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Job Search Strategies and Unemployment Duration

  • Isabel Baumann
Open Access
Chapter
Part of the Life Course Research and Social Policies book series (LCRS, volume 5)

Abstract

About 90 % of the reemployed and unemployed workers in our study indicated they had searched for a job. While finding a job after displacement is challenging for all workers, some manage to return quickly to the active labor force whereas others remain unemployed for over a year or even arrive at the end of eligibility for unemployment benefits. These differences may translate into diverging career outcomes and quality of life. In particular, since in modern societies individuals’ social status strongly depends on their participation in the economic production system, unemployment, especially if it is extended, may trigger a feeling of failure and a downgrading of workers’ social status (Gallie and Paugam 2000: 1).

Keywords

Unemployment Benefit Unemployment Insurance Labor Market Status Temporary Employment Unemployed Worker 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

About 90 % of the reemployed and unemployed workers in our study indicated they had searched for a job. While finding a job after displacement is challenging for all workers, some manage to return quickly to the active labor force whereas others remain unemployed for over a year or even arrive at the end of eligibility for unemployment benefits.1 These differences may translate into diverging career outcomes and quality of life. In particular, since in modern societies individuals’ social status strongly depends on their participation in the economic production system, unemployment, especially if it is extended, may trigger a feeling of failure and a downgrading of workers’ social status (Gallie and Paugam 2000: 1).

Earlier studies have suggested that activation of the social network improves job seekers’ reemployment prospects, not only in terms of reemployment chances but also of job quality. This leads to our hypothesis H3 (see Sect.  1.4) that workers who found their new job through former colleagues, acquaintances, friends or family manage to return more quickly to jobs of better quality.

In this chapter we analyze the use of different job search strategies and their effect on workers’ success to find a job. We then examine how long displaced workers spent on their job search and which factors are most strongly related to short spells of unemployment. Finally, we briefly discuss the transition out of the labor force to training, childcare and disability.

5.1 Job Search Strategies

Workers are not passive victims of job loss but can actively try to influence their labor market outcomes (Kalleberg 2009: 14). Activating one’s social network, an intensive job search, or increasing the geographical job search radius are assumed to increase the likelihood of a successful job search – everything else being constant (Granovetter 1995 [1974]; Marsden and Gorman 2001: 470; Burgess and Low 1998: 242; Kaufmann et al. 2004).

In order to maintain their financial security while they search for a job, workers can apply for unemployment benefits on condition that they paid into the mandatory unemployment insurance for at least 1 year within the preceding 2 years while they were employed. We find for our sample that the likelihood of applying for benefits depends on the workers’ labor market status. Among the still or again unemployed workers 98 % had applied, but only 66 % of the reemployed and 73 % of the labor force dropouts. The difference between the unemployed and the reemployed suggests that the reemployed either anticipated their rapid reemployment or that they found a job even before they became unemployed.

5.1.1 The Application Process

With respect to information channels we find that 16 % of the workers found their new job through a job offer in the press and the same proportion through their former colleagues (see Fig. 5.1). 14 % managed to get back into the active labor force with the aid of a private employment agency and the same proportion found their new job through a job offer in the Internet. 10 % of the workers found their job due to a spontaneous application. 9 % of the workers found their job because of information provided by family or friends and 8 % by acquaintances. The public employment agency directly helped 4 % of the workers to find their new job.
Fig. 5.1

Information channel for finding new job. Note: N = 499. Dark gray: informal search methods, light gray: formal search methods

A common distinction in the literature is between formal and informal job search methods (Granovetter 1973: 1372). Formal methods represent those where job seekers use formal intermediaries such as agencies or advertisements, while informal methods consist in job search through personal contacts. Among the workers in our sample who found a new job, 34 % of the workers used informal methods such as former colleagues, family, friends or acquaintances (dark gray in Fig. 5.1).

