Preliminary, please do not quote.
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HOW WERE CANADIAN LABOUR MARKET
TRANSITIONS AFFECTED BY 1996 EMPLOYMENT
INSURANCE REFORM?
KAILING SHEN
Abstract. The 1996 Employment Insurance (EI) reform involved
one of the most significant changes of the Canadian EI program in
its history. It was designed to speed up the re-employment process
of job losers.
This paper is an evaluation of this reform’s impact on labour
market transitions. In particular, I examine the impact of this
reform on the distributions of both employment and unemployment
spells of non-seasonal and seasonal sectors, taking the multiple
changes in this reform as a package.
I use an underlying discrete proportional hazard model combined with a heaping effect measurement model. The spell data
comes from the confidential Survey of Labour and Income Dynamics. This study found some evidence that the 1996 EI reform might
have discouraged tailoring behaviour and on average, the probability for an employment spell to last more than 50 weeks increased
and the probability for an unemployment spell to last more than
50 weeks decreased after the reform, both in non-seasonal as well
as seasonal sectors. This study also revealed the impact of the
1996 EI reform distributed unevenly across different worker and
job types.
Date: May 18 2004.
I would like to thank David A. Green for answering my numerous questions and
providing continuous guidance in this work. I would also like to thank Craig Riddell,
Shinichi Sakata, Thomas Lemieux, Nicole Fortin, and Paul Beaudry for continuous
guidance, help, and encouragement. Assistance of Lee Grenon, James P. Croal
of the Research Data Centre at the University of British Columbia in using the
SLID data is highly appreciated. Special thank for Michel Y Bédard of the Human
Resources Development Canada(HRDC) for helping me get EI-region related data.
Any remaining errors are my own. The research and analysis are based on data from
Statistics Canada and the opinions expressed do not represent the views of Statistics
Canada. email: kailings@interchange.ubc.ca address: Department of Economics,
the University of British Columbia.
1
2
1. Introduction
First introduced in 1940, Employment Insurance (EI) program has
been the most prominent federal policy in Canada’s labour market
since then. Statistics shows there are 1.9 million new EI claims for
income benefits in year 2000/01, and 88% of paid workers were EI
benefit eligible as of December 20001.
To accommodate economic growth and fluctuation, Canada’s EI program has experienced a series of changes over the years. Studies on the
EI program’s impact have provided important evidence for these EI
program changes. Evaluation of these changes is also an important
part of EI studies. The 1996 EI reform involved one of the most significant changes of the Canadian EI program in its history. This paper
examines the impact of 1996 EI reform on both employment and unemployment spell distributions.
The 1996 EI reform is known as shifting the focus of EI program from
subsidizing unemployed workers to speeding up the re-employment
process of job losers. Four of the most noticeable changes in this
reform are: replacement of week system by hour system, reduction
of maximum benefit duration, longer entrance requirement for new
entrants/re-entrants, and introduction of worker-side experience rating.
While there have been many empirical studies on Canada’s EI program’s impacts on labour market transitions, for example, Green and
Riddell(1993, 1997), Baker and Rea (1998), Ham and Rea (1987). Most
of them focus on only one type of spell, either employment or unemployment. But, evidence on both of these two types of spells is needed
to understand the impact of the 1996 EI reform’s impact. Why? First,
the overall EI program expense depends on these two types of spells
jointly. While the distribution of employment spells determines the
entry rate of EI benefit collection spells, the distribution of these EI
collection spells are closely correlated with unemployment spells. Second, workers’ welfare and the health of the economy depend on both
types of spells. Theoretically, this EI reform could reduce unemployment spell duration but still indirectly hurt subsequent employment
stability if job-match quality deteriorates due to poorer search.
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EI Monitoring and Assessment Report
EI REFORM AND LABOUR MARKET TRANSITIONS
3
Therefore, the key feature of this study is that I study employment
and unemployment spells jointly. In this study, employment and unemployment spells are defined coherently, constructed from a common
data source, and analyzed using the same econometric setup. Thus,
this paper allows us to combine evidence from both types of spells to
evaluate the impact of the 1996 EI reform on the overall labour market
transitions in a straightforward way.
The second feature of my study is that the multiple simultaneous
changes in this reform are treated as a package. Most empirical EI
studies have been focused on the marginal effect of individual EI parameters. While others, such as Clark and Summers (1982), simultaneously consider multiple aspects of an EI program. I choose, instead, to
take the multiple changes in 1996 EI reform as a single package because
it is infeasible to separate individual impacts as they interact with each
other.
The third feature of this study is that spells are grouped according
to seasonality. Seasonal2 and repeated3 EI users, as defined by the
EI administration, make far more frequent EI claims than the average
workers, whose probability to make EI claims in one year period is only
12%4. Furthermore, seasonal users constitute about 80% of repeated
EI users. These seasonal repeated EI users5 alone made 30%6 of the
EI claims in 1995/96. In this sense, EI financial resources have been
allocated to seasonal industries disproportionately.
Green and Sargent (1998) pointed out that seasonal workers, unlike
non-seasonal ones, face a known, high probability of job separation and
re-employment and they tend to deal with same employers repeatedly.
These seasonal workers counts on EI benefits to be a substantial and
regular part of their annual income. The pre-reform EI program is often
seen to have indirectly subsidized seasonal firms without fulfilling an
insurance purpose, thus it prevented both physical and human capital
relocation. To address this problem, the 1996 EI reform introduced
2Seasonal
EI uses are defined as “people who had started previous claims at
about the same time of year as their current one”.
3Frequent EI users are defined as “individuals who had three or more claims
for regular or fishing benefits within the previous five years”.
4This is calculated as the ratio of new claims over the labour force size in a
typical year in 1990s.
5Seasonal repeated EI users are defined as both seasonal and frequent EI users.
These definitions are used for official EI program annual evaluation.
6This figure is derived from 1998 EI Monitoring and Assessment report.
4
KAILING SHEN
worker-side experience rating, which made repeated EI usage much
less profitable. Thus, the 1996 EI reform is expected to have different
impacts on seasonal and non-seasonal spells.
This paper’s data comes from confidential Survey of Labour and Income Dynamics (SLID) data. Eight groups of sample spells are created:
employment and unemployment spells in non-seasonal and seasonal
sectors during the pre-reform and post-reform periods. An underlying discrete proportional hazard model combined with a heaping effect
measurement model is estimated in each of these eight group of sample
spells.
The major problem in identifying the EI reform’s impacts using a
pre- and post- reform comparison is due to the improving macroeconomic conditions over the study period. The difference in labour
market transitions between post-reform and pre-reform periods could
be due to gradually improving macroeconomic conditions as well as
changes made in the 1996 EI reform. In this study, I used raw unemployment rate derived from Labour Force Survey as control for macroeconomic conditions. Lemieux and MacLeod (2000) found evidence that
workers propensity of making EI claims is affected by their own experience after the change in the system in earlier 1970s. If similar things
happened in this 1996 EI reform, then the labour market might need
several years to adjust to changes of 1996 EI reform fully, although
majority of the EI reform’s changes have come into effect in January
1997. In this sense, the evidence found in my study only reflect the
1996 EI reform’s impacts on the labour market transitions in the short
run.
This study found some evidence that the 1996 EI reform might have
discouraged tailoring behaviour and on average, the probability for an
employment spell to last more than 50 weeks increased and the probability for an unemployment spell to last more than 50 weeks decreased
after the reform, both in non-seasonal as well as seasonal sectors. This
study also revealed the impact of the 1996 EI reform distributed unevenly across different worker and job types.
The rest of this paper is organized in six parts. Section 2 outlines
1996 EI reform’s four major changes and their expected impacts; section 3 discussed how I construct sample spells from SLID as well as
descriptive statistics of these sample spells; section 4 discuss the hazard
model with heaping effect that I used in estimation; section 5 presents
the estimation results; section 6 presented the simulation results using
EI REFORM AND LABOUR MARKET TRANSITIONS
5
the actual worker and job types in the data; section 7 briefly review
related studies and concludes.
2. Four major changes in the 1996 EI reform
Based on Variance Entrance Requirement (VER) system, 1996 EI
reform made four major changes that affect EI benefit duration in the
regular benefit part of EI program.
VER is a feature that distinguish Canada’s EI program from similar
programs of other countries. In this VER system, unemployed workers EI benefit durations depend on both previous employment history
as well as local EI region’s unemployment rate. Higher local unemployment rate will lead to longer EI benefit duration and shorter EI
entrance requirement.
VER leads to build-in correlation between EI generosity and local
labour market, which makes controlling for local labour market much
difficult in EI studies. It also leads to build-in lagged duration dependence between employment duration and subsequent unemployment
duration, which means any change in the EI program will potentially
affect distributions of both employment and unemployment spells as
well as their correlation.
It is based on such VER system that the 1996 EI reform introduced
the following four major changes that substantially altered the incentives of EI program.
2.1. replacement of week system by hour system. In the prereform week system, when applying for EI benefit, workers’ employment history is measured by calender week. Among the four types of
EI insurable jobs, (a)less than 15 hours per week, (b)15 to 34 hours
per week, (c)35 hours per week, and (d) more than 35 hours per week,
in the week system, group (a) jobs weren’t counted in EI benefit calculation. Moreover, for all calendar weeks with jobs whose weekly hours
above the 15 hours threshold, number of hours worked did not affect EI
benefit duration. To recognize development of workplace practice, the
new hour system counts every hour worked in every EI insurable jobs
in EI benefit calculation. The post-reform benefit duration schedule
is rewritten in terms of hours using 35 hours/week ratio based on the
pre-reform schedule.
6
KAILING SHEN
Although this revision lead to no difference in the EI benefit calculation for single-job holders with group (c) jobs, the revision did affect
other workers. The benefit of hour system is most obvious for multiple
job holders with only group (a) jobs. Single job-holders, workers whose
jobs belong to group (a) and (d), also benefit from the change. At the
same time, those who have jobs in group (b) now will need to work
longer to get same EI benefit as they could pre-reform. Besides these,
this week to hour change could also lead to higher participation rate
as individuals who are indifferent about whether to participate or not
could be attracted to the labour force if the jobs available to them are
mostly group (a) jobs.
2.2. Reduction of maximum benefit duration. The up-limit of
EI benefit duration pre-reform was 50 weeks and it is now 45 weeks.
Since EI benefit duration depends on workers preceding employment
history as well as local EI unemployment rate, only workers with more
than 40 EI employment weeks in a region with at least 10.1% unemployment rate would be eligible for more than 45 weeks of benefit
pre-reform. Therefore, from a static point of view, only these workers
are affected by this reduction of maximum benefit duration. But in a
dynamic view, the employment behaviour is endogenously determined
and longer EI benefit set in the EI schedule constitute contributes to
workers’ incentive for longer employment spells. Thus, the reduction
of maximum benefit duration could result in less number of cases that
are eligible for more than 45 weeks of EI benefit and thus to less stable
employment. In other words, the whole distribution of employment
spells could be negatively affected by this change.
