County Presidential Data Source Reference

County Presidential Election Returns 2000-2024 from MIT Election Data and Science Lab (MEDSL). Use when analyzing county-level presidential vote shares, party trends, turnout, or geographic voting patterns. Key join column county_fips enables linking to census, education (CCD/SAIPE), and demographic datasets. Requires Harvard Dataverse API key (HARVARD_DATAVERSE_API_KEY env var). Categorical variables use uppercase strings, not Portal integer codes. Critical caveat: naive mode='TOTAL' filtering silently drops ~1,000 counties in 2020+ data — use 3-pattern reconstruction.

The authoritative source for county-level U.S. presidential election returns spanning 2000-2024. Provides candidate-level vote counts across all 50 states and DC, enabling vote share analysis, partisan trend mapping, and cross-domain geographic research via FIPS code joins.

CRITICAL: Value Encoding

This dataset uses uppercase string codes for categorical variables (party, mode, candidate, state) rather than integer codes. Empty strings ("") appear as undocumented values in party (501 rows, 2024) and mode (2,795 rows, 2024).

Context	party	mode	candidate
Standard values	DEMOCRAT, REPUBLICAN	TOTAL	BARACK OBAMA
Aggregate/meta values	OTHER, ""	"", ELECTION DAY	OTHER, UNDERVOTES

See ./references/variable-definitions.md for complete encoding tables.

Prerequisites

API Key Required: This data source requires a Harvard Dataverse API key to fetch data. Unlike education data sources (which use the Urban Institute's free, unauthenticated API), election data is hosted on Harvard Dataverse and requires authentication.

Setup instructions:

1. Create a free Harvard Dataverse account at https://dataverse.harvard.edu/
2. Log in, navigate to your account name (top-right) → API Token
3. Click "Create Token" and copy it
4. Add it to the environment_settings.txt file in your daaf-docker/ folder on the host machine:

   HARVARD_DATAVERSE_API_KEY=your_token_here
   

   If you don't have an environment_settings.txt file yet, copy the template: cp environment_settings_example.txt environment_settings.txt (or Copy-Item environment_settings_example.txt environment_settings.txt on Windows). Then recreate the container: docker compose down followed by run_daaf. Alternatively, set it manually inside the container: export HARVARD_DATAVERSE_API_KEY="your_token_here"

If the key is missing, any fetch script will fail with a KeyError: 'HARVARD_DATAVERSE_API_KEY'. The orchestrator should check for this variable's existence before dispatching Stage 5 fetch tasks that use this data source.

What is the MEDSL County Presidential Returns Dataset?

• Producer: MIT Election Data and Science Lab (MEDSL)
• Coverage: County-level presidential election returns, 50 states + DC
• Frequency: Every 4 years (presidential election cycle)
• Available years: 2000, 2004, 2008, 2012, 2016, 2020, 2024
• Primary identifier: county_fips (5-digit FIPS code, stored as integer)
• Record unit: One row per county-year-candidate-party-mode combination
• Total records: 94,151 rows x 12 columns (~8.4 MB). Note: rows per year vary dramatically — 2020/2024 have ~2x rows due to mode breakdowns (~22K vs ~9.5K for earlier years)
• Source: Harvard Dataverse (DOI: 10.7910/DVN/VOQCHQ)

Reference File Structure

File	Purpose	When to Read
variable-definitions.md	Complete column specs, party/mode/candidate value tables	Interpreting specific columns or coded values
coded-values.md	All categorical value mappings with frequencies	Filtering or recoding party, mode, candidate
columns.md	Detailed per-column profiling (types, nulls, ranges)	Understanding column characteristics
quality-notes.md	Known issues, anomalies, duplicates, null patterns	Assessing data reliability
mode-reconstruction.md	3-pattern TOTAL mode reconstruction for 2020+ data	Cleaning any 2020+ analysis (CRITICAL)
interpretations.md	Preliminary semantic interpretations (flagged for review)	Understanding column meanings

Decision Trees

What analysis do I need?

