metadata: authors: 0: author: Mavridis, Dimitris
count: World,United Kingdom
docna: 0: docna: Can subjective well-being predict
            unemployment length ?
docty: Policy Research Working Paper
subtopic: Labor Markets,Gender and Development,Employment and Unemployment,Rural Labor Markets
teratopic: Macroeconomics and Economic Growth,Governance,Social Protections and Labor,Health, Nutrition and Population
abstracts: cdata!: This paper uses 16 waves of panel data
            from the British Household Panel Survey to evaluate the role
            of subjective well-being in determining labor market
            transitions. It confirms a previous finding in the
            literature: individuals report a fall in their happiness
            when they lose a job, but they report a smaller fall when
            they are surrounded by unemployed peers, an effect called
            the "social norm". The main results of interest
            are that job search effort and unemployment duration are
            affected by the utility differential between having a job
            and being unemployed. Since this differential is also
            affected by the social norm, it implies that when
            unemployment increases, the unemployed are happier and they
            reduce their search effort. These results indicate that
            unemployment hysteresis has labor supply causes.
geo_reg_key: 80475,80461
repnme: repnme: Can subjective well-being predict
            unemployment length ?
display_title: Can subjective well-being predict
            unemployment length
listing_relative_url: /research/2010/05/12327719/can-subjective-well-being-predict-unemployment-length
topicv3: Labor Markets m1326488 855,Gender m1327621 615,Jobs m1503931 2670,Unemployment m1504051 2747,Labor Supply m1504055 2751
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Governance,Social Protections and Labor,Health, Nutrition and Population
no_of_pages: 52
model_extractions: WPS5293 Policy Research Working Paper 5293 Can Subjective Well-Being Predict Unemployment Length?

_Dimitris Mavridis_ The World Bank Development Economics Vice Presidency Operations & Strategy Unit May 2010
Policy Research Working Paper 5293

# **Abstract**

This paper uses 16 waves of panel data from the British Household Panel Survey to evaluate the role of subjective well-being in determining labor market transitions. It confirms a previous finding in the literature: individuals report a fall in their happiness when they lose a job, but they report a smaller fall when they are surrounded by unemployed peers, an effect called the “social norm”. The main results of interest are that job search effort and unemployment duration are affected by the utility differential between having a job and being unemployed. Since this differential is also affected by the social norm, it implies that when unemployment increases, the unemployed are happier and they reduce their search effort. These results indicate that unemployment hysteresis has labor supply causes.

This paper—a product of the Operations & Strategy Unit, Development Economics Vice Presidency—is part of a larger effort in the department to study the determinants of unemployment and labor market transitions. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmavridis@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Can Subjective Well-Being Predict Unemployment Duration?

Dimitris Mavridis<sup>_∗_</sup> This version :May 1, 2010

> _∗_ Junior Professional Associate at DEC-OS, The World Bank. Email : mavridis.dimitris@gmail.com This paper is a revised and condensed version of my master’s thesis at the Paris School of Economics. I would like to thank Andrew Clark for invaluable supervision and comments.

1
# **Contents**

|**1**|**Intr**|**oduction**|**3**|
|---|---|---|---|
|**2**|**Lite**|**rature Review**|**4**|
||2.1|Subjective Well-Being and Labor Market Status<br>. . . . . . . . . . . . . . .|4|
||2.2|The Social Norm Efect of Unemployment . . . . . . . . . . . . . . . . . . .|5|
||2.3|Unemployment Duration and Subjective Well-Being<br>. . . . . . . . . . . . .|6|
|**3**|**Dat**|**a Description**|**7**|
|**4**|**Det**|**erminants of Subjective Well-Being**|**9**|
||4.1|Labor Market Status, Age, Income, Civic Status, Education and Health . .|11|
||4.2|The Social Norm Efect of Unemployment . . . . . . . . . . . . . . . . . . .|13|
|||4.2.1<br>Theory : a binary choice model with externalities . . . . . . . . . . .|13|
|||4.2.2<br>Empirical evidence : pooled data regressions<br>. . . . . . . . . . . . .|14|
|||4.2.3<br>Empirical evidence : results with panel data specifcation<br>. . . . . .|16|
||4.3|A note on the use of Pooled data vs Fixed Efect . . . . . . . . . . . . . . .|17|
|||4.3.1<br>Pooled Data<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|17|
|||4.3.2<br>Fixed Efects Regressions : individual fxed-efects<br>. . . . . . . . . .|18|
|**5**|**Un**|**employment Duration**|**20**|
||5.1|Determinants of duration<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|20|
|||5.1.1<br>Unemployment duration and well-being: how to fnd the causality<br>direction ? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|21|
||5.2|The role of social norms in duration<br>. . . . . . . . . . . . . . . . . . . . . .|24|
|||5.2.1<br>Social norms and duration : empirical evidence from the duration<br>model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|27|
||5.3|Is search intensity related to change in GHQ ?<br>. . . . . . . . . . . . . . . .|28|
||5.4|Conclusion on unemployment duration . . . . . . . . . . . . . . . . . . . . .|29|
|**6**|**Con**|**clusion**|**30**|
|**7**|**Ref**|**erences**|**31**|
|**8**|**Ap**|**pendix**|**34**|
||8.1|Is the GHQ-12 a good measure of Well-Being?<br>. . . . . . . . . . . . . . . .|34|
||8.2|Duration model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|35|
|||8.2.1<br>Cox Proportional Hazard<br>. . . . . . . . . . . . . . . . . . . . . . . .|36|
||8.3|Regression Results and tables . . . . . . . . . . . . . . . . . . . . . . . . . .|37|

2
# **1 Introduction**

This paper aims at showing that labor supply decisions are often made taking into account other’s labor supply, due to the existence of strong comparison effects. It also goes one step further in showing that job search effort is affected by comparisons with others. The dataset used is the BHPS (British Household Panel Survey). It is a representative sample of the British population, from which labor market status and a composite measure of self-reported well-being (to proxy for utility) are used.

Three distinctive results are presented. First, it is shown that upon losing their job, individuals report a fall in their well-being. This fall is reduced when there are more unemployed in one’s comparison group (household and region), referred to as the ”Social Norm Effect”, as in Clark (2003). This effect persists in panel data estimations were the unobserved individual heterogeneity is controlled for. The second finding is that unemployment duration is affected by this social norm effect. The more an individual reports feeling hurt when losing his job -(the happiness difference)- the shortest will be his duration in unemployment. The happiness difference is a good predictor of the duration of unemployment, even after controlling for demographic characteristics also affecting duration. The third result shows that job search effort is itself dependent on the happiness difference. An individual searches with more intensity when he reports a large happiness drop when entering unemployment.

