjects receive income from several

sources simultaneously, these categories are not mutually exclusive, and the proportions

will not sum to unity. With the exception of sources with trivial amounts, we enumerate

every source of income that is received by any subject, so the coverage should be

exhaustive. These potential sources are listed in text table 2.

Text Table 2 – Possible sources of income and other destinations for laid-off workers

1) labour market earnings 2) self-employment income 3) employment insurance 4) social assistance 5) workers’ compensation 6) CPP/QPP and/or OAS and/or GIS (public pension income) 7) private pension income 8) CPP – Disability regime 9) other type of income (property income, capital income) Missing or censored observations 10) zero income 11) non-filer

8 With the exception of the latest two cohorts, we can follow all of them for five years after the point of layoff. Cohorts # 6 and # 7 can be followed for 4 and 3 years, respectively, after the point of layoff, until 2005.

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12) attrition from sample due to age restrictions or FT student status

The values for these incidence rates are presented in Tables 2 and 3 of Finnie and

Gray (2009). This procedure generates a great deal of empirical detail; many workers

exhibit complex profiles. For any given post-separation year, there are potentially many

distinct states resulting from combinations of income received simultaneously from those

nine different sources. If one were to enumerate all of the possible combinations, the

permutations would number in the thousands. In the interests of econometric tractability

and ease of exposition, these states of receiving a certain type of income are collapsed

into a typology of income configurations.

For the typology analysis, the central thrust is the following question: during the

post-displacement years, what was the principal source of income? How is the worker

getting by, if indeed he/she has managed to replace a substantial share of his/her pre-

displacement income? The principal source of income is defined as the source that

accounts for 50 % or more of the subject’s total income, provided that such a source

exists. For each type of income, we calculate the proportion of the group of laid-off

workers that received it as their primary income source. For any subject-year observation

for which there are either one or two sources of income, there must necessarily be a

principal source.

The nine categories of income listed above are not mutually exclusive for the

purposes of the typology analysis, as there are many cases in which the income sources

are diversified such that no single source accounts for more than 50 % of the subject’s

total income during that year. Out of all the residual observations that do not fit into one

of those categories according to the 50 % criterion, we define further types that are

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defined according to whether the following combinations of sources when combined

account for 50 % or more of the total income: i) earnings plus self-employment income

plus any combination of social insurance benefits (EI, SA, WC, CPPD), and ii) public

pension plus private pension. Finally, there is a residual category. The observations are

assigned into these categories in the sequence that is listed in text table 2, noting that the

composition of the groups, and especially the catch-all, residual category, is sensitive to

that order. All of the possible configurations are partitioned into 12 different types (listed

in text table 3). The types are constructed to be mutually exclusive and exhaustive, and

thus the shares sum to unity.

The residual category accounts for between 0 and 4 % of all observations

(depending on the year), but in most years, fewer than 1 % of the observations did not fall

into the 11 specific classifications. Observations are omitted, and are thus not assigned a

type, for any of the three following reasons: they have total income falling below the

$5,000 threshold, they did not file a return that year, or they aged out of the working

sample.

Text Table 3: Types of Income Patterns

Configurations of income received, defined by 50 % of more of the total received from: 1. earnings 2. EI 3. public pension 4. private pension 5. workers’ compensation (WC) 6. CPPD 7. social assistance 8. investment income 9. earnings and/or self-employment 10. earnings and/or self-employment plus any combination of EI, SA, WC, and/or CPPD (mostly EI) 11. any combination of pension income, public and/or private

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12. residual category

We extend the scope of the descriptive typology analysis presented in Finnie and

Gray (2011) by estimating an econometric model of the shares of subjects relying on a

given source of income. A multi-variate equation can account for some of the

compositional effects that would confound uni-variate analysis, in particular the impact

of age, which is critical in the determination of retirement behaviour. We are particularly

interested in the influence of three variables, namely the number of years elapsed since

layoff, the calendar year, and regional labour market conditions. The coefficients for the

first set of variables should be identified through variation of years elapsed since layoff

across individuals of the same age. The second set of regressors is specified as a flexible

form of year-specific binary variables, while the effect of age is specified in flexible form

as a set of binary indicator for each year between the ages of 45-63).

In the interests of econometric tractability, we estimate the multi-nomial logit

model based on fewer and hence broader categories than the twelve that are enumerated

above. The outcomes for the dependent variable (collapsed from the twelve types

enumerated above) are reliance on: i) social insurance (3.4 % of person-year

observations), ii) earnings (69.6 % of cases), iii) earnings plus self-employment (2.3 % of

cases), iv) public and/or private pension (13.7 % of cases), and v) the residual group (2.8

% of cases).9 The post-displacement profiles are captured by a set of binary variables for

the number of elapsed years since layoff (T2, T3, T4, T5 for years T + 2, T + 3, T + 4,

and T + 5, respectively). The remaining exogenous variables are listed in text table 4.

Text Table 4 – List of Covariates for the Regression equation

9 6.5 % of the observations are not included in the regression analysis because they are non-filers, and 1.7 % of cases are excluded because their total incomes fell below $5,000.

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Variable Description and Categories Calendar year 1997-2004 (1997 omitted) Age at point of layoff in years Separate effects for 45-63 years (45

omitted) Gender Separate equations Family structure Single (omitted), married no children,

married with children, lone parent Union membership before layoff Binary indicator SMA size (area size of residence) greater than 500,000 (omitted), 100,000-

499,999, 30,000-99,999, 15,000-29,999, small urban area, rural area

English/French speaker (minority language status)

English in Quebec, French outside of Quebec, majority language (omitted)

Province 10 provinces (Ontario omitted) Regional unemployment rate Continuous value for each of 51 regions

The second component of the econometric analysis seeks to assess the impact of

the event of layoff on a post-displacement outcome utilizing a treatment group of

displaced workers and a control group of counterparts who were not laid off during the

first year of the respective cohort. This specification is somewhat similar to the one in

Ichino et al. (2007), in which the dependent variable is binary and is related to post-

displacement employment activity. Whereas those authors model the employment status

that is observed during a quarter, we address the somewhat narrower question of whether

the worker relied primarily on employment activity instead of receipt of income from

alternative sources) during a post-displacement year. If he/she derived over half of their

total income from employment activity, this variable assumes a value of unity.

As mentioned in our previous work, approximately 2 % of the total worker-year

observations in any calendar year represent layoffs. Although the values may well be

higher during recession years, during our interval the event of layoff for a highly attached

worker is low. The inclusion of all the observations that do not involve displacement

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results in huge estimating samples: almost 6,000,000 for men and approximately

4,700,000 for women. By sample selection procedures, the treatment group and the

comparison group have labour market histories (before the reference year) that are

roughly similar, albeit according to our broad definition of exhibiting the ‘clean

employment window’. Because there is no difference in the outcome variable (which is

essentially labour market attachment) during the time period prior to the reference year,

we do not estimate a difference-in-difference specification. In order to control to some

extent for pre-displacement variation in labour market activity, we include the level of

prior earnings (using a set of categorical variables) observed during the 4-year interval

leading up to the year of displacement.10 The impact of layoff on post-displacement

income sources is identified by a comparison of the conditional mean values of the

dependent variable between the two groups. The indicator is a binary variable assuming

a value of unity if the worker was laid off during the first year of his/her cohort, and zero

otherwise. This dummy variable enters as an intercept shift effect, but it is also interacted

with the number of elapsed years since the point of layoff in order to discern whether the

impact of being laid dissipates along that dimension. The layoff dummy variable is also

interacted with the age-bracket of the worker at the point of layoff. We estimate both the

linear probability model via least squares and the logit model.

