# The Accuracy of Job Seekers’ Wage Expectations††thanks: We thank Patrick
Arni, Adeline Delavande, Ingo Isphording, Nina Roussille, Amelie Schiprowski,
Daphne Skandalis, Wilbert van der Klauuw, and participants at the annual
meetings of the European Society for Population Economics 2023, the European
Association of Labor Economics 2023, and the Verein für Socialpolitik 2022, as
well as at the Berlin Network of Labor Market Research Winter Workshop 2022
for their valuable comments and suggestions. We are grateful to the Institute
of Labor Economics (IZA) and the Research Data Center (FDZ) of the Federal
Employment Agency at the Institute of Employment Research (IAB) for access to
the survey and administrative data from the IZA/IAB Evaluation Dataset. Marco
Caliendo gratefully acknowledges funding from the German Research Foundation
(Deutsche Forschungsgemeinschaft, DFG, project 405629508).

Marco Caliendo Robert Mahlstedt Aiko Schmeißer Sophie Wagner

University of Potsdam, IZA, DIW, IAB<EMAIL_ADDRESS>of
Copenhagen, IZA<EMAIL_ADDRESS>of Potsdam, Berlin
School of Economics<EMAIL_ADDRESS>of Potsdam;
<EMAIL_ADDRESS>
(Working Paper
September 2023
)

###### Abstract

Job seekers’ misperceptions about the labor market can distort their decision-
making and increase the risk of long-term unemployment. Our study establishes
objective benchmarks for the subjective wage expectations of unemployed
workers. This enables us to provide novel insights into the accuracy of job
seekers’ wage expectations. First, especially workers with low objective
earnings potential tend to display excessively optimistic beliefs about their
future wages and anchor their wage expectations too strongly to their pre-
unemployment wages. Second, among long-term unemployed workers, overoptimism
remains persistent throughout the unemployment spell. Third, higher extrinsic
incentives to search more intensively lead job seekers to hold more optimistic
wage expectations, yet this does not translate into higher realized wages for
them. Lastly, we document a connection between overoptimistic wage
expectations and job seekers’ tendency to overestimate their reemployment
chances. We discuss the role of information frictions and motivated beliefs as
potential sources of job seekers’ optimism and the heterogeneity in their
beliefs.

Keywords: Subjective expectations, objective benchmarks, job search,
unemployment, reemployment wages

JEL codes: D83, D84, J64

##  1 Introduction

Job search is a challenging process, in which unemployed workers encounter
significant uncertainty about their future outcomes. It is well-recognized
that workers often have an incomplete understanding of their labor market
prospects (see, e.g., Adams-Prassl et al., 2023; Balleer et al., 2021; Mueller
et al., 2021; Spinnewijn, 2015) and potential job matches (see, e.g., Belot et
al., 2019; Jäger et al., 2023; Krueger and Mueller, 2016). This, in turn, can
distort their decision-making during job search and may increase the risk of
long-term unemployment. However, despite the increasing evidence indicating
the presence of systematic biases in job seekers’ beliefs (see Mueller and
Spinnewijn, 2023, for an overview), there is still a limited understanding of
the underlying causes and, in particular, which groups of workers are affected
most by these misperceptions.

In our study, we examine the accuracy of job seekers’ expectations about their
wages upon reemployment and analyze heterogeneity in the extent to which they
exhibit overly optimistic or pessimistic beliefs about their potential
earnings. To that end, we explore a unique combination of survey and
administrative data on job seekers in Germany. The large-scale survey provides
insights into the perceptions of more than 5,000 newly unemployed workers
about their future wages. Simultaneously, we leverage administrative records
to establish objective benchmarks for their actual earnings potential based on
the realized wages of comparable workers in a similar situation. To
approximate job seekers’ objective wage potential, we account for a rich set
of socio-demographic characteristics, regional information, and detailed
employment biographies and employ flexible LASSO regressions.

While job seekers, on average, overestimate their future wages by about 17%,
the comparison of subjective beliefs and objective benchmarks allows us to
show that there is significant heterogeneity in the levels of overoptimism
among different groups of workers. We find that especially job seekers with
lower objective earnings potential tend to hold disproportionately optimistic
views regarding their future income. Those positioned in the lowest decile of
the objective benchmark distribution overestimate their potential wages by
approximately 36%, whereas the level of overoptimism is comparatively modest
at around 6% among individuals in the top decile of the distribution.
Moreover, we observe considerable heterogeneity in the accuracy of job
seekers’ wage expectations concerning their personal characteristics. For
example, we find that men and high-skilled workers overestimate their wages
relative to their objective earnings potential more strongly than women and
low-skilled workers.

In examining the influence of job seekers’ pre-unemployment wages, we
demonstrate that expected wage changes are more tightly compressed around zero
compared to the distribution of objectively predicted wage changes. This
observation suggests that job seekers tend to anchor their wage expectations
more strongly to their pre-unemployment wages than what is objectively
justified. At the same time, we find that this anchoring effect is asymmetric.
While the beliefs of job seekers who can reasonably expect a wage increase
compared to their previous salary are relatively accurate, those who are
predicted to face a wage penalty anchor their expectations too strongly to
their pre-unemployment wage. This finding indicates that job seekers do not
sufficiently account for the potential scarring effects of unemployment when
forming their wage expectations.111Existing evidence suggests that the
experience of unemployment is associated with wage penalties upon reemployment
(see, e.g, Arulampalam, 2001; Gregory and Jukes, 2001). Consistent with this
notion, our objective benchmarks indicate that job seekers’ average wage
potential decreases by about 12% compared to their pre-unemployment wage.

In addition, we explore repeatedly elicited wage expectations for job seekers
who are still searching for a job about one year after becoming unemployed. We
find that the overoptimism among this group of long-term unemployed remains
persistent throughout the unemployment spell. This suggests that those facing
challenges in securing a job are hesitant to adjust their wage expectations
despite the feedback they receive during the job search process. This
reluctance to adapt might be one factor hindering their successful
reintegration into the labor market.

It is essential to recognize that job seekers’ beliefs can also be influenced
by their own actions. To shed light on the causal impact of individuals’
search behavior on the accuracy of their wage expectations, we exploit
exogenous variation in the incentives of unemployed workers to search for
jobs. Specifically, we leverage regional differences along the administrative
borders of local employment agency (LEA) districts, where job seekers face
varying risks of being subject to punitive benefit sanctions. This variation
arises because LEAs have the discretion to decide how strictly they punish job
seekers for non-compliance with their search requirements. Consequently,
caseworkers in LEA districts with more stringent sanction regimes may exert
greater pressure on job seekers, leading them to perceive stronger incentives
to apply for and accept jobs.

Supporting this notion, we find that a 10 percentage point higher sanction
intensity – this is equivalent to an increase of approximately one standard
deviation – raises the number of weekly job applications by about 9.8%.
Simultaneously, a stricter sanction regime fosters greater optimism among job
seekers regarding their earnings potential. Raising the sanction intensity by
10 percentage points increases job seekers’ wage expectations relative to the
objective benchmark by about 1.8%. In contrast, we find no evidence that the
sanction intensity has a positive effect on job seekers’ realized wages upon
reemployment. The rise in overoptimistic beliefs may appear somewhat
surprising, given that an enhanced sanction risk is presumed to directly
influence job seekers to become less selective, leading them to lower their
wage expectations. However, the increased search incentives may induce
indirect effects that foster heightened levels of optimism. For instance, job
seekers may adopt more optimistic wage expectations as a way to enhance their
motivation to search for jobs and to cope with the increased threat of benefit
sanctions.

In the final part of our analysis, we provide descriptive evidence on the
labor market implications of wage optimism. The matched survey-administrative
data enable us to examine the extent to which job seekers’ belief accuracy
predicts their search behavior, realized wages, and perceived and actual job
finding rates. It turns out that being overly optimistic about the potential
wage one could earn is positively related to the number of job applications
and realized wages upon finding employment. At the same time, we observe a
wedge between the perceived and actual job finding rates for increasing levels
of wage optimism. On the one hand, job seekers who are most optimistic about
their potential wages also report the highest perceived chances of finding a
job. On the other hand, job seekers’ actual prospects of finding a job decline
as their wage optimism rises. This suggests that the more optimistic workers
are about the wages they can earn upon reemployment, the more likely they are
to overestimate their job finding prospects. This pattern is in line with the
idea that optimistic job seekers who receive wage offers lower than their
expectations tend to be excessively selective and reject offers more
frequently than warranted, thereby prolonging unemployment (see also Conlon et
al., 2018; Dubra, 2004; Mueller et al., 2021).

What causes the overall optimism of unemployed workers and the heterogeneity
in their beliefs? It is often argued that job seekers are not fully informed
about the job finding process and learn about their labor market prospects
during job search (Burdett and Vishwanath, 1988; Gonzalez and Shi, 2010).
Conlon et al. (2018) show that job seekers’ wage expectations increase when
they receive an offer exceeding their initial belief. Aligning with this
notion, our findings suggest that job seekers with greater unemployment
experience and those who receive more advice from their caseworker tend to
hold more accurate earnings expectations. Against this backdrop, one may
expect that providing job seekers with information about their objective
earnings potential may reduce their tendency to hold excessively optimistic
wage expectations.222Related to this idea, various studies have investigated
the causal effects of informing unemployed workers about potentially promising
job matches (Altmann et al., 2022; Behaghel et al., 2022; Belot et al., 2019,
2022; Ben Dhia et al., 2022) or the search process in general (Altmann et al.,
2018), which can have positive effects on job seekers’ labor market
integration. Moreover, Jäger et al. (2023) provide employed workers with
information about their outside options, that is, the average wage of workers
with similar characteristics in the same labor market, leading treated
individuals to revise their wage expectations, as well as their job search and
wage negotiation intentions.

However, an alternative view is that misperceptions can arise from the way
individuals process the information available to them. The literature on
motivated reasoning suggests that the desire to maintain a positive self-image
can significantly impact how individuals form their beliefs (Bénabou and
Tirole, 2002, 2004, 2016). For this reason, they may selectively retrieve
certain information and deliberately suppress negative feedback (Bordalo et
al., 2020, 2021; Gennaioli and Shleifer, 2010). Several of our findings speak
to the empirical relevance of these ideas for the accuracy of job seekers’
wage expectations. In particular, we find higher levels of overoptimism among
individuals with the lowest objective earnings potential, especially among
workers who are predicted to experience a wage decline compared to their past
salary. This group of workers may have a heightened desire for motivated
beliefs. Furthermore, we find that job seekers who initially overestimate
their wage potential by up to 17% continue to increase their wage expectations
throughout the unemployment spell, despite receiving (most likely) negative
feedback. Additionally, job seekers who are encouraged to search more
intensively due to extrinsic incentives hold more optimistic wage
expectations, yet this does not translate into higher realized wages for them.
This aligns with the notion that these individuals adopt a more optimistic
outlook to motivate themselves to comply with their search requirements.

Our study is the first to establish objective benchmarks for the subjective
wage expectations of unemployed workers. By doing so, we contribute to a
growing body of literature demonstrating job seekers’ average tendency to be
overly optimistic about their job finding prospects (Balleer et al., 2021;
Mueller et al., 2021; Spinnewijn, 2015; Van den Berg et al., 2023) and their
reluctance to update their wage expectations over time (Conlon et al., 2018;
Drahs et al., 2018; Krueger and Mueller, 2016). In this context, our approach
enables us to document significant heterogeneity concerning the accuracy of
job seekers’ wage expectations. In addition, we empirically establish a
connection between optimistic wage expectations and job seekers’ tendency to
overestimate their reemployment prospects. These findings support the
theoretical notion that overly optimistic beliefs are associated with being
excessively selective and rejecting offers more frequently than justified
(see, e.g., Mueller et al., 2021; Mueller and Spinnewijn, 2023).

Moreover, when comparing our findings to those of Jäger et al. (2023), who
employ a similar approach to study the beliefs of employed workers about their
outside options, we note that the extent of anchoring seems to be less
pronounced for the wage expectations of unemployed workers in contrast to
employed workers. This difference could potentially be attributed to
unemployed job seekers having already acquired information about their
earnings potential during their job search.

