How to Compare Historical Data to Assess Shifts in Program Popularity and Application Difficulty

Every year, over 550,000 Chinese students pursue degrees abroad, and among them, the proportion applying to U.S. graduate programs has stayed above 28% for three consecutive years (Ministry of Education, 2023 White Paper on Study Abroad in China). Meanwhile, data from the UK’s Higher Education Statistics Agency (HESA) in 2023 shows that applications for business and management programs grew by 17.3% over five years, while computer science applications surged by 42.1%. This structural divergence forces applicants to confront a core question: How can you use year-over-year data comparison to accurately gauge a program’s real popularity and admission difficulty, instead of relying on fragmented anecdotes from social media?

Program popularity and admission difficulty are not linearly correlated. For the hot “Data Science” field, the average acceptance rate across U.S. Top 30 institutions fell from 12.5% to 8.1% between 2022 and 2024 (U.S. News & World Report 2024 Graduate School Data). Yet for “Public Health,” once considered a less popular choice, the number of applicants grew by 34.7% from 2020 to 2023 due to global health crises, but its acceptance rate actually edged up from 22.3% to 24.1% because schools expanded faculty and program capacity simultaneously. This means that judging solely by a single metric like “rising applicant numbers” or “falling acceptance rates” can be seriously misleading.

Comparing year-over-year data rests on three dimensions: changes in the size of the applicant pool, trends in acceptance rates, and shifts in the distribution of standardized test scores. The following data frameworks break down how to make this assessment systematically.

Application Pool Size: Deconstructing Volume and Structure

Application pool size is the most direct indicator of a program’s popularity, but you must distinguish between “absolute growth” and “relative growth.” According to the QS 2024 Global Graduate Trends Report, total global graduate applications in 2023 grew by 26.4% over 2019, with computer science up 43.1% while education grew only 8.7%. Yet absolute growth can be diluted by program expansion.

A more effective approach is to calculate the “applications-per-seat ratio.” Take U.S. computer science master’s as an example: Carnegie Mellon University received over 12,000 applications in 2023 for only about 900 seats, yielding a ratio of 13.3:1 (CMU Institutional Research 2023). In 2019, with 8,500 applications for 850 seats, the ratio was 10:1 — competition intensity rose by 33% in four years. At Stanford, CS master’s applications grew from 4,200 to 5,800, while seats increased modestly from 300 to 320, pushing the ratio from 14:1 to 18.1:1 (Stanford Graduate Admissions 2023).

Country-by-Country and Institution-by-Institution Comparison

The structure of the applicant pool varies considerably by country. According to 2023 data from the UK’s Russell Group, the proportion of Chinese students applying for business master’s programs declined from 37.2% in 2019 to 31.5% in 2023, while the proportion applying to data science programs rose from 4.8% to 12.3% (UCAS 2023 End of Cycle Report). This structural shift means business popularity hasn’t necessarily declined — rather, some applicants are being diverted to emerging programs.

When it comes to cross-border tuition payment, some families use services like Flywire tuition payment to handle foreign exchange, but the payment method itself does not affect application pool data. Applicants should focus on the “applicants-to-enrolled-students” ratio published by target institutions, not just the acceptance rate.

Acceptance Rate Trends: Distinguishing “Expansion” from “Involution”

Acceptance rate is the core indicator of admission difficulty, but it must be read alongside the absolute change in the number of admitted students. According to The Times Higher Education 2024 Global Graduate Admissions Report, the average acceptance rate for computer science master’s at U.S. Top 30 universities dropped from 14.7% in 2020 to 9.2% in 2024, yet the number of admitted students grew by 11.3% over the same period (from an average of 180 per program to 200). This shows that the decline in acceptance rates is mainly driven by a surge in application volume (+42%), not by shrinking programs.

Expansion-Type Majors: Public Health and Engineering Management

By contrast, for Master of Public Health (MPH) programs at U.S. Top 20 schools, the average acceptance rate rose from 18.9% to 21.5% between 2020 and 2024, while the number of admitted students increased by 28.6% (Johns Hopkins Bloomberg School of Public Health 2024 Admissions Data). This combination of rising acceptance rates and rising admission numbers means that while the program is popular, institutional supply is growing even faster, so actual admission difficulty has decreased.