VI. Results The results of a typology analysis consisting only of descriptive statistics are

presented in Finnie and Gray (2008,2011). As a lead-in to our multi-variate analysis, a

10 The set of categorical variables corresponds to the following earnings brackets denominated in constant 2005 $ (annual values averaged over the 4-year period): below 25,000, 25,000-40,000, 40,000-55,000, 55,000-70,000, 70,000-85,000, 85,000-100,000, above 100,000.

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brief synopsis is presented here. During the estimation interval, approximately two-thirds

of the 45-59 year olds and but less than one-third of the over-59 group gained over 50 %

of their income from the labour market. As might be expected, these proportions

diminish with the elapsed time since layoff. The values for women are slightly lower

than those for their male counterparts. While a significant minority of workers does file

at least one EI claim subsequent to the initial layoff, in any given year, less than 2 % of

the group becomes dependent on the EI regime as their primary source of income.

Hardly any subject meets our criterion of dependence on the social insurance programs of

social assistance (SA), workers’ compensation (WC), or Canada Pension Plan Disability

(CPPD). Only about 1 % of the group of laid-off workers becomes dependent on the

CPPD regime, although the incidence is slightly higher among those over 59 years of age.

These findings are not consistent with the literature drawn from the USA that was cited

above, but the eligibility conditions for the Canadian program are extremely stringent.

Instead of that option, the displaced workers in our sample are far more likely to be

dependent on either early retirement income financed by the public pension schemes (up

to 20 % and 28 % of the older group of men and women, respectively), or by private

pension schemes (up to 39 % and 37 % of the older group of men and women,

respectively). A further 1 to 10 % derives more than half of their post-layoff income

from a combination of public and private pension benefits.

The primary regression results for the multi-nomial logit model (those consisting

of the estimated coefficients) are available from the authors. The calculations for the

more intuitive marginal probability effects are reported in Tables 1 and 2 for men and

women, respectively. The values are interpreted as absolute deviations of probabilities

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relative to the average caused by a unit increase in that regressor. Each column

corresponds to one of the five possible types, and these marginal probabilities

horizontally sum to zero. These magnitudes can be compared to the univariate shares for

each type that were listed above: 0.034 for social insurance, 0.696 for earnings, 0.023 for

self-employment income, 0.137 for pension income, and 0.028 for the residual category.

Due to the abundance of estimates, our discussion is limited to the more marked

patterns that we discern. Relative to childless single workers, married couples are less

likely to rely on social insurance income and more likely to rely on self-employment

income. The impact of the age variable for both genders is essentially as expected; the

younger workers are more likely to rely on earnings or self-employment income, but are

less likely to rely on pension income. Starting at around age 52, a quasi-monotonic trend

of decreasing probabilities for that outcome is discerned for both genders, and the

magnitude is higher among women. Starting at around age 48, the opposite pattern is

discerned for the type of relying on pension income, and for many ages the magnitudes

are almost offsetting. The estimated coefficients pertaining to the social insurance

outcome (not including pensions) are mostly insignificant among both men and women.

Many of the point estimates for the area-size-of-residence variables are significant

for the outcomes of reliance on self-employment income and pension income. Relative

to regions with more than 500,000 people, being situated in a smaller SMA is associated

with a lesser reliance on self-employment income, and either a greater reliance on

pension income or on labour market income (not statistically significant). No patterns are

discerned for reliance on social insurance income. The provincial effects are most

notable for Quebec and to a lesser extent, Alberta and BC. Relative to Ontario, both men

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and women in Quebec are less likely to rely on either earnings or self-employment

income, and more likely to rely on pension income. In BC the opposite pattern applies,

and in Alberta the propensity to retire on either public or private pensions is relatively

low. The rate of unemployment prevailing in the economic region has the expected

positive impact on reliance on social insurance and retirement income, and the expected

negative impact on reliance on self-employment income for both genders. On the other

hand, reliance on labour market earnings surprisingly increases with the unemployment

rate, and is even significant for men.

The impact of union representation is very similar for both genders. Relative to

their non-union counterparts, unionized workers are less likely to rely on income received

from either social insurance programs, pension regimes, or self-employment, but are

more likely to rely on employment earnings. This pattern is suggestive of a higher degree

of labour force attachment among previously unionized workers for reasons that are not

apparent. A possible interpretation is that some of them were recalled to their previous

jobs.

We do not expect to capture much in the way of cyclical effects from the calendar

year covariates, as the period from 1997 to 2005 was characterized by favourable

macroeconomic conditions, with the exception of a slowdown in 2002. There are certain

time trends that are discerned which are interpreted as deviations from 1997. Among

women there appears to be a declining trend in reliance on social insurance income over

this time period, and among men to a lesser extent. For both genders there is a more-or-

less monotonic trend towards greater reliance on earnings over this period, although this

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is often estimated imprecisely. Among men there is a monotonic trend toward less

reliance on pension income, but for women this pattern emerges only after 2001.

The rows at the bottom of Tables 1 and 2 contain the point estimates for the

regressor of the elapsed years since layoff, which are interpreted as deviation from year T

+ 1 (the year after the layoff). Net of age effects, for both genders there is a tendency to

rely less on social insurance income during years T + 2 through T + 5, but there is very

little estimated difference between those years. The probability of that outcome does not

rise with elapsed time since the point of layoff. For both genders, reliance on earnings is

greater two years after layoff, but that effect reverts back to the values in year T + 1

thereafter. There is some evidence of both men and women turning to self-employment

during years T + 2 through T + 5. As one might expect, we discern a steadily increasing

tendency to rely of pension income for both men and women.

The regression results for the equations that include a control group are presented

in tables 3-5. The full set of estimates for the logit model as well as the marginal

probability effects are listed in table 3, while those for the linear probability model are

listed in table 4. The dependent variable is the event of relying primarily on either

earnings or self-employment income during the reference year (we label this state

‘working’ below). The treatment variable is the layoff indicator, which assumes a value

of unity if the worker was laid off during the cohort year and a value of zero otherwise.

In the specifications listed in columns 3,4,7, and 8 of Table 3, it is interacted with the

variables of the number of years elapsed since layoff as well as age. For the sake of

parsimony, the age variable is collapsed into four categories: 40-44 (omitted), 45-49, 50-

54, and 55-59.

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With the exception of the age variables, the results from the logit model are

qualitatively and (in the case of the marginal probability effects) often quantitatively

similar to those of the linear probability model. Controlling for prior earnings has little

impact on the estimates, but it does raise the degree of explanatory power slightly. We

first mention the estimated coefficients not involving the layoff variable, which are

identified overwhelmingly by the non lay-off group. For men, all other (pre-layoff)

earnings categories other than the omitted category of 39-52 K are less likely to work,

with the largest impact between 55-100 K. Among women there is a similar pattern, but

the magnitudes are higher. The pure (non interacted) age effects are monotonic and as

expected; working becomes less likely at older ages, and the magnitude is almost

identical across genders. The impact of area size of residence is usually increasing; the

higher the density, the more likely they are to be working. The impact of residing in

Western Canada relative to Ontario tends to be positive, but it is remarkably negative in

Quebec and Newfoundland. The unemployment rate has the expected negative impact,

although the magnitude is low. The other regional indicators (i.e. province and area size

of residence) might be capturing some of the impact of local labour market conditions.

The impact of having a unionized job is positive, but that is a partially mechanical effect

given the predominance of non-laid off workers in our sample. The trait of speaking

English in Quebec has high positive impact, while the trait of speaking French outside of

Quebec has the opposite effect. The effect associated with the calendar year is gradually

increasing relative to the baseline year of 1996. The monotonic pattern is similar across

genders.