2.3. Longer entrance requirement for new-entrants/re-entrants.
New-entrants/re-entrants constitute a special group of EI users. They
are defined as having less than 14 weeks pre-reform, or 490 hours postreform, of EI insurable employment and EI benefit collection, during
the 52 weeks preceding their current EI qualification period7. Under VER, new-entrants/re-entrants are treated differently as to their
entrance requirement8 . All other EI users’ entrance requirements depend on local unemployment rate but new-entrants/re-entrants face
uniform entrance requirement. Pre-reform, they would need 20 weeks
7EI
qualification period is used to calculated EI employment for benefit calculation purpose. It is defined as the 52 weeks period preceding the employment
separation, or since the beginning of last EI claim, whichever is shorter.
8The minimum EI insurable employment required to get any EI benefit.
EI REFORM AND LABOUR MARKET TRANSITIONS
7
of employment to be eligible for EI benefit. Post-reform, the entrance
requirement for new-entrants/re-entrants is 910 hours of employment,
which is equivalent to 26 weeks using 35 hours per week ratio. It is
in this sense, the entrance requirement for new-entrants/re-entrants
became longer post-reform.
The increased entrance requirement for new-entrants/re-entrants partly
offsets the positive labour market participation effect of hour system
as discussed above. It also partly offsets the potential negative impact of reduced maximum benefit weeks on employment stability as it
reenforce the incentives for strong labour market attachment.
Jointly these first three changes encourage workers to take occasional/irregular jobs or even less stable jobs while at the same time keep
strong labour market attachment. The whole distribution of employment and unemployment spells could be affected by these changes.
Moreover, these changes will bring differential changes across worker
types.
2.4. Introduction of worker-side experience rating. Worker-side
experience rating discouraged repeated EI users. It punished EI users
in two aspects, 1) replacement ratio9 by intensity rule; and, 2) EI benefit repayment conditions10 in clawback rule. The intensity rule cut
1% from the replacement ratio for each additional 20 weeks of benefit
workers collected in the last five years. Repeated EI users’s replacement ration could be cut to a minimum 50% from the standard 55%.
Since there is a upper limit on the absolute amount of weekly EI benefit
payment at $413, the intensity rule only has impact on workers whose
weekly earning is less than $826.
High weekly earning workers are mostly affected by the clawback
rule, which could make EI collection less profitable or even undesirable
for them. According to the clawback rule, every EI user who isn’t a
repeated EI user, is required to repay 30% of their EI benefit, or 30%
of their net income above the threshold of $48, 750, whichever is less
through the taxation system. The repayment condition is different for
EI users with more than 20 weeks of benefit, they will need to make
repayment at a lower threshold of $39, 000 and a maximum of 50% to
100% of EI benefit could need to be repayed.
9EI
weekly benefit is the product of average weekly earnings and the applicable
replacement rate.
10From the taxation system.
8
KAILING SHEN
Since most non-seasonal workers could not plan on their EI collection, worker-side experience rating affects seasonal workers most. seasonal workers will have to go back to work every year about the same
time, otherwise they will lose their jobs. Their planning horizon is finite — one year. Under pre-reform system, it makes sense for seasonal
workers to work just year maximum weeks, which are the number of
employment weeks that they could use to collect EI benefit till the
start of their next job season. Each additional week of employment
beyond their year maximum weeks means one less week of EI benefit
collection. The nature of seasonal jobs and the pre-reform EI program
jointly provide incentive for seasonal workers not to work longer than
their year maximum weeks.
Post-reform, the incentives changed and each additional week of employment of high weekly earning seasonal workers means not only one
less week of EI benefit collection but also less repayment in subsequent
years. Indeed, in some situations, they might be better off not to collect
EI benefit even their job season hasn’t come yet. Although experience
rating was completely cancelled in May 2001, its impact is expected
to be substantial to high-income repeated users for even the first few
years 11.
11To
demonstrate the impact of clawback rule on high weekly income seasonal
workers, let’s assume a seasonal worker who typically work 28 weeks each year and
then collect 22 weeks of EI benefit in the rest of the year. If this worker normally
receives $2, 000 per week on his job, then he will get EI benefit at the maximum of
$413. Let’s call this option 1.
In this option, his EI benefit is $413×22 = $9, 086 in both years and his net annual
income before repayment is $20, 00×28+$9, 086 = $65, 086 in both years. The year
one clawback will take min{($65, 086 − $48, 750) × 30% = 4900, $9, 086 × 30% =
$2, 725} = $2, 725 away and left him with $62, 361. In year two, since he had
collected more than 20 weeks, he will face more severe clawback rule, which will
take min{($65, 086 − $39, 000) × 30% = 7825.8, $9, 086 × 50% = 4543} = $4543
away and left him with $60, 543.
In option two, suppose he only collect 19 weeks in each year. Then his EI benefit
is now $413×19 = $7, 847 in both years and his net annual income before repayment
is $20, 00 × 28 + $7, 847 = $63, 847 in both years. The year one clawback will take
min{($63, 847 − $48, 750) × 30% = $4, 529.1, $7, 847 × 30% = $2, 354.1} = $2, 354.1
away and left him with $61, 492.9. In year two, since he had collected less than 20
weeks, he will face same clawback rule as year one, which will left him with another
$61, 492.9.
In terms of total income in two years, option two will give him $81 more than
option one. The benefit from less severe clawback rule in option two will continue
to realize in year three and onward. Each year, he face more severe clawback rate
in option one than option two, thus he will get even less with choosing option one.
Moreover, with option two, he will have 3 weeks with no regular seasonal job, no
EI REFORM AND LABOUR MARKET TRANSITIONS
9
Seasonal workers are often regarded as having a higher potential to
improve their labour market attachment. Therefore, the four changes
could have a stronger impact on seasonal sector than on non-seasonal
sector.
In short, each of the four major changes in the regular EI benefit
program has its own targeted group and expected impacts upon that
group. At the same time, they also interact with each other, sometimes
offset each other. Jointly, they are meant to strength workers’ attachment to the labour market and discourage repeated usage of EI program. It is infeasible to separate individual impacts in such situation.
For example, study on the impact of increased entrance requirement
on new-entrants/re-entrants has to be considered together with easier
access to EI benefit due to the week to hour system change. Moreover,
there is reason to expect they have different impacts on seasonal and
non-seasonal sectors. Therefore, this study will take all the changes
in the 1996 EI reform as a package and study the net impact of this
reform on the distributions of employment spells and unemployment
spells for non-seasonal and seasonal sectors.
3. Data
Empirical studies on Canada’s EI program using Survey of Labour
Income Dynamics (SLID) have just started recently. Labour Market Activity Survey (LMAS), Canadian Out of Employment Panel
(COEP), and administrative data of EI program have been the major data sources used in EI studies in the literature.
In general, administrative data has more accurate EI collection information than survey data like SLID. As any other survey data, SLID
does not have as accurate EI information as administrative data. Researchers have to derive their own EI information based on EI rules
and labour market experience of the correspondents. But for all unemployed workers, only those made EI claims are included in the administrative data. After workers left EI payment, the administrative data
have no further information on their labour market activities unless
they come back to claim EI again. If an EI user has stopped collecting
EI benefit, we wouldn’t know whether the person, is still unemployed,
EI benefit. The opportunity cost of working is zero, so he has much more incentive
to work post year maximum week.
10
KAILING SHEN
becomes self-employed, has returned to school, or has left the country, etc. In this sense, EI administrative data provides incomplete and
selected information on the labour market transitions.
On the other hand, survey data usually has far more information
about each correspondents than administrative data. For example,
SLID not only has correspondents detailed job and job absence data, it
also has education, martial, mobility, family, and income data. Among
all survey data, SLID is the only one covers both pre- and post- 1996 EI
reform period. Therefore, SLID has strong potential in 1996 EI reform
evaluation.
I constructed eight groups of sample spells from SLID, employment
and unemployment spells in non-seasonal and seasonal sectors during
the pre-reform and post-reform periods. Given only paid employment is
EI insurable, these spells are constructed to focus on paid employment
labour force’ transitions between two states:
(1) employed with paid jobs only;
(2) unemployed due to loss of paid jobs.
My process of SLID data could be summarized in four steps: 1)construct person-specific observation windows; 2) create employment and
unemployment spells; 3) designate person, job, seasonality information
to each spell; 4) derive EI parameters for each week of each spell.
3.1. Construct person-specific observation windows. Although
this study focuses on paid-employment labour force and only studies
two states of the workers, employed with only paid jobs, and unemployed due to loss of paid jobs, each correspondents could experience
many other states during survey period. For example, schooling, retirement, self-employed, home-production, etc.
In order to make the final sample as accurate as possible, and at the
same time, representative of the experience of ‘ordinary’ workers who
are actively participating the paid employment labour market — the
group of workers that EI is meant to serve, person-specific observation
windows are constructed by excluding five types of periods from the sixyear survey panel time horizon. As a result, a correspondent could have
zero, one, or multiple observation windows, each of which is defined by
a start calender date and an end calender date.
These five types of excluded periods are:
EI REFORM AND LABOUR MARKET TRANSITIONS
11
(1) years out of the ten provinces of Canada or missing labour information;
(2) periods worked in non-paid jobs;
(3) from first year to last year of work disabled;
(4) from first month to last month of some schooling;
(5) from the date that a job spell censored due to inconsistency
follow-up information to panel end.
3.2. Create employment and unemployment spells. By construction, employment spells are spells of paid employment spells and unemployment spells are potential EI benefit collection spells that following
the paid employment spells. For each correspondent, each calender
date that he/she is recorded as working on paid jobs is flagged using
both job and job absences information. Employment spells are then
constructed using these flags,
According to Canada’s EI program, when unemployed workers’ applications for EI benefit are approved, they will not get EI payment for
the first 2 weeks of their unemployment spells. This is called waiting
period. For each EI user, the 3rd week of unemployment spell is actually his/her first week of EI benefit. Therefore, any two employment
spells separated by no more than 14 days are connected as one which
starts at the start date of the first spell and ends at the end date of
the second spell. On the other hand, unemployment spells following
employment spells are at least 15 days long. Therefore, the first 14
days, which aren’t informative, are deleted for estimation purpose.
Only fresh employment spells and unemployment spells started within
observation windows are selected. Fresh spells are selected mainly for
two technical reasons: 1) it is problematic to assume behaviour of ongoing spells while out of observation windows are the same as when
they are inside observation windows under duration dependence assumption. 2) It is impossible to derive EI data for ongoing spells at
the start of the panel because EI benefit duration calculation needs
minimum 52 weeks of previous labour market history, which means
any spells started within the first year of the panel could not be used
and they weren’t used in the estimation.
Each selected spell is further censored if its end date is beyond its
corresponding observation window’s end date. In this manner, the
study is focused on the majority of workforce who works on paid jobs
only while recognizing transitions into and out of this workforce.
12
KAILING SHEN
The sample scheme used here is ‘endogenous’ or ‘choice-based’ according to Lancaster and Imbens (1990) which examined how different
sampling schemes could affect the inference. Using the current sample
scheme, the sample spells are not representative for the whole population. Instead, they are representative of workers who experience
labour market transitions. In other words, these spells are weighted
according to workers’ probabilities of experience transitions between
employment and unemployment states. Workers who tend to have
more frequent transitions are weighted more and their behaviours have
more important impact on the results of this study. Those workers who
stay employed all the time are least represented in the results. This
sample scheme thus allow the results closer to the real composition of
EI users.
The sample weight could also be affect by the panel nature of SLID.