The implications of these results are twofold. First, they shed light on our understanding of job search effort. The results suggest that search effort is positively dependent on _Ve − Vu_ , the difference in well-being an individual reports between being employed and jobless. As the payoff from being employed rises (falls), the unemployed will search more (less). Second, they provide a labor-supply explanation of unemployment hysteresis. Due to comparison effects, when an individual loses his job, he feels less bad if there are more unemployed around him. His utility is hence affected by other’s employment status. This will reduce his search effort and increase his unemployment duration, affecting then the search behavior of others. In the case of an exogenous macroeconomic shock that reduces labor demand, our findings suggest that labor supply will also shift to the left, _increasing unemployment_ and causing hysteresis.

The remaining of the paper is organized as follows. Section II provides a review of the literature on subjective well-being (SWB henceforth) and labor market status. Section III describes the 16 waves of the BHPS. Section IV shows the first results of importance. It first presents the determinants of SWB, and then the social norm effect. Both pooled and panel data specifications are explained. Section V introduces the determinants of unemployment duration and search effort. Section VI concludes.

3
# **2 Literature Review**

This section reviews the literature on subjective well-being (SWB) and labor market status. It summarizes two main findings, both relevant for the present thesis. The first finding is that individuals’ happiness is affected by their employment status. Those who lose their job feel significantly worse than when employed, far worse than their income loss would predict<sup>1</sup> . The second finding is that aggregate unemployment is also affecting individuals. There is a so called ”Social Norm” effect of unemployment, through which unemployed feel less hurt the higher the unemployment is in their reference group. Unemployment also affects those who are employed, although contradictory effects are found in the literature<sup>2</sup> . Based on these findings, this section presents the possibility that the social norm effect from unemployment might affect the search behaviour and the duration of unemployment. It then asks what policy questions arise.

## **2.1 Subjective Well-Being and Labor Market Status**

A large stream of research has been interested in the relationship between subjective wellbeing and labor market status. The social psychology literature precedes economics in this field. The idea conveyed in most of the works is that there are many non-pecuniary benefits from working. Unemployment deprives the former workers from latent functions such as social interactions, purposefulness, a time structure and a certain construction of identity (Jahoda, 1982). Hence, unemployed are worse off not just because of the loss of their wage income<sup>3</sup> . Earlier empirical work by Jackson, Stafford and Warr (1983) shows that well-being rises with the transition from unemployment to paid work. Although the sample used is not representative of the population, it is useful at highlighting the effect of transitions on happiness. Darity and Goldsmith (1996) provide an extensive summary of the social psychology literature on unemployment.

Empirical work from economists testing this view has been conducted since the early 1990s, when data on SWB became available through national household surveys including a section on well-being. Clark and Oswald (1994) use the first wave of the BHPS to find that unemployed are, on a raw average, half as happy as the employed. This result is corroborated by the studies of Korpi (1997), who uses Swedish data, Winkelmann and Winkelmann (1998), who use the German GSOEP, Woittiez and Theeuves (1998) who have Dutch data, and Frey and Stutzer (2000) who use a Swiss household survey. Data on other countries have also been available through the World Values Surveys (WVS) and through

> 1Clark (1998) finds that the income loss from losing a job explains only a quarter of the drop in well-being

> 2Di Tella _et al_ , (2001) find that unemployment negatively affects SWB (in developed countries), whereas Eggers _et al_ (2006) find a positive effect using Russian data.

> 3Proponents of this idea highlight the negative psychological impact of being unemployed. Summaries of the literature can be found in Fryer and Payne (1986), Warr _et al_ (1988), Feather (1990), Burchell (1992), Murphy and Athanasou (1999). Argyle (1989) is a reference book in social psychology with an extensive chapter on the GHQ measure and another one on unemployment

4
other European studies such as the ones used by Blanchflower (1996) and Di Tella (2001). What all these studies have in common is the result of lower levels of well-being for the unemployed.

A reverse causality issue can arise if one is limited to cross section data, as it might be easier for happy people to find a job. If inherently happy people are also more productive, better at work or simply more desirable to employers, then it is happiness that positively influences the chances of finding a job, and not the reverse. One way to isolate the causal impact is to use panel data and observe what happens to individuals’ happiness as they change status. This identification strategy is followed in this paper, and the panel data evidence on labor market transitions proves that the causality goes from labor status to happiness rather than the other way around.

# **2.2 The Social Norm Effect of Unemployment**

Individuals are affected by their employment status, but also by others’ employment. Di Tella _et al_ (2001) are among the first to test the impact of aggregate unemployment on individual’s well-being. They find that people have a preference for lower levels of aggregate unemployment. Their results, to be interpreted in a context of a trade-off between inflation and unemployment (a Phillips curve), show that individuals are also hurt by inflation although its effects are much lower. The finding is consistent with the literature on happiness in the sense that it provides evidence of strong comparison effects.

The classical analysis of Akerlof (1980) has been instrumental in the way economists think about social norms, their sustainability and their effect on individual’s behavior. In his model, a social norm precluding transactions at the market-clearing wage can cause unemployment and still be sustainable if deviation from the norm is costlier, in terms of reputation, than the monetary benefit from adhering to it. The higher the proportion of followers of the norm, the more sustainable it is - as it becomes more costly to deviate. Following this theoretical conclusion, the question that arose was whether or not employment can be considered as a social norm. Supposing it can be, then it can be tested whether unemployment hurts more if one’s reference group has little of it - that is if the norm is not followed.

Clark (2003) provides strong evidence supporting this hypothesis. He finds that unemployment at regional, partner and household level positively, strongly affects well-being when the respondent is unemployed, the effect being higher for men. Backing this hypothesis one finds results from Russian data (Eggers _et al_ , 2006) and from South Africa (Powdthavee, 2006).

Stutzer and Lalive (2004) also test Akerlof’s theory. They instrument for the unobserved social norms by using a referendum on unemployment benefits to extract the voting patterns across localities in Switzerland, to proxy for social norms. To correct for the po5
tential reversed causation (regional unemployment causes the norm) they use a stratified approach, which is the variation in the proxy accounts for variation within regions. Their results suggest that, indeed, in cantons voting to reduce benefits (strong work ethic), the unemployed were more likely to find a job than in cantons voting for a rise in benefits (weaker work ethic). For those not having the same native language as the canton, the effect was lower. These results emphasize the view that unemployment can be interpreted as a social norm.

Given that unemployment reduces happiness, but that an increase in unemployment in the reference group attenuates this negative impact, the question has been raised to know whether the duration of unemployment is affected by the level of it in the individual’s reference group, broadly defined. This will be the main focus of this paper.

# **2.3 Unemployment Duration and Subjective Well-Being**

To know how SWB is related to unemployment duration, many papers ask if duration affects SWB; because it is relevant to know whether or not individuals adapt to unemployment. Clark (2006) finds that there is no (or little) evidence of habituation. After the initial drop in well-being when losing a job, individuals do not become happier with time unless they change status.

