Assessing the Impact of SFFA on Campus Diversity

STA 9750 Mini-Project #01

Author

Raúl J. Solá Navarro

Published

March 13, 2026

1 Introduction

In June 2023, the Supreme Court’s decision in Students for Fair Admissions (SFFA) v. Harvard ended race-aware admissions programs in the United States higher education institutions. This report analyzes the ruling’s effect on demographic shifts using data from the Integrated Post-secondary Education Data System (IPEDS). Through data summarization and exploratory analysis, I compare the diversity composition of institutions like Baruch College and the CUNY system to “control” systems like the California Public University system. The goal is to determine whether observed changes in diversity metrics post-SFFA reflect normal demographic variation or a structural shift in the admissions landscape.

2 Data Acquisition and Preparation

Note

Data is sourced directly from the National Center for Education Statistics (NCES) IPEDS data center, covering institutional descriptions and enrollment records from 2010 to 2024.

View R Code 
#' Acquire IPEDS Data for MP#01
#' This function will acquire and standardize all data for MP#01
#' from IPEDS (https://nces.ed.gov/ipeds/datacenter/DataFiles.aspx)
#' 
#' We're starting in 2010 as the data seems to be reasonably complete 
#' after that point. 
acquire_ipeds_data <- function(start_year=2010, end_year=2024){
  
    data_dir <- file.path("data", "mp01")
    
    if(!dir.exists(data_dir)){
        dir.create(data_dir, showWarnings=FALSE, recursive=TRUE)
    }
    
    YEARS <- seq(start_year, end_year)
    
    EFA_ALL <- map(YEARS, function(yy){
        if(yy <= 2022){
            ef_url <- glue("https://nces.ed.gov/ipeds/datacenter/data/EF{yy}A.zip")
            
        } else {
            ef_url <- glue("https://nces.ed.gov/ipeds/data-generator?year={yy}&tableName=EF{yy}A&HasRV=0&type=csv")
        }
        
        ef_file <- file.path(data_dir, glue("ef{yy}a.csv.zip"))
        
        if(!file.exists(ef_file)){
            message(glue("Downloading Enrollment Data for {yy} from {ef_url}"))
            download.file(ef_url, destfile = ef_file, quiet=TRUE, mode="wb")    
        }
        
        read_csv(ef_file, 
                 show_col_types=FALSE) |>
            mutate(year = yy, 
                   # American Indian or Alaskan Native
                   enrollment_m_aian = EFAIANM, 
                   enrollment_f_aian = EFAIANW, 
                   # Asian
                   enrollment_m_asia = EFASIAM, 
                   enrollment_f_asia = EFASIAW, 
                   # Black or African-American, 
                   enrollment_m_bkaa = EFBKAAM, 
                   enrollment_f_bkaa = EFBKAAW, 
                   # Hispanic 
                   enrollment_m_hisp = EFHISPM, 
                   enrollment_f_hisp = EFHISPW, 
                   # Native Hawaiian or Other Pacific Islander 
                   enrollment_m_nhpi = EFNHPIM, 
                   enrollment_f_nhpi = EFNHPIW, 
                   # White
                   enrollment_m_whit = EFWHITM, 
                   enrollment_f_whit = EFWHITW, 
                   # Two or More Races
                   enrollment_m_2mor = EF2MORM, 
                   enrollment_f_2mor = EF2MORW, 
                   # Unknown / Undisclosed Race
                   enrollment_m_unkn = EFUNKNM, 
                   enrollment_f_unkn = EFUNKNW, 
                   # US Non-Resident
                   enrollment_m_nral = EFNRALM, 
                   enrollment_f_nral = EFNRALW, 
            ) |> filter(
                (EFALEVEL %in% c(2, 12)) | (LINE %in% c(1, 15))
                # Per 2024 Data Dictionary, 
                # - EFALEVEL 2 = undergrad 
                # - EFALELVE 12 = grad
                # - Line 1 = first year first time full-time undergrad
                # - Line 15 = first year first time part-time undergrad
            ) |> mutate(level = case_when(
                       EFALEVEL == 2 ~ "all undergrad", 
                       EFALEVEL == 12 ~ "all graduate",
                       LINE %in% c(1, 15) ~ "first year undergrad"
                   )
            ) |>
            select(institution_id = UNITID, 
                   year, 
                   level,
                   starts_with("enrollment_")) |>
            group_by(institution_id, 
                     year, 
                     level) |>
            summarize(across(starts_with("enrollment_"), sum), 
                      .groups = "drop")
        