Additionally, our study sheds light on how job seekers’ incentives to search
for employment influence individuals’ behavior and beliefs. In doing so, we
contribute to a limited body of research that examines the response of job
search behavior to changes in the benefit environment. Aligning with existing
studies focusing on the generosity of UI benefit payments (see, e.g., Lichter
and Schiprowski, 2021; Marinescu, 2017), we find that more restrictive policy
regimes encourage unemployed workers to apply for jobs more intensively.
Simultaneously, our finding that an enhanced sanction risk fosters greater
wage optimism among unemployed workers challenges the notion that a stricter
regime makes job seekers less selective in their job choices. Rather, the
heightened incentives to search for jobs appear to induce indirect effects,
leading to a more optimistic outlook regarding the wages individuals can earn
upon reemployment. This phenomenon may contribute to the observation that, in
various settings, reservation wages do not respond to variation in benefit
payments or changes in the benefit rules as predicted by standard job search
theory (Le Barbanchon et al., 2019; Lichter and Schiprowski, 2021; Krueger and
Mueller, 2016; Schneider, 2008).333Using French administrative data on
reservation wages and changes in UI rules, Le Barbanchon et al. (2019)
estimate a null effect of the potential benefit duration on reservation wages,
which is consistent with the estimates by Lichter and Schiprowski (2021) based
on German survey data. Similarly, Krueger and Mueller (2016) cannot reject
that the elasticity of reservation wages to benefit levels in the U.S. is
equal to zero, while Schneider (2008) finds no significant effect of imposed
benefit sanctions on the reservation wages of unemployed workers in Germany.
It is worth noting that there is no indication that job seekers’ increased
wage optimism, which comes with the higher search intensity, is warranted. On
the contrary, realized wages upon reemployment tend to be (insignificantly)
lower when job seekers are subject to a more restrictive sanction regime. This
aligns with previous evidence suggesting that benefit sanctions often lead job
seekers to eventually accept lower-quality jobs (Arni et al., 2013; Van den
Berg and Vikström, 2014; Van den Berg et al., 2019). Against this backdrop, it
is conceivable that job seekers facing an elevated risk of sanctions only
revise their wage expectations as time progresses.

The remainder of this paper proceeds as follows. In the next section, we
discuss our empirical setting, while Section 3 illustrates some of the
theoretical issues related to the wage expectations of unemployed workers.
Section 4 presents empirical evidence on the accuracy of job seekers’ wage
expectations and Section 5 concludes.

##  2 Empirical Setting

To examine the accuracy of job seekers’ wage expectations, our analysis builds
on different complementary data sources providing information on unemployed
workers in Germany. To begin with, we rely on a large-scale survey involving
17,400 workers who became unemployed between June 2007 and May 2008 and were
eligible for unemployment insurance (UI) benefits (see Arni et al., 2014). The
first interview was conducted within 7 to 14 weeks after entering
unemployment, followed by a second interview wave 12 months later. The survey
encompasses detailed data on socio-demographic characteristics, personality
traits, job search behavior, and, notably for our study, subjective beliefs
about labor market prospects, especially wages upon reemployment.

In addition to the survey, we leverage administrative records to access highly
reliable data regarding job seekers’ actual labor market outcomes and their
employment histories prior to unemployment. We utilize the administrative data
for two purposes. Firstly, we can directly link the survey information with
the administrative records at the individual level for about 87% of the survey
respondents (Eberle et al., 2017). Secondly, we incorporate administrative
information from a larger sample of unemployed workers to establish objective
benchmarks for job seekers’ earnings potential. Importantly, both datasets
consist of individuals randomly sampled from the same population of unemployed
workers. This ensures that job seekers within the different datasets can be
directly compared and that we have access to similar information regarding
their labor market biographies.444The IZA/IAB Administrative Evaluation
Dataset offers administrative data for a 4.7% random sample of individuals who
entered unemployment between 2001 and 2008 (Caliendo et al., 2011; Eberle and
Schmucker, 2015), while the the IZA Evaluation Dataset Survey provides survey
data for a representative subset of individuals who entered unemployment
between June 2007 and May 2008. Furthermore, the IZA/IAB Linked Evaluation
Dataset combines survey and administrative data, linking them for 87% of the
survey respondents.

###  2.1 Subjective wage expectations and objective benchmarks

The survey elicits job seekers’ beliefs about the monthly net salary (in €)
they expect to receive upon starting a new job, using the following question:

> “Now, I am interested in the salary you anticipate receiving in your next
> job. What is your expected monthly net income in €?”

The question is asked during the initial survey interview, which takes place 7
to 14 weeks after entry into unemployment, and is directed at all individuals
who are still unemployed at this stage and are actively searching for a job.
Moreover, our analysis focuses exclusively on individuals who previously held
full-time positions to minimize the influence of variation in working hours on
monthly wage expectations. This results in an estimation sample of 5,376
survey respondents who can be linked to the administrative records.

The objective of our empirical analysis is to examine the accuracy of
individuals’ subjective wage expectations, which is notoriously challenging
for two reasons. First, we need to compare subjective beliefs to realizations
of the same outcome. However, realized wages of the job seekers observed in
our survey data represent only one instance of the objective ex-ante
distribution of wages, and they might be affected by unforeseeable labor
demand shocks that individuals cannot be aware of when reporting their
subjective beliefs. Second, job seekers’ expectations may impact their job
search behavior, which in turn can influence actual labor market outcomes. In
order to address these concerns, we adopt an approach where we estimate
objective benchmarks for job seekers’ earnings potential based on the realized
wages of comparable individuals in similar situations.

To that end, we utilize administrative data from a larger sample of 84,617
workers who became unemployed between January 2005 and May 2007 (see Appendix
A for additional information regarding the prediction of objective
benchmarks). This time period was chosen to avoid any overlap with the survey
sample, ensuring that the objective predictions are not influenced by the
beliefs and behaviors of survey respondents.555As a robustness check, we also
employ a random sample comprising 80% of all entries into unemployment between
June 2007 and May 2008, which aligns with the survey period. Detailed summary
statistics for both samples can be found in Appendix Table A.1. In order to
ensure comparability with the survey sample, we apply similar restrictions to
the administrative data. Specifically, we focus on newly unemployed
individuals who are eligible for unemployment insurance (UI) benefits and were
previously employed in non-subsidized full-time positions for at least three
months. Moreover, we restrict the sample to job seekers who have not found
regular employment within three months after entry, which is the average time
until the first interview of the survey.

We employ flexible LASSO regressions to predict reemployment wages, accounting
for a comprehensive set of pre-determined covariates available in the matched
survey-admin data. This includes socio-demographic characteristics,
information on the last job before unemployment, labor market history over the
past ten years, and local labor market characteristics. The dependent variable
is the first monthly salary received in a regular job within 24 months after
entry into unemployment, and we test the robustness of our findings using
different time horizons.666Specifically, we utilize wages of individuals
reemployed within nine months of unemployment, which is, on average, six
months after the initial interview. This is motivated by the fact that job
seekers answered the question about their wage expectations shortly after
discussing their anticipated likelihood of finding a job during the next six
months, suggesting a consistent time frame for wage expectations. To compare
the objective benchmarks with subjective wage expectations, we convert the
realized wages recorded in administrative records from gross to net terms by
deducting social security contributions and income taxes. The exact procedure
is described in Appendix A.

To evaluate the quality of the benchmarks, we estimate the out-of-sample
$R^{2}$ by regressing realized wages on predicted wages using distinct test
datasets, i.e., samples not utilized during the prediction generation process.
As shown in Table A.3, we find values of $R^{2}$ within the range of 0.48 to
0.53, suggesting that we are equipped with meaningful objective benchmarks for
individuals’ wages. Further supporting this notion, Table A.4 reveals that the
objective benchmarks derived from wages of comparable workers exhibit greater
predictive power for survey respondents’ realized wages compared to their own
subjective wage expectations.

###  2.2 Summary statistics

Figure 1 shows the distributions of subjective wage expectations and objective
benchmarks. The average expected net income is € 1,407 per month, which is
substantially greater than the average income that workers could reasonably
expect. For the average job seeker in our sample, the objective benchmarks
suggest a monthly net wage of only € 1,173. This closely aligns with the
average realized wage of € 1,190 per month that we observe among the survey
respondents (see Panel A of Appendix Table B.1).

Figure 1: Distribution of subjective wage beliefs and objective benchmarks

A. Subjective wage beliefs B. Objective benchmarks • Note: The figure shows
the distribution of subjective beliefs (Panel A) and objective benchmarks
(Panel B) for individuals’ monthly net income upon reemployment among the
sample of survey respondents ($N=5,376$). Objective benchmarks are generated
from realized outcomes of similar individuals observed in the administrative
records (see Section 2.1 for details). Panel A excludes individuals with an
expected reemployment wage greater than 4,000€ ($<1\%$ of sample).

In addition to job seekers’ wage expectations, we also explore information on
individuals’ perceived and actual job finding prospects. The perceived chances
of reemployment are elicited over a six-month horizon and responses are
provided using four options: “very likely”, “likely”,“unlikely”, or “very
unlikely”.777Specifically, survey respondents answer the following question:
“When you think of the future, how likely is it from your perspective that you
will find a job within the next six months?” Overall, unemployed workers in
our survey sample tend to be remarkably optimistic about their reemployment
prospects. In particular, 89% of the survey population consider themselves
“likely” or “very likely” to find a job within six months, while only 56%
actually do so (see Panel B of Appendix Table B.1). This observation is in
line with existing evidence from, e.g., Balleer et al. (2021), Mueller et al.
(2021), and Spinnewijn (2015), suggesting that a majority of job seekers hold
overly optimistic beliefs about their job finding probabilities.

##  3 Theoretical Considerations

Before presenting the results of our empirical analysis, we illustrate some of
the theoretical issues related to the accuracy of job seekers’ wage
expectations. To do so, we sketch a random job search framework in which job
seekers face uncertainty about their labor market prospects. While individuals
are unemployed, they receive a flow of benefits, $b$. In each period $t$, they
make decisions about the number of job applications they send out, $s_{t}$,
and their reservation wage, $\phi_{t}$. The probability of a successful job
application, resulting in a job offer, is denoted by $\lambda_{t}$, while the
effort costs incurred during the job search are captured by the increasing and
convex function $\gamma(s_{t})$. Each job offer is associated with a wage,
denoted by $w$, which is a random draw from the wage offer distribution
$F(w)$. Upon receiving multiple job offers in a given period, individuals
accept the highest wage offer $y=\max\\{w_{1},w_{2},...,w_{n}\\}$ if it
exceeds their reservation wage. The distribution of this maximum offer can be
described as $F_{yt}(y)=F(y)^{n_{t}}$, where $n_{t}=\lambda_{t}s_{t}$
represents the total number of job offers received in period $t$.

Inspired by Mueller and Spinnewijn (2023), we assume that job seekers hold
subjective beliefs about their success probability, $\widehat{\lambda}_{t}$,
and the wage distribution, $\widehat{F}(w)$, which may differ from the true
functions. When choosing their search strategy, individuals maximize their
perceived present value of income in period $t$:

$\displaystyle
U_{t}=\max_{s_{t},\phi_{t}}b-\gamma(s_{t})+\rho\left\\{EU_{t+1}+\left[1-(1-\widehat{\lambda}_{t})^{s_{t}}\right]\int_{\phi}^{\infty}\left(EV_{t+1}(y)-EU_{t+1}\right)d\widehat{F}_{yt}(y)\right\\}$
(1)

where future income is discounted at rate $\rho$ and $V_{t+1}$ denotes the
value of being employed at wage $y$ when a job is found in the future. The
corresponding uncertainty is captured by the expectation operator $E$. The
reservation wage and the optimal search effort can be expressed as functions
of the (perceived) model primitives:

$\phi_{t}=\phi_{t}[b,\gamma(\cdot),\widehat{\lambda}_{t},\widehat{F}(\cdot),V_{t+1}(\cdot)]$
$s_{t}^{*}=s_{t}^{*}[b,\gamma(\cdot),\widehat{\lambda}_{t},\widehat{F}(\cdot),V_{t+1}(\cdot)]$

At their reservation wage, $\phi_{t}$, job seekers are indifferent between
accepting a job and remaining unemployed, $U_{t+1}=V_{t+1}(\phi_{t})$, while
they choose the optimal effort level, $s_{t}^{*}$, trading off the cost of
search and the perceived returns to search.

