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The Employment Insurance (EI) Act, which came into force with the passage of Bill C-12 in June 1996, was the most fundamental restructuring of the Unemployment Insurance program in the past 25 years. Such substantial changes are likely to affect the behaviour of many people. Given the variety of provisions contained in the Bill, various demographic groups are likely to be impacted differently and to different extents by these changes.
This research uses the fact that the implementation of Bill C-12 proceeded in two separate phases. With each phase being separated by a full quarter, it is in principle possible to estimate the impact of each by quasi-experimental methods using appropriate COEP surveys. Furthermore, given the numerous surveys available, it should also be possible to provide two separate estimates of the total impact of Bill C-12. One of the estimates relies on surveys that were relatively close to the implementation date of the Bill. It is thus important to investigate whether strategic behaviour can be detected in the data. If unaccounted for, such behaviour can seriously bias the parameter estimates obtained from quasi-experimental methods.
In general, the econometric and non-parametric results indicate that the new EI legislation has had an important negative impact on the duration of both the unemployment spells and the benefits recipiency spells. On the other hand, various demographic groups have reacted differently to the new provisions. When focusing on the duration of unemployment spells, we find that men have shortened their spells considerably more than women, and that part-time workers have reacted similarly compared to full-time workers. On the other hand, young workers do not seem to have reacted to the new provisions, whereas seasonal and non-seasonal workers have adjusted their behaviour similarly.
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The Employment Insurance (EI) Act, which came into force with the passage of Bill C-12 in June 1996, was the most fundamental restructuring of the Unemployment Insurance program in the past 25 years. Entrance requirements for new entrants and re-entrants, benefit levels, length of claims, repayment on benefits (“Clawback”), and maximum insurable earnings have all been substantially revised. In addition, an “intensity rule” that decreases the benefit rate in proportion to the number of weeks of regular benefits claimed since June 30th, 1996 has been introduced.
Such substantial changes are likely to affect individual behaviour. Previous legislative changes to the unemployment insurance system have been shown to have a substantial impact on workers’ behaviour.1 Despite all the successive UI and EI reforms that have taken place during the 1990s, the Canadian EI system arguably provides workers with the greatest control over their expected benefits. The number of weeks worked, the wage rates, the time chosen to file, etc., all strongly affect the amount and length of benefits a worker can expect to claim if or when he becomes unemployed. It is therefore not surprising that the previous literature found Canadian workers to be sensitive to these parameters.
The main goal of this research is to study the effects of the new EI legislation on unemployment and recipiency durations. The changes brought by the new EI legislation have been phased in gradually. Some changes were implemented as of June 30th, 1996 (Phase I). A second series of changes to insurance parameters were phased in as of January 1997 (Phase II). The effects of the changes implemented in each phase can be estimated separately by using two separate Canadian Out of Employment Panel (COEP) surveys for the appropriate quarters. Phase I corresponds to the third quarter of 1996 (Q96/03) and Phase II to the first quarter of 1997 (Q97/01). Thus, the impact of Phase I can be assessed by using the Q96/04 and Q95/04 COEP surveys. Phase II can be similarly assessed with the Q96/04 and Q97/04 COEP surveys. In implementing such a quasi-experimental approach in evaluating the EI legislation, it must be noted that the design may poorly approximate a true experiment with random assignment to treatments. Consequently, it will be important to investigate whether other explanatory variables have to be accounted for to properly measure the impact of the EI legislation.
The total impact (Phase I + Phase II) of the EI legislation can be measured using two different sets of COEP surveys: Q97/04 and Q95/04 or Q97/02 and Q96/02. A priori the two sets should yield very similar results. Both sets will be used in assessing the impact of Bill C-12 and their respective results will be contrasted.
The main difference between the quasi-experiment studied here and a true experiment with random assignment lies in the possibility of “strategic filing,” in the terminology of Jones (1998). The term strategic filing usually refers to the possibility that workers may exert some control over the date at which they file a claim. The existence of such behaviour is normally assumed away when using quasi-experimental estimators. Here we will consider the date at which a claim is filed to be purely exogenous to the workers. On the other hand, we will investigate a different situation that is akin to strategic behaviour. We will look at the possibility that unemployed individuals may delay their exit as the implementation date of Bill C-12 nears in order to benefit as much as possible from the UI regime. As we will see later, the new EI legislation introduced provisions such as the “Clawback” and the “intensity rule” that impose penalties to claimants that are commensurate to the number of weeks of regular claim following June 30th, 1996. It is thus conceivable that spells in the previous quarter may be “strategically” increased somewhat. In fact, the closer the starting date of a claim is to June 30th, 1996, the more likely it will be affected by such “strategic” exit. Unless this possibility is explicitly taken into account, the quasi-experimental evaluation of Bill C-12 will be severely biased. Fortunately, quasi-experimental evaluations that rest on the quarters Q97/04 and Q95/04 will provide a check on estimates based on quarters Q97/02 and Q96/02.
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The changes brought by the new Employment Insurance (EI) legislation were introduced in two phases. The impact of each phase will be evaluated separately. The following outlines the main features of each phase.
