Beliefs, Expectations, and Job Search

Learning Objectives¶

By the end of Week 3, students should be able to:

1. write a benchmark job-search choice problem and locate where beliefs enter it;

2. distinguish subjective beliefs about job finding, wages, competitiveness, and fit from nonstandard preferences;

3. explain why unemployment duration, weak updating, and optimism matter for search effort and reservation behavior;

4. compare belief elicitation, survey expectations, information experiments, posting experiments, certification designs, and structural search models;

5. evaluate whether an empirical result identifies a behavioral search wedge, a standard search friction, rational learning, or unobserved heterogeneity.

Opening Orientation¶

Week 2 treated worker behavior as a preference problem. Week 3 shifts to beliefs and expectations in job search: how workers perceive job-finding probabilities, wage offers, competition, skill fit, and the payoff to applications. The economic question is whether search behavior reflects correct optimization in a hard environment, or whether mistaken, sticky, or incomplete information changes effort, applications, reservation behavior, and unemployment duration.

Bridge¶

DellaVigna’s taxonomy separates nonstandard preferences from nonstandard beliefs and nonstandard decision-making DellaVigna, 2009DellaVigna, 2018. Week 2 focused on preferences: present bias, reference points, risk, commitment demand, and social preferences. Week 3 asks when a similar observed outcome instead comes from the worker’s perceived labor market.

Job search is the cleanest labor domain for this shift. A job seeker makes repeated choices under uncertainty, receives noisy feedback, learns slowly about the distribution of offers, and may not know how firms evaluate her skills. A standard search model already contains beliefs about offer arrival and wages. Behavioral labor asks whether those beliefs are correct, updated, and used in the way the benchmark assumes.

The week stays focused on job search. Wage posting, skill certification, hiring frictions, and information provision enter only because they shape the worker’s search problem. Later weeks broaden the frame to attention, salience, complexity, policy delivery, and firm response.

Field Core¶

Benchmark Search Problem¶

A compact benchmark job-search object is:

where $s_t$ is search effort, $\bar{w}_t$ is the reservation wage, $p_t(s_t)$ is the perceived job-finding probability generated by effort, $V^E(w)$ is the value of employment at wage $w$, $V^U$ is the value of remaining unemployed, and $c(s_t)$ is the cost of search. In the benchmark, the worker chooses effort and reservation behavior using correct beliefs about offer arrivals and the wage-offer distribution.

This benchmark is useful because it tells us where to look. A low application rate can reflect high search costs, a high reservation wage, low offer probability, low perceived returns to effort, or a tight constraint. A behavioral claim starts only after the analyst says which object is misperceived or insufficiently updated.

Subjective Beliefs And Behavioral Search¶

The behavioral version replaces objective beliefs with subjective ones:

The wedge can enter through $p_t^{b}(s_t)$, the perceived wage-offer distribution inside $\mathbb{E}^{b}$, perceived competitiveness for vacancies, or perceived fit with jobs. It need not be a preference wedge. A worker with standard preferences may search too little if she underestimates the returns to applications, search too narrowly if she misreads fit, or keep a high reservation wage if she overestimates the probability of a good offer.

Job-Finding Beliefs, Duration Dependence, And Weak Updating¶

The main empirical anchor is Mueller et al., 2021. The central object is not simply whether the unemployed find jobs. It is whether elicited beliefs about job-finding probabilities predict future employment and whether those beliefs update enough as unemployment spells lengthen.

Beliefs can be predictive and biased at the same time. A job seeker may know private information about her prospects, so subjective expectations can forecast exits better than a coarse analyst model. But the same beliefs can remain optimistic relative to realized hazards, especially when feedback is noisy and unsuccessful search is hard to interpret. A failed application may mean the worker was unlucky, chose the wrong vacancy, lacked an observable skill, or overestimated the market. The behavioral issue is weak updating in a state where the signal is repeated but ambiguous.