The result on the proportion of jobs found through personal contacts corresponds to the findings from other Swiss and European studies. A literature review shows that across Europe between 31 and 47 % of job seekers in Europe find their employment through their social network (Marsden and Gorman 2001: 479). Yet these job seekers are not necessarily unemployed as is the case in our study. However, a Swiss study that focuses on unemployed workers’ use of their social ties in job search also reveals that about a third of the workers learned about their new job from their personal contacts (Bonoli et al. 2013: 67). Accordingly, this job search strategy seems to be one of the most important instruments to help workers return to the active labor force.

A descriptive analysis of the determinants of the use of different job search channels reveals that workers aged over 55 tend to find their job more frequently (42 %) though their social network than workers aged under 30 (28 %). The differences are statistically significant, but the relationship is not linear. In contrast, there are no significant differences in the use of personal contacts by nationality, gender or collar.

Granovetter (1995 [1974]: 13–14) – focusing on managers, professionals and technicians – argues that job search through social contacts leads to employment in jobs of better quality. For our data, we find that workers who found their new job through social networks experience a stronger wage loss than workers who found their job through other channels (−6 % versus −2 %). If we distinguish between jobs found through colleagues on the one hand and jobs found through other contacts on the other hand, we find that workers who used the first method experienced wage losses of −5 % and those who used the second method experienced a decline in wages of −6 %. With respect to contract type, risk of job loss or skill mismatch in the new position, finding a job through personal contacts did not seem to have a positive effect on the quality of the new job. The result remains unchanged if we restrict the analysis to the managers, professionals and technicians in our sample (the group of occupations analyzed by Granovetter 1995 [1974]).

Moreover, we find no evidence for a link between job search through the social network and reduced unemployment duration either. Although a larger proportion of the workers who found their job through colleagues, friends or acquaintances were reemployed within less than 1 month (34 %) as compared to the workers who found their job through other channels (28 %), the opposite is the case if we consider the proportion of workers who found their job within 4 months: 57 % of the workers who found their job through the personal network versus 65 % of the workers who found their job through other channels.

It may be assumed that workers who are active in associations are more likely to find a job or to find a job within short notice, as they have access to a larger network of individuals. Among the workers in our sample, 77 % are member of at least one association. Many are members of several associations (see Fig. 5.2). In fact, 46 % are members of a sports club, 37 % of a trade union, 28 % of a church, 14 % of a charity organization, 10 % of a neighbors’ association, 6 % of a political party, 5 % of a migrant association and 18 % of other types of associations.
Fig. 5.2

Membership of associations. N = 677. Multiple answers per respondent possible; lines do not sum up to 100 %. Reading example: 39 % of the respondents indicated being an active member of a sports club, 7 % indicated being a passive member of a sports club

A descriptive analysis of the workers’ labor market status by membership in an association reveals that workers who are members of at least one association are not more likely to be reemployed than workers who are not members of any association. With respect to the unemployed, there is a small difference: among the individuals who did not belong to any association 19 % were unemployed, while among the workers who were members only 15 % were unemployed. The differences may stem from age differences as we find that among the non-members 10 % retired but among the members 13 % went into retirement. The difference is, however, not significant. Turning to the duration of job search, we find that the differences between the non-members and members in associations are even smaller. Accordingly, membership of an association does not seem to be linked to the success of job search.

Figure 5.3 illustrates the number of job applications workers had written by their labor market status at the moment of the survey. In all labor market status categories we find workers who have written over 100 applications. While it is less surprising that 44 % of the unemployed and 13 % of the reemployed sent out over 100 applications, it is interesting that this was also the case for 36 % of the labor force dropouts and 6 % of the retired workers. This shows that some of the labor force dropouts and retirees tried hard to find a job but probably were not successful and therefore quit the labor market. At the same time, 43 % of the labor force dropouts and 22 % of the retirees applied less than six times and thus quit the labor market without making much effort to search for a job. Unsurprisingly, we find from Fig. 5.3 that the unemployed applied more frequently for jobs than the reemployed. One reason for this finding is that workers who applied for unemployment benefits are obliged to actively search for jobs. Moreover, it indicates that most of the unemployed workers were not voluntarily without employment.
Fig. 5.3