These sweeping changes are likely to impact various groups differently. For instance, the new entrance requirements are likely to impact take-up rates of part-time workers and individuals with low labour market attachment more than others. The new provisions regarding the entitlement schedule are likely to impact most unemployed workers by reducing recipiency durations.3 Finally, the changes made to the MIE and the “Clawback” will likely affect full-time workers or individuals with strong attachments to the labour market.
The more stringent entrance requirements for new entrants and re-entrants will likely seriously impact the take-up rates of many individuals who depict low labour market attachment. On the other hand, the “intensity rule” and the “Clawback” will mostly affect seasonal workers. To the extent that various socio-demographic groups have traditionally depicted more or less attachment to the labour market, the provisions implemented in both phases will impact them differently. For instance, if women or youths have traditionally been more concentrated in services sectors or the tourist industry, which fluctuate considerably over a year, then they are likely to be more affected by some of the new provisions of the EI legislation than other groups. On the other hand, individuals who have traditionally had very strong attachments to the labour market or who receive good wages will be mostly affected by the “Clawback” introduced in Phase II. It will thus be important in the ensuing empirical analysis to distinguish between broad socio-demographic groups to verify the extent to which they have been affected by provisions implemented under either Phase I or Phase II.
The data used in this work is drawn from nine successive so-called “1996 Canadian Out of Employment Panel (COEP) surveys”. The target population is composed of Canadians aged 15 and over, living in the ten provinces or the territories, who had “job separations” between October 1995 and December 1997 inclusive.4 Each COEP survey includes individuals who experienced job separations in a particular quarter. The surveys, based on a 10 percent random sample of individuals with Records of Employment (ROE) that are filed whenever a job separation occurs, are linked to administrative UI data and related records. The surveys contain detailed information on respondents’ dates in and out of employment, reasons for break in employment, job search activities, demographics of the respondent and his/her household, etc. The fact that each survey uses a similar sampling methodology and identical survey instruments ensures that the empirical analysis is not flawed with biases that may arise for methodological reasons. In fact it is probably true that the COEP surveys constitute the best resource available in Canada, and perhaps more widely, to study the effects of program changes.
For the purpose of this study, it should be mentioned at the outset that not all job separations are included in the ensuing analysis. Separations that occurred for any of the following reasons have not been included in the final sample: injury, illness, disability leave, maternity or parental leave, other family responsibilities, return to school, retirement, or dismissal by employer. Table 1 presents descriptive statistics for each of the nine cohorts separately. The two light-shadowed columns correspond to Phase I and Phase II, respectively. Some variables show considerable variations across quarters, while others remain remarkably constant. The take-up rate represents the proportions of eligible workers that actually did claim benefits. Not surprisingly, take-up decreases as we move from the fourth quarter (October-December) to the first quarter (July-September). Notice however that the take-up rates are generally lower for equivalent quarters following Phase II. Eligibility, on the other hand, represents the proportion of workers that did qualify for benefits following a separation. It was computed using Status Vector information for workers who actually applied for benefits. In cases where workers did not apply for benefits, it was estimated using ROE information, the unemployment rate in the economic region at the time, and the benefit table at the time. A careful examination of the table indicates that the proportion of eligible workers decreased only very slightly following Phase II. The decrease certainly is less than one might have expected a priori given the nature of the changes that were implemented. The next couple of lines report information about the mean and median duration of unemployment spells and weeks of entitlement. As expected, the mean (and median) duration of unemployment spells varies considerably across quarters. The duration is at its highest in the second quarter and its lowest in the fourth quarter of each year.
The middle portion of the table reports descriptive statistics for groups of workers that may be of particular interest. These are presented so as to highlight the significant changes in sample composition from one quarter to the next. For instance, the proportion of male claimants usually peaks in the fourth quarter (about 61 percent) and reaches its lowest level in the second quarter of any given year (about 45 percent). The proportion of youths is roughly constant year-round at approximately 17 percent, except in the third quarter where it reaches as much as 27 percent on average. Finally, the proportion of both seasonal and part-time workers varies in a predictable manner across the different quarters. These four groups (men–women, young–older workers, seasonal–non-seasonal workers and part-time–full-time workers) will be analysed separately in the empirical analysis.
The last portion of the table presents the mean value of various demographic variables. Although there is some variation across quarters, the mean values are relatively stable on a quarter-per-quarter basis. Notice that individuals in the third quarter are usually younger and proportionately less are married. This is naturally related to the fact that the proportion of youths increases significantly in that quarter, as reported in the bottom portion of the table.
Table 1 illustrates the importance of using comparable quarters when conducting a quasi-experimental analysis on the impact of program changes. On one hand, the figures show that there are considerable compositional changes in the samples across quarters. On the other hand, comparable quarters also depict variations in candidate explanatory variables. It is thus likely that the pure quasi-experimental effects will be affected once these variables (as well as others) are controlled for.
