What Jobs Come to Mind? Stereotypes About Fields of Study

Conlon and Patel test whether students stereotype the link between college majors and occupations — that is, whether they exaggerate the likelihood that majors lead to their “representative” careers (those most overrepresented among a major’s graduates relative to other majors, as measured by a likelihood ratio in US census data). The representative career for each major is intuitive: doctors for biology/chemistry, lawyers for political science, counselors for psychology, journalists for communications, artists for art, and so forth.

The authors draw on three bodies of evidence. First, surveys of first-year undecided undergraduates in Ohio State University’s Exploration program (primarily Fall 2020 and Fall 2021 cohorts, ~80% response rate), asking students their beliefs about the share of US graduates in various careers conditional on major, as well as their beliefs about their own likely career. Beliefs are benchmarked against true career shares computed from the 2017–2019 American Community Survey restricted to college graduates aged 30–50. Second, 40+ years (1975–2018) of the CIRP Freshman Survey from UCLA, covering more than nine million nationally representative US college freshmen, which records intended major and intended career. Third, a field experiment embedded in the 2021 OSU survey with an RD design, in which treated students were shown the true share of their top major’s representative career before reporting beliefs, intentions, and — via administrative records — actual course enrollments and major declarations up to three years later.

The main finding is large, systematic overestimation of representative careers. In the OSU survey, students believe 53% of art majors work as artists (true: 17%), 47% of journalism majors work as journalists (true: 4%), 38% of political science majors work as lawyers (true: 16%), and 43% of psychology majors work as counselors (true: 21%). OLS regressions of beliefs on true career frequency and a representative-career indicator yield a stereotyping coefficient θ of 0.32 p.p. (p < 0.01) without career fixed effects and 0.28 p.p. (p < 0.01) with them, meaning students believe representative careers are roughly 28–32 percentage points more common than equally prevalent non-representative careers. These patterns are similar across gender, ethnicity, and first-generation status, replicate in an MTurk sample (θ = 0.30, p < 0.01) and a nationally representative US adult sample (θ = 0.33, p < 0.01).

In the CIRP data, 63% of biology freshmen expect to become doctors (true: 23%), 62% of psychology freshmen expect to be counselors (true: 21%), 65% of art freshmen expect to be artists (true: 17%), and 42% of communications/journalism freshmen expect to be writers or journalists (true: 4%). The average gap between expected and actual representative-career attainment is 36 p.p., and this gap has been roughly stable since at least the 1970s.

An implicit association test (IAT) administered to 434 OSU students shows that implicit associations between representative major–career pairs are 0.30–0.36 standard deviations stronger than for non-representative pairs (p < 0.01), and remain 0.24–0.28 SDs stronger (p < 0.01) after controlling for true career frequency. A one-SD increase in individual IAT scores predicts 2.8–4.1 p.p. greater stereotyped beliefs (p < 0.01). Knowing someone with a non-representative major–career combination predicts beliefs 16 p.p. lower for the representative career (p < 0.01) — more than half the stereotyping effect — and also predicts lower IAT scores, suggesting associations arise from personal experience.

An equilibrium model shows that stereotyping causes students to infer that representative careers have unusually favorable unobservable attributes, and that this inflates enrollment in the representative major among marginal students who are poorly suited to it. Correlational evidence from the NSCG, SIPP, and SHED confirms that majors subject to greater stereotyping are associated with more job dissatisfaction (+6.0% per SD, p < 0.01), greater job-skill mismatch (+3.1%, p < 0.05), more major-career mismatch (+5.4%, p < 0.05), and more regret about field of study (+4.8%, p < 0.05).

The field experiment shows that correcting beliefs reduces stereotyping and shifts major choices. A 10 p.p. reduction in beliefs about the top major’s representative career lowers intentions toward that major by 3.5 p.p. (p < 0.01), reduces enrollment in that major’s courses by 0.22 credits in the next semester (p < 0.05), and reduces the probability of declaring that major within one year by 6.1 p.p. (p = 0.23). The same information boosts intentions toward students’ second-ranked major by 2.1 p.p. (p = 0.17), increases second-major course enrollment by 0.20 credits (p < 0.10), and raises the probability of declaring the second major within a year by 9.9 p.p. (p < 0.01). Treated students also spend on average 0.21 more semesters undecided before declaring a major (p < 0.05). Effects are concentrated in the first year and partially fade over the two-to-three-year follow-up window.

