Claudia Sahm on the Sahm Rule and Using Big Data to Inform Policymaking

Episode Summary

Executive Summary: Claudia Somm discusses her path into economics, Fed culture, mentoring, and two major policy contributions: the SOM rule for automatic recession-triggered stimulus payments and big-data methods for real-time economic measurement. She argues that simple, transparent rules and better data can improve stabilization policy and Fed decision-making.

Main Topics: Career path and mentorship in economics (Priority: 5/5): Somm explains how liberal arts training, strong mentors, and exposure to applied macro and labor economics shaped her career, emphasizing the importance of encouragement and open-minded job choices for PhD students. Fed culture and internal dynamics (Priority: 4/5): She reflects on 12 years at the Federal Reserve, describing its formal culture, training, communication norms, and gradual efforts to improve respect and inclusivity inside the institution. The SOM rule and automatic fiscal stabilization (Priority: 5/5): Somm details her recession-triggered direct-payment proposal, designed to automate stimulus based on the unemployment rate so fiscal support arrives quickly without waiting for Congress to act during a downturn. Communication and writing in economics (Priority: 4/5): She stresses that strong technical work can fail if papers are poorly written or unclear, and she uses her mentoring of job-market candidates to improve abstracts, introductions, and presentation of contributions. Big data and real-time economic statistics (Priority: 5/5): Somm describes Fed work using credit/debit transaction data to build high-frequency, geographically detailed measures of consumer spending, useful for tracking shocks like hurricanes and data gaps during shutdowns. Public-private data partnerships and policy challenges (Priority: 4/5): She argues that big data can complement surveys and official statistics, but partnerships with private firms are fragile because corporate incentives differ from public-good goals.

Key Arguments: Economic careers benefit from broad training, strong mentors, and willingness to consider unexpected job paths rather than narrowing too early. Mentorship does not require demographic similarity; effective mentors can support students across backgrounds and career stages. The SOM rule is designed to be simple, transparent, and fast: unemployment is the clearest recession signal and can trigger automatic payments before official recession dating is available. Rules-based fiscal stimulus could reduce recession severity by providing predictable backstops and avoiding legislative delays. Communication matters as much as technical soundness in economics; clear writing and strong introductions are essential for papers to be noticed and survive referee or desk review. High-frequency transaction data can improve real-time policy analysis, especially when official statistics are delayed or disrupted. Big data should complement, not fully replace, surveys and official statistics because each has comparative advantages and conceptual limitations. Public-private data collaboration is valuable but unstable because firms optimize for profit, not public goods, so long-term access is a recurring challenge.

Data Points: Years at the Federal Reserve: 12 years - Somm says she worked at the Board of Governors for 12 years before moving to Equitable Growth. Low recession trigger threshold: 0.5 percentage point rise in the 3-month average unemployment rate - Core trigger in the SOM rule for identifying a recession and starting direct payments. Measurement window for SOM rule: 13 months - She compares the current 3-month average unemployment rate to the low over the prior 12 months. NBER recession dating lag: About 12 months later - She notes the NBER usually dates recessions well after they begin, too late for timely stimulus. Direct payment examples: $500 in 2001; $1,000 in 2008 - Historical discretionary stimulus payments that informed the SOM rule proposal. Big recessions threshold: More than a 2 percentage point increase in unemployment in the first year - For large downturns, her proposal would send annual checks until unemployment meaningfully improved. Fed briefing training: 1 week - She describes a week-long briefing school for staff before they can brief in the boardroom. Consumer spending data latency: Within 2-3 days - First Data transaction data arrive quickly enough for near-real-time tracking. Hurricane tracking example: 2017 - The transaction data were used to track the effects of Hurricanes Harvey and Irma for FOMC briefings. Government shutdown example: 2019 - Fed transaction data helped fill in when Census retail sales were delayed during a shutdown. Estimated market job-matching insight: 3 tenths rule vs 5 tenths rule - She contrasts the Fed's internal labor-market heuristic with the SOM rule threshold and notes some false positives for smaller thresholds.

Pivotal Quotes: "If I'm falling asleep on page five, this is not going to help you." — Claudia Somm: On why economists need to write clearer introductions and communicate contributions early in a paper. "You can be a good mentor and an ally regardless of whether on some demographic dimension or life experience dimension you match up." — Claudia Somm: On the value of mentorship across differences in identity or background. "This is not a forecasting device. This is an indicator that says we're in a recession and we're early in it." — Claudia Somm: Explaining the purpose of the SOM rule as a timely trigger for automatic stimulus payments.

Implications: The episode suggests macro policy can improve through simple rules, faster fiscal triggers, and better real-time data. For listeners, the key takeaway is that both stabilization policy and economic measurement are moving toward more automated, transparent, and data-rich systems.