Duration dependence matters because it changes choices. If workers stay optimistic as unemployment lengthens, they may keep reservation wages high, search in the same channels, or delay strategy changes. If pessimism becomes excessive, they may reduce effort too much or apply to jobs with poor fit. Welfare depends on whether the belief path helps workers smooth search in a genuinely uncertain market or traps them in strategies that no longer match their prospects.

Information Provision And Search Strategy¶

Information interventions ask whether search changes when job seekers receive advice, encouragement, or facts about effective strategies. In Altmann et al., 2018, the intervention is labor-facing because it targets search behavior and employment outcomes, not because it demonstrates a generic messaging effect.

The key design question is what the information changes. A brochure or advice treatment may update beliefs about job-finding prospects, make search channels more salient, motivate effort, reduce planning costs, or signal that the agency is monitoring the worker. A clean interpretation states the observed margin first: search effort, applications, interviews, unemployment exits, or job quality. The behavioral object is then interpreted with caution.

Policy relevance is immediate. Search assistance can be useful even when benefits and formal incentives are unchanged, but the welfare statement depends on whether information helps workers choose better strategies, merely raises pressure, or shifts applications toward already crowded vacancies.

Two-Sided Limited Information And Skill Certification¶

Job search is not a one-sided application problem. Workers often lack information about wages, fit, skill requirements, and their own marketable strengths. Firms lack information about worker skills, reliability, and match quality. This makes search a behavioral-information environment: beliefs, signals, and screening decisions move together.

Carranza et al., 2022 is the anchor for this two-sided view. Skill assessment and certification can affect workers by changing self-knowledge and application targeting. It can affect firms by making a worker’s skills more credible. The same certificate can therefore shift beliefs, search behavior, employer screening, hiring, and earnings.

The identification challenge is to avoid calling every signal a worker belief effect. A certification design may identify the joint effect of information to workers and credible information to firms. That is valuable, but it is different from a pure belief intervention. Students should ask who learns what, who observes the signal, and which search margin changes.

Perceived Competition And Wage Announcements¶

Posted wages affect expected payoffs, but they can also signal competition. In a standard search model, a higher wage raises the value of a vacancy, holding the probability of success fixed. In a behavioral search environment, a worker may infer that a high-wage vacancy attracts stronger applicants, is harder to obtain, or has hidden requirements. The application response can then be muted or even reversed for some workers.

Belot et al., 2022 uses wage announcements to make this distinction concrete. The empirical object is not only wage sensitivity. It is how posted wage information changes vacancy choice, perceived selectivity, and self-selection into applications. This is still a job-search question: the wage signal changes the perceived return to applying.

Application Costs, Procrastination, And Talent Selection¶

Application costs can be monetary, time-based, cognitive, or psychological. Even small costs can change who applies, especially when workers are uncertain about fit or success probabilities. Abebe et al., 2021 shows why application frictions matter for talent selection: changing costs can alter the applicant pool and what firms learn from applications.

The behavioral interpretation is subtle. A large response to a small cost can reflect liquidity, transport constraints, attention, procrastination, low perceived returns, or discouragement. The labor question is which workers are screened out and whether the lost applications are low-value, high-value, or informationally important for firms.

Historical Bridge: Impatience In Job Search¶

Week 3 is primarily about beliefs, but the earlier search literature already recognized that behavioral objects can enter search effort and reservation behavior. DellaVigna & Paserman, 2005 links impatience to job-search outcomes. Its role here is a bridge: search behavior can deviate from the exponential-discounting benchmark through preferences, while modern work adds direct measurement and manipulation of beliefs and information.

The distinction matters empirically. Low effort can be caused by impatience, pessimistic beliefs about returns, application costs, weak attention, or genuine lack of opportunities. Behavioral job search is a family of mechanisms, not one explanation.

Welfare And Policy Implications¶

Behavioral job search has a welfare problem before it has a policy solution. If job seekers hold distorted beliefs, observed search choices may not reveal what they would choose under accurate information. If they rationally learn slowly in a noisy market, apparent optimism may be an efficient response to ambiguous feedback. If firms use limited information to screen, worker-side advice alone may not fix the relevant friction.