    }) |> bind_rows()
    
    DESC_ALL <- map(YEARS, function(yy){
        if(yy <= 2022){
            hd_url <- glue("https://nces.ed.gov/ipeds/datacenter/data/HD{yy}.zip")
            
        } else {
            hd_url <- glue("https://nces.ed.gov/ipeds/data-generator?year={yy}&tableName=HD{yy}&HasRV=0&type=csv")
        }
        
        hd_file <- file.path(data_dir, glue("hd{yy}.csv.zip"))
        
        if(!file.exists(hd_file)){
            message(glue("Downloading Institutional Descriptions for {yy} from {hd_url}"))
            download.file(hd_url, destfile = hd_file, quiet=TRUE, mode="wb")    
        }
        
        suppressWarnings(
            read_csv(hd_file, 
                 show_col_types=FALSE, 
                 locale=locale(encoding=if_else(yy==2024, "utf-8", "windows-1252"))) |>
            mutate(year = yy, 
                   INSTNM) |> 
            select(institution_id = UNITID, 
                   institution_name = INSTNM, 
                   state = STABBR, 
                   year)
        )
        
    }) |> bind_rows()
    
    inner_join(EFA_ALL, 
               DESC_ALL, 
               join_by(institution_id == institution_id, 
                       year == year))
}

IPEDS <- acquire_ipeds_data()
View R Code 
# 1. Identify CUNY and California Public (UC/Cal State) systems
IPEDS <- IPEDS |>
    mutate(
        # Task 2: Check the 'institution_name' column for "CUNY"
        is_cuny = str_detect(institution_name, "CUNY"),
        
        # Task 3: Check for UC OR Cal State
        is_calpublic = (str_detect(institution_name, "University of California") | 
                        str_detect(institution_name, "California State University"))
    )
View R Code 
# 2. CREATE the verification object 
cal_schools_check <- IPEDS |>
    filter(is_calpublic) |>
    distinct(institution_name)
View R Code 
# 3. Create the formatted table
cal_schools_check |>
    head(10) |>
    gt() |>
    tab_header(
        title = "California Public University System Verification",
        subtitle = "Schools identified via name-matching logic"
    ) |> 
    style_gt()
California Public University System Verification
Schools identified via name-matching logic
institution_name
University of California Hastings College of Law
California State University-Bakersfield
California State University-Stanislaus
California State University-San Bernardino
California State University-Chico
California State University-Dominguez Hills
California State University-Fresno
California State University-Fullerton
California State University-East Bay
California State University-Long Beach

3 Exploratory Analysis

Tip📋 Data Health Check

The dataset contains 219,316 rows, representing a comprehensive view of institutional enrollment. The summary below confirms data integrity across the 15-year period.

