Methodological Rift Over California's $20 Fast-Food Wage: Job Losses vs. Null Employment Effects

Methodological Rift Over California's $20 Fast-Food Wage: Job Losses vs. Null Employment Effects

The academic and policy debate over the employment impacts of California’s $20 fast-food minimum wage (AB 1228, effective April 1, 2024) remains deeply divided along methodological and data lines. Two major, highly rigorous research camps have produced conflicting results, illustrating how the literature is weaponized by both sides.

1. The Disemployment Camp: BLS QCEW Macro-Data

Lead researchers Jeffrey Clemens (UC San Diego/Hoover Institution), Olivia Edwards (Texas A&M), and Jonathan Meer (Texas A&M) published a landmark study analyzing the policy's employment effects. Using the Quarterly Census of Employment and Wages (QCEW) from the Bureau of Labor Statistics, they compared California's fast-food employment from September 2023 (enactment) to September 2024 against national trends, utilizing difference-in-differences (DiD) and triple-difference (DDD) designs.

They found a significant negative employment impact, estimating that AB 1228 reduced fast-food employment by 2.3% to 3.9% (with a median estimate of 3.2%), translating to approximately 18,000 lost jobs that would have otherwise been retained.

As summarized by the authors:

"Following AB 1228’s enactment, employment in California’s fast-food sector fell, with estimates ranging from 2.3% to 3.9% across specifications, even as employment in other sectors of the California economy tracked national trends."

2. The Null-Effect Camp: Granular Multi-Source Data

Conversely, Michael Reich and Denis Sosinskiy of the UC Berkeley Institute for Research on Labor and Employment (IRLE) released an updated working paper on March 31, 2026, using a vastly different, highly granular dataset. Instead of traditional administrative BLS data, they compiled payroll records from Square, job postings from Glassdoor, DoorDash menu prices, and cell phone location data from Advan Research to track restaurant foot traffic.

They concluded that the policy successfully raised average weekly wages for covered fast-food workers by 11% with no adverse impact on employment.

From their abstract:

"We find that the policy increased average weekly wages for covered fast food workers by about 11 percent and did not reduce employment. Compared to controls, prices increased by 1.5 percent, equivalent to 6 cents for a $4 item."

Adjudicating the Rift: Data and Design Differences

The divergence between these studies highlights how data selection shapes the policy narrative:

• Data Sources: Clemens et al. rely on the official BLS QCEW, which captures comprehensive, administrative employment headcounts across all registered employers. Reich & Sosinskiy rely on alternative, high-frequency private datasets (Square, Advan, Glassdoor). While private data offers real-time granularity, critics argue it may suffer from selection bias (e.g., Square payrolls skew toward smaller, tech-forward merchants, whereas AB 1228 applies strictly to large chains with over 60 national locations).
• Control Groups: Clemens et al. compare California to national fast-food trends and control for overall state labor market performance. Reich & Sosinskiy use a mix of local and national control groups, arguing that administrative macro-data fails to isolate fast-food-specific shocks from state-wide macroeconomic dynamics.
• Headcount vs. Hours: A critical bridge between these two findings is that macro-level headcount stability does not equal labor stability. While headcounts may remain relatively flat in some datasets, actual worker hours and shifts have been aggressively cut¹, a phenomenon explored in The Franchise-Level Reality: Headcount Stability vs. Drastic Labor Hour Cuts.