What Is a Degree Really Worth? Earnings, Lifetime Pay, and Learning in the AI Era

1. The conclusion: a degree can expand your options without defining your worth

“Education does not matter.” There are examples that make this tempting to say: people who do excellent work without attending university, and graduates of selective institutions who struggle at work. But those examples do not establish that further education has no value.

“Get into a good university and you will be fine” is equally misleading. A credential does not certify judgment, integrity, creativity, and commercial ability in one convenient package.

Measured through earnings and occupational opportunities, university education still has average advantages. But a degree, an institution’s name, a field of study, and a person’s actual abilities need to be evaluated separately. That is the starting point supported by the evidence.

We should also distinguish different meanings of “necessary.” A prescribed course of study leading toward a regulated profession, an employer’s graduate requirement, and education that improves someone’s ability to work are different things. University can be a formal gateway in some cases and one valuable way of expanding options in others.

Money cannot measure a person’s worth or every aspect of happiness. It does, however, help people sustain a household, choose how to spend their time, support others, and survive an unsuccessful attempt at something new. This article therefore focuses on earnings, lifetime pay, and educational costs before turning to knowledge, environment, confidence, and intuition.

The information cutoff is September 10, 2026. Official statistics, estimates from a private employment platform, and calculations created for this article are identified separately. The university figures, in particular, are not promises about the futures of students enrolling today.

2. How much difference does education make to pay in Japan?

Monthly pay differs—but multiplying by twelve does not give total annual earnings

Japan’s Ministry of Health, Labour and Welfare reports the following scheduled monthly earnings for general workers in its 2025 Basic Survey on Wage Structure. “General workers” is not synonymous with permanent employees, and scheduled earnings exclude overtime and bonuses. These comparisons do not hold age or occupation constant.^[1][2]

The new-graduate figures also come from the 2025 survey. High-school and university graduates enter employment at different ages, so this is not a comparison of equally aged people’s abilities. It does show that pay structures differ from the beginning of working life.^[3]

Across all ages, university graduates’ scheduled monthly earnings are about 1.33 times those of high-school graduates; graduate-school completers’ earnings are about 1.74 times as high. This does not mean that sending the same person to university would increase their pay by 33%. Industries, age profiles, employer size, tenure, and gender composition all contribute to the observed difference.

Lifetime earnings are a model assembled from today’s age-specific wages

JILPT’s Useful Labor Statistics 2025 uses 2024 wage data to estimate earnings for someone who starts work immediately after graduation and remains a full-time regular employee until age 60. Across employer sizes, excluding retirement allowances, the estimates are as follows.^[4]

The high-school-to-university difference is ¥47.2 million for men and ¥52 million for women. Notice that the male vocational-college figure falls below the high-school figure: longer schooling does not produce an orderly earnings increase along every educational route.

These figures do not follow a single person for forty years. They connect the wages of younger, middle-aged, and older workers observed at the same time. Nor are they lifetime income estimates for the entire population allowing for unemployment, lengthy career breaks, or changes in working patterns. Lower figures for women do not demonstrate lower ability; job assignments, promotion, and working arrangements are among the differences embedded in the data.

Employer characteristics matter as well. In the same publication, the male university-graduate estimate is ¥301.3 million at employers with at least 1,000 employees and ¥214.2 million at employers with 10–99 employees: a gap of ¥87.1 million. This is not a causal estimate proving that joining a larger employer guarantees a gain. It does show why focusing only on a university’s name can obscure industry, employer, and occupation.

What has changed over the past thirty years?

