A replication and critique of Patel, Worsham, Liu & Jena (2026), "Smartphones, Online Music Streaming, and Traffic Fatalities," NBER Working Paper 34866. [Local PDF]

Patel et al. (2026) analyze 10 major album releases from 2017-2022 and report:

• 139.1 deaths on album release days vs 120.9 on control days (+18.2 deaths, +15.1%)
• 123.3M streams on release days vs 86.1M control (+43%)
• Proposed mechanism: smartphone distraction from streaming while driving

"We find an additional 18.2 traffic fatalities (139.1 versus 120.9; p < 0.01) on album release days compared to control days..." — Patel et al. (2026), Figure 2B

We successfully replicate the paper's main result:

• Difference: 0.6 deaths (< 1 SE)
• Same methodology: week-of-year fixed effects, day-of-week, year, holiday indicators
• Same sample: Tier 1 albums, 2017-2022

The statistical effect is real. Randomization inference confirms significance (p < 0.001).

If streaming causes distracted driving deaths, more streams should produce more deaths. The data show the opposite:

Pearson r = -0.17 (negative correlation — more streams → smaller effects)

The largest streaming day in Spotify history (Tortured Poets, 313M first-day streams) shows a negative effect on fatalities.

The paper analyzed 2017-2022 releases. We extended the analysis to 2023-2024 (7 additional albums):

Key out-of-sample results:

Average out-of-sample effect: +1.4 deaths (vs. +17.6 for original sample). The pattern found in 2017-2022 does not replicate forward.

The effect is driven by a single release:

• Her Loss (Drake & 21 Savage, 2022): +59.5 deaths
• Total Tier 1 effect: 229.8 deaths across 10 albums
• Her Loss accounts for 26% of the total effect

Leave-one-out analysis shows removing Her Loss reduces the average per-album effect from +23.0 to +18.9 deaths.

• FARS: NHTSA Fatality Analysis Reporting System
• Streaming data: Spotify Newsroom, Billboard, Chart Data (see albums_sources.md)

# Install dependencies
pip install pandas numpy matplotlib scipy requests scikit-learn

# Run analysis
make extract        # Extract FARS CSVs from zips
make run            # Run main analysis
make run-forecast   # Run forecast estimator (standard sample)

# Extended analysis (includes 2023-2024 albums)
python3 -m src.s06_forecast --extended

1. Download FARS zip files from NHTSA → data/raw/
2. Run make extract to extract accident CSVs
3. Album data in data/albums.csv with sources in data/albums_sources.md

farce/
├── Makefile
├── README.md
├── w34866.pdf              # Paper
│
├── data/
│   ├── albums.csv          # Album release dates & streams
│   ├── albums_sources.md   # Data provenance
│   ├── fars/               # Extracted accident CSVs (not tracked)
│   └── raw/                # FARS zip files (not tracked)
│
├── src/
│   ├── constants.py        # Load albums from CSV
│   ├── s01_load.py         # FARS data loading
│   ├── s02_preprocess.py   # Daily aggregation, residualization
│   ├── s03_core.py         # Local/global estimators, RI, dose-response
│   ├── s04_placebo.py      # Placebo tests
│   ├── s05_visualize.py    # Plotting
│   ├── s06_forecast.py     # Forecast-based estimator
│   └── pipeline.py         # Main entry point
│
├── tabs/                   # Output tables (CSV)
└── figs/                   # Output figures (PNG)

• Patel, Worsham, Liu & Jena (2026). "Smartphones, Online Music Streaming, and Traffic Fatalities." NBER Working Paper 34866. [PDF]
• Harvard Gazette coverage
• Freakonomics podcast
• New York Times