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The effects associated with the event of lay off are summarized in table 5. All of

these values are derived from figures contained in Tables 3 and 4. The intercept layoff

parameter – corresponding to the year after layoff (T + 1) - has a strong negative impact.

The marginal probability effect is estimated to be -0.39 for men and -0.47 for women in

the logit equation and -0.38 and -0.49 in the linear probability model. This point estimate

is much higher when we include all of the interacted terms.

The interacted effect with elapsed time since layoff (T2,T3,T4, and T5) is relative

to year T + 1. For the laid-off group (columns 3-4 and 7-8, bottom panel), the probability

of working in year T + 2 is higher than it is in year T + 1. From year T + 2 and

thereafter, we discern monotonic decreases in the probability of working for both genders

across both specifications. The point estimate in year T + 5 is close to the baseline value

in year T + 1. The gap between the layoff group and the non-layoff group, as captured in

the interacted term, grows wider and wider with elapsed time. The patterns among

women are somewhat sharper

The interacted effects for age groups are relative to the oldest category of 60-64

years. The oldest group of laid-off workers is the least likely to be working relative to

their non-laid off counterparts, and for both genders effects are monotonically decreasing

in age. The gap between the layoff and the non layoff group, actually becomes negative

in the case of 45-49 year-old group; the laid-off workers of this age group are actually

estimated to be more likely to be working than their non laid-off counterparts in the

baseline year of T + 1. For the laid-off group (columns 3-4 and 7-8, top panel), the

negative impact of that event emerges after workers have turned 50. The gap between

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laid-off and non laid-off workers is highest for the 50-54 years old group with the

exception of the logit equation based on the male sample.

V. Conclusions

The central objective of this study is to investigate the income sources and

income-receipt patterns of prime-age and older workers after having suffered a layoff.

We focus on a set of cohorts comprised of both male and female workers who are

deemed to have a high degree of attachment to the labour force and uninterrupted

employment activity preceding that event. Using a unique administrative data base, we

track all of their sources of income over a five-year interval after the layoff. This

provides ample information on the sources of income which are typically not considered

in the existing displaced workers literature, such as pension income, social insurance

payments, self-employment income, and labour market earnings.

The most common destination states for prime-age and older laid-off workers

who have not yet reached normal retirement age are reliance on private pension benefits

and continued labour market activity. It is relatively rare for them to draw heavily on

social insurance benefits. In contrast to the case of the USA, we find little evidence that

disability benefits and workers compensation are functioning as disguised unemployment

insurance benefits.

A multinomial logit regression analysis of a typology of the principal source of

income for the laid off worker revealed a number of empirical patterns. Workers situated

in larger SMAs are more likely to rely on earnings and self-employment activity than are

their counterparts in more rural areas. Workers situated in Quebec are less likely than

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their Ontario-based counterparts to rely on earnings or self-employment income, and are

more likely to depend on retirement income, while the opposite pattern applies to the two

western-most provinces. Higher regional unemployment rates are associated with a

higher reliance on social insurance income, but no significant relationship was discerned

for the outcomes involving earnings or early retirement. Unionized workers are more

likely than their non-union counterparts to rely on earnings, and less likely to rely on

income received from either pensions or social insurance regimes. Over the interval from

1997 to 2005, we discerned monotonic trends of an increasing degree of reliance on

earnings and self-employment income, and a decreasing reliance on social insurance

income. There is no evidence of a tendency to rely more on social insurance income with

the passage of time since layoff, but for the period between 3 to 5 years after layoff, there

is a marked decline in reliance on earnings coupled with an increase in the degree of

reliance on pensions.

The existing literature on older and middle-aged displaced workers has addressed

the evolution of their employment rates and pathways into early retirement for the United

States and several European countries. Our analysis that includes a comparison group of

non-displaced workers indicates that laid-off workers are much less likely to be working

in the (baseline) year right after the year of lay off: the marginal probability effect is

approximately – 0.4 men and almost - 0.5 for women. Two years after the point of

layoff, the marginal probability effect (relative to the baseline year) is positive, but five

years after the year of layoff, they are no more likely to rely on labour market or self-

employment income than they were during the baseline year. The discrepancy in the

employment rates between the laid-off group and the comparison group widens over

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elapsed time since layoff. Once workers turn 50, the event of suffering a layoff renders

them less and less likely to work as they age. Workers who are laid off between the ages

of 45-50, however, are actually more likely to be working than their counterparts who

were not laid-off.

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References

Abe, P. Kuhn, Y. Higuchi, M. Nakamura, A. Sweetman (2002) “Worker Displacement in Japan and Canada” in Kuhn, P., editor, Losing Work, Moving On: International Perspectives on Worker Displacement W.E. Upjohn Institute for Employment Research

Autor, D. and M. Duggan (2006) “The Growth in the Social Security Disability Rolls: A

Fiscal Crisis Unfolding” Journal of Economic Perspectives 20, 3, pp. 71-96 Chan, S. and A. Huff-Stevens (1999) “Employment and Retirement Following a Late-

Career Job Loss” American Economics Association Papers and Proceedings, Vol. 89, May, pp. 211-16

Chan, S. and A. Huff-Stevens (2001) “Job Loss and Employment Patterns of Older

Workers” Journal of Labor Economics Vol. 19, 2, pp. 484-521 Chan, S. and A. Huff-Stevens (2004) “How Does Job Loss Affect the Timing of

Retirement?” Berkeley Journal of Economic Analysis and Policy, Vol. 3, 1, article 5

Chen, W-H and R. Morissette (2010) “Have Employment patterns of Older Displaced

Workers Improved Since the Late 1970s?” Canadian Labour Market and Skills Researcher Network, WP No. 61, May

Finnie, R. and D. Gray (2009) “Displacement of older workers: re-employment, hastened retirement, disability, or other destinations?” Canadian Labour Market and Skills Research Network working paper, No. 15

Finnie and Gray (2011) “ Labour-Force Participation of Older Displaced workers in Canada: Should I Stay or Should I go?” IRPP (Institute for Research on Public Policy) Study No. 15, February

Ichino, A., G. Schwerdt, R. Winter-Ebmer, and J. Zweimuller (2007) “Too Old to Work,

Too Young to Retire?” Institute for the Study of Labour (IZA) Working Paper 3110

Jacobson, L., R. Lalonde, and D. Sullivan (1993) “Earnings Losses of Displaced Workers” American Economic Review 83, 4, pp. 685-709

Kuhn, P. (2003) “Effects of Population Aging on Labour Market Flows in Canada: Analytical Issues and Research Priorities” Issues paper, Skills Research Initiative Partnership – Expert Roundtable on Labour Market Adjustments due to population aging in Canada”

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Morissette, R., X. Zhang, and M. Frenette (2007) “Earnings Losses of Displaced Workers: Canadian Evidence from a Large Administrative Database on Firm Closures and Mass Layoffs” working paper No. 291, Statistics Canada, Business and Labour Market Analysis Division

Neill, C. and T. Schirle (2008) “Remain, Retrain, or Retire: Options for Older Workers

Following Job Loss” in Abbot, Beach, Boadway, and MacKinnon, eds. Retirement Policy Issues in Canada Montreal: McGill-Queens’ University Press, pp. 277-308

OECD Employment Review (2003) Transforming Disability into Ability; Policies to Promote Work and Income Security for Disabled People Paris: Organization for Economic Cooperation and Development

Tatsiramos, K. (2010) “The Effect of Job Displacement on the Transitions to

Employment and Early Retirement for Older Workers in Four European Countries” European Economic Review 54,4, pp. 517-535

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Table 1

Regression Results: Multinomial Logit Model of Principal Source of Income Marginal Probability Effect, Men

reliance on income earned from

social

insurance earnings Self-empl.