University attendance has become less unusual. MEXT’s figures as originally published put university entry at 32.1% in 1995 and 44.2% in 2005. The final 2025 figure is 58.6%. Recent revisions include special-needs-school graduates in the denominator, so older published figures should not be spliced into the newer series to make precise decimal-point comparisons. The broad movement is from roughly one in three to more than half.^[5][6][7]

Has the wage gap disappeared along the way? JILPT’s retrospective series puts male new-graduate pay in 2006 at ¥167,300 for high-school graduates and ¥211,900 for university graduates, a ratio of approximately 1.27. In 2014 the figures were ¥169,100 and ¥210,800, or about 1.25. In 2025 they were ¥210,100 and ¥264,900, or about 1.26. This particular entry-pay indicator still shows a gap across the mid-2000s, mid-2010s, and the present. The earlier observations are retrospective estimates, which also deserves attention.^[8][3]

No consistently measured national series tracking the strength of employer screening by university name from the 1990s to today was identified for this article. Entry rates and pay ratios cannot be repurposed as direct measurements of changes in employers’ attitudes toward credentials.

3. What do graduates of Tokyo, Kyoto, Waseda, and Keio earn?

University earnings figures are not a government census of all graduates

Institution-specific differences are an obvious point of interest. OpenWork’s 2022 study provides one source with stated comparison conditions. It covers 246,134 people who registered earnings and university information between March 2018 and June 2022, across 291 institutions with at least 100 observations. Graduate schools are excluded. Its age-specific figures are estimated annual earnings based on platform data, not average earnings measured among all graduates in 2026.^[9]

The age-30 and age-40 columns below reproduce the published estimates. The last column is a calculation made for this article. It is not published university-specific lifetime earnings: it assumes someone can follow the same earnings curve for a 38-year working period.

Calculation method. Connect the source’s earnings estimates at ages 25, 30, 35, 40, 45, 50, and 55 with straight lines. For each five-year interval, multiply the average of its two endpoints by five. For the missing three years from 22 to 25, hold earnings at the age-25 estimate; for the five years from 55 to 60, hold them at the age-55 estimate. Sum these amounts for 38 years of gross earnings. Exclude retirement allowances, pensions, investment income, share-sale proceeds, educational costs, tax, social insurance, career breaks, inflation, and future wage growth. Starting at 22 and assuming flat earnings after 55 are our assumptions, not source findings.

For example, the Tokyo calculation is approximately ¥394 million. Reduce the assumed pay level at every age by 20%, and it becomes approximately ¥315 million. This is a sensitivity example, not a confidence interval. “Admission to Tokyo will earn you ¥390 million” would therefore be an incorrect claim. These totals also should not be subtracted from JILPT’s nationwide estimates: the populations and methods differ.

OpenWork users may disproportionately include people interested in changing jobs. Industries, regions, and gender composition are not held constant across institutions, and the data may not adequately capture medical careers or self-employment income. Career pathways are more useful than small ranking differences.

The Kansai shorthand “Kankandoritsu” refers to Kansai, Kwansei Gakuin, Doshisha, and Ritsumeikan universities. Doshisha appears in the published top thirty at age 30, but that table cannot supply the other three institutions’ figures. The Tokyo-area MARCH grouping is not a single earnings category either. A collective nickname does not determine each graduate’s income.

The employer’s name and the employee’s job are different variables

Official university information reveals multiple pathways rather than one standard destination for selective-university graduates. Reporting years and degree levels differ below, so these are concrete examples, not a ranking of institutions.

Sources are the universities’ own publications. The industry distribution of employed graduates and the proportion of all graduates pursuing further study have different denominators.^{[10][11][12][13][14][15][16]}

Prestigious employers in a destination list do not mean everyone holds the same job or receives the same pay. An employer’s reported average annual compensation is not its new graduates’ starting salary; experienced managers are included in a company-wide average.

Occupation-level figures offer another perspective. MHLW’s job tag pages, processing the 2025 wage survey, show annual earnings of ¥5.785 million and an average age of 37.1 for the web-services systems-engineer page. The management-consultant page shows ¥11.346 million and age 39.4. The statistical occupation group can be broader than the page title. These are not new-graduate salaries, earnings of graduates from particular universities, or business income earned by independent practitioners.^[17][18]

A university does not deposit a salary in your bank account. What you do afterward—the field, the job, the conditions, and how long you continue—produces the pay.

