Team,
I have reviewed the latest research output from the Federal Reserve districts. While some entries were incomplete, there are several critical pieces of structural analysis that we need to integrate into our current models.
Here are the most analytically significant publications:
1. [NY] The R–Labor Share Nexus: This paper explores the correlation between the declining labor share of income and the downward drift of the natural rate of interest (R). If the structural decline in R* is tied to labor's diminishing share of GDP, it suggests that long-term neutral rates may remain lower than pre-pandemic norms unless there is a significant shift in labor bargaining power.
2. [NY] Use of Gen AI in the Workplace and the Value of Access to Training: This study examines the diffusion of generative AI and the productivity gap created by uneven access to training. For our equity analysts, this highlights that the "AI dividend" will be concentrated in firms with robust internal upskilling, rather than simply those with the best software licenses.
3. [NY] What Millions of Homeowner’s Insurance Contracts Reveal about Risk Sharing: By analyzing $48 trillion in housing assets, this research assesses how insurance contracts mitigate (or fail to mitigate) climate-related shocks. This is a critical signal for systemic risk; if insurance gaps are widening, we should expect a higher reliance on federal fiscal transfers following natural disasters.
4. [RIC] Reserve Demand Estimation: A Proposal: This technical proposal aims to refine how the Fed estimates the demand for reserves. While granular, any change in how the Fed views reserve demand directly impacts the predictability of the federal funds rate and the timing of balance sheet runoff (QT).
5. [STL] An Empirical Analysis of the Cost of Borrowing: This analysis provides a fresh look at the transmission of monetary policy into actual borrowing costs. It is essential for our forecasting to understand if the "lag" in policy transmission is shortening or lengthening in the current high-rate environment.
Synthesis: The research suggests a tension between long-term structural headwinds (declining R* and insurance gaps) and short-term productivity catalysts (Gen AI). We must monitor whether AI-driven productivity gains can offset the secular decline in labor's share of income to support a higher neutral rate.
The paper proposes a model to analyze the simultaneous decline of the natural rate of interest (R*) and the labor share of income. It argues that these two macroeconomic trends are fundamentally linked rather than isolated phenomena.
Over the past quarter century, the U.S. economy has experienced significant declines in both the labor share of income and the natural rate of interest, referred to as R*. Existing research has largely analyzed these two developments in isolation. In this post, we provide a simple model that captures the joint evolution of the labor share and R*, which we call the R*–labor share nexus. Our key finding is that structural changes affecting R* also influence the evolution of the labor share, and thereby wages and prices. This highlights a potentially important channel, absent from many macroecono
This study examines the integration of generative AI in the workplace, focusing on tool accessibility and productivity gains. It highlights the significant value workers place on specialized training to utilize these technologies effectively.
The rapid spread of generative AI (AI) tools is reshaping the workplace at a remarkable rate. Yet relatively little is known about whether workers have access to these tools, how the tools affect workers’ daily productivity, and how much workers value the training needed to use the tools effectively. In this post, we shed light on these issues by drawing on supplemental questions in the November 2025 Survey of Consumer Expectations (SCE), fielded to a representative sample of the U.S. population. We find that adoption of AI tools at work is heterogeneous, that a sizable share of workers see AI
This paper examines the role of homeowner's insurance as a primary mechanism for risk sharing and asset protection for U.S. households. It highlights the systemic importance of insurance in mitigating the financial shocks caused by natural disasters to the nation's largest household asset class.
Housing is the largest component of assets held by households in the United States, totaling $48 trillion in 2025. When natural disasters strike, the resulting damage to homes can be large relative to households’ liquid savings. Homeowner’s insurance is the primary financial tool households use to protect themselves against property risk. Despite the economic importance of homeowner’s insurance, we know surprisingly little about how insurance contracts are actually designed with respect to property risk. In this post, which is based on our new paper, “Economics of Property Insurance,” we exami
The author presents a new methodology for estimating reserve demand to enhance the precision of monetary policy implementation. The proposal aims to resolve practical difficulties inherent in current estimation techniques.
Reserve-demand estimation is central to monetary policy implementation but tricky in practice. This discusses a proposal that could improve it.
The paper provides an empirical investigation into the determinants and trends of borrowing costs. It analyzes the factors influencing the pricing of credit across different market segments.
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