##### Accuracy of wage expectations:

Within this framework, the object of our empirical analysis, the accuracy of
job seekers’ wage expectations, is characterized by the difference between the
perceived and actual expected maximum of all wage offers that the worker
receives in a given period:

$\displaystyle E\widehat{F}_{yt}(y)-EF_{yt}(y).$ (2)

This illustrates that disparities between job seekers’ subjective beliefs and
objective benchmarks can have different origins. It is straightforward that
workers may hold overly optimistic wage expectations due to misperceptions
about the distribution of wage offers, $F(w)$. Specifically, overly optimistic
workers may perceive the wage offer distribution as shifted toward higher
values compared to the actual distribution, or they may perceive the
distribution to be more dispersed than it truly is. The latter can induce
optimistic beliefs because job seekers are inclined to accept the offer with
the highest pay. On the other hand, overestimating the success probability of
an application can also induce wage optimism because it may lead job seekers
to overestimate their prospects of attracting offers that come with
particularly high wages (i.e. overestimating $n_{t}$ causes individuals to
perceive a higher expected maximum offer).

##### Anchoring and belief updating:

Workers who have incomplete information, for instance, about the statistical
properties of the wage offer distribution have to form their beliefs based on
the signals they have received. A signal that typically comes at no cost and
is at their disposal is the wage job seekers earned in their prior job. This
can lead individuals to anchor their wage expectations to their previous
salary (see, e.g., Jäger et al., 2023, for a formal illustration). As a
consequence, workers who should reasonably anticipate a wage decrease
(increase) relative to their pre-unemployment wage would overestimate
(underestimate) their reemployment wages relative to the objective benchmarks.

At the same time, individuals may acquire additional signals about their
objective earnings potential over the course of their job search from the job
offers they receive (see, e.g., Burdett and Vishwanath, 1988; Conlon et al.,
2018; Gonzalez and Shi, 2010). This should enable them to adjust their wage
expectations accordingly as time progresses. In situations involving Bayesian
learning, the beliefs of job seekers who receive repeated feedback are
anticipated to converge toward the actual mean of the wage distribution.

Conversely, when individuals hold motivated beliefs, they may want to believe
that they can earn a particular wage, possibly driven by motivational reasons
(Bénabou and Tirole, 2002) or ego-related satisfaction (Köszegi, 2006). In
such instances, individuals may selectively process available signals, such as
their prior salary and incoming wage offers, and thus deceive themselves
(Bordalo et al., 2021; Gennaioli and Shleifer, 2010; Tversky and Kahneman,
1973). Therefore, it is conceivable that the anchoring and updating of beliefs
is skewed toward excessively optimistic expectations (see, e.g., Heidhues et
al., 2018; Huffman et al., 2022; Zimmermann, 2020).

##### The role of extrinsic incentives:

Clearly, job seekers’ beliefs can also be influenced by their own actions. For
example, unemployed individuals who engage in a more intensive job search may
gather a greater amount of information, effectively increasing the number of
signals they receive about their potential earnings. As a result, a higher
search intensity could lead to more accurate wage expectations. Concurrently,
by submitting a larger number of job applications, job seekers expand the pool
of available offers, denoted as $n_{t}$, within a given period. This wider
array of opportunities increases their chances of receiving a particularly
well-paying offer, which makes it reasonable to anticipate higher wages.
Against this backdrop, we expect that variations in job search incentives –
such as differing benefit levels $b$ or benefit payments conditioned on a
minimum effort level – will also impact the accuracy of job seekers’ wage
expectations. Furthermore, individuals who adopt beliefs that enhance their
effort motivation may become more optimistic when confronted with extrinsic
incentives to intensify their job search.

##### Labor market implications:

Lastly, it is evident that the beliefs held by job seekers play a significant
role in shaping their decisions, ultimately influencing their integration into
the labor market. For instance, if individuals are optimistic regarding their
chances of attracting a well-paying job offer, they perceive particularly high
returns to search. As a result, optimistic workers might be willing to exert
more effort during job search, potentially enhancing their actual job finding
prospects. On the other hand, optimistic workers may set higher reservation
wages, leading them to be more inclined to reject job opportunities offering
comparatively lower salaries. This in turn could lead to extended periods of
unemployment compared to utility-maximizing job seekers with accurate wage
expectations. In addition, this mechanism can result in job seekers who have
overly optimistic wage expectations also overestimating their actual job
finding rates (see, e.g. Conlon et al., 2018; Dubra, 2004; Mueller et al.,
2021).

##  4 Empirical Evidence on the Accuracy of Wage Expectations

In this section, we compare job seekers’ subjective wage expectations to the
objective benchmarks. This enables us to uncover heterogeneity in the accuracy
of job seekers’ wage expectations, taking into account their objective
earnings potential (see Section 4.1) and individual background characteristics
(see Section 4.2). In this context, we place particular emphasis on
investigating the influence of job seekers’ pre-unemployment wages to
understand the extent to which they anchor their wage expectations to their
previous salary and whether such anchoring is justified (see Section 4.3).
Moreover, we leverage the longitudinal nature of our data by examining
repeatedly elicited wage expectations from job seekers who are still in search
of employment approximately one year after becoming unemployed. This allows us
to investigate how job seekers revise their subjective expectations over the
course of their unemployment spell (see Section 4.4). We also take into
consideration the interdependence between job seekers’ beliefs and their
actions. To that end, we exploit exogenous variation in individuals’ job
search incentives and study the consequences for the accuracy of their wage
expectations (see Section 4.5). Finally, we present descriptive evidence on
the labor market implications of inaccurate beliefs (Section 4.6) and discuss
how our results speak to different theoretical mechanisms related to job
seekers’ belief formation (see Section 4.7).

###  4.1 Heterogeneity by objective earnings potential

To begin with, we consider the relationship between subjective expectations
and objective benchmarks based on the following regression model (see Jäger et
al., 2023):

$S_{i}=\beta_{0}+\beta_{1}\widehat{O_{i}}+\epsilon_{i}$ (3)

where $S_{i}$ denotes the subjective belief of job seeker $i$ about their
reemployment wage and $\widehat{O}_{i}$ refers to the corresponding objective
prediction. The intercept $\beta_{0}$ captures biases in beliefs that are
common to all job seekers, while the slope parameter $\beta_{1}$ describes how
strongly beliefs respond to variation in objective benchmarks. In this
context, we can think about different scenarios depending on the values of
$\beta_{0}$ and $\beta_{1}$.

First, when $\beta_{1}=1$ and $\beta_{0}=0$, individuals’ expectations
perfectly correspond to the objective benchmark, which indicates unbiased
beliefs throughout the distribution. Second, when $\beta_{1}=1$ and
$\beta_{0}\neq 0$, job seekers’ beliefs are subject to homogeneous biases,
that is, individuals are overly optimistic ($\beta_{0}>0$) or pessimistic
($\beta_{0}<0$), but job seekers with different objective predictions share
the same degree of bias. Lastly, when $\beta_{1}\neq 1$, beliefs do not
exhibit a one-to-one response to objective variation, resulting in
heterogeneous biases. In particular, $\beta_{1}<1$ means that beliefs do not
adjust sufficiently strongly to changes in objective predictions, which
implies that overoptimism is more pronounced among job seekers with a
relatively low objective wage potential.

Figure 2: Comparison of subjective beliefs and objective benchmarks

A. Relationship B. Distribution • Note: The figure compares subjective beliefs
and objective benchmarks for reemployment wages among the sample of survey
respondents ($N=5,376$). Panel A illustrates Equation (3) by plotting
individuals’ subjective beliefs against objective benchmarks (each of the 20
dots represents a ventile of the distribution) and the red line represents the
unbiased benchmark if subjective expectations were to equal objective
predictions. Panel B illustrates the distribution of individual-level log
differences between subjective and objective benchmarks.

Panel A of Figure 2 displays a binned scatter plot based on Equation (3). It
illustrates the prevalence of overoptimism regarding reemployment wages across
all levels of the corresponding objective distribution. In each of the 20
bins, the average expected wage exceeds the corresponding objective benchmark.
In other words, both job seekers with low and high objective earnings
potential tend to overestimate their reemployment wage compared to our
objective benchmarks. At the same time, the magnitude of job seekers’ optimism
varies across the objective distribution. Our analysis reveals a slope
coefficient of $\widehat{\beta}_{1}=0.74$ (SE: 0.01) compared to an objective
benchmark slope of one. This suggests that beliefs do not adequately respond
to variations in the objective wage potential. As a result, job seekers with
lower predicted wages exhibit a greater tendency to overestimate their
earnings potential compared to those with higher objective predictions. To be
precise, individuals within the bottom decile of the objective benchmark
distribution overestimate their earnings potential by approximately 36%,
whereas the overoptimism is only about 6% among individuals in the top decile
of the distribution.

##### Robustness:

A series of robustness checks confirms our result that job seekers with lower
objective earnings potential exhibit the highest levels of optimism. First, we
vary the sample used to estimate the objective benchmark based on the
administrative records. In particular, we rely on alternative training data
including individuals who became unemployed in 2007 and 2008 (i.e. the same
time period during which the survey was conducted), and we only include job
seekers who find a job within nine rather than 24 months. Second, we address
concerns that the estimated slope coefficients may suffer from attenuation
bias due to measurement error in the objective predictions. Therefore, we
conduct an instrumental variable (IV) regression, where we use objective
predictions from the alternative training data as an instrument for objective
predictions from our baseline training data. This approach is similar to a
split-sample IV measurement error correction (see, e.g., Drenik et al., 2020;
Jäger et al., 2023). Third, we impose two additional restrictions on the
survey sample, considering only (i) individuals who search for and expect to
find a full-time job and (ii) unmarried individuals. This enables us to
examine whether differences in expected working hours (full-time) and
measurement error arising from the conversion of gross to net wages (unmarried
individuals) affect our estimates. As summarized in Appendix Table B.2, we
obtain similar slope coefficients between 0.64 and 0.75 across the various
specifications.

###  4.2 Determinants of optimism and pessimism

Another way to illustrate heterogeneity in the accuracy of beliefs is to
consider deviations between subjective beliefs and objective benchmarks:
$S_{i}-O_{i}$. Panel B of Figure 2 shows the resulting distribution. We see
that job seekers overestimate their wage potential, on average, by about 17%.
While a majority of about 57% of the survey respondents expect their wage to
be at least 10% higher than the objective prediction, only 13% underestimate
their wage potential by more than 10%.888In Appendix Table B.2, we report the
mean bias and the share of job seekers who over- and underestimate their wage
potential for the alternative specifications explained above. Across all
specifications, we find that overly optimistic wage expectations are
widespread among unemployed workers.

Building upon this individual-level measure, we can explore correlations
between the accuracy of beliefs and job seekers’ characteristics. To that end,
we first regress subjective wage expectations on a set of covariates without
accounting for the objective predictions (column 1 of Table 1). In a second
step, we analyze to what extent these differences in expectations are
justified by heterogeneity in job seekers’ actual wage potential. Therefore,
we regress the deviation between subjective beliefs and objective predictions,
$S_{i}-O_{i}$, on the objective benchmarks and the same set of individual-
level characteristics (column 2).999One should note that the covariates
included in the regression model represent a subset of all individual-level
characteristics that are explored to generate the objective benchmarks. This
means the estimated correlations cannot be explained by heterogeneity in
rational expectations. Lastly, in columns (3) and (4), we distinguish between
individuals who overestimate and underestimate their wage potential.101010In
particular, we consider the deviation between $S_{i}$ and $O_{i}$ and set
negative (positive) values to zero and thus only exploit variation in positive
(negative) deviations.

