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<title>Federal Reserve Bank of Philadelphia publications</title>
<description>Economic research and commentary from Federal Reserve Bank of Philadelphia</description>
<link>https://fedinprint.org/search?facets[]=provider_literal_array:Federal+Reserve+Bank+of+Philadelphia</link>
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<pubDate>Mon, 11 May 2026 12:12:46 +0000</pubDate>
<item>
<title>Flight to Safety: Evaluating Stablecoin’s Role as a Safe-Haven Asset in DeFi Markets</title>
<link>https://fedinprint.org/item/fedpwp/103156/original</link>
<description>
<![CDATA[This study examines the impact of the stablecoin Tether (USDT) on systemic liquidity across the Ethereum and Bitcoin markets, utilizing an event study approach that integrates on-chain wallet data, pricing, and financial metrics. By analyzing cryptocurrency market responses to key protocol and market-moving events, augmented by nonlinear volatility models, we identify distinct, chain-specific flight-to-safety behaviors. Our results show that USDT acts as a primary liquidity lifeline for Ethereum holders during stress, particularly among retail investors, whereas its role for Bitcoin holders is more muted and stabilizing. Notably, we find stronger flight-to-safety evidence in Wrapped Bitcoin (Ethereum-based) than in native Bitcoin, highlighting that USDT’s function is network dependent. These findings imply that effective regulatory frameworks must be differentiated, accounting for chain-specific liquidity, investor composition, and risk dynamics, as a uniform approach would likely be systematically miscalibrated.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/103156/original</guid>
<dc:creator>Chernoff, Alan; Yoshida, Nathaniel; Jagtiani, Julapa</dc:creator>
<dc:date>2026-05-07</dc:date>
<rdau:hasExtent>37</rdau:hasExtent>
<dc:subject>Cryptocurrency; Stablecoins; Bitcoin; Ethereum; Tether; Flight to safety; BTC; ETH; USDT</dc:subject>
<swpo:hasNumber>26-24</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.24</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>Are Fiscal Transfers Inflationary?</title>
<link>https://fedinprint.org/item/fedpwp/103152/original</link>
<description>
<![CDATA[We assess the inflationary effects of fiscal transfers by leveraging advances in the identification of fiscal policy shocks within the recently proposed rotation-invariant time-varying structural vector autoregression. Our analysis suggests that fiscal transfer shocks account for a sizable share of the early post-pandemic increase in the price level through mid-2021. Thereafter, the rise in the price level is dominated by adverse supply shocks (especially supply-chain disruptions), while demand shocks mainly matter later for the lift-off in short-term interest rates. In addition, we find that fiscal transfers were essential for preventing a decline in real output per capita similar to the one experienced during the Great Depression.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/103152/original</guid>
<dc:creator>Shin, Minchul; Arias, Jonas E.; Rubio-Ramirez, Juan F.</dc:creator>
<dc:date>2026-05-05</dc:date>
<rdau:hasExtent>55</rdau:hasExtent>
<dc:subject>fiscal policy; structural vector autoregressions; identification</dc:subject>
<swpo:hasNumber>26-23</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.23</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>Do Recent Auto Loan Delinquency Rates Overstate Borrower Distress?</title>
<link>https://fedinprint.org/item/p00001/103107</link>
<description>
<![CDATA[Headlines about record-high auto loan delinquencies paint a worrying picture of American consumers under increasing financial strain. But how much of that picture reflects a genuine increase in distress — and how much reflects how we measure it? After considering several possible explanations, we focus on deconstructing the severe delinquency rate — the number of auto loans that are 60 or more days delinquent — to better understand what is driving the increase in this rate. We find that while the stock of severe auto delinquencies is rising, the flow of new delinquencies into this stage is fairly stable. A possible explanation for the difference between these two trends may be that account management (e.g., forbearance practices) for distressed auto loans has evolved. An open question is whether further adjustments will be made to these practices if the U.S. market experiences a deterioration in the macroeconomic environment.]]>
</description>
<guid>https://fedinprint.org/item/p00001/103107</guid>
