unemployment hypothesis

Natural Rate of Unemployment

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Milton Friedman defined the natural rate of unemployment as the level of unemployment that resulted from real economic forces, the long-run level of which could not be altered by monetary policy. Macroeconomic policymakers continue to view the natural rate as a key benchmark due to the belief that monetary policy can counter short-run deviations of the unemployment rate from the natural rate. It is important, however, that policymakers focus as much attention on understanding the real determinants of the natural rate, and the policies that can affect it, as they do on trying to identify and counteract deviations from it.

This chapter was originally published in The New Palgrave Dictionary of Economics , 2nd edition, 2008. Edited by Steven N. Durlauf and Lawrence E. Blume

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Pries, M.J. (2008). Natural Rate of Unemployment. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95121-5_716-2

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A Crisis of Long-Term Unemployment Is Looming in the U.S.

by Ofer Sharone

Summary .   

The stigma of long-term unemployment can be profound and long-lasting. As the United States eases out of the Covid-19 pandemic, it needs better approaches to LTU compared to the Great Recession. But research shows that stubborn biases among hiring managers can make the lived experiences of jobseekers distressing, leading to a vicious cycle of diminished emotional well-being that can make it all but impossible to land a role. Instead of sticking with the standard ways of helping the LTU, however, a pilot program that uses a wider, sociologically-oriented lens can help jobseekers understand that their inability to land a gig isn’t their fault. This can help people go easier on themselves which, ultimately, can make it more likely that they’ll find a new position.

Covid-19 has ravaged employment in the United States, from temporary furloughs to outright layoffs. Currently, over 4 million Americans have been out of work for six months or more , including an estimated 1.5 million workers in white-collar occupations, according to my calculations. Though the overall unemployment rate is down from its peak last spring, the percent of the unemployed who are long-term unemployed (LTU) keeps increasing and is currently at over 40%, a level of LTU comparable to the Great Recession but otherwise unseen in the U.S. in over 60 years.

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The social stigma of unemployment: consequences of stigma consciousness on job search attitudes, behaviour and success

• Gerhard Krug   ORCID: orcid.org/0000-0002-6952-0579 1 , 2 ,
• Katrin Drasch 2 &
• Monika Jungbauer-Gans 3  

Journal for Labour Market Research volume  53 , Article number:  11 ( 2019 ) Cite this article

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Studies show that the unemployed face serious disadvantages in the labour market and that the social stigma of unemployment is one explanation. In this paper, we focus on the unemployed’s expectations of being stigmatized (stigma consciousness) and the consequences of such negative expectations on job search attitudes and behaviour. Using data from the panel study “Labour Market and Social Security” (PASS), we find that the unemployed with high stigma consciousness suffer from reduced well-being and health. Regarding job search, the stigmatized unemployed are more likely to expect that their chances of re-employment are low, but in contrast, they are more likely to place a high value on becoming re-employed. Instead of becoming discouraged and passive, we find that stigmatized unemployed individuals increase their job search effort compared to other unemployed individuals. However, despite their higher job search effort, the stigma-conscious unemployed do not have better re-employment chances.

1 Introduction

Unemployment is associated with adverse consequences. Empirical evidence has been presented for social exclusion (Hirseland and Ramos Lobato 2014 ), network withdrawal (Jones 1988 ), marital dissolution (Hansen 2005 ), financial shame (Rantakeisu et al. 1999 ), ill health (Krug and Eberl 2018 ), as well as reduced wage levels (Gangl 2004 ), reduced well-being (Mousteri et al. 2018 ), even after re-employment. For many of these consequences, social stigma is considered one of the central mechanisms (for an overview, see Brand 2015 ). The social stigma literature, in contrast, rarely addresses the stigma of unemployment, instead focussing on the stigma of mental or physical illness (Baumann 2007 ; Scambler 2009 ), race (Mosley and Rosenberg 2007 ; Pinel et al. 2005 ; Sigelman and Tuch 1997 ), ethnicity (Binggeli et al. 2014 ), sexual orientation (Herek 2010 ; Mattocks et al. 2015 ), etc. If at all, unemployment is only addressed as a potential consequence of other social stigmas such as mental illness or history of incarceration (cf., Link and Phelan 2001 ; LeBel 2008 ; Karren and Sherman 2012 ).

However, there is no doubt that in modern welfare states, there is a number of stereotypical beliefs regarding the attitudes of the unemployed to work and other personal shortcomings that are seen as the main reason for why individuals are getting and remain unemployed (Oschmiansky et al. 2003 ; McFadyen 1998 ). One strand of literature in labour market research explicitly addresses unemployment as a social stigma and shows that it might be these stereotypical beliefs that can hinder the unemployed from getting a job. This literature focusses on the discrimination of the unemployed, especially by firms during the hiring process. This research consistently shows that even if they had the same qualifications and competences as employed applicants, the unemployed and especially the long-term unemployed have significantly lower chances of getting hired. In a recent survey using German data, Rebien and Rothe ( 2018 ) showed that discrimination against the unemployed is very common. The authors found that only 14% of German firms would fill current vacancies with unemployed applicants irrespective of their unemployment duration. Thirty-four percent of these firms would accept such applications only if the applicants were unemployed for less than 1 year. The unemployment discrimination literature mostly focusses on firms’ behaviour towards the unemployed. As a result, we have ample empirical evidence regarding the demand side of the matching process but not so much on the supply side. This empirical one-sidedness can leave the impression that the targets of unemployment stigma are only passive victims of potential employers’ discriminatory hiring behaviour.

In this paper, we aim to contribute to the existing literature by illuminating the role of individuals’ experience with social unemployment stigma in shaping their behavioural responses towards being stigmatized. Specifically, we ask whether this experience helps to co-create the adverse re-employment chances by influencing the job search behaviour and job search success, as is suggested by several authors. To do this, we apply the concept of stigma consciousness to the context of unemployment. This concept explicitly focusses on whether stigmatized individuals internalize the expectation of being stereotyped in social interactions. Stigma consciousness is defined as the extent to which individual targets of specific stereotypes “focus on their stereotyped status and believe it pervades their life experiences” (Pinel et al. 2005 : 482). In other contexts of social stigma (e.g., gender, sexual orientation, or disabilities), it has been shown that the degree to which individuals perceive themselves to be subjected to stigmatization significantly influences their behaviour. For our analysis in the context of unemployment, we use data from the panel study “Labour Market and Social Security” (PASS) (Trappmann et al. 2013 ). A new scale was developed and implemented in 2013 to measure stigma consciousness among the unemployed (Gurr and Jungbauer-Gans 2013 ). We first corroborate that a higher stigma consciousness is associated with lower subjective well-being and lower health satisfaction. Based on expectancy-value theory, we find that those who are more stigma conscious have lower expectations of finding a job but highly value obtaining a job. However, our main result is that instead of leading to a reduced job search effort, those unemployed with a higher stigma consciousness are more likely to engage in an active job search, use more job search methods, spend more time searching for jobs, etc. Despite these positive associations with job search effort, we find that high stigma consciousness is not correlated with re-employment chances.

The remainder of this paper proceeds as follows. Section  2 discusses our definition of social stigma and stigma consciousness. Section  3 presents our theoretical considerations and how we derived our hypotheses. Section  4 presents a literature review regarding the role of unemployment stigma in labour market outcomes. Section  5 outlines our data, operationalization and analytical strategy. Section  6 presents the results of our study and discusses some limitations. Section  7 concludes with a summary and discussion of our results.

2 Definitions of stigma and stigma consciousness

The concept of stigma has received considerable interest in social science research, but as Link and Phelan ( 2001 ) remark, there is great variability in the definitions applied by different researchers. According to the seminal treatment of the topic by sociologist Erving Goffman ( 1963 : 3), stigma is “an attribute that is deeply discrediting” and leads to negative and often hostile behaviour towards the stigmatized. Goffman distinguishes three types of stigmatizing conditions: tribal identities (e.g., ethnicity, religion, nationality or gender), abominations of the body (e.g., physical disabilities or deformities) and blemishes of character (e.g., mental illness, addiction, or previous incarceration). Unemployment can be regarded as an example of stigma of character, where the stigmatized are considered individuals with a “weak will, domineering or unnatural passions, treacherous and rigid beliefs, and dishonesty” (Goffman 1963 : 4).

Several researchers have expanded upon Goffman’s definition of stigma. For example, Link and Phelan ( 2001 ) propose conceptualizing stigma as the interrelation of four processes. First, the differences among members of society are distinguished and labelled. Second, these differences are associated with negative attributes. Third, the labels attached to differences imply a separation of “them” from “us”. Fourth, the labelled person experiences a loss of status and discrimination. “Thus, we apply the term stigma when elements of labelling, stereotyping, separation, status loss and discrimination co-occur in a power situation that allows them to unfold” (Link and Phelan 2001 : 367). This definition has been criticized by Deacon ( 2006 ) because it sees discrimination as an integral part of the stigma concept. In contrast, she defines stigma independent of discrimination. If we apply the definition to the context of unemployment, stigma can be defined as the following social process: Unemployment is construed as preventable and controllable; “immoral” behaviours causing unemployment are identified; these behaviours are associated with carriers of the characteristic in other groups, drawing on existing social constructs of the “other”; the unemployed are thus blamed for their situation; status loss is projected onto the “other”, which may (or may not) result in disadvantage to them (adapted from Deacon 2006 : 421). In contrast to Link and Phelan ( 2001 , see also Besley and Coate 1992 ) Deacon ( 2006 : 421) points out that being stigmatized is not automatically associated with disadvantages caused by discrimination. Furthermore, stigmatization can lead to disadvantages even in the absence of discrimination because it can have negative consequences for the self-concept and actions of the stigmatized individuals.

The reason for disadvantages that are independent of discrimination is that the stigma is internalized by the stigmatized individuals and manifests itself in self-stigma Footnote 1 (Bos et al. 2013 ; Pryor and Reeder 2011 ). According to Stuber and Schlesinger ( 2006 ), self-stigma combines two aspects, i.e., identity and treatment stigma. Identity stigma refers to the internalization of negative labels and stereotypes by the stigmatized individual, resulting in negative self-characterizations. Treatment stigma, in turn, refers to expectations about negative treatment by others. Both identity and treatment stigma are part of the self-stigma and therefore to be distinguished from actual discrimination because both are based on the perceptions of the stigmatized themselves. “For example, administrative practices that are not inherently discriminatory (such as questions about personal finances or living arrangements) may be interpreted by potential recipients as such” (Stuber and Schlesinger 2006 : 935).