$S_{i}$ $S_{i}-O_{i}$ $S_{i}-O_{i}$ $S_{i}-O_{i}$ $S_{i}-O_{i}$ $S_{i}-O_{i}$
Pos. values Neg. values Pos. values Neg. values (1) (2) (3) (4) (5) (6) Socio-
demographic characteristics Female -0.172∗∗∗ -0.086∗∗∗ -0.067∗∗∗ -0.019∗∗∗
-0.064∗∗∗ -0.018∗∗∗ (0.008) (0.007) (0.006) (0.003) (0.006) (0.003) Age (ref.
16-24 years) 25 - 34 years 0.039∗∗∗ 0.022∗∗ 0.012 0.010∗∗ 0.014∗ 0.011∗∗
(0.011) (0.010) (0.008) (0.004) (0.008) (0.004) 35 - 44 years 0.057∗∗∗ 0.014
0.005 0.009∗∗ 0.011 0.010∗∗ (0.011) (0.010) (0.008) (0.004) (0.009) (0.005) 45
- 55 years 0.072∗∗∗ 0.027∗∗ 0.016∗ 0.012∗∗∗ 0.026∗∗∗ 0.015∗∗∗ (0.012) (0.011)
(0.009) (0.004) (0.009) (0.005) German citizen -0.026∗ -0.046∗∗∗ -0.032∗∗∗
-0.014∗∗∗ -0.037∗∗∗ -0.014∗∗∗ (0.016) (0.014) (0.012) (0.005) (0.012) (0.005)
Educational level (ref. no higher education) Vocational certificate 0.055∗∗∗
0.018 0.003 0.015∗∗ -0.003 0.014∗∗ (0.013) (0.012) (0.010) (0.006) (0.010)
(0.006) University degree 0.266∗∗∗ 0.135∗∗∗ 0.118∗∗∗ 0.017∗∗∗ 0.106∗∗∗ 0.017∗∗
(0.016) (0.016) (0.013) (0.007) (0.013) (0.007) Married -0.022∗∗∗ 0.002
0.015∗∗ -0.013∗∗∗ 0.017∗∗ -0.012∗∗∗ (0.008) (0.008) (0.007) (0.003) (0.007)
(0.003) Any children -0.026∗∗∗ -0.012 -0.004 -0.007∗∗ -0.003 -0.007∗∗ (0.009)
(0.008) (0.007) (0.003) (0.007) (0.003) East Germany -0.081∗∗∗ -0.015∗ -0.012∗
-0.003 -0.010 -0.002 (0.008) (0.008) (0.007) (0.003) (0.007) (0.003) Labor
market history Last wage (ln) 0.325∗∗∗ 0.212∗∗∗ 0.176∗∗∗ 0.036∗∗∗ 0.169∗∗∗
0.035∗∗∗ (0.013) (0.013) (0.011) (0.004) (0.011) (0.004) Last job was quit
-0.025 -0.003 0.004 -0.007 0.001 -0.007 (0.018) (0.016) (0.013) (0.006)
(0.013) (0.006) Number of unemployment spells in last 2 years (ref. 0 spells)
1 spell 0.032∗∗∗ -0.015 -0.017∗∗ 0.002 -0.019∗∗ 0.002 (0.010) (0.009) (0.008)
(0.003) (0.008) (0.003) 2 spells 0.011 -0.025∗∗∗ -0.031∗∗∗ 0.005 -0.030∗∗∗
0.006∗ (0.010) (0.009) (0.008) (0.003) (0.008) (0.003) $\geq$ 3 spells 0.011
-0.024∗∗ -0.027∗∗∗ 0.003 -0.028∗∗∗ 0.003 (0.011) (0.010) (0.008) (0.004)
(0.008) (0.004) Last unemployment duration -0.001∗ 0.001∗∗ 0.001 0.001∗∗∗
0.001∗ 0.001∗∗∗ (0.001) (0.001) (0.000) (0.000) (0.000) (0.000) Personality
traits Internal locus of control 0.010∗∗∗ 0.001 (0.003) (0.001)
Conscientiousness -0.008∗∗ 0.000 (0.003) (0.001) Openness 0.017∗∗∗ 0.000
(0.003) (0.001) Extraversion 0.006∗ 0.003∗∗ (0.003) (0.001) Neuroticism
-0.009∗∗∗ -0.002 (0.003) (0.001) Caseworker counseling Number of caseworker
meetings (ref. 0 - 2 meetings) 3 - 5 meetings -0.015∗∗ -0.003 (0.006) (0.003)
$\geq$ 6 meetings -0.004 0.001 (0.009) (0.004) Number of vacancy referrals
0.001 0.008∗∗∗ (0.006) (0.003) Objective benchmark $O_{i}$ -0.544∗∗∗ -0.456∗∗∗
-0.088∗∗∗ -0.456∗∗∗ -0.088∗∗∗ (0.019) (0.016) (0.007) (0.016) (0.007) No. of
observations 5,376 5,376 5,376 5,376 5,200 5,200 $R^{2}$ 0.458 0.239 0.248
0.067 0.258 0.070 Mean dep. variable 7.183 0.170 0.205 -0.035 0.205 -0.036 •
Note: The table reports the results of OLS regressions. In column (1), the
dependent variable is individuals’ subjective belief $S_{i}$ about their net
monthly reemployment wage (in log). In column (2), we consider the log
difference between the subjective belief $S_{i}$ and the objective benchmark
$O_{i}$. In columns (3) and (5), we set negative values to zero and thus only
exploit variation in positive deviations (“optimism”), while in columns (4)
and (6), we set positive values to zero and thus only exploit variation in
negative deviations (“pessimism”). Robust standard errors are shown in
parentheses. ***/**/* indicate statistical significance at the
1%/5%/10%-level.

Table 1: Determinants of optimism and pessimism

Overall, we observe intuitive correlations between the accuracy of job
seekers’ beliefs and their background characteristics. For instance, it can be
seen in column (1) that women expect to earn about 17% less than men upon
reemployment. This is in line with existing evidence that men generally have
higher levels of self-confidence (Barber and Odean, 2001; Cortés et al., 2022)
and set higher reservation wages (Caliendo et al., 2017) than women. When we
take into account the heterogeneity in job seekers’ objective wage potential
in column (2), the gender gap remains negative. In particular, the difference
between the subjective wage expectation and the objective prediction is about
9 percentage points lower for women than for men. Comparing these estimates
suggests that about half of the gender gap in wage expectations is due to the
fact that men actually earn higher wages than women and it is therefore
rational that men have also higher wage expectations. At the same time, the
other half of the gender gap in wage expectations cannot be explained by the
objective predictions suggesting that men tend to be more confident even in
comparison to the rational benchmark. Considering the decomposition of results
for optimistic and pessimistic beliefs in column (3) and column (4), we find
that the gender differences are mainly driven by men exhibiting more overly
optimistic beliefs rather than women being too pessimistic.

Moreover, we observe several other noteworthy correlations. First, it may not
be surprising that German citizens have more accurate beliefs compared to
foreigners, as they may possess a better understanding of the German labor
market dynamics. Second, high-skilled workers, specifically those with a
university degree, exhibit a greater tendency toward overoptimism regarding
their wage potential compared to low-skilled workers. Quantitatively, job
seekers with a university degree tend to overestimate their earnings potential
by approximately 12 percentage points more compared to those without any
higher education. This result is in line with earlier findings that higher
levels of education are associated with individuals being more overconfident
about their investment decisions (Bhandari and Deaves, 2006; Trejos et al.,
2019). This pattern could reflect that individuals’ beliefs about their
abilities (Stinebrickner and Stinebrickner, 2012; Wiswall and Zafar, 2015) or
about the returns to schooling (Attanasio and Kaufmann, 2014; Jensen, 2010)
affect educational or occupational choices early on in their
careers.111111Note that, at first glance, this finding contradicts recent
evidence from Balleer et al. (2021) who find that overconfidence among job
seekers in the U.S. decreases with their skill level. However, it should be
noted that we condition on the level of objective predictions and various
other covariates. This allows us to disentangle the effect of education from
differential misperceptions along other characteristics that are correlated
with job seekers’ education. Lastly, we find that wage optimism tends to be
less pronounced among job seekers who have experienced more frequent periods
of unemployment in the past. This observation aligns with the notion that
individuals with greater job search experience may have accumulated more
accurate information about their potential earnings.

In addition to the covariates that we use to estimate the objective benchmarks
based on the administrative records, the survey provides information on a
variety of other worker characteristics that are typically related to their
labor market integration. In specifications (5) and (6), we additionally
account for personality traits and caseworkers’ counseling activities.
Notably, we find that job seekers who hold an internal locus of control,
believing that their life outcomes are primarily determined by their own
actions rather than external factors, tend to hold more optimistic beliefs
about their future wages.121212This finding aligns with previous studies by
Caliendo et al. (2015) and McGee (2015), who show that individuals with a more
internal locus of control exert more search effort and tend to have higher
reservation wages. At the same time, higher levels of openness and
extraversion, as well as lower levels of neuroticism, are associated with
greater optimism among job seekers. Furthermore, we find that job seekers who
have had several meetings with their caseworker since becoming unemployed tend
to exhibit less optimistic beliefs. On the other hand, a higher number of
vacancy referrals, where caseworkers direct unemployed workers toward specific
job postings, comes with less pessimistic wage expectations.

While the observed patterns appear intuitive, it is essential to acknowledge
that the disparity between subjective wage expectations and objective
benchmarks might be partly attributed to job seekers having private
information about their personal situations. Since our prediction model does
not account for individuals’ personality traits and caseworkers’ counseling
activities, it remains uncertain whether the observed patterns regarding these
factors truly indicate heterogeneity in misperceptions about workers’ earnings
potential. In particular, if job seekers understand that they exhibit certain
traits that come with higher earnings, it would be reasonable for them to
adjust their wage expectations accordingly.131313For example, several studies
document statistically significant correlations between workers’ personality
traits and their earnings (e.g. Andrisani, 1977; Heckman et al., 2006; Heineck
and Anger, 2010; Mueller and Plug, 2006; Semykina and Linz, 2007). Similarly,
previous research has demonstrated that caseworker counseling plays a
substantial role in the labor integration of unemployed workers (see, e.g.,
Behncke et al., 2010; Schiprowski, 2020).

###  4.3 Anchoring of beliefs to pre-unemployment wages

Various studies indicate that individuals often rely on anchoring heuristics
(Kahneman et al., 1982) when forming their expectations. Closely related to
our setting, it is commonly observed that unemployed job seekers anchor their
reservation wages to their previous salary before becoming unemployed (see,
e.g., Feldstein and Poterba, 1984; Krueger and Mueller, 2016; Le Barbanchon et
al., 2019; Koenig et al., 2021). However, due to the absence of objective
benchmarks in the previous literature, it is challenging to determine the
extent to which this type of anchoring is justified. In light of this, we now
compare both job seekers’ subjective wage expectations and the objective
predictions to their pre-unemployment wages.

Figure 3: Subjective beliefs and objective benchmarks relative to pre-
unemployment wages

A. Distribution of changes B. Relationship between changes • Note: Panel A
shows a histogram of individuals’ subjectively expected reemployment wage
changes and objectively predicted reemployment wage changes (i.e. both in
comparison to their pre-unemployment wages). Panel B depicts a binned scatter
plot (with 20 bins) for the individual-level relation between the two
variables. $N=5,376$.

To begin with, Panel A of Figure 3 shows the distributions of the subjective
beliefs and objective predictions relative to pre-unemployment wages. It turns
out that job seekers overestimate their wage potential not only relative to
the objective benchmarks generated from wages of similar workers, but also in
comparison to their own previous salary. On average, job seekers anticipate a
5.1% increase in salary compared to their last job, whereas objective
predictions indicate an average decrease of approximately 12.1% in actual
wages compared to the pre-unemployment wage. While periods of unemployment
often come with wage penalties upon reemployment (Arulampalam, 2001; Gregory
and Jukes, 2001), it appears that job seekers, on average, do not account for
these adverse effects of unemployment when forming their wage expectations.