Q: How do the authors define a major’s “representative career”? A: The representative career of major M is the career c that maximizes the likelihood ratio R(c, M) = p_{c|M} / p_{c|not-M}, where p_{c|M} is the true share of major-M graduates working in career c and p_{c|not-M} is the share of graduates from all other majors working in c. This ratio captures how much more common a career is among one major’s graduates relative to all other graduates. For example, the representative career of communications/journalism is “writers and journalists,” whose graduates are between 155% and 1,751% more likely to hold their major’s representative career than graduates of other majors, even though the absolute frequency of such careers is often modest (ranging from 2% to 60% across fields).

Q: What is the core model of stereotyped belief formation? A: The model draws from Bordalo et al. (2016). Let p_{c|M} be the true career share and π_{c|M} the student’s belief. The model specifies π_{c|M} = (1 − θ) p_{c|M} + θ · 1[c = c*(M)], where c*(M) is the representative career and θ ∈ [0,1] measures the extent of stereotyping. When θ = 0 the student holds rational beliefs; when θ = 1 beliefs assign all probability mass to the representative career. This formulation implies that students overweight representative careers because those careers come to mind more easily, grounded in a representativeness heuristic based on likelihood ratios.

Q: What does the regression test for stereotyping find in the OSU survey? A: The authors regress individual beliefs π_{c|M} on the true frequency p_{c|M} and an indicator for c being the representative career of M, clustering standard errors at the individual and career-by-major level. The estimated θ is 0.32 (p < 0.01) without career fixed effects (Column 1 of Table 1) and 0.28 (p < 0.01) with career fixed effects (Column 2). For self-beliefs about students’ top-ranked major, the estimates are 0.36–0.43 p.p. (p < 0.01 both with and without career fixed effects). These estimates imply that students regard a major’s representative career as 28–43 percentage points more common than an equally prevalent non-representative career for the same major.

Q: Do the OSU results replicate in other samples? A: Yes. An MTurk convenience sample of 430 current college students yields a stereotyping coefficient of 0.30 (p < 0.01). A nationally representative sample of US adults yields a coefficient of 0.33 (p < 0.01); this pattern holds separately for college-educated and non-college-educated respondents and for both younger respondents (aged 18–29) and older respondents (aged 30+). The authors also ran a pre-registered 2021 replication survey in a new OSU Exploration cohort and found similar results.

Q: What does the CIRP Freshman Survey data show about the persistence and scale of stereotyping? A: Pooling more than nine million US college freshmen surveyed from 1975 to 2018, the CIRP data show that students systematically intend to enter their major’s representative career far more often than graduates actually do. Among students who have decided on a major, 63% intend to have their major’s representative career while only 27% of college graduates actually attain it — a gap of 36 p.p. (p < 0.01). The specific examples include: 63% of biology freshmen intend to become doctors (true: 23%), 62% of psychology freshmen expect to be counselors (true: 21%), 65% of art freshmen expect to be artists (true: 17%), and 42% of communications/journalism freshmen expect to be writers or journalists (true: 4%). The gap has been stable over the full 40+ year window, with no sign of convergence, and amounts to 40,000–200,000 students per year expecting careers in representative fields that they will not attain.

Q: Can alternative mechanisms such as overconfidence or motivated reasoning explain the results? A: The authors argue no, for two reasons. First, students overestimate the prevalence of representative careers not only for majors they plan to pursue (where overconfidence or motivated reasoning might apply) but also for majors they do not plan to pursue — the pattern holds for the gray (population belief) bars across all ten majors in Figure 1. Second, a Shapley-Sharrocks decomposition reported in Table A.V shows that the stereotyping mechanism accounts for a larger share of variance in beliefs than any other mechanism tested. A pre-registered survey also rules out unawareness of non-representative occupations as a driver: students are aware of the overwhelming majority of the 100 most common non-representative occupations, and such unawareness as exists is uncorrelated with stereotyped beliefs.

