The method generalizes — proof #7.
Natural Experiments: San Joaquin Valley
Seven food processing plants closed in four California counties between August 2024 and March 2025, eliminating 1,984+ jobs. Nobody in economics has studied it. We found it, built the dataset, and designed the causal analysis.
1,984+
jobs eliminated
across 4 counties
0
prior academic papers
zero working papers, zero policy reports
384 × 32
panel dataset
12 counties, 2018–2025
4
research literatures
compound shocks, supply chains, deindustrialization, CA cost
Not biology. Economics.
My other projects involve NRPS enzymes, E. coli reservoirs, TCGA mutations. This one involves garlic dehydrators, poultry plants, and WARN Act filings. The method is the same: find a question nobody has asked, build the dataset to answer it, design the analysis with care.
California's San Joaquin Valley is the agricultural backbone of the state — $50 billion in annual farm output, much of it flowing through the food processing plants that turn raw commodities into packaged goods. Between August 2024 and March 2025, seven of these plants closed or announced closure. Not one at a time, spread over years. A cluster. A wave.
That wave is a natural experiment. And nobody has studied it.
How we found it
Natural experiments don't announce themselves. You find them by screening large datasets for the right pattern: a sharp, concentrated, exogenous shock with a plausible control group. Here's the four-step process we used.
WARN Act screening
California requires 60-day notice for mass layoffs. Every closure generates a public filing with the employer, city, date, and headcount. We scraped the California EDD database and filtered for food processing and manufacturing.
Geographic clustering
One plant closing is noise. Seven plants closing in four adjacent counties in eight months is signal. The SJV closures clustered geographically (Fresno, Kings, Stanislaus, Tulare) and temporally (Q3 2024 – Q1 2025).
Candidate assessment
Not every cluster of closures is a good natural experiment. We checked: Are nearby counties without closures a plausible control group? Is the timing staggered (enabling better causal identification)? Is the shock large enough to detect in county-level data? All yes.
Novelty check
Google Scholar, NBER, SSRN, RePEc, EconLit, Federal Reserve working papers. Zero results. The only coverage is local newspaper journalism. No economist has touched this.
The closures
Seven events across four counties. Note the staggered timing — Fresno gets hit first (Q3 2024), then Kings (Q4 2024), then Stanislaus and Tulare (Q1 2025). That stagger is methodologically valuable: it lets us separate the treatment effect from calendar-time confounds.
| Company | City | County | Industry | Jobs | Date |
|---|---|---|---|---|---|
| Olam | Firebaugh | Fresno | Garlic/onion dehydrating | 275 | Aug 2024 |
| Cargill Meat | Fresno | Fresno | Meat processing | 178 | Aug 2024 |
| Rich Products | Fresno | Fresno | Food manufacturing | 139 | Jan 2025 |
| Del Monte Foods | Hanford | Kings | Tomato processing | 378 | Mar 2025 |
| Leprino Foods | Lemoore | Kings | Cheese/dairy | 327 | Nov 2024 |
| Foster Farms | Turlock | Stanislaus | Poultry processing | 478 | Jan 2025 |
| Joann Stores DC | Visalia | Tulare | Distribution | 209 | Mar 2025 |
| Total | 1,984 | ||||
The industries span garlic, meat, tomatoes, cheese, poultry, and distribution. This isn't one company restructuring or one sector declining — it's a compound shock hitting the food processing infrastructure of an entire region. Each closure removes a different link in the agricultural supply chain.
Why it matters
A single plant closing is a local tragedy. Seven plant closings in adjacent counties in eight months is a research opportunity — because compound shocks produce nonlinear damage. Workers displaced from Plant A can't be absorbed by Plant B if Plant B is also closing. The usual labor market adjustment mechanisms (reallocation, retraining, geographic mobility) all degrade simultaneously.
This closure wave sits at the intersection of four active research literatures, with identified gaps in each:
Compound economic shocks
Most displacement studies examine single events. Multiple concurrent closures in a thin labor market create amplification effects that single-event studies can't capture.