There is however another channel through which well-being and duration might be related. Looking for a job entails a costly effort, needing investment in readings ads, writing applications, mobilizing one’s network, etc. If the utility differences between states (employed and unemployed) is small, it might not be worth suffering the search cost for an outcome that is uncertain. This paper shows that in high unemployment regions, SWB falls less when individuals lose their job. Hence, the incentive to return to work is reduced, since the utility difference is smaller. This might affect search behavior, and unemployment duration might be longer, affecting in turn the behaviour of others in a self-reinforcing fashion. This mechanism is very similar to the one described in Akerlof (1980). If the norm is employment, and it is not much followed, there are less bad reputation effects from not following it. It suggest that shocks matter because they affect the labor supply behaviour. If this story holds, then there is a continuum of equilibria - as one’s status affects other’s search behaviour. The present thesis attempts to find whether or not the job search behaviour (and unemployment duration) is affected by the social norm effect. Future research should aim at modeling labor supply and job search including externalities - to capture the effect mentioned above.

6
# **3 Data Description**

The data set used in this paper is the British Household Panel Survey (BHPS). The data is collected on households and individuals aged 16 or older, once a year between the months of September and May. It covers a representative sample of the British population during 16 years (1991-2006), providing information on almost 10.000 households over the period. For waves 1 to 8 there are around 10.000 individual observations by year, whereas for waves 9-16 there are on average 15.000 yearly individual observations<sup>4</sup> . This means that the panel is unbalanced: during the sixteen years of the survey, some households and individual leave while others enter the sample.

We keep in the sample individuals aged between 21 and 65 years who are active in the labour force - either working or actively looking for a job. Given that we keep only working age population, the mean age in the sample changes to around 40 years old, as opposed to 45 for the whole survey. Table A.1 in the appendix provides a summary of statistics for the variables in the survey.

The measure of well-being used is derived from the General Health Questionnaire (GHQ). It has been designed by Goldberg (1972), and is widely used by psychiatrists to assess a person’s well-being. The 12 questions asked are provided in the appendix. As presented by Argyle (1989, 2002) the GHQ is one of the most reliable indicators of psychological distress. I use an ”inverted Caseness score”<sup>5</sup> . The distribution of this variable is given in table A.2, in the appendix. It is observable that the distribution is highly skewed to the top, with most of the respondents scoring 12, the highest possible grade. Only a quarter (26 %) scores less than 10. Looking at the distribution of GHQ by employment status, one can note the following two facts. First, the mean of GHQ is significantly lower for unemployed persons, if one compares them to the employed or self-employed population (9,1 for the unemployed vs. 10,4 for the employed and 10,1 for the whole population sample.). Second, the distribution of GHQ has a larger variance in the unemployed group, where 26% of the respondents declare a low well-being (defined as a score inferior to 8), against 13% and 12% for the employed and self-employed. These distributional characteristics are clearly presented in Table 1 and Figure 1, below.

> 4In 1999, 1500 households were added from both Scotland and Wales. In 2001, another 2000 households were added from Northern Ireland. More information can be found on the BHPS at http://www.iser.essex.ac.uk/survey/bhps

> 5There are 12 questions, and the score ranges from 0 to 12. Individuals start with a score of 12, and for each question in which they are fairly or highly stressed, they lose a point. Psychiatrists use the GHQ : individuals with low levels of Caseness are eligible for their treatment (Argyle, 2002).

7
<!-- Start of picture text --> norma50.210EmployedUnemployedDensityFigureGHQ12465HQ 1distributionGHQ12 by employment Figure 1 status<br>GHQ distribution by employment status<br>Employed Unemployed<br>0 5 10 15 0 5 10 15<br>GHQ12<br>Density<br>normal GHQ12<br>.6<br>.4<br>Density<br>.2<br>0<br><!-- End of picture text --> 8
# **4 Determinants of Subjective Well-Being**

Subjective well-being is obviously not only related to employment status. A multivariate approach is necessary. Income, health, age, education, and civic status are all likely to directly affect an individual’s well-being. As the GHQ caseness score is an ordinal measure (it is not a continuous variable), a linear probability model is not the best tool to estimate its determinants<sup>6</sup> . An ordered probit regression is used to complement the results of the OLS regression, and the results from both methods will be discussed below. The results on the main determinants of SWB are in regression table 2, which shows the ordered probit estimation. Table A.3 in the appendix shows the results from the OLS regressions. Three specifications are presented for both cases, in which the explained variable is subjective well-being.

The three OLS equations estimated are shown below. _Si_ stands for labor market status; _Yi_ for income; _Educi_ for educational achievement, the reference category being no diploma. _Healthi_ stands for the health status, the reference being excellent health. The reference dummy for civic status is never married and _Xi_ stands for the control of all previous variables.

In the first specification, labor market status is the only regressor, for which the reference category is ”Employed”. The coefficients indicate that the self-employed are slightly but significantly happier than the employed, when other life variables are <u>not</u> controlled for. The unemployed are the least happy. In the OLS regression, they have around 1 _._ 2 points less of well-being than the employed. The coefficients of the ordered probit indicate that Unemployed are 40% less likely to have a score of 12 than the employed, when controlling for other factors. In the second specification controls are added for the list of individual characteristics mentioned above. In the third specification, children dummies, year and region fixed effects are included. The results in all three specifications are in line with those found in Clark (2003) using the first seven waves of the BHPS.

> 6because the distribution of the disturbance term is not normal anymore - standard errors and t-stats are thus invalid. An ordered probit increases the fit and provides reliable z-stats, instead of the t-stats.

9
Table 2 : Well-Being and Own Labor Force Status Ordered Probit Regressions, BHPS Waves 1-16 Pooled

||Simple|With Some|Broad|
|---|---|---|---|
||Specification|Controls|Specification|
|Self-Employed|0.04|-0.046|-0.04|
||(3.70)**|(4.09)**|(3.57)**|
|Unemployed|-0.433|-0.412|-0.409|
||(31.29)**|(28.29)**|(28.00)**|
|Annual Income in ‘k||0|0|
|||-1.37|(2.46)*|
|Male||0.209|0.215|
|||(28,97)"*|(29,23)**|
|Age||-0.021|-0.021|
|||(8.70)**|(8.19)**|
|Age2/100||0.319|0.308|
|||(11.13)**|(10.09)**|
|Married||0.019|0.03|
|||(1.99)*|(2.78)**|
|Separated||-0.32|-0.329|
|||(14.45)**|(14.67)**|
|Divorced||-0.08|-0.07|
|||(5.51)**|(4.69)**|
|Widowed||-0,194|-0.18|
|||(6.13)**|(5.64)**|
|Health<br>: Good||-0.244|-0.245|
|||(28.98)**|(28.86)**|
|Health<br>: Poor||-0.775|-0.779|
|||(77.65)**|(77.40)**|
|Education<br>: High||-0.174|-0.186|
|||(18,22)**|(19,17)**|
|Education<br>: Medium||-0.084|-0.093|
|||(8.57)**|(9.40)**|
|Children Dummies|No|No|Yes|
|Wave Dummies|No|No|Yes|
|Regional Dummies|No|No|Yes|
|Observations|111761|110140|109390|
|Absolute value of z statistics i|n parentheses|* significant at 5%; *|* significant at 1%|
|Reference Dummy for Labor M|arket Status is<br>Paid Emp|loyment||
|Reference Dummy for Civic Sta|tus is Never Married|||
|ReferenceDummyforHealthi|sPoorandLowforEduca|tion||
# **4.1 Labor Market Status, Age, Income, Civic Status, Education and Health**

In all specifications, the coefficient for males is positive and significant (0.5 points higher than females). This indicates that men self-report higher levels of well-being than women. This is also visible in the raw mean, where men are 0.5 points happier than women. The effect of age is U-shaped, confirming the results found in the literature<sup>7</sup> , bottoming in the late thirties.