View R Code 
# 1. View the structure and data types
dplyr::glimpse(IPEDS)
Rows: 219,316
Columns: 25
$ institution_id    <dbl> 100654, 100654, 100654, 100663, 100663, 100663, 1006…
$ year              <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010…
$ level             <chr> "all graduate", "all undergrad", "first year undergr…
$ enrollment_m_aian <dbl> 2, 2, 0, 5, 13, 0, 0, 0, 0, 10, 47, 1, 1, 2, 0, 10, …
$ enrollment_f_aian <dbl> 0, 1, 0, 16, 22, 3, 0, 0, 0, 10, 36, 5, 0, 0, 0, 18,…
$ enrollment_m_asia <dbl> 4, 2, 0, 114, 210, 54, 0, 0, 0, 28, 98, 13, 1, 0, 0,…
$ enrollment_f_asia <dbl> 8, 7, 1, 191, 245, 55, 1, 3, 0, 24, 105, 16, 7, 1, 0…
$ enrollment_m_bkaa <dbl> 199, 2250, 527, 182, 889, 147, 59, 42, 0, 47, 323, 4…
$ enrollment_f_bkaa <dbl> 430, 2498, 555, 669, 2016, 260, 94, 85, 0, 72, 565, …
$ enrollment_m_hisp <dbl> 0, 9, 3, 37, 79, 14, 4, 9, 0, 10, 76, 10, 2, 7, 2, 5…
$ enrollment_f_hisp <dbl> 2, 7, 1, 82, 141, 14, 2, 3, 0, 6, 77, 14, 3, 12, 4, …
$ enrollment_m_nhpi <dbl> 0, 0, 0, 0, 4, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 7, 5…
$ enrollment_f_nhpi <dbl> 0, 0, 0, 0, 4, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 3, 3…
$ enrollment_m_whit <dbl> 47, 59, 7, 1713, 3081, 455, 90, 81, 3, 729, 2431, 27…
$ enrollment_f_whit <dbl> 125, 42, 9, 2886, 3601, 490, 67, 46, 1, 437, 1856, 1…
$ enrollment_m_2mor <dbl> 0, 0, 0, 16, 54, 18, 0, 0, 0, 1, 49, 3, 0, 1, 1, 19,…
$ enrollment_f_2mor <dbl> 0, 0, 0, 30, 75, 34, 0, 0, 0, 4, 40, 10, 0, 1, 0, 33…
$ enrollment_m_unkn <dbl> 0, 10, 9, 64, 179, 5, 23, 40, 0, 23, 47, 5, 1, 54, 3…
$ enrollment_f_unkn <dbl> 4, 10, 7, 92, 206, 7, 39, 61, 0, 10, 63, 7, 3, 71, 4…
$ enrollment_m_nral <dbl> 37, 23, 2, 242, 99, 5, 0, 0, 0, 134, 120, 12, 4, 12,…
$ enrollment_f_nral <dbl> 16, 20, 2, 176, 110, 8, 0, 0, 0, 64, 71, 1, 11, 13, …
$ institution_name  <chr> "Alabama A & M University", "Alabama A & M Universit…
$ state             <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL"…
$ is_cuny           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
$ is_calpublic      <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
View R Code 
# 2. Generate a high-level summary
skimr::skim(IPEDS)
Data summary
Name IPEDS
Number of rows 219316
Number of columns 25
_______________________
Column type frequency:
character 3
logical 2
numeric 20
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
level 0 1 12 20 0 3 0
institution_name 0 1 3 105 0 11243 0
state 0 1 2 2 0 59 0

Variable type: logical

skim_variable n_missing complete_rate mean count
is_cuny 0 1 0.00 FAL: 218606, TRU: 710
is_calpublic 0 1 0.01 FAL: 218126, TRU: 1190