Pension income

Other sources

Family Status

Single (omitted category)

Couple no Kids -0.033*** 0.04 0.010*** -0.009 -0.008***

[0.004] [0.031] [0.002] [0.008] [0.003]

Couple with Kids -0.029*** 0.045 0.014*** -

0.023*** -0.007***

[0.004] [0.044] [0.003] [0.004] [0.002]

Lone Parent -0.002 -0.018 0 0.018 0.002

[0.013] [0.107] [0.007] [0.020] [0.009]

Age Category

45 (omitted category)

Age40 -0.006 0.017 0.002 -0.007** -0.005

[0.006] [0.095] [0.006] [0.003] [0.003]

Age41 -0.007 0.014 -0.008 -0.002 0.003

[0.006] [0.096] [0.005] [0.004] [0.005]

Age42 -0.002 0.013 0 -0.007* -0.005

[0.007] [0.095] [0.006] [0.003] [0.003]

Age43 0.004 0.003 -0.004 -0.001 -0.002

[0.007] [0.094] [0.006] [0.004] [0.004]

Age44 -0.002 0.005 -0.002 -0.002 0.001

[0.007] [0.095] [0.006] [0.004] [0.004]

Age46 0 -0.001 0.001 0 0.001

[0.007] [0.094] [0.006] [0.004] [0.004]

Age47 -0.002 0 -0.002 0.005 -0.001

[0.007] [0.097] [0.006] [0.005] [0.004]

Age48 0.005 -0.016 -0.008 0.017** 0.001

[0.008] [0.095] [0.006] [0.007] [0.004]

Age49 0.01 -0.05 0.002 0.028*** 0.010*

[0.008] [0.089] [0.007] [0.009] [0.005]

Age50 0.006 -0.052 -0.009* 0.046*** 0.009*

[0.008] [0.089] [0.006] [0.011] [0.005]

Age51 0.002 -0.097 -0.008 0.091*** 0.012**

[0.007] [0.082] [0.006] [0.016] [0.006]

Age52 0.008 -0.122 -0.013** 0.112*** 0.015**

[0.008] [0.079] [0.005] [0.018] [0.006]

Age53 0.009 -0.159** -0.012** 0.144*** 0.018***

[0.008] [0.074] [0.006] [0.021] [0.006]

Age54 0.007 -

0.196*** -0.012** 0.175*** 0.026***

[0.008] [0.068] [0.006] [0.024] [0.007]

Age55 0.020** -

0.228*** -

0.022*** 0.207*** 0.023***

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[0.009] [0.067] [0.005] [0.027] [0.007]

Age56 0.004 -

0.277*** -

0.028*** 0.269*** 0.031***

[0.008] [0.063] [0.004] [0.033] [0.008]

Age57 0.004 -

0.318*** -

0.022*** 0.297*** 0.040***

[0.008] [0.058] [0.006] [0.036] [0.009]

Age58 0.007 -

0.362*** -

0.028*** 0.346*** 0.037***

[0.008] [0.054] [0.005] [0.040] [0.009]

Age59 0.009 -

0.420*** -

0.033*** 0.394*** 0.050***

[0.009] [0.048] [0.005] [0.045] [0.010]

Age60 0.011 -

0.467*** -

0.033*** 0.431*** 0.058***

[0.009] [0.042] [0.005] [0.047] [0.011]

Age61 0.009 -

0.421*** -

0.035*** 0.386*** 0.062***

[0.010] [0.056] [0.005] [0.049] [0.013]

Age62 0.017 -

0.464*** -

0.040*** 0.440*** 0.047***

[0.012] [0.062] [0.005] [0.066] [0.013]

Age63 0.016 -

0.453*** -

0.043*** 0.401*** 0.080***

[0.015] [0.083] [0.004] [0.074] [0.021]

Area Size of Residence

500 000+ (omitted category)

100 000 499 999 -0.003 -0.004

-0.012*** 0.020*** -0.002

[0.004] [0.039] [0.003] [0.007] [0.003]

30 000 - 99 999 0.004 -0.007 -

0.014*** 0.022** -0.006**

[0.005] [0.048] [0.003] [0.009] [0.003]

15 000 - 29 999 -0.002 0.006 -

0.015*** 0.009 0.003

[0.007] [0.077] [0.005] [0.013] [0.005]

1 000 - 14 999 -0.001 -0.001 -

0.013*** 0.016* -0.002

[0.005] [0.051] [0.003] [0.009] [0.003]

Less than 1000 0.003 -0.02 -0.005* 0.018** 0.005

[0.004] [0.040] [0.003] [0.007] [0.003]

Province

ON (omitted category)

NF -0.003 -0.075 0.01 0.051** 0.018*

[0.010] [0.130] [0.013] [0.026] [0.010]

PE -0.013 -0.064 0.02 0.039 0.018

[0.014] [0.218] [0.026] [0.038] [0.015]

NS -0.008 -0.067 -0.006 0.071*** 0.011**

[0.006] [0.073] [0.005] [0.017] [0.005]

NB 0.005 -0.034 -0.003 0.024 0.008

[0.008] [0.097] [0.007] [0.016] [0.006]

PQ 0.009** -0.054 -

0.007*** 0.036*** 0.015***

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[0.004] [0.038] [0.003] [0.007] [0.003]

MB 0 -0.004 -

0.015*** 0.021 -0.003

[0.007] [0.087] [0.005] [0.015] [0.004]

SK -0.005 0.016 -0.005 -0.012 0.006

[0.008] [0.107] [0.007] [0.015] [0.006]

AB -0.012*** 0.039 -

0.013*** -0.018** 0.003

[0.004] [0.057] [0.003] [0.007] [0.003]

BC 0 -0.002 0.016*** -

0.018*** 0.004

[0.003] [0.041] [0.004] [0.006] [0.003]

Language

Majority Lang. (omitted category)

English in Qc 0.001 0.03 -0.007 -0.027** 0.003

[0.006] [0.079] [0.005] [0.011] [0.005]

French out. QC. -0.007 -0.013 -0.012 0.004 0.028*

[0.013] [0.174] [0.010] [0.028] [0.016]

Regional labour market

unemployment rate 0.006*** 0.021**

-0.002*** 0.003** 0.002***

[0.001] [0.008] [0.001] [0.001] [0.000]

Union Status

Non-Union (omitted category)

Union -0.057*** 0.208*** -

0.049*** -

0.086*** -0.017***

[0.002] [0.047] [0.002] [0.005] [0.002]

Year

1997 (omitted category)

1998 -0.003 0.021 0.003 -0.004 -0.016***

[0.006] [0.039] [0.003] [0.009] [0.004]

1999 -0.009* 0.038 0 -0.015 -0.014***

[0.005] [0.046] [0.004] [0.010] [0.004]

2000 -0.012*** 0.062 -0.001 -

0.037*** -0.012***

[0.004] [0.052] [0.004] [0.010] [0.004]

2001 -0.014*** 0.082 0 -

0.056*** -0.012***

[0.004] [0.053] [0.004] [0.010] [0.004]

2002 -0.013*** 0.093 0 -

0.063*** -0.017***

[0.005] [0.057] [0.004] [0.010] [0.003]

2003 -0.012** 0.105* -0.002 -

0.073*** -0.017***

[0.005] [0.060] [0.004] [0.009] [0.003]

2004 -0.007* 0.110* 0 -

0.090*** -0.013***

[0.004] [0.067] [0.005] [0.010] [0.004]

2005 -0.008* 0.124* 0 - -0.012***

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0.104***

[0.004] [0.073] [0.005] [0.010] [0.004]

T2 -0.082*** 0.052*** 0.007*** 0.040*** -0.017***

[0.002] [0.018] [0.001] [0.003] [0.002]

T3 -0.079*** 0.036 0.008*** 0.054*** -0.018***

[0.002] [0.022] [0.002] [0.004] [0.002]

T4 -0.076*** 0.012 0.006*** 0.076*** -0.018***

[0.003] [0.027] [0.002] [0.006] [0.002]

T5 -0.074*** -0.017 0.007*** 0.102*** -0.018***

[0.003] [0.029] [0.002] [0.008] [0.002]

Standard errors in brackets

*** p<0.01, ** p<0.05, * p<0.1

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Table 2

Regression Results: Multinomial Logit Model of Principal Source of Income: Marginal Probability Effect, Women

reliance on income earned from

social

insurance Earnings Self-empl.