The table is also useful to illustrate how the quasi-experimental estimation of the program effects will be carried out. As mentioned earlier, Phase I corresponds to the third quarter of 1996. From Table 1 it is easy to see that using data for Q96/04 and Q95/04 will yield an appropriate estimate. Similarly, the effects of Phase II, which correspond to the first quarter of 1997, can be estimated using data for Q97/04 and Q96/04. Finally, the compounded effect of both phases can be estimated using either data for Q97/04 and Q95/04 or Q97/02 and Q96/02. In the former case, one could argue that the estimate may be more or less reliable given the data is taken from quarters relatively far apart (2 years). This should be no cause for concern given that differences in the economic environments between the two quarters can be adequately dealt with through regression analysis. In fact, the estimate that uses data for the Q97/04 and Q95/04 quarters should be the preferred one. Indeed, it can safely be argued that all behavioural adjustments to the implementation of the new EI legislation have taken place by 1997/04. Furthermore, claims that occurred during Q95/04 are sufficiently far from Phase I so as to be considered void of any strategic behaviour.5 On the other hand, an unknown proportion of claims that occurred in Q96/02 may be the result of such strategic filing. We should thus expect the duration of some spells to have been stretched somewhat during the Q96/02 quarter in anticipation of the new provisions. Exits out of unemployment should thus decrease just prior to Phase I as the result of this, but also chiefly as a result of the intensity rule implemented in Phase II which decreases the benefit rate in proportion to the number of weeks of regular claims starting in Phase I.
Simple comparisons of mean spell durations provide prima facie evidence of the impact of the new legislation.6 For instance, a simple comparison of the first and last columns of Table 1 indicates that the unconditional average spell length decreased by 1.5 months between 1997 and 1995. Thus, a pure quasi-experimental estimate would suggest that the new legislation induced an 11.7 percent drop in mean spell duration over that period. The total impact can be further decomposed into two components. By comparing the mean durations of the fourth quarter of 1996 and 1995, it is found that the average spell length decreased by 0.8 of a month following the implementation of Phase I. Similarly, by comparing the mean durations of the fourth quarter of 1996 and 1997, it is found that the implementation of Phase II is associated with a further 0.7 month drop in mean duration.
As mentioned earlier, the total impact of the new legislation can also be assessed by comparing the mean durations of the second quarter of 1996 and 1997. The figures reported in Table 1 show that the average duration increased slightly in the months that followed the implementation of Bill C-12. This result is in stark contrast to the one reported above. However, these estimates are reliable only to the extent that our quasi-experimental design adequately mimics a true experiment with random assignment. It is thus likely that changes in the economic environment or individual behaviour across quarters have sufficiently impacted mean spell durations so as to render pure quasi-experimental estimates inappropriate.
The estimates reported above are based on simple mean durations that do not explicitly account for right censoring.7 It is customary in the literature to focus on survival rates to avoid any bias that may arise due to right censored spells. Figures 1–9 plot Kaplan-Meier survival functions of unemployment spells for various socio-demographic groups before and after the implementation of Bill C-12. In each case, cohorts Q95/04 and Q97/04 are used to compute the “Before” and “After” functions, respectively. Figure 1 plots the survival function for the entire sample. As shown, the “After Bill C-12” survival function lies everywhere below the “Before Bill C-12” function. This naturally translates into shorter average spells in the “After Bill C-12” period and is consistent with the relevant mean duration reported in Table 1. Figures 2–9 report results for women, men, seasonal workers, non-seasonal workers, youths, adult workers, and part-time and full-time workers. Inspection of these figures reveals that the “After Bill C-12” survival functions always lie entirely below the “Before Bill C-12” functions. Although these figures are based on unconditional durations, they provide prima facie evidence that Bill C-12 has had a negative impact on the duration of unemployment spells.
Figures 10–18 plot the Kaplan-Meier survival functions of recipiency durations for the same socio-demographic groups as in Figures 1–9. Using the complete sample (Figure 10) we find that the “After Bill C-12” curve lies everywhere below the “Before Bill C-12” curve. It thus appears that the average recipiency duration has decreased following the implementation of Bill C-12. In fact, inspection of the remaining figures reveals that the mean recipiency duration of most demographic groups has increased rather than decreased following Bill C-12. Only adult and part-time workers have experienced somewhat shorter durations.
The null assumption that the survival functions depicted in each figure are the same can be tested formally with a Log-rank test.8
This test statistic is distributed under the null assumption. Note that the test is not particularly good at detecting differences when survival curves cross and should be used with caution when they do. Table 2 provides a series of log-rank tests based on unemployment durations as well as recipiency durations. None of the unemployment survival curves depicted in Figures 1–9 cross. The Log-rank tests can thus be used to test the null assumption. It turns out that the null assumption is strongly rejected in all cases, except for young adults. On the other hand, the survival curves based on recipiency durations cross in several cases. Because most cross near the right extremity of the curves, the Log-rank test is likely not too severely affected. The statistics reported in Table 2 indicate that the average recipiency duration for the whole sample has decreased following Bill C-12. The test is unable to strongly reject the null assumption when using samples restricted either to women, seasonal workers, and part-time workers. Finally, the test statistics strongly reject the null assumption when using samples restricted to men, adult workers, and full-time workers.
