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<channel>
<title>Federal Reserve Bank of San Francisco publications</title>
<description>Economic research and commentary from Federal Reserve Bank of San Francisco</description>
<link>https://fedinprint.org/search?facets[]=provider_literal_array:Federal+Reserve+Bank+of+San+Francisco</link>
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<pubDate>Fri, 04 Sep 2026 19:52:09 +0000</pubDate>
<item>
<title>Consumer Vibe Check: Gas Prices and Inflation Expectations</title>
<link>https://fedinprint.org/item/fedfel/103717</link>
<description>
<![CDATA[According to household surveys, inflation expectations tend to move together with expectations for gasoline price changes. The relationship is not symmetrical, though: Inflation expectations rise when households revise up their outlook for gas price growth but change little when they revise it down. This pattern is strongest among lower-income and less-educated households. Analysis also suggests that increases in expected gas price growth are associated with greater perceived inflation uncertainty. However, quantitatively, the evidence suggests a moderate relationship between changes in expected gasoline prices and households’ inflation expectations.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103717</guid>
<dc:creator>Sung, Yeji; Kim, Taerin</dc:creator>
<dc:date>2026-08-31</dc:date>
<rdau:hasExtent>5</rdau:hasExtent>
<dc:subject>gas prices; inflation expectations; consumer vibes</dc:subject>
<bibo:volume>5</bibo:volume>
<bibo:issue>2026-24</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>Can Fiscal, AI, or Monetary News Explain the Rise in r∗?</title>
<link>https://fedinprint.org/item/fedfwp/103705/original</link>
<description>
<![CDATA[Following decades of secular decline, many estimates of r∗—the natural or steady-state short-term real interest rate—have risen roughly 1 percentage point since 2020 in the United States. The most prominent explanations attribute this reversal to heightened expectations of rising government debt and faster productivity growth from artificial intelligence (AI). However, a high-frequency event study finds that news about fiscal and AI developments does not explain this increase. Furthermore, contrary to earlier evidence that persistent shifts in longer-term yields occurred around monetary policy meetings, we find that monetary policy news does not account for the recent rise in r∗.]]>
</description>
<guid>https://fedinprint.org/item/fedfwp/103705/original</guid>
<dc:creator>Rudebusch, Glenn D.; Christensen, Jens H. E.</dc:creator>
<dc:date>2026-08-27</dc:date>
<rdau:hasExtent>32</rdau:hasExtent>
<dc:subject>r star; fiscal policy; artificial intelligence; monetary policy</dc:subject>
<swpo:hasNumber>2026-19</swpo:hasNumber>
<identifiers:doi>10.24148/wp2026-19</identifiers:doi>
<bibo:series>Working Paper Series</bibo:series>
</item>
<item>
<title>Quality, Inequality and Understanding Housing Markets</title>
<link>https://fedinprint.org/item/fedfwp/103704/original</link>
<description>
<![CDATA[Most housing research relies on a single-margin model where a single housing price relates to a single housing quantity. We generalize this approach by introducing income inequality and housing quality. Two important challenges to studying housing supply (and demand) emerge: First, there is no singular housing supply function, instead there is a continuum of unit-supply functions indexed by quality. This implies that supply elasticity estimates are local average treatment effects (LATEs), not structural elasticities. Second, spillovers common in spatial settings may preclude even the LATE interpretation of estimates, which are instead local general equilibrium objects. Critically, these issues cannot be bypassed by using the land share of value to measure supply constraints as it is not even a diagnostic for supply constraints in the standard model. We suggest that understanding heterogeneity in incomes and housing services quality is likely critical to understanding housing markets more generally.]]>
</description>
<guid>https://fedinprint.org/item/fedfwp/103704/original</guid>
<dc:creator>Najjar, Rami; Mondragon, John; Louie, Schuyler; Wieland, Johannes F.</dc:creator>
<dc:date>2026-08-27</dc:date>
<rdau:hasExtent>16</rdau:hasExtent>
<dc:subject>quality; inequality; housing markets</dc:subject>
<swpo:hasNumber>2026-18</swpo:hasNumber>
<identifiers:doi>10.24148/wp2026-18</identifiers:doi>
<bibo:series>Working Paper Series</bibo:series>
</item>
<item>
<title>A Dual Mandate Can Support Price Stability</title>
<link>https://fedinprint.org/item/fedfwp/103696/original</link>
<description>