Consistent with most of the literature findings, the effect of marriage is slightly positive, while being separated, divorced or widowed is on average associated with a lower well-being. This should not be seen as a causal effect, as it could also be possible that being inherently happy favors one’s marriage prospects. To isolate the causal effect of marriage or divorce we need to look at transitions between civic states.<sup>8</sup> This is done in Clark and Lucas (2006). Using the GSOEP, they find that marriage increases happiness but that a habituation effect exists. The prospect of marriage (cohabitation) raises wellbeing 3 years before marriage, but this increase in happiness does not last longer than 3 years after the marriage date<sup>9</sup> . The channels through which marriage _causes_ the rise in well-being are explained in Argyle (2002). An intimate relationship enhances self-esteem and it can attenuate stress from other life activities like one’s job.

The effect of income on SWB is particularly interesting. As found in Easterlin (1974, 2001), income is a poor estimator of happiness. The results found in the literature suggest that relative income matters more than income itself, and income growth is important but not its level (above a certain threshold). These results highlight the role of comparisons, to others and to oneself in the past. The coefficients we find are negative and insignificant, confirming this story. However, when one goes from specification 2 to specification 3, the coefficient remains negative and becomes slightly significant. This suggests that a higher income might be associated with other variables that we are not controlling for (such as hours of work) that are negatively correlated with SWB. In the appendix a specification controlling for relative income is presented. Being in the top quarter of the wage distribution has no significant impact on well being. The effect of education is also interesting. The higher the achievement the lower the SWB. The current explanation is that a higher diploma leads also to higher income expectation, which reduces satisfaction for a given level of income. (see Argyle, 2002, and Frey and Stutzer, 2001, on expectations).

> 7See Clark and Oswald(1994), Blanchflower and Oswald (2007), Frey and Stutzer (2002), Winkelmann and Winkelmann (1998)

> 8Which gender benefits more from marriage has been subject to intense debate. Bernard (1972) proposed that men benefit much more from marriage than women. Glenn (1975) shows the opposite. More recent findings using subjective well-being data confirm Bernard (Fowers, 2004), while others show that marriage increases happiness equally between genders.

> 9Stutzer and Frey (2006), using the same data, find exactly the same results

11
Table 3. Well-Being and Labor Market Transitions

|Transition Matrix:<br>Change in L<br>|abor Force Status and Change<br>Labor Force Status <br><br>|in Well-Being<br> int<br>|
|---|---|---|
|Labor Force Status in t-1|Employed<br>Unemployed|Self-Employed|
|Employed<br>|||
|Mean|-0.05<br>-1.08|0.03|
|Std.Err|0.011<br>0.106|0.073|
|N|70,175<br>1,419|1,486|
|Unemployed|||
|Mean|1.41<br>-0.01|1.20|
|Std.Err|0.095<br>0.079|0.236|
|N|1,552<br>1,928|239|
|Self-Employed|||
|Mean|0.22<br>-0.91|-0.04|
|Std.Err|0.085<br>0.280|0.030|
|N|1,299<br>184|8,401|
1 point drop for females. The transition from self-employment to employment gives much greater rewards to females (0.4 points more) than to males (0.1), which could be interpreted as a more risk-loving behavior of men.

# **4.2 The Social Norm Effect of Unemployment**

The literature on happiness highlights the important role of comparisons in individuals’ well-being. It is income relative to others that matters or to oneself in the past. A slightly different comparison mechanism is at play when it comes to labor market status, but comparisons are still present. The previous section shows that the transition from employment to unemployment is causing a drop in SWB. Furthermore, there is no habituation effect : the unemployed feel on average significantly worse than those in employment, even after controlling for other factors<sup>11</sup> . Aware of this pattern, a relevant question arises. How does other’s employment affects one’s well-being ? Are those losing a job also comparing themselves to others in unemployment? If yes, in what ways ? Finally, do these comparisons affect their job search behavior ?

## **4.2.1 Theory : a binary choice model with externalities**

As in Akerlof’s (1980) social norm model, we can add others’ behavior and beliefs in the utility function. Whereas previous well-being estimations were only accounting for personal unemployment ( _Ue_ ) and were of the form _Wi_ = _W_ ( _Ue, X_ ), we are now interested in a utility function including a norm, beliefs and reputation effects, such that

## _Wi_ = _W_ ( _G, R, A, d_<sup>_c_</sup> _, ε_ )

Agent’s utility _Wi_ is dependent on their private consumption _G_ , their reputation _R_ , their belief in the norm _d_<sup>_c_</sup> , obedience to the norm _A_ (1,0), and personal tastes _ε_ . Let us suppose that reputation depends itself on the proportion of believers _μ_ and one’s own actions _A_ , such that _R_ = _R_ ( _μ, A_ ). If everyone believes in the norm ( _μ_ = 1) and agent _i_ does not follow it (A=0), he suffers from reputation effects. As less people believe in the norm, the reputation effect from not following it is reduced and this in turn pushes more people not to obede. As Akerlof explains, if there are no reputation effects, the only possible equilibria are derived from the traditional utility function with tastes _ε_ and consumption _G_ . However, if deviation from the norm is costly in terms of reputation, we may have a stable equilibrium in which the norm is self-sustained and agents follow it. It is a simple example of a binary choice model with externalities, in which two equilibria are sustainable.

> 11Clark (2006) finds that unhappiness does not decrease with unemployment length using the GSOEP and the ECHP, but has mixed results using the BHPS

13
<!-- Start of picture text --> Figure 2 diff and Urate.pdf<br>4F-2026GHQ.05RegionaldiffWell-b1ttedghqdifferencevaluesUnemployming gapbetweenandnt RateemployedRegionaland Unemploymentunemployed Rate<br>Well-being gap and Regional Unemployment Rate<br>0 .05 .1 .15 .2<br>Regional Unemployment Rate<br>diff_ghq Fitted values<br>In this case the norm is employment. The adherence to the norm, μ is the employment<br>rate. This follows Akerlof’s model in the sense that if all other adhere to the norm, there<br>is a bad reputation effect from not following it. As the number of unemployed rises, the<br>stigma from being unemployed falls. Clark (2003) considers a linear form for reputation :<br>R = ( −Uei (1 − Ue ∗ i ). We follow his steps here, and the main equation is :<br>6<br>4<br>2<br>0<br>-2<br>GHQ difference between employed and unemployed<br><!-- End of picture text --> This allows for the following effects. Being unemployed hurts (through the first term), a rise in the unemployment rate hurts (through the second term), but it improves well-being if one is unemployed (again first term).