The above test statistics are based on unconditional recipiency and unemployment durations and as such, do not account for changes that may have occurred in the economic environment, in individual behaviour, or in sample composition between the two periods. In order to better assess the true impact of Bill C-12 we must now turn to econometric analysis.
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It has become customary to follow Cox (1972) and to specify a so-called proportional hazard function. Let
The term on the left-hand side, 
, is the individual exit rate at time t. The first term on the right-hand side, 
, is the baseline hazard, i.e. the hazard common to all individuals. The second term captures the effect of the explanatory variables whose values may, or may not, change over time, and ß is an appropriately dimensioned vector of parameters to be estimated. The exponential term constrains the hazard rate to be positive. This model is said to be proportional since the exogenous variables simply multiply the baseline hazard.9
Intuitively, this model states that the individual hazard rate can be written as the product of a component that is identical for each individual [] and a person specific component (exp(xi(t)B)). It is assumed that individual circumstances, as captured by xi(t) (age, benefits during the spell, unemployment rates during the spell, etc.), are responsible for differences in hazard rates for individuals within the same socio-demographic group.
This econometric model allows for right censoring, i.e. the existence of ongoing spells at the end of the sample period. The main difficulty in specifying a statistical model lies in the choice of a particular functional form for the baseline hazard. There are essentially three ways to model . First, one can rely on well-known parametric models (Weibull, log-logistic, etc.). Second, one can approximate non-parametrically to avoid having to choose a particular functional form. Third, one can turn to Cox’s partial likelihood model and avoid having to specify a function for altogether. This remarkable result implies that the B coefficients can be estimated without having to specify any functional form for . In what follows, we will use Cox’s partial likelihood estimator to assess the impact of Bill C-12.
The econometric assessment of the new Employment Insurance (EI) legislation can be conducted on the basis of two different indicators: (1) duration of unemployment spells following job separation; (2) Unemployment Insurance (UI) recipiency durations. Both indicators provide different insights into the adjustments to the legislation. Furthermore, when studying both indicators the analyst must keep in mind that the samples at his disposal are not representative of the same underlying population. Indeed, some unemployed individuals may or may not qualify for benefits, while others may qualify but elect not to claim benefits. Thus, the analysis of unemployment durations is more relevant within the context of a theoretical job-search model. The analysis of the recipiency durations, on the other hand, rests on individuals that both qualify for benefits and have elected to claim benefits. The main interest for studying recipiency durations relates to the budgetary implications of policy changes. Because they rely on fundamentally different samples, both indicators have very little in common and are thus of interest for their own sake.10 Results for both indicators will be presented in turn. We first start with the duration of unemployment spells.
A common feature of conducting quasi-experimental evaluations of policy change is to start by specifying the simplest model possible, i.e. by incorporating a single dummy variable that captures the treatment effect (“After Bill C-12” period in our case). To the extent the samples in both the “Before” and “After” periods are homogeneous, and to the extent the economic environment has remained stable over time, the parameter estimate can be interpreted as a pure treatment effect. The empirical strategy consists of gradually introducing explanatory variables into the model to study the robustness of the initial estimate. This is precisely the strategy we follow in this research.
As there are numerous tables in the appendix, it is perhaps worthwhile to explain at this stage how the results are structured. In all, there are eight separate socio-demographic groups that are studied separately. These groups were deemed more likely to be affected by the new EI legislation or to be of particular interest. These groups are: women, men, youths, adults, part-time workers, full-time workers, seasonal workers and non-seasonal workers. In addition, the first set of results concerns the entire sample. There are thus nine different sets of results pertaining to unemployment durations. Each set of results contains three different tables. The first table looks at the impacts of Phase I and Phase II separately. The second and third tables provide estimates of the total impact of Bill C-12. The second table uses data from Q97/04 and Q95/04, while the third uses data from Q97/02 and Q96/02. Finally, the first column of each table presents a “pure” quasi-experimental estimate and additional columns simply introduce additional explanatory variables into the model to investigate the robustness of the “pure” estimate.
It would be unreasonable to discuss each single table in turn. Instead, we will highlight the most salient results in each set and underline regularities that are found across most tables.
Tables 3–5 report results for the entire sample. The variable C-12 is a dummy indicator that equals 1 in the period after Bill C-12 and 0 in the period before. As mentioned previously, the effects of Phase I are estimated with cohorts from Q96/04 and Q95/04. The dummy indicator is thus equal to 1 for spells that occurred during Q96/04 and 0 for those that occurred during Q95/04. The significantly positive coefficient of 0.115 on the C-12 variable in the first column of Table 3 means that the hazard rate out of unemployment is higher in Q96/04 than Q95/04. Recall that the explanatory variables operate on the baseline hazard rate through exp(x'B ). The point estimate thus implies that the baseline increased by a proportion of exp(0.115)= 1.1218, i.e. a 12.18 percent increase between quarters. Recall from Table 1 that a simple comparison of mean durations between Q96/04 and Q95/04 yielded a decrease of 0.8 of a month, equivalent to 5.1 percent. The difference between 5.1 percent and 12.18 percent is entirely attributable to the fact that the latter accounts for censored spells whereas the former does not.