<![CDATA[Since employment dynamics are persistent, a central bank’s dual mandate to promote maximum employment and price stability naturally generates history dependence in monetary policy. This history dependence under a dual mandate flattens the reduced-form Phillips curve, reduces the volatility of inflation in response to demand shocks, and improves outcomes at the zero lower bound. Moreover, we show that a dual mandate can be observationally equivalent to average inflation targeting following a demand shock. However, this equivalence breaks down in the presence of supply shocks. We first illustrate these findings analytically and then examine their quantitative importance in a model with nominal rigidities and labor search frictions calibrated to match U.S. business-cycle moments. An employment mandate can naturally provide the benefits associated with history-dependent policy frameworks.]]>
</description>
<guid>https://fedinprint.org/item/fedfwp/103696/original</guid>
<dc:creator>Bundick, Brent; Petrosky-Nadeau, Nicolas</dc:creator>
<dc:date>2026-08-26</dc:date>
<rdau:hasExtent>36</rdau:hasExtent>
<dc:subject>inflation; monetary policy; dual mandate</dc:subject>
<swpo:hasNumber>2026-17</swpo:hasNumber>
<identifiers:doi>10.24148/wp2026-17</identifiers:doi>
<bibo:series>Working Paper Series</bibo:series>
</item>
<item>
<title>What’s Behind the Declining Trend Unemployment Rate?</title>
<link>https://fedinprint.org/item/fedfel/103677</link>
<description>
<![CDATA[The U.S. unemployment rate has trended down for decades. Estimates after removing business cycle fluctuations show that the trend rate fell from 7.8% in 1976 to 4.8% in 2024. This decline reflects in part a more-educated and older workforce—that is, a shift in composition towards demographic groups with traditionally lower unemployment rates. It also reflects newer cohorts entering the labor force with lower unemployment rates. Projections suggest that future demographic changes will gradually lower trend unemployment about 0.4 percentage point further over the next 20 years.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103677</guid>
<dc:creator>Kudlyak, Marianna; Avaradi, Greeshma; Hornstein, Andreas; Kim, Taerin</dc:creator>
<dc:date>2026-08-24</dc:date>
<rdau:hasExtent>6</rdau:hasExtent>
<dc:subject>unemployment rates; demographic groups</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>23</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>Assessing a Medium-Run Natural Rate of Interest</title>
<link>https://fedinprint.org/item/fedfel/103655</link>
<description>
<![CDATA[The natural rate of interest is the inflation-adjusted interest rate consistent with the economy operating at full capacity. Although this rate helps gauge the economy’s health, empirical estimates of it are imprecise and volatile. A medium-run measure that focuses on responsiveness to persistent economic factors while removing short-term volatility may provide more reliable guidance. Analysis suggests that monetary policy using this measure could stabilize inflation and achieve maximum employment more effectively than standard benchmarks. Current medium-run estimates suggest that monetary policy is accommodative, although uncertainty around this estimate remains high.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103655</guid>
<dc:creator>Curdia, Vasco</dc:creator>
<dc:date>2026-08-17</dc:date>
<rdau:hasExtent>5</rdau:hasExtent>
<dc:subject>natural rate of interest; interest rates; real interest rates</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>22</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>Financial Markets, Oil Prices, and Supply-Side Risks</title>
<link>https://fedinprint.org/item/fedfel/103632</link>
<description>
<![CDATA[The relation between stocks and bonds indicates whether supply or demand shocks dominate the risks to economic activity. After two decades of concerns primarily about changes in demand, the stock-bond correlation recently flipped, suggesting that the perceived source of risk to the economy has shifted towards supply shocks. Other financial correlations, such as the stock-oil correlation, also changed accordingly and thus agree with this interpretation. In line with this evidence, financial market pricing now indicates that elevated oil prices and potential inflation are prominent sources of risk.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103632</guid>
<dc:creator>Mertens, Thomas M.; Wasserburger, Wesley</dc:creator>
<dc:date>2026-08-10</dc:date>
<rdau:hasExtent>6</rdau:hasExtent>
<dc:subject>oil prices; financial markets</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>21</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>Reaching for Duration</title>
<link>https://fedinprint.org/item/fedfwp/103631/original</link>
<description>