Moreover, the distribution of expected wage changes is much more compressed
around zero compared to the distribution of changes in objective predictions.
This suggests that job seekers anchor their wage expectations more strongly to
their past wage than is objectively justified. Panel B of Figure 3 further
illustrates this trend by showcasing the relationship between job seekers’
subjectively expected wage growth and the objectively predicted wage growth.
The estimated slope coefficient of 0.71 (SE: 0.02), which is significantly
smaller than the objective benchmark slope of one, indicates that job seekers
perceive their reemployment wage to be closer to their pre-unemployment wage
than it actually is. In particular, when job seekers should reasonably
anticipate a 10 percentage point larger wage decline, they expect the wage
decrease to be, on average, only 7.1 percentage points larger. That is, they
tend to anchor their beliefs too strongly to their pre-unemployment salary.

Lastly, we find that the anchoring to the pre-unemployment salary is
asymmetric, as further illustrated in Appendix Figure B.1. Here, we depict
differential slope coefficients for positive and negative variations in
objective wage changes. The beliefs of job seekers who can reasonably expect a
wage increase compared to their previous salary are relatively accurate, as
indicated by the slope coefficient of 1.07 (SE: 0.03). Conversely, among
individuals who are predicted to face a wage penalty compared to their
previous wage, we estimate a slope coefficient of 0.66 (SE: 0.02). This
finding suggests that this particular group of workers tends to hold overly
optimistic beliefs and anchors their wage expectations too strongly to their
pre-unemployment salaries.

We can directly compare our results on the wage expectations of unemployed job
seekers with the estimates of Jäger et al. (2023), who analyze beliefs of
employed workers regarding their outside options, that is, the wages they
could earn with other employers. Jäger et al. (2023) estimate a slope
coefficient of 0.089 for the relationship between workers’ subjectively
expected wage changes and an objective benchmark for their actual wage changes
(generated from observed wage changes of their coworkers). This coefficient is
almost an order of magnitude lower than our corresponding estimate for the
reemployment wages of unemployed workers. This indicates that the distorting
effects of anchoring might be less severe for wage beliefs of unemployed job
seekers compared to employed workers. A potential explanation might be that
unemployed job seekers possess more knowledge about their earnings potential
than employed workers, perhaps because they have already gathered information
during their current or previous spells of unemployment.

###  4.4 Belief updating over the unemployment spell

To further explore the idea that job seekers acquire relevant information
during the job search process and update their beliefs accordingly (see, e.g.,
Burdett and Vishwanath, 1988; Conlon et al., 2018; Gonzalez and Shi, 2010, for
learning models of the labor market), we now investigate the evolution of job
seekers’ beliefs throughout their unemployment spell. Therefore, we utilize
wage expectations that were repeatedly elicited during the first and second
survey waves. We observe this information for 459 individuals who were still
unemployed and actively searching for a job at the time of the follow-up
interview, conducted one year after entry into unemployment.

Appendix Figure B.2 compares the distributions of wage expectations over time.
Consistent with the patterns observed by Mueller et al. (2021) and Krueger and
Mueller (2016), job seekers’ wage expectations remain remarkably stable
throughout their unemployment spell. More specifically, we find no
statistically significant changes in the distribution of wage expectations
between the two waves ($p=0.872$ based on a Kolmogorov-Smirnov test for equal
distributions in both waves).141414Additionally, Appendix Figure B.3 depicts
variation in the accuracy of wage expectations with respect to the timing of
the first survey interview. In line with our results based on the second
survey wave, we find no evidence for systematic differences across job seekers
interviewed at different points of their unemployment spell (between seven and
14 weeks after becoming unemployed).

Having established objective benchmarks, we can also examine whether there is
a systematic relationship between job seekers’ initial level of wage optimism
or pessimism and the way they update their wage expectations over time. To
that end, Panel A of Figure 4 illustrates the change in respondents’
expectations from the first to the second interview against the deviation
between their subjective expectations and the objective benchmark as measured
during the first interview. The estimated slope coefficient of -0.26 indicates
that beliefs are not updated perfectly. Specifically, job seekers who
initially overestimate their reemployment wage by an additional 10% only
decrease their wage expectations by 2.6% more during the course of their
unemployment spell.151515It is worth noting that the estimated negative slope
may be influenced to some extent by statistical mean reversion resulting from
measurement error in belief elicitation, which suggests that the estimated
slope coefficient might represent a lower bound for the true relationship.
Additionally, we observe that only job seekers who exhibit significant initial
wage optimism in the first interview revise their wage expectations downwards.
Conversely, job seekers who overestimate their wage potential by up to 17%
actually increase their wage expectations over time. These individuals most
likely have received negative feedback from the jobs they encountered during
their job search, yet they appear to resist updating their wage expectations
accordingly.

Figure 4:  Belief updating over the unemployment spell

A. Change in wage expectations B. Change in accuracy of wage expectations •
Note: The figure depicts binned scatter plots of individuals’ change in wage
beliefs between waves 1 and 2 (Panel A) and wage overconfidence in wave 2
(Panel B) against the overconfidence in wave 1. Wave 1 was conducted 7 - 14
weeks after unemployment entry and wave 2 was collected 12 months after entry.
The sample only includes individuals who are still in the same unemployment
spell in wave 2. $N=459$.

Finally, we account for changes in objective benchmarks over time by
predicting the reemployment wages of job seekers who remain unemployed for at
least 12 months following their entry into unemployment, as observed in the
administrative data. Previous research shows that job seekers’ reemployment
prospects (Eriksson and Rooth, 2014; Kroft et al., 2013) and the quality of
wage offers (Schmieder et al., 2016) decrease with prolonged unemployment
spells. Consistent with this notion of negative duration dependence, we find
that the objectively predicted wage for respondents who are still unemployed
at the second interview decreases, on average, by about 6% over the course of
one year. Combining the observation that many job seekers are reluctant to
revise their wage expectations downward with the decline in the objective
earnings potential during the unemployment spell, it becomes apparent that a
majority of job seekers who remain unemployed display an even greater degree
of overoptimism as time progresses. This trend is illustrated in Panel B of
Figure 4, which showcases the relationship between the deviations of
subjective beliefs and objective benchmarks during the first and second
interview waves. Specifically, the estimates suggest that the degree of wage
optimism increases for job seekers who initially overestimate their wage
potential by up to 34% (i.e. the intersection of the red and blue lines in
Panel B of Figure 4) and only decreases for those who exhibit an even higher
level of optimism during the first interview.

Taken together, our results suggest that job seekers’ overoptimism remains
persistent throughout the unemployment spell and they update their wage
expectations only to a very limited extent in response to the feedback
received during the search process. In this context, it is important to note
that the group of individuals who are still unemployed during the second
interview after one year consists of job seekers with poor overall labor
market prospects (reflecting dynamic selection over the unemployment spell).
It appears plausible that their reluctance to revise their wage expectations
is one factor that hinders the labor market integration of these job seekers,
ultimately contributing to their extended unemployment spell. On the flip
side, it is possible that job seekers who revised their wage expectations as
time progressed may have successfully found employment before the second
interview, making it difficult to draw conclusions as to how their beliefs
were updated over time.

###  4.5 How do search incentives shape job seekers’ beliefs?

Next, we study how the accuracy of job seekers’ wage expectations depends on
their search behavior. This analysis is subject to a non-trivial
identification problem, because individuals’ choices might be influenced by
their subjective beliefs. To address this concern, we explore exogenous
variation in the incentives of unemployed workers to search for jobs and
analyze their impact on job seekers’ wage optimism or pessimism. We exploit
regional variation in the risk that job seekers will be subject to punitive
sanctions along the administrative borders of local employment agency (LEA)
districts. These sanctions involve temporary reductions in unemployment
benefit payments and are imposed by caseworkers when they detect that job
seekers are not complying with job search requirements.161616Instances of non-
compliance include insufficient job applications, rejecting job offers from
the employment agency, and voluntary termination of employment. The regional
variation arises because LEAs have autonomy in deciding about the local policy
style (see, e.g., Boockmann et al., 2014; Doerr and Kruppe, 2015; Fertig et
al., 2006), including how strictly they punish job seekers for inadequate
search behavior.

As a result, caseworkers in LEA districts with higher sanction intensities may
exert greater pressure on job seekers, leading them to perceive stronger
incentives to apply for and accept jobs. This can yield different implications
for job seekers’ wage expectations. On the one hand, an enhanced sanction risk
may prompt job seekers to reduce their selectivity, leading them to lower
their wage expectations. On the other hand, job seekers intensifying their
search activities can induce indirect effects fostering heightened levels of
optimism. For example, a larger number of applications may increase the
probability of encountering job offers with particularly high wages.
Alternatively, job seekers might adopt more optimistic beliefs in order to
motivate themselves.

####  4.5.1 Econometric strategy

To capture variations in the local sanction regime, we utilize regional data
on the annual number of benefit sanctions imposed in each of the 178 LEA
districts (indexed by $j$) and normalize this information by the average
annual stock of unemployed workers in each LEA district. The resulting
sanction intensity, $SI_{j}$, can be linked to the administrative and survey
data, as explained in Section 2, both of which include identifiers for job
seekers’ place of residence.171717Due to data security restrictions, we are
unable to utilize regional identifiers for the linked survey-administrative
data in our analysis. Consequently, in this section, we rely on the survey and
administrative data without linking them at the individual level. This
requires us to re-estimate the objective benchmarks using a reduced set of
covariates available in both the survey and administrative records. This
includes socio-demographic characteristics, previous wage, regional
information, and month of entry into unemployment. Despite this adjustment,
our model demonstrates strong out-of-sample predictive power (with an $R^{2}$
of 0.39), and the re-estimated objective predictions closely align with the
measure employed in the previous sections ($\rho=0.75$).  To ensure that our
estimation sample does not contribute to the sanction intensity measure, we
rely on the corresponding numbers as observed in the year before a job seeker
entered unemployment. In the Appendix, we illustrate the distribution of the
sanction intensity across survey respondents (see Figure B.4), as well as LEA
districts in Germany (see Figure B.5).

While the local sanction intensity serves as a proxy for the personal risk of
being exposed to a benefit sanction, LEA districts imposing more sanctions
might face a different composition of the unemployed workforce. This makes it
unlikely that a simple regression of job seekers’ outcomes on the local
sanction intensity will identify the causal effect of job seekers’ personal
sanction risk. Therefore, we exploit discontinuities with respect to the
sanction intensity along the administrative borders of the LEA districts
(similar to Caliendo et al., 2022; Dube et al., 2010). Specifically, we
estimate border-pair fixed-effects models of the following form:

$\displaystyle Y_{ijb}$ $\displaystyle=\alpha+\delta SI_{j}+\beta X_{i}+\phi
R_{j}+\kappa_{b}+\varepsilon_{ijb},$ (4)

where $i$ denotes the individual job seeker, $j$ the LEA district in which the
individual is located at the beginning of the unemployment spell, and $b$ a
pair of bordering LEA districts such that $\kappa_{b}$ denotes the border-pair
fixed effects for any combination of two neighboring LEA districts. Since one
LEA district usually has several neighboring districts, an individual living
in region $j$ can belong to different sets of boarder pairs $b$ and therefore
enters the estimation multiple times (depending on the number of neighboring
regions). Therefore, we use sampling weights referring to the inverse of the
number of neighboring LEA districts. The parameter of interest $\delta$
identifies the effect of sanction intensity on the outcome variables $Y$ by
comparing individuals living in similar, neighboring LEA districts but facing
varying risks of being sanctioned. Moreover, $R_{j}$ captures regional
characteristics including the local unemployment rate, vacancy rate, gross
domestic product, industry structure, and federal state fixed effects, and
$X_{i}$ accounts for individual-level characteristics. Standard errors are
clustered at the LEA district level.