Number of applications by labor market status. N = 499. Reading example: 30 % of the reemployed respondents indicated that they applied less than six times to a job before they were accepted. 10 % of the reemployed indicated that they applied 6–10 times

5.2 Other Strategies of Job Search: Commuting, Training, Temporary Jobs

One strategy to increase the number of potential jobs available is geographical mobility, either in the form of moving house or commuting longer distances. With respect to the first strategy, our analysis shows that only 4 % of the workers in our sample relocated. This result may, however, be biased since those who had moved house more than 1 year before we conducted the survey did not receive our questionnaire.2 But since only about 10 % of the addresses were invalid, relocation seems to have little importance as job search strategy.

By contrast, a large number of workers accepted a job in a more distant location than their pre-displacement job. Among the reemployed workers, 50 % had a longer journey to their new workplace than before they lost their job. More precisely, 23 % traveled more than 30 min longer per day and 27 % traveled between 5 and 30 min longer. 22 % commuted daily for about the same time as before displacement. Finally, 18 % traveled between 5 and 30 min less per day and 9 % traveled more than 30 min less. Data for the US suggests that commuting is one of the activities that individuals most strongly dislike (Kahneman et al. 2004: 432). Accordingly, many workers seemed willing to accept the drawback of longer traveling distances in order to be able to return to a job.

The change in commuting time seems to be related to workers’ education. 32 % of the workers with a tertiary degree accepted a job further away from home than their former job, while this was the case for only 19 % of the reemployed with upper secondary education or less. With respect to the question whether commuting longer distances pays off in terms of wage gains, a descriptive analysis reveals that workers who commute longer distances experience on average a wage loss of 4 % while workers who commute about the same distance only experience a loss of 1 %. Workers who commute shorter distances also earn on average 4 % less in the new job as compared to the former job. An OLS regression does not reveal a significant correlation between change in commuting distance and change in wages. We thus do not find any evidence in our data that commuting is compensated for by wage gains.

Workers may enhance their labor market prospects if they complete a training course. Among the participants of our survey, 23 % attended training while searching for a job and for 57 % of them the training lasted longer than 1 month. In 41 % of the cases the workers learned new skills, 38 % attended a training course to update their skills and 18 % did some vocational retraining. Our analysis reveals a link between longer unemployment durations and having completed a training course – possibly a consequence of active labor market policies implemented by the public job placement agencies (RAV/ORP). This suggests that workers experiencing longer phases of unemployment decide or are obliged to update their skills or learn another occupation in order to enhance their labor market prospects. Furthermore we find that workers who completed a training course did not have significantly better chances of being be reemployed than workers who did not (77 % as compared to 75 %). Workers who attended a training course also did not have higher earnings or a better job match in their new job than other workers. These findings may be the result of a selection effect: we do not know if the workers who attended a training course improved their labor market outcome as compared to a counterfactual situation.

The transition from the pre-displacement position to a stable job may not be direct; workers may be displaced again or accept temporary jobs. With respect to temporary employment there is a large debate as to whether it functions as a stepping stone into regular employment by serving as screening for employers and as a means to gain work experience for employees. While some studies found evidence for this mechanism for some countries (e.g. Booth et al. 2002 for the UK; Gebel 2013 for Germany and the UK), the results for other countries are more ambiguous (e.g. De Graaf-Zijl et al. 2011 for the Netherlands; Gebel 2013 for Switzerland).

Our analysis shows that – taking all workers together – 18 % (n = 106) indicated that they were employed in a temporary job. 69 % (n = 73) of them were reemployed and 31 % (n = 33) were either unemployed, retired or out of the labor force when we surveyed them. It would be interesting to examine whether having been employed in a temporary job is related to reemployment in a higher quality job. However, as Gebel (2013) has pointed out it is highly probable that there is a selection effect leading less employable workers into temporary jobs. Indeed, we find that workers who have not been in temporary employment are significantly better off than those who have been in temporary employment in terms of unemployment duration (controlling for age, sex and education). Nonetheless, we cannot exclude that for the workers who were employed in a temporary job this position may have improved their employability and subsequently may have served them as a means to leave unemployment.