Analyzing presidential election data?
├─ County-level vote shares → Use 3-pattern mode reconstruction (./references/mode-reconstruction.md)
│   └─ Longitudinal (cross-year) → MUST reconstruct TOTAL for 2020+ (naive filter drops ~1,000 counties)
│   └─ Single year (pre-2020) → Safe to filter mode='TOTAL'
│   └─ Single year (2020/2024) → Reconstruct unless analyzing a known TOTAL-only state
├─ Party trends → Group by year + party, use party column (not candidate name)
│   └─ Third parties → See ./references/coded-values.md (GREEN/LIBERTARIAN vary by year)
├─ Turnout analysis → Use totalvotes column (dedup per county-year before summing!)
├─ Joining with other data → Use county_fips as join key (zero-pad to 5 chars first!)
│   └─ Census/ACS data → Join on county_fips (standard 5-digit string)
│   └─ Education data (CCD/SAIPE) → Join on county_fips
│   └─ Null FIPS? → See ./references/quality-notes.md (CT, ME, RI)
└─ Voting method analysis → 2020 and 2024 only, see mode breakdown

Is this a data quality issue?

Unexpected values?
├─ county_fips is null → CT/ME/RI in pre-2020 years (52 rows)
├─ county_fips > 72999 → Kansas City MO (FIPS 2938000, non-standard)
├─ county_fips join failures → Zero-pad to 5 chars! (AR codes = 4 digits as int)
├─ AR FIPS 5135 has two counties → Source data error: St. Francis under Sharp County
│   └─ See ./references/quality-notes.md #arkansas-fips-contamination (BLOCKER)
├─ CT counties missing from shapefile → 2022+ TIGER uses planning regions, not counties
│   └─ See ./references/quality-notes.md #connecticut-fips-geography-mismatch
├─ ~1,000 counties missing after mode filter → Use 3-pattern reconstruction, not naive filter
│   └─ See ./references/mode-reconstruction.md
├─ candidate is not a person → UNDERVOTES/OVERVOTES/SPOILED/TOTAL VOTES CAST
│   └─ Filter these OUT for candidate-level analysis
├─ party is empty string → 501 rows in 2024, undocumented
├─ mode is empty string → 2,795 rows in 2024 (may be totals OR breakdowns per state)
├─ candidatevotes is null → 37 rows (NM 2024, mode breakdown)
├─ sum(candidatevotes) > totalvotes → 49 county-years (minor rounding)
├─ Duplicate rows → 83 exact duplicates exist
└─ Alaska 2004 → District-level data, not county; FIPS = 2001-2099
    └─ See ./references/quality-notes.md #alaska-2004

Quick Reference: Election Variables

Party Values by Year

Year	DEMOCRAT	REPUBLICAN	LIBERTARIAN	GREEN	OTHER	""
2000	Y	Y	-	Y	Y	-
2004-2016	Y	Y	-	-	Y	-
2020	Y	Y	Y	Y	Y	-
2024	Y	Y	Y	-	Y	Y

Mode Values by Year

Year	Modes Available
2000-2016	TOTAL only
2020	TOTAL + 15 breakdown modes (11 states)
2024	TOTAL + 9 breakdown modes + "" (varies by state)

For complete mode values see ./references/coded-values.md.

Key Identifiers

ID	Format	Level	Example	Notes
county_fips	Int64 (5-digit)	County	6037 (LA County, CA)	52 nulls (CT/ME/RI); join key for census/education data
state_po	String (2-char)	State	CA	USPS abbreviation; 1:1 with state
state	String	State	CALIFORNIA	Full uppercase name

WARNING: FIPS Zero-Padding Required for Joins

county_fips is stored as Int64. When converting to string for joins with Census, SAIPE, CCD, or other datasets, zero-pad to 5 characters or Arkansas and other small-FIPS states will produce 4-digit codes that fail to match.