####  4.5.2 Validity of the empirical approach

The underlying assumption of this approach is that two LEA districts with a
common border are similar in all relevant characteristics except the sanction
intensity. LEA districts represent relatively small geographical entities and
delineations of functional local labor markets in Germany typically result in
larger geographical entities (see, e.g., Kropp and Schwengler, 2016, who
identify 50 local labor market regions, compared to 178 LEA districts). For
example, the two largest metropolitan areas in Germany – the Rhine-Ruhr region
and the Berlin-Brandenburg area – are home to approximately 10.9 million and
6.2 million residents, respectively. At the same time, they encompass 13 and
eight distinct LEA districts each. Multiple LEA districts being part of larger
local labor markets makes it likely that bordering LEA districts will exhibit
similar characteristics. To empirically support this premise, Table B.3
contrasts disparities in regional labor market indicators – such as
unemployment rates, vacancy rates, GDP, etc. – within 487 pairs of neighboring
LEAs with differences in randomly selected LEA district pairs (see also
Caliendo et al., 2022). For instance, the average disparity in unemployment
rates between two randomly chosen LEA districts is approximately 4.0
percentage points. In contrast, when examining pairs of LEAs that share a
common border, this disparity is markedly reduced by about 70%, resulting in a
mere 1.2 percentage point difference.

Moreover, we conduct balancing tests regressing the local sanction intensity
on a rich set of individual-level characteristics to further examine the
validity of our approach. As in our main analysis, we condition on border-pair
fixed effects, as well as the set of regional characteristics, and we explore
the predictive power of socio-demographic characteristics, labor market
histories and personality traits, all variables that have been proven to be
important for individuals’ labor market success. As shown in Appendix Table
B.4, we find very little evidence that individual characteristics as observed
in our data are correlated with the conditional sanction intensity (i.e. see
$p$-values at the bottom of Table B.4).

Another concern relates to the possibility that LEAs with more restrictive
sanction regimes also adjust other dimensions of their policy style. In that
case, any effect of the sanction intensity could possibly reflect changes in
the usage of other policy instruments rather than sanctions. To test this, we
exploit survey data on various dimensions of caseworkers’ counseling
activities including notifications about labor market programs (i.e. training,
workfare programs, job creation schemes, and start-up subsidies), the number
of caseworker meetings, and the provision of vacancy referrals. These
variables are the most direct measures of the LEA’s policy style, since they
reflect the caseworkers’ information strategy. The findings presented in
Appendix Table B.5 provide no evidence that the sanction intensity is related
to caseworkers’ counseling activities.

####  4.5.3 Effect of sanction risk on behavior and beliefs

Table 2 shows the effect of the sanction risk on job seekers’ search effort,
wage expectations, and realized wages. In line with standard search-
theoretical arguments, a stricter sanction regime seems to motivate unemployed
workers to exert more search effort. Specifically, as shown in column (1) of
Table 2, a 10 percentage point higher sanction intensity – equivalent to an
increase of approximately one standard deviation – raises the number of weekly
job applications by about 9.8% ($p=0.016$). At the same time, the estimates in
column (2) reveal that a stricter sanction regime fosters greater optimism
among job seekers regarding their earnings potential. Raising the sanction
intensity by 10 percentage points increases job seekers’ wage expectations
relative to the objective benchmark by about 1.8% ($p=0.006$). When we
differentiate between individuals who overestimate and underestimate the
potential wages they could earn, we observe that the sanction intensity
impacts both dimensions. Specifically, it significantly enhances optimism (as
indicated in column (3)) while concurrently reducing pessimism (as shown in
column (4)).

Accuracy of wage expectations(a) Dependent variable Log no. of job
$S_{i}-O_{i}$ $S_{i}-O_{i}$ $S_{i}-O_{i}$ Log realized net applications Pos.
values Neg. values monthly wage(b) (1) (2) (3) (4) (5) Effect of sanction
intensity 0.098∗∗ 0.018∗∗∗ 0.011∗∗ 0.007∗∗∗ -0.009 (0.040) (0.007) (0.005)
(0.003) (0.008) No. of observations 5,669 5,669 5,669 5,669 17,973 Mean dep.
variable 1.716 0.125 0.167 -0.042 7.001 • Note: The table reports the effect
of the local sanction intensity (measured in 10%-points) on job seekers’
search effort (column 1), their subjective wage expectations (columns 2-4),
and realized wages (column 5). In all specification, we account for socio-
demographic and regional characteristics, as well as border-pair fixed
effects. Standard errors clustered at the LEA district level are shown in
parentheses. ***/**/* indicate statistical significance at the
1%/5%/10%-level. • (a) In column (2), the dependent variable is the log
difference between subjective belief $S_{i}$ and objective benchmark $O_{i}$.
In column (3), we set negative values to zero and thus only exploit variation
in positive deviations (“optimism”), while in column (4), we set positive
values to zero and thus only exploit variation in negative deviations
(“pessimism”). • (b) In column (5), the dependent variable is the log realized
wage of individuals observed in the administrative sample who start regular
employment within 24 months after entry into unemployment.

Table 2: Effect of sanction risk on search behavior and accuracy of wage
expectations

At first glance, these findings may appear somewhat surprising, as an enhanced
sanction risk would be presumed to directly influence job seekers to become
less selective, leading them to reduce their wage expectations. However, the
increased incentives to search can induce indirect effects that foster
heightened levels of wage optimism. For example, by submitting a greater
number of job applications, job seekers enhance their prospects of attracting
job offers that come with particularly higher wages. Moreover, this effect
might be reinforced in the presence of negative duration dependence, e.g., due
to skill depreciation during the unemployment spell. Job seekers who
anticipate finding a job after a shorter period of unemployment due to their
intensified search efforts may also anticipate receiving more favorable job
offers, since their skills are perceived to have depreciated at a lesser rate
(see, e.g., Nekoei and Weber, 2017). Lastly, it is often emphasized that
confidence can be valuable, as it enhances an individual’s motivation to exert
effort (see, e.g., Bénabou and Tirole, 2002). In our context, it is
conceivable that job seekers who perceive increased pressure from their
caseworkers to submit a greater number of job applications may adjust their
perception of the returns from this strategy, thereby holding more optimistic
expectations about their potential earnings. This mechanism also aligns with
the observation that a greater sanction risk increases job seekers’ wage
optimism, yet does not translate into higher realized wages for
them.181818Note that the effects of the sanction intensity on realized wages
are estimated based on a larger sample of job seekers as observed in the
administrative records. This is because we cannot utilize regional identifiers
for job seekers’ LEA district and therefore do not observe the local sanction
intensity when analyzing the linked survey-administrative data (see also
footnote 17). On the contrary, as shown in column (5) of Table 2, realized
wages upon reemployment tend to be (insignificantly) lower when job seekers
are subject to a more restrictive sanction regime. Therefore, it is possible
that job seekers exposed to an increased risk of sanctions only revise their
wage expectations as time progresses.

###  4.6 Labor market implications: descriptive evidence

In the final part of our analysis, we take a closer look at the potential
labor market implications of wage optimism. As elaborated upon in Section 3,
inaccurate beliefs can yield different consequences for the labor market
integration of unemployed workers. On the one hand, optimistic wage
expectations can motivate job seekers to exert more effort, thereby
facilitating job finding. On the other hand, unemployed individuals who
possess unrealistically optimistic wage expectations might exhibit an
excessive degree of selectivity and reject job offers more frequently than
justified. This in turn may lead to higher realized wages, but may prolong
unemployment and cause job seekers to overestimate their reemployment
prospects. In what follows, we present descriptive evidence illustrating the
empirical significance of these mechanisms. As previously discussed,
individuals’ search behavior and their beliefs are closely intertwined, making
it notoriously challenging to identify the causal effects of optimistic wage
expectations on job seekers’ search and labor market outcomes. Nonetheless, as
we shall see, the data patterns reveal insightful perspectives regarding the
importance of the distinct mechanisms at play.

Figure 5 illustrates correlations of job seekers’ wage optimism or pessimism
with their search effort, realized wages, and perceived and realized job
finding rates. Specifically, we depict binned scatter plots conditioning on
socio-demographic characteristics and objective benchmarks (Appendix Table B.6
reports the corresponding regression results). We find that the level of wage
optimism is positively related to the number of job applications (see Panel A
of Figure 5). In particular, job seekers who overestimate their wage by an
additional 10% sent out, on average, 1.6% more job applications ($p=0.001$).
Moreover, it turns out that workers who exhibit a greater level of optimism
also earn higher wages upon finding a job within two years after the start of
the unemployment spell (see Panel B of Figure 5). A wage expectation that is
inflated by 10% comes along with approximately 3.1% higher monthly wages
($p<0.001$) in comparison to individuals who provide an accurate assessment of
their earnings potential.

Figure 5: Descriptive evidence on labor market implications

A. Log no. of job applications B. Log realized net monthly wage C. Perceived
job finding rate D. Realized job finding rate • Note: The figure shows binned
scatter plots (with 20 bins) of individuals’ search effort, realized
reemployment wage, and perceived and realized six-months-ahead job finding
rates against their wage belief inaccuracy (defined as the log difference
between subjective belief $S_{i}$ and objective benchmark $O_{i}$). The blue
line shows a quadratic fit for the conditional means after residualizing
against socio-demographic characteristics (gender, age categories, German
citizen, education categories, married, any children, East Germany) and the
objective benchmark. The corresponding regression results are reported in
Appendix Table B.6.

Finally, we examine the relationship between the accuracy of job seekers’ wage
expectations and their perceived and actual job finding rates, both measured
over a period of six months following the interview. This comparison reveals a
remarkable pattern. On the one hand, as illustrated in Panel C of Figure 5,
there is a positive and almost linear relationship between wage optimism and
the perceived job finding probability. This suggests that job seekers who are
most optimistic about their reemployment wages also report the highest
perceived chances of finding a job. On the other hand, Panel D of Figure 5
shows a non-linear connection between the accuracy of individuals’ wage
expectations and their actual job finding rates. Specifically, job seekers who
hold relatively accurate beliefs about their reemployment wage have the
highest likelihood of finding a job within six months, whereas those who over-
or underestimate their earnings potential face reduced reemployment rates.
Consequently, our results suggest that the more optimistic workers are about
the wages they can earn upon reemployment, the higher the likelihood that they
will overestimate their prospects of finding a job.

Taken together, the observed pattern aligns with the notion that optimistic
job seekers, upon receiving wage offers they deem to be “too low”, exhibit an
excessive degree of selectivity. Therefore, they may earn higher wages, but
prolong the duration of unemployment beyond their initial expectations,
resulting in a wedge between the true and perceived job finding rates.
Concurrently, wage optimism can serve as a motivation to search more
intensively, potentially explaining the positive correlation between
optimistic beliefs and search effort. However, it appears that the impact of
heightened effort on job finding is outweighed by the increased selectivity
linked to job seekers’ excessive optimism.

###  4.7 What causes overly optimistic wage expectations?

Gaining insight into the reasons behind the observed average optimism and the
heterogeneity in belief inaccuracies is essential to draw meaningful
conclusions about how to assist the unemployed in making better decisions
during their job search. With this in mind, we will now discuss our results
against existing theoretical and empirical evidence related to job seekers’
belief formation.

##### Limited information:

While psychologists and economists offer numerous explanations for why
individuals make systematic judgment errors (see, e.g, Benjamin, 2019, for an
overview), it is often emphasized in the job search literature that unemployed
workers may have incomplete information about the labor market (Burdett and
Vishwanath, 1988; Gonzalez and Shi, 2010). After all, job search is a complex
endeavor and there is relatively little information and feedback that may help
job seekers to navigate this process. Various pieces of existing evidence
support the notion that information frictions about workers’ earnings
potential play a significant role and may contribute to job seekers’ overly
optimistic wage expectations. For example, Conlon et al. (2018) demonstrate
that job seekers’ expectations of wage offers increase when they receive an
offer exceeding their initial expectations. Additionally, information
regarding the salaries of comparable individuals has been shown to impact
workers’ beliefs about their outside options (Jäger et al., 2023) and women’s
willingness to ask for higher wages (Roussille, 2022). Related to this notion,
a growing body of literature suggests that providing workers with information
about the salaries of their peers narrows co-worker wage gaps (Baker et al.,
2023; Cullen and Pakzad-Hurson, 2023) and affects workers’ motivation (Breza
et al., 2018; Card et al., 2012; Cullen and Perez-Truglia, 2022).