Pension income Others

Family Status

Single (omitted category)

Couple no Kids -0.011*** -0.007 -0.001 0.013* 0.005**

[0.004] [0.033] [0.002] [0.008] [0.003]

Couple with Kids -0.014*** 0.01 0.003 0.002 -0.002

[0.005] [0.053] [0.003] [0.009] [0.003]

Lone Parent 0.020** 0.017 0.002 -0.037** -0.002

[0.010] [0.075] [0.004] [0.015] [0.006]

Age

45 (omitted category)

Age40 -0.002 0.009 0.01 -0.007* -0.010***

[0.009] [0.118] [0.006] [0.004] [0.004]

Age41 0 0.011 0.007 -0.006 -0.011***

[0.009] [0.121] [0.006] [0.004] [0.004]

Age42 -0.002 0.004 0.007 -0.006 -0.004

[0.009] [0.121] [0.006] [0.004] [0.005]

Age43 -0.007 0.013 0.004 -0.004 -0.007

[0.009] [0.122] [0.006] [0.004] [0.004]

Age44 0.001 -0.001 0.001 0.005 -0.006

[0.009] [0.119] [0.005] [0.006] [0.004]

Age46 0.002 -0.008 0.005 0.005 -0.004

[0.009] [0.116] [0.006] [0.006] [0.005]

Age47 0.01 -0.022 -0.001 0.011* 0.001

[0.010] [0.114] [0.005] [0.007] [0.005]

Age48 0 -0.027 0.005 0.022*** 0

[0.009] [0.113] [0.006] [0.008] [0.005]

Age49 0.006 -0.055 0.001 0.035*** 0.013*

[0.009] [0.109] [0.005] [0.010] [0.007]

Age50 0.008 -0.089 -0.001 0.072*** 0.01

[0.010] [0.101] [0.005] [0.015] [0.007]

Age51 0.004 -0.164* -0.005 0.156*** 0.009

[0.009] [0.089] [0.005] [0.024] [0.006]

Age52 0.001 -0.192** -0.006 0.179*** 0.019**

[0.009] [0.086] [0.005] [0.027] [0.008]

Age53 0.007 -0.230*** 0 0.204*** 0.018**

[0.009] [0.074] [0.005] [0.028] [0.007]

Age54 0.012 -0.262*** -0.002 0.222*** 0.031***

[0.010] [0.072] [0.006] [0.030] [0.009]

Age55 0.005 -0.328*** -0.007 0.291*** 0.039***

[0.009] [0.063] [0.005] [0.036] [0.010]

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Age56 -0.001 -0.381*** -0.017*** 0.357*** 0.042***

[0.009] [0.059] [0.004] [0.044] [0.011]

Age57 0.002 -0.407*** -0.010* 0.371*** 0.044***

[0.009] [0.056] [0.005] [0.045] [0.011]

Age58 -0.001 -0.483*** -0.017*** 0.436*** 0.066***

[0.009] [0.047] [0.004] [0.051] [0.013]

Age59 0.005 -0.542*** -0.024*** 0.518*** 0.043***

[0.010] [0.041] [0.003] [0.063] [0.011]

Age60 -0.016** -0.550*** -0.020*** 0.530*** 0.056***

[0.008] [0.041] [0.004] [0.065] [0.013]

Age61 -0.017* -0.533*** -0.025*** 0.516*** 0.059***

[0.009] [0.057] [0.004] [0.079] [0.016]

Age62 -0.015 -0.551*** -0.020*** 0.513*** 0.072***

[0.011] [0.060] [0.006] [0.085] [0.020]

Age63 0.013 -0.520*** -0.024*** 0.486*** 0.045**

[0.021] [0.104] [0.006] [0.122] [0.022]

Area Size of Residence

500 000+ (omitted category)

100 000 499 999 0.006 -0.027 -0.004* 0.030*** -0.005

[0.005] [0.046] [0.003] [0.009] [0.003]

30 000 - 99 999 0.008 -0.032 -0.012*** 0.040*** -0.003

[0.006] [0.056] [0.002] [0.012] [0.004]

15 000 - 29 999 0.011 -0.032 -0.006 0.031 -0.004

[0.010] [0.091] [0.005] [0.019] [0.006]

1 000 - 14 999 0.009* -0.046 -0.004 0.040*** 0

[0.006] [0.050] [0.003] [0.012] [0.004]

Less than 1000 0.014*** -0.064 0.002 0.043*** 0.005

[0.005] [0.041] [0.003] [0.010] [0.004]

Province

ON (omitted category)

NF 0.005 0 -0.014* 0.014 -0.006

[0.021] [0.227] [0.008] [0.033] [0.012]

PE -0.023 0.082 -0.011 -0.033 -0.014

[0.020] [0.379] [0.012] [0.042] [0.015]

NS 0.006 -0.013 0 0.01 -0.002

[0.010] [0.104] [0.006] [0.017] [0.007]

NB -0.003 0.015 -0.003 0.004 -0.012*

[0.012] [0.151] [0.007] [0.022] [0.007]

PQ 0 -0.090** -0.007*** 0.106*** -0.009***

[0.005] [0.043] [0.002] [0.011] [0.003]

MB -0.007 0.021 -0.005 0.004 -0.014***

[0.009] [0.107] [0.005] [0.016] [0.005]

SK -0.013 0.037 0.017** -0.036** -0.006

[0.009] [0.113] [0.009] [0.015] [0.008]

AB -0.011** 0.033 0.005 -0.028*** 0.001

[0.005] [0.058] [0.003] [0.008] [0.004]

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BC 0.002 0.003 0.017*** -0.028*** 0.007*

[0.004] [0.047] [0.004] [0.007] [0.004]

Language

Majority Lang. (omitted category)

English in Qc 0.012 0.024 -0.001 -0.054*** 0.020**

[0.010] [0.099] [0.005] [0.013] [0.008]

French out. QC. 0.005 -0.045 -0.009 0.054 -0.005

[0.019] [0.177] [0.008] [0.046] [0.010]

Regional labour market

unemployment rate 0.005*** 0.011 -0.001** 0.002 0.002***

[0.001] [0.010] [0.001] [0.002] [0.001]

Union Status

Non-Union (omitted category)

Union -0.058*** 0.198*** -0.030*** -0.093*** -0.016***

[0.003] [0.056] [0.001] [0.006] [0.003]

Year

1997 (omitted category)

1998 -0.027*** -0.008 -0.005** 0.059*** -0.020***

[0.008] [0.042] [0.003] [0.012] [0.004]