<![CDATA[Using historical data on U.S. commercial bank balance sheets, we show that banks’ maturity mismatch has more than tripled since the mid-1980s, moving in close lockstep with declining interest rates and term premia. We rationalize these trends in a model of bank portfolio choice in which banks must cover operating costs out of current earnings. When term premia or short-term rates decline, banks extend the duration of their assets to remain profitable. This “reaching for duration” effect is convex in the degree of term premium compression. The resulting maturity mismatch renders banks increasingly vulnerable to self-fulfilling runs by uninsured depositors. Consistent with the model, less profitable banks subsequently raise their asset maturities, particularly in periods of low term premia and large Federal Reserve asset holdings. Quantitative easing, designed to remove duration risk from the private sector, may thus paradoxically concentrate it on bank balance sheets and undermine financial stability.]]>
</description>
<guid>https://fedinprint.org/item/fedfwp/103631/original</guid>
<dc:creator>Mertens, Thomas M.; Schneider, Andrés; Paul, Pascal</dc:creator>
<dc:date>2026-08-10</dc:date>
<rdau:hasExtent>105</rdau:hasExtent>
<dc:subject>maturity mismatch; term premium; quantitative easing; financial stability; bank runs; deposit franchise</dc:subject>
<swpo:hasNumber>2026-16</swpo:hasNumber>
<identifiers:doi>10.24148/wp2026-16</identifiers:doi>
<bibo:series>Working Paper Series</bibo:series>
</item>
<item>
<title>Will AI Intensify or Weaken Market Competition?</title>
<link>https://fedinprint.org/item/fedfwp/103630/original</link>
<description>
<![CDATA[We study how AI affects market competition based on a general equilibrium framework with heterogeneous firms facing idiosyncratic productivity and variable markups. Firms choose the AI technology subject to fixed costs, where AI production requires data and energy inputs. Our model predicts a non-monotonic relation of AI diffusion with industry concentration. As AI usage rises from an initially low level, large incumbent users gain market share. When AI usage is sufficiently diffused, entry of new and smaller adopters erodes the market share of incumbents, reducing industry concentration. The non-monotonic relations are robust when firms can complement AI with their own data. Our calibrated model predicts that industry concentration is likely to fall if AI adoption increases relative to the current level. In comparison, the relation of AI with the average markup depends on whether increased AI usage is driven by demand or supply factors. Our model also predicts that a modest subsidy of about 3 percent for AI adopter revenues maximizes social welfare, reflecting a tradeoff between aggregate productivity and the average markup associated with AI usage.]]>
</description>
<guid>https://fedinprint.org/item/fedfwp/103630/original</guid>
<dc:creator>Liu, Zheng; Firooz, Hamid; Leduc, Sylvain</dc:creator>
<dc:date>2026-08-10</dc:date>
<rdau:hasExtent>47</rdau:hasExtent>
<dc:subject>artificial intelligence; data; heterogeneous firms; industry concentration; markup; productivity; welfare</dc:subject>
<swpo:hasNumber>2026-15</swpo:hasNumber>
<identifiers:doi>10.24148/wp2026-15</identifiers:doi>
<bibo:series>Working Paper Series</bibo:series>
</item>
<item>
<title>Job-Finding Anomalies of the Current Expansion</title>
<link>https://fedinprint.org/item/fedfel/103594</link>
<description>
<![CDATA[Job-finding rates have declined over the past three years for people who are unemployed or are out of the labor force. Analysis shows that the decline in job finding for unemployed people has been pronounced for prime-age and college-educated individuals, while the decline in job finding for people who are out of the labor force has been driven by younger and less-educated individuals. These demographic patterns differ from job-finding rates during typical economic expansions and imply both a cooling and a restructuring within the labor market.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103594</guid>
<dc:creator>Kudlyak, Marianna; Mikhlin, Riva; Chen, Ingrid</dc:creator>
<dc:date>2026-08-03</dc:date>
<rdau:hasExtent>6</rdau:hasExtent>
<dc:subject>job finding; unemployment rate; labor market health</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>20</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>AI Adoption Among Small Businesses: Qualitative Insights from the Small Business Credit Survey</title>
<link>https://fedinprint.org/item/fedfcb/103580</link>
<description>
<![CDATA[Nearly 40% of small business respondents to the 2024 Small Business Credit Survey (SBCS) reported either using or planning to use artificial intelligence (AI), revealing a rapidly evolving landscape of adoption among the nation’s smaller firms.