The second column adds a series of demographic control variables. Eligibility is a dummy variable whose value is 1 if the individual is entitled to benefits. Entitlement represents the number of weeks of benefits entitlement. Minority is a dummy indicator equal to 1 if the individual is part of a “visible” minority. Unemployment rate refers to the rate in the individual’s region of residence. Next is a series of nine provincial dummy variables. Ontario has been omitted from the list.11 Consequently, the parameter estimates must be interpreted with respect to Ontario. Finally, the table contains a series of six school dummy indicators. The omitted group is “less than high-school”. The parameter estimates must be interpreted accordingly. The inclusion of demographic variables slightly decreases the magnitude of the C-12 coefficient even though few control variables are statistically significant. Being part of a visible minority decreases the hazard rate considerably. Being married also decreases the exit rate, although the parameter is only marginally significant. Interestingly, individuals living in PEI, Saskatchewan or Alberta have higher exit rates than those living in Ontario. None of the parameter estimates on the school dummy variables come out statistically significant.
In the third column four additional dummy variables are added to the model. These variables investigate whether men behave differently than women and whether there are any differences between youths (25 years of age or less) and adults, part-time workers and full-time workers, and seasonal versus non-seasonal workers. As mentioned above, these four categories were chosen because of their expected sensitivity to the new EI legislation or for their intrinsic interest. Note that the introduction of the additional variables brings the parameter estimate of C-12 to its original value in the first column. All new four variables are statistically significant. The results indicate that, all else being equal, men have much higher exit rates than women, youths exit faster than adults, full-time workers have higher exit rates than part-time workers, and finally, seasonal workers have much higher exit rates than non-seasonal workers. According to the parameter estimate, seasonal workers have exit rates that are 62 percent higher than non-seasonal workers (=1-exp(0.483)).
The specification in column 4 investigates whether the exit rates are sensitive to the number of entitlement weeks left in a claim. Exhaust (8,4,2) are time-varying dummy indicators that equal 1 whenever there are between 4-8, 2-4, or less than 2 weeks of entitlement left, respectively. These variables are meant to capture the “exhaustion” effect found in the literature.12 Although none of the variables are statistically significant, they all bear a negative sign, which is somewhat surprising.13
Finally, the fifth column of the table presents the full specification. Most parameter estimates are relatively robust and in particular, the parameter estimate of C-12 is nearly identical to the quasi-experimental estimate in column 1. One notable result concerns the school dummy variables. In the full specification, some are now statistically significant and have the expected sign. In particular, those who have some college or university training have systematically higher exit rates.
The lesson to be drawn from so far is that the provisions of Bill C-12 that were implemented in Phase I seem to have affected the exit rates in the expected direction but the order of magnitude is relatively modest. We now turn to the next five columns of the table, which focus on the provisions implemented under Phase II. The results show little evidence that Phase II has had any impact on the exit rates. The parameter estimate of C-12 ranges between 0.05 and 0.08 and its statistical significance is sensitive to the choice of a particular specification. Note that most explanatory variables are not statistically significant. On the other hand, the parameter estimates associated with PEI, Nova Scotia, and New Brunswick are significant and are quite sensitive to the inclusion of dummy indicators for men, youth, full-time, and seasonal workers. Given the prevalence of seasonal work in these provinces, once this is controlled for, these provinces are not much statistically different from Ontario.14
The next table provides estimates of the total impact of Bill C-12 based on cohorts 1997/04 and 1995/04. The cohort 1995/04 is sufficiently remote from the implementation of Phase I to assume that no strategic claims occurred during that quarter. Furthermore, cohort 1997/04 is sufficiently far from Phase II to assume that all behavioural adjustments have taken place and that individuals experiencing unemployment spells have fully integrated the new provisions of the EI legislation. Consequently, the comparison of these cohorts should provide the best possible estimate of the total impact of Bill C-12.
The first column in Table 4 shows that the total impact of Bill C-12 was to raise the exit rates considerably. Not surprisingly, this is roughly equal to the sum of the corresponding parameter estimates of Phase I and Phase II. The parameter estimate is highly significant and translates into an overall increase of 18.4 percent (=1-exp (0.169)).15 The set-up of the next four columns is identical to that of the previous table. Notice that the parameter estimate of C-12 is very robust and hardly varies across specifications. As before, the dummy indicator for PEI is sensitive to the inclusion of the seasonal dummy and the parameter estimate of Saskatchewan is large and significant. Interestingly, once we control for men, youth, full-time and seasonal work, the parameter estimates associated with schooling variables all become statistically significant.