1999 -0.021*** 0.004 -0.007** 0.043*** -0.019***

[0.006] [0.051] [0.003] [0.013] [0.004]

2000 -0.018*** 0.032 -0.008** 0.009 -0.014***

[0.006] [0.059] [0.004] [0.013] [0.005]

2001 -0.021*** 0.061 -0.010*** -0.012 -0.017***

[0.005] [0.063] [0.004] [0.013] [0.004]

2002 -0.020*** 0.077 -0.011*** -0.023* -0.023***

[0.006] [0.067] [0.004] [0.013] [0.004]

2003 -0.014** 0.098 -0.011*** -0.047*** -0.025***

[0.007] [0.073] [0.004] [0.011] [0.004]

2004 -0.008* 0.108 -0.013*** -0.066*** -0.021***

[0.005] [0.085] [0.005] [0.011] [0.005]

2005 -0.009* 0.126 -0.014** -0.084*** -0.019***

[0.005] [0.092] [0.005] [0.011] [0.005]

T2 -0.139*** 0.099*** 0.012*** 0.049*** -0.020***

[0.003] [0.023] [0.001] [0.005] [0.002]

T3 -0.136*** 0.072** 0.014*** 0.069*** -0.019***

[0.003] [0.028] [0.002] [0.006] [0.002]

T4 -0.137*** 0.039 0.017*** 0.101*** -0.019***

[0.003] [0.031] [0.002] [0.008] [0.003]

T5 -0.139*** 0.005 0.015*** 0.136*** -0.017***

[0.004] [0.035] [0.002] [0.011] [0.003]

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Standard errors in brackets

*** p<0.01, ** p<0.05, * p<0.1

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Table 3

Regression Results for Dependence on Labour Market, Treatment versus Control Groups Logit equation Coefficients Marginal Effects

No Interaction Term

Interaction of Treatment with Age

and Time since layoff No Interaction Term

Interaction of Treatment with Age

and Time since layoff

Male Female Male Female Male Female Male Female family status couple no kids 0.146*** -0.009** 0.145*** -0.009** 0.018*** -0.001** 0.017*** -0.001** [0.004] [0.004] [0.004] [0.004] [0.000] [0.000] [0.000] [0.000] couple with kids 0.549*** 0.234*** 0.549*** 0.234*** 0.059*** 0.022*** 0.057*** 0.020*** [0.006] [0.008] [0.006] [0.008] [0.000] [0.001] [0.000] [0.001] lone parent 0.035 0.161*** 0.037* 0.163*** 0.004 0.015*** 0.005* 0.015*** [0.022] [0.015] [0.022] [0.016] [0.003] [0.001] [0.003] [0.001] age group age4549 2.885*** 2.979*** 2.902*** 2.995*** 0.148*** 0.111*** 0.143*** 0.104*** [0.006] [0.007] [0.006] [0.007] [0.000] [0.000] [0.000] [0.000] age5054 1.547*** 1.628*** 1.546*** 1.632*** 0.118*** 0.092*** 0.115*** 0.086*** [0.005] [0.006] [0.005] [0.006] [0.000] [0.000] [0.000] [0.000] age5559 0.445*** 0.569*** 0.439*** 0.573*** 0.049*** 0.046*** 0.047*** 0.044*** [0.005] [0.006] [0.005] [0.006] [0.000] [0.000] [0.000] [0.000] dage4549 (interaction) -0.359*** -0.088** -0.050*** -0.009** [0.032] [0.038] [0.005] [0.004] dage5054 (interaction) 0.174*** 0.205*** 0.020*** 0.018*** [0.030] [0.037] [0.003] [0.003] dage5559 (interaction) 0.285*** 0.120*** 0.032*** 0.011*** [0.030] [0.037] [0.003] [0.003] area size of residence 100,000-499,999 -0.289*** -0.370*** -0.289*** -0.370*** -0.040*** -0.042*** -0.039*** -0.040*** [0.004] [0.005] [0.004] [0.005] [0.001] [0.001] [0.001] [0.001] 30,000-99,999 -0.368*** -0.493*** -0.369*** -0.494*** -0.052*** -0.058*** -0.051*** -0.056*** [0.005] [0.006] [0.005] [0.006] [0.001] [0.001] [0.001] [0.001] 15,000-29,999 -0.303*** -0.542*** -0.304*** -0.542*** -0.042*** -0.065*** -0.041*** -0.062*** [0.008] [0.009] [0.008] [0.009] [0.001] [0.001] [0.001] [0.001] 1,000-14,999 -0.349*** -0.503*** -0.350*** -0.504*** -0.049*** -0.060*** -0.048*** -0.057*** [0.005] [0.006] [0.005] [0.006] [0.001] [0.001] [0.001] [0.001] less than 1,000 -0.438*** -0.587*** -0.439*** -0.587*** -0.063*** -0.071*** -0.062*** -0.068*** [0.004] [0.005] [0.004] [0.005] [0.001] [0.001] [0.001] [0.001] Province NF -0.402*** -0.450*** -0.404*** -0.452*** -0.058*** -0.052*** -0.056*** -0.050*** [0.013] [0.016] [0.013] [0.016] [0.002] [0.002] [0.002] [0.002] PE -0.128*** 0.166*** -0.128*** 0.166*** -0.017*** 0.016*** -0.017*** 0.015*** [0.021] [0.024] [0.021] [0.024] [0.003] [0.002] [0.003] [0.002]