Q: What does the IAT reveal about the mechanism behind stereotyping? A: The IAT was run on 434 OSU Exploration students in Fall 2021, measuring implicit associations between five major–career pairs (Humanities-Writers and Journalists, Sciences-Healthcare, STEM-Business, Social Science-Law, Social Science-Counseling/Education). Participants sorted stimuli faster in “matched” blocks (where the representative career shares a response key with its major) than in “unmatched” blocks, yielding DID-IAT effects of 0.30–0.36 SDs (p < 0.01) for all five pairs. After controlling for true career frequency with career and major fixed effects, the effect shrinks only slightly to 0.24–0.28 SDs (p < 0.01), confirming that associations are driven by representativeness beyond base rates. At the individual level, a one-SD increase in DID-IAT scores predicts 4.1 p.p. greater stereotyped beliefs (p < 0.01) without career-by-major fixed effects and 2.8 p.p. (p < 0.01) with them.

Q: What does the role-model heterogeneity analysis show? A: Students were asked which major–career combinations they knew personally. Controlling for career-by-major fixed effects, knowing someone with a non-representative major–career combination (i.e., a non-default path) predicts beliefs about the representative career that are 16 p.p. lower (p < 0.01). This is more than half the size of the baseline stereotyping effect (28–32 p.p.). Knowing such a person also predicts lower IAT scores (p < 0.01), implying that personal exposure can reduce both implicit associations and explicit stereotyped beliefs.

Q: What does the equilibrium model predict about misallocation? A: The model embeds stereotyped beliefs in a two-stage choice framework: students choose a major first, then choose a career after graduation. It shows two main results (Propositions 1 and 2 in Online Appendix A.1). First, students who perceive the representative career as more common than it is will infer — through a rational expectations mechanism — that the unobservable amenities of that career are particularly favorable, so they will be surprised upon graduation. Second, stereotyping raises misallocation because it draws in marginal students whose career preferences make them poorly matched to the major’s representative career, while the inframarginal students who would have chosen the major anyway are better matched. The misallocation effect increases in the extent of stereotyping.

Q: What correlational evidence links stereotyping to post-graduation mismatch outcomes? A: Using major-level stereotyping estimates from the OSU data merged with three nationally representative surveys (NSCG, SIPP, SHED), the authors find: a one-SD increase in major-level stereotyping is associated with 6.0% more job dissatisfaction (p < 0.01, NSCG), 3.1% more reports that the job does not fit the worker’s skills and experience (p < 0.05, NSCG), 5.4% more reports that the job is unrelated to the field of study (p < 0.05, SIPP), and 4.8% more regret about field of study choice (p < 0.05, SHED). The authors note these are correlational and cannot rule out confounders such as underlying complexity of the career mapping.

Q: How does the field experiment work and what is its identifying strategy? A: The experiment was embedded in the second 2021 OSU survey, with students in the treatment group shown the true share of their top major’s representative career before reporting beliefs and intentions; control students answered the same questions without receiving this information. The main regression relates outcomes to (True Share − Prior Belief), set to zero for controls. Because students with less accurate prior beliefs may be more likely to choose the relevant major, OLS is potentially inconsistent; the authors use an RD design where the running variable is the information shock (True Share − Prior Belief), with the threshold at zero. Students just above (who overestimated) receive negative news; students just below (who underestimated) receive positive news. The RD estimates are combined with a first-stage estimate of belief updating to produce IV estimates of the effect of a 10 p.p. change in beliefs. Balance tests on predetermined demographics confirm no discontinuities at the threshold.

Q: What are the first-stage belief-updating results? A: Students update their posterior beliefs in response to the treatment: in response to information that the representative career is 1 p.p. less likely, students update their posterior beliefs down by 0.37 p.p. (p < 0.01). This under-reaction is consistent with Bayesian updating when priors are informative (Mobius et al. 2022). Students also update beliefs about non-representative careers: a 1 p.p. reduction in the representative career’s stated likelihood increases the expected probability of other careers by 0.27 p.p. (p < 0.01).