Table 4 shows that Bill C-12 has had some impact on the exit rates. As mentioned previously, given the timing of the COEP surveys it is possible to obtain a second estimate of the impact of Bill C-12. For this second estimate, the “Before” cohort includes spells that occurred during 1996/02 while the “After” cohort includes those that occurred during 1997/02. In theory, the two estimates should be similar.16 Yet, given the proximity of the “Before” cohort and Phase I it is conceivable that the estimate will be contaminated by unusual behavioural adjustments. Indeed, recall that the tracking of benefits for both the “Clawback” and the “intensity rule” start with Phase I. To the extent that some individuals are likely to experience future unemployment spells (insured or not), they may elect to extend their spell as much as possible before the tracking is implemented. As a result, some of the claims that occurred during 1996/02 may be unusually lengthy, and the quasi-experimental estimate may be biased downward.
Table 5 presents the results based on the 1997/02-1996/02 cohorts. The first five columns of the table are identical to that of the previous table. The last four columns introduce an additional time-varying covariate. The Remain (8,4,2) variable is a time-varying indicator that equals 1 whenever there are between 4-8, 2-4, or 0-2 weeks left before the implementation of C-12, respectively. We will first focus on the first five columns. A striking feature of these columns is that the parameter estimate of Bill C-12 is not statistically different from zero. Hence, using these cohorts it appears that Bill C-12 has had no impact whatsoever on the duration of unemployment spells. These results are also consistent with those reported in Table 1. The remaining parameters are qualitatively similar to those of the previous table. Indeed, the parameter estimate associated with PEI is sensitive to the inclusion of the seasonal variable and individuals living in Saskatchewan (and Alberta) appear to have much shorter spells than any one else in Canada. Note also that the schooling variables have the expected sign and most are statistically significant.
On the whole, the results of Table 5 are relatively similar to those of Table 4, except for the parameter estimate of interest, C-12. We now turn to the last four columns of the table. The various specifications of these columns investigate the robustness of the parameter estimates of C-12 to the inclusion of different sets of explanatory variables. The striking feature of these columns is that the C-12 parameter is now statistically significant and very close to the one obtained using the two previous cohorts. The change in the parameter estimates of Bill C-12 is entirely due to the inclusion of the Remain (8,4,2) covariates. The table indicates that all three parameter estimates are negative, although only Remain-2 is ever statistically significant. In other words, it appears as though the individuals who entered a claim near the implementation of Bill C-12 may have purposely postponed their exit so as to claim as much benefits as possible without incurring future penalties. The remaining parameter estimates of the last four columns are remarkably similar to those of the first five columns and to those of the previous table.17
The conclusions to be drawn from the results presented so far are twofold. First, it seems that the provisions implemented under Phase I have had more impact than those implemented under Phase II. This should not be surprising since the benefits tracking for both the “Clawback” and the “intensity rule” were implemented under Phase I. Second, it appears that some claimants may have the ability to postpone their exit from unemployment in order to benefit as much as possible from the older UI legislation. These results pertain to the whole population of claimants. As such, the parameter estimates of all socio-demographic groups are constrained to be identical. In what follows, we will present results for each group separately. Doing so will allow us to determine which group has been most affected by the new EI legislation.
Tables 6–11 present the results pertaining to women and men separately. Each set of three tables is set up in the same manner as the tables pertaining to the whole sample. We will start by discussing the results for women.
The impact of Phase I is presented in the first five columns of Table 6. All five columns indicate that the provisions implemented under Phase I have had no statistical impact on their exit rates. The results for PEI and Saskatchewan are qualitatively similar to those of the whole sample. Very few parameter estimates are statistically significant. As before, being young (Youth) or being a seasonal worker (Seasonal) increases significantly the exit rates. The last five columns provide some evidence that Phase II has had some impact on the exit rates. The parameter estimates are all statistically significant at 5 percent, except for the pure quasi-experimental estimate which turns out not to be significant.
Table 7 provides estimates of the total impact of Bill C-12 for women using cohorts 1997/04 and 1996/04. The C-12 parameter estimates are all statistically significant and very similar to those concerning Phase II. This is not surprising given that Phase I was found not to have any impact. Table 8 provides an alternative estimate of the total impact using cohorts 1997/02 and 1996/02. The first five columns indicate that Bill C-12 has had no impact on the exit rates of women. This result is very similar to the one obtained for the complete sample. The specifications of the last four columns include the Remain (8,4,2) variables. It turns out that their inclusion has a direct impact on the parameter estimates of C-12. Indeed, both Remain-4 and Remain-2 are negative and statistically significant. Hence, it must be concluded that some female claimants managed to postpone their exit from unemployment in the weeks preceding the implementation of C-12. Consequently, a comparison of the two cohorts that does not account for such behavioural adjustment will yield a downward biased estimate of the true impact of Bill C-12. As it turns out, both Tables 7 and 8 yield very similar estimates of the impacts of Bill C-12 on women’s exit rates.