Policy interventions therefore need a target object. Information provision can help workers update job-finding beliefs, search channels, or expected wages. Advice and coaching can reduce planning costs and improve strategy. Skills signals can help workers and firms learn. Wage transparency can change perceived payoffs but may also change perceived competition. Application-cost reductions can expand the applicant pool, but the composition of new applicants determines the welfare and productivity effects.

For applied work, the discipline is the same across designs: name the belief or information object, name the search margin, define the counterfactual, and state which standard explanations remain.

Research Lab¶

The Week 3 lab uses a bounded synthetic path to practice Reproduce -> Diagnose -> Transfer. The primary anchor is Mueller et al., 2021. Students reproduce a small factbook on subjective job-finding beliefs, realized exits, and duration dependence. The point is to see why beliefs can be predictive while still updating too weakly during unemployment.

The challenge anchor is Carranza et al., 2022. Students transfer the logic to a synthetic certification design where a skill signal can change worker targeting and firm screening. The optional extension anchor is Belot et al., 2022, where students classify how wage announcements can shift applications through perceived competition rather than expected pay alone.

The lab asks students to diagnose four objects:

1. what belief or information object is measured;

2. how duration dependence is interpreted;

3. how the design separates beliefs from unobserved heterogeneity or pure search frictions;

4. how to transfer the logic to another search setting.

The bounded transfer ideas are deliberately narrow: adapt belief elicitation to another search margin, adapt certification to another hiring context, or study how wage and quality signals change application behavior in a reduced pedagogical simulation.

Methods Box¶

Reading Ladder And References¶

Core framing. Start with DellaVigna, 2009 for the field taxonomy and DellaVigna, 2018 for structural behavioral economics.

Behavioral job search foundations. Read DellaVigna & Paserman, 2005 for impatience in job search and Mueller et al., 2021 for beliefs, duration dependence, and bias.

Information provision and search strategy. Use Altmann et al., 2018 to study how advice and information can change search outcomes.

Two-sided information and skill signaling. Read Carranza et al., 2022 for limited information about skills, certification, and hiring.

Perceived competition and wage posting. Use Belot et al., 2022 to separate wage announcements as payoff information from wage announcements as competition signals.

Application costs and selection. Use Abebe et al., 2021 to connect application frictions, applicant composition, and firm learning.

Exercises And Discussion Prompts¶

1. In equation (2), choose one subjective object and explain which search margin it should move.

2. A job seeker reports a high job-finding probability after six months of unemployment. Give one rational-learning interpretation and one weak-updating interpretation. What extra data would help distinguish them?

3. Design a field experiment that changes information about search strategy. What does it identify, and what does it fail to identify?

4. In a skill-certification experiment, who learns: the worker, the firm, or both? How does your answer change the interpretation?

5. Suppose posted wages reduce applications among low-experience workers. State a price-effect explanation, a perceived-competition explanation, and a selection explanation.

Reproducibility And Code Lab Note¶

The Week 3 lab lives at labs/03-beliefs-expectations-and-job-search/. It creates deterministic synthetic data for a Mueller-Spinnewijn-Topa-style belief panel and a Carranza-Garlick-Orkin-Rankin-style certification transfer design. The smoke path builds the data, runs the reproduction script, and runs the transfer script. The lab is not an official replication package; it is a bounded pedagogical path for converting belief and information objects into applied job-search designs.

Slide Companion Note¶

The Week 3 slide deck lives at slides/week3/03-beliefs-expectations-and-job-search.tex. The deck is a conceptual and empirical map rather than a duplicate of this chapter: it bridges from Week 2, introduces benchmark and subjective search problems, highlights duration dependence and weak updating, maps information and certification designs, and ends with wage announcements, identification, and the bridge to Week 4.

Bridge Forward¶

Week 3 has treated job search as a behavioral-information environment driven by beliefs, expectations, perceived competition, and two-sided limited information. Week 4 widens the lens from job search to attention, salience, complexity, and labor-market policy design.