In the empirical analysis below, we apply the concept of stigma consciousness to the context of unemployment. Several authors (e.g., Taylor et al. 1994 , cited after LeBel 2008 ) have shown that there is a discrepancy between group discrimination and the extent to which individuals personally experience discrimination. While some stigmatized individuals do, others do not attribute negative outcomes to stereotypes and discriminations. To cover these differences, Pinel ( 1999 ) developed and validated a 10-item ‘Stigma Consciousness Questionnaire’ (SCQ) for several stereotyped groups (e.g., women, lesbians, gay men, African Americans, and Latinos/Latinas). Stigma consciousness reflects the extent to which individual targets of specific stereotypes “focus on their stereotyped status and believe it pervades their life experiences” (Pinel et al. 2005 : 482). According to Pinel ( 1999 ), consciousness of the stigma is a key determinant of the stigmatized individual’s behavioural reactions. Stigma consciousness is argued to increase the perception of being discriminated against and to heighten the belief that group membership influences social interactions and experience (Guyll et al. 2010 ). Negative feedback from others is more often interpreted as discriminatory. Thus, stigma consciousness is viewed as a mechanism mediating the association between group membership and negative outcomes.

3 Consequences of stigma of unemployment: theoretical considerations and hypotheses

Several scholars have examined the consequences of internalizing stigmatizing stereotypes, but the bulk of research focusses on contexts such as ethnicity, gender, medical conditions, sexual orientation or history of incarceration (see e.g., LeBel 2008 ). In this paper, we are interested in the stigma of unemployment and its consequences. Changes in subjective well-being and health are among the most obvious consequences of any type of stigma (Hatzenbuehler et al. 2013 ; Markowitz 1998 ; Rosenfield 1997 ), and the stigma of unemployment should be no exception. However, empirical evidence is scarce, but O’Donnell et al. ( 2015 ) found that anticipated stigma, which is a measure similar to stigma consciousness, has a negative impact on psychological distress and physical health.

We follow this strand of research and argue that stigmatization consciousness is directly connected to subjective well-being and health. Therefore, our first two hypotheses are concerned with the proposition that the stigmatized suffer from their status as unemployed more than the non-stigmatized as follows:

The higher the stigma consciousness among the unemployed, the lower their subjective well-being.

The higher the stigma consciousness among the unemployed, the lower their subjective health.

However, the main concern of the present analysis is the role of stigma consciousness for job search. According to Goffman ( 1963 ), an important dimension of the stigma influencing how it is perceived by the respective targets, is its visibility. As an example of a stigma of character (e.g., Gurr and Jungbauer-Gans 2017 ), unemployment is a stigma that is not highly visible and therefore often concealable. The unemployed are therefore “discreditable” instead of “discredited”. Thus, in social situations, the unemployed can often choose whether they disclose information regarding their status. This is not the case during the job search because to obtain re-employment, per definition, the unemployed have to disclose their status to other individuals.

Job search activities such as visiting the unemployment agency, asking friends for job leads, and attending job interviews make it difficult to conceal one’s status as unemployed and are experienced as humiliating and potentially lead to rejection (Letkemann 2002 ). Therefore, reducing job search effort could be a viable strategy to avoid being stigmatized. The literature on welfare stigma (cf., Andrade 2002 ) even assumes that some of the unemployed will forego welfare benefits they are entitled to in order to avoid their stigma being made visible (Yaniv 1997 ; Moffitt 1983 ; Loewenberg 1981 ). According to Sherman ( 2013 ), for those who do not have other financial options or find themselves unable to get a job despite their best efforts, the eventual acceptance of welfare benefits often leads to self-hatred, shame, and depression. Kerbo ( 1976 ) observes that welfare benefit claimants exhibit lower job search activity than non-claimants and attribute this behaviour to discouragement. He argues that those who felt highly stigmatized because they received welfare were also most likely to be the most passive. Heslin et al. ( 2012 ) develop a theoretical model that relates the labour market experience of members of ethnic minorities to the becoming discouraged workers, i.e., wanting to work but not looking for employment due to negative experiences. They argue that due to the stigma attached to the minority status (lazy, untrustworthy, etc.), they fare worse in the recruitment and selection process of employers. This experience will make them prone to become discouraged workers because among others, it leads to learned helplessness (Seligman 1975 ), that is, they become passive and no longer try to improve the negative situation that they perceive as uncontrollable. According to Abramson et al. ( 1978 ), individuals are more disposed to react with learned helplessness if they see the reason for their negative experience in themselves.

According to Pinel ( 1999 ), an important predictor of whether individuals avoid situations in which stigma is salient is stigma consciousness. She found that those with higher stigma consciousness are more likely to avoid situations where they expect to be stereotyped. For example, compared to those with low stigma consciousness, female workers with high stigma consciousness were more likely to intend to and actually leave their job (Pinel and Paulin 2005 ). Stigma consciousness can cause greater experience of stereotype threat, raise the level of perceived prejudices and feelings of rejection, and reduce the person’s sense of control and self-esteem (Wang et al. 2012 ). Wang and her colleagues show that a person with high stigma consciousness more often views subtle bias as discrimination and becomes angrier.

Thus, there seems to be a consensus in the literature that stigmatization and specifically stigma consciousness should result in reduced job search efforts. However, there are also some indications in the literature that this view might be too one-sided. Based on stress theory, Miller and Kaiser ( 2001 ) note that individuals tend to have two options that the authors call engagement and disengagement behaviour. While the above discussion highlights the potential of social stigma of unemployment to result in disengagement or avoidance behaviour, in other contexts of stigmatization (e.g., mental health), it has been shown that some stigmatized individuals choose engagement behaviour, e.g., raising awareness of social stigma or in the form of problem solving. For example, the above cited Wang et al. ( 2012 ) also find that the high stigma conscious are more often willing to engage in collective action. By directly referencing to the stigma of unemployment, Bretschneider ( 2014 ) draws a similar conclusion using group identity theory (Tajfel and Turner 1979 ). She argues that to the degree that group boundaries are perceived as permeable and social mobility between groups is possible, individuals are more likely to attempt to obtain a more positive sense of self by changing groups. An important way for the unemployed to achieve this goal is by proactively engaging in job search.

To develop testable predictions regarding the potentially ambiguous effect of stigma consciousness on the job search effort, we can draw upon the expectancy-value theory (Vroom 1964 ). This theory was first applied to the job search process by Feather ( 1982 ), and here we extend this theory to incorporate stigma consciousness among the unemployed. Expectancy-value theory assumes that the level of job search effort is determined by two factors, i.e., expectations and value. The first determinant of job search effort expectation refers to the expectations that specific behaviour, such as the job search in our case, will result in the desired outcome, such as obtaining gainful employment. Expectancy-value theory predicts that individuals with high expectation that their job search will be successful will exert more effort in the job search. Regarding the social stigma of unemployment, we hypothesize that their expectations of meeting negative stereotypes during the job search lead the unemployed with higher ratings on the stigma consciousness scale to have lower expectations of succeeding in the job search.

The higher the stigma consciousness among the unemployed, the lower their expectations of their successful re-employment chances.

Value is the second determinant of effort and refers to the degree to which the desired outcome of the job search, i.e., becoming re-employed, is valued by the unemployed. The central behavioural assumption is that the higher the subjective value of the desired outcome, the higher the effort exerted to obtain this outcome. We assume that unemployed individuals with high levels of stigma consciousness are more likely to place a high value on re-employment possibly because the stigmatized are more likely to suffer from adverse consequences due to their status as unemployed compared to other unemployed persons. Our next hypothesis is as follows:

The higher the stigma consciousness among the unemployed, the more value they place on employment.

Given that expectancy-value theory assumes that both expectations and value determine the job search effort, we consider predictions regarding the effect of stigma consciousness on job search effort ambiguous. Regarding expectations, high stigma consciousness should result in lower job search effort; however, regarding the value of re-employment, a higher job search effort is expected. Thus, the overall effect depends on the relative importance of either of the two factors, resulting in two competing hypotheses.

The higher the stigma consciousness among the unemployed, the lower the job search effort.

The higher the stigma consciousness among the unemployed, the higher the job search effort.

Several scholars assume that the actual re-employment chances are crucially determined by the intensity of the job search effort as follows: the higher the job search effort, the higher the probability of obtaining adequate and acceptable job offers (see e.g., Mortensen 1986 ). Therefore, for job search success, we also posit two opposing hypotheses. We expect that stigma consciousness has negative effects on job search success in the case of reduced effort, but in contrast, we expect positive effects in the case of increased job search effort.

The higher the stigma consciousness among the unemployed, the lower is the job search success.

The higher the stigma consciousness among the unemployed, the higher is the job search success.

4 Literature review

Empirical evidence regarding the effect of unemployment stigma on job search behaviour is scarce. In contrast, there is ample and convincing evidence regarding discrimination of the unemployed in employers’ hiring behaviour and so-called true unemployment state dependence. In the following, we review the part of the literature that explicitly connects their results to unemployment stigma as the key explanatory mechanism.

Several studies find that firms are reluctant to fill vacancies with an unemployed job seeker and explain this finding by the prevalence of unemployment stigma. In a correspondence experiment, Oberholzer-Gee ( 2008 ) observes that the callback rates for short-term unemployed individuals are even higher than those for employed job seekers, but if applicants are long-term unemployed, the callback rates decline. He finds that even after controlling for further characteristics of the supply side of a job offer, the duration of unemployment has a crucial negative effect on the likelihood of being invited to a job interview. Several studies have replicated and extended his results. Nüß ( 2017 ) finds that callbacks decline after 10 months of unemployment. Eriksson and Rooth ( 2014 ) do not find that past unemployment spells will lead to differential treatment regarding callbacks, nor will current short-term unemployment for up to 9 months. However, after this point, stigmatization effects arise, and callback rates decline. The authors also observe stronger stigma effects for men than for women. Ghayad ( 2014 ) reports that after more than 6 months of unemployment, work experience will no longer matter. The generally positive effect of an unemployed applicant’s industry-specific human capital disappears, and callback rates are similar to those for unemployed persons without industry-specific human capital. Kroft et al. ( 2013 ) find that discrimination against the unemployed is common if labour markets are tight, and callback rates already start to decline after 6 months of unemployment. However, some studies do not observe any stigma effects. Nunley et al. ( 2017 ) find no effects of unemployment on callback, irrespective of labour market tightness. Similarly, Farber et al. ( 2015 ) do not find that unemployment reduces callback, but they do find reduced callback rates for applicants over the age of 50. In a recent experiment, van Belle et al. ( 2017 ) also conclude that unemployment duration serves as a sorting criterion because employers view it as a signal of low motivation.