Although each panel consists of randomly selected individuals at the
start of the panel, the composition of the group of workers that experience labour market transitions could be changing over time if more
and more workers have found stable jobs and there aren’t enough new
entrants to the labour force. To prevent such composition-caused bias
in estimation, pre-reform and post-reform samples are selected from a
common time-frame relative to the panel they belong.
Specifically, I use both panels of currently available SLID in this
study. Pre-reform sample are those panel one (1993–1998) spells started
in the period from 1994 July, the time of a previous substantial EI
program change, to 1996 June, and post-reform are those panel two
(1996–2001) spells started in the period from 1997 July to 1999 June.
3.3. Designate person, job, seasonality information to each
spell. Personal characteristics, such as age, gender, martial status,
education, kids, etc, were matched with each spell given the year of
the spell. Then, only those spells with age in the range of [20, 54] are
selected.
To designate job property to each spell, a single most relevant job/job
absence was chosen for each spell even though it could contain multiple
jobs/job absences.
For any employment spell, among all jobs that started at the beginning of the spell, called start jobs, the one last the longest was chosen.
In case there are job absences ended at the beginning of an employment
spell, among the jobs corresponding to those job absences, the one that
EI REFORM AND LABOUR MARKET TRANSITIONS
13
started the earliest was chosen and its job property at the start of that
employment spell was used.
For any unemployment spells, among all jobs that ended at the beginning of the spell, called end jobs, the one last the longest was chosen.
In case there are job absences started at the beginning of an unemployment spell, the job absence that ended the latest was chosen and the
job property of its corresponding job before the start of that unemployment spell was used.
Seasonality of each spell is determined by the seasonality of the
job/job absence chosen for job properties. Seasonality of each job depends on the job ending reason. A job is seasonal if and only if it ended
due to seasonal reason. Seasonality of each job absence depends on the
job absence reason. A job absence is seasonal if and only if it started
due to seasonal reason.
As a result, seasonal employment spells are those employment spells
who started with a seasonal job which lasted the longest among all start
jobs, or started due to an ending of a seasonal job absences whose corresponding job has lasted the longest. Seasonal unemployment spells are
those unemployment spells who started because a seasonal job ended,
and this seasonal job is the longest one among all end jobs, or started
due to a seasonal job absence.
The choice made here regarding seasonality might be the simplest
one possible. In theory, such seasonality designation is problematic for
employment spells because most job’s seasonality depends on whether
they are censored. If censored, jobs are flagged as non-seasonal unless
they have a previous seasonal absence. For unemployment spells, since
all end jobs already ended, their seasonality are clear.
But in practice, the problem for employment spells’ seasonality is
much less severe. The pre-reform spells are those started within the
period from 1994 July to 1996 June in panel one. Only those employment spells whose start jobs didn’t end in December 1998 have the
seasonality flag problem. Given those jobs should be at least 2 and
half years long, they are most probably non-seasonal jobs rather than
seasonal jobs. Similarly, the post-reform spells are those started within
the period from 1997 July to 1999 June in panel two — exact the same
position and length as pre-reform group in panel one. The probability
of a seasonal job to last more than 2 and half years long is considered
to be negligible.
14
KAILING SHEN
Spell’s seasonality designation rule used here is very similar as in
Green and Sargent(1998) which also designate seasonality according to
job separation reason. It is obviously quite different in nature from
seasonality classifications often used to describe the extent of repeat
EI usage, as in EI Monitoring and Assessment report and Gray and
Sweetman (2001), where EI users’ seasonality depends on posterior
knowledge about labour market transitions pattern. Considering most
workers know whether their chosen jobs will end for seasonal reasons
or not, spells’ seasonality as defined in this study is effectively set a
priori, thus a proper explanatory for labour market transitions.
3.4. Derive EI information for each week of each spell. Derivation of EI variables for each week of each spell is necessary even though
SLID does allow identification of whether each worker has received EI
benefit in monthly term. Even if this information is in the desired
weekly term and there is reasonable accuracy in such posterior knowledge, it would be affected by actual take-up rates and still miss the
potential EI benefit duration available for each week employed and unemployed, which is critical to study how EI incentives affect labour
market transitions.
The contribution of each additional week to EI benefit is not necessarily the same because each employment spells could be formed by
several jobs and weekly hours of each job could vary over time. Therefore, I used weekly hours of every related job within each employment
spell to derive the EI employment accumulated at each week. For prereform spells, the EI employment is in terms of calender weeks, for
post-reform spells, hours.
For all employment spells, the number of EI benefit weeks that
worker could get if the spell ended at that week is then calculated week
by week using local EI unemployment rate used by the EI administration and applicable schedule. For each week in each employment spell,
I also calculated the applicable EI entrance requirement, EI year maximum flag, and EI maximum flag. These two flags signal the workers
have accumulated the maximum number of EI benefit weeks that are
useful for them. Seasonality matters in the employment spells estimation setup in the sense that EI year maximum flag is used for seasonal
spells only and EI maximum flag for non-seasonal spells only.
For seasonal workers, assuming they will have to return back to work
the same time of the year every year, then they only need enough EI
benefit to cover them till the start of next season. Therefore, when
EI REFORM AND LABOUR MARKET TRANSITIONS
15
the number of weeks already worked and the number of EI benefit
weeks accumulated reached 53, a seasonal worker is regarded as having
reached year maximum and EI year maximum flag is set to be 1 for
that week. Non-seasonal workers do not have 1 year planning horizon.
When the number of EI benefit weeks accumulated has reached maximum given the EI schedule, that non-seasonal worker is regarded as
having reached EI maximum week, therefore EI maximum flag is set
to be 1 for that week.
It should be point out that, at this point, special provisions of the
EI system for new-entrants/re-entrants, repeaters and renewals as considered in Kidd and Shannon (1996) are still to be incorporated in the
EI variable constructions.
Following Green and Sargent (1998), I then used these three timevarying EI variables to capture how EI incentives could affect the probability of employment separation.
For unemployment spells, the number of EI benefit weeks eligible
was derived first. Then following Ham and Rea (1987), for each week,
the number of EI benefit weeks left are calculated. Based on these
remaining EI weeks, several time-varying dummies are created to capture how the average level of the probability to leave unemployment
are related with the number of remaining weeks.
It has to be noted that workers’ local EI region is not readily available
in the original SLID data. To derive EI region, with special help from
SLID, I matched each worker’s corresponding end of year household’s
postal code with a conversion table kindly generated by HRDC. This
approach assume three things: 1)the worker didn’t change household;
2)that household didn’t move over the year; 3)postal code boundaries
remain the same over the year.
After these four steps, eight groups of spells are created for examination, employment and unemployment spells in non-seasonal and seasonal sectors during the pre-reform and post-reform periods. Workers
are studied only when they were active in the labour market. After all,
it is EI reform’s impact on these flows of workers that the evidence of
this study tries to evaluate. Different from conventional interpretation
of unemployment spells, the unemployment spells here are best interpreted as potential EI collection spells and used to capture workers’
re-employment process.
16
KAILING SHEN
3.5. Descriptives statistics. Descriptives statistics in Table 1 and 2
captured the composition variation among the eight groups of spells.
Among these groups, the most obvious difference in sample size and
composition happened is across seasonality groups, not across states
or periods groups, and the across seasonality differences are relatively
independent of the state and period combination. which shows the
comparability of pre-reform samples with post-reform samples.
Given any state and period combination, 20% of the sample are seasonal ones which, in general, have less female, less education, more
concentrated in agriculture and manufacture industries, less public industry cases, in smaller firms12, much higher proportion to have worked
for the same employer in previous jobs, a bit less union coverage rates,
and less immigrants. Such pattern reflected in the descriptive statistics
makes it reasonable to expect different impacts of 1996 EI reform on
labour market transitions across seasonality groups.
4. Econometric model
Spells are assumed to be generated in two steps: first, the discrete
proportional hazard model generates the true underlying distribution
of spells; and, second a heaping model taking account of measurement
error that might generate the observed distribution of spells. The measurement model in step two explicitly models the heaping effect, which
means, in this study, with respect to “a priori expectations about the
smoothness of the distribution”, the observed data shows “an abnormal concentration of duration at certain dates”— 15th and last day of
each month (Torelli and Trivellato (1993)).
4.1. Underlying model: discrete proportional hazard model.
Four related functions could represent the distribution of the length of
a spell, T : 1) probability density function (pdf), ft , gives the probability of a spell to be t weeks; 2) cumulative distribution function
(cdf), Ft , gives the probability of a spell to end at or before week t; 3)
complementary cumulative distribution function (ccdf), F t , gives the
probability of a spell to be more than t week long; and finally, 4) hazard
function (hf), ht , gives the probability of a spell to end at week t given
it is at least t − 1 week long.
12This
is measured by firm size, i.e., number of employees at all locations.
EI REFORM AND LABOUR MARKET TRANSITIONS
17
Given any one of them, the other three distributional functions are
determined as well. The mathematical definitions and relationships of
these four functions are as follows,
(4.1)
(4.2)
ft ≡ P rob{T = t} =
ht
if t = 1
Qt−1
ht · s=1 (1 − hs ) if t ∈ {2, 3, . . . }
Ft ≡ P rob{T ≤ t} = 1 −
t
Y
(1 − hs )
s=1
(4.3)
F t ≡ P rob{T > t} =
t
Y
(1 − hs )
s=1
(4.4)
ht ≡ P rob{T = t|T ≥ t − 1} = ft /(1 − Ft ) = ft /F t
The discrete hazard model I used in this study directly modelled ht
for each week of each spell. But after estimation was done, ccdf and
pdf as well as hazard function are used to analysis the distributional
impact of 1996 EI reform.
In the discrete hazard model, ∀t ∈ {1, 2, . . . , 50}, hazard rate for
spell i at week t, hi,t , is given by H(.) as follows,
(4.5) hi,t = H(xi,. , t; θ, β) ≡ exp(− exp(θt ) · exp(cxi · βcx + tvxi,t · βtvx ))
Therefore, the baseline hazard rate for week t is given by exp(− exp(θt )).
Set of covariates that remain the same value within each spell is cx. Set
of covariates that are allowed to change weekly is tvx. In both cases,
covariates for spell i at week t shifts the hazard rate proportionally
relative to the baseline while still in the range of [0, 1].
Among others, Meyer (1990), Green and Sargent (1998), Green and
Riddell (2000), also chose such proportional hazard model to study EI’s
impacts on labour market transitions. The advantage of this model is
that time-varying EI variables could be entered into the model easily.
But different from above studies where the baseline, θ, was estimated
non-parametric using dummy variables, I imposed a parametric form on
the baseline. The baseline vector θ captures the duration dependency
18
KAILING SHEN
of the hazard function. By imposing parametric form on θ, I am able to
get explicit results on the slope and curvature of this baseline. Since the
measurement error will be taken care of in the second step of modelling,
it is reasonable to believe that the movement of the true behaviour
hazards from week to week follows some smooth pattern. Moreover,
parametric baseline specification is a more parsimonious setup.
The conventional parametric baseline specification on θt is to use
direct polynomial approximation approach, or “direct approach” as
called in Cooper (1972) which strongly recommended “Lagrangian interpolation polynomials” over “direct approach”. Cooper(1972) compared these two approaches and demonstrated their “algebraic equivalence and computational differences”, where it was shown that “the
direct approach can be hampered by multicollinearity in the artificial
variables created for computational purpose.” Following his suggestion, I use the ‘Lagrangian interpolation polynomials” in the baseline
specification of this study. The θt in (4.5) is defined as follows,
(4.6)
A
  
 

P

−1  α 
θ1
θ(α, 1)
1 1 1 12
1
2
1
1
1
α1
 θ2   θ(α, 2)   1 21 22 
1
2
 . =





1
26
26
α2 
=
×
×
..