Tables 9–11 concern men. Table 9 shows that Phase I has had a considerable impact on men’s exit rates. The parameter estimates are robust with respect to various specifications. On the other hand, Phase II appears not to have had any impact on their exit rates. Not a single parameter estimate is significant at conventional levels. The total impacts reported in Table 10 are sizeable and approximately correspond to the compounded impacts of Phase I and Phase II reported in the previous table. There is no contradiction in the fact that the total impact is significant while that of Phase II is not. What is more puzzling on the other hand, is that the measure of Bill C-12 obtained from cohorts 1997/02 and 1996/02, as reported in Table 11, is not significant even when accounting for weeks remaining prior to implementation. The parameter estimate of Remain-2 is statistically significant and its inclusion in the model increases the parameter estimate of C-12, but the significance level of the latter remains very weak.
The overall conclusions of these results are that men have been impacted somewhat more than women from the new EI legislation, and that women have reacted more to Phase II while men seem to have reacted more to Phase I.
Our next set of results concerns seasonal and non-seasonal workers and is presented in Tables 12–17. The first column of Table 12 indicates that Phase I has increased the exit rates of seasonal workers. Once we control for various explanatory variables the impact is reduced somewhat and loses some of its statistical significance (P-value = 0.071). Consequently, it must be concluded that the economic environment of seasonal workers has changed sufficiently between cohorts so as to affect their overall exit rates upwardly. Although the evidence is statistically weak, Phase I still has had a small, albeit not very significant, impact. As an indication that seasonal workers were responsible for PEI having a large parameter estimate, notice that when we focus on the sample of seasonal workers the PEI parameter estimate is small in absolute value and no longer significant.
Phase II also appears to have had little impact on the exit rates of seasonal workers. Indeed, not a single parameter estimate associated with Bill C-12 is statistically significant. Table 13 reports that the overall impact of Bill C-12 has increased the exit rates of seasonal workers by approximately 17 percent (1-exp(0.16)). This is roughly equal to the compound impact of Phases I and II. The result is robust and highly statistically significant. Turning to the alternate estimate in Table 14, we notice once again that the total impact is not statistically different from zero in the first five columns. When controlling for weeks remaining before implementation, the total impact of Bill C-12 is roughly equal to that of Table 12 but is not statistically significant.
The results pertaining to non-seasonal workers are presented in Tables 15–20. The impact of Phase I, as reported in Table 15, is relatively weak and imprecise. The statistical significance of the C-12 parameter estimate is sensitive to the choice of a particular specification. As for seasonal workers, the parameter estimate decreases somewhat once we control for various explanatory variables. Phase II, on the other hand, appears not to have had any impact on the exit rates. The total impact measured by cohorts 1997/04 and 1995/04 is reported in Table 16. The parameter estimate is large, significant and corresponds to the compounded effects of Phases I and II. The alternate estimate reported in Table 17 is equal to zero when no account is made of time remaining before implementation of Phase I. When controlled for, the total impact is significant and closely matches the impact of Phase I. Once again it must be concluded that non-seasonal workers have somehow managed to postpone their exits from unemployment in the weeks prior to Phase I.
The samples are next broken down according to age groups. The “younger” workers include those that were 25 years of age or less while unemployed and the “older” workers include those that were aged more than 25. The results for these two groups are included in Tables 18–23.
The results in Table 18 clearly indicate that Phase I has had no impact on young workers’ exit rates. Notice also that apart from Men and Seasonal, not a single parameter estimate is statistically significant. In particular, PEI and Saskatchewan are no longer significant. The other panel of the table also indicates that Phase II has had no statistically significant impact on their exit rates. It should thus come as no surprise that the total impact, as measured by cohorts 1997/04 and 1995/04 in Table 19, is also not statistically significant. On the other hand, the total impact measured by cohorts 1997/02 and 1996/02 (Table 20) is marginally statistically significant when we control for weeks remaining prior to Bill C-12.
Older workers, on the other hand, appear to have been more sensitive to the new EI legislation. As reported in Table 21, Phase I has had a significant impact on their exit rates, but not Phase II. The total impact measured by cohorts 1997/04 and 1995/04 in Table 22 is large and statistically significant. When measured with cohorts 1997/02 and 1996/02, the total impact is not significant, even when controlling for weeks remaining before implementation.
The last set of results concern part-time and full-time workers and is presented in Tables 24–29. An individual is considered a part-time worker if the average weekly number of hours on the last job before separation was less or equal to thirty. The first panel of Table 24 shows that Phase I has had no statistically significant impact on the exit rates of part-time workers. On the other hand, Phase II has had a significant impact that is relatively robust across specifications. The parameters associated with the explanatory variables behave as they did for the other demographic groups. The next table provides estimation of the total impact of Bill C-12 based on quarters 1997/04 and 1995/04. Not surprisingly, the results essentially replicate those of Phase II in the previous table. Finally, Table 26 provides an alternative estimate of the total impact based on quarters 1997/02 and 1996/02. As before, the first five columns do not take into account the possibility of strategic behaviour. It is thus found that Bill C-12 has had no impact on exit rates of part-time workers. Surprisingly, once we do control for strategic behaviour, the parameter estimates still indicate that Bill C-12 has had no impact.