In a related stream of research, stigma is considered the reason for the so-called true state dependence in the duration of unemployment, i.e., the effect of past unemployment on one’s current labour market status (e.g., Arulampalam 2001 , 2002 ; Arulampalam et al. 2001 ; Heckman and Borjas 1980 ). Spurious state dependence means that unobserved differences between the unemployed create the impression that re-employment chances diminish with longer unemployment durations. By contrast, true state dependence means that the longer an individual is unemployed, the lower the chances of finding re-employment (e.g., van den Berg and van Ours 1996 ). One explanation provided by the literature for how true state dependence arises is the stigmatization of the (long-term) unemployed by employers because they consider unemployment as a signal of low motivation or productivity (Vishwanath 1989 ).

For example, Biewen and Steffes ( 2010 ) find significant effects of past unemployment on the present unemployment risk; these effects decrease when unemployment rates are high. The authors consider this to be evidence of stigma effects because individual unemployment is less likely to be interpreted as a negative signal if unemployment is high and vice versa (see also Omori 1997 ). Ayllón ( 2013 ) reports similar results but also finds that if unemployment rates are high, discouragement effects counterbalance the lower stigma effect to some extent.

A third strand of literature is addressing the unemployed social experience as stigmatized but is more strongly focussed on psychological coping mechanisms or communication strategies of the unemployed as a reaction to their status as stigmatized. Knabe et al. ( 2018 ) analyse whether social networks can be a substitute for stigmatized unemployed to feel respected and appreciated. Gurr and Jungbauer-Gans ( 2017 ) focus on whether or not the unemployed have internalized society’s view that the unemployed themselves are to be blamed for their situation. Similar, but with a stronger focus on job search requirements, Hirseland and Ramos Lobato ( 2014 ) found that the unemployed react to stigmatizing media discourse (“lazy unemployed”) by either taking over the public opinion, by seeing themselves as an exception to the rule or by complying with the public demands for intensified job search effort. Research using the same data as that used in the following analysis yielded the following results: Lang and Gross ( 2017 ) find that unemployment stigma consciousness is determined by the strength of deviation, the scope of the norm’s application and the intensity of formal social control; Gurr et al. ( 2018 ) find no effects of unemployment benefit sanctions on stigma consciousness; and Linden et al. ( 2018 ) find that being exempted from job search requirements due to ill health does not reduce stigma consciousness among the unemployed.

Overall, empirical evidence regarding how the social stigma of unemployment is related to job search attitudes, behaviour and re-employment success is scarce. To the best of our knowledge, the only studies intersecting with ours are a qualitative data analysis performed by Hirseland and Ramos Lobato ( 2014 ) and a quantitative paper published by Kerbo ( 1976 ); however, in both studies, no formal tests of these relationships were conducted.

5 Data and method

5.1 data and operationalizations.

In the following analysis, we use the German household panel study PASS (Trappmann et al. 2013 ), which began in 2007, and at the time of the writing of this manuscript, ten waves were available. PASS consists of two almost equally large subsamples, a probability sample drawn from all long-term unemployed persons registered with the German federal employment services and a random population sample. We use both samples for our analysis, but because our focus is on the unemployed, the registered unemployed sample dominates our analytical sample. Both subsamples of the PASS were refreshed several times and survey-provided weights are used in the analysis below to account for panel attrition. PASS collected data regarding (un-)employment histories retrospectively, and for each wave, detailed information about the current employment or unemployment situation is available.

Our operationalization of unemployment stigma relies on a scale that measures stigma consciousness among the unemployed who were part of wave 7 of PASS (Gurr and Jungbauer-Gans 2013 ). Footnote 2 This scale builds upon Goffman’s stigma concept and adapts a rather general psychological concept of gender stigmatization for the case of the unemployed (Pinel 1999 ). However, it also uses insights from other concepts and definitions described in the theoretical section of this article (e.g., Link and Phelan 2001 ). We exclude the item “I am trying to find a job as quickly as possible” because this item measures a concept similar to one of our dependent variables job search effort. We construct an index by aggregating all but one of the above mentioned stigma item. We normalize the values of the scale to range from 0 (no stigmatization) to 10 (maximum stigmatization). Table  3 in the appendix provides an overview of the items used in the scale. With a value of Cronbach’s alpha of 0.73, this scale exhibits acceptable reliability. The average value of the scale amounted to 5.09 with a standard deviation of 1.80, indicating on average medium stigmatization and considerable variation between individuals.

To test Hypotheses 1 and 2, we use the PASS questions regarding life and health satisfaction, both of which are measured on an 11-point scale (“In general, how satisfied are you currently with your life overall?”; “How satisfied are you today with your health?”). To test Hypothesis 3, we use a self-assessment of the unemployed’s employment chances (“What do you think are your chances to find a new job in the next 6 months? good/quite good/quite bad/bad”), which was presented to all unemployed individuals regardless of whether they searched for a job. Footnote 3 To test Hypothesis 4, we use a factor score obtained from a set of four items measured on a four-point scale concerning non-monetary and monetary motivations to work as the dependent variable. We maintained the three items Footnote 4 reflecting non-monetary motivation because these items loaded on a common factor, whereas the fourth item constituted an independent factor.

In Hypothesis 5a/b, job search effort is the dependent variable. We use several different measures available in PASS to cover a wide range of potential indicators of higher or lower job search effort (see Table  4 in Appendix for a detailed overview). Our first and most basic indicator uses binary information regarding whether the respondent actively searched for a job within the prior 4 weeks. A second indicator is the sum of all job search methods actively used in the last 4 weeks. The third indicator uses additional information on the intensity with which a specific job search method was used. We use a sum score of all job search intensities from all job search methods used by the respondent. If a method is not used, intensity is coded as 0. Indicator number four is the number of hours spent searching for a job. Indicator five measures the number of times during the last 4 weeks a respondent used one of the following ways to apply for a job: replied to job advertisements, placed an “employment wanted” advertisement with the newspaper, asked for a job at the company itself or submitted an application even though no job opening had been advertised. We set all indicators of the job search effort to zero for those respondents who left the labour force (e.g., “homemaker”) because per definition, these individuals are not searching for a job, and not including them would systematically excludes the most discouraged unemployed.

In Hypotheses 6a/b, we are interested in the job search success. This is measured first as the number of job interviews a respondent had during the last 4 weeks. Second, we construct a dummy variable that assumes the value of one for those who hold a job and zero for those who are unemployed, have withdrawn from the labour force or are in any other state (e.g., retirement).

5.2 Sample selection and analytical design

Because stigma consciousness is measured only in one single wave, our analytical sample is in principle a cross-section. However, the stigma consciousness scale is embedded in an ongoing panel study. Therefore, while stigma consciousness is measured in wave 7, the outcome variables are obtained from wave 8 of the PASS study. We include several important covariates (often time-constant or measured at wave 7) as control variables to account for socio-demographic information, including age, age squared, gender, marital status, migration background, educational attainment, place of residence (East/West Germany), household size, and household income, and a dummy variable representing the general population and the welfare benefit sample. The descriptive statistics (mean, standard deviation, minimum and maximum, and number of cases) of the dependent and independent variables are shown in Table  5 in Appendix .

In addition, we include information regarding previous employment status (unemployed, employed, or out of the labour force), which was measured in wave 6. Furthermore, we included unemployment duration and the number of previous unemployment spells, which were measured at wave 7, as controls because these variables might influence the level of stigma consciousness. However, because we have no information regarding the level of stigma consciousness at the beginning of the unemployment episode, the current unemployment duration can also be an outcome of stigma consciousness, especially if reduced job search effort is the dominant reaction of the stigmatized. Alternatively, we can also consider unemployment duration and previous unemployment as alternative measures of unemployment stigma, albeit potentially confounded by the depreciation of human capital. Because of these alternative views, we also test our hypotheses without including the unemployment duration variables in the model. We find that the choice of including unemployment duration did not substantially influence our results (see Tables  6 and 7 in Appendix ). Footnote 5

In summary, our analytical sample is selected as follows. From the 14,449 respondents in wave 7, we restrict ourselves to 2448 respondents who were eligible to answer the stigma consciousness scale items. The main criterion for eligibility is registered unemployment during wave 7. We exclude individuals with missing stigma scale values, reducing the sample to 2286 individuals. Finally, we exclude those who dropped out in wave 8 (we used the PASS provided weights to account for this panel attrition), resulting in 1779 cases remaining. For Hypothesis 1, we arrive at our analytical sample of 1278, which included individuals who were still unemployed or moved to the silent reserve (“homemaker”) at the time of wave 8. For Hypothesis 2, the same sample is used if the number of job interviews is the dependent variable. If job-finding is the dependent variable, the sample size increases to 1573 because those who found employment between waves 7 and 8 will be included also. To avoid any further loss of cases and, thus, precision in our estimate, we multiply impute (Rubin 1987 ) the missing data in any of two cases. First, we impute the data if the data were missing due to item non-response. Second, we impute the data if the data were missing for respondents who entered the panel survey only during wave 7 because in this case, no information on several variables from wave 6 was available.

With our estimation strategy we follow standard procedures using liner regression, except for that we also use linear regression analyses for binary and ordinal outcome variables because the estimation of marginal effects after multiple imputations is cumbersome (see STATA multiple imputation reference manual release 13: 77). Thus, linear regressions are conducted to analyse life and health satisfaction (ordinal), re-employment expectation (ordinal), non-monetary employment motivation (continuous), active job search (binary), re-employment (binary) and job search intensity (continuous). All other outcomes are count data variables, i.e., hours spent searching, the number of job search methods, the number of applications and the number of job interviews, and we use negative binomial count data regressions, a method similar to Poisson regression, but more generally applicable (Cameron and Trivedi 2010 ).