Sentiments around AI adoption ranged from enthusiasm to opposition. Reported applications of AI among small business respondents were just as varied, spanning both core operations and support functions. Common applications included:

-productivity tasks
-marketing, social media, and search engine optimization
-developing written communications
-visuals generation and graphic design
-customer service
-analytics and forecasting
-programming machines and sensors
-developing custom AI tools for specific business needs

The sophistication of these applications varied considerably, from basic task assistance to complex, integrated systems.

Many firms expressed interest in AI adoption but reported facing barriers, including policy and regulatory limitations, financial costs associated with appropriate tools, time and capacity constraints related to staff training and system upgrades, and knowledge gaps regarding implementation strategies. Other firms deliberately opted not to adopt AI, citing concerns about accuracy and intellectual property rights, the centrality of human interaction to their business model, or an absence of perceived applicability to their operations.

These findings from the 2024 SBCS’s initial qualitative inquiry into AI adoption establish a baseline understanding of how small businesses are experiencing this technological transformation and complement forthcoming analysis of the 2025 SBCS, which included a comprehensive supplemental survey module on AI adoption.]]>
</description>
<guid>https://fedinprint.org/item/fedfcb/103580</guid>
<dc:creator>Simms, Sarah; Holmes, Natalie; Sanchez-Moyano, Rocio</dc:creator>
<dc:date>2026-07-15</dc:date>
<rdau:hasExtent>19</rdau:hasExtent>
<dc:subject>artificial intelligence; small business credit; AI adoption</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>01</bibo:issue>
<identifiers:doi>10.24148/cdrb2026-01</identifiers:doi>
<bibo:series>Community Development Research Brief</bibo:series>
</item>
<item>
<title>Prices and Monetary Policy: The Role of Financial Constraints</title>
<link>https://fedinprint.org/item/fedfwp/103550/original</link>
<description>
<![CDATA[Firm heterogeneity in financial constraints is a quantitatively important driver of how monetary policy transmits to inflation. Using detailed microdata on Swedish public and private firms, and high-frequency monetary policy surprises around Riksbank announcements, we document that smaller, financially constrained firms adjust prices significantly less than larger firms in response to changes in monetary policy. This heterogeneous price response materially dampens the aggregate PPI inflation response to monetary policy. Models of customer markets and financial frictions can explain our findings: because the external finance premium rises after a monetary contraction, constrained firms cut prices less to preserve cash flows, sacrificing future market share. Additional evidence on heterogeneous sales, debt, marginal cost, and markup responses further supports this channel. We consider several alternative explanations, including differences in price adjustments, working capital, market share, and export share, but these cannot rationalize our main heterogeneity result.]]>
</description>
<guid>https://fedinprint.org/item/fedfwp/103550/original</guid>
<dc:creator>Czarnota, Alexander; Klein, Mathias; Bauer, Michael D.</dc:creator>
<dc:date>2026-07-17</dc:date>
<rdau:hasExtent>58</rdau:hasExtent>
<dc:subject>prices; monetary policy; financial constraints; firm heterogeneity</dc:subject>
<swpo:hasNumber>2026-13</swpo:hasNumber>
<identifiers:doi>10.24148/wp2026-13</identifiers:doi>
<bibo:series>Working Paper Series</bibo:series>
</item>
<item>
<title>Recency Effects in Perceived Uncertainty</title>
<link>https://fedinprint.org/item/fedfwp/103549/original</link>
<description>
<![CDATA[Firms frequently revise not only their expectations, but also how uncertain they feel about those expectations. Using the U.S. Survey of Business Uncertainty, we study perceived uncertainty about firms’ own sales and employment growth. Reported uncertainty rises after larger revisions to firms’ point forecasts, with the strongest response to the most recent revision. This recency pattern remains visible outside elevated sectoral-volatility episodes. We develop a model in which agents learn about a constant-volatility process but recall older observations noisily. Noisy recall gives recent surprises disproportionate influence, even when objective volatility is constant.]]>
</description>
<guid>https://fedinprint.org/item/fedfwp/103549/original</guid>
<dc:creator>Sung, Yeji; Acosta, Miguel</dc:creator>
<dc:date>2026-07-10</dc:date>
<rdau:hasExtent>50</rdau:hasExtent>
<swpo:hasNumber>2026-12</swpo:hasNumber>
<identifiers:doi>10.24148/wp2026-12</identifiers:doi>
<bibo:series>Working Paper Series</bibo:series>
</item>
<item>
<title>Firms’ Inflation Expectations During the Pandemic-Era Surge</title>