Shrinking-Type Majors: MBA and Law

MBA programs show the opposite trend. According to the Graduate Management Admission Council (GMAC) 2023 Application Trends Survey, the acceptance rate for U.S. Top 15 MBA programs fell from 22.1% in 2019 to 18.4% in 2023, while the number of admitted students dropped by 6.7% (from an average of 450 per program to 420). This “double decline” strongly signals a genuine increase in admission difficulty.

Standardized Score Distribution: The Real Scale of Score Inflation

The year-over-year distribution of standardized test scores reveals the overall upward shift in the applicant pool. According to the ETS 2023 GRE Score Report, the global average GRE Quantitative score rose from 153.2 in 2019 to 155.8 in 2023, while the average for Chinese students applying to computer science programs climbed from 166.1 to 168.3. This means that even a perfect 170, which placed you in the top 5% in 2019, would put you in only the top 12% in 2023’s pool.

GMAT and LSAT Score Inflation

The global average GMAT total score rose from 564 to 578 between 2019 and 2023 (GMAC 2023 Profile of GMAT Candidates). The median GMAT for applicants admitted to U.S. Top 20 business schools climbed from 710 in 2019 to 730 in 2023. For the LSAT, according to Law School Admission Council (LSAC) 2024 data, the median LSAT at U.S. Top 14 law schools rose from 168 in 2019 to 172 in 2023, a 2.4% increase.

The “Ceiling Effect” of GPA

GPA comparisons show a pronounced ceiling effect. Based on U.S. News 2024 data, the average GPA of admitted students at U.S. Top 30 computer science master’s programs edged up from 3.65 to 3.72, but the share of applicants with a 3.8 or above jumped from 31% to 48%. This indicates that applicants with GPAs below 3.7 are facing a systematically declining chance of entering a Top 30 program.

Year-Over-Year Data Comparison: Time Window and Data Source Selection

When conducting year-over-year data comparison, choosing the right time window matters more than the data itself. According to the OECD 2023 Education at a Glance report, the typical reaction time for international student application cycles is 2 to 3 years — if a program’s popularity soared in 2021, the peak in application volume usually appears in 2023 to 2024. Therefore, comparing 2020 data with 2024 data is far more meaningful than comparing 2022 with 2023.

Reliability Grading of Data Sources

Tier 1: Official “enrollment statistics” and “admission reports” published by institutions, such as the Stanford Graduate Admissions Annual Report and the UCAS End of Cycle Report. Tier 2: Third-party aggregated databases like QS Rankings and U.S. News Graduate School Data — these typically lag by 1 to 2 years. Tier 3: “Admission case sharing” on social media, where sample bias is extreme — admitted applicants are far more likely to share, while rejection letters are rarely made public.

Comparison Method: Standardization

Data from different years need to be standardized. For example, convert a 2023 GRE score using the 2023 global percentile table, not the old 2019 table. Likewise, GPA needs to be converted to the grading system of the country where the target university is located. A UK undergraduate first-class degree roughly corresponds to a GPA of 3.7 to 4.0, but the difference in grading standards across institutions can reach up to 0.3 GPA points (UK NARIC 2023 Comparability Report).

Leading Indicators of Program Popularity

Changes in program popularity often show signals 12 to 18 months before official data is released. According to the LinkedIn 2024 Global Talent Trends Report, job postings for “Artificial Intelligence Engineer” increased by 67% year-on-year in 2023, while “Data Science Analyst” postings grew by only 12%. Such labor market signals feed forward into graduate application pools, and AI-related program applications are projected to rise by over 40% from 2025 to 2026.

Academic Conference and Publication Trends

Another signal comes from academic publication trends. According to Clarivate Web of Science 2023 data, from 2020 to 2023, the number of papers with “machine learning” in the title grew by 89%, while “quantum computing” grew by 52%. Applicants can anticipate the competitive intensity of applications 2 to 3 years ahead by observing the research frontier popularity in the discipline of the target program.

