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A. Sample characteristics Table 1 summarizes the characteristics of the 1995 COEP sample. The three columns of this table divide the sample into three groups: all individuals, persons who succeed in becoming re-employed and the subset of the re-employed who did not return to their previous job. Several features of the sample are worthy of note. First, the individuals examined are rather young since the average age is just under 29 years for the entire sample. This compares to an average age of 37 years reported for the 1993 COEP by Crémieux et al (1995b). The percentage of persons who found a new job was 82.4 percent for the 1995 COEP versus 70.2 percent for the 1993 sample. This success rate and the percentage finding a job may be indicative of more favourable conditions in 1995. Comparing information across the columns of Table 1, it is apparent that persons who find a new job are slightly more likely to have a seasonal job than the overall average and also have marginally more experience in the job lost. Persons who do not return to the same job are much less likely to have had a seasonal job and also have less experience on the job lost than persons who do return to the former job. Persons who did not return to the same job are less likely to be eligible for UI benefits and this shows up as a lower rate of receipt of benefits. Table 2 looks at some features of the individuals in the sample. This table looks at statistical determinants of the probability that a survey participant answers the second part of the survey (the "wave 2"interview - roughly one year after the date of job loss). The probability of response increases with factors associated with stability such as age, marital status, employment status and participation in the labour force. There is no statistically significant relationship between the fact that the lost job was seasonal in nature and the probability of continuing in the survey through to the second-wave interview. This is important because if such a relationship had been found, then results regarding the effect of C-17 on weeks worked per year could be biased due to greater sample attrition of persons in seasonal jobs. Table 3 provides information regarding exhaustion of benefits in the 2 COEP samples. It is significant that the percentage of job-finders who exhaust their benefits was 19 percent in 1993 versus 43 percent in 1995. The corresponding figures are 29 percent and 84 percent for those not finding a new job. This points to a significant increase in the number of persons exhausting benefits, much as would be expected given that C-17 reduced benefit entitlements for many persons. On the other hand, job-finding rates actually rose between 1993 and 1995 for both exhausters and non-exhausters. This suggests that re-employment outcomes were not significantly harmed by reduced benefit entitlements under C-17. This issue is analysed in greater detail later in this report where statistical methods are used to control for the effects of many observable determinants of the probability of finding a job.
The analysis of wage losses reveals an interesting difference between the 1993 and 1995 COEP samples. While average wage losses are comparable for the full samples (-0.9 percent in 1993 and -4.1 percent in 1995), differences become apparent if "extreme" variations are removed from the sample3. For the 1993 COEP, this has no change on the average wage loss while in the 1995 COEP the wage change is positive (+0.7 percent) without extreme variations. This seems to suggest that the more negative wage loss for the full sample in 1995 reflects the influence of these extreme variations. The presence of such extreme variations in the 1995 COEP could be evidence that large wage losses are linked to a higher exhaustion rate of UI benefits . B. UI and the probability of remaining in the labour force The results presented in Table 4 allow us to judge whether the presence of unemployment insurance benefits has an impact upon the likelihood that a worker leaves the labour force after losing a job. It is possible that one effect of the benefit reductions implied by C-17 was to push some workers out of the labour force and perhaps onto social assistance. The four columns of the table present results both with and without temporary layoffs and also allow for the UI effects to differ for those who actually claimed benefits versus those who did not claim the benefits to which they were entitled. Variables with a positive coefficient in this table increase the probability that a person will have left the labour force by the time of the follow-up survey. As expected, several factors other than unemployment insurance benefits also have an impact upon the probability of leaving the labour force. The probability of leaving the labour force is higher for older workers, for the disabled and for men. On the other hand, persons who had a recall date and those with higher wages on the former job had lower probabilities of leaving the labour force. Unemployment insurance benefits do have a negative impact upon the probability of leaving the labour force although the effect is of roughly the same