The results pertaining to full-time workers are presented in Tables 27–29. The impacts of Phase I and Phase II are opposite to those of part-time workers. Indeed, Phase I appears to have had a significant impact on their exit rates, whereas Phase II appears not to have had any. The total impact of Bill C-12, as reported in the next table, is naturally approximately equal to that of Phase I, since Phase II was found to have no effect. Finally, the last table reports the total impact of Bill C-12 based on the quarters 1997/02 and 1996/02. The results of the first five columns, in which we do not control for strategic behaviour, indicate that Bill C-12 has had no impact on the exit rates of full-time workers. When we do control for the latter, we find the total impact to be approximately equal to the one obtained from quarters Q97/4 and Q95/04. This is strong evidence of strategic behaviour.
We have conducted the same analysis as above for recipiency durations. The results are contained in Tables 30–56. It would be rather tedious to discuss all the results in detail. Given that they are qualitatively similar to those concerning unemployment spells, we will instead focus on what follows in broad results.
As a general rule, the impact of the new EI legislation on recipiency durations is smaller in absolute value than its impact on the duration of unemployment spells. Given smaller sample sizes, the results are also usually less precise than previously.
Phase II has had no impact on the exit rates of any of the demographic groups considered, except for seasonal workers. In the latter case, Phase II has significantly increased their exit rates. Interestingly, Phase I has had a noticeable impact on the exit rates of men, adult workers, full-time workers, and seasonal workers, but none on women, young workers, part-time workers, and non-seasonal workers. These results are relatively robust. The total impact of Bill C-12 measured by the Q97/04 and Q96/04 quarters are consistent with these findings. On the other hand, the results based on quarters Q97/02 and Q96/02 perform relatively poorly, even when accounting for strategic behaviour. Indeed, only for seasonal workers do both estimators yield sensibly similar results.
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The Employment Insurance (EI) Act, which came into force with the passage of Bill C-12 in June 1996, was the most fundamental restructuring of the Unemployment Insurance program in the past 25 years. Such substantial changes are likely to affect the behaviour of many people. Given the variety of provisions contained in the Bill, various demographic groups are likely to be impacted differently and to different extents by these changes.
This research uses the fact that the implementation of Bill C-12 proceeded in two separate phases. With each phase being separated by a full quarter, it was possible to estimate the impact of each by quasi-experimental methods using appropriate Canadian Out of Employment Panel (COEP) surveys. Furthermore, given the numerous surveys available, it was also possible to provide two separate estimates of the total impact of Bill C-12. One of the estimates relied on surveys that were relatively close to the implementation date of the Bill. We have thus investigated whether strategic behaviour could be detected in the data. If unaccounted for, such behaviour can seriously bias the parameter estimates obtained from quasi-experimental methods.
In general, the econometric results indicate that the new EI legislation has had a negative impact on the duration of both the unemployment spells and the benefits recipiency spells. On the other hand, various demographic groups have reacted differently to the new provisions. When focusing on the duration of unemployment spells we find that men have shortened their spells considerably more than women, and that part-time workers have reacted similarly compared to full-time workers. On the other hand, young workers do not seem to have reacted to the new provisions, whereas seasonal and non-seasonal workers have adjusted their behaviour similarly.
Although the results using the duration of benefits recipiency usually agree with those using the duration of unemployment spells, it must be stressed that the recipiency durations of women and non-seasonal workers have not been affected by the new provisions of Bill C-12. This is in contrast to the results pertaining to the duration of unemployment spells and has important budgetary implications.
Finally, the data on unemployment spells revealed that a number of individuals have somehow managed to postpone their exit to some extent in order to benefit as much as possible from the Unemployment Insurance (UI) regime and to avoid being penalized eventually if and when they experience another spell. The econometric treatment of such strategic behaviour is not fully satisfactory. It nevertheless allowed us to identify the existence of such behaviour and was sufficient in many cases to reconcile the results from using two different sets of COEP surveys.
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Bertrand, J.-F., J.-Y. Duclos and M.Van Audenrode, “Unemployment Insurance Take-Up and Reemployment”, mimeo, Université Laval, 1999.
Browning, M., Income and Living Standards During an Unemployment Spell, Final Report, prepared for Human Resources Development Canada, 1998.
Cox, D.R., “Regression Models and Life Tables (with discussion)”, Journal of the Royal Statistical Society B, 34, pp. 187-220, 1972.
Jones, S.R.G., Evidence on the Labour Market Effects of Bill C-111: The Effects of the Benefit Rate Reduction and the Changes in Entitlement Regulations on Unemployment, Job Search Behaviour and New Job Quality, Final Report, prepared for Human Resources Development Canada, 1994.
Jones, S.R.G., Unemployment And Benefit Durations, Final Report, prepared for Human Resources Development Canada, 1998.
Meyer, B., “Unemployment Insurance and Unemployment Spells”, Econometrica, 58, pp. 757-782, 1990.
Storer, P. and M. Van Audenrode, The Uncertainty of Displacement, Working Paper 9823, Université Laval, 1998.
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