Earlier studies have shown that it is not unusual that new employment relations are dissolved quickly – simply because many of them turn out to be bad matches (Farber 1998). Repeated job separations have proved to be painful if they are involuntary because they are accompanied by great uncertainty about the workers’ career (Stevens 1997: 176). 66 % of the reemployed workers in our sample did not lose their new job. In contrast, 4 % were dismissed again from their new job, 12 % quit their post-displacement job of their own will and – as we have seen earlier – the remaining 18 % accepted a job with a temporary contract. Among the small number of workers (4 % or n = 26) who found a job but were dismissed again, two-thirds were reemployed at the moment of the survey. The other third, however, were still unemployed. For the workers who quit their post-displacement job of their own will, the pattern is different: only 10 % of them were unemployed or out of the labor force at the moment of the survey; the other 90 % were reemployed. This suggests that those quitting voluntarily left their first post-displacement job for a better job. If we look at the socio-demographic characteristics of workers with multiple job separations, we find that younger workers are substantially more likely to voluntarily quit their new job than older workers (17 % for workers aged under 40 as compared to 3 % for workers aged over 55). With respect to involuntary repeated job loss there are no differences between older and younger workers.

5.3 Unemployment Duration

In Switzerland, about half of all the job seekers who receive unemployment benefits from the public unemployment insurance are reemployed within 6 months (OECD Statistics).3 At the same time about 20 % of the unemployed do not find a job within 1 year and are thus defined as long-term unemployed (Babey 2011, 2012).4 About 15 % of the job seekers reach the end of the eligibility for unemployment benefits, which occurs for a typical worker after 18 months of receiving unemployment benefits (SECO 2010, 2011).5

The analysis of the duration of job search of the workers in our sample reveals similar results. The variable “unemployment duration” is a construct of survey and register data. In the survey, we asked workers how long they searched for a job. This question is somewhat ambiguous since workers may have started job search before they actually lost their job or only some weeks after. In the register data we may be confronted with similar ambiguities: workers may first have tried to find a job without signing up for unemployment benefits. Thus, this variable is not totally unambiguous. Our analysis only includes workers who either searched for a job or who described themselves as unemployed at the moment of the survey. In contrast, workers who directly transited into retirement or dropped out of the labor force are not considered.

Figure 5.4 shows that 36 % of the workers were reemployed within 2 months and 47 % within 4 months. Thus almost half of all the workers in our sample managed to return quickly to the labor force. At the same time 12 % of the workers were unemployed for over 12 months and thus long-term unemployed. 20 % of the workers were still unemployed when we surveyed them. From these still or again unemployed workers, 88 % were unemployed for more than 1 year and 19 % for more than 2 years. Most critical is probably the situation of 3 % of the respondents (representing 21 % of the unemployed workers) who arrived at the end of their eligibility for unemployment benefits (not shown in Fig. 5.4).
Fig. 5.4

Unemployment duration. N = 755. The analysis only includes workers who were either reemployed or unemployed when we surveyed them

Regarding the workers’ unemployment duration, a Finnish study on plant closure in manufacturing shows that 44 % of the workers were reemployed after 3 months, and 57 % after 11 months (Jolkkonen et al. 2012: 87). Another Swiss study of job loss in the manufacturing sector reports that 68 % of the workforce was reemployed within 3 months but 2 years after displacement still 20 % of the workers were searching for a job (Wyss 2009: 28, 40). In our sample 36 % of the workers were reemployed after 2 months and 47 % after 4 months, but after 12 months only 69 % of the job seekers were back in a job. Common to all the results is the pattern of rapid reemployment of large proportions of displaced workers within the first few months which gives then way to a slowing down rate of return to work.

In our survey we asked workers whether the duration of job search corresponded to their expectations. While 55 % of the workers indicated that the job search took less time than they estimated, for 33 % the duration was longer than anticipated. Only for 13 % of the respondents did the duration correspond to their expectations. We tested whether the individual factors age, sex, education, occupation and locus of control6 predicted the correspondence between unemployment duration and expectation, but found no evidence for an association.