magnitude regardless of the duration of benefits. Coefficients are quite similar for the entire sample and for the sub-sample excluding workers who were laid-off temporarily. When UI coefficients are allowed to differ for those who did or did not claim benefits, the effects are stronger and more significant for the claimants. This is an interesting result since it suggests that claimants and non-claimants who both have the right to exactly the same benefits do behave differently nevertheless. When interpreting this, however, it must be noted that in formal statistical tests the hypothesis of identical behaviour cannot be rejected. C. UI and the probability of finding a new job This section looks at the probability that an unemployed worker actually goes on to find a new job. The results relating this probability to Unemployment Insurance and other observable characteristics are found in Table 5. The first column of the table includes all job losers while the second only uses individuals who stay in the labour force. The third column removes both persons who leave the labour force and workers who return to their former employer after a temporary layoff (labelled "recalls" in Table 5). In many ways, the results of the table reflect the well-known disincentive effect of UI: persons with UI benefits can be more demanding when searching for a new job and as a consequence the presence of UI benefits lowers the probability that a job will be found by the survey date. The results in the table support this interpretation. Workers with long benefit durations have lower probabilities of finding a new job. It is interesting to combine this information with the observation from Table Three that both benefit exhaustion and job-finding rates rose between 1993 and 1995. There is some evidence in Table 5 that workers with short benefit entitlement periods (precisely the workers whom we might expect to see exhausting benefits) are actually more likely to find a new job than workers who were not eligible for benefits. This could provide further support for the hypothesis that workers with short benefit entitlement periods under C-17 did not suffer a reduced ability to obtain a new job. Related to the issue of finding a new job is the length of time that the job will last. When evaluating the impact of the UI disincentive effect on the probability of finding a new job, it is important to examine some measure of job duration. While UI benefits could make some workers less likely to find a new job, it should also be true that those who actually do find a job will be more likley to be happy with it and thus less likely to leave after only a short spell. To incorporate this factor, we conduct a statistical analysis of the probability that persons who find a new job will lose it again by survey time. If UI is playing a positive role in job quality, then this probability should vary inversely with the number of weeks of UI benefits received. The six columns of Table 6 contain results for both the entire sample and the sub-sample with persons returning to their old job removed. There are also results with binary variables added to indicate whether UI benefits were exhausted (columns three and four) as well as whether UI benefits were actually claimed (columns five and six). In all cases, longer UI benefits are associated with a lower probability that the job will be lost by survey time. The fact that benefits were exhausted does not have a significant impact upon the probability that a new job is lost again by survey time. The act of claiming benefits does have a significant positive impact upon the probability of losing a job again. This coexistence of high claim probabilities and short job durations may be consistent with the presence of workers who only work just long enough to claim benefits. D. UI and the wage earned inthe new job Table 7 presents results that indicate how the logarithm of the wage earned in the new job varies with available observable factors and, in particular, the length of the period of UI benefit entitlement. As has been the case throughout this study, one set of results are for the full sample while another removes workers who return to the same job. The first two columns calculate UI benefit entitlement weeks using C-17 rules. The second set of columns uses a hypothetical number of benefit weeks calculated under the assumption that pre-C-17 rules still applied. These counter-factual results were included to allow for the possibility that the unemployed may have estimated their benefit entitlement based on their experience with pre-C-17 rules. If this were the case, these erroneous calculations might still be linked more closely with wage effects than are actual C-17 weeks. To interpret the coefficients obtained from this analysis, it is important to note that the variables used are defined so that persons with 50 weeks of benefits have a coefficient of zero. Coefficients for other groups then indicate whether they do better or worse than persons entitled to 50 weeks of benefits. For example, the first column of Table 7 gives a coeficient of 0.016 for the non-eligible and this means that persons not eligible for benefits had, on average, re-employment