4. Neither the United States nor Europe has become a society that ignores education

The U.S. Bureau of Labor Statistics reports 2025 median weekly earnings for full-time wage and salary workers aged 25 and older of $966 for high-school graduates, $1,578 for bachelor’s graduates, $1,876 for master’s graduates, and $2,307 for doctoral graduates. Their unemployment rates were 4.3%, 2.8%, 2.6%, and 1.8%, respectively. The 2025 estimates average eleven months because October data were not collected during the federal government shutdown.^[19]

These definitions differ from Japan’s mean monthly earnings, so converting dollars into yen and placing the figures side by side would not create a clean comparison. They do show that associations between educational attainment, pay, and unemployment persist in the United States.

Europe is not one uniform system either. OECD’s Education at a Glance 2025 reports that tertiary-educated adults aged 25–64 earn approximately 50% more than upper-secondary graduates in Germany and 60% more in France. Tertiary education includes short-cycle and postgraduate qualifications as well as bachelor’s degrees. These figures cannot be called a direct measure of university-brand value.^[20][21]

Germany’s unemployment rates for those same broad attainment groups and ages are close: 2.5% for tertiary graduates and 2.6% for upper-secondary graduates. Routes incorporating vocational education can also connect people to employment. An earnings advantage and protection against unemployment are distinct outcomes.

What requirement has Google actually relaxed?

Current Google job postings still include a bachelor’s degree “or equivalent practical experience” among minimum qualifications. Accepting applicants without a degree is different from removing school names from résumés and never considering them. Some research jobs request a PhD or equivalent practical experience. The evidence does not establish company-wide educational blindness.^[22]

A 2024 study by Harvard Business School and the Burning Glass Institute similarly distinguishes removing degree requirements from changing hiring behavior. Non-degree hiring increased by an average of 3.5 percentage points in affected roles. Once the small share of roles was considered, the estimated additional opportunities amounted to fewer than roughly one in 700 hires in the preceding year. This does not mean only one in 700 people hired lacked a degree.^[23]

Even the causal effects of elite U.S. institutions are not a simple story. Dale and Krueger found that adjusting for applicants’ underlying differences using information such as application and acceptance patterns substantially reduced average earnings advantages associated with college selectivity. Benefits remained for groups including students from lower-income families.^[24]

Chetty and colleagues used variation in waitlist admissions to compare Ivy-Plus attendance with attendance at a state flagship institution. Their results include an approximately 50% increase in the probability of reaching the top 1% of earnings. That is neither a 50-percentage-point increase nor a claim that everyone earns 50% more. Populations and research designs change the question being answered.^[25]

“Japanese universities are hard to enter; American universities are hard to leave” also becomes crude when it ignores selective and open-access institutions, fields of study, transfers, and career breaks. Completion rates alone cannot identify academic difficulty. Importing one element of U.S. hiring practice is not a complete answer to Japan’s situation.

5. If capable workers often have strong credentials, is screening by education rational?

Someone may form a high opinion of a colleague’s work and later learn that the colleague attended a selective university. That experience is possible. Memorable matches between an impression and a credential do not, however, establish that a screening procedure works well.

A population-level relationship is different from accurately assessing the individual in front of you. Admissions selectivity and school names are not individual IQ test results. Even an actual IQ score would not measure every aspect of domain knowledge, persistence, cooperation, and customer understanding.

Four mechanisms deserve separate consideration:

• Selection: applicants differ before university in attainment, persistence, family support, and other characteristics.
• Education: practice in reading, explanation, calculation, and verification develops capabilities.
• Signaling: employers infer some learning experience from degrees and institution names.
• Environment: peers, teachers, alumni, equipment, and access to vacancies differ.

Observed outcomes combine these mechanisms. Assigning everything to innate ability—or everything to branding—misreads that combination. Ritchie and Tucker-Drob’s meta-analysis of quasi-experimental research also finds evidence that education improves intelligence-test performance. The relationship does not run only from ability to education; learning can expand what people can do.^[26]

Genetics and family environment require careful distinctions

Books at home, someone to discuss options with, time and space to study, and the ability to recover from a setback are meaningful differences. Ignoring them and treating admission or income as the product of effort alone is not justified.