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NS -0.194*** -0.047*** -0.194*** -0.047*** -0.026*** -0.005*** -0.026*** -0.004*** [0.008] [0.010] [0.008] [0.010] [0.001] [0.001] [0.001] [0.001] NB -0.122*** -0.187*** -0.122*** -0.187*** -0.016*** -0.020*** -0.016*** -0.019*** [0.009] [0.011] [0.009] [0.011] [0.001] [0.001] [0.001] [0.001] PQ -0.422*** -0.523*** -0.422*** -0.525*** -0.061*** -0.062*** -0.059*** -0.060*** [0.004] [0.005] [0.004] [0.005] [0.001] [0.001] [0.001] [0.001] MB -0.184*** -0.155*** -0.184*** -0.154*** -0.025*** -0.016*** -0.024*** -0.015*** [0.007] [0.009] [0.007] [0.009] [0.001] [0.001] [0.001] [0.001] SK 0.109*** 0.117*** 0.109*** 0.117*** 0.013*** 0.011*** 0.013*** 0.011*** [0.009] [0.010] [0.009] [0.010] [0.001] [0.001] [0.001] [0.001] AB 0.212*** 0.168*** 0.212*** 0.168*** 0.025*** 0.016*** 0.025*** 0.015*** [0.005] [0.006] [0.005] [0.006] [0.001] [0.001] [0.001] [0.001] BC 0.085*** 0.048*** 0.085*** 0.049*** 0.011*** 0.005*** 0.010*** 0.005*** [0.005] [0.005] [0.005] [0.005] [0.001] [0.000] [0.001] [0.000] Language English in Qc. 0.461*** 0.471*** 0.462*** 0.473*** 0.051*** 0.040*** 0.049*** 0.038*** [0.009] [0.010] [0.009] [0.010] [0.001] [0.001] [0.001] [0.001] French out Qc. -0.334*** -0.463*** -0.334*** -0.465*** -0.047*** -0.054*** -0.046*** -0.052*** [0.014] [0.015] [0.014] [0.015] [0.002] [0.002] [0.002] [0.002] regional UR -0.018*** -0.021*** -0.018*** -0.021*** -0.002*** -0.002*** -0.002*** -0.002*** [0.001] [0.001] [0.001] [0.001] [0.000] [0.000] [0.000] [0.000] union status Union 1.372*** 1.479*** 1.372*** 1.479*** 0.125*** 0.131*** 0.125*** 0.131*** [0.003] [0.004] [0.003] [0.004] [0.000] [0.000] [0.000] [0.000] Year 1998 0.023** -0.088*** 0.020* -0.094*** 0.003** -0.009*** 0.002* -0.009*** [0.011] [0.013] [0.011] [0.013] [0.001] [0.001] [0.001] [0.001] 1999 0.109*** 0.037*** 0.106*** 0.023* 0.013*** 0.004*** 0.013*** 0.002* [0.011] [0.013] [0.011] [0.013] [0.001] [0.001] [0.001] [0.001] 2000 0.203*** 0.163*** 0.199*** 0.145*** 0.024*** 0.015*** 0.023*** 0.013*** [0.011] [0.013] [0.011] [0.013] [0.001] [0.001] [0.001] [0.001] 2001 0.271*** 0.255*** 0.267*** 0.237*** 0.032*** 0.023*** 0.030*** 0.021*** [0.010] [0.012] [0.010] [0.013] [0.001] [0.001] [0.001] [0.001] 2002 0.312*** 0.324*** 0.310*** 0.308*** 0.036*** 0.029*** 0.035*** 0.026*** [0.010] [0.012] [0.010] [0.013] [0.001] [0.001] [0.001] [0.001] 2003 0.351*** 0.372*** 0.349*** 0.358*** 0.040*** 0.033*** 0.039*** 0.030*** [0.010] [0.012] [0.010] [0.013] [0.001] [0.001] [0.001] [0.001] 2004 0.392*** 0.401*** 0.390*** 0.389*** 0.044*** 0.035*** 0.043*** 0.032*** [0.011] [0.013] [0.011] [0.013] [0.001] [0.001] [0.001] [0.001] 2005 0.445*** 0.446*** 0.443*** 0.436*** 0.049*** 0.038*** 0.048*** 0.035*** [0.011] [0.013] [0.011] [0.013] [0.001] [0.001] [0.001] [0.001] event of layoff -0.968*** -1.313*** -2.033*** -2.649*** -0.159*** -0.195*** -0.386*** -0.470*** [0.009] [0.009] [0.029] [0.037] [0.002] [0.002] [0.006] [0.008] years since layoff T2 -0.503*** -0.537*** -0.540*** -0.605*** -0.074*** -0.064*** -0.078*** -0.070***

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[0.005] [0.006] [0.005] [0.006] [0.001] [0.001] [0.001] [0.001] T3 -0.909*** -0.973*** -0.955*** -1.050*** -0.148*** -0.132*** -0.154*** -0.140*** [0.005] [0.006] [0.005] [0.006] [0.001] [0.001] [0.001] [0.001] T4 -1.267*** -1.358*** -1.317*** -1.440*** -0.221*** -0.204*** -0.228*** -0.212*** [0.005] [0.006] [0.005] [0.006] [0.001] [0.001] [0.001] [0.001] T5 -1.593*** -1.710*** -1.647*** -1.796*** -0.292*** -0.275*** -0.300*** -0.285*** [0.005] [0.006] [0.005] [0.006] [0.001] [0.001] [0.001] [0.001] dT2 (interacted) 0.912*** 1.263*** 0.083*** 0.076*** [0.024] [0.025] [0.002] [0.001] dT3 (interacted) 1.250*** 1.586*** 0.102*** 0.085*** [0.024] [0.025] [0.001] [0.001] dT4 (interacted) 1.436*** 1.763*** 0.110*** 0.089*** [0.025] [0.026] [0.001] [0.001] dT5 (interacted) 1.587*** 1.907*** 0.116*** 0.092*** [0.027] [0.027] [0.001] [0.001] prior earnings earnings 25-40 k -0.079*** -0.434*** -0.080*** -0.435*** -0.010*** -0.050*** -0.010*** -0.048*** [0.010] [0.006] [0.010] [0.006] [0.001] [0.001] [0.001] [0.001] earnings 40-55 k -0.482*** -0.879*** -0.482*** -0.880*** -0.071*** -0.116*** -0.069*** -0.112*** [0.009] [0.006] [0.009] [0.006] [0.002] [0.001] [0.002] [0.001] earnings 55-70 k -0.850*** -1.251*** -0.850*** -1.252*** -0.136*** -0.183*** -0.133*** -0.176*** [0.009] [0.006] [0.009] [0.006] [0.002] [0.001] [0.002] [0.001] earnings 71-85 k -0.942*** -1.511*** -0.942*** -1.513*** -0.154*** -0.234*** -0.151*** -0.226*** [0.010] [0.008] [0.010] [0.008] [0.002] [0.002] [0.002] [0.001] earnings 85-100 k -0.689*** -0.966*** -0.689*** -0.966*** -0.106*** -0.131*** -0.104*** -0.126*** [0.010] [0.012] [0.010] [0.012] [0.002] [0.002] [0.002] [0.002] earnings 100+ k -0.346*** -0.562*** -0.346*** -0.563*** -0.049*** -0.068*** -0.048*** -0.065*** [0.010] [0.013] [0.010] [0.013] [0.002] [0.002] [0.001] [0.002] Constant 1.357*** 1.665*** 1.400*** 1.741*** [0.015] [0.015] [0.015] [0.016] Observations 5983380 4724375 5983380 4724375 5983380 4724375 5983380 4724375 (Pseudo) R-squared 0.221 0.230 0.223 0.232 0.221 0.230 0.223 0.232 Standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1

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Table 4

Regression Results for Dependence on Labour Market treatment versus control goups

linear probability model

No Interaction Term

Interaction of Treatment, Age, and

Time since layoff Male Female Male Female family status couple no kids 0.016*** -0.001*** 0.016*** -0.001*** [0.000] [0.000] [0.000] [0.000] couple with kids 0.033*** -0.003*** 0.033*** -0.002*** [0.000] [0.000] [0.000] [0.000] lone parent 0.007*** -0.003*** 0.007*** -0.002*** [0.002] [0.001] [0.002] [0.001] age group age4549 0.315*** 0.329*** 0.310*** 0.323*** [0.001] [0.001] [0.001] [0.001] age5054 0.227*** 0.237*** 0.223*** 0.232*** [0.001] [0.001] [0.001] [0.001] age5559 0.059*** 0.080*** 0.055*** 0.077*** [0.001] [0.001] [0.001] [0.001] dage4549 (interaction) 0.200*** 0.255*** [0.006] [0.006] dage5054 (interaction) 0.164*** 0.163*** [0.006] [0.006] dage5559 (interaction) 0.108*** 0.057*** [0.006] [0.007] area size of residence 100,000-499,999 -0.030*** -0.037*** -0.030*** -0.037*** [0.000] [0.000] [0.000] [0.000] 30,000-99,999 -0.039*** -0.050*** -0.039*** -0.050*** [0.000] [0.001] [0.000] [0.001] 15,000-29,999 -0.033*** -0.056*** -0.033*** -0.056*** [0.001] [0.001] [0.001] [0.001] 1,000-14,999 -0.036*** -0.050*** -0.036*** -0.050*** [0.001] [0.001] [0.001] [0.001] les than 1,000 -0.049*** -0.061*** -0.048*** -0.061*** [0.000] [0.001] [0.000] [0.001] province NF -0.050*** -0.047*** -0.049*** -0.047*** [0.001] [0.002] [0.001] [0.002] PE -0.016*** 0.013*** -0.016*** 0.013*** [0.002] [0.002] [0.002] [0.002]