# **4.2.2 Empirical evidence : pooled data regressions**

Figures 2 shows a simple plot of regional/yearly unemployment rates and the average wellbeing difference between employed and unemployed. The correlation is quite visible at eye-level. In regions/years where unemployment is high, people report being less hurt when they lose a job. Appendix A shows the regression results : a 1% increase in unemployment corresponds to a 0.08 points drop in the loss of happiness. A first empirical glimpse of this relationship was found in Clark and Oswald (1994), though they used only the first wave 14
of the BHPS. They were unable to reject the shift-share hypothesis<sup>12</sup> . Clark (2003) uses the first seven waves of the BHPS to confirm this early prediction. He finds that in regions with high unemployment, SWB falls less when losing a job. The same result is found in this paper, extending the sample to 16 waves of the BHPS.

This finding suggests that unemployment hurts - but it hurts less the more there is of it around. It suggest that labor market status is one important comparison affecting well-being. The question often posed is who do people compare to ? Who are the relevant others ? Is it the whole population or the people on the neighborhood? Is those of same sex, age, income and educational achievement? Data on relevant others from the survey can be used to test if employment in the reference group is also affecting well-being when losing a job. The OLS estimations to test the hypothesis are the following.

Where _Wit_ stands for well-being of individual _i_ at period t. _Xi_ stands here for all other determinants, year, region and children fixed fixed effects. _Uit_ = 1 when the respondent is unemployed. _RkU_ stands for the regional unemployment rate; and _U_<sup>_∗_</sup> = 1 when the relevant other is unemployed. The interaction terms are expected to be positive.

Following Moulton (1980) the estimation uses clustered standard errors for the regional unemployment rates. This is because the regional unemployment rate is the same for all individuals within the region. If the clustering is ignored, the repetition of the same value in one variable is biasing downwards the standard errors.

The results of the regressions on pooled data are very straightforward. Table 4, below, shows the ordered probit results. Table A.5, in the appendix shows the OLS results. In the simple specification, one can see that the regional unemployment rate has no effect on well-being. However, when one adds an interaction term between own unemployment and regional unemployment rate, the coefficient is positive and significant. This reflects the finding of the previous graph: unemployment hurts, but it hurts less the more there is of it around at the regional level.

The effect of spouse unemployment is also straightforward. Having an unemployed spouse significantly lowers one’s well-being. The effect is 35 times higher than the regional unemployment interaction. But the interaction term between own unemployment and the

> 12The shift-share hypothesis would hold if a rise in unemployment pushes the happier of the working force into unemployment.

15
|Well-Bei<br>Ordered P|ng and Other<br>robit Regressions|'s Labor Force S<br>, BHPS Waves 1-16 P|tatus<br>ooled||
|---|---|---|---|---|
||;<br>Specification|Spouse<br>Employment<br>Interaction|Regional<br>Employment<br>Interaction|Specification|
|Self-Employed|-0.04|-0.04|-0.045|-0.045|
||(3.55)**|(3.53)**|(3.68)**|(3.66)**|
|Unemployed|-0.409|-0.552|-0.549|-0.68|
||(23.80)**|(13.19)**|(20.78)**|(12.18)**|
|Regional Unemployment Rate|0|-0.002|-0.002|-0.003|
||(-0.2)|(-0.98)|(-0.54)|(-0.98)|
|Spouse Employed=1|||0.074|0.074|
||||(6.55)**|(6.51)**|
|Interaction<br>: Regional|||||
|Unemployment*Respondant Unemployed||0.021||0.02|
|||(3.45)**||(2.70)**|
|Interaction<br>: Spouse Unemployed *|||||
|Respondant Unemployed|||0.268|0.258|
||||(6.49)**|(6.25)**|
|All Other Controls|Yes|Yes|Yes|Yes|
|Observations|109461|109461|80850|80850|
|Absolutevalueofzstatisticsinparenth|eses|*significantat5%;|**significantat1%||
<!-- Start of picture text --> Well-Being and Other's Labor Force Status : OLS Regressions w/ Fixed Effects<br>ALL MEN WOMEN ALL MEN WOMEN<br>-13 -14 -15 -16 -17 -18<br>—_ GHQI2GHQI2<br>Self-Employed -0.064 -0.112 GHQI20.025 SC GHQU2-0.072 TC GHQU2-0.143 GHQU20.053<br>-1.5 (2.27)* -0.31 -1.48 (2.61)** -0.56<br>Unemployed -1.234 -1.559 -0.744 -1.444 -1.783 -1.02<br>(8.60)** (9.21)** (2.86)** (15.70)** (15.30)** (6.94)**<br>Regional Unemp. Rate -0.004 0 -0.01<br>-0.71 -0.04 -1<br>Spouse Employed=1 0.178 0.078 0.413<br>(5.11)** (2.06)* (5.24)**<br>Spouse<br>Unemployed*Unemployed 0.497 0.793 0.145<br>(3.30)** (4.89)** -0.44<br>Unemployment*Regional Unemployed 0.005 0.043 -0.058<br>-0.24 (2.01)* -1.53<br>All Other controls Yes Yes Yes Yes Yes Yes<br>Constant 10.228 11.698 8.808 11.979 12.157 11.627<br>(7.04) ** (9.43) ** (4.02) ** (41.45)** (38.13)** (28.63)**<br>Observations 109461 56860 52601 81478 43082 38396<br>R-squared 0.45 0.45 0.44 0.45 0.45 0.44<br>Absolute value of t statistics in parentheses<br><!-- End of picture text -->