Earlier studies have consistently shown that with increasing unemployment duration, workers’ likelihood of exiting unemployment decreases (e.g. Steiner 2001: 103 and Gebel 2009: 677 for West Germany or van den Berg et and van Ours 1996 for the US). Regarding the explanation of this phenomenon there is, however, a debate in the literature (Machin and Manning 1999: 12): according to some studies, unobserved heterogeneity explains this outcome, whereby the most employable workers leave unemployment quickly while the least employable remain in the group of the unemployed. Other authors have argued that there is true duration dependence, meaning either that long spells of unemployment reduce reemployment prospects because the workers lose their knowhow, motivation and self-confidence or that employers perceive long unemployment spells as a negative signal of workers’ abilities (Pissarides 1992; Flückiger 2002: 15; Eriksson and Rooth 2014: 1029; Kroft et al. 2013: 1128).

It is likely that the same factors that are associated with workers’ reemployment prospects – as discussed in Chap.  3 – are relevant for rapid reemployment. A descriptive analysis (not shown) reveals that there are substantial differences in the duration of job search by age, district unemployment rate and plant. In a next step we therefore first analyze the workers’ outflow of unemployment by age before we look into a larger range of factors associated with exit from unemployment. The workers’ outflow from unemployment by age is demonstrated by means of a Kaplan-Meier survival analysis. The analysis is based on the same subsample as Fig. 5.4 above. The result – which simply shows how the proportion of job seekers decreases over time – is illustrated in Fig. 5.5. The different lines represent six different age groups.
Fig. 5.5

Survival curve for job seekers according to age (Kaplan-Meier survival estimate). N = 747. Note: Job search duration 1 = less than 1 month, 2 = 1–2 months, 3 = 3–4 months, 4 = 5–6 months, 5 = 6–9 months, 6 = 10–12 months, 7 = 13–18 months, 8 = 18–24 months, 9 = more than 24 months, 10 = still unemployed. The x-axis represents the unemployment duration, 1 standing for an unemployment duration of less than 1 month, 2 for an unemployment duration of 1–2 months and so forth. The y-axis represents the proportion of workers searching for a job, 1.00 meaning 100 %, 0.75 for 75 % and so forth. Reading example: Among the displaced workers over 60 years, no one went from unemployment to employment within less than 1 month. 1–2 months after job loss, about 4 % of the entire group of workers over 60 had found a job and after 3–4 months about 6 % of them. 5–6 months after job loss another 2 % of the entire group of workers over 60 had found a job and after 6–9 months another 2 %. 10–12 months after job loss about 2 % of the entire group of workers over 60 had found a job and after 13–18 months another 3 %. After 18 months, no more workers of this age group managed to transit from unemployment to employment and the survival curve stabilizes at 75 %, meaning that 25 % of the workers of this age group had found a job while the remaining 75 % has not

For the youngest age group of the under-30s the curve is steep: within less than 1 month, already 40 % had left the group of the job seekers because they had found a job or quit the labor market for other reasons. One to two months after job loss, another 30 % of the youngest age group had exited the group of the job seekers. About 6 months after displacement less than 25 % of the youngest workers were still searching for a job. Then, the curve flattens and after more than 24 months only about 10 % of the workers below the age of 30 remained unemployed. In strong contrast, the survival curve of the oldest worker cohort (aged 60 and over) is flat. 5–6 months after displacement only about 15 % of those older workers who were searching for a job were successful, and at the moment of the survey still about 75 % of them were searching for a job. This oldest age group clearly is an outlier, which is unsurprising given that the workers in this group have less than 5 years until they become retired and employers may therefore be reluctant to hire them. However, we also observe long unemployment durations for the second oldest age group of the 55 to 59 year olds. It takes 9–12 months of job search for 50 % of them to have found new employment. The other age cohorts lie in between the two extremes of the youngest and the two oldest age groups. It is noteworthy that the curves are aligned in a strictly linear pattern with respect to age.