df = df.with_columns(pl.col("county_fips").cast(pl.Utf8).str.zfill(5).alias("county_fips_str"))

library(dplyr)
library(stringr)  # str_pad()
df <- df |> mutate(county_fips_str = str_pad(county_fips, width = 5, pad = "0"))

Missing Data Codes

Code	Column(s)	Meaning	Frequency
null	county_fips	FIPS not assigned	52 rows (CT, ME, RI pre-2020)
null	candidatevotes	Vote count unavailable	37 rows (NM 2024 mode breakdowns)
0	totalvotes	No votes recorded	50 rows
0	candidatevotes	Zero votes for candidate	3,908 rows (4.15%)
""	party	Party not specified	501 rows (2024 only)
""	mode	Mode not specified	2,795 rows (2024 only)

Non-Candidate Entries in candidate Column

Value	Rows	Meaning
OTHER	27,548	Aggregate of minor candidates
TOTAL VOTES CAST	427	County total (redundant with totalvotes)
UNDERVOTES	402	Ballots with no presidential selection
OVERVOTES	380	Ballots with multiple presidential selections
SPOILED	14	Invalidated ballots

Filter these out for candidate-level vote share analysis.

Best Practice: Party-Based Identification

Always use party column for identifying party affiliation, never candidate name. Candidate names are inconsistent across years (e.g., "DONALD TRUMP" in 2016 vs "DONALD J TRUMP" in 2020/2024). The party column (DEMOCRAT, REPUBLICAN) is stable across all years.

# CORRECT — stable across years
dem = df.filter(pl.col("party") == "DEMOCRAT")

# WRONG — misses 2016 or 2020/2024 depending on which name you use
trump = df.filter(pl.col("candidate") == "DONALD TRUMP")

# CORRECT — stable across years
dem <- df |> filter(party == "DEMOCRAT")

# WRONG — misses 2016 or 2020/2024 depending on which name you use
trump <- df |> filter(candidate == "DONALD TRUMP")

Data Access

Dataset Paths

Topic	Type	Path
County presidential returns	Single file	Harvard Dataverse DOI: 10.7910/DVN/VOQCHQ
Codebook	Single file	Bundled: County Presidential Returns 2000-2024.md
Sources per state	Single file	Bundled: sources-president.tab

Codebooks

Dataset	Codebook Path
County presidential returns 2000-2024	County Presidential Returns 2000-2024.md (bundled in Dataverse)

Codebook is a Markdown file bundled in the Harvard Dataverse deposit. For human reference. The QA methodology paper is at: https://www.nature.com/articles/s41597-022-01745-0

Truth Hierarchy: When interpreting variable values, apply this priority:

1. Actual data file (what you observe in the TSV) -- this IS the truth
2. Live codebook (Markdown file in Dataverse) -- authoritative documentation, may lag
3. This skill documentation -- convenient summary, may drift from codebook

If this documentation contradicts the codebook, trust the codebook. If the codebook contradicts observed data, trust the data and investigate.

Example Fetch

# Fetch from Harvard Dataverse API
import os, requests, polars as pl, io

api_key = os.environ["HARVARD_DATAVERSE_API_KEY"]
# Get file ID from dataset metadata first, then download
# File: countypres_2000-2024.tab (TSV format)
file_url = "https://dataverse.harvard.edu/api/access/datafile/{file_id}"
r = requests.get(file_url, params={"key": api_key, "format": "original"})
df = pl.read_csv(io.BytesIO(r.content), separator='\t')

# Filter to California, 2020, TOTAL mode only
ca_2020 = df.filter(
    (pl.col("state_po") == "CA") &
    (pl.col("year") == 2020) &
    (pl.col("mode") == "TOTAL")
)

# Fetch from Harvard Dataverse API
library(httr2)
library(readr)
library(dplyr)

api_key <- Sys.getenv("HARVARD_DATAVERSE_API_KEY")
# Get file ID from dataset metadata first, then download
# File: countypres_2000-2024.tab (TSV format)
file_url <- paste0("https://dataverse.harvard.edu/api/access/datafile/{file_id}")
resp <- request(file_url) |>
  req_url_query(key = api_key, format = "original") |>
  req_perform()
tmp <- tempfile(fileext = ".tsv")
writeBin(resp_body_raw(resp), tmp)
df <- read_tsv(tmp)