There is no need to avoid research on genetic contributions either. But even a 2022 study of educational attainment involving approximately three million people emphasizes that genetic predictors can capture associations operating through family environments, making within-family analysis important. Explaining variation within a population does not identify the ceiling of an individual child’s future.^[27]

A parent’s high income does not establish biological superiority. Income also reflects occupation, wealth, institutions, health, historical circumstances, and luck. A family’s income and one child’s success cannot be used to allocate percentages to genes and environment. “Both matter” is a starting point for investigation, not a conclusion for sorting people.

Lower processing costs can come with higher costs of missed talent

For an employer receiving many applications, educational information is inexpensive to obtain. Using it may reduce initial processing costs. That does not prove it offers the best total return: the cost of rejecting people who perform well elsewhere, or of turnover after hiring, may never have been measured.

Looking back at a candidate’s high-school selectivity can also generate a hypothesis about whether they once worked toward a demanding goal. But admission routes, regions, and educational opportunities differ. A historical school ranking should not be equated with current job ability.

A practical experiment begins by identifying the capabilities the job requires and creating a short common task with explicit scoring criteria. Ask every candidate the same core interview questions, scoring their evidence, explanation, and response to correction. The U.S. Office of Personnel Management discusses the validity of structured interviews based on job analysis.^[28]

Then compare assessments made with credentials hidden and disclosed, relating them to later performance, retention, and recruitment effort. This is a proposal from this article, not a statistic about how many companies already do it. Rather than settling the issue through a slogan, test whether educational information improves prediction in your own workplace.

6. Educational costs, graduate school, and entrepreneurship change the calculation

Having a degree and paying to acquire one are different decisions

An existing credential may be useful, but acquiring it requires time and money.

For University of Tokyo undergraduates enrolling from FY2025 onward, annual tuition is ¥642,960 and admission costs ¥282,000. Holding fees constant for four years gives approximately ¥2.85 million, excluding living expenses and other costs. MEXT’s FY2025 private-university survey puts first-year tuition, admission, and facilities charges at approximately ¥1.21 million for humanities/social-science programs and ¥1.60 million for science-related programs. National universities should not be assumed to charge identical fees; check the institution itself.^[29][30]

The comparison also needs forgone earnings. Consider an entirely fictional scenario: four years of direct university costs total ¥3 million, while starting work after high school would produce ¥2 million per year after tax and additional expenses associated with working. Add four years of forgone earnings and the comparison amount is ¥11 million.

If the later annual after-tax earnings advantage is ¥500,000, simple recovery takes 22 years; at ¥1 million it takes 11 years; at ¥1.5 million, about 7.3 years. Ordinary living costs are assumed equal between routes and excluded. Additional accommodation costs, fee waivers, scholarships, student earnings, loan interest, discounting, and unemployment risk are not included. This is an exercise in exposing assumptions, not an estimated rate of return on university attendance.

The gross lifetime earnings differences in the earlier chapter cannot simply be subtracted from this after-tax comparison. Tax treatment, time periods, and the scope of living expenses need to match.

A master’s, PhD, or MBA is not a universal upgrade

A master’s can provide training and access relevant to research, development, and advanced technical work. Graduate-school progression is a major route in the science and engineering examples above. But Japan’s “graduate school” wage category combines master’s and doctoral graduates, so its ¥517,400 monthly average cannot identify the extra return to a doctorate.

A PhD trains people to develop methods for answering questions whose answers are not yet known. It can open research and specialist careers. NISTEP’s Japan Doctoral Human Resource Profiling studies also show that destinations differ between universities, private companies, and other sectors, with variation by field. Examine where a lab’s recent graduates went, whether positions are fixed-term, what funding is available, how supervision works, and how long completion takes.^[31]

For an MBA, look beyond the credential’s sound and examine recruitment into the intended employer, country, and role. Costs include forgone income, exchange-rate exposure, and the conditions governing post-graduation employment. A school’s median salary may cover only graduates reporting particular employment outcomes. “An overseas MBA will pay for itself” is too broad a conclusion.

