166 lines
6.6 KiB
Python
166 lines
6.6 KiB
Python
"""
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analyze_asst_prof_comp.py
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==========================
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One-off analysis: median total compensation (salary + benefits) for
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non-medical Assistant Professors across the whole UC system, 2024.
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Data source: CA State Controller's Government Compensation in California
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(GCC) dataset, restored from this repo's git history (gcc_data/GCC_*_2024.csv)
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because the UC-wages source uc_salary_pipeline.py normally uses has no
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benefits field at all.
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"Total compensation" = TotalWages (cash pay) + TotalRetirementAndHealthContribution
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(employer-paid health/dental/vision + retirement + deferred comp) -- the
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standard total-comp figure used for this dataset (e.g. by Transparent
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California).
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"Non-medical" reuses classifier.py's existing SENATE_FACULTY / is_medical_center
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logic, which already flags HCOMP / HS-CLIN-PROF / clinical-workforce titles
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as medical regardless of campus.
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"Assistant Professor" is a rank-level filter this repo doesn't otherwise
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do (classify_title() only buckets at the Senate-faculty-vs-not level) --
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_ASST_RANK_RX below mirrors classifier.py's own _SENATE_RX patterns,
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restricted to the assistant-rank variants.
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"""
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import csv
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import glob
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import re
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import statistics
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from classifier import classify_title, is_medical_center, normalise
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EMPLOYER_TO_CAMPUS = {
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"University of California, Berkeley": "UCB",
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"University of California, Los Angeles": "UCLA",
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"University of California, Davis": "UCD",
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"University of California, Irvine": "UCI",
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"University of California, Santa Barbara": "UCSB",
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"University of California, Riverside": "UCR",
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"University of California, Santa Cruz": "UCSC",
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"University of California, Merced": "UCM",
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"University of California, Office of the President": "UCOP",
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"University of California, San Diego": "UCSD",
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"University of California, San Francisco": "UCSF",
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}
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# Assistant-rank Senate faculty titles only -- mirrors classifier.py's
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# _SENATE_RX but excludes bare/ASSOC/full-professor variants.
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# "ASST ACT PROF" (reversed order = non-Senate, per classifier.py's own
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# docstring) is NOT matched here and is excluded anyway by the
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# SENATE_FACULTY-only filter in main().
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_ASST_RANK_RX = [re.compile(p, re.IGNORECASE) for p in (
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r"^ASST\s+PROF-(?:AY|FY|HCOMP|SFT-VM)\b",
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r"^ASST\s+PROF-AY-",
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r"^ASST\s+PROF-FY-",
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r"^ASST\s+PROF OF TEACH\b",
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r"^ASST\s+PROF OF TEACH-",
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r"^ASST\s+PROF OF CLIN\b",
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r"^ASST\s+PROF OF CLIN-",
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r"^ASST\s+PROF IN RES\b",
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r"^ASST\s+PROF IN RES-",
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r"^ACT\s+ASST\s+PROF\b",
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r"^ACT\s+ASST\s+PROF-",
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r"^ACT\s+ASST\s+PROF OF TEACH\b",
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r"^ACT\s+ASST\s+PROF OF TEACH-",
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r"^ACT\s+ASST\s+PROF OF CLIN\b",
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r"^ACT\s+ASST\s+PROF OF CLIN-",
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r"^ACT\s+ASST\s+PROF IN RES\b",
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)]
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# Other Senate-faculty patterns (non-assistant) so we can flag anything
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# that matched SENATE_FACULTY but neither this nor the assistant patterns
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# recognise -- a diagnostic for titles the rank regex list above missed.