6.1 Hypotheses tests

This section presents the results of our empirical analysis. Please note that in the following, the terms “effect” and “coefficient” are used interchangeably and without intending to imply causality. See Sect. 7 for a discussion regarding what speaks for or against a causal interpretation of our regression results. In Table  1 , Models 1–4 present the results of Hypotheses 1–4. Hypotheses 1 and 2 posit that people suffer from unemployment stigma, and therefore, higher stigma consciousness is associated with lower subjective well-being and lower health. As shown by Model 1 in Table  1 , the coefficient of stigma consciousness is negative and statistically significant. The coefficient is  −0.319, indicating that a one point (or equivalently a 10%) increase in stigma consciousness is associated with ca. 0.32-point reduction on the 11-point life-satisfaction scale. This reduction is only a slight one, because it accounts for a small part of the standard deviation of the dependent variable (see Table  5 in Appendix ). The results of health satisfaction (Model 2 in Table  1 ) are similar, although the coefficient (0.196) is smaller. As shown, the respective coefficient is also statistically significant and negative, indicating that high stigma consciousness is associated with lower health satisfaction. Therefore, the data support hypotheses 1 and 2.

Hypothesis 3 focusses on the unemployed’s job expectations and posits that a negative association exists between stigma consciousness and self-perceived re-employment chances. Based on Model 3 in Table  1 , the coefficient of the stigma consciousness scale on the 4-point scale of self-assessed chances of re-employment is − 0.047, which as predicted, is negative and statistically significant. The higher the unemployed’s stigma consciousness, the lower their expectations of transitioning from unemployment to employment. Hypothesis 4 concerns the positive relationship between unemployment stigma and the value placed on re-employment. The respective coefficient (Model 4 in Table  1 ) is 0.125, which is statistically significant. Thus, Hypotheses 3 and 4 are also supported by our data.

In Table  2 , the results of Hypotheses 5a/b and 6a/b are presented. In both hypotheses, the theoretical prediction is ambiguous, allowing for both positive and negative associations between unemployment stigma and job search effort and job search success.

In Model 1 in Table  2 , the dependent variable is active job search. Instead of the negative regression coefficient expected from Hypothesis 3a, Model 1 reports that stigma consciousness has a positive effect on whether unemployed individuals actively search for a job, and this effect is significant at the 0.1% level. For every additional point on the ten-point stigma consciousness scale, the probability of actively searching for a job increases by 2.6% points. Thus, two hypothetical individuals who are located at the opposite ends of the stigma consciousness scale could differ by 26% points in their probability of engaging in active job search, whereas the average probability of an active job search is approximately 52% in our analytical sample (see Table  5 in Appendix ).

Model 2 in Table  2 extends the binary outcome variable by not only looking at active search but at the number of job search methods used during job search. With each additional point on the stigma consciousness scale, the number of methods significantly increases on average by 0.067, which is also only a slight increase. Model 3 then takes into account that job search intensity can vary within each method used for job search and uses the sum score of all the respondents’ values for each method used as the dependent variable. Again, we observe a significantly positive but rather small effect. Our next indicator of job search effort is the number of hours spent searching for a job, where the coefficient of stigma consciousness is significant only at the 5% level. On average, an additional point on the stigma scale results in approximately 4.6 min per week (60 min * 0.077) more time spent searching for a job. For Model 5, the number of times the unemployed applied for a job is the dependent variable. We observe a positive and statistically significant coefficient of 0.069, indicating that a one-point increase in the stigma variable is associated with ca. 0.07 more applications.

Our empirical analysis based on several different indicators of job search effort finds empirical support for Hypothesis 5b, i.e., stigma-conscious unemployed individuals increase their job search effort.

Turning to Hypothesis 6, we have two different indicators of an unemployed individual’s job search success. The first indicator focusses on the number of job interviews during the last 4 weeks of those still unemployed (Model 6). Here, the association with stigma consciousness is positive but the coefficient is small and statistically insignificant. Considering Model 7 in which the dependent variable is actual re-employment, we find that stigma consciousness is slightly negatively related to re-employment probability, but again, this effect is not statistically significant.

6.2 Robustness checks

Here, we report some robustness checks and tests of potential alternative explanations for our empirical results.

A first alternative explanation concerns the relationship between stigma consciousness and job-search effort. We cannot exclude the possibility that the level of job search effort could lead to high stigma consciousness and not the other way around. For example, we might assume that those who are starting their job search with above average job search effort are more prone to interpret their experience of continued unemployment as the result of stigmatization. In both cases, a positive regression coefficient of job search effort on stigma consciousness will arise in cross-sectional data. However, if the regression coefficients reflect such differences in job search effort at the beginning of the unemployment spell, the positive relationship should disappear once these initial differences are accounted for. Thus, as a robustness check, we measure the job search effort at the start of the unemployment episode. Specifically, we use the job search effort from the start of the unemployment spell or if observations remain censored because the respondents entered the panel survey during unemployment, job search effort from the first observed wave. Footnote 6 The underlying idea here is that job search effort during the early stage of the unemployment spell is not influenced by stigma consciousness because it is defined as the expectation of being subjected to negative stereotypes during the job search. To the degree that these expectations are based on actual experiences, measuring job search behaviour at the beginning of the job search suggests that only minimal experiences have been gathered and that the level of job search effort should still be relatively independent of such negative experiences.

Unfortunately, information regarding the initial job search effort is only available for active job search, number of job search methods and number of job interviews. However, at least for these outcomes, we can perform a robustness check by including the respective initial values as additional control variables. We extend this procedure to the analysis of life and health satisfaction and we control for the initial value at the first observed wave in unemployment, too. As shown in Appendix , Table  8 , including the initial levels of the dependent variables reduces the size of the coefficients of stigma consciousness in all cases, but the basic conclusions remain unchanged. The coefficients tend to be smaller but are still positive and statistically significant. However, using past values of the dependent variable (so-called lags) has been criticized not only in the context of panel data (Nickell 1981 ) but also, more recently, in pooled cross-sectional data analysis by Vaisey and Miles ( 2017 ). Thus, this strategy might not be able to remove the bias entirely. Footnote 7

A second alternative explanation is concerned with whether increasing the job search effort is really based on the autonomous decision of the unemployed as suggested by our theoretical framework. In contrast, as posited by self-determination theory (Ryan and Deci 2000 ), increased effort can also be the result of externally controlled behaviour. Following Hirseland and Ramos Lobato ( 2014 ), we might assume that the increased job search effort of those unemployed experiencing high stigma consciousness could be the result of their attempts to comply with the demands placed upon them by case workers at local unemployment offices. To “activate” the unemployed, case workers often monitor their job search effort and sanction those who do not comply with what in Germany is called “Mitwirkungspflicht” (duty to cooperate). If this monitoring is successful such that it leads to increased job search efforts but simultaneously makes the unemployed feel depreciated and stereotyped as the “lazy unemployed”, high stigma consciousness could arise as a by-product of such monitoring practices. Consequently, the positive effect of stigma consciousness could be spurious and solely based on the level of monitoring through unemployment offices and their case workers as a common cause. To test this alternative explanation, we included information on whether an integration agreement was signed between the unemployed and the case worker as an additional covariate. Furthermore, we included a factor score measuring the self-perceived quality of the unemployeds’ experiences with the job center and their staff members. The factor sore was derived from a factor analyses on items of a respective items set. Footnote 8 We found that the inclusion of these variables did not substantially change the results (see Tables  9 and 10 in Appendix ).

Third, a further alternative explanation, especially for the results concerning job search effort, is social desirability bias. Social desirability bias refers to survey respondents’ tendency to adapt their answers towards what they perceive to be the social norm. To explain the positive association between stigma consciousness and effort, social desirability bias must upward bias the reporting of both job search effort and stigma consciousness, net of all covariates, such as age, gender and education. Clearly, there is a danger that individuals tend to overstate their job search effort in an interview situation because given the public debate regarding the lazy unemployed, high effort to end unemployment is normatively more acceptable than low effort. However, regarding stigma consciousness, the nature of social stigma is that it is rather hidden in social interactions than overemphasized. Therefore, social desirability bias is more likely to lead to a downward bias in reporting the level of stigma experienced during unemployment. Footnote 9 Overall, this logic argues against social desirability bias as an alternative explanation.

Fourth, in accordance with our theoretical framework, many of our dependent variables are based on self-assessment and measure respondent’s perceptions, e.g., of being subjected to stereotypes or of their labour market chances. Therefore, personality traits such as self-efficacy or the “Big 5” personality traits might be a common cause for the negative relationship of stigma consciousness and subjective well-being and/or with the positive association with job search effort. Therefore, in a further robustness check, we conducted our analysis controlling for these traits. Self-efficacy is defined as generalized self-efficacy and was measured as sum score obtained from five items Footnote 10 measured in wave 7. These items focus on the personal assessment of one’s own competences to deal with difficulties and barriers in everyday life (Schwarzer and Jerusalem 1995 , 1999 ) and were used as sum indices. Further personality traits were measured using the 21-item version of the Big Five Inventory (BFI-K) which covers rather broad personality dimensions extraversion, agreeableness, conscientiousness, neuroticism and openness to experience (Rammstedt and John 2005 ). Those traits are only available in wave 5 and for those respondents in our sample who did not already participate in that wave, values had to be multiply imputed. We found that even if some of the personality traits are significantly correlated with some outcomes, including self-efficacy or the “Big 5” personality traits does not substantially change the results. Whereas the coefficients of stigma consciousness for well-being and health become smaller, those for job-search effort even slightly increase (see Tables  11 and 12 in Appendix ).

Finally, the Appendix also documents that the results without multiple imputation are, except for higher standard errors, similar to those after multiple imputation (see Tables  13 and 14 in Appendix ).