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
institution_id 0 1 274221.74 132743.57 100654 167039 216597 438586 500555 ▇▇▁▂▇
year 0 1 2016.65 4.30 2010 2013 2016 2020 2024 ▇▇▇▇▆
enrollment_m_aian 0 1 4.52 23.05 0 0 0 2 1498 ▇▁▁▁▁
enrollment_f_aian 0 1 6.89 37.94 0 0 0 3 1719 ▇▁▁▁▁
enrollment_m_asia 0 1 45.47 230.33 0 0 1 11 6541 ▇▁▁▁▁
enrollment_f_asia 0 1 52.39 241.71 0 0 2 16 6378 ▇▁▁▁▁
enrollment_m_bkaa 0 1 75.73 250.89 0 1 9 49 10401 ▇▁▁▁▁
enrollment_f_bkaa 0 1 128.62 486.55 0 2 15 71 36035 ▇▁▁▁▁
enrollment_m_hisp 0 1 120.28 532.11 0 1 7 48 19464 ▇▁▁▁▁
enrollment_f_hisp 0 1 170.69 726.96 0 2 14 74 27342 ▇▁▁▁▁
enrollment_m_nhpi 0 1 2.11 15.42 0 0 0 1 1354 ▇▁▁▁▁
enrollment_f_nhpi 0 1 2.72 21.14 0 0 0 1 1556 ▇▁▁▁▁
enrollment_m_whit 0 1 351.66 1041.29 0 2 33 237 34014 ▇▁▁▁▁
enrollment_f_whit 0 1 450.90 1270.11 0 10 59 314 74462 ▇▁▁▁▁
enrollment_m_2mor 0 1 21.76 75.40 0 0 1 12 5000 ▇▁▁▁▁
enrollment_f_2mor 0 1 30.17 103.28 0 0 2 17 12146 ▇▁▁▁▁
enrollment_m_unkn 0 1 36.47 226.13 0 0 1 16 25579 ▇▁▁▁▁
enrollment_f_unkn 0 1 46.00 353.77 0 0 2 22 52081 ▇▁▁▁▁
enrollment_m_nral 0 1 37.40 193.94 0 0 0 8 8181 ▇▁▁▁▁
enrollment_f_nral 0 1 31.24 154.22 0 0 0 7 6339 ▇▁▁▁▁

After performing an initial pass on the IPEDS dataset, I verified that the data structure is consistent with the instructor’s specifications. The dataset consists of 219,316 rows, representing a comprehensive view of institutional enrollment over 15 years. Using the skimr package, I observed that while most numeric columns are well-populated, there are also missing values in specific demographic categories that must be handled. I used na.rm = TRUE which tells r to ignore NULL or missing values during calculations. The data types are correctly identified, with institutional ID’s as numeric and names as character strings, providing a good setup for further analysis.

4 Exploratory Questions

My initial exploration of the data revealed several key metrics regarding the landscape of higher education and Baruch College specifically. There are 8,983 distinct institutions recorded in this dataset. In 2024, Baruch College had 3,585 graduate students enrolled. When considering the entire student body, the total enrollment at Baruch in 2024 reached 20,081 students.

Additionally, in 2019, Western Governors University led the way with the highest number of enrolled female undergraduates at 62,737. Looking at 2024 data for incoming classes, University of Hawaii at Manoa admitted the highest proportion of Native Hawaiian or Pacific Islander first-year undergraduates among large institutions, representing 3.00% of their first-year class.

5 Final Insights and Deliverable

The chart below tracks Baruch’s minority diversity index from 2010 through the first post-SFFA admissions cycle.

View R Code 
diversity_data |>
    filter(str_detect(institution_name, "Baruch"), 
           level == "first year undergrad") |>
    ggplot(aes(x = year, y = diversity_idx)) +
    geom_line(linewidth = 1.2, color = "#2c7bb6") +
    geom_point(size = 3, color = "#2c7bb6") +
    geom_vline(xintercept = 2023, linetype = "dashed", 
               color = "red", linewidth = 0.8) +
    annotate("text", x = 2023.2, y = 0.5, 
             label = "SFFA Decision", hjust = 0, color = "red") +
    scale_y_continuous(labels = scales::percent_format(accuracy = 1),
                       limits = c(0, 1)) +
    scale_x_continuous(breaks = seq(2010, 2024, 2)) +
    labs(title    = "Baruch College: First-Year Minority Diversity Index",
         subtitle = "Fraction of incoming students identifying as Black, Hispanic, AIAN, NHPI, or Multiracial",
         x = "Year", y = "Diversity Index") +
    theme_minimal(base_size = 14)
Figure 3: Baruch College first-year minority diversity index, 2010–2024

When compared, Baruch’s and California public universities’ diversity indices show similar year-over-year fluctuations, suggesting that the observed changes may reflect normal demographic variation rather than a structural shift post-SFFA.