 ..  
 . . . . . . . . . 
.
1
2
1 50 50
α3
1 501 502
θ50
θ(α, 50)
The only difference from conventional direct polynomial approximation is that I add matrix P in the transformation from α to θ. After
estimation, the derived baseline θ̂ should be the same whether direct
polynomial approximation is used or Lagrangian interpolation polynomials is used.
Using (4.6), (4.5) could be rewritten in terms of (α, βcx, βtvx ) as
(4.7)
hi,t = G(xi,. , t; α, βcx, βtvx ) ≡ exp(− exp(θ(α, t)·exp(cxi ·βcx +tvxi,t ·βtvx ))
Having specified hazard function hi,t , the underlying behaviour distribution for spell i is then fully determined. fi,t , Fi,t , F i,t could be
derived from hi,t using (4.1), (4.2) and (4.3) respectively.
4.2. Measurement model: heaping at certain dates. The measurement model then translates the true underlying distribution of
spells to observed distribution assuming a specific form of heaping
EI REFORM AND LABOUR MARKET TRANSITIONS
19
at 15th and last day of each month. The heaping assumed in this
study could be interpreted as a result of correspondents’ rounding-off
behaviour in , or institutional, for example, bi-weekly/monthly salary
system.
Given EI data is organized in weekly unit, heaping caused spikes in
hazard function could potentially obscure the “true” EI spikes. For
example, suppose a worker leaves employment as he has just reached
year maximum EI week in the underlying data generation process. But
due to heaping at monthly term, he is observed to leave at the end of
month which contains that year maximum EI week. The true behaviour
spike at year maximum EI week will not be observed unless we explicitly
model heaping effect.
Let γ1 be the probability of spells ending only been observed in the
week containing 15th and last day of the month, γ2 be the probability
of spells ending only been observed in the week containing last day of
the month. Then γ0 = 1 − γ1 − γ2 is the probability that spells ending
been reported in the week they ended.
Generally, heaping could happen in two directions, forward to the
next neighboring heaping weeks, and backward to the previous neighboring heaping weeks. This paper assumed forward heaping only.
Given fi,t from (??f1))and (γ0 , γ1 , γ2 )13, the probability density function of observed distribution of spell i, fei,t , is then given as follows,
(4.8)
fei,t = γ0 · fi,t + γ1 ·
X
s∈SB(t)
fi,s + γ2 ·
X
fi,s
s∈SM (t)
Where SM (t) is empty for all t except if week t contains the last
day of a month. If SM (t) is non-empty, then it is equal to the set of
weeks no later than week t and in the same calender month as week t.
For example, assume week 5, 11, 15, .. in a spell are the only weeks that
have the last of a month, then SM (1) = SM (2) = SM (3) = SM (4) =
∅, SM (5) = {1, 2, 3, 4, 5}, SM (6) = SM (7) = SM (8) = SM (9) =
SM (10) = ∅, SM (11) = {6, 7, 8, 9, 10, 11}, SM (12) = SM (13) = SM (14) =
∅, SM (15) = {12, 13, 14, 15}, . . .
SB(t) is similarly defined where heaping effect takes place in both
15th of the month and last day of the month. Again, using above example, weeks that have 15th of a month are week 3, 8, 13, .., then SB(1) =
13By
construction, (γ0 , γ1 , γ2 ) have to be in the range of [0, 1].
20
KAILING SHEN
SB(2) = ∅, SM (3) = {1, 2, 3}, SB(4) = ∅, SM (5) = {4, 5}, SB(6) =
SB(7) = ∅, SB(8) = {6, 7, 8}, . . .
The identification of heaping probabilities (γ0 , γ1 , γ2 ) is thus from
the smooth parametric baseline hazard function and variations in the
calendar properties of each spell, that is variations in SM (t) and SB(t).
4.3. Loglikelihood function. Given the pdf function of observed distribution of spells, fe(i, t), the contribution to the loglikelihood function
of observing spell i is then,
(4.9)
l(i, t) = fe(i, t)δi · (1 −
t
X
s=1
fe(i, s))1−δi
Where δi = 1 if spell i is complete and 0 if censored.
Obviously, if set {γ1 , γ2 } be zeros, the model will be the traditional
hazard model. In this sense, the combined model used here is more
flexible. Further, given most EI impact is captured using time-varying
covariates, this approach will allow more clear identification of EI spikes
from the calender heaping spikes.
5. Estimation results
In this part, the impacts of 1996 EI reform is discussed using the
estimation results directly. It is shown that the 1996 EI reform’s impacts on labour market transitions are different across seasonal and
non-seasonal sectors as well as different across different worker and job
types. Several patterns of the labour market transitions in the first 50
weeks are also shown to remain the same during the period.
In all estimation, a common omitted group is used for comparability
reason. They are workers who are male, married, household heads,
high school educated, in management industry, with firm less than 20
employees, have not worked for their current employers in previous jobs,
not covered by union of collective bargaining, no kids, not immigrants,
36 year old, with $13 hourly wage, local EI region’s unemployment rate
is 7%.
This omitted group looks like a mixture of typical seasonal workers and typical non-seasonal workers. The impacts of 1996 EI reform on this omitted group, as reflected by baseline hazard rates shift
EI REFORM AND LABOUR MARKET TRANSITIONS
21
post-reform, might not be representative of the sample, whether nonseasonal or seasonal. Therefore, simulation results are presented in
later section as well.
Estimated coefficients are presented in four tables (3-6). All spells
are censored at week 50 in estimations since EI incentives only explicitly change in the first year of spells. In each estimation, the set of
time-varying covariates in each estimation are used to capture EI effects only, while the set of time-constant covariates in each estimation
are used to capture worker and job characteristics’ impacts on spells’
distribution– winners and losers of the reform as well as sensitivity to
local unemployment rate. The estimated coefficients of these two sets
are thus summarized in different tables. Definitions of most of the variables used in the estimation are self-evident, except one, the EI region
monthly unemployment rate, which is in the raw form derived from
Labour Force Survey. Relative to the 3-month moving average seasonally adjusted unemployment rate used by EI administration, this
raw form of unemployment rate is a better control for macroeconomic
conditions with less correlation with EI variables.14
Assuming the omitted group and 12% local EI administrative unemployment rate, the impacts of EI incentives on hazard rates of prereform and post-reform employment spells are illustrated in figure 1.
Assuming the omitted group and 24 weeks of EI benefit available, the
impacts of EI incentives on hazard rates of pre-reform and post-reform
unemployment spells are illustrated in figure 2.
As another way of showing the estimated coefficients, F 50 for default types of spells and marginal impact on F 50 by changing covariate
are included in table 7 to 10. The results are mostly consistent with
estimated coefficients but in a more intuitive manner.
5.1. Non-seasonal Employment spells. For employment spells, positive coefficients will means higher probability of employment separation. The major impacts of 1996 EI reform on employment stability
(see table 3 and 4) are:
14The
unemployment rate that I derived for estimation is just the raw number
which equals to the ratio of number of unemployed worker over total number of
workers in the labour force. The administrative rate is problematic as control for
macroeconomic conditions as it is correlated with workers’ EI incentives under VER
and, moreover, the seasonal fluctuation of labour market condition is smoothed out
in the administrative rate.
22
KAILING SHEN
5.1.1. winners. The group of non-seasonal workers whose employment
stability improved more than the omitted group are those who are
single, not household head, less than high school educated, employed
in service and public industries, employed by firms whose sizes are in
[20, 100) and [100, 500) categories, worked for the employer previously,
had pre-school kids.
5.1.2. losers. The group of non-seasonal workers whose employment
stability improved less than the omitted group are those who worked
in manufacture industry, immigrants.
5.1.3. sensitivity to local labour market. Moreover, employment stability in the first 50 weeks became more sensitive to local unemployment
rate after the reform. As table 7 shows, probability for non-seasonal
employment spell to last more than 50 weeks, 4F 50 , will be 2.1% lower
for each 1% increase in local EI region’s unemployment rate after the
reform. This inverse correlation between employment stability and unemployment rate could be interpreted as showing “negative cyclical
impact on job match quality” as Bowlus(1993) has found using the
National Longitudinal Survey of Youth data. But this change could
also be caused by 1996 EI reform.
5.1.4. EI impacts. According to table 4, the current estimation didn’t
find any statistically significant EI effect within pre- or post-reform
group for non-seasonal employment spells. Any seeming impacts showed
in figure 1(a) and 1(b) are not statistically significant.
But this isn’t a complete proof for no impacts of 1996 EI reform on
non-seasonal employment spells. Although raw unemployment rate is
used in my study to control for macroeconomic conditions, the changes
in labour market transitions after the reform as shown in this study
could still be partially driven by macroeconomic conditions. Unemployment rate is only one commonly used measure of macroeconomic
conditions which could affect labour market transitions in many dimensions linearly or non-linearly. For example, Baker (1992) suggests
the interaction between worker type and hazard rate could be affected
by business cycle. If this is true, then the winners and losers in nonseasonal sector’s employment spells as discussed earlier could be caused
by the 1996 EI reform as well as the overall improvement of macroeconomic conditions. Another study using periods with same EI program
but different macroeconomic condition is then needed to complement
the current study.
EI REFORM AND LABOUR MARKET TRANSITIONS
23
5.2. Seasonal Employment spells.
5.2.1. winners. The group of seasonal workers whose employment stability improved more than the omitted group are those who are employed in agriculture, manufacture industries, employed by firms whose
sizes are above 20, immigrants.
5.2.2. losers. The group of seasonal workers whose employment stability improved less than the omitted group are those who worked for the
employer previously.
5.2.3. sensitivity to local labour market. Unlike non-seasonal case, the
sensibility of employment stability in the first 50 weeks with regard to
local unemployment rate didn’t change after the 1996 EI reform.
5.2.4. EI impacts. According to table 4, EI effects are statistically significant in both pre- and post- reform for seasonal employment spells.
And the EI effects changed over time (figure 1(c), 1(d)). Increasingly
seasonal workers are working beyond year maximum weeks now.
Pre-reform, seasonal workers tend to postpone leaving their employment before they could get EI benefit, which is consistent with theoretical prediction on EI entrance requirement effect. As shown in figure
1.c, employment hazard rate — conditional probability to exit employment state — is below the baseline before week of entrance requirement.
Furthermore, the closer to entrance requirement week from the left, the
larger the difference from the baseline. Then, once entrance requirement is met, the hazard rate jump back to the baseline quickly. Once
they passed the entrance requirement week, their employment separation process will no long be affected by EI incentives except at the year
maximum week. There is a significant spike at the year maximum week
that signals that seasonal workers did have tendency to leave employment once they have accumulated enough EI benefit to collect till their
next job season.
Post-reform, EI entrance requirement effect was still significant but
the sharp decrease of hazard rate just before entrance week disappeared
and the magnitude is smaller in weeks before entrance weeks as well.