6.3 Limitations

An important caveat of our analysis is that our results might only apply to the German context and need not necessarily extend to countries with different systems of social security or welfare traditions. In general, Germany is assumed to be characterized by a social security system that has a strong focus on status maintenance. For example, Paugam and Russell ( 2000 ) argue that due to the high importance of employment for social status in Germany, unemployment is likely to lead to social stigma. Within the German context, our analytical sample is characterized by a high share of long-term unemployed, mostly recipients of welfare benefits. In Germany, unemployment insurance benefit receipt is limited to 12 months for the general population and 24 month for workers 55 years or older. After the insurance benefits expire, the unemployed can receive means-tested basic income support. Basic income support consists of a flat rate, and the unemployed are only eligible for support if their household income is below a certain threshold. However, our indicator of stigma consciousness does not focus on the stigma of welfare receipt but on the general stigma of unemployment. Therefore, we cannot answer the question regarding whether a measure of stigma consciousness focusing more on welfare state dependency could lead to different results. In addition, due to our analytical sample restrictions, we focus on unsuccessful job searches, increasing the tendency to over represent the long-term unemployed. However, the long-term unemployed should be subject to stronger stigma than the short-term unemployed, and unsuccessful job searches should be more likely to lead to passivity. Therefore, notably, even in this analytical sample (average unemployment duration in wave 6 is slightly over 5 years), the association with job search effort is positive.

A second limitation concerns the results on actual re-employment. The results for job search effort and the number of job interviews on the one hand and re-employment chances on the other hand are observed on systematically divergent populations (the former is only observed among those who did not find re-employment), the interpretation that increased job search effort does not increase re-employment chances can thus be challenged. In addition for re-employment, a duration analysis might have been the more informative and appropriate method. However, even if our focal independent variable stigma consciousness is in principle time-varying, it was measured in PASS at only one point in time. This point in time is different for all respondents with respect to their previous unemployment duration. Given that unemployment duration influences stigma consciousness, it is crucial for our analysis to control for this variable. In a duration analysis, unemployment duration would already be the dependent variable, therefore controlling for elapsed unemployment duration until the stigma consciousness was measured would induce endogeneity. Therefore, we rely on the simpler logistic regression model, where we can control for unemployment duration. We acknowledge, however, that this discards a lot of information and is only a workaround.

7 Discussion and conclusion

An important strand of literature in labour market research is concerned with the effect of unemployment stigma on re-employment chances. This literature shows that the unemployed are stigmatized in the sense that they face serious disadvantages on the labour market, irrespective of their actual motivation, skills and behaviour and that unemployment can create a vicious cycle where unemployment begets further unemployment. In contrast, literature on the behaviour of the unemployed themselves is scarce and prone to assume that the typical reaction of the unemployed to being stigmatized is passivity and withdrawal behaviour.

Our paper is the first to present an empirical test of how stigma consciousness relates to job search attitudes and behaviour. We tested several hypotheses and interpreted the empirical evidence as follows. First, we corroborated the results of other studies showing that being stigmatized has negative consequences on individual well-being and health. Those who rated higher on the stigma consciousness scale also showed significantly lower life and health satisfaction. Based on expectancy-value theory, we found that being subjected to the social stigma of unemployment leads the unemployed to have lower expectations of successfully leaving unemployment. In contrast, the value of employment increases most likely because re-employment is an effective way to free the unemployed from unemployment stigma.

While the literature on unemployment suggests discouragement or withdrawal among the unemployed, we posit two possible reactions of the unemployed towards perceiving themselves stigmatized with respect to job search behaviour. If the low expected employment chances dominate the stigmatized’s behaviour, these individuals should decrease their job search effort. In contrast, if a higher value of employment dominates, the stigmatized should actually increase their job search effort. By presenting empirical evidence from several different indicators of job search effort, we found that high values of stigma consciousness were associated with more rather than less effort, e.g., in terms of engaging in an active job search, the number of hours spent searching or the number of job applications. We interpret this as evidence that the stigmatized unemployed are not characterized by passiveness or learned helplessness as the literature sometimes suggests. In contrast, we interpret this finding as evidence that the stigmatized suffer from their experience of joblessness even more than the average unemployed and aim to leave unemployment to change their social status and eliminate the social stigma. However, for individuals subjected to unemployment stigma, such increased effort does not lead to better actual re-employment chances. Despite its positive association with job search effort, we found no statistically significant association between stigma consciousness and the number of job-interviews or even re-employment probability.

Overall, we interpret our results as evidence that those who experience unemployment stigma during their job search suffer more from their experience of joblessness but do not tend to react with withdrawal and passivity. These individuals do not quit the job search and instead increase their effort by utilizing more methods to search for a job, spend more hours searching and send more job applications to potential employers. However, no empirical evidence supports that this increased effort helps the unemployed improve their situation by leaving unemployment. This result is in line with Gielen and van Ours ( 2014 ), who find that even if the unhappy unemployed search more actively for a job, it does not impact their unemployment duration. The results are also in line with Hohmeyer and Wolff ( 2018 ) who find that One-Euro-Job announcements increase job search effort but does not lead to higher employment probability.

Because we mainly rely on cross-sectional data, alternative explanations of the observed pattern of associations are possible. For example, high stigma consciousness could instead be a reaction of high-effort job seekers who become frustrated by the absence of re-employment success. Furthermore, pressure from employment offices might be a common cause of both high stigma consciousness and high job search effort. We attempted to test these alternative explanations as much as possible and found no evidence supporting these explanations over our own. However, we must acknowledge that these alternative explanations cannot be entirely dismissed given that there seems to be no bulletproof solution, especially regarding reverse causality, which is particularly true for cross-sectional data but in many regards also extends to longitudinal data (see e.g., Vaisey and Miles 2017 ). With longitudinal data, more sophisticated methods are available (cf., Leszczensky and Wolbring 2018 ), but these methods are also not without their own problems. Therefore, further research is needed to corroborate our results. Such research should preferably be based on longitudinal data that includes the measurement of unemployment stigma at several points in time, including the beginning of unemployment. To gain such data, refining the existing scale could be worth the effort to obtain a shorter scale that is more easily incorporated into panel surveys to measure unemployment stigma.

Availability of data and materials

The datasets analysed in the current study are available in the Forschungsdatenzentrum der Bundesagentur für Arbeit (BA) im Institut für Arbeitsmarkt- und Berufsforschung (IAB), https://fdz.iab.de/de/FDZ_Individual_Data/PASS.aspx .

Other manifestations are public stigma (shared attitudes and behaviour of a society towards the stigmatized), structural stigma (legitimization of a social stigma by being embedded in a society’s institutions) and stigma by association (people’s reaction of being associated with a stigmatized person) (Pryor and Reeder 2011 ).

The scale was originally designed to derive different distinct factors of stigmatization and distinguish between factors pertaining expectations with respect to other unemployed persons (in-group) and the general population (out-group) and strategies of action (Gurr and Jungbauer-Gans 2013 ). However, neither the pretest of the study nor the main study of PASS wave 7 confirms these theoretical expectations, though other non-congruent factors (social relations, avoidance of situations, pressure to act, awareness of prejudices) develop. In the PASS dataset factor analyses only confirms these factors to some extent. This could either be due to the low number of cases in the pretest (N = 104) or the fact that distinct factors are difficult to measure within the stigmatization framework. Therefore, and in line with Gurr et al. ( 2018 ) and Linden et al. ( 2018 ) we use a sum score for our analyses.

For several other items regarding job search expectations and attitudes, this was not the case.

Let us now deal with the topic of work and gainful employment. Regardless of whether you currently work or not: To what extent do you agree to the following opinions on work? Please think very generally about working in a job. Please tell me whether you “strongly agree”, “somewhat agree”, “somewhat disagree” or “strongly disagree” with these opinions. “Having work is the most important thing in life.”; “Work is important because it gives you the feeling of being a part of something/belonging.”; “I would also like to work if I didn’t need the money”. (Official translation provided by PASS).

Because unemployment duration is often seen to be negatively related to job-search effort, this stability might seem surprising. However, Schels and Bethmann ( 2018 ) found that for most unemployed, job search effort remains stable over time.

For example, if an individual entered unemployment during wave 4 and is still observed as unemployed during wave 7 (our main sample selection criterion), the indicator measures job search effort during wave 4. If the individual entered the panel survey during wave 5 as unemployed and was unemployed at least until wave 7, the job search effort is measured at wave 5. If the individual was employed before wave 7, the job search effort on the job is measured at wave 6.

In our application, lagging the dependent variable could pose a problem if in technical terms, large reverse causality bias and large bias due to unobserved confounders coincide. According to results from a Monte Carlo simulation by Vaisey and Miles ( 2017 ), if bias due to unobserved confounders is low to medium, the results of a regression with and without lagged dependent variables can be considered the lower and upper bounds, e.g., for the dependent variable “active job search (yes)”, the upper bound is 0.026 (Table  2 , Model 1) and the lower bound is 0.014 (see Table  8 , Model 3). However, we emphasize that there is an important difference between our strategy and the strategy criticized by Vaisey and Miles ( 2017 ). We do not simply lag the dependent variable for one period, which was the approach used by Vaisey and Mills ( 2017 ). In that case, we would still use a value of the dependent variable (job search effort) that has potentially been influenced by prior values of the focal independent variable (stigma consciousness). Instead, we aim to control for the initial (or at least the earliest observed) value of job search effort. Thus, we aim to measure the dependent variable at a point in its history when it is still “uncontaminated” by the focal independent variable. In the simulated data reported by Vaisey and Miles ( 2017 ), there is never such a point in the common history of the dependent and independent variables, which may explain why lagging does not work as intended. To clarify this distinction, we refer to “initial values” instead of “lags” of the dependent variables (see Table  8 in Appendix ).

How far do the following statements apply to your personal experience with the Job centre and their staff members? Please tell me whether these statements “Apply completely”, “Tend to apply”, “Tend not to apply” or “Do not apply at all”. (A) The staff dictate too much what I am to do; (B) They really want to help me there; (C) I expect that my situation will improve through the counselling; (D) They support me in finding a job again; (E) Only demands are put forward by them, but I don’t get any support; (F) I trust the staff; (G) My ideas are taken into consideration in counselling; (v) The staff members are friendly and helpful to me; One item (C) had to be excluded from the scale because of positive and negative correlations with the other items, two items (A and E) were reversed so that higher values indicate a more positive experience. The scale proved to be one-dimensional and is sufficiently reliable (Cronbach’s alpha 0.85). The scale was only presented to those unemployed that were actually registered with the job center, i.e., recipients of welfare benefits, thus the number of observations are slightly lower compared to the main analysis.