Q: What are the effects of the information intervention on major intentions? A: A 10 p.p. reduction in beliefs about the top major’s representative career reduces intentions (stated probability of graduating with that major) by 3.5 p.p. (p < 0.01). This effect is similar across subgroups (Columns 2–4 of Table 2). For students’ second-ranked major, a 10 p.p. reduction in stereotyping boosts intentions by 2.1 p.p. (p = 0.17), which is imprecisely estimated but consistent in sign with all other outcomes.

Q: What are the effects on actual course enrollments? A: In the semester immediately following the experiment, learning that the representative career of the first major is 10 p.p. less likely causes students to enroll in 0.22 fewer credits in that major’s field (95% CI: [−0.41, −0.02], p < 0.05), relative to a mean of 0.85 credits. Learning that the representative career of the second major is 10 p.p. less likely causes students to enroll in 0.20 more credits in the second major’s field (95% CI: [0.004, 0.40], p < 0.10), relative to a mean of 0.36 credits.

Q: What are the effects on official major declarations? A: Within one year of the experiment, students who learned the representative career of their top major is 10 p.p. less likely are 6.1 p.p. less likely to have declared that major (95% CI: [−16.0, 3.8], p = 0.23) and 9.9 p.p. more likely to have declared their second major (95% CI: [2.5, 17.4], p < 0.01); the difference between these two effects is 16.0 p.p. (p < 0.01). By two years out, the effects are more attenuated. Treated students also spend on average 0.21 more semesters undecided before declaring a major (95% CI: [0.02, 0.40], p < 0.05). Effects do not appear to be driven by dropout: treated students are if anything slightly more likely to still be taking classes two to three years later.

Representativeness (likelihood ratio): The representativeness R(c, M) of career c for major M is defined as the ratio p_{c|M} / p_{c|not-M} — how much more common career c is among major-M graduates than among graduates of all other majors. This is a relative, not absolute, frequency measure. The representative career (or exemplar) of a major is the career that maximizes this ratio.

Stereotyping (as exaggeration of a kernel of truth): In this paper’s framework, stereotyping means overweighting the representative career when forming beliefs about a major’s career distribution. The belief model is π_{c|M} = (1 − θ) p_{c|M} + θ · 1[c = c*(M)], where θ > 0 implies beliefs exaggerate how common the representative career is relative to equally prevalent non-representative careers. This is distinct from overconfidence, motivated reasoning, or simple noise.

DID-IAT score (difference-in-differences implicit association test): The paper’s adaptation of the standard IAT to measure relative implicit associations between major and career groups. For a focal major–career pair, the DID-IAT score is the difference in the matched-vs-unmatched IAT D-score for the focal major (relative to a comparison major). A positive score indicates the focal major is more strongly associated with the focal career than the comparison major is. This measures implicit memory-based associations rather than deliberate beliefs.

Misallocation (as used in the model): The welfare loss arising because stereotyped beliefs draw marginal students — those on the margin between choosing the representative major and not — who have career preferences close to the average rather than being the students best suited to that major. These marginal students end up choosing careers other than the representative career after graduation at higher rates, producing major-career mismatch. Misallocation is shown (Proposition 2) to increase in the extent of stereotyping θ.

Information shock: In the field experiment, the information shock for a given student and major is the difference between the true share of the major’s representative career and the student’s prior belief about that share. Positive shocks correspond to students who overestimated (and thus receive bad news); negative shocks correspond to students who underestimated (and receive good news). The RD design uses the threshold at shock = 0 to generate quasi-experimental variation.

Source text origin (implicit in the paper’s design): The paper measures beliefs about career distributions benchmarked against American Community Survey data on actual career outcomes of college graduates aged 30–50, restricting to respondents born 1958–1997. This defines the objective ground truth against which stereotyping is measured throughout the paper.