wages 1.6 percent higher than did persons entitled to 50 weeks of benefits. Looking at the results using actual C-17 rules (the first two columns of the table), we find that weeks of UI benefit entitlement of up to 40 weeks lead to higher re-employment wages than for the ineligible. Interestingly, this effect is strongest for quite low levels of benefit weeks: the new wage is 10.7 percent higher with 10 to 19 weeks than with 50 weeks. Using the incorrect pre-C-17 weeks variables yields lower UI wage effects and gives quite negative effects for persons who would have had 30 to 50 weeks under the pre-C-17 regime. This may reflect the fact that persons who had high pre-C-17 weeks but lower entitlements under C-17 did poorly in terms of wage outcomes. It also seems that this hypothetical variable has relatively little explanatory power. Table 8 illustrates how the relationship between weeks of UI benefits and wages in the re-employment job has changed under a series of UI policy regimes. Three situations are compared in this table: the results of the currrentstudy based on the 1995 COEP and those obtained when the same methodology was applied to the 1993 COEP data and the National Employment Services Survey (NESS) data used in Crémieux et al (1995a). The results using the two previous survey data sets show roughly similar results but the C-17 results show a departure from past trends. This is readily apparent in Figure One in which illustrates the trends in coefficient values from Table 8 using a graph. For the pre-C-17 studies, coefficients are most negative for the ineligible and then gradually rise toward the value of zero imposed for persons entitled to 50 weeks of benefits. This is consistent with the view that longer benefit entitlement periods have beneficial effects on wages in the new job, perhaps because the existence of longer benefit periods translates into a more thorough and selective search of new job opportunities. For example, Figure One shows the ineligble with new wages roughly 8 percent lower than those with 50 weeks of benefits while persons with 30 to 39 weeks of benefits had roughly 3 percent lower wages than the 50 week group. For the 1993 COEP sample, the better wage performance as benefit entitlements increase is constant throughout the entitlement categories while the relationship is more erratic for the NESS data. Overall, though, these two sample tell the same story. An entirely different pattern to the relationship between weeks of benefit entitlement and wages was found in 1995. While it was still true for the 1995 COEP sample that the ineligible fared the most poorly in terms of re-employment wages, the previous pattern of persons doing better and better as more weeks of benefits are available is no longer found. Instead, it is persons with 30 to 39 weeks of benefits who do the best in terms of new wages (about 7 percent better than those with 50 weeks). Of all the groups eligible for benefits, it is those entitled to 50 weeks who fare the poorest in terms of new wage outcome. The value of UI benefits is normalized to zero for the group with 50 weeks and we find positive values for all other groups except the ineligible. The change observed for the benefit weeks / new wage relationship may be related to the way that C-17 modified the distribution of weeks of benefits to which workers are entitled. Figure Two compares the distributions of weeks of benefit entitlement in the 1993 and 1995 COEP samples. The NESS and 1993 COEP samples show the highest concentration of numbers of benefit weeks at 50 weeks while the 1995 sample shows a distribution over the range of weeks of benefits with some concentration in the vicinity of 35 weeks of benefits. The distribution of insurable weeks changed far less dramatically so that the changes in benefit entitlements are attributable to policy changes in the rules determining benefit entitlements. It seems that the radical change in the distribution of weeks of benefit entitlement induced by C-17 had a corresponding effect on the relationship between UI weeks and wages. Interestingly, in both 1993 and 1995 the range of UI benefit weeks associated with the best wage outcomes in the new job was also roughly the range in which the heaviest concentration of numbers of benefit weeks was found. In the 1993 COEP this was found for the 50 weeks group while in the 1995 COEP it was around 30 weeks. This suggests a link between wage outcomes and the position of an unemployed person within the distribution of benefit weeks rather than wages and the actual number of weeks. It is possible that the changes to the rules determining benefit entitlement periods may have taken some workers by surprise. Such workers may have failed to plan their job search correctly and accepted a low wage when they discovered that their benefits were terminated. Indeed, such behaviour could potentially explain the larger number of extreme wage variations in the 1995 COEP data. To examine this possibility, a variable equal to the change in the number of benefit weeks under the pre-C-17 and C-17 regimes was created. When included in new wage regressions, however, this measure of "C-17 surprise potential" did not have