Since the PASS is a dual mode survey, one way to test for social desirability bias is to determine whether both job-search effort and stigma consciousness are higher among those engaged in face-to-face interviews compared to those engaged in telephone interviews (under the assumption that social desirable answers are more common face-to-face). However, we observe lower instead of higher job-search effort in face-to-face interviews, which is not consistent with social desirability bias.

Whenever unexpected difficulties or problems show up, there are different ways of reacting to that. We grouped some opinions about that topic here. Please tell me, whether to you those opinions “Apply completely”, “Tend to apply”, “Tend not to apply” or “Do not apply at all”. (A) I have a solution for every problem. (B) Even when things happen surprisingly, I believe that I can cope with them. (C) I have no difficulties in achieving my aims. (D) I always know how to act in unforeseeable situations. (E) I can always solve difficult problems if I try to.

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Acknowledgements

We thank the guest editor Katrin Auspurg, two anonymous reviewers, Katrin Hohmeyer and Jens Stegmaier for the helpful and constructive comments. Previous versions of the manuscript have also profited from comments by the participants of the Second PASS user conference in Nürnberg and the participants of the Session of the Sektion ‘Sozialpolitik’ at the 2018 DGS Kongress in Göttingen, especially Sigrid Betzelt and Carolin Freier. We also thank Huyen Nguyen Ngoc and Luca Reinold for help with preparing the manuscript.

Monika Jungbauer-Gans received funding for this article from the Deutsche Forschungsgemeinschaft under grant DFG JU 414/15-1.

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Krug, G., Drasch, K. & Jungbauer-Gans, M. The social stigma of unemployment: consequences of stigma consciousness on job search attitudes, behaviour and success. J Labour Market Res 53 , 11 (2019). https://doi.org/10.1186/s12651-019-0261-4

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Journal of Economic Perspectives

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Should We Reject the Natural Rate Hypothesis?

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The Natural Rate of Unemployment

Reflections on 25 years of the hypothesis.

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For 25 years, theory about the causes of, and possible solutions to, the problem of unemployment has been dominated by Phelps' and Friedman's natural rate of unemployment hypothesis. This postulates that the equilibrium rate of unemployment consistent with steady inflation is determined by structural variables: sustainable reductions in unemployment can be achieved only by measures to change underlying microeconomic structures, such as benefit and pay bargaining systems. Belief in the hypothesis has faltered since the 1980s, the hypothesis being unable to explain the dramatic upward shifts in European unemployment rates. These essays reflect upon the fundamental structures underlying the hypothesis, assess the related evidence, and look forwards, suggesting possible modifications. In contrast to the single rate postulated by the natural rate hypothesis, several of the contributors propose that there are ranges of unemployment rates consistent with steady inflation.

" The 17 essays in this volume discuss the theoretical foundations, dynamics, empirical status, and political economy of the natural rate hypothesis. They reflect the vigor with which we might expect both proponents and critics to discuss a concept of such central importance. Cross should be applauded for the breadth of his perspective on the natural rate. He has marshalled a wide variety of papers into a volume that is particularly good at highlighting recent advances and which, ultimately, no macroeconomist will want to be without." Mark Setterfield, Eastern Economic Journal

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Frontmatter pp i-viii

• Get access Check if you have access via personal or institutional login Log in Register

Contents pp ix-x

List of contributors pp xi-xii, preface pp xiii-xiv.

• By Olivier Blanchard , Massachusetts Institute of Technology and NBER

1 - Introduction pp 1-12

• By Rod Cross , ICMM, Strathclyde

Part I - The theoretical framework pp 13-14

2 - the origins and further development of the natural rate of unemployment pp 15-31.

• By Edmund Phelps , Columbia

3 - The natural rate as new classical macroeconomics pp 32-42

• By James Tobin , Yale

4 - Theoretical reflections on the ‘natural rate of unemployment‘ pp 43-56

• By Frank Hahn , Cambridge

5 - Of coconuts, decomposition, and a jackass: the genealogy of the natural rate pp 57-74

• By Huw Dixon , York and CEPR

Part II - Adjustment, ranges of equilibria and hysteresis pp 75-76

6 - the economics of adjustment pp 77-89.

• By Andrew Caplin , Columbia, John Leahy , Harvard

7 - Hysteresis and memory in the labour market pp 90-100

• By G.C. Archibald , University of British Columbia

8 - Models of the range of equilibria pp 101-152

• By Ian McDonald , Melbourne

9 - Hysteresis revisited: a methodological approach pp 153-180

• By Bruno Amable , INRA and CEPREMAP, Paris, Jérôme Henry , Banque de France, Frédéric Lordon , CEPREMAP, Paris, Richard Topol , CNRS and OFCE, Paris

10 - Is the natural rate hypothesis consistent with hysteresis? pp 181-200

Part iii - empirical tests and macro models pp 201-202, 11 - the natural rate hypothesis and its testable implications pp 203-230.

• By Hashem Pesaran , Cambridge and UCLA, Ron Smith , Birkbeck

12 - Non-linear dependence in unemployment, output and inflation: empirical evidence for the UK pp 231-255

• By David Peel , Aberystwyth, Alan Speight , Aberystwyth

13 - Prices, wages and employment in the US economy: a traditional model and tests of some alternatives pp 256-298

• By Albert Ando , Pennsylvania and NBER, Flint Brayton , Board of Governors of the Federal Reserve System

14 - The natural rate in empirical macroeconomic models pp 299-312

• By Simon Wren-Lewis , ICMM, Strathclyde and CEPR

Part IV - Political economy pp 313-314

15 - is the natural rate of unemployment a useful concept for europe pp 315-345.

• By Maria Demertzis , Strathclyde, Andrew Hughes Hallett , Princeton, CEPR and ICMM, Strathclyde

16 - The natural rate of unemployment: a fundamentalist Keynesian view pp 346-361

• By Meghnad Desai , London School of Economics and Political Science

17 - Politics and the natural rate hypothesis: a historical perspective pp 362-373

• By Bernard Corry , Queen Mary and Westfield College

Index pp 374-382

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The Natural Rate of Unemployment

• Definition: The natural rate of unemployment is the rate of unemployment when the labour market is in equilibrium. It is unemployment caused by structural (supply-side) factors. (e.g. mismatched skills)

Diagram showing the natural rate of unemployment

• The natural rate of unemployment is the difference between those who would like a job at the current wage rate – and those who are willing and able to take a job. In the above diagram, it is the level (Q2-Q1)
• Frictional unemployment
• Structural unemployment . For example, a worker who is not able to get a job because he doesn’t have the right skills
• The natural rate of unemployment is unemployment caused by supply-side factors rather than demand side factors

What Determines the Natural Rate of Unemployment?

Milton Freidman argued the natural rate of unemployment would be determined by institutional factors such as.

• Availability of job information . A factor in determining frictional unemployment and how quickly the unemployed find a job.
• The level of benefits . Generous benefits may discourage workers from taking jobs at the existing wage rate.
• Skills and education. The quality of education and retraining schemes will influence the level of occupational mobilities.
• The degree of labour mobility. See: labour mobility
• Flexibility of the labour market E.g. powerful trades unions may be able to restrict the supply of labour to certain labour markets
• Hysteresis . A rise in unemployment caused by a recession may cause the natural rate of unemployment to increase. This is because when workers are unemployed for a time period they become deskilled and demotivated and are less able to get new jobs.

Explaining Changing Natural Rates of Unemployment

In the post-war period, structural unemployment was very low. During the 1980s, the natural rate of unemployment rose, due to rapid deindustrialisation and a rise in geographical and structural unemployment.

Since 2005, the natural rate of unemployment has fallen.

• Increased labour market flexibility, e.g. trade unions less powerful.
• Privatisation has helped increased competitiveness of industry, leading to more flexible labour markets.
• Rise in self-employment and gig economy, have created new types of jobs.
• Increased monopsony power of employers, who have kept wage growth low, enabling firms to employ more workers.
• Harder to claim unemployment benefits.

Natural Rate of Unemployment in EU

Even during the period of economic growth 2000-2007, unemployment in Eurozone is higher than US and UK. This suggests the Eurozone has a higher natural rate of unemployment.

• Rigidity in EU labour markets e.g. minimum wages and the maximum working week
• Restrictions on closing factories and mandatory severance pay for workers made unemployed, and this makes firms more reluctant to set up in these countries.
• Higher degrees of unionisation resulting in wage rigidity.
• Generous benefits which lessen the pain of unemployment.
• Hysteresis effects . The cyclical recessions of the 1970s and 1980s had long-lasting effects resulting in more unemployment. However, this does not appear to have affected the UK
• Growing competition from Asian countries, lead to structural unemployment from increased job competition.

During 2012-14, the higher unemployment was partly due to lower rates of economic growth – caused by austerity, and deflationary pressures of the Eurozone single currency.

Reducing the natural rate of unemployment

To reduce the natural rate of unemployment, we need to implement supply-side policies, such as:

• Better education and training to reduce occupational immobilities.
• Making it easier for workers and firms to relocated, e.g. more flexible housing market and greater supply in areas of high job demand.
• Making labour markets more flexible, e.g. reducing minimum wages and trade unions.
• Easier to hire and fire workers.

NAIRU and Non-Accelerating Rate of Unemployment

• A very similar concept to the natural rate of unemployment is the NAIRU – the non-accelerating rate of unemployment.
• This is the rate of unemployment consistent with a stable rate of inflation. If you try to reduce unemployment by increasing aggregate demand, then you will get a higher rate of inflation, and the fall in unemployment will prove temporary.