|Well-<br><br>|Being and Other'<br>ALL<br>-13<br>—_<sup>GH</sup>|s Labor Force<br>MEN<br>-14<br><sup>Q</sup>|Status<br>: OLS <br>WOMEN<br>-15<br><sup>I2</sup><br>|Regressions w/ <br>ALL<br>-16<br><sup>GH</sup><br>|Fixed Effects<br>MEN<br>-17<br><sup>QI</sup><br> <br>|WOMEN<br>-18<br><sup>2</sup><br>|
|---|---|---|---|---|---|---|
|Self-Employed|-0.064|-0.112|GHQI2 <br>0.025|CGHQU2 <br>-0.072|CGHQU2<br><br>-0.143|HQU2<br>0.053|
||-1.5|(2.27)*|<br>-0.31|<br>-1.48|<br><br>(2.61)**|-0.56|
|Unemployed|-1.234|-1.559|-0.744|-1.444|-1.783|-1.02|
||(8.60)**|(9.21)**|(2.86)**|(15.70)**|(15.30)**|(6.94)**|
|Regional Unemp. Rate|-0.004|0|-0.01||||
||-0.71|-0.04|-1||||
|Spouse Employed=1||||0.178|0.078|0.413|
|||||(5.11)**|(2.06)*|(5.24)**|
|Spouse|||||||
|Unemployed*Unemployed||||0.497|0.793|0.145|
|||||(3.30)**|(4.89)**|-0.44|
|Regional<br>Unemployment*Unemployed|0.005|0.043|-0.058||||
||-0.24|(2.01)*|-1.53||||
|All Other controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|10.228|11.698|8.808|11.979|12.157|11.627|
||(7.04)**|(9.43)**|(4.02)**|(41.45)**|(38.13)**|(28.63)**|
|Observations|109461|56860|52601|81478|43082|38396|
|R-squared<br>Absolutevalueoftstatisti|0.45<br>csinparentheses|0.45|0.44|0.45|0.45|0.44|
where _αi_ =<sup>�</sup><sup>_s_</sup> _p_ =1<sup>_γpZpi_;and</sup><sup>_αi_standsfortheunobservedeffectofall</sup><sup>_Z_on</sup><sup>_Yi_.It</sup> measures the individuals’ specific unobserved fixed effect. This unobserved, individual-specific fixed effect can be dropped if it is not significant. This could be done if all relevant characteristics affecting GHQ are captured by the controls _X_ . If this is the case, then the _αij_ term can be dropped and the data set can be used as a **pooled data set** , in which all observations from the different years are used in the same sample. The present paper presents the pooled data results before presenting the next step, the panel data results.

# **4.3.2 Fixed Effects Regressions : individual fixed-effects**

Well-being levels reported by one person can be thought of being subjectively different that those reported by another person. If one cannot compare a well-being of 10 between two individuals, then we might want to control for individual fixed effects, as we are interested in understanding how _changes_ in explanatory variables relate to _changes_ in well-being. To do so, we must subtract the mean values of all variables (of individual i) to each observation. The mean variables are found by the equation below : Where _Y_<sup> ̄</sup> _i_ accounts for the mean happiness of individual _i_ during the years surveyed. When subtracting the individual’s mean observation to their yearly observation, we obtain an equation in which the unobserved individual fixed effect _αi_ disappears, as also does the intercept _β_ 1. The regression controlling for individual fixed effects is shown below : This within-group regression measures how the variation in observable explanatory variables affects the variation in happiness.

Three main problems arise when using individual fixed effects. First, the intercept and any explanatory variable reporting no change during the time of the survey is dropped. Since we are measuring how change in variables explain change in happiness, then gender, educational achievement and civic status will not be taken into account unless they change : their value will be zero. Second, the variation of explanatory variables within-individuals is much smaller than the variation across them. The model will have large disturbance terms and low precision in the estimates. Third, and most important, using individual fixed effect means we are controlling for _every_ single individual. This leads to the loss of 18
many degrees of freedom (as many as individuals). It significantly reduces the precision of the 19
# **5 Unemployment Duration**

The BHPS contains a detailed module on each employment/unemployment episode from every individual in the sample. Merging this data with the previous yearly household survey makes it possible to run a duration model of unemployment spells in order to find the main determinants of its duration. The sample is right-censored: information is not available on spells finishing after wave 16. Some random censoring also occurs as individuals are randomly lost during the 16 waves.

Over the 16 years of the sample, we have data on 5700 unemployment spells<sup>13</sup> . The average duration of a spell is 23 months, but there is a great heterogeneity as half of the spells end before the 12<sup>_th_</sup> month and 70% of them before the 24<sup>_th_</sup> month. The distribution is distinctly skewed to the right, artificially pushing the mean to 23 months. Table 5.1 summarizes some descriptive statistics on the spells. Figure 5.2 shows the survival function of spells, decomposed by gender.

## **5.1 Determinants of duration**

The differences between genders is very clear: women spend on average less time in unemployment. Selection issues are very likely to be the cause of this difference. Participation rates are higher for men than for women<sup>14</sup> , so it is possible that working women differ significantly from their non-working peers, whereas this is less the case for men. Differences across regions are also evident. Unemployment duration is much higher in Scotland, Northern Ireland and Wales than in London and in the South. Age is a major determinant as younger individuals have shorter spells than older ones, in both genders. Finally, individuals with a higher educational achievement have on average shorter spells (15.4 months) than the low achievers (32.1 months).

Figures 5.1 and 5.2 provide visual evidence of two clear discontinuities in the rate of return to work. The first discontinuity is found in the months 10 and 11, in which the rate of return to work is significantly higher than in the immediate preceding or following months. The main suspect for causing this discontinuity is the reduction in benefits occurring on the 12<sup>_th_</sup> month. The same story applies for months 22 and 23, as the benefits are also reduced on the 24<sup>_th_</sup> month. For some individuals, monetary incentives seem to play a significant role in the rate of return to work, as the anticipation of a drop in benefits pushed them to return to a paid job. These results are in line with the literature on job search behavior. The theoretical model of Mortensen (1977) predicts a rise in the hazard

> 13Spells longer than 12 years are (arbitrarily) taken out of the sample. They are outliers irrelevant to the present analysis and bias the results

> 14Women’s participation rates have increased from 70% to almost 75% between 1991 and 2006 (the years covered in this survey). Men participation rates are above 85%

20
ratio as one gets closer to the benefits exhaustion time. Meyer (1990) finds evidence of large spikes in the hazard in the prior weeks before exhaustion, a result that is also shared by a large amount of literature<sup>15</sup> .

To summarize: age, gender, region and educational achievement all seem to be correlated with unemployment duration; while monetary incentives also play a significant role as the deadline of benefit exhaustion approaches. In the next subsection it will be asked how unemployment duration and well-being are related. As duration spent in unemployment and well-being can influence each-other, it is difficult to isolate the impact of one on the other. Individuals might suffer when they lose their job, but perhaps they also get used to be unemployed. Hence, we could observe that time spent on unemployment has a positive effect on well-being. A reverse mechanism could also exist. The well-being difference between the two states can play as an incentive to return to work. As the gap of well-being increases between being jobless and employed, the incentive to find a job increases, thus reducing duration. Understanding which of these stories is true (both can be) is important for both policy and research reasons, as it will be explained below.

# **5.1.1 Unemployment duration and well-being: how to find the causality direction ?**

Unemployment duration might affect well-being through a habituation effect. In their review of unemployment-related psychology findings, Darity and Goldsmith(1996) describe three phases of emotional response after a job loss. A first shock phase where optimism still predominates, followed by a phase of pessimism and helplessness, and finally a phase of fatalism feelings with habituation.