In a next step we examine the effect of a larger array of factors on unemployment duration. We again run logistic regression analyses for being reemployed within two and within 12 months, respectively, controlling for sex, civil status, country of residence, district unemployment rate, plant, education, occupation, and nationality.7 The results are presented in Figs. 5.6 and 5.7.
Fig. 5.6

Average Marginal Effects (AME) for a binomial logistic regression for being reemployed within 2 months. N = 567. Note: Significance levels: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors are clustered at the plant level. Pseudo R2 = 0.22. Reading example: As compared to women, men were 6 percentage points more likely to be reemployed within 2 months

Fig. 5.7

Average Marginal Effects (AME) for a binomial logistic regression for being reemployed within 12 months. N = 567. Note: Significance levels: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors are clustered at the plant level. Pseudo R2 = 0.32. Reading example: As compared to women, men were 12 percentage points more likely to be reemployed within 12 months

We find strong differences across age groups for reemployment both within two and within 12 months. Workers aged over 30 are substantially less likely to be reemployed in a short time than younger workers. More precisely, workers aged 30–39 are 20 percentage points less likely to be reemployed within 2 months and 10 percentage points less likely to be reemployed within 12 months than workers below the age of 30. Workers aged 40–49 are 12 percentage points less likely to be reemployed within 12 months than the youngest age group. For workers over the age of 50 the reemployment prospects within two and 12 months are even lower. As an example, workers between 55 and 59 are 39 and 37 percentage points less likely to be reemployed within two and 12 months. The result that older workers are much more at risk of long unemployment durations confirms earlier findings for Switzerland (Wyss 2009: 40, 2010: 31) and the US (Kruse 1988: 411).

The nonlinear outcome for the effect of district unemployment rate on reemployment within 2 months with higher rates of reemployment in areas with higher unemployment is counterintuitive and difficult to interpret. Probably this effect comes about because of strong collinearity between plant and district unemployment rate: while the unemployment rate in the district of Plant 1 (Geneva) was never below 6 %, the unemployment rate in the district of Plant 4 (Bern) was never above 4 %. Our result for the likelihood of reemployment within 12 months is more plausible, being 7 percentage points lower for workers who were displaced in a macroeconomic context of over 6.6–7.2 % of unemployment as compared to workers displaced in a context of 1.6–2.5 % of unemployment.

We observe that rapid reemployment varies massively across plants. Workers from Plants 2, 3, 4 and 5 located in the canton of Bern and in North-Western Switzerland are 20–42 percentage points more likely to be reemployed within two or within 12 months than workers from Plant 1 located in Geneva. This suggests that unobservable factors specific to the plants trigger these differences.

Our analysis reveals a positive effect of education on reemployment within 2 months. Workers with tertiary education are 12 percentage points more likely to be reemployed within this time than workers with less than upper secondary education. A similar result has been found by Wyss (2009, 2010) for Switzerland and by Kruse (1988) for the US, who showed that low-qualified workers are more likely to experience long spells of unemployment than high-qualified workers.

With respect to occupation, we observe that machine operators and workers in elementary occupations are 19 and 20 percentage points less likely to be reemployed within 2 months respectively, and 21 and 29 percentage points less likely to be reemployed within 12 months as compared to clerks. This result is interesting in the light of our finding from Chap.  3, which only provides weak evidence for lower reemployment prospects for blue-collar workers as compared to white-collar workers. However, when it comes to unemployment duration, low-qualified blue-collar workers are confronted with considerable disadvantages. This finding corresponds to earlier results by Kruse (1988), who reports for the US that sales workers have shorter spells of unemployment than blue-collar workers. Our analysis also shows that professionals have a 19 percentage points lower likelihood of being reemployed within 2 months as compared to clerks, which possibly shows that the search and recruitment process for highly qualified jobs takes longer.

With respect to nationality, we find that workers with German or Austrian nationality have a 9 and 14 percentage points higher probability of being reemployed within two and 12 months respectively, as compared to Swiss workers. At the same time, workers with a citizenship from outside the European Union have a 16 or 21 percentage points lower likelihood of being reemployed within two and 12 months than those with Swiss citizenship.