The most dramatic change in EI effects for seasonal employment spells
happened is the way year maximum week affect the hazard rate. After
the reform, most workers will postpone their employment separation
until they have reached year maximum weeks. This phenomenon is
24
KAILING SHEN
absence in pre-reform period. Moreover, the sharp increase of hazard
rate in year maximum week disappeared post reform.
Overall, the shape of hazard function with EI incentives incorporated changed dramatically after the reform. One major factor behind
such change could be the introduction of experience rating which effectively force seasonal workers’ planning horizon to be five year long
and take away some of the disincentives of working long season. If
this hypothesis is proved, then the change in seasonal workers’ employment behaviour demonstrates the tailoring behaviour did exist in the
pre-reform period.
5.3. Non-seasonal unemployment spells. For unemployment spells,
positive coefficients mean higher probability of re-employment. According to table 5 and 6, the major changes in re-employment process
given seasonality after the 1996 EI reform are:
5.3.1. winners. The group of non-seasonal workers whose re-employment
process speed increased more than the omitted group are those who are
not household head.
5.3.2. losers. The group of non-seasonal workers whose re-employment
process speed increased less than the omitted group are those who are
female, in the service industry. Overall most of the covariates do not
affect re-employment speed whether pre-reform or post-reform.
This is different from employment separation process, where a much
larger set of covariates affect the process significantly.
5.3.3. sensitivity to local labour market. Also different from employment separation process, in non-seasonal sector, re-employment process
in the first 50 weeks is not statistically correlated with local unemployment rate after the reform.
5.3.4. EI impacts: EI eligibility. According to table 6, pre-reform in
non-seasonal sector, re-employment process is much quicker for workers
who have no EI benefit then for those who have EI benefit pre-reform.
After the reform, the difference in re-employment speed caused by EI
benefit eligibility disappeared.
EI REFORM AND LABOUR MARKET TRANSITIONS
25
5.3.5. EI impacts: EI benefit weeks. The current estimation didn’t find
any statistically significant EI effect within pre- or post-reform group
for non-seasonal employment spells. Any seeming impacts showed in
figure 2(a) and 2(b) are not statistically significant.
5.4. Seasonal unemployment spells.
5.4.1. winners and losers. Most types of seasonal workers’ re-employment
process speed increased less than the omitted group, except for single,
union covered workers. Before reform, re-employment process speed
was not affected by age, but after the reform, age is positively correlated
with re-employment process speed — older workers get re-employed
faster now. This is different form the non-seasonal case, where age is
negatively correlated with re-employment process speed whether prereform or post-reform.
5.4.2. EI impacts: EI eligibility. According to table 6, pre-reform in
seasonal sector, re-employment process is much slower for workers who
have no EI benefit then for those who have EI benefit pre-reform. After
the reform, the difference in re-employment speed caused by EI benefit
eligibility disappeared.
5.4.3. EI impacts: EI benefit weeks. But the pattern EI benefit weeks
affect re-employment process changed dramatically after the reform.
Another peak in hazard function emerged.
While pre-reform, a substantial proportion of seasonal workers will
just exhaust their EI benefit before return back to work, which is consistent with seasonal workers’ tendency to collect just year maximum
weeks when employed in the pre-reform period.
After the reform, a substantial proportion of seasonal workers will
leave at least 5 weeks of benefit unused when they return back to
work. The emergence of this second, larger peak might be a result of
the introduction of worker-side experience rating.
5.5. Estimated heaping probabilities. The estimated coefficients
show statistically significant heaping effect. Probability for ending of
employment spells to be reported in monthly term is estimated to be
above 30% and probability for employment spells to be reported in biweekly term is estimated to be 10% to 27%. This means less than half
of ending of employment spell were reported in weekly term. But with
26
KAILING SHEN
help of heaping effect measure model, I was still able to capture the EI
effect in weekly term.
It is interesting to notice that the estimated probability for ending
of unemployment spells to be reported in monthly term is zero, which
could be explained as workers were much more exact in reporting the
start of their jobs then reporting the end of their jobs. Further, the
estimated probability for ending of unemployment spells to be reported
in bi-weekly term is substantially higher in seasonal sector than in nonseasonal sector.
5.6. Estimated duration dependency by state and seasonality.
Not only 1996 EI reform have different impacts on labour market transitions across seasonality groups, the estimated duration dependencies15
of hazard function turned out to be very different across seasonality
groups as well, as illustrated in the baselines of figure 1 and 2.
There is little duration dependency in non-seasonal sector’s employment separation process but non-seasonal sector’s re-employment process still shows considerable duration dependency. The longer some
one unemployed, the lower his/her chance of getting re-employed.
For seasonal spells, the duration dependency shows obvious peak
in the middle of the study period which might due to the nature of
seasonal jobs.
On the other hand, we could not then conclude the hazard is higher
post-reform for overall even though the baseline hazard function shifted
up for the omitted group after the reform in non-seasonal employment
and unemployment spells. The composition of spells have to be considered.
6. Simulation using actual spells
Simulation using actual spells is mainly to understand how 1996
EI reform affected labour market transitions to the observed samples,
taking account of their composition in terms of worker and job. Such
results are especially important when the omitted group isn’t representative.
15As
mentioned in Lancaster(1989), estimated duration dependency will be affected by to what extend heterogeneity of the sample is accounted for.
EI REFORM AND LABOUR MARKET TRANSITIONS
27
6.1. simulation design. The observed impacts of 1996 EI reform on
pre-reform spells could be decomposed into two parts:
1. The true impacts of 1996 EI reform given pre-reform spells, which
could be measured by comparing simulated distributions of pre-reform
sample’s worker and job types using pre-reform set of coefficients,
1
1
2
2
(α1 , βcx
, βtvx
) to post-reform set of coefficients, (α2 , βcx
, βtvx
).
2. The change due to worker and job types composition change,
which could be measured by comparing simulated distributions of prereform sample’s worker and job types with post-reform sample’s worker
and job types using a common set of coefficients, pre-reform or postreform.
Average magnitude of each decomposed part of the impacts is captured in the differences of the weighted mean of pdf,f t , and ccdf, F t of
simulated distributions (figure 3 and 4).
The winners and losers in each decomposed part of the impacts of
1996 EI reform is then analyzed using OLS(table 11 to 14), which used
F 50 from two sets of F t as independent variables and using pre-reform
sample worker and job types x1 as explanatory variables. The two sets
of F t comes from simulated distributions of using pre-reform sample
spells’ worker and job types and two different sets of coefficients: prereform and post-reform. As a result, I got two sets of OLS coefficients,
b1 and b2 . Using these two OLS coefficients and means of pre-reform
and post-reform sample worker and job types variables, x̄1 and x̄2 , I
then captured the winners and losers in each steps of decomposition
(table 11-14).
6.2. simulation results.
6.2.1. Non-seasonal employment spells. After the reform, entrance requirement effect of EI disappeared which lead to the disappearance of
the peak in the pdf function. The observed increase in non-seasonal sector’s employment stability, 4F 50 = 7.47% is mainly caused by more
stable employment of workers, who are single, not household head,
union or collective bargaining coverage, which offset the less stable employment of workers, who are in the manufacture industry, female and
have post-secondary education.
28
KAILING SHEN
6.2.2. Seasonal employment spells. After the reform, a substantial workers in the seasonal sector choose to work more than their year maximum week. The obvious peak in the pre-reform seasonal employment
spells’ pdf function thus became considerably weaker. This change is
the major factor that lead to more stable employment post-reform in
the seasonal sector, 4F 50 = 5.99%.
6.2.3. Non-seasonal unemployment spells. After the reform, the disappearing of EI benefit exhaustion effect is the major factor that lead to a
quick re-employment process in non-seasonal sector, 4F 50 = −4.24%.
6.2.4. Seasonal unemployment spells. Seasonal workers’ re-employment
process became quick after the reform, 4F 50 = −3, 43%, mainly due
to the fact that after the reform, a substantial seasonal workers got reemployed when they still have some remaining EI benefit uncollected.
In short, the 1996 EI reform did have positive impacts on enforcing
workers labour market attachment in seasonal and non-seasonal sectors after controlling for composition change. At the same time, this
reform’s impacts are distributed unevenly across worker and job types.
7. Related studies, future works and conclude
This study is related to at least three areas in the literature. First,
empirical EI studies as surveyed in Welch (1977) and more recently
Holmlund (1997); Second, hazard model studies. Besides those already mentioned earlier, some important research in this area are Lancaster (1990), Heckman and Singer (1984), and Van den Berg (2000);
and third, economic theoretical models on labour market transitions.
Earlier studies in this area include Burdett (1978), Mortensen (1977),
Jovanovic (1984). There are also many other studies related to this
study, but it is beyond this paper to give a proper review of all the
studies that related to my study in a broader sense.
One group of studies that are closely related to this paper are those
which evaluate 1996 EI reform’s impacts from other directions. Their
results are generally consistent with findings of this study. For example, Green and Riddell (2000) studies 1996 EI reform’s impacts on
employment spells caused by week to hour system. They found that
seasonal employment spells didn’t become shorter after the reform if
comparing spells ended during first 3 quarters of 1997 with first 3 quarters of 1996. They also found non-seasonal employment spells weren’t
EI REFORM AND LABOUR MARKET TRANSITIONS
29
affected much by EI program either pre-reform or post-reform. Fortin
and Audenrode (2000) found some evidence of worker-side experience
rating on workers’ unemployment spells.
Based on these studies, there are several directions that this current study could be expanded. For example, in this current study
the multiple spells nature of the sample is not modelled, each spell’s
seasonality is treated as fixed, lagged duration dependence is ignored.
Extra elements could be added to the current econometric model to
accommodate a richer data generation process.
In summary, this current study shows the impacts of 1996 EI reform
on employment and unemployment spells are related and its impacts
differ between seasonal and non-seasonal spells, between different workers and job types. After controlling for composition change, macroeconomic condition change, this study found some evidence that the 1996
EI reform might have discouraged tailoring behaviour and on average,
the probability for an employment spell to last more than 50 weeks
increased and the probability for an unemployment spell to last more
than 50 weeks decreased after the reform, both in non-seasonal as well
as seasonal sectors. This study also revealed the impact of the 1996 EI
reform distributed unevenly across different worker and job types.