If individuals adapt to being jobless, we should observe a higher well-being in long-term unemployed than in those who recently lost their job. Controlling for individual effects we should observe, among the unemployed, a rise in well-being with time spent in unemployment. However, upon comparing well-being across different categories of unemployed one cannot rule out a sample selection issue - arising in pooled regressions.<sup>16</sup> That is why pooled regressions yield different estimates than panel data regressions. Using panel data (individual fixed effects) is the proper way to estimate the impact of duration on well-being. Clark (2006) does this exercise using three European panel data sets. For the pooled data, he uses an interaction term between duration and unemployment. He finds that panel data

> 15Moffit (1985), Meyer (1990), Vodopivec (1995), Dormont _et al_ (2001) all find, using data from different countries, that the exit rate from unemployment to employment rises sharply as the end of the entitlement period approaches. See Card, Chetty and Weber (2007) for a review of findings.

> 16Through a shift-share mechanism. Those who stay unemployed longer are different : those suffering the most might have left to inactivity or back to work. Those suffering less from unemployment stay jobless. A selection bias arises in cross-section

21
Table 5.1 Table 1: Unemployment Spells - Descriptive Statistics by Demographics

|**Variable**|**Men (Std.Dev)**|**Women (Std.Dev)**|**All(Std.Dev)**|
|---|---|---|---|
|Average|26.9 - (31.9)|18.0 - (23.7)|23.7- (29.6)|
|6 months or less %|29.2|35.0|31.2|
|12 months or less %|46.3|61.5|51.7|
|24 months or less %|65.4|79.6|70.4|
|Region||||
|Inner London|28.3 - (26.1)|18.0 - (21.8)|24.3 - (25)|
|Rest SE|21.9 - (27.6)|13.2 - (15.7)|18.2- (23.7)|
|South West|21.0 - (27.7)|12.1 - (12.8)|17.9- (24.0)|
|Scotland|30.9 - (37.6)|20.6 - (26.7)|27.0- (34.3)|
|Wales|27.6 - (32.5)|24.0 - (32.7)|26.3 - (32.6)|
|N.Ireland|27.2 - (33.6)|25.6 - (32.6)|26.6 - (33.2)|
|Age||||
|Age _≥_50|34.9 - (37.1)|22.3- (25.8)|30.6 - (34.2)|
|Age _≤_35|22.1 -(27.3)|15.9 -(22.3)|19.9-(25.8)|
|Educational Achievement||||
|Low|36.2 - (36.6)|23.7- (28.3)|32.1 - (34.6)|
|Medium|20.7 -(24.7)|17.5 -(22.5)|19.5-(23.9)|
|High|18.1 -(24.6)|10.5 -(13.7)|15.4-(21.7)|

22
<!-- Start of picture text --> Figure 5.2 Hazard -Survival function - unemployment by gender no CI.pdf<br>Theresex145Data0.0023MonthsBHPSSurviv05725is=isaFemaleMale-clearrightspentWdiscontil vesfunctioncensoredinuity1-16unemploymentin months- -all10Uandspells11mployment(beforendtheatfirstwavered line)16.Spells; and in month 23 (before the second red line).<br>Survival function - Unemployment Spells<br>BHPS - Waves 1-16<br>0 10 20 30 40 50<br>Months spent in unemployment<br>sex = Female sex = Male<br>There is a clear discontinuity in months 10 and 11 (before the first red line) ; and in month 23 (before the second red line).<br>Data is right censored - all spells end at wave 16.<br>1.00<br>0.75<br>0.50<br>0.25<br>0.00<br><!-- End of picture text --> does not support the hypothesis of habituation, when using the GSOEP and the ECHP<sup>17</sup> , but the results are not significant using the BHPS. The same exercise is executed here.

Using pooled data, the interaction term between unemployment and its duration, it is found that unemployment duration has a positive and significant effect on well-being, when one does not control for the other life variables. However, if one controls for life variables, then duration has no effect on well-being. This hints that duration is associated with the life variables mentioned above, but not directly with well-being. The transition matrix shown before provides evidence that staying in unemployment has no different effect on well-being than staying in employment. As pooled data is not rigourous, panel data specifications are used. Introducing individual fixed effects makes it possible to regress the change in well-being to the change in duration. A fixed effect logit is used to estimate this effect of duration on well-being, for the unemployed. The results show that the coefficient of duration is not significant. Hence, it can be said that the effect of unemployment on well-being is independent from its duration. These results are shown in the appendix. Table A.8 shows the pooled results, and Table A.9 the fixed effects results.

> 17GSOEP is the German Socio-Economic Panel ; ECHP is the European Community Household Panel

23
<!-- Start of picture text --> FigureDifferTherePercent01023-10atai f arerencef5.4ceom1438in BGHQinobservations.PSGHQreportedW vesin uponTransition1-16ThetheMeantransitioni equalfromfromtoEmpl Figure 5.4 -1Employmentand thementstd.errto toUnemploymentequalsUnemployment.10<br>Difference in GHQ in Transitions from Employment to Unemployment<br>Data from BHPS Waves 1-16<br>-10 0 10<br>Difference in GHQ reported upon the transition from Employment to Unemployment<br>There are 1438 observations. The Mean is equal to -1 and the std.err equals .10<br>30<br>20<br>Percent<br>10<br>0<br><!-- End of picture text -->

# **5.2 The role of social norms in duration**

This section tests whether the length of the unemployment spell is affected by the change in well-being reported when losing one’s job. A variable is created that calculates the reported change in happiness when the individual is made redundant. This variable is named ”Difference in happiness”. It stands for _Ve − Vu_ , which in the job search theory literature is the utility difference between being employed ( _Ve_ ), and unemployed, ( _Vu_ ). Even though information on 5700 unemployment spells are available, there are only 1400 observations for the reported changes in well-being. This is due to the fact that data for SWB is yearly, whereas the unemployment spells are coded monthly. The distribution of this variable is presented in figure 5.4.

The figure 5.4 has to be read as follows : 10% of the individuals report a drop of -1 in well-being, when losing their job. 31% report feeling no change in their well-being. As it is observable, a significant proportion of the individuals record feeling _happier_ when losing a job (almost 18%). The majority, however, reports feeling worse off (42%). The average _change_ in well-being is equal to _−_ 1, on a scale going from _−_ 12 to 12. This is a considerable drop, given that SWB is highly skewed to the top. .

To estimate the determinants of duration, two regression model are performed. The first is a standard OLS regression, and the second a proportional hazard model. The results from these models are shown in table 5.2. As shown before, there are large differences in duration, by gender, age, regions and educational achievement; so these are used as controls. The regional jobless rate is also likely to affect time spent looking for work: if more people compete to get jobs, average search duration should increase. As shown in the em24
pirical literature, unemployment benefits should also increase duration, because they have an effect on the reservation wage. We also add a dummy for unemployed spouse. Finally, we test two different measures of well-being. First, we add the coefficient ”difference in happiness”. Second, we create a dummy when the initial drop in well-being is larger than 1, and we call it _bigloss_ . The results are presented in table 5.2, and all the details about the modeling of duration and the significance of the coefficients are explained on the section 8.2 of the appendix.

The results from the OLS regression (columns 1 to 4) are in line with those from the duration model (columns 5 to 5). Women, the young and the highly educated spend less time in unemployment. When more people are jobless in one’s region, duration increases. Benefits also push individuals to keep looking for a job longer (their reservation wage is higher).