NAIRU explained

• If there is an increase in AD, firms pay higher wages to workers in order to increase in output, this increase in nominal wages encourage workers to supply more labour and therefore unemployment falls.
• However, the increase in AD also causes inflation to increase and therefore real wages do not actually increased but remain the same. Later workers realise that the increase in wages was only nominal and not a real increase.
• Therefore they no longer work overtime. Therefore the supply of labour falls, and unemployment returns to its original or Natural rate of unemployment. It is only possible to reduce unemployment by causing an increase in the rate of inflation. Therefore the natural rate is also known as the NAIRU (non accelerating rate of unemployment.
• This model assumes workers do not correctly predict the rate of inflation but have adaptive expectations .
• Some economists argue workers will correctly predict higher AD causes higher inflation and therefore there will not be even a short term fall in unemployment; this is known as rational expectations .

Example of NAIRU

• In the above example, the natural rate of unemployment is 6%. If you try to reduce unemployment through increased demand, we get a temporary fall in unemployment, but higher inflation. (point A)
• However, this fall in unemployment is unsustainable and the short-run Phillips Curve shifts to SRPC2, and we move to (point C) and unemployment of 6%.
• Causes of Unemployment
• Voluntary unemployment
• Essay on: Natural Rate of Unemployment

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• Natural Unemployment Rate

Understanding Natural Unemployment

• Inflation and Unemployment

The Bottom Line

What is the natural unemployment rate.

Julia Kagan is a financial/consumer journalist and former senior editor, personal finance, of Investopedia.

• What Is Unemployment? Causes, Types, and Measurement
• Termination of Employment
• Unemployment Claim
• Unemployment Compensation
• Severance Pay
• Severance Agreements
• Negotiating Severance
• Preparing for a Layoff
• Unemployment Insurance
• Applying for Unemployment Insurance
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• Paying Your Bills When You're Unemployed
• 401(k) Withdrawals and Unemployment
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• Unemployment Rate
• Calculating the U.S. Monthly Unemployment Rate
• Highest and Lowest Rates in the World
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• What The Unemployment Rate Doesn't Reveal
• The Unemployment Rate Affects Everybody
• How Inflation and Unemployment Are Related
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• Cost of Unemployment to the Economy
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• Inflation and Unemployment Correlation
• Downside of Low Unemployment
• Frictional vs. Structural Unemployment
• Structural and Cyclical Unemployment
• Cyclical Unemployment
• Disguised Unemployment
• Employment-to-Population Ratio
• Frictional Unemployment
• Full Employment
• Labor Force Participation Rate
• Labor Market
• Natural Unemployment CURRENT ARTICLE
• Structural Unemployment

The natural unemployment rate is the minimum unemployment rate resulting from real or voluntary economic forces. Natural unemployment reflects workers moving from job to job, the number of unemployed replaced by technology, or those lacking the skills to gain employment.

Key Takeaways

• The natural unemployment rate is the minimum unemployment rate resulting from real or voluntary economic forces.
• It represents the number of people unemployed due to the structure of the labor force, such as those replaced by technology or those who lack the skills to get hired.
• Natural unemployment is commonplace in the labor market as workers flow to and from jobs or companies.
• Unemployment is not considered natural if it is cyclical, institutional, or policy-based unemployment.
• Because of natural unemployment, 100% full employment is unattainable in an economy.

Investopedia / Theresa Chiechi

The term “ full employment ” is often a target to achieve when the U.S. economy is performing well. The term is a misnomer because there are always workers looking for employment, including new college graduates or those displaced by technological advances. There is always movement of labor throughout the economy that represents natural unemployment.

Unemployment is not considered natural if it is cyclical, institutional, or policy-based unemployment. An economic crash or steep recession might increase the natural unemployment rate if workers lose the skills necessary to find full-time work or if certain businesses close and are unable to reopen due to excessive loss of revenue. Economists call this effect “ hysteresis .”

Important contributors to the theory of natural unemployment include Milton Friedman , Edmund Phelps , and Friedrich Hayek , all Nobel prize recipients. The works of Friedman and Phelps were instrumental in developing the non-accelerating inflation rate of unemployment (NAIRU).

Natural unemployment can occur from both voluntary and involuntary factors. Hysteresis often occurs following extreme or prolonged economic events such as a recession, where the unemployment rate may continue to increase despite economic growth.

Causes of Natural Unemployment

Economists commonly held that if unemployment existed, it was due to a lack of demand for labor or workers and the economy would need to be stimulated through fiscal or monetary measures. However, history reveals the natural flow of workers to and from companies even during robust economic periods.

Full employment means 100% of the workforce is employed. History shows that this is unattainable as workers move from job to job. A zero unemployment rate is also undesired as it requires an inflexible labor market, where workers cannot quit their current job or leave to find a better one.

According to the general equilibrium model of economics, natural unemployment is equal to the level of unemployment in a labor market at perfect equilibrium. This is the difference between workers who want a job at the current wage rate and those willing and able to perform such work. Under this definition of natural unemployment, it is possible for institutional factors, such as the minimum wage or high degrees of unionization, to increase the natural rate over the long run.

Effects of Inflation on Unemployment

John Maynard Keynes wrote The General Theory of Employment, Interest and Money in 1936, leading many economists to believe there is a direct relationship between the level of unemployment in an economy and the level of inflation.

This direct relationship was formally codified in the Phillips curve , which showed that unemployment moved in the opposite direction of inflation . If the economy was to be fully employed, there must be inflation, and conversely, with periods of low inflation, unemployment must increase or persist.

The Phillips curve fell out of favor after the great stagflation of the 1970s. During stagflation, unemployment and inflation both rise , questioning the implied correlation between strong economic activity and inflation, or between deflation and unemployment.

What Is Natural vs. Cyclical Unemployment?

The cyclical unemployment rate is the difference between the natural unemployment rate and the current rate of unemployment as defined by the U.S. Bureau of Labor Statistics.

Why Is the Natural Unemployment Rate Significant?

The natural rate of unemployment is considered the lowest acceptable level that a healthy economy can sustain without creating inflation.

How Does a Recovering Economy Impact the Natural Unemployment Rate?

The natural rate of unemployment typically rises after a downturn in the economy or a recession as workers become more confident that they can move from job to job.

The natural unemployment rate is the minimum unemployment rate stemming from real or voluntary economic forces. It is common in the labor market as workers flow to and from jobs or companies, and because of natural unemployment, full employment is unattainable in an economy. Unemployment is not considered natural if it is cyclical, institutional, or policy-based unemployment.

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Chapter 4: Testing hypotheses with t-statistics