Vocational institutions can offer specific practical facilities, qualifying courses, and links with employers. It is not justified to assume that every practical profession can be learned independently while attending an unrelated university. In the arts, production time, mentors, collaborators, and opportunities to present work matter. Admissions selectivity cannot directly reveal the value of a piece of music or art.

A list of successful founders does not establish the educational profile of entrepreneurship

Japan Finance Corporation’s FY2025 Survey on Business Start-ups reports that 35.9% of founders had university or graduate-school qualifications, 27.9% attended specialized or miscellaneous schools, and 27.3% had high-school qualifications. High-school graduates were not the largest group in this survey.^[32]

The sample consists of recently established firms financed by JFC. It does not cover every entrepreneur, only listed-company founders, or only successful businesses. Composition also cannot tell us which educational group is most likely to succeed. A success rate needs the number of attempts within each group and a definition of success. University-origin startups also require distinguishing faculty and researcher founders from students.

Successful founders who started businesses while at university prove neither that university is unnecessary nor that attendance guarantees success. They may have benefited from knowledge, co-founders, equipment, or credibility; they may also have had substantial capabilities and opportunities beforehand.

People can leave university, enter another field, and eventually earn well. Yet identical earnings late in a career do not imply identical cumulative earnings along the way. Conversely, high early pay may not continue. A photograph taken at the end is not the same thing as the full film of a working life.

7. If AI can explain everything, does remembering anything still matter?

Historical dates, place names, definitions: many facts are easy to retrieve through search or AI. The economic value of merely reproducing stored information can therefore change.

But retrieving information and knowing what to investigate are different abilities.

Someone aware that Germany and the United States connect education and employment differently can ask for a comparison that takes those institutional differences into account. Without knowing about the comparison, that question is less likely to arise. AI can suggest alternatives, of course; existing knowledge still helps evaluate whether those alternatives are suitable and what has been omitted.

“Which universities have high-earning graduates?” differs from “Control for age, field, and gender composition; separate self-reporting bias from causal effects.” The second request is not a magical prompt. It reflects knowledge of the ways a comparison can go wrong.

That does not establish that AI will only make highly educated people more powerful. Research in customer support found larger productivity improvements among inexperienced and lower-skilled workers. AI can transmit useful practices from experienced workers and narrow performance gaps.^[33]

The design of a learning tool matters too. In a randomized trial involving high-school mathematics, Bastani and colleagues found that an AI tool freely supplying help could improve practice performance while harming subsequent performance when students had to work without AI. A version with learning-oriented guardrails produced different results. This is evidence from a particular subject and experimental setting, not proof that all AI-assisted learning is harmful.^[34]

The purpose of remembering can shift from storing more facts than a machine to understanding, questioning, and checking for yourself. Vocabulary, number sense, broad historical context, and the difference between correlation and causation help you work out where newly retrieved facts belong.

One practical routine is to write your own short hypothesis first, ask AI for counterexamples and weaknesses, check the sources, and finally close the tool and explain the idea again. The goal is to turn “I could do it with AI” into “I retained something myself.”

8. Confidence, peers, and intuition: benefits that should not be confused with a credential

Success can build confidence—but a selective institution can also challenge it

Setting a goal, working toward it, and achieving it can support the belief that another difficult task is manageable. This is consistent with self-efficacy theory, in which mastery experiences are one source of efficacy beliefs. An entrance examination is not the only way to have that experience; producing a work, competing, or improving a process at work can also provide it.^[35]

Capable peers can be stimulating, but comparison can also lower someone’s view of their own ability. Research on the big-fish-little-pond effect documents how selective academic environments can reduce academic self-concept.^[36]

Conversation may feel easier, shared interests may be explored more deeply, and ambitious projects may become normal. Those are understandable reasons to value an environment. They are not determined by admissions rankings alone. A university laboratory may provide that environment for one person; a creative community or workplace may provide it for another.