30
KAILING SHEN
References
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EI REFORM AND LABOUR MARKET TRANSITIONS
31
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32
KAILING SHEN
List of Tables
1 Descriptive statistics for employment spells
2 Descriptive statistics for unemployment spells
3 Estimators for employment spells: Non-EI effects
4 Estimators for employment spells: EI effects
5 Estimators for unemployment spells: Non-EI effects
6 Estimators for unemployment spells: EI effects
7 simualted probability for employment spells: non EI effects
8 simualted probability for employment spells: EI effects
9 simualted probability for unemployment spells: non EI effects
10 simualted probability for unemployment spells: EI effects
11 Decomposition of 4F 50 using OLS: non-seasonal employment
spells
12 Decomposition of 4F 50 using OLS: seasonal employment spells
13 Decomposition of 4F 50 using OLS: non-seasonal unemployment
spells
14 Decomposition of 4F 50 using OLS: seasonal unemployment
spells
33
34
35
36
37
38
39
40
41
42
43
44
45
46
List of Figures
1 Estimated impacts of EI parameters on hazard functions of
employment spells
2 Estimated impacts of EI parameters on hazard functions of
unemployment spells
3 Simulated pdf function ft
4 Simulated ccdf function F t
47
48
49
50
EI REFORM AND LABOUR MARKET TRANSITIONS
33
Table 1. Descriptive statistics for employment spells
number of spells
female
single
not household head
less than high school
high school
post-secondary
university or above
agriculture
manufacture
management
service industry
public industry
service industry
firm size [1,20)
firm size [20,100)
firm size [100,500)
firm size [500,+∞)
worked for the employer previously
union or collective
bargaining coverage
present of pre-school
kids
present of school-age
kids
present of young
adult kids
immigrant
age
aStandard
Employment spells
Non-seasonal
Seasonal
prepostprepostreform
reform
reform
reform
2829
2656
665
472
54.1%
54.5%
37.7%
31.8%
33.5%
35.5%
33.8%
36.7%
48.3%
49.0%
47.4%
42.9%
18.4%
16.0%
43.2%
30.8%
30.1%
31.5%
24.4%
34.7%
35.6%
36.7%
25.6%
28.3%
16.0%
15.8%
6.8%
6.2%
2.5%
3.4%
19.5%
23.1%
45.2%
43.0%
46.9%
47.3%
31.0%
29.5%
16.5%
10.5%
14.4%
13.9%
13.5%
15.1%
6.7%
4.9%
3.7%
1.7%
14.4%
13.9%
13.5%
15.1%
33.7%
32.6%
44.2%
54.7%
18.3%
15.9%
22.6%
17.4%
13.8%
13.3%
16.9%
9.4%
34.2%
38.2%
16.3%
18.5%
18.4%
12.6%
56.3%
36.4%
32.3%
32.8%
29.4%
30.2%
25.1%
25.9%
23.2%
17.2%
34.4%
34.6%
38.7%
31.5%
12.0%
11.9%
11.4%
11.1%
13.8%
13.3%
8.9%
10.1%
35.3 (9.3) 35.8 (9.3) 36.4 (9.6) 37.3 (9.3)
deviations in parentheses.
34
KAILING SHEN
Table 2. Descriptive statistics for unemployment spells
number of spells
female
single
not household head
less than high school
high school
post-secondary
university or above
agriculture
manufacture
management
service industry
public industry
service industry
firm size [1,20)
firm size [20,100)
firm size [100,500)
firm size [500,+∞)
worked for the employer previously
union or collective
bargaining coverage
present of pre-school
kids
present of school-age
kids
present of young
adult kids
immigrant
age
aStandard
Unemployment spells
Non-seasonal
Seasonal
prepostprepostreform
reform
reform
reform
2504
2400
788
565
55.0%
54.7%
35.2%
28.6%
32.0%
32.2%
34.2%
33.8%
49.6%
47.4%
43.9%
41.4%
19.7%
17.2%
44.4%
32.2%
29.9%
32.8%
24.8%
37.0%
34.8%
34.2%
23.8%
27.0%
15.6%
15.8%
7.0%
3.8%
2.8%
3.1%
18.7%
25.1%
44.4%
43.7%
48.0%
47.6%
30.7%
28.8%
14.9%
7.8%
14.4%
12.7%
14.7%
14.6%
7.7%
5.1%
3.6%
2.4%
14.4%
12.7%
14.7%
14.6%
34.3%
30.8%
46.1%
50.3%
17.1%
15.4%
22.8%
21.2%
13.5%
15.1%
15.5%
9.6%
35.1%
38.7%
15.6%
18.9%
18.9%
10.4%
53.3%
33.6%
34.4%
35.1%
30.1%
26.7%
25.1%
26.1%
24.4%
18.2%
34.7%
34.5%
37.4%
31.1%
12.1%
12.4%
11.0%
10.3%
13.5%
12.0%
8.1%
8.5%
35.6 (9.3) 36.6 (9.3) 36.1 (9.3) 36.0 (9.6)
deviations in parentheses.
EI REFORM AND LABOUR MARKET TRANSITIONS
35
Table 3. Estimators for employment spells: Non-EI effects
female
single
not household head
less than high school
post-secondary
university or above
agriculture
manufacture
service industry
public industry
firm size [20,100)
firm size [100,500)
firm size [500,+∞)
worked for the employer previously
union or collective
bargaining coverage
present of pre-school
kids
present of school-age
kids
present of young
adult kids
immigrant
age
hourly wage
EI region monthly
unemployment rate
bi-weekly reporting
probability
monthly
reporting
probability
aStandard
Employment spells
Non-seasonal
Seasonal
prepostprepostreform
reform
reform
reform
-0.157 (0.066)* -0.113 (0.074)
0.028 (0.120)
0.140 (0.141)
0.237 (0.074)* 0.046 (0.085)
0.033 (0.132)
0.096 (0.155)
0.391 (0.063)* 0.137 (0.072)† -0.043 (0.100) 0.017 (0.136)
0.374 (0.079)* 0.092 (0.095)
0.593 (0.120)* 0.131 (0.138)
-0.029 (0.073)
0.100 (0.076)
0.230 (0.133)† -0.254 (0.149)†
-0.037 (0.098) -0.025 (0.115)
0.294 (0.233) -0.284 (0.269)
0.514 (0.178)* 0.451 (0.169)* 0.272 (0.164)† 0.828 (0.215)*
0.188 (0.077)* 0.356 (0.081)* -0.048 (0.144)
0.234 (0.195)
0.186 (0.095)* 0.031 (0.108)
0.035 (0.167)
0.046 (0.231)
0.377 (0.124)* 0.164 (0.167)
0.159 (0.261)
0.140 (0.461)
-0.024 (0.080) -0.188 (0.092)* 0.038 (0.119)
-0.488 (0.155)*
-0.060 (0.094) -0.452 (0.115)* 0.252 (0.132)† -0.357 (0.210)†
-0.252 (0.077)* -0.279 (0.083)* -0.018 (0.148) -0.391 (0.189)*
0.204 (0.071)* 0.011 (0.094) -0.266 (0.095)* 0.169 (0.123)
0.088 (0.076)
-0.129 (0.084)
-0.055 (0.115)
-0.108 (0.167)
-0.015 (0.077)
-0.007 (0.084)
-0.171 (0.126)
0.275 (0.182)
0.011 (0.072)
-0.151 (0.102)
-0.200 (0.134)
-0.188 (0.117)
-0.141 (0.155)
-0.218 (0.198)
0.192 (0.065)*
-0.068 (0.101)
0.089
-0.003
-0.024
0.019
(0.085)
0.198 (0.091)* 0.108 (0.162) -0.431 (0.218)*
(0.004) -0.004 (0.004) -0.008 (0.006)
0.016 (0.008)*
(0.006)* -0.027 (0.006)* -0.047 (0.011)* -0.052 (0.015)*
(0.018)
0.066 (0.019)* 0.033 (0.024)
0.034 (0.030)
0.100 (0.027)*
0.167 (0.031)* 0.166 (0.041)*
0.270 (0.053)*
0.340 (0.022)*
0.350 (0.025)* 0.374 (0.033)*
0.300 (0.043)*
errors in parentheses. * denotes statistically different from zero at 5% significant
level. † denotes statistically different from zero at 10% significant level.
36
KAILING SHEN
Table 4. Estimators for employment spells: EI effects
=11 weeks before EI
entrance requirement
10-6 weeks before EI
entrance requirement
5-0 weeks before EI
entrance requirement
1-0 weeks before EI
entrance requirement
just get satisfied EI
entrance requirement
1 week after EI entrance requirement to
year maximum week
just get satisfied year
maximum
requirement
just get satisfied maximum requirement
1 week after EI entrance requirement to
maximum week
aStandard
Employment spells
Non-seasonal
Seasonal
prepostprepostreform
reform
reform
reform
0.047 (0.269) 0.254 (0.236) -0.356 (0.332) 0.213 (0.431)
-0.089 (0.270)
0.368 (0.260)
0.130 (0.213) -0.380 (0.302)
-0.020 (0.354)
0.127 (0.215) -0.502 (0.258)† -0.578 (0.328)†
-0.232 (0.261) -0.102 (0.292) -0.786 (0.614)
-0.053 (0.501)
0.054 (0.372) -0.420 (0.482) 0.686 (0.704)
0.277 (0.530)
-0.085 (0.230)
-0.040 (0.155)
-0.674 (0.198)*
0.478 (0.283)†
0.026 (0.447)
0.105 (0.160)
0.342 (0.540) -0.013 (0.442)
errors in parentheses. * denotes statistically different from zero at 5% significant
level. † denotes statistically different from zero at 10% significant level.
EI REFORM AND LABOUR MARKET TRANSITIONS
37
Table 5. Estimators for unemployment spells: Non-EI effects
female
single
not household head
less than high school
post-secondary
university or above
agriculture
manufacture
service industry
public industry
firm size [20,100)
firm size [100,500)
firm size [500,+∞)
worked for the employer previously
union or collective
bargaining coverage
present of pre-school
kids
present of kids
immigrant
age
hourly wage
EI region monthly
unemployment rate
bi-weekly reporting
probability
monthly
reporting
probability
aStandard
Unemployment spells
Non-seasonal
Seasonal
prepostprepostreform
reform
reform
reform
-0.105 (0.055)† -0.277 (0.057)* -0.100 (0.112) -0.346 (0.133)*
-0.018 (0.062) -0.005 (0.065) -0.160 (0.126)
0.272 (0.132)*
-0.337 (0.054)* -0.252 (0.056)* 0.191 (0.103)† -0.072 (0.114)
-0.155 (0.074)* -0.060 (0.074) -0.150 (0.111) -0.303 (0.125)*
0.070 (0.061)
0.084 (0.059)
0.110 (0.125) -0.061 (0.128)
0.017 (0.083) -0.014 (0.082)
0.591 (0.185)* 0.667 (0.256)*
-0.062 (0.155)
0.014 (0.139)
0.322 (0.168)† -0.720 (0.198)*
-0.035 (0.064) -0.067 (0.059)
0.313 (0.152)* -0.298 (0.176)†
-0.164 (0.085)† -0.069 (0.084)
0.557 (0.179)* -0.655 (0.212)*
-0.025 (0.094)
0.055 (0.109) -0.409 (0.267) -1.324 (0.379)*
-0.041 (0.074) -0.019 (0.077) 0.017 (0.117)
-0.160 (0.131)
0.143 (0.078)† 0.097 (0.077) 0.314 (0.129)* -0.398 (0.172)*
0.196 (0.063)* 0.081 (0.064) 0.191 (0.133)
-0.154 (0.153)
-0.043 (0.061) -0.068 (0.077) -0.026 (0.093) -0.066 (0.108)
0.325 (0.057)*
0.307 (0.058)* 0.255 (0.115)*
0.413 (0.140)*
-0.216 (0.071)* -0.218 (0.073)* -0.294 (0.122)* -0.040 (0.166)
-0.044
-0.055
-0.014
0.009
0.014
(0.063) -0.052 (0.063) 0.129 (0.117)
0.160 (0.136)
(0.074)
0.018 (0.078)
0.353 (0.161)* -0.070 (0.194)
(0.003)* -0.009 (0.003)* -0.005 (0.006)
0.018 (0.006)*
(0.004)* 0.013 (0.004)* 0.050 (0.011)* 0.022 (0.011)†
(0.015)
0.004 (0.014)
0.003 (0.023) -0.066 (0.026)*
0.093 (0.021)*
0.132 (0.020)* 0.318 (0.036)*
0.221 (0.042)*
0.000 (0.000)
0.000 (0.000)
0.000 (0.000)
0.000 (0.000)
errors in parentheses. * denotes statistically different from zero at 5% significant
level. † denotes statistically different from zero at 10% significant level.