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_NON_ASST_SENATE_RX = [re.compile(p, re.IGNORECASE) for p in (
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r"^(?:ASSOC\s+)?PROF-(?:AY|FY|HCOMP|SFT-VM)\b",
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r"^(?:ASSOC\s+)?PROF-AY-",
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r"^(?:ASSOC\s+)?PROF-FY-",
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r"^(?:ASSOC\s+)?PROF OF TEACH\b",
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r"^(?:ASSOC\s+)?PROF OF TEACH-",
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r"^(?:ASSOC\s+)?PROF OF CLIN\b",
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r"^(?:ASSOC\s+)?PROF OF CLIN-",
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r"^(?:ASSOC\s+)?PROF IN RES\b",
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r"^(?:ASSOC\s+)?PROF IN RES-",
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r"^ACT\s+PROF\b", r"^ACT\s+PROF-",
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r"^ACT\s+ASSOC\s+PROF\b", r"^ACT\s+ASSOC\s+PROF-",
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r"^ACT\s+PROF OF TEACH\b", r"^ACT\s+PROF OF TEACH-",
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r"^ACT\s+ASSOC\s+PROF OF TEACH\b", r"^ACT\s+ASSOC\s+PROF OF TEACH-",
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r"^ACT\s+PROF OF CLIN\b", r"^ACT\s+PROF OF CLIN-",
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r"^ACT\s+ASSOC\s+PROF OF CLIN\b", r"^ACT\s+ASSOC\s+PROF OF CLIN-",
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r"^ACT\s+PROF IN RES\b", r"^ACT\s+ASSOC\s+PROF IN RES\b",
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r"^INSTR\b", r"^INSTR-", r"^INSTRUCTOR\b",
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r"^(?:ASSOC\s+)?INSTR IN RES\b",
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r"^REGENTS'? PROF\b",
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r"\bPROF(?:ESSOR)?[-\s]EMERITUS\b", r"\bPROF(?:ESSOR)?\s+EMERITUS\b",
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r"^RECALL FACULTY\b",
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r"^(?:ASSOC\s+)?AGRON AES(?:-AY|-FY|-B/E/E|-SFT-VM)?$",
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r"^(?:ASSOC\s+)?ASTRONOMER(?:-AY|-FY)?$",
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)]
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def is_assistant_rank(raw_title: str) -> bool:
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t = normalise(raw_title)
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return any(rx.search(t) for rx in _ASST_RANK_RX)
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def is_recognised_non_assistant_senate(raw_title: str) -> bool:
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t = normalise(raw_title)
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return any(rx.search(t) for rx in _NON_ASST_SENATE_RX)
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def parse_money(s: str) -> float:
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s = (s or "").replace(",", "").replace("$", "").strip()
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return float(s) if s else 0.0
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def main():
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rows = []
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unrecognised_senate_titles = set()
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for path in sorted(glob.glob("gcc_data/GCC_*_2024.csv")):
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if path.endswith("GCC_ALL_2024.csv"):
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continue
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with open(path, newline="", encoding="utf-8-sig") as f:
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for row in csv.DictReader(f):
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employer = row["EmployerName"]
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campus = EMPLOYER_TO_CAMPUS.get(employer)
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if campus is None:
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raise ValueError(f"Unmapped EmployerName: {employer!r} in {path}")
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title = row["Position"]
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if classify_title(title) != "SENATE_FACULTY":
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continue
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if is_medical_center(title, campus):
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continue
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if not is_assistant_rank(title):
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if not is_recognised_non_assistant_senate(title):
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unrecognised_senate_titles.add(title)
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continue
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total_comp = (parse_money(row["TotalWages"])
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+ parse_money(row["TotalRetirementAndHealthContribution"]))
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rows.append({"campus": campus, "title": title, "total_comp": total_comp})
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if unrecognised_senate_titles:
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print("NOTE: Senate-faculty titles not matched by any rank pattern "
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"(not counted as assistant, but check these aren't mis-missed asst titles):")
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for t in sorted(unrecognised_senate_titles):
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print(f" - {t}")
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print()
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all_comp = [r["total_comp"] for r in rows]
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print(f"Non-medical Assistant Professors, UC system-wide, 2024: n={len(rows)}")
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print(f"Median total compensation (salary + benefits): ${statistics.median(all_comp):,.2f}")
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print(f"Mean total compensation: ${statistics.mean(all_comp):,.2f}")
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print()
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print(f"{'Campus':<8}{'N':>6}{'Median':>16}")
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for campus in sorted(EMPLOYER_TO_CAMPUS.values()):
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campus_comp = [r["total_comp"] for r in rows if r["campus"] == campus]
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if campus_comp:
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print(f"{campus:<8}{len(campus_comp):>6}{statistics.median(campus_comp):>16,.2f}")
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else:
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print(f"{campus:<8}{0:>6}{'--':>16}")
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if __name__ == "__main__":
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main()
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