<link>https://fedinprint.org/item/fedfel/103523</link>
<description>
<![CDATA[Inflation expectations among businesses can affect how they set current prices. Firms’ expectations diverged from those of professional forecasters during the pandemic-era inflation surge and moved closer to household expectations. Analyzing firms’ survey data from 2018 to 2025 reveals three main patterns behind this shift: Businesses became more sensitive to current inflation perceptions, their longer-term expectations temporarily drifted up, and their perceptions of the Federal Reserve’s inflation goal increased. However, when inflation eventually moderated, the survey data show that firms’ inflation expectations largely returned to their characteristics from before the pandemic.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103523</guid>
<dc:creator>Reinelt, Timo; Malhotra, Simar; Hajdini, Ina</dc:creator>
<dc:date>2026-07-13</dc:date>
<rdau:hasExtent>6</rdau:hasExtent>
<dc:subject>inflation expectations; inflation; pandemic; firms</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>19</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>Using Inflation Shock Patterns to Help Forecast Inflation</title>
<link>https://fedinprint.org/item/fedfel/103483</link>
<description>
<![CDATA[A new indicator—the Inflation Shock Momentum Index—can help identify emerging inflationary or disinflationary pressures in real time. The index tracks the shares of consumer spending categories that are experiencing consecutive positive or negative monthly inflation shocks, allowing detection of shifts in the underlying inflation environment. The index improves inflation forecasts at one-year to three-year horizons and responds to macroeconomic shocks in line with accepted theory. Recent index readings have fluctuated above and below zero, indicating that inflation may remain near current levels in the near to medium term.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103483</guid>
<dc:creator>Lansing, Kevin J.; Shapiro, Adam Hale</dc:creator>
<dc:date>2026-07-06</dc:date>
<rdau:hasExtent>6</rdau:hasExtent>
<dc:subject>inflation; inflationary trends</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>18</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>Calibrating Monetary Policy</title>
<link>https://fedinprint.org/item/fedfel/103472</link>
<description>
<![CDATA[The new SF Fed Policy Calibration Tool is designed to help construct a monetary policy path that aligns with one’s views of the economy and policy objectives. Applying the tool to recent tariff increases shows that preferred policy paths vary depending on one’s assessment of the economic effects of tariffs. If tariffs predominantly affect demand, more policy accommodation may be warranted; if they predominantly affect supply, less accommodation may be appropriate. The high uncertainty surrounding these effects implies a wide range of possible scenarios for the best course of action.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103472</guid>
<dc:creator>Singh, Aayush; Barnichon, Régis</dc:creator>
<dc:date>2026-06-29</dc:date>
<rdau:hasExtent>5</rdau:hasExtent>
<dc:subject>monetary policy; tariffs</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>17</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>Central Bank Bond Purchases and the Price of Safety</title>
<link>https://fedinprint.org/item/fedfel/103412</link>
<description>
<![CDATA[Central banks purchase bonds and other securities with their own reserves. In doing so, they expand the supply of safe assets in the economy, which should lower the premium investors are willing to pay for safety. Analysis confirms that bond purchases by the European Central Bank in 2015–2021 lowered safety premiums for investors, partially offsetting declines in bond yields as much as 30 basis points. The results suggest that such transactions essentially reduce a central bank’s effectiveness in using asset purchases to lower interest rates in safe bond markets.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103412</guid>
<dc:creator>Zhang, Xin; Christensen, Jens H. E.; Mirkov, Nikola</dc:creator>
<dc:date>2026-06-22</dc:date>
<rdau:hasExtent>5</rdau:hasExtent>
<dc:subject>central banking; government bonds</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>16</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>How Labor Force Participation Has Diverged Across Genders</title>
<link>https://fedinprint.org/item/fedfel/103347</link>
<description>
<![CDATA[U.S. labor force participation rose for decades until the mid-1990s but has fallen steadily since then. This general pattern masks different paths for men and women in the workforce. Aging and rising education explain much of the long-run changes but do not account for the divergence by gender. Men’s trend participation has fallen steadily since the late 1970s, while women’s participation rose through 2000 before flattening. The difference mainly reflects younger male cohorts participating less than earlier ones, whereas younger female cohorts—especially those with more education—have higher participation.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103347</guid>