Finally, while civil status has no effect, men are 12 percentage points more likely to find a job within 12 months than women. This result may be due to the fact that among the married workers, men tend to be the main breadwinner and subsequently more often go back to employment within 1 year. Indeed, some earlier studies have found that married workers have shorter spells of unemployment because of higher financial responsibilities (Kruse 1988: 411; Teachman et al. 1994). Although the effect of being married in Figs. 5.6 and 5.7 goes in the expected direction, we do not find a significant result with respect to the workers’ marital status.8

5.4 Conclusion

Our analysis shows that workers use a variety of job search strategies to find a new job. While commuting longer distances is an option that many workers accepted, only a few relocated. Over a third of all new positions were found through social contacts, mainly through former colleagues. However, our hypothesis H3 that jobs found through individuals’ social network are better in terms of quality is not confirmed: Workers who found a job through their social network experienced higher wage cuts than workers who used other channels . Also with respect to other aspects of job quality such as contract type, risk of job loss or skill mismatch, using informal job search channels does not seem to be an advantage.

Most displaced workers either tended to return very quickly to the labor force or remained long-term unemployed. We found that a third of the workers were reemployed within 2 months and almost half of them within 4 months. At the same time 12 % of the workers were unemployed for over a year and 20 % were still unemployed when we surveyed them. Unemployment duration is principally driven by workers’ age. From the age of 55, unemployment durations were substantially longer and from the age of 60 even longer as compared with younger workers. Moreover, blue-collars, workers from non-EU countries and workers in Plant 1 (Geneva) encountered particularly strong difficulties in returning to employment within a short time.

The duration of unemployment is not only important because the job search phase is often difficult and accompanied by financial insecurity, but also because it seems to have an effect on the reemployment conditions. This may be due to depreciation of workers’ skills during long phases of unemployment or due to the stigma employers attach to spells of unemployment. In this context it is important to link the duration of unemployment to the quality of the job in which workers are reemployed, a question which we pursue – among others – in the next chapters. From a policy perspective it seems to be crucial to manage to bring workers back to employment quickly, preventing the negative impact of long unemployment spells. Providing workers with advice for job search, motivating them to apply intensely and promoting their participation in active labor market policies have proven to be the most effective ways to address this challenge.

Footnotes

  1. 1.

    Eligibility for unemployment benefits expires in Switzerland for the median unemployed worker after 18 months.

  2. 2.

    The Swiss post office forwards the mail of people who have relocated to their new address for one year.

  3. 3.

    Over the period of our survey the figures were 52 % (2009), 44 % (2010) and 42 % (2011).

  4. 4.

    More precisely, 13 % in 2009, 21 % in 2010 and 20 % in 2011.

  5. 5.

    Over the period of our survey the figures were 15 % (2009) and 18 % (2010). For the year 2011 there is no information available because the revision of the unemployment insurance in this year led to a particularly high number of job seekers who reached the end of eligibility for unemployment insurance. Source: SECO (2010, 2011) .

  6. 6.

    The concept of locus of control describes people’s belief about how much they can control events that affect them (Goldsmith et al. 1996: 337).

  7. 7.

    We tested other models where we included tenure, pre-displacement wage, job search channel for new job and locus of control. But since these variables did not provide significant effects we did not include them in the model presented. The finding that personality traits do not have a significant effect on the transition from unemployment to employment is in line with some of the results from a German longitudinal study on the transition from school to vocational training (Protsch and Dieckhoff 2011: 83–4).

  8. 8.

    We additionally estimated an interaction effect between sex and civil status. This effect was however not significant.

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Authors and Affiliations

  • Isabel Baumann
    • 1
    • 2
  1. 1.Center for Health SciencesZurich University of Applied SciencesWinterthurSwitzerland
  2. 2.National Centre of Competence in Research “Overcoming Vulnerability - Life Course Perspectives” - NCCR LIVESLausanneSwitzerland

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