a statistically significant effect. E. UI and non-wage compensation in the new job Table 9 permits analysis of how non-wage characteristics of jobs change due to job-to-job transitions. For each characteristic, the group of 2,448 job finders is classified according to the status of the old and new job. This table looks at characteristics such as the seasonal nature of a job, whether the job is unionized, whether a pension plan is provided, whether medical or dental benefits are provided and finally whether the job was full- or part-time. These indices of job quality give additional insights into the desirability of a job beyond that contained in the wage alone. A striking feature of these data is that the number or workers in seasonal jobs falls dramatically. While 618 workers reported that their old job was seasonal, the figure is only 124 for the new job. This could be due to C-17 since we find that 91 percent of persons in a seasonal job before the moment of loss do not classify their new job as seasonal. What is not clear is whether workers intend to stay long in these new non-seasonal jobs. There is also evidence that a large proportion of persons (23 percent) who previously were employed full-time lose this full-time status after the job-loss episode that earned them a place in the COEP sample. While this is partly compensated for by the high proportion of part-time jobs that become full-time, the net effect is a fall from 3,076 full-time jobs in the first job to only 2,775 full-time jobs in the new job. This effect manifests itself later in the study as a fall in weekly hours worked that may have a negative impact on weekly income. Table 10 provides evidence regarding the relationship between UI and non-wage job characteristics. The top half of the table is for the entire sample while the bottom portion only considers job loss other than temporary layoffs. Eligibility for UI benefits does not seem to increase the probability that a new job will be unionized or that it will have a pension plan. As a person becomes entitled for UI benefits through the 20-29 week range, the probability that a new job will be seasonal rises with the length of the benefit entitlement period. After this point, the probability that a new job will be seasonal in nature falls as UI benefits are available for a longer time. The category of persons entitled to 50 weeks as benefits has the lowest probability of finding a seasonal job. There are roughly positive impacts of the existence of longer benefits for job attributes such as the presence of medical plans, dental plans, and full-time status. This suggests that UI can have a positive effect upon both weekly wages and the value of total compensation when benefits as well as wages are measured.
F. UI and the expected duration of the new job In Table 11, we present results linking observable characteristics to the expected number of weeks to be spent in the new job. Results are presented for both the full sample and for the sub-sample of persons who do not return to their previous job. For each of these two samples, we first present results without the duration of the previous job and then add this duration. Omitting the duration on the previous job allows us to link personal characteristics with stability on the new job without allowing some of these stability effects to be captured through their impact in the previous job. The effect of some characteristics are unaffected by the absence or presence of temporary layoffs. As might be expected, persons with a longer duration on the past job have a higher anticipated number of weeks in the new job. For example, persons losing seasonal jobs, unionized jobs or living in regions with a higher regional unemployment rate tend to expect shorter durations for their new jobs. Relative to the province of Quebec, persons in all other provinces expect shorter job durations. This effect is most significant from a statistical viewpoint for the provinces other than Ontario. The effect of variables such as sex and the language of the interview are only found for the full sample. While men tend to expect shorter new jobs, this effect is smaller and not statisticallysignificant once temporary layoffs are excluded. Similarly, the positive effect of a language being conducted in English also becomes insignificant without temporary layoffs. Finally, effects of education are only significant without temporary layoffs. The only real pattern observed for the education variables is that university-educated persons anticipate shorter spells. For variables capturing the number of weeks of UI benefit entitlement, the general pattern observed here is that longer benefits increase the expected duration of the new job. This is at variance with the popular association of short employment spells and long periods of UI benefits due to the stereotypical "10/42" pattern of UI use. While results presented earlier in this study associated claiming UI with higher probabilities of new job loss, the key to the resolution of this puzzle may lie in the observation that (self-reported) expected job duration and actual outcomes may not always be consistent.
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