<dc:creator>Kim, Taerin; Avaradi, Greeshma; Kudlyak, Marianna; Hornstein, Andreas</dc:creator>
<dc:date>2026-06-01</dc:date>
<rdau:hasExtent>6</rdau:hasExtent>
<dc:subject>Labor Force Participation; gender; women; Women; employment</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>15</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>Trends in Labor Force Participation and Unemployment, 1976-2024</title>
<link>https://fedinprint.org/item/fedfwp/103327/original</link>
<description>
<![CDATA[Using CPS microdata, 1976-2024, we estimate trend and cyclical components of unemployment and labor force participation for 44 age-gender-education groups. We fit a parsimonious state-space model in which each series is the sum of latent cohort and time-varying age effects and a latent cyclical factor shared across unemployment and participation, without imposing structural covariates. Aggregating group trends with observed population shares, we find that population aging and educational upgrading explain most long-run movements in aggregate trends, while cohort effects drive large gender differences in participation. Combining our estimates with demographic projections and an estimated cohort model of education shares, we forecast that over the next two decades, trend participation declines by about 1.5 pp and trend unemployment falls by about 0.4 pp, remaining historically low.]]>
</description>
<guid>https://fedinprint.org/item/fedfwp/103327/original</guid>
<dc:creator>Hornstein, Andreas; Kudlyak, Marianna</dc:creator>
<dc:date>2026-05-08</dc:date>
<rdau:hasExtent>36</rdau:hasExtent>
<dc:subject>labor force participation rate; unemployment rates; Demographic Composition; Age Effects; cohort effects</dc:subject>
<swpo:hasNumber>2026-11</swpo:hasNumber>
<identifiers:doi>10.24148/wp2026-11</identifiers:doi>
<bibo:series>Working Paper Series</bibo:series>
</item>
<item>
<title>Have We Entered an Era of High Productivity Growth?</title>
<link>https://fedinprint.org/item/fedfel/103308</link>
<description>
<![CDATA[Labor productivity gains over the past three years helped the U.S. economy expand steadily, even with near-zero employment growth. Combined with substantially increased business investment in artificial intelligence technology, these conditions have raised the question of whether the economy is entering a high-productivity growth period. Two well-known productivity measures do not yet provide strong evidence of this shift. However, recent patterns resemble the mixed signals during the early stages of the 1990s productivity surge before a sustained high-growth period materialized, giving reason for cautious optimism about future productivity growth.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103308</guid>
<dc:creator>Foerster, Andrew; Abdelrahman, Hamza</dc:creator>
<dc:date>2026-05-26</dc:date>
<rdau:hasExtent>5</rdau:hasExtent>
<dc:subject>productivity; productivity growth; labor productivity; artificial intelligence</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>14</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>Is Optimism for Artificial Intelligence Boosting Investment?</title>
<link>https://fedinprint.org/item/fedfel/103265</link>
<description>
<![CDATA[U.S. business spending related to artificial intelligence (AI) grew substantially in 2025 among publicly traded firms, which account for the bulk of overall investment. Analyzing sentiment data from quarterly company earnings calls can help infer current and evolving optimism towards AI. Public firm data show growth in capital spending and research and development funding has come entirely from the largest companies that are positive about AI. While market concentration among large firms raises some challenges, optimism measures suggest that AI investment will continue to contribute to future overall investment growth.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103265</guid>
<dc:creator>Li, Huiyu; Kalyani, Aakash</dc:creator>
<dc:date>2026-05-18</dc:date>
<rdau:hasExtent>6</rdau:hasExtent>
<dc:subject>artificial intelligence; investment; optimism</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>13</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>AI-Powered Algorithmic Pricing and Monetary Policy</title>
<link>https://fedinprint.org/item/fedfel/103235</link>
<description>