An institution’s name or an MBA on an executive biography can plausibly communicate expertise or credibility. But a published biography is selected information. It cannot establish that the credential caused the promotion. A reputation that helps someone enter a room and the work needed to sustain trust inside it are different things.

A feeling that something is wrong may compress experience

An experienced practitioner notices something odd before being able to explain why. Later inspection reveals small cues recognized from several previous situations. There are ways of understanding this without treating intuition as magic.

Kahneman and Klein emphasize conditions for dependable intuitive expertise: an environment with sufficient regularity and an opportunity to learn those regularities. Experience is more useful when outcomes become known and judgments can be corrected. Simply spending many years somewhere does not make intuition accurate.^[37]

Biological research also investigates processing of potentially threatening stimuli. Human brain-imaging studies using snake pictures examine structures including the amygdala, superior colliculus, and pulvinar. But responses to those stimuli are not the same experimental phenomenon as an uneasy feeling during a negotiation or interview. An explanation involving ancestral survival cannot establish that a present-day business judgment is correct.^[38]

Imagining a component for understanding emotion alongside a CPU or GPU is an interesting analogy. It is not an anatomical description: the brain cannot be divided neatly into computer parts, and the analogy does not identify a single dedicated emotion-reading device.

How far AI can substitute for experience-based judgment also needs to be tested task by task. The existence of judgments people cannot immediately verbalize establishes neither permanent human superiority nor a timetable for AGI. The defensible point is that reflecting on experience, correcting mistakes, and improving the next judgment have value beyond a credential itself.

The scientist’s or engineer’s sense that something is unusual can be understood similarly. It becomes productive when followed by a hypothesis, measurement, and a test of reproducibility. Moving between an idea and verification turns experience into discovery. University is one setting for that training; a graduation certificate does not complete the habit on its own.

9. What might still matter ten, twenty, or thirty years from now?

No one can responsibly provide precise future education premiums today. Work in 2036, 2046, and 2056 will depend on AI, demographics, qualification systems, and employers’ investment in training. The following are conditional scenarios, not statistical forecasts.

A 2036 hypothesis. As AI makes applications and portfolios easier to polish, employers may want stronger evidence of who did what and how. Some may retain institution-based signals; others may use demonstrations, oral explanations, and internships more extensively. The balance depends on assessment costs and predictive accuracy.

A 2046 hypothesis. Later learning and a person’s work history may need to supplement a degree earned decades earlier. Universities can still retain value if they remain places where specialized equipment and expert communities are concentrated.

A 2056 hypothesis. Rankings of specific occupations become even less certain. It remains reasonable to expect that decomposing unfamiliar problems, assessing evidence, collaborating, and learning again will be useful. People may acquire those capabilities through different combinations of school, work, and independent study.

The timing of a first job matters as well. Research comparing Japan and the United States finds persistent effects of entering employment in a recession, particularly for less-educated Japanese men. A first gateway can matter without failure to pass through it demonstrating low ability.^[39]

For a secondary-school student, dismissing education as meaningless can close possibilities too early. If a career is not yet settled, preserving broad learning options has value. Compare prospective institutions through costs and support, subject area, graduate destinations, and whether the environment will help you continue learning—not just through the name.

For someone already working, a school attended in the past should not define the ceiling of a future career. Build outcomes you can explain, deepen knowledge, and obtain a qualification or degree when it opens a relevant door. Work backward from the role: does it require a credential, or can documented experience open the way?

For employers, neither declaring educational blindness nor asserting faith in prestigious institutions is enough. Measure which information predicts the actual job and which candidates the process misses. The cost of that investigation belongs in any serious discussion of recruitment efficiency.

A degree can be a record of effort that its holder is entitled to value. It should not become a device for reducing the worth of those who lack one. Nor does everyone need to pursue the highest possible qualification regardless of cost and career fit.

Do not dismiss an opportunity to learn. If you obtain it, do not stop at the name on the certificate. Education’s value is not finalized on graduation day; it continues to change with what you learn afterward and the work you connect it to.

References

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