We also find evidence supporting the idea that information frictions impact
the formation of beliefs among unemployed workers in our context. As discussed
in Section 4.2, we observe that job seekers with greater unemployment
experience and those who receive more advice from their caseworkers tend to
hold more accurate earnings expectations. Moreover, the anchoring of beliefs
to pre-unemployment wages, as documented in Section 4.3, may stem from
workers’ having incomplete information, leading them to utilize their previous
wage as a signal for their wage upon reemployment. These observations align
with the notion that acquiring additional information can mitigate job
seekers’ tendency to be overly optimistic. Against this backdrop, one may
expect that a policy offering job seekers precise information about their
potential earnings may improve the accuracy of job seekers’ beliefs.

##### Motivated reasoning and selective recall:

An alternative explanation for the divergence between perceived and actual
outcomes relates to the way individuals process the available information. It
is commonly argued that subjective beliefs serve essential psychological needs
(see, e.g., Bénabou and Tirole, 2016, for an overview). For instance,
individuals may hold unrealistically positive beliefs because they derive
direct utility from maintaining a positive self-image (Brunnermeier and
Parker, 2005; Köszegi, 2006), they aim to enhance their motivation and
overcome self-control problems (Bénabou and Tirole, 2002), or they use
optimism as a signal to convince others about one’s abilities (Burks et al.,
2013). Consistent with these ideas, our findings indicate higher levels of
overoptimism among job seekers with a lower objective earnings potential. It
appears plausible that this specific group of unemployed workers may have a
heightened desire for motivated beliefs compared to individuals who can
reasonably anticipate higher wages.

Motivated beliefs may impact not only job seekers’ overall tendency to be
overly optimistic, but also how they process feedback. In particular, Bénabou
and Tirole (2002, 2004) suggest selective recall – that is, individuals
deliberately managing to forget or suppress negative feedback – as a way to
sustain overly optimistic beliefs even when faced with repeated negative
feedback.191919Aligning with these theoretical ideas, Zimmermann (2020)
demonstrates in a lab experiment that positive feedback regarding individuals’
relative performance has a lasting impact on their beliefs, while negative
feedback affects subjective beliefs only in the short term. Moreover, Huffman
et al. (2022) provide empirical evidence, both through reduced-form analysis
and structural modeling, indicating that managers who participate repeatedly
in high-powered tournament incentive systems tend to make overly optimistic
predictions about their future performance. These predictions are associated
with an inclination toward overly positive recollections of their past
performance. In this context, two of our results are particularly noteworthy.
First, especially job seekers who are predicted to experience a wage decline
compared to their previous job tend to exhibit overly optimistic beliefs and
anchor their wage expectations too strongly to their past salary, whereas job
seekers who have reasons to anticipate a wage increase have relatively
accurate expectations (see Section 4.3). Second, we find that job seekers who
remain unemployed for an extended period are reluctant to revise their wage
expectations downwards. Those who initially overestimate their earnings
potential by up to 17% even increase their wage expectations over time (see
Section 4.4). These findings are consistent with the notion that job seekers
do not fully internalize the negative feedback they receive during the search
process.

Lastly, the result that job seekers who perceive greater extrinsic incentives
to apply for jobs hold more optimistic wage expectations (see Section 4.5),
while not experiencing an increase in actual wages, can be explained by models
of motivated beliefs as well. Specifically, when job seekers face an elevated
risk of sanctions for non-compliance with search requirements, they may adjust
their expectations about the potential returns of submitting job applications
as a way to motivate themselves. In other words, they may adopt a more
optimistic outlook to compensate for the expected loss in utility resulting
from the threat of financial penalties.

The existence of motivated beliefs carries implications for policymakers
seeking to assist job seekers in their search process. First, it is not
immediately evident whether implementing policies to reduce overoptimism is
advisable if optimistic beliefs help sustain high levels of motivation among
job seekers. Second, interventions aiming to correct belief inaccuracies may
not be effective if job seekers forget or suppress negative feedback in order
to sustain optimistic beliefs. To reduce selective processing of information,
it may help to provide feedback in a way that is less likely to be perceived
by job seekers as “ego threatening”.

##  5 Conclusion

Job seekers’ misperceptions about the labor market can distort their decision-
making and prolong unemployment. In our study, we have established objective
benchmarks for the subjective wage expectations of unemployed workers, which
provide intriguing insights along four important dimensions.

First, we have discovered significant variation in the levels of optimism and
pessimism among different groups of unemployed individuals. Notably, those
with lower objective earnings potential, particularly individuals expected to
experience a wage decline compared to their previous job, tend to display
remarkably overoptimistic beliefs. Second, when exploring repeatedly elicited
wage expectations, we find that the overoptimism among the group of long-term
unemployed workers remains persistent throughout the unemployment spell. This
reluctance to adapt expectations might be one factor hindering their labor
market integration. Third, our results show that unemployed workers who face
greater incentives to search for and accept jobs – and who consequently
increase the number of job applications – become even more optimistic about
the potential wages they can earn upon reemployment. This heightened optimism
potentially serves as an additional factor motivating them to intensify their
job search efforts.

Finally, we have established a clear connection between overly optimistic wage
expectations and job seekers’ tendency to overestimate their prospects of
reemployment. This finding holds significant implications for policymakers
committed to preventing long-term unemployment. To be more precise, our
results suggest a wedge between the perceived and actual job finding rates for
increasing levels of wage optimism. This aligns with the idea that inaccurate
beliefs may incur decision-making costs, because overly optimistic job seekers
may prolong unemployment by being excessively selective with respect to the
jobs they accept. Against this backdrop, encouraging job seekers to revise
their overly ambitious aspirations could present an attractive path for labor
market policy focused on enhancing the reemployment prospects of unemployed
workers.

While the combination of survey data and administrative records offers
valuable insights into the interrelation of individuals’ beliefs, their job
search behavior, and their actual labor market outcomes, it is clear that our
setting does not come without limitations. First of all, it is important to
note that individuals may possess private information about their earnings
potential that is not accounted for in our benchmarks. This poses a
significant challenge in identifying misperceptions at the individual level,
and has the potential to influence the relationship between subjective beliefs
and job search or labor market outcomes. The ideal survey should elicit
individuals’ beliefs not only about their own earnings potential, but also
about primitives, such as the perceived returns to search and the distribution
of wage offers, to reduce issues of reverse causality. Moreover, one could
hope to improve the accuracy of objective benchmarks by conditioning on a
richer set of commonly unobserved individual characteristics. These could, for
instance, include workers’ personality traits, their non-cognitive skills, or
their preferences over non-wage job characteristics.

Acknowledging these limitations, we consider our analysis as an important step
toward opening the black box of how job seekers form their beliefs and how
beliefs can affect their decisions while searching for jobs. Notably, various
pieces of evidence substantiate the idea that motivated beliefs have a
significant influence on job seekers’ tendency to be overly optimistic about
their labor market prospects. Simultaneously, we recognize that other factors,
such as information frictions, may be at work as well, and untangling these
distinct explanations presents an intriguing avenue for future research. For
instance, it would be particularly interesting to provide a randomly selected
group of job seekers with information about their objective earnings potential
and analyze the consequences for their behavior and reemployment prospects.
Such an approach would help to assess the significance of information
frictions in influencing job seekers’ decision-making and outcomes during the
job search process. At the same time, analyzing how job seekers recall the
provided information and update their beliefs depending on their priors could
offer further insights into the role of motivated beliefs.

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## Online Appendix

##  Appendix A Further Details on Objective Benchmarks

In the following, we present details about the prediction of objective
benchmarks for job seekers’ reemployment wages.

##### Covariates:

Monthly wages are modeled as a function of covariates which are pre-determined
at the time of unemployment entry. We exploit a rich set of characteristics
that are available in both the IZA/IAB Linked Evaluation Dataset (LED) and the
IZA/IAB Administrative Evaluation Dataset (AED). These include
sociodemographic information (e.g. gender, age, education, family status),
characteristics of the last job before unemployment (wage, part- versus full-
time job), detailed information on the labor market biography in the last 10
years (e.g. months employed, unemployed, and in labor market programs), and
local labor market characteristics (unemployment rate, East versus West German
residency). To allow for a flexible functional form, we also use third-order
polynomial terms of all continuous variables and first-order interaction terms
of all variables with some important characteristics (gender, age, education,
East German residency, last wage). See Table A.2 for a complete list of all
the 717 included covariates.

##### LASSO regression:

To address the high-dimensional nature of the data and to avoid over-fitting,
we estimate LASSO regressions, which add a regularization term to the
objective function and shrink some coefficients to zero. Specifically, we
estimate a linear LASSO regression for wages and a logistic LASSO regression
for job finding probabilities.202020We use the Stata commands lasso2 from the
lassopack developed by Ahrens et al. (2018, 2020). We optimized the
regularization parameter $\lambda$ using five-fold cross-validation in the
training data and considering the following values for $\lambda$: 0.001, 0.01,
0.1, 1, 10, 100, 1000. In the estimated models, 66% of all covariates are
selected.

##### Prediction quality:

In Table A.3, we evaluate the out-of-sample predictive performance of the
LASSO model. Results are presented for the different training samples (AED
01/2005 - 06/2007 or 80% of AED 06/2007 - 06/2008) and test samples (80% of
AED 06/2007 - 06/2008 or LED). We calculate the out-of-sample $R^{2}$ obtained
from a regression of realized gross wages on predicted wages in the test data.
The predictions explain between 48% and 53% of the out-of-sample variation.
Note that the predictive performance is similar for the two different training
samples. The correlation of predictions between both samples is also very high
(0.976).

Results in Table A.3 are based on gross wages. For our analyses, we convert
the predicted gross wage into net terms (see procedure below). For the
converted net wage predictions in the LED sample, we can compare the
explanatory power of objective predictions with that of respondents’
subjective beliefs. The results in Table A.4 demonstrate that our lasso model
is better at predicting realized wages than job seekers themselves are: while
job seekers’ beliefs explain 41% of the variation in realized net wages, the
objective benchmarks explain 56%. 212121This result is in line with the
evidence provided by Van den Berg et al. (2023) who find that machine learning
predictions have higher explanatory power for reemployment probabilities than
job seekers’ self assessments.

##### Converting gross to net wages:

Wage beliefs in the survey are elicited in net terms, while the administrative
data provide realized wages in gross terms. We therefore convert gross into
net wages by deducting social security contributions and wage taxes.

Wage taxes are withheld by the employer and deducted from the monthly wage
payment. They qualify as a pre-payment of the income tax in case the employee
files an annual income tax declaration. We do not perform a complete income
tax calculation since we do not have information on individual-specific
deductions and other income sources. Moreover, individuals most likely think
about their monthly payroll when asked about their net wage. Rather, we
calculate withheld wage taxes utilizing contribution and tax schedules of 2008
and taking into account variation in rates according to partnership status,
number of children, age, and East versus West German residency.

Exemption thresholds in the wage tax schedule depend on the tax class of the
individual. While single individuals without children are always in class I
and single parents are always in class II, married couples may choose between
a combination of classes IV/IV or III/V. With IV/IV, the standard exemption
threshold is applied to both spouses, whereas with III/V the higher-earning
spouse obtains twice the standard exemption rate while the second earner is
already taxed at lower earnings. Although we do not directly observe the
chosen tax classes for married couples, we can infer them from the observed UI
benefit payments.222222We thank Jacob Wegmann for this suggestion. UI benefits
are generally calculated by the following formula: Monthly benefit = 0.6
$\times$ (Average monthly gross wage in last 12 months before unemployment -
Wage tax - SolZ - Social security payment). Thus, the relation between
previous gross wages and UI benefits depends on the chosen tax class. We
exploit this relation by calculating the hypothetical benefits under tax
classes III, IV, and V and then choose the tax class that minimizes the
difference between the actual observed and the hypothetical benefit payments.
The derived tax classes match well-known descriptives about taxation of
married couples in Germany. For instance, tax class V is chosen more
frequently by women than men and this type of splitting is more prevalent in
West than in East Germany.