38
KAILING SHEN
Table 6. Estimators for unemployment spells: EI effects
20-1 weeks before EI
benefit exhaustion
10-1 weeks before EI
benefit exhaustion
5-1 weeks before EI
benefit exhaustion
2-1 weeks before EI
benefit exhaustion
EI benefit exhaustion
week
Not EI benefit eligible
aStandard
Unemployment spells
Non-seasonal
Seasonal
prepostprepostreform
reform
reform
reform
0.081 (0.100)
0.146 (0.096) -0.308 (0.128)* -0.101 (0.171)
-0.075 (0.195)
-0.263 (0.200) -0.187 (0.198)
0.870 (0.222)*
0.405 (0.272)
0.330 (0.296) -0.049 (0.297)
-1.064 (0.520)*
0.228 (0.317)
-0.125 (0.402) 0.776 (0.316)*
0.847 (0.645)
0.159 (0.436)
0.364 (0.444) 0.612 (0.287)*
0.360 (0.722)
0.184 (0.056)*
0.033 (0.057) -0.261 (0.111)* -0.056 (0.124)
errors in parentheses. * denotes statistically different from zero at 5% significant
level. † denotes statistically different from zero at 10% significant level.
EI REFORM AND LABOUR MARKET TRANSITIONS
39
Table 7. simualted probability for employment spells: non EI effects
Employment spells
Non-seasonal
Seasonal
prepostprepostreform
reform
reform
reform
⇒ female
⇒ single
⇒ not household
head
⇒ less than high
school
⇒ post-secondary
⇒ university or above
⇒ agriculture
⇒ manufacture
⇒ service industry
⇒ public industry
⇒ firm size [20,100)
⇒ firm size [100,500)
⇒ firm size [500,+8)
⇒ worked for the employer previously
⇒ union or collective
bargaining coverage
⇒ present of preschool kids
⇒ present of schoolage kids
⇒ present of young
adult kids
⇒ immigrant
⇒ age +1
⇒ hourly wage +1
⇒ EI region monthly
unemployment rate
+1
F 50 for default group
66.3 (3.3)
57.6 (4.1)
15.4 (6.0)
21.7 (8.4)
4.1 (1.8)*
-6.9 (2.1)*
-11.8 (1.9)*
4F 50
3.5 (2.3)
-1.5 (2.7)
-4.5 (2.4)†
-0.8 (3.4)
-0.9 (3.8)
1.2 (2.9)
-4.5 (4.4)
-3.1 (5.1)
-0.6 (4.5)
-11.3 (2.5)*
-3.0 (3.1)
-12.0 (4.4)*
-4.2 (4.4)
0.8
1.0
-16.0
-5.4
-5.4
-11.4
0.6
1.6
6.4
-5.9
(2.0)
-3.2 (2.5)
(2.6)
0.8 (3.6)
(5.9)* -15.5 (6.0)*
(2.2)* -12.1 (2.7)*
(2.7)* -1.0 (3.5)
(4.0)* -5.4 (5.6)
(2.1)
5.7 (2.8)*
(2.5)
12.8 (3.1)*
(2.0)*
8.3 (2.5)*
(2.1)* -0.4 (3.0)
-5.9
-7.3
-6.8
1.4
-1.0
-4.2
-1.1
-6.4
0.5
8.4
(3.7)
8.9 (5.2)†
(5.5)
10.0 (9.8)
(4.6) -18.7 (7.4)*
(4.2)
-7.2 (6.4)
(4.7)
-1.5 (7.6)
(6.7)
-4.5 (14.2)
(3.4)
17.4 (5.7)*
(3.5)† 12.6 (7.6)†
(4.3)
13.9 (6.8)*
(3.2)* -5.3 (3.9)
-2.5 (2.2)
4.0 (2.6)
1.6 (3.5)
3.7 (5.8)
0.4 (2.1)
0.2 (2.7)
5.3 (4.0)
-8.3 (5.5)
-5.5 (1.9)*
-0.4 (2.3)
4.6 (3.2)
6.9 (4.7)
4.3 (4.9)
7.6 (7.0)
1.8 (2.7)
-2.5
0.1
0.6
-0.5
(2.4)
(0.1)
(0.2)*
(0.5)
5.7 (3.5)†
-6.5
0.1
0.9
-2.1
(3.1)*
(0.1)
(0.2)*
(0.6)*
-3.0
0.2
1.4
-0.9
(4.3)
(0.2)
(0.4)*
(0.7)
15.3
-0.5
1.8
-1.1
(8.2)†
(0.3)†
(0.5)*
(1.0)
t , ≡ 1 − Ft , is defined as 1 minus the cumulative probability at week t. It is the probability
that a spell is at least t weeks long. All numbers in perentage term.
b
Standard errors in parentheses. * denotes statistically different from zero at 5% significant
level. † denotes statistically different from zero at 10% significant level.
c
default is for married, male, non-immigrant, high-school worker in management industry, less
than 20 workers firm, household head, no kid, no union coverage, with local EI region unemployment rate 12% and hourly wage equals $13.
aF
40
KAILING SHEN
Table 8. simualted probability for employment spells: EI effects
Employment spells
Non-seasonal
Seasonal
prepostprepostreform
reform
reform
reform
F 50 for default group
66.3 (3.3) 57.6 (4.1) 15.4 (6.0)
⇒ if EI entrance requirement increase by
1 week, 4F̄20
⇒ if EI entrance requirement increase by
1 week, 4F̄50
⇒ if EI year maximum increase by 1
week, 4F̄20
⇒ if EI year maximum increase by 1
week,4F̄50
21.7 (8.4)
-0.1 (0.1)
4F t
-0.1 (0.2)
0.8 (0.4)*
-1.1 (0.5)*
-0.1 (0.1)
-0.1 (0.1)
0.2 (0.1)*
-0.3 (0.1)*
0.0 (0.0)‡
0.0 (0.0)
0.0 (0.0)‡
0.0 (0.0)‡
0.0 (0.1)
-0.1 (0.1)
0.0 (0.1)
0.5 (0.2)*
t , ≡ 1 − Ft , is defined as 1 minus the cumulative probability at week t. It is the probability
that a spell is at least t weeks long. All numbers in perentage term.
b
Standard errors in parentheses. * denotes statistically different from zero at 5% significant
level. † denotes statistically different from zero at 10% significant level.
c
default is for married, male, non-immigrant, high-school worker in management industry, less
than 20 workers firm, household head, no kid, no union coverage, with local EI region unemployment rate 12% and hourly wage equals $13.
aF
EI REFORM AND LABOUR MARKET TRANSITIONS
41
Table 9. simualted probability for unemployment spells: non EI effects
Unemployment spells
Non-seasonal
Seasonal
prepostprepostreform
reform
reform
reform
⇒ female
⇒ single
⇒ not household
head
⇒ less than high
school
⇒ post-secondary
⇒ university or above
⇒ agriculture
⇒ manufacture
⇒ service industry
⇒ public industry
⇒ firm size [20,100)
⇒ firm size [100,500)
⇒ firm size [500,+8)
⇒ worked for the employer previously
⇒ union or collective
bargaining coverage
⇒ present of preschool kids
⇒ present of kids
⇒ immigrant
⇒ age +1
⇒ hourly wage +1
⇒ EI region monthly
unemployment rate
+1
F 50 for default group
14.5 (3.0)
9.2 (2.3)
22.0 (7.3)
0.7 (0.9)
3.1 (1.6)†
0.5 (1.7)
10.7 (1.8)*
4F 50
7.2 (1.7)*
0.1 (1.4)
6.5 (1.6)*
3.4 (3.9)
5.5 (4.2)
-6.0 (3.3)†
2.4 (2.2)
-0.6 (0.7)
0.3 (0.5)
4.6 (2.2)*
1.4 (1.7)
5.2 (3.8)
1.9 (1.6)
-1.9
-0.5
1.8
1.0
4.9
0.7
1.2
-3.7
-4.9
1.2
(1.7)
(2.3)
(4.5)
(1.8)
(2.6)†
(2.7)
(2.1)
(2.0)†
(1.7)*
(1.8)
-1.7
0.3
-0.3
1.5
1.6
-1.2
0.4
-2.0
-1.7
1.6
(1.3)
(1.8)
(3.0)
(1.3)
(1.9)
(2.3)
(1.7)
(1.6)
(1.3)
(1.8)
-7.6 (1.6)* -5.3 (1.3)*
6.6 (2.4)*
1.2
1.6
0.4
-0.2
-0.4
5.5 (2.2)*
-3.5
-15.5
-9.6
-9.4
-14.9
14.6
-0.6
-9.4
-6.0
0.9
(4.1)
0.2 (0.6)
(5.1)* -0.7 (0.9)
(5.5)† 8.5 (3.7)*
(5.1)† 1.9 (1.3)
(5.7)* 7.1 (3.5)*
(9.6) 26.4 (13.1)*
(3.9)
0.8 (1.0)
(3.9)* 3.0 (2.8)
(4.1)
0.8 (1.0)
(3.1)
0.3 (0.5)
-7.8 (3.6)*
-0.7 (0.8)
10.4 (4.5)*
0.2 (0.7)
(1.8)
1.2 (1.4)
-4.1 (3.9)
(2.1) -0.4 (1.7) -10.4 (4.4)*
(0.1)* 0.2 (0.1)*
0.2 (0.2)
(0.1)* -0.3 (0.1)* -1.6 (0.4)*
(0.4) -0.1 (0.3)
-0.1 (0.8)
-0.4
0.3
-0.1
-0.1
0.3
(0.6)
(1.0)
(0.1)
(0.1)
(0.3)
t , ≡ 1 − Ft , is defined as 1 minus the cumulative probability at week t. It is the probability
that a spell is at least t weeks long. All numbers in perentage term.
b
Standard errors in parentheses. * denotes statistically different from zero at 5% significant
level. † denotes statistically different from zero at 10% significant level.
c
default is for married, male, non-immigrant, high-school worker in management industry, less
than 20 workers firm, household head, no kid, no union coverage, with local EI region unemployment rate 12% and hourly wage equals $13.
aF
42
KAILING SHEN
Table 10. simualted probability for unemployment spells: EI effects
Unemployment spells
Non-seasonal
Seasonal
prepostprepostreform
reform
reform
reform
F 50 for default group
14.5 (3.0) 9.2 (2.3) 22.0 (7.3)
⇒ if EI benefit weeks
is zero instead, 4F̄20
⇒ if EI benefit weeks
is zero instead, 4F̄50
⇒ if EI benefit weeks
increase from 24 to
25, 4F̄20
⇒ if EI benefit weeks
increase from 24 to
25, 4F̄50
-2.3 (2.7)
4F t
2.2 (2.5)
-2.1 (3.0)
0.7 (0.9)
8.9 (4.1)*
-2.6 (1.5)† 0.7 (1.2)
6.7 (3.5)† 0.9 (0.9)
1.0 (0.5)* 0.3 (0.4)
1.2 (0.7)† 0.8 (0.9)
0.1 (0.1)
0.1 (0.1)* 0.0 (0.0)
0.1 (0.1)
t , ≡ 1 − Ft , is defined as 1 minus the cumulative probability at week t. It is the probability
that a spell is at least t weeks long. All numbers in perentage term.