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NS -0.020*** -0.005*** -0.020*** -0.005*** [0.001] [0.001] [0.001] [0.001] NB -0.016*** -0.022*** -0.016*** -0.022*** [0.001] [0.001] [0.001] [0.001] PQ -0.048*** -0.057*** -0.048*** -0.057*** [0.000] [0.000] [0.000] [0.000] MB -0.022*** -0.020*** -0.022*** -0.020*** [0.001] [0.001] [0.001] [0.001] SK 0.010*** 0.009*** 0.010*** 0.009*** [0.001] [0.001] [0.001] [0.001] AB 0.018*** 0.011*** 0.018*** 0.011*** [0.000] [0.001] [0.000] [0.001] BC 0.007*** 0.001*** 0.007*** 0.001*** [0.000] [0.000] [0.000] [0.000] language l English in Qc. 0.053*** 0.053*** 0.052*** 0.052*** [0.001] [0.001] [0.001] [0.001] French out Qc. -0.036*** -0.048*** -0.036*** -0.048*** [0.001] [0.002] [0.001] [0.002] regional UR -0.002*** -0.002*** -0.002*** -0.002*** [0.000] [0.000] [0.000] [0.000] union status union 0.131*** 0.137*** 0.131*** 0.137*** [0.000] [0.000] [0.000] [0.000] year 1998 -0.001 -0.010*** -0.001 -0.010*** [0.001] [0.001] [0.001] [0.001] 1999 0.005*** -0.001 0.005*** -0.002** [0.001] [0.001] [0.001] [0.001] 2000 0.014*** 0.010*** 0.013*** 0.009*** [0.001] [0.001] [0.001] [0.001] 2001 0.020*** 0.017*** 0.019*** 0.016*** [0.001] [0.001] [0.001] [0.001] 2002 0.024*** 0.025*** 0.024*** 0.024*** [0.001] [0.001] [0.001] [0.001] 2003 0.029*** 0.030*** 0.028*** 0.029*** [0.001] [0.001] [0.001] [0.001] 2004 0.034*** 0.035*** 0.034*** 0.034*** [0.001] [0.001] [0.001] [0.001] 2005 0.040*** 0.040*** 0.040*** 0.039*** [0.001] [0.001] [0.001] [0.001] event of layoff -0.132*** -0.192*** -0.382*** -0.491*** [0.001] [0.001] [0.006] [0.006] years since laoff T2 -0.040*** -0.040*** -0.042*** -0.043*** [0.000] [0.000] [0.000] [0.000]

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T3 -0.079*** -0.081*** -0.082*** -0.084*** [0.000] [0.000] [0.000] [0.000] T4 -0.119*** -0.122*** -0.122*** -0.125*** [0.000] [0.000] [0.000] [0.000] T5 -0.159*** -0.164*** -0.161*** -0.168*** [0.001] [0.001] [0.001] [0.001] dT2 (interacted) 0.106*** 0.159*** [0.004] [0.004] dT3 (interacted) 0.135*** 0.186*** [0.004] [0.004] dT4 (interacted) 0.146*** 0.193*** [0.004] [0.004] dT5 (interacted) 0.154*** 0.199*** [0.005] [0.005] prior earnings earnings 25-40 k -0.017*** -0.047*** -0.017*** -0.047*** [0.001] [0.001] [0.001] [0.001] earnings 40-55 k -0.061*** -0.091*** -0.061*** -0.090*** [0.001] [0.001] [0.001] [0.001] earnings 55-70 k -0.097*** -0.124*** -0.097*** -0.124*** [0.001] [0.001] [0.001] [0.001] earnings 71-85 k -0.106*** -0.150*** -0.105*** -0.150*** [0.001] [0.001] [0.001] [0.001] earnings 85-100 k -0.077*** -0.093*** -0.077*** -0.093*** [0.001] [0.001] [0.001] [0.001] earnings 100+ k -0.040*** -0.052*** -0.040*** -0.051*** [0.001] [0.001] [0.001] [0.001] Constant 0.732*** 0.745*** 0.738*** 0.752*** [0.002] [0.002] [0.002] [0.002] Observations 5983380 4724375 5983380 4724375 R-squared 0.171 0.175 0.172 0.178

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Table 5

summary of interacted effects - treatment versus control group logit equation (point estimates) linear probability equation Male Female Male Female Male Female Male Female layoff dummy -2.033 -2.649 -0.382 -0.491 age groups no layoff laid off no layoff laid off age4549 2.902 2.995 0.51 0.258 age4549 0.31 0.323 0.128 0.087age5054 1.546 1.632 -0.313 -0.812 age5054 0.223 0.232 0.005 -0.096age5559 0.439 0.573 -1.309 -1.956 age5559 0.055 0.077 -0.219 -0.357age6064 (omitted) -2.033 -2.649 age6064 -0.382 -0.491 interactions dage4549 -0.359 -0.088 dage4549 0.2 0.255 dage5054 0.174 0.205 dage5054 0.164 0.163 dage5559 0.285 0.12 dage5559 0.108 0.057 years since layoff no layoff laid off no layoff laid off t1 (omitted) -2.033 -2.649 -0.382 -0.491t2 -0.54 -0.605 -1.661 -1.991 -0.042 -0.043 -0.318 -0.375t3 -0.955 -1.05 -1.738 -2.113 -0.082 -0.084 -0.329 -0.389t4 -1.317 -1.44 -1.914 -2.326 -0.122 -0.125 -0.358 -0.423t5 -1.647 -1.796 -2.093 -2.538 -0.161 -0.168 -0.389 -0.46 interactions dt2 0.912 1.263 0.106 0.159 dt3 1.25 1.586 0.135 0.186 dt4 1.436 1.763 0.146 0.193 dt5 1.587 1.907 0.154 0.199

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Appendix Table 1

Sample Exclusions for Construction of Cohorts of Workers at Risk of Layoff

1996 1997 1998 1999 2000 2001 2002 Full LAD 4186120 4255015 4319175 4412095 4483410 4594920 4628290 Filed return 2615045 2664310 2681825 2708715 2769995 2774175 2798765 % of original sample (62.47) (62.62) (62.09) (61.39) (61.78) (60.37) (60.47) Age range 1038425 1082915 1121050 1165545 1221775 1259520 1306645 % of original sample (24.81) (25.45) (25.96) (26.42) (27.25) (27.41) (28.23) Still Living 1033935 1078590 1116765 1161135 1217245 1254930 1301955 % of original sample (24.70) (25.35) (25.86) (26.32) (27.15) (27.31) (28.13) Residing in Canada 1030290 1074800 1112890 1157000 1212680 1250200 1296740 % of original sample (24.61) (25.26) (25.77) (26.22) (27.05) (27.21) (28.02) Exclude Self-Employed 870320 904555 932900 967160 1011575 1042635 1082960 % of original sample (20.79) (21.26) (21.60) (21.92) (22.56) (22.69) (23.40) Earnings>15k 469630 483760 500120 517020 540300 571045 603245 % of original sample (11.22) (11.37) (11.58) (11.72) (12.05) (12.43) (13.03) Exclude receipt of Transfer Income 318485 332280 346465 362900 383465 409510 432170 % of original sample (7.61) (7.81) (8.02) (8.23) (8.55) (8.91) (9.34) Exclude Students 316345 330250 344445 360865 381360 407380 430195 % of original sample (7.56) (7.76) (7.97) (8.18) (8.51) (8.87) (9.29) LAD-EI Check 314685 328415 342525 359275 379810 405165 427970 For regular EI (7.52) (7.72) (7.93) (8.14) (8.47) (8.82) (9.25)

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