<dc:creator>Santucci, Larry; Zhou, Justin; Cheney, Julia S.; Hunt, Robert M.; Lambie-Hanson, Lauren</dc:creator>
<dc:date>2026-05-01</dc:date>
<rdau:hasExtent>5</rdau:hasExtent>
<bibo:series>Consumer Finance Institute Research Briefs and Special Reports</bibo:series>
</item>
<item>
<title>Is Household Financial Health Improving?</title>
<link>https://fedinprint.org/item/p00001/103106</link>
<description>
<![CDATA[In this CFI Research Brief, we use anonymized credit report data on consumer delinquency to assess the recent trajectory of households' financial health. Do recent improvements in consumer delinquency trends reflect strengthening household financial health, or are delinquency trends driven by other factors such as changes in the composition of borrowers? Using nationally representative consumer credit panel data and fixed-effects regression methods, our analysis points to a continued rise in the likelihood of delinquency, consistent with further deterioration in household financial health. This deterioration appears to be most pronounced for low- to moderate-income consumers, even as their debt burdens have declined significantly.]]>
</description>
<guid>https://fedinprint.org/item/p00001/103106</guid>
<dc:creator>Bhutta, Neil; Doubinko, Valeria Zeballos</dc:creator>
<dc:date>2026-05-01</dc:date>
<rdau:hasExtent>7</rdau:hasExtent>
<bibo:series>Consumer Finance Institute Research Briefs and Special Reports</bibo:series>
</item>
<item>
<title>It’s You, Not Me – Survey Data on AI’s Impact on Employees</title>
<link>https://fedinprint.org/item/p00001/103105</link>
<description>
<![CDATA[While the effect of artificial intelligence (AI) on the workplace has received a significant amount of attention in recent years, the nature of that effect on employees and on the job market appears to be mixed among respondents to the LIFE Survey. Generally and across most demographic groups, employed respondents are very likely to disagree that AI is directly affecting their jobs or career opportunities. At the same time, they are very likely to agree that AI is affecting the job market as a whole]]>
</description>
<guid>https://fedinprint.org/item/p00001/103105</guid>
<dc:creator>Akana, Tom</dc:creator>
<dc:date>2026-05-01</dc:date>
<rdau:hasExtent>4</rdau:hasExtent>
<bibo:series>Consumer Finance Institute Research Briefs and Special Reports</bibo:series>
</item>
<item>
<title>How Quantum Computing Threatens Cryptography</title>
<link>https://fedinprint.org/item/fedpei/103077</link>
<description>
<![CDATA[Quantum computers may one day decrypt our data, but banks can mitigate this risk.]]>
</description>
<guid>https://fedinprint.org/item/fedpei/103077</guid>
<dc:creator>Sanches, Fabio</dc:creator>
<dc:date>2026-04-21</dc:date>
<rdau:hasExtent>10</rdau:hasExtent>
<dc:subject>Quantum computing</dc:subject>
<bibo:series>Economic Insights</bibo:series>
</item>
<item>
<title>International Currency Dominance</title>
<link>https://fedinprint.org/item/fedpwp/103040/original</link>
<description>
<![CDATA[We present a micro-founded monetary model of the world economy to study international currency competition. Our model features both “unipolar” equilibria, with a single dominant international currency, and “multipolar” equilibria, in which multiple currencies circulate internationally. Long-run equilibria are highly history-dependent and tend towards the emergence of a dominant currency. Governments can compete to internationalize their currencies by offering attractive interest rates on their sovereign debt, but large economies have a natural advantage in ensuring the dominance of their currencies. We calibrate the model to assess the quantitative importance of these mechanisms and study the dynamics of the international monetary system under counterfactual scenarios.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/103040/original</guid>
<dc:creator>Sanches, Daniel R.; Abadi, Joseph; Fernández-Villaverde, Jesús</dc:creator>
<dc:date>2026-04-15</dc:date>
<rdau:hasExtent>52</rdau:hasExtent>
<dc:subject>dominant currency; international monetary system; strategic complementarities; history dependence</dc:subject>
<swpo:hasNumber>26-21</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.21</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>Time-Consistent Individuals, Time-Inconsistent Households</title>
<link>https://fedinprint.org/item/fedpwp/103023/original</link>
<description>