b
Standard errors in parentheses. * denotes statistically different from zero at 5% significant
level. † denotes statistically different from zero at 10% significant level.
c
default is for married, male, non-immigrant, high-school worker in management industry, less
than 20 workers firm, household head, no kid, no union coverage, with local EI region unemployment rate 12% and hourly wage equals $13.
aF
EI REFORM AND LABOUR MARKET TRANSITIONS
43
Table 11. Decomposition of 4F 50 using OLS: nonseasonal employment spells
x¯1
x¯2
b1
(b2 −b1 )·x¯1 b2 ·(x¯2 − x¯1 ) b2 · x¯2 −b1 · x¯1
b2
average difference
constant
EI region monthly
unemployment
rate
single
female
immigrant
less than high
school
post-secondary
university
or
above
age
agriculture
manufacture
service industry
public industry
firm size [20,100)
firm size [100,500)
firm size [500,+8)
not
household
head
present of preschool kids
present of schoolage kids
present of young
adult kids
worked for the
employer previously
union or collective
bargaining coverage
hourly wage
1.00 1.00
-0.66 -1.26
5.14
2.33
7.47
65.82
-0.67
57.08
-2.02
-8.74
0.89
-0.00
1.21
-8.74
2.10
0.34
0.54
0.14
0.18
0.35 -7.87
0.54
4.92
0.13 -2.61
0.16 -13.05
-1.70
3.22
-5.77
-2.81
2.07
-0.92
-0.44
1.88
-0.03
0.01
0.03
0.07
2.04
-0.91
-0.41
1.95
0.36
0.16
0.37
0.16
-3.02
0.42
-1.47
-0.13
-0.03
-0.00
-1.51
-0.13
-0.75 -0.21
0.10
0.13
0.02 0.03 -16.93 -13.31
0.45 0.43 -5.95 -10.59
0.14 0.14 -5.95
0.01
0.07 0.05 -12.57 -4.16
0.18 0.16
0.71
6.41
0.14 0.13
2.19 13.87
0.34 0.38
8.53
9.25
0.48 0.49 -12.92 -4.60
-0.02
0.09
-2.10
0.86
0.56
1.05
1.62
0.25
4.02
0.07
-0.12
0.23
-0.00
0.07
-0.16
-0.07
0.37
-0.03
0.05
-0.03
-1.87
0.86
0.64
0.89
1.54
0.62
3.99
1.12
1.23
0.25
0.26
0.54
0.23
-0.08
0.00
-0.08
0.34
0.35
-6.32
-0.58
1.98
-0.00
1.98
0.12
0.12
2.22
5.84
0.44
-0.01
0.43
0.18
0.13
-6.93
-0.45
1.20
0.03
1.22
0.32
0.33
-2.64
3.96
2.13
0.02
2.15
0.34
1.22
0.72
0.76
0.02
0.67
0.69
44
KAILING SHEN
Table 12. Decomposition of 4F 50 using OLS: seasonal
employment spells
x¯1
x¯2
b1
(b2 −b1 )·x¯1 b2 ·(x¯2 − x¯1 ) b2 · x¯2 −b1 · x¯1
b2
average difference
constant
EI region monthly
unemployment
rate
single
female
immigrant
less than high
school
post-secondary
university
or
above
age
agriculture
manufacture
service industry
public industry
firm size [20,100)
firm size [100,500)
firm size [500,+8)
not
household
head
present of preschool kids
present of schoolage kids
present of young
adult kids
worked for the
employer previously
union or collective
bargaining coverage
hourly wage
1.00 1.00
-0.06 -0.31
4.89
1.10
5.99
18.26
-0.85
25.23
-0.75
6.97
-0.01
0.00
0.19
6.97
0.18
0.34
0.38
0.09
0.43
0.37 -0.85
0.32 -1.00
0.10 -2.22
0.31 -13.30
-2.64
-3.88
11.70
-3.03
-0.60
-1.09
1.23
4.44
-0.08
0.23
0.14
0.38
-0.68
-0.86
1.37
4.81
0.26
0.07
0.28
0.06
-6.53
-5.30
5.43
7.75
3.06
0.89
0.15
-0.05
3.21
0.84
0.40
0.19
0.47
0.13
0.04
0.23
0.17
0.16
0.47
1.28
0.23
0.47
0.15
0.02
0.17
0.09
0.18
0.43
0.14 -0.23
-5.10 -18.74
0.50 -10.00
-0.58 -5.31
-1.90 -8.08
-0.34 10.10
-3.33
6.00
1.22
7.76
1.11
0.24
-0.15
-2.66
-4.92
-0.64
-0.23
2.36
1.57
1.06
-0.41
-0.20
-0.68
-0.04
-0.09
0.16
-0.53
-0.45
0.17
-0.01
-0.36
-3.34
-4.96
-0.72
-0.07
1.83
1.13
1.23
-0.42
0.23
0.17
3.56
-5.76
-2.17
0.35
-1.82
0.39
0.32
3.47
3.40
-0.03
-0.24
-0.27
0.11
0.11
3.73
3.80
0.01
-0.01
-0.01
0.56
0.36
4.59
-3.48
-4.55
0.69
-3.85
0.29
0.30
0.79
3.38
0.76
0.03
0.79
-0.72
0.16
1.12
1.14
-0.02
1.00
0.98
EI REFORM AND LABOUR MARKET TRANSITIONS
45
Table 13. Decomposition of 4F 50 using OLS: nonseasonal unemployment spells
x¯1
x¯2
b1
b2
average difference
constant
EI region monthly
unemployment
rate
single
female
immigrant
less than high
school
post-secondary
university
or
above
age
agriculture
manufacture
service industry
public industry
firm size [20,100)
firm size [100,500)
firm size [500,+8)
not
household
head
present of preschool kids
present of kids
worked for the
employer previously
union or collective
bargaining coverage
hourly wage
1.00 1.00 16.00 10.45
-0.69 -1.24 -0.39 -0.08
(b2 −b1 )·x¯1 b2 ·(x¯2 − x¯1 ) b2 · x¯2 −b1 · x¯1
-3.83
-0.41
-4.24
-5.55
-0.21
0.00
0.05
-5.55
-0.17
0.32
0.55
0.14
0.20
0.32
0.55
0.12
0.17
0.56
3.09
2.75
4.88
0.01
8.23
0.44
1.59
-0.18
2.83
-0.31
-0.65
0.00
-0.02
-0.01
-0.04
-0.18
2.80
-0.32
-0.68
0.35
0.16
0.34
0.16
-1.93
-0.43
-2.45
0.38
-0.18
0.13
0.02
0.00
-0.17
0.13
0.63 0.41
0.03 1.47
0.44 1.36
0.13 6.10
0.05 0.84
0.15 0.75
0.15 -4.50
0.39 -6.13
0.47 10.63
0.26
0.44
2.52
2.91
-1.27
0.20
-2.96
-2.29
7.58
0.07
-0.03
0.52
-0.46
-0.16
-0.09
0.21
1.35
-1.51
0.28
0.00
-0.02
-0.05
0.03
-0.00
-0.05
-0.08
-0.17
0.35
-0.03
0.50
-0.51
-0.13
-0.10
0.16
1.26
-1.68
-0.44
0.03
0.44
0.14
0.08
0.17
0.14
0.35
0.50
0.25
0.26
6.44
6.14
-0.07
0.06
-0.01
0.58
0.19
0.59
0.10
1.17
1.40
1.53
1.83
0.21
0.08
0.01
-0.15
0.22
-0.07
0.34
0.35
-9.23
-8.41
0.28
-0.05
0.23
0.98
1.65
-0.23
-0.32
-0.09
-0.22
-0.30
46
KAILING SHEN
Table 14. Decomposition of 4F 50 using OLS: seasonal
unemployment spells
x¯1
x¯2
b1
b2
average difference
constant
EI region monthly
unemployment
rate
single
female
immigrant
less than high
school
post-secondary
university
or
above
age
agriculture
manufacture
service industry
public industry
firm size [20,100)
firm size [100,500)
firm size [500,+8)
not
household
head
present of preschool kids
present of kids
worked for the
employer previously
union or collective
bargaining coverage
hourly wage
1.00 1.00
-0.43 -0.69
(b2 −b1 )·x¯1 b2 ·(x¯2 − x¯1 ) b2 · x¯2 −b1 · x¯1
-1.57
-1.86
-3.43
21.45
-0.06
2.33
1.73
-19.13
-0.77
0.00
-0.45
-19.13
-1.22
0.34
0.35
0.08
0.44
0.34
0.29
0.09
0.32
4.02
3.38
-9.04
4.11
-6.75
8.49
1.43
7.82
-3.68
1.80
0.85
1.65
0.03
-0.56
0.01
-0.96
-3.66
1.24
0.85
0.69
0.24
0.07
0.27
0.04
-2.54
-9.36
1.45
-8.32
0.95
0.07
0.05
0.26
1.00
0.33
0.07
0.19
0.48
0.15
0.04
0.23
0.15
0.16
0.44
0.03
0.11 -0.45
0.25 -6.67 11.49
0.48 -7.39 1.11
0.15 -12.80 11.90
0.02 15.37 29.90
0.21 -0.52 3.75
0.10 -8.06 11.01
0.19 -4.38 4.29
0.41 -4.37 2.12
-0.04
3.40
4.08
3.63
0.53
0.97
2.95
1.35
2.85
0.02
0.74
-0.00
-0.02
-0.37
-0.06
-0.64
0.14
-0.05
-0.02
4.13
4.08
3.62
0.16
0.91
2.30
1.49
2.80
0.24
0.18
7.33
0.81
-1.59
-0.05
-1.64
0.57
0.53
0.50
0.34
-3.62
0.63
-4.01
1.56
-0.22
0.49
0.26
-0.31
0.04
0.18
0.30
0.27
-5.63
-9.76
-1.24
0.33
-0.91
-0.88 -0.36
-0.96
-0.43
-0.47
-0.22
-0.69
EI REFORM AND LABOUR MARKET TRANSITIONS
47
Figure 1. Estimated impacts of EI parameters on hazard functions of employment spells
aAssume
ommitted type of spell with EI entrance requirement just satisfied at
week 14, get year maximum EI benefit weeks at week 24, get maximum EI benefit
weeks at week 49. These critical weeks are emphasized using the dotted vertical
lines in each figure.
48
KAILING SHEN
Figure 2. Estimated impacts of EI parameters on hazard functions of unemployment spells
aAssume
omitted type of spell with 24 weeks of EI benefit. The EI benefit
exhaustion week is emphasized using the dotted vertical line in each figure.
EI REFORM AND LABOUR MARKET TRANSITIONS
49
Figure 3. Simulated pdf function ft
a{α1 , β 1
1
cx , βtvx }
2
2
refers to pre-reform estimates; {α2 , βcx
, βtvx
}refers to post-reform estimates.
50
KAILING SHEN
Figure 4. Simulated ccdf function F t
a{α1 , β 1
1
cx , βtvx }
2
2
refers to pre-reform estimates; {α2 , βcx
, βtvx
}refers to post-reform estimates.