Column 4 shows that the coefficient _difference in happiness_ is positive and significant at the 1% level. Since the change in well-being is negative, it suggests that the more an individual says it suffers from losing a job, the quicker he returns to work. Column 3 confirms this intuition: those who suffered a big loss in well-being when losing their job are spending less time in unemployment. The results suggest that a one point increase in happiness (when losing a job) is associated with a 0.3 months increase in unemployment duration, even after controlling for other factors.

Finally, having an unemployed spouse increases one’s unemployment duration, possibly due to the comparison effect presented earlier. The proportional hazard model confirms the OLS results. Those who suffered a big loss have a higher hazard rate, while the _diff-hap_ coefficient is negative : the less one is hurt by unemployment, the lower the hazard rate out of unemployment.

25
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# **5.2.1 Social norms and duration : empirical evidence from the duration model**

Another piece of evidence suggesting that those who suffer more from the job loss return to work quicker is provided in the Figure 5.5, below, where a Kaplan-Meier survival function is estimated. The sample of unemployment spells is split in 2 different sub-samples. Those for whom the loss is high (drop of 2 or more in well-being) are grouped together in the high-loss sample. Those for whom the loss is low (no change or increase in well-being) are grouped in the low-loss group. Then, a Kaplan-Meier survival function is estimated for each sub-sample. The results are shown in the graph 5.5 below. The graph shows 2 lines. The blue line (above) estimates the rate of return to work for those who suffered _less_ from the job loss. The red line (below) estimates the rate for those for suffered _more_ . For the least happy, the rate of return to work is higher (red line) than for those who suffer less (blue line).

The difference in both survival functions is significant at the 80% level from the 5th month onwards. Hence, this figure provides some visual evidence that the rate of return to work is lower for individuals who report being less hurt from unemployment<sup>18</sup> . In the next

> 18This result also confirms the prediction of Clark (2006) that sample selection bias arises if one uses pooled data OLS regressions to estimate the impact of unemployment duration on well-being. It seems that those feeling worse leave unemployment sooner than the others

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subsection, it is shown that the channel through which their return rate is lower could be their search

# **5.3 Is search intensity related to change in GHQ ?**

The BHPS has a section designed in a similar way to the LFS (Labor Force Survey), intended to measure unemployment by the ILO standards. In this section, unemployed persons are being asked if they have been searching for a job in the last week, and/or in the last month, their response being limited to Yes/No. Their answer can provide some information on the search intensity of the group as a whole. Using the previous sample of GHQ differences, two groups are created. The first group consists of those who report a large utility loss (higher than 1 point of SWB) when they lose their job. The second group consists of those who report either no utility change or are happier being unemployed.

Search intensity varies slightly between the two groups. It is observed that 62% of the group suffering from being unemployed searched for work last week, whereas this drops to 55% for the other group. These results suggest that the unemployed modify their search behavior according to how much they report being hurt by the job loss<sup>19</sup> . A means test confirms that the two means are different, at the 95% confidence level, the results of the test are in table A.11 of the appendix.

We provide in the appendix a probit of ”search last week”, in which we use the same controls as for duration. Unfortunately, our variables of SWB do not seem to be significant, exept for _bigloss_ , whose effect is positive and significant, but only at the 10% level. Further data could confirm our prediction, and show that those suffering a bigloss are indeed searching more frequently. Table A.10 presents the results of the search probit.

Table 2: Search intensity : ”Have you looked for any kind of paid job in the last week?” - population mean by <u>groups</u>

|**Variable**|**Mean**|**Std. Err.**|**N.Obs**|
|---|---|---|---|
|High GHQ drop|0.625|.021|554|
|No GHQ drop|0.55|.028|324|

> 19An reverse causality issue can arise : maybe search is causing unhappiness. As soon as search stops, happiness increases (true when they find a job or when they search less). If this holds, then all the results/conclusions in the present thesis are wrong. The reason why this does not hold is because we are using the _initial_ drop in happiness.

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# **5.4 Conclusion on unemployment duration**

The length of time an individual spends being unemployed depends on numerous factors and affects an equally large number of outcomes. Gender and age can be thought of exogenous but one cannot distinguish between supply or demand factors through which they affect the rate of return to work. Is it that the women and the young are better at looking for jobs, more motivated (supply side), or is it that demand for them is higher ?

The interrelation between SWB and search intensity provides -at last - a pure labor supply story. Upon losing their job, people self-report a drop in their subjective well-being. This drop depends on how many other around are unemployed, and it also affects their search behaviour. When those around are jobless, being unemployed hurts less and job search effort is less intensive. Hence, the rate of return to work is also lower. These results suggest that the job search intensity and the rate of return to work are both dependent on the difference of well being between being employed and unemployed. In standard models of job search, the intensity of search is also derived from the utility differences from the two states. However, in those models, utility is only determined by the differences in the flow of income. The results presented here suggest that comparison effects play a significant role in the rate of return to work.

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# **6 Conclusion**

Previous literature (Clark, 2003) found that the higher the unemployment rate among an individuals’ reference group, the less that individual reported being hurt by unemployment, which was called the ”social norm effect”. In this paper, we provide more evidence of the ”social norm effect”. We show that albeit the comparison effect is strong, it differs by gender. Women suffer more than men when their spouse is made redundant. But men compare more, both to their spouses and to their regional peers. When men they lose their job, they suffer, but they suffer less if their spouse is also unemployed.

This paper goes one step further. It provides evidence that the duration of unemployment is affected by this ”social norm”. It is suggested here that job search behaviour is also dependent on the social norm, on the individual’s perception of how socially stigmatising is unemployment.

The lesson to be drawn by these findings is that one’s employment decisions have a strong externality on other’s labor supply and job search effort, through comparison effects. Upon losing a job, if a relevant other is also jobless then both individuals search with less intensity. In the opposite scenario, if all relevant others are employed, search intensity increases for the unemployed. It suggest that participation to the labor market should be seen as a binary model with externalities. One’s decision affects the others. As in Neumark and Postlewaite (1998) this mechanism helps in explaining the rise in women’s participation rates in the 20th century, and complements the standard model of labor supply. It also explains why in high unemployment regions the jobless rate might remain higher than in low regions, or why it may take longer to fall.

Explanations of unemployment hysteresis have been so far centred around labor demand. The present findings suggest unemployment hysteresis might also come from the labor supply behaviour. If others are unemployed, I will search less and extend my unemployment duration, in turn affecting other’s return to work. Many policy implications arise. One concerns the responses to exogenous macroeconomic shocks. Following a labor demand shock, as unemployment rises, the labor supply might also fall (shift to the left) through a comparison effect. It suggest that a policy response should correct for this externality in times of high unemployment. Future empirical research should examine in more detail the link between search intensity and the social norm effect. Future theoretical research should asses what policy responses arise now that we know how one’s participation the others.

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