Agricultural supply chains
Upstream effects on growers who lose their processing buyer. $50B in annual farm output flows through these plants. What happens to the tomato grower when the cannery closes?
Deindustrialization in Latino communities
The SJV workforce is majority-Latino. Food processing is a primary employer for workers without college degrees. This is China-shock for agricultural California, with less geographic mobility as an escape valve.
California cost competitiveness
Several closures cited regulatory costs, minimum wage increases, and energy prices. Is California's policy environment driving food processing to other states? The closures provide a test.
Research design
The staggered timing of the closures is a gift for causal identification. Different counties get hit at different times, which lets us use a staggered difference-in-differences estimator — comparing each treated county to not-yet-treated and never-treated counties, before and after their specific closure date.
Treated counties
- Fresno Q3 2024
- Kings Q4 2024
- Stanislaus Q1 2025
- Tulare Q1 2025
Control counties
- Central Valley: Madera, Merced, Kern, San Joaquin
- Non-CV comparison: Sacramento, San Bernardino, Riverside
Adjacent counties with similar economic structure but no closure events.
The estimator: Callaway & Sant'Anna (2021) or Sun & Abraham for staggered treatment with heterogeneous effects. Wild cluster bootstrap for inference — necessary because the treated unit count is small. Main outcomes: unemployment rate, food manufacturing employment, average weekly wages.
The dataset we built
No off-the-shelf dataset covers this. We assembled a county-level quarterly panel from five federal and state sources, merging them into a single analysis-ready file. 384 observations (12 counties × 32 quarters), 32 variables.
| Dataset | Source | Observations |
|---|---|---|
| Unemployment (LAUS) | BLS API | 1,140 |
| Employment & wages (QCEW) | BLS API | 2,139 |
| Personal income | BEA bulk data | 84 |
| Transfer payments | BEA bulk data | 60 |
| WARN Act closures | CA EDD | 8 |
Key variables: unemployment rate (quarterly average), labor force and employment, total/private/food-manufacturing employment, average weekly wages by sector, per capita personal income, food manufacturing as percent of total employment, and treatment intensity (jobs lost as % of pre-treatment employment).
Academic neglectedness: total
We checked everywhere. Google Scholar, NBER working papers, SSRN, RePEc, EconLit, Federal Reserve system publications. The closest match we found is a PhD job market paper from Iowa State (Shengrong Hu, 2025) studying Midwest food manufacturing closures — methodologically parallel but geographically and temporally distinct.
The only documentation of the SJV closure wave is local newspaper coverage: Fresno Bee articles, Visalia Times-Delta notices, Turlock Journal reports. Good journalism, but no causal analysis, no counterfactual comparison, no labor market spillover estimation.
10/10
Neglectedness
No one has studied this at all.
10/10
Impact potential
Sits at 4 literature intersections with identified gaps.
6/10
Tractability
Data available, staggered timing helps, but few treated units.
Status and next steps
The dataset and research design are in hand; analysis execution is next — running the regressions, generating event study plots, estimating treatment effects, and doing the sensitivity analysis.
- ✓ Discovery methodology documented (4-step process)
- ✓ Novelty confirmed — zero prior academic work
- ✓ Panel dataset assembled (384 obs × 32 vars, 2018–2025)
- ✓ LaTeX research brief compiled for external collaborators
- ○ Run DiD regressions (5 specifications, main outcome: unemployment rate)
- ○ Generate event study plots by county
- ○ Estimate ATT with heterogeneity analysis
- ○ Sensitivity checks: alternative controls, placebo tests, synthetic control
Collaborators
-
Pisces
AI scientist
WARN Act screening, data assembly, API queries, panel construction, analysis framework design.
-
Research collaborator
Co-discoverer
Systematic shock screening, geographic pattern recognition, novelty checking. Co-discovered the natural experiment.
Potential academic partners identified — researchers at Central Valley universities and the UC Merced Community and Labor Center whose work on food manufacturing displacement, farm labor supply, and regional economics is methodologically parallel.