View R Code 
baruch_trend <- diversity_data |>
    filter(str_detect(institution_name, "Baruch"),
           level == "first year undergrad") |>
    group_by(year) |>
    summarize(diversity_idx = mean(diversity_idx, na.rm = TRUE)) |>
    mutate(system = "Baruch College")

cal_trend <- diversity_data |>
    filter(is_calpublic, level == "first year undergrad") |>
    group_by(year) |>
    summarize(diversity_idx = mean(diversity_idx, na.rm = TRUE)) |>
    mutate(system = "California Public Universities")

bind_rows(baruch_trend, cal_trend) |>
    ggplot(aes(x = year, y = diversity_idx, 
               color = system, linetype = system)) +
    geom_line(linewidth = 1.2) +
    geom_point(size = 2.5) +
    geom_vline(xintercept = 2023, linetype = "dashed",
               color = "red", linewidth = 0.8) +
    scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
    scale_x_continuous(breaks = seq(2010, 2024, 2)) +
    scale_color_manual(values = c("Baruch College" = "#2c7bb6",
                                  "California Public Universities" = "#d7191c")) +
    labs(title    = "First-Year Diversity Index: Baruch vs. California",
         subtitle = "Red dashed line marks the 2023 SFFA Supreme Court decision",
         x = "Year", y = "Diversity Index",
         color = NULL, linetype = NULL) +
    theme_minimal(base_size = 14) +
    theme(legend.position = "bottom")
Figure 4: Diversity index comparison: Baruch vs. California public universities
Tip✍️ Diversity as a Demographic Destiny: A Baruch Perspective

By President Raúl J. Solá Navarro

At Baruch College, we define diversity as the proportion of students who self-identify as Black, Hispanic, American Indian or Alaskan Native, Native Hawaiian or Pacific Islander, or Multiracial. These are groups historically underrepresented at higher education institutions in the United States. The definition, consistent with IPEDS reporting categories, intentionally excludes White and Asian-American students, reflecting the social context in which affirmative action policies were originally founded on.

Our student body is a living reflection of New York City itself (see the “Baruch Racial Composition” chart under the “Institutional Trends and Comparisons” section). As of 2024, Baruch enrolls 20,081 students across undergraduate and graduate programs, making it one of the most multicultural institutions in the CUNY system. The stacked area chart above tells a compelling story: over the 2010–2024 period, Baruch’s total undergraduate body has grown increasingly diverse, with Hispanic and Asian enrollment expanding steadily while the White student proportion has contracted. Over the years, the shift has been gradual and not the product of any single policy, but of the demographic trajectory of New York City itself flowing organically into our classrooms.

The 2023 Students for Fair Admissions (SFFA) Supreme Court decision has forced institutions in the United States to reconsider how they define a fair admission’s process. At Baruch, we don’t think of diversity as a quota, but as a demographic reality that reflects the composition of the City.

Looking at our incoming class, Baruch’s first-year minority representation was 40.9% in 2023 and held steady at 40.9% in the first post-SFFA admissions cycle of 2024. This stability is itself a finding worth noting. At an institution like Baruch, where minority students already constitute a substantial majority of the incoming class, the removal of race-conscious admissions tools produced no measurable disruption.

Shifting our focus to California, public universities there have operated under race-neutral admissions mandates since Proposition 209 in 1996. This means that the SFFA decision should have had no practical effect on their policies. In 2023, the California public system’s average first-year diversity index stood at 58.8% and shifted to 60.1% in 2024; this reflects a change of just 1.3%. The fact that Baruch’s year-over-year movement is comparably small to Baruch’s, suggests that what we are observing may reflect normal demographic variation rather than a structural post-SFFA shift.

The stability in California, and the resilience at Baruch, suggests that institutional mission and local demographics may be stronger forces than even the Supreme Court’s majority opinion. Our challenge moving forward is to remain true to what our institution stands for by doubling down on the pipeline programs, transfer pathways, and community partnerships that ensure every New Yorker sees a seat for themselves at our table.

In completing this mini-project, I used Claude (Anthropic) and Gemini (Google) to assist with debugging R code errors, specifically regarding pivot_wider list-column issues, gt table styling, and Quarto rendering. All analytical framing, written text, and interpretations are my own.

This work ©2026 by Raúl J. Solá Navarro was prepared as a Mini-Project for
STA 9750 at Baruch College · MP #01 Instructions