# Filter to California, 2020, TOTAL mode only
ca_2020 <- df |> filter(state_po == "CA", year == 2020, mode == "TOTAL")

Filtering

# Common filter patterns for county presidential data

# 1. Cross-year analysis: WARNING — naive filter drops ~1,000 counties in 2020+!
#    Use 3-pattern mode reconstruction instead. See ./references/mode-reconstruction.md
#    The single-line filter below is ONLY safe for single-year analysis on a state
#    known to have TOTAL rows (e.g., 2000-2016 data, or a confirmed TOTAL-only state).
longitudinal = df.filter(pl.col("mode") == "TOTAL")  # UNSAFE for 2020+ multi-state!

# 2. Remove non-candidate rows (UNDERVOTES, OVERVOTES, etc.)
candidates_only = df.filter(
    ~pl.col("candidate").is_in(["TOTAL VOTES CAST", "UNDERVOTES", "OVERVOTES", "SPOILED"])
)

# 3. Major party analysis — RECOMMENDED: use party column, not candidate name
#    Candidate names change across years (e.g., "DONALD TRUMP" vs "DONALD J TRUMP")
two_party = df.filter(pl.col("party").is_in(["DEMOCRAT", "REPUBLICAN"]))

# 4. Exclude Alaska 2004 anomaly
clean = df.filter(~((pl.col("state_po") == "AK") & (pl.col("year") == 2004)))

# 5. Exclude rows with null county_fips (for join operations)
joinable = df.filter(pl.col("county_fips").is_not_null())

# Common filter patterns for county presidential data

# 1. Cross-year analysis: WARNING — naive filter drops ~1,000 counties in 2020+!
#    Use 3-pattern mode reconstruction instead. See ./references/mode-reconstruction.md
longitudinal <- df |> filter(mode == "TOTAL")  # UNSAFE for 2020+ multi-state!

# 2. Remove non-candidate rows (UNDERVOTES, OVERVOTES, etc.)
candidates_only <- df |> filter(
  !candidate %in% c("TOTAL VOTES CAST", "UNDERVOTES", "OVERVOTES", "SPOILED")
)

# 3. Major party analysis — RECOMMENDED: use party column, not candidate name
two_party <- df |> filter(party %in% c("DEMOCRAT", "REPUBLICAN"))

# 4. Exclude Alaska 2004 anomaly
clean <- df |> filter(!(state_po == "AK" & year == 2004))

# 5. Exclude rows with null county_fips (for join operations)
joinable <- df |> filter(!is.na(county_fips))

Common Pitfalls

Pitfall	Issue	Solution
Cross-year mode mismatch	2000-2016 has only TOTAL; 2020/2024 have breakdowns. Mixing modes inflates counts	Always filter mode == 'TOTAL' for longitudinal analysis
Non-candidate rows	UNDERVOTES, OVERVOTES, SPOILED, TOTAL VOTES CAST appear as "candidates"	Filter out before computing candidate vote shares
Alaska 2004	District-level data with non-standard FIPS codes (2001-2099); vote counts overstated	Exclude AK 2004 or handle separately; do not join on county_fips
Null FIPS for joins	CT, ME, RI have null county_fips in some years; breaks joins	Use state_po + county_name as fallback join key
Kansas City MO FIPS	Uses non-standard FIPS 2938000 (not a real county FIPS)	Handle as special case in joins; Kansas City is an independent city
Empty string party/mode	2024 has "" in party (501 rows) and mode (2,795 rows)	Treat as missing/undocumented; filter or investigate by state
Vote share > 100%	49 county-years where sum(candidatevotes) > totalvotes	Minor rounding; use caution with strict validation
Duplicate rows	83 exact duplicate rows exist in dataset	Deduplicate before analysis
FIPS zero-padding	Int64 → string without padding gives 4-digit codes for AR and other small-FIPS states	pl.col("county_fips").cast(pl.Utf8).str.zfill(5) before joins
Name-based party ID	Candidate names change across years ("DONALD TRUMP" vs "DONALD J TRUMP")	Always use party column, never candidate name
CT FIPS mismatch	2022+ Census TIGER uses CT planning regions, not legacy counties	Use 2020-vintage TIGER shapefiles for CT county joins
totalvotes duplication	totalvotes is repeated per candidate row; summing without dedup inflates by ~3-4x	Deduplicate to one row per (county_fips, year) before aggregating