<![CDATA[The business practice of adjusting prices using algorithms powered by artificial intelligence—known as AI pricing—has grown rapidly and spread across many sectors in the economy. Unlike traditional price setting, AI pricing uses predictive analysis of large data sets to incorporate real-time changes in supply and demand conditions into pricing decisions. This enables businesses to adjust prices more quickly in response to unexpected changes in market conditions and monetary policy. Industry-level evidence suggests that price adjustments are more sensitive to monetary policy in sectors where AI pricing is more prevalent.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103235</guid>
<dc:creator>Avaradi, Greeshma; Zhao, Steven; Liu, Zheng</dc:creator>
<dc:date>2026-05-11</dc:date>
<rdau:hasExtent>5</rdau:hasExtent>
<dc:subject>artificial intelligence; algorithmic pricing; monetary policy</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>12</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>What Do Financial Officers Predict for Price Growth?</title>
<link>https://fedinprint.org/item/fedfel/103117</link>
<description>
<![CDATA[Survey responses from chief financial officers and other financial decisionmakers yield a new measure of inflation expectations. Rather than asking about expectations for overall inflation, this survey asks about expected price growth at each respondent’s business. Aggregating survey responses provides an economy-wide indicator that tracks well with actual core consumer price index inflation. Survey responses collected before and during the recent oil shock imply that core inflation could remain elevated this year if the historical relationship between financial officer expectations and realized core inflation persist.]]>
</description>
<guid>https://fedinprint.org/item/fedfel/103117</guid>
<dc:creator>Fried, Stephie; Singh, Sanjay R.; Malhotra, Simar; Graf, Tobin</dc:creator>
<dc:date>2026-05-04</dc:date>
<rdau:hasExtent>6</rdau:hasExtent>
<dc:subject>inflation; consumer price index; inflation expectations</dc:subject>
<bibo:volume>2026</bibo:volume>
<bibo:issue>11</bibo:issue>
<bibo:series>FRBSF Economic Letter</bibo:series>
</item>
<item>
<title>Measuring Inflation Shock Momentum</title>
<link>https://fedinprint.org/item/fedfwp/103112/original</link>
<description>
<![CDATA[We develop a non-parametric filter that identifies sustained directional runs in shocks to monthly inflation—a concept we define as “inflation shock momentum.” By assessing the shocks to over 100 disaggregated Personal Consumption Expenditures (PCE) inflation categories, we isolate the share of categories experiencing positive or negative inflation shock momentum in a given month. We define the “Inflation Shock Momentum” (ISM) index as the net positive momentum share of expenditure-weighted categories (positive minus negative) in a given month. We show that the ISM index helps to forecast aggregate PCE inflation at horizons of 1 to 3 years, even after controlling for a variety of other inflation predictor variables. The ISM index is particularly useful in capturing emerging disinflationary pressure and can be used to help forecast future inflation movements in real time.]]>
</description>
<guid>https://fedinprint.org/item/fedfwp/103112/original</guid>
<dc:creator>Shapiro, Adam Hale; Lansing, Kevin J.</dc:creator>
<dc:date>2026-04-30</dc:date>
<rdau:hasExtent>41</rdau:hasExtent>
<dc:subject>PCE Inflation; Non-parametric filter; Forecasting</dc:subject>
<swpo:hasNumber>2026-10</swpo:hasNumber>
<identifiers:doi>10.24148/wp2026-10</identifiers:doi>
<bibo:series>Working Paper Series</bibo:series>
</item>
<item>
<title>Stabilization vs. Growth</title>
<link>https://fedinprint.org/item/fedfwp/103111/original</link>
<description>
<![CDATA[Should firms in financial distress be saved to stabilize an economy, even if less productive ones are kept alive, possibly reducing economic growth? To assess this fundamental stabilization-vs. growth trade-off, we develop a new dynamic general equilibrium model with business cycles, endogenous growth, and innovation externalities. We discipline key parameters using microeconomic data and an instrumental-variable approach that links firm productivity growth to R&D expenditure. Based on the calibrated model, we find that economies that save distressed firms with credit guarantees, debt restructuring, or loan evergreening experience lower volatility but also slower growth. Even though welfare is higher in an economy without such interventions, the various “soft credit” regimes can still arise as equilibrium outcomes when a benevolent government intervenes in credit markets under discretion.]]>
</description>
<guid>https://fedinprint.org/item/fedfwp/103111/original</guid>
<dc:creator>Faria-e-Castro, Miguel; Paul, Pascal; Sánchez, Juan M.</dc:creator>
<dc:date>2026-04-29</dc:date>
<rdau:hasExtent>71</rdau:hasExtent>
<dc:subject>business cycles; endogenous growth; financial frictions</dc:subject>
<swpo:hasNumber>2026-09</swpo:hasNumber>
<identifiers:doi>10.24148/wp2026-09</identifiers:doi>
<bibo:series>Working Paper Series</bibo:series>
</item>
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