<![CDATA[I present a model of consumption and savings for a multi-person household in which members are imperfectly altruistic, derive utility from both private and shared public goods, and share wealth. I show that, despite having standard exponential time preferences, the household is time-inconsistent: members save too little and overspend on private consumption goods. The household remains time-inconsistent even when members save separately, because the possibility of voluntary transfers or joint contribution to the public good preserves the dynamic commons problem. The household will choose to share wealth when the risk sharing benefits outweigh the utility cost of overconsumption.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/103023/original</guid>
<dc:creator>Hertzberg, Andrew</dc:creator>
<dc:date>2026-04-13</dc:date>
<rdau:hasExtent>63</rdau:hasExtent>
<dc:subject>Time-Inconsistency; Savings; Families; Intra-Household Decision Making</dc:subject>
<swpo:hasNumber>26-20</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.20</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>Why Is Manufacturing Productivity Growth So Low?</title>
<link>https://fedinprint.org/item/fedpwp/103003/original</link>
<description>
<![CDATA[We examine the recent slow growth in manufacturing productivity. We show that nearly all measured TFP growth since 1987 — and its post-2000s decline — comes from a few computer-related industries. We argue conventional measures understate manufacturing productivity growth by failing to fully capture quality improvements. We compare consumer to producer and import price indices. In rapidly changing industries, consumer price indices indicate less inflation, suggesting mismeasurement in standard industry deflators. Using an input-output framework, we estimate that TFP growth is understated by 1.4 percentage points in durable manufacturing and 0.3 percentage points in nondurable manufacturing and is slightly overstated in nonmanufacturing industries.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/103003/original</guid>
<dc:creator>Atalay, Enghin; Kimmel, Nicole; Syverson, Chad; Hortacsu, Ali</dc:creator>
<dc:date>2026-04-07</dc:date>
<rdau:hasExtent>78</rdau:hasExtent>
<dc:subject>manufacturing; productivity measurement; ICT</dc:subject>
<swpo:hasNumber>26-19</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.19</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>Temporal Focal Points and Economic Outcomes Evidence from U.S. Mortgage Lending</title>
<link>https://fedinprint.org/item/fedpwp/102981/original</link>
<description>
<![CDATA[Temporal focal points shape high-stakes economic outcomes. We investigate this proposition in the U.S. mortgage market by documenting novel within-month patterns in lending. Using confidential Home Mortgage Disclosure Act (HMDA) data, we show that applications arrive smoothly throughout the month, yet approximately half of all originations occur in the final week. The Black approval gap, 2.4 percentage points unexplained at the start of the month, narrows to zero by month’s end. Underperforming loan officers and high-turnover lending institutions amplify this convergence. Demand-side factors, such as borrower financial constraints, also increase end-of-month volume, but the sensitivity does not vary meaningfully by applicant group]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/102981/original</guid>
<dc:creator>Yu, Edison; Heimer, Rawley; Giacoletti, Marco</dc:creator>
<dc:date>2026-04-01</dc:date>
<rdau:hasExtent>71</rdau:hasExtent>
<swpo:hasNumber>26-18</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.18</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>Monetary Policy and Productivity</title>
<link>https://fedinprint.org/item/fedpsp/102949</link>
<description>
<![CDATA[Anna Paulson delivered a keynote speech at the Federal Reserve Bank of San Francisco's Macroeconomics and Monetary Policy Conference, where she addressed key challenges facing monetary policymakers in the current economic environment. Speaking to an audience of academic and central bank researchers, financial market practitioners, and policymakers, President Paulson discussed the possible impacts of artificial intelligence on economic productivity and the implications for monetary policy to achieve the Fed’s mandate.]]>
</description>
<guid>https://fedinprint.org/item/fedpsp/102949</guid>
<dc:creator>Paulson, Anna L.</dc:creator>
<dc:date>2026-03-27</dc:date>
<rdau:hasExtent>8</rdau:hasExtent>
<bibo:series>Speech</bibo:series>
</item>
<item>
<title>Bank Loans to NBFIs: Evidence of Specialization, Part II</title>
<link>https://fedinprint.org/item/fedpei/102943</link>
<description>
<![CDATA[Is lending to nonbanks posing a systemic risk? We take a closer look at which bank characteristics correlate with NBFI specialization.]]>
</description>
<guid>https://fedinprint.org/item/fedpei/102943</guid>