In this chapter we examine two procedures for testing whether an observed difference in averages (means) is statistically significant. The first case looks at the difference between unemployment rates for blacks and whites. In this example, we ask whether the observed differences are large enough and systematic enough to give us a high degree of confidence that unemployment affects the black population more severely than whites. This form of inquiry is an attempt to rule out the possibility that the observed differences are solely a reflection of random variation in unemployment rates for both groups. The second case asks a general question about the US's experience with supply side economics during the 1980s. Supply side policy makers and journalists made extravagant claims about the positive effects of supply side economics. In particular, they argued that deep tax cuts and extensive deregulation would improve incentives for working, investing, and saving. It is well known and widely accepted that higher savings and investment rates are associated with faster growth in real GDP and productivity. In its most extreme form, supply siders argued that the tax cuts would help to shrink the federal deficit. Their flawed reasoning was based on a serious overestimate of the growth stimulus provided by tax cuts and deregulation. In simple terms, they argued that when government revenue becomes a smaller percentage of GDP, the economy grows so much that the dollar size of revenue is actually more in dollar terms. In order to examine these issues, we must conceptualize the economy as a process which generates many different outcomes. The outcomes are the measured values of the variables in the dataset. The measured values, however, are not entirely determined by the systematic operation of our economic system. There are also random factors that play a role, as well as a certain (unknown) amount of measurement error. The value of every variable in the data set is a result of all three of these factors: the systematic processes of the economy, random and uncontrollable factors external to the economy, and measurement error. Recognition of a randomness and measurement error complicates the simple act of comparing variables. For example, we would like to compare black and white unemployment rates in order to determine the average difference. We have already calculated averages for both races, and black rates are higher. The problem, however, is that we cannot say for certain if there is a systematic component to the difference given that the higher unemployment rates for blacks could be due to a couple of years of random events or a couple of years of measurement errors. Hypothesis tests for a difference in means enables us to test this possibility. As you might imagine, the procedure depends on both the average unemployment rates, and the amount of variation they exhibit over time. We may also want to compare the values of a single variable measured at different points in time. For example, the 1980s look different from the 1970s. Deficits were higher, inflation was lower, real rates of interest were higher, and so forth. Once again, however, the differences may not be large enough to rule out the possibility that they are due to measurement error or random, non-repeated processes. What we really want to know is whether these differences are systematic enough to give us a high degree of confidence that they cannot be fully explained by the normal amount of variation which is always occurring. 4-1 Paired means: Differences in black and white unemployment rates In the following, we are trying to determine if the observed difference in black and white unemployment rates is large enough and persistent enough so that we can rule out the possibility that the "true" underlying difference is zero. Formally, let m B represent the true average rate of unemployment for blacks, and m W the rate for whites. Our hypothesis is m B = m W , or alternatively, m B - m W = 0. If we rule this out, then it must be the case that m B ¹m W , which we will designate our alternative hypothesis. Formally we call these the null and alternative hypotheses, where the term "null" conveys the idea of no difference. Symbolically, they can be written: H 0 : m B = m W , H 1 : m B ¹m W , where H 0 : is the symbol for the null hypothesis. In fact, however, we never observe the true averages. Instead, we have sample averages which are based on the available data for a group of years. The sample averages are subject to measurement error and random variation due to unique events in particular years. In addition, they are due to the systematic and persistent factors that determine unemployment rates for each group. The relationship between the sample and average and the true average is: Sample average = x-bar = m± (t statistic)(standard error of the sample average), where the standard error of the sample average is the standard deviation of the unemployment rate (s ur ) divided by the square root of the sample size (Ö n). The t-statistic is the relevant value of a student's t distribution for n-1 degrees of freedom, and (usually) .025 in each tail. (See a statistics text for a complete treatment.) The procedure for carrying out this test in SPSS is straightforward. We will test three pairs of unemployment rates, those for black and white men, women, and teens. Select Statistics from the menu bar, choose Compare Means, and Paired Samples t test; Highlight bm20u in the variable list box (this clicks it into the Current Selections box); Highlight wm20u in the variable list box, and click the arrow to put them into the Paired Variables list box; Do the same for bw20u and ww20u; Do the same for btu and wtu; Click Okay. The SPSS output for black and white men is in Table 5 . SPSS prints two tables for each pair of variables. In the upper part of the table, it prints a set of descriptive statistics, including means, standard deviations, and standard error of the estimate of the mean (SE Mean). The latter is an estimate of the possible range for the "true" population mean, given that this is a sample based on 25 observations. Between the descriptive statistics for bm20u and wm20u, SPSS prints the number of observations (25), the correlation coefficient (0.949--see Chapter 5), and a test statistic to determine if bm20u and wm20u are significantly correlated. Table 5 T-tests for Paired Samples Variable Number  of pairs Corr 2-tail Sig Mean SD SE of Mean BM20U 11.3147 2.894 0.579 25 0.949 0.000 WM20U 4.9840 1.263 0.253 Paired differences Mean SD SE of Mean t-value df 2-tail Sig 6.3307 1.742 0.348 18.17 24 0.000 In the second part of the table, SPSS puts the results of the test H 0 : m B = m W . This is the most important information, and the point at which interpretation of results becomes important. The average difference is 6.3307; the t-statistic for the test is 18.17. The 2-tail Sig is the probability of a t-statistic which is 18.17, or larger, in absolute value. To three decimal places, it has a zero probability. Another way to look at the t-statistic is as the value of the mean difference (6.3307) when it is transferred to a t-distribution scale under the assumption that the null hypothesis is true (no difference in the "true" population mean). Since the t has a zero probability, we can conclude that there is also a zero probability of getting a sample difference of 6.3307 when the true difference is zero. Hence, we reject the null hypothesis. What about women? Is the difference between black and white women significant (i.e. significantly different from zero)? What about teens? In general, should we reject the idea that the underlying "true" rates are the same? How confident can you be about this? 4-2 Independent samples: Supply side and the 1980s economy Proponents of supply side economics appeared on the scene in the late 1970s, at a time when the traditional Keynesian consensus was in disarray. Growth had fallen in the 1970s, inflation had continued to creep up, unemployment rates were consistently higher than they had been in the 1960s, and Keynesian policy prescriptions seemed to hold little promise for improving the situation. Compounding these macroeconomic problems were several microeconomic ones. The US automobile industry experienced some of its worst years ever and the onslaught of more fuel efficient and reliable Japanese imports began to swamp Detroit. The US steel industry, consumer electronics, machine tools, and a number of other traditional manufacturing strengths also experienced their first real challenge in domestic markets. Some of these industries disappeared from the US altogether (consumer electronics) while others were forced to make painful choices in order to restructure over a period of years (steel). Given the turmoil in domestic markets and the macroeconomy, it is not surprising that radical alternatives to mainstream economic analysis suddenly began to appear. The supply siders were the most successful of the radical views. They managed to win the support of an extremely popular president and were blessed (or cursed) with the opportunity to enact major parts of their program. During the 1970s, mainstream conservative economists began to examine the macroeconomic effects of taxes and regulations. They came up with a number of widely accepted and credible empirical studies which showed that various taxes and business regulations had become obstacles to economic growth. The conclusion of many of their studies was that if these disincentives to work and invest were addressed, then there would probably be modest improvements in the overall rate of economic growth. In no way did this body of work support the idea that the much higher rates of growth of the 1950s and 1960s would return; rather it showed a potential for relatively modest increases in economic growth. In the hands of the supply siders, conservative ideas about taxes and regulation were turned into a panacea for every economic problem, including inflation, budget deficits, trade deficits, productivity growth, GDP growth, loss of manufacturing, low savings and investment, and so on. The key promise they made, however, was that with a cut in taxes, saving and investment rates would rise. They argued that when people were allowed to keep a larger piece of future income, they would work, save, and invest more. The rise in work effort, savings and investment would raise the rate of growth of GDP and productivity (output per hour worked). In 1981, President Reagan took office on the promise that he would enact many of the supply side proposals. The cornerstone of his policy was an across the board income tax cut. Legislation was quickly passed cutting everyone's income taxes by 10% in 1981, 10% in 1982, and 5% in 1983. In addition, he continued the trend that was begun under his predecessor, President Carter, of deregulating various sectors of the economy. We will examine a number of variables to see if their is any evidence to support the supply siders' claims. In Chapter 3 we created the variable "is," the share of investment in GDP. According to the proponents of supply side economics, this variable should have increased in the 1980s. Similarly, the variable psp, personal savings as a share of disposable personal income should have risen. The growth rates of productivity (prod1 and/or prod2) and GDP should have risen and the size of the average deficit should have shrunk. In each case, we can test for the predicted effects by testing the hypothesis that the mean value (is, psp, GDP growth, productivity growth, deficit as a share of GDP) for 1970 is different from the 1980 mean. The steps to do this first require the computation of the variables not already in the data set: Select Transform from the menu bar, then choose Compute . . .; If you have not already done so, create new variables: growth rate of GDP; deficits/GDP; growth rate of productivity; investment/GDP; Use the recode function to create a marker for the 1970s and 1980s (if you did not do this in the last chapter). Select Transform from the menu bar, then Recode, and Into Different Variable; Highlight year in the variable list and use the arrow to move it into the Numeric Variable -> Output box; Type sside in the Output Variable box and click Change; Click Old and New Values; In the Old Value box, click the Range button and put 1971 and 1980 in the two boxes; In the New Value box type 1 and click Add; Go back to the Range boxes and type 1981 and 1990; In the New Value box type 2 and click Add; Click Continue and then click OK. Test the hypothesis for each variable, H 0 : m 70s = m 80s , H 1 : m 70s ¹m 80s , using the Independent Samples t test: Select Statistics from the menu bar, choose Compare Means, then Independent Samples T-Test; Highlight psp and click the arrow to put it into the Test Variable(s) box; Do the same for the other variables (investment share, rate of growth of GDP and productivity, deficits as a share of GDP); Highlight sside and click the arrow to put it into the Grouping Variable box, then click Define Groups . . .; In Group 1, type 1 and in Group 2, type 2; Click Continue, then OK; SPSS will perform t-tests on each variable, comparing the mean value for the 1970s to the mean for the 1980s. For each variable, there are two tables, one with the means and standard deviations, and the second with the t value for the tests. Note that SPSS also automatically performs a test to see if the variances are the same during the two periods (Levene's test) and calculates separate t values for each case (equal variances, unequal variances). If the variances are the same, then the procedure pools all the data from both periods to calculate a pooled variance. This makes the t-test slightly more powerful if it is valid to pool the data. What can you conclude? Did the growth rate of real GDP increase? Did any of the variables perform as predicted by supply side politicians? Why do you suppose supply side theory is ignored by mainstream economists? 4-3 Sources Krugman, Paul. Peddling Prosperity: Economic Sense and Nonsense in the Age of Diminished Expectations. New York: WW Norton. 1994. Krugman is a leading American economist who has written an in-depth critique of supply side economics that is accessible to non-economists.

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Another reason why unemployment can remain high even in a recovery

A study suggests recruiters struggle to sift through the many applications from job-seekers.

WHEN THE supply of houses for sale is high, and demand for them is low, pundits often speak of a “buyers’ market”, as it is quicker and cheaper for people to find a new home. It follows, then, that when there is lots of unemployment economists assume it is quicker and cheaper for employers to find new staff. As both the cost of recruitment and wages fall, in theory firms should feel encouraged to hire more workers, which would reduce unemployment quickly after an economic shock. But does this happen in practice?

The data suggest that joblessness can remain stubbornly high after a recession. After the global financial crisis of 2007-09 it took nearly eight years for America’s unemployment rate to fall from a peak of 10% to beneath its pre-recession low of 4.4%. Since the onset of the covid-19 pandemic, unemployment has fallen sharply from 14.8% to 6.1%. But that is still nearly twice its pre-pandemic level. A loss of skills during periods out of work may make it harder for some job-seekers to find employment again. Pressure from existing workers may be keeping wages for new hires artificially inflated, blunting the incentive that exists to recruit more when labour is cheaper (though declining trade-union membership has dampened this effect in recent recessions).

New research suggests an additional explanation for why unemployment should stay high for so long—termed “contagious unemployment”—after an economic shock. The author, Niklas Engbom of New York University’s Stern School of Business, says that economists should ask personnel managers whether recessions make their job easier. The answer, to the surprise of economists, will often be “no”. That is because they are faced with sifting through a flood of applications from job candidates, many of whom will not be suitable for the position. That costs firms time and money. During recessions managers also find it harder to distinguish the best applicants from the duds. With a higher share of applicants unemployed, it is more difficult for employers to determine who is a good fit, particularly as many of them will be changing industries.

To prove the theory, Mr Engbom used data from the New York Federal Reserve’s survey of consumer expectations, along with other sources, from 2006 to 2015. The data show that the number of applications per vacancy and the hours spent by the employer recruiting for each job opening increased rapidly during the recession of 2008-09 (see left-hand chart). Unemployed people submitted over ten times as many job applications each month as job-switchers did. But the success rate per application of the unemployed is less than half that of those already in work. This additional cost of recruitment may discourage companies from hiring new staff as the economy recovers.

More data are needed to prove Mr Engbom’s hypothesis. But it may provide an intriguing explanation if America’s next jobs report, due on June 4th, throws up yet another month of contradictory trends . Job openings in America reached a record high of 8.1m in March and the number of job-seekers per vacancy is still much higher than before the pandemic (see right-hand chart). Although many firms claim that they are struggling to find workers, the unemployment rate ticked up slightly in April, from 6.0% to 6.1%. Could the labour market be struggling to match the right workers to the right job openings?

Some companies, such as McDonald’s , are raising wages to attract more staff. More generous states are providing bonuses for claimants who switch from jobless benefits to work, while thriftier ones are using the labour shortage as an excuse to axe the handouts earlier. If Mr Engbom’s suppositions are correct, these policies may encourage the unemployed to flood recruiters with ever more CVs, but may not lead to the quick hiring of more workers.

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Unemployment Rate in California (CAUR)

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Units:   Percent , Seasonally Adjusted

Frequency:   Monthly

Suggested Citation:

U.S. Bureau of Labor Statistics, Unemployment Rate in California [CAUR], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CAUR, .

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