LED AED AED 01/2005 - 05/2007 06/2007 - 05/2008 No. of observations 5,376
84,617 21,715 Reemployment wage (gross, per month)(a) 1,782 1,704 1,716 Socio-
demographic characteristics Female 0.41 0.35 0.37 Age 36.16 35.11 34.90 School
leaving degree lower secondary degree 0.31 0.38 0.35 middle secondary degree
0.44 0.39 0.40 upper secondary degree 0.23 0.18 0.21 Further education
vocational certificate 0.72 0.68 0.68 university degree 0.20 0.16 0.19 German
citizen 0.94 0.91 0.91 Married 0.39 0.45 0.43 Number of children one child
0.18 0.16 0.17 two or more children 0.12 0.14 0.13 Last job Wage in last job
(gross, per month) 1,726 1,688 1,696 Last job was quit by individual 0.06 0.07
0.09 Labor market history # of employers in last 2 years 2.55 1.49 1.61 # of
employers in last 10 years 4.64 3.13 3.45 # of UE spells in last 2 years 1.41
0.59 0.44 # of ALMP programs in last 2 years 0.42 0.17 0.21 Duration of last
UE spell in months 5.09 5.34 5.80 # of months employed in year t-1 8.64 10.74
10.92 # of months employed in year t-2 7.86 9.27 9.14 # of months employed in
year t-3 7.50 8.38 7.73 # of months employed in years t-4 to t-10 46.12 43.18
40.20 # of months unemployed in year t-1 0.79 0.86 0.61 # of months unemployed
in year t-2 1.07 1.57 1.49 # of months unemployed in year t-3 1.29 1.75 2.19 #
of months unemployed in years t-4 to t-10 8.81 8.37 10.42 # of months in ALMP
in year t-1 0.60 0.22 0.25 # of months in ALMP in year t-2 0.68 0.53 0.57 # of
months in ALMP in year t-3 0.63 0.61 0.61 # of months in ALMP in years t-4 to
t-10 1.35 1.20 1.42 Average wage in year t-1 (quintiles) 3.17 3.25 3.19
Average wage in year t-2 (quintiles) 2.97 3.00 2.89 Average wage in year t-3
(quintiles) 2.78 2.81 2.59 Average wage in years t-4 to t-10 (quintiles) 17.88
17.01 16.41 Local labor market West, UE rate $<$3% 0.02 0.00 0.01 West, UE
rate 3-6% 0.29 0.07 0.26 West, UE rate 6-9% 0.25 0.24 0.26 West, UE rate $>$9%
0.11 0.39 0.18 East, UE rate $<$12% 0.07 0.00 0.04 East, UE rate 12-14% 0.10
0.01 0.08 East, UE rate 14-16% 0.10 0.05 0.12 East, UE rate $>$16% 0.04 0.23
0.05 • Note: The table compares the mean characteristics of the survey sample
(LED) with those of the two adminstrative samples used for the prediction of
objective benchmarks (AED). • (a)Realized wages are observed for individuals
who start a regular job within 24 months after unemployment entry.

Table A.1: Summary statistics: LED versus AED samples

Type Covariate Socio-demographic characteristics Continuous Age Categorical
Female School degree (4 categories: none, lower sec. degree, middle sec.
degree, upper sec. degree) Further education (3 categories: none, vocational
certificate, university degree) German citizen Married # of children (3
categories: 0, 1, $\geq$ 2) Last job Continuous Wage in last job (gross, per
month) Categorical Last job was quit by individual Labor market history
Continuous Duration of last UE spell in months # of months employed in year
t-1 # of months employed in year t-2 # of months employed in year t-3 # of
months employed in years t-4 to t-10 # of months unemployed in year t-1 # of
months unemployed in year t-2 # of months unemployed in year t-3 # of months
unemployed in years t-4 to t-10 # of months in ALMP in year t-1 # of months in
ALMP in year t-2 # of months in ALMP in year t-3 # of months in ALMP in years
t-4 to t-10 Categorical # of employers in last 2 years (5 categories) # of
employers in last 10 years (5 categories) # of UE spells in last 2 years (5
categories) # of ALMP programs in last 2 years (5 categories) Average wage in
year t-1 (5 categories) Average wage in year t-2 (5 categories) Average wage
in year t-3 (5 categories) Average wage in years t-4 to t-10 (5 categories)
All months regularly employed in year t-1 Zero months regularly employed in
year t-2 All months regularly employed in year t-2 Zero months regularly
employed in year t-3 All months regularly employed in year t-3 Zero months
regularly employed in years t-4 to t-10 All months regularly employed in years
t-4 to t-10 Zero months unemployed in year t-1 Zero months unemployed in year
t-2 Zero months unemployed in year t-3 Zero months unemployed in years t-4 to
t-10 Zero months in ALMP in year t-1 Zero months in ALMP in year t-2 Zero
months in ALMP in year t-3 Zero months in ALMP in years t-4 to t-10 Local
labor market Categorical Unemployment rate in employment agency district at
time of UE entry (8 categories: West $<$3%, 3-6%, 6-9%, $>$9%; East $<$12%,
12-14%, 14-16%, $>$16%) Timing of unemployment spell Categorical Calendar
month of entry into unemployment (12 categories) • Note: The table reports the
list of covariates used in the lasso regression to predict reemployment wages.
We also include second- and third-order polynomials of all continuous
variables, as well as interaction terms of all variables with female, age,
upper secondary degree, East German residency, and the monthly gross wage in
the last job, yielding a total of 717 covariates included.

Table A.2: List of covariates used to generate objective benchmarks

Test dataset(a) Administrative data Survey data Training dataset(b) Baseline
Robustness Baseline Robustness Out-of-sample $R^{2}$ .525 .516 .511 .481 •
Note: The table reports the out-of-sample $R^{2}$, which is obtained based on
a regression of realized reemployment wages as observed in the respective test
dataset on predictions of objective benchmarks generated in the training
dataset. • (a)The administrative test dataset includes a 20% sample of all
entries into unemployment between June 2007 and May 2008, while the survey
test dataset includes all individuals observed in the matched survey-
administrative data. • (b)The baseline training dataset includes all entries
into unemployment between January 2005 and May 2007 as observed in the
administrative records. As a robustness check, we use an 80% sample of all
entries into unemployment between June 2007 and May 2008 (i.e. the same period
during which the survey was conducted) excluding observations from the
respective test dataset.

Table A.3: Prediction performance based on out-of-sample $R^{2}$

Dependent variable Log realized net monthly wage (1) (2) Objective benchmark
(log) 0.951∗∗∗ (0.016) Subjective belief (log) 0.602∗∗∗ (0.017) No. of
observations 4,098 4,098 $R^{2}$ 0.559 0.405 • Note: The table reports the
results of an OLS regressions of log realized reemployment wages on objective
benchmarks and subjective wage expectations, respectively. The sample includes
survey respondents as observed in the matched survey-administrative data who
found anew job within 24 months after the beginning of the unemployment spell.
Standard errors reported in parenthesis. ***/**/* indicate statistical
significance at the 1%/5%/10%-level.

Table A.4: Predictive power of objective benchmarks and subjective beliefs

##  Appendix B Additional Figures and Tables

Figure B.1: Asymmetric relation between subjective beliefs and objective
benchmarks for wage changes

• Note: The figure depicts a binned scatter plot (with 20 bins) for the
relation between individuals’ subjectively expected wage changes and
objectively predicted wage changes (i.e. both in comparison to their pre-
unemployment wages). Slope coefficients are estimated separately for positive
and negative variation in objective wage changes. $N=5,376$.

Figure B.2:  Distribution of wage beliefs in survey waves 1 and 2

• Note: The figure shows the distribution of individuals’ beliefs about net
monthly reemployment wages in survey waves 1 and 2. Wave 1 was conducted 7 -
14 weeks after unemployment entry and wave 2 was collected 12 months later.
The sample only includes individuals who are still in the same unemployment
spell in wave 2 ($N=459$). We do not show individuals with an expected
reemployment wage in wave 1 or 2 larger than 4,000€ ($<2\%$ of sample).

Figure B.3:  Wage belief inaccuracy in survey wave 1 across interview weeks

• Note: The figure shows the mean wage belief inaccuracy (defined as the log
difference between subjective belief $S_{i}$ and objective benchmark $O_{i}$),
together with 95% confidence intervals, of respondents in survey wave 1 by
interview week relative to unemployment entry.

Figure B.4:  Distribution of local sanction intensity across survey
respondents

• Note: The figure shows the distribution of local sanction intensity across
all survey respondents ($N=5,669$).

Figure B.5:  Distribution of local sanction intensity across LEA districts

• Note: The figure shows the geographical distribution of local sanction
intensity across LEA districts in Germany.

Mean No. of observations 5,376 A. Monthly net wage in € Subjective wage
expectation ($S_{i}$) 1,407 Objective benchmark(a) ($O_{i}$) 1,173 Realized
wage(b) 1,190 Accuracy of wage expectation: $ln(S_{i})-ln(O_{i})$ 0.17 B. Job
finding within six months Realized job finding rate 0.56 Perceived job finding
rate Very likely 0.52 Likely 0.37 Unlikely 0.08 Very unlikely 0.03 • Note: The
table reports summary statistics among the sample of survey respondents. •
(a)Objective benchmarks are generated from realized outcomes of similar
individuals observed in the administrative records as described in Section
2.1. • (b)Realized wages are observed for individuals who start a regular job
within 24 months after unemployment entry ($N=4,098$).

Table B.1: Summary statistics: survey respondents Table B.2: Robustness:
relationship between subjective beliefs and objective benchmarks

| Mean | Share with | Share with | $\widehat{\beta}_{1}$ | $N$
---|---|---|---|---|---
| difference | diff. $>$ 0.1 | diff. $<$ -0.1 |  |
Baseline | 0.170 | 0.569 | 0.130 | 0.735∗∗∗ (0.012) | 5,376
Prediction from alternative training data (survey period) | 0.154 | 0.536 | 0.152 | 0.708∗∗∗ (0.013) | 5,376
Instrument prediction from baseline training data with | - | - | - | 0.740∗∗∗ (0.013) | 5,376
prediction from alternative training data |  |  |  |  |
Wage prediction based on all individuals reentering | 0.153 | 0.540 | 0.147 | 0.736∗∗∗ (0.012) | 5,376
employment within 9 months after unemployment entry |  |  |  |  |
Restrict to individuals searching for full-time employment | 0.179 | 0.579 | 0.110 | 0.684∗∗∗ (0.014) | 4,284
Restrict to individuals expecting between 35 and 45 | 0.165 | 0.560 | 0.118 | 0.639∗∗∗ (0.013) | 4,396
working hours |  |  |  |  |
Gross-to-net conversion: restrict to unmarried | 0.155 | 0.566 | 0.117 | 0.747∗∗∗ (0.017) | 3,229
Note: Robustness tests for comparisons between subjective beliefs $S_{i}$ and
objective predictions $O_{i}$ for net monthly reemployment wages (both in
log). The table reports the mean difference between $S_{i}$ and $O_{i}$, the
shares of individuals who overestimate and underestimate wages by more than
10% (difference $>$ 0.1 / $<$ 0.1), the slope coefficient (with its robust
standard error in parenthesis) from a regression of $S_{i}$ on $O_{i}$, and
the number of individuals in the sample. ***/**/* indicate statistical
significance at the 1%/5%/10%-level.

Absolute difference within pair of LEA districts %-change Simulated Actual
Simulated - border pairs border pairs actual (1) (2) (3) Unemployment rate
0.040 0.012 -70.4% GDP per capita in € 1,000 7.576 4.948 -34.7% Vacancy rate
0.066 0.028 -58.6% Share of working population in agriculture sector 0.017
0.008 -49.6% in manufacturing sector 0.079 0.043 -45.1% in service sector
0.084 0.047 -43.9% Migration rate 0.009 0.005 -36.4% No. of LEA districts 178
178 No. of border pairs 1,068 487 • Note: The table compares absolute
differences in regional indicators within the 487 actual border pairs to a set