Critical: Mode Column Behavior Change

WARNING: Naive mode == "TOTAL" filtering drops ~1,000 counties in 2020+ data. Multiple states report ONLY mode breakdowns (no TOTAL rows). A simple filter silently removes all their counties. Use 3-pattern mode reconstruction instead. See ./references/mode-reconstruction.md for the full code pattern and validation.

The mode column behavior changed significantly starting in 2020:

• 2000-2016: All rows have mode = 'TOTAL' (aggregate county totals only)
• 2020: 11 states report by voting method alongside TOTAL; 10 states have ONLY breakdowns (AR, AZ, GA, IA, KY, MD, NC, OK, SC, VA)
• 2024: Additional breakdown states; some have empty string mode (which may represent totals OR breakdowns depending on the state)

For any cross-year or multi-county analysis, use 3-pattern mode reconstruction:

1. Pattern 1: TOTAL present → keep TOTAL, drop breakdowns
2. Pattern 2: Only breakdowns, no TOTAL → sum candidatevotes across modes
3. Pattern 3: Empty-string mode = totals → reclassify after per-state verification

Empty-string detection must be per-state — NC 2024 empty-string rows are breakdowns (multiple per county-candidate), while other states' empty-string rows are totals (one per county-candidate). See ./references/mode-reconstruction.md for detection logic and code.

Related Data Sources

Source	Relationship	When to Use
Census/ACS	Join via county_fips	County demographics, population, income
SAIPE (education-data-source-saipe)	Join via county_fips	County poverty estimates (cross-domain)
CCD (education-data-source-ccd)	Join via county_fips	School district data (cross-domain)
MEDSL Precinct Returns	Finer geographic resolution	When county-level is insufficient
MEDSL Senate/House Returns	Same producer, different office	When analyzing down-ballot races

Note: This is the first election domain dataset in DAAF. Cross-domain joins with education data are possible via county_fips. No election-specific explorer or query skills exist yet.

Topic Index

Topic	Reference File
Column specifications	./references/columns.md
Column types and ranges	./references/columns.md
Party values and year coverage	./references/coded-values.md
Mode values and year behavior	./references/coded-values.md
Candidate name mapping	./references/coded-values.md
Non-candidate entries	./references/coded-values.md
Complete encoding tables	./references/variable-definitions.md
Null county_fips patterns	./references/quality-notes.md
Alaska 2004 anomaly	./references/quality-notes.md
Kansas City MO FIPS	./references/quality-notes.md
Duplicate rows	./references/quality-notes.md
Null candidatevotes	./references/quality-notes.md
Per-state data sources	./references/quality-notes.md
Missing votes flags	./references/quality-notes.md
Mode reconstruction (3-pattern)	./references/mode-reconstruction.md
States without TOTAL rows	./references/mode-reconstruction.md
Empty-string mode detection	./references/mode-reconstruction.md
Row count estimation by year	./references/mode-reconstruction.md
FIPS zero-padding for joins	./references/columns.md
AR FIPS contamination (Sharp/St. Francis)	./references/quality-notes.md
CT geography mismatch (2022+)	./references/quality-notes.md
Party-based identification (best practice)	./references/coded-values.md
totalvotes deduplication	./references/variable-definitions.md
Preliminary interpretations	./references/interpretations.md
Data profiling scripts	./scripts/