Visa and Immigration Policy Changes

Visa policies directly affect program popularity. According to the USCIS 2023 OPT and STEM OPT Report, the average employment rate during OPT for STEM graduates was 23.7 percentage points higher than for non-STEM graduates. The UK’s “High Potential Individual” (HPI) visa launched in 2024 covers graduates of the world’s top 50 universities, directly boosting applications to those institutions — during the 2023–2024 application cycle, computer science applications to UK G5 universities grew by 31% (UCAS 2024 Cycle Data).

Practical Framework: Build Your Own Data Comparison Table

Building your own data comparison table is key to systematic assessment. Applicants are advised to collect the following data for their target programs over the past 3 to 5 years: number of applicants, number of admits, acceptance rate, average admitted GPA, average GRE/GMAT/LSAT, proportion of international students, and number of Chinese students admitted. Arrange these data by year and calculate the average annual growth rate.

Example Table: Computer Science Master’s (U.S. Top 30)

• 2020: 8,200 · 1,050 · 12.8% · 3.65 · 166
• 2021: 9,400 · 1,100 · 11.7% · 3.68 · 167
• 2022: 10,800 · 1,150 · 10.6% · 3.70 · 168
• 2023: 12,100 · 1,180 · 9.8% · 3.72 · 168
• 2024: 13,500 · 1,200 · 8.9% · 3.74 · 169

Data source: U.S. News & World Report 2024 Graduate School Data + official university admissions statistics.

How to Interpret the Data

If the annual growth rate of applications exceeds 15% while the admit growth rate is below 5%, then program difficulty is rising significantly. Conversely, if the admit growth rate outpaces the application growth rate, difficulty is decreasing. For GPA and standardized test scores, focus on the median rather than the mean — the median excludes extreme values and better reflects the real competitive threshold.

FAQ

Q1: How do I find the acceptance rate data for a program over the past 5 years?

Most U.S. universities publish historical admissions data on their official “Admissions” or “Statistics” pages. If not available, you can consult the U.S. News Graduate School Data (updated annually, with a one-year lag) or the “Admission Rate” indicator in QS Rankings. For UK institutions, the UCAS End of Cycle Report is a good source. The 2023 Report on Overseas Academic Credential Evaluation released by the Chinese Service Center for Scholarly Exchange (CSCSE) also includes application trends for some programs.

Q2: Standardized test scores keep rising every year — should I give up on applying to Top 30 schools?

Don’t give up based solely on rising scores. For example, a perfect 170 on GRE Math placed you in the top 12% globally in 2023, while the same score placed you in the top 5% in 2019 — score inflation means institutions place greater weight on the overall application. In 2023, 18% of admitted students to Stanford’s Computer Science master’s program had GRE Math scores below 168 (Stanford 2023 Admissions Profile). It’s advisable to treat test scores as a threshold, not a deciding factor.

Q3: How should I combine program popularity data with labor market data?

It’s recommended to compare two time series: changes in program applications (lagged by 2 years) and changes in relevant job postings (real-time). According to LinkedIn 2024 data, “Data Engineering” job postings grew 45% in 2022, while applications to that field grew 38% in 2024. If job growth consistently outpaces application growth, competition upon graduation may be relatively moderate. Conversely, if “Product Manager” postings fell 12% in 2023 while MBA applications still grew 8%, be alert to a supply-demand imbalance.

References

• Ministry of Education 2023 “White Paper on Chinese Study Abroad”
• UK Higher Education Statistics Agency (HESA) 2023 “Student Data”
• U.S. News & World Report 2024 “Graduate School Data”
• QS 2024 “Global Graduate Trends Report”
• Graduate Management Admission Council (GMAC) 2023 “Application Trends Survey”
• ETS 2023 “GRE Score Report”
• OECD 2023 “Education at a Glance”
• Unilink Education 2024 “Global Admissions Database”