<dc:creator>Conroy, Andrew; D'Erasmo, Pablo</dc:creator>
<dc:date>2026-03-26</dc:date>
<rdau:hasExtent>7</rdau:hasExtent>
<bibo:series>Economic Insights</bibo:series>
</item>
<item>
<title>Bank Loans to NBFIs: Evidence of Specialization, Part I</title>
<link>https://fedinprint.org/item/fedpei/102942</link>
<description>
<![CDATA[Should we be worried about bank lending to nonbanks? That depends on the degree of specialization.]]>
</description>
<guid>https://fedinprint.org/item/fedpei/102942</guid>
<dc:creator>D'Erasmo, Pablo; Conroy, Andrew</dc:creator>
<dc:date>2026-03-26</dc:date>
<rdau:hasExtent>10</rdau:hasExtent>
<bibo:series>Economic Insights</bibo:series>
</item>
<item>
<title>Building Credit Histories</title>
<link>https://fedinprint.org/item/fedpwp/102941/original</link>
<description>
<![CDATA[This paper investigates how new borrowers expand their credit access. In particular, we examine the role that consumers’ credit choices, not just repayment behavior, play in building their credit histories. Using credit bureau data, we document that incumbent lenders typically increase credit limits for borrowers who open additional credit cards. This effect is especially pronounced for new borrowers. Our interpretation of this evidence is that lenders perceive credit offered by other lenders as revealing favorable information about the borrower. We build a novel model consistent with this hypothesis and show that the model’s predictions are consistent with the data.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/102941/original</guid>
<dc:creator>Livshits, Igor; Zetlin-Jones, Ariel; Kovrijnykh, Natalia</dc:creator>
<dc:date>2026-03-26</dc:date>
<rdau:hasExtent>108</rdau:hasExtent>
<dc:subject>Emerging Borrowers; Credit History; Information Aggregation; Debt Dilution</dc:subject>
<swpo:hasNumber>26-17</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.17</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>Off the Beaten Tract: Constructing a New Neighborhood Geography Using Revealed Preference</title>
<link>https://fedinprint.org/item/fedpwp/102934/original</link>
<description>
<![CDATA[We construct a new neighborhood geography using a revealed preference intuition: If people disproportionately move within neighborhoods, their boundaries can be backed out from migration flows. Our “districts,” which consist of about nine census tracts each, correspond to recognizable local areas, as their boundaries align with physical barriers, sharp demographic changes, and local government borders. To illustrate applications, we first show that tract-level analyses of neighborhood sorting miss important broader patterns. Second, aggregating tract-level intergenerational mobility estimates to the district level increases precision threefold while introducing little aggregation bias, resulting in improved predictive power in a hold-out sample.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/102934/original</guid>
<dc:creator>Mast, Evan; Barca, Alaina</dc:creator>
<dc:date>2026-03-24</dc:date>
<rdau:hasExtent>67</rdau:hasExtent>
<dc:subject>Neighborhood definition; residential mobility; residential sorting</dc:subject>
<swpo:hasNumber>26-16</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.16</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>Evaluating Transportation Improvements Within Cities Using Quantitative Spatial Models</title>
<link>https://fedinprint.org/item/fedpwp/102896/original</link>
<description>
<![CDATA[I describe the use of quantitative spatial models (QSMs) to evaluate the effects of transportation infrastructure within cities. After discussing the motivation for QSMs relative to other economic measurement techniques, I develop a simple QSM and detail the components that enter into the model. Next, I consider identification challenges and practical implementation. Finally, I highlight several shortcomings common in applications of QSMs, as well as growth areas where QSMs show promise for future development.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/102896/original</guid>
<dc:creator>Severen, Christopher</dc:creator>
<dc:date>2026-03-05</dc:date>
<rdau:hasExtent>31</rdau:hasExtent>
<dc:subject>quantitative spatial models; transportation infrastructure; transit; urban economics</dc:subject>
<swpo:hasNumber>26-13</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.13</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>The Impact of Early Investments in Urban School Systems in the United States</title>
<link>https://fedinprint.org/item/fedpwp/102897/original</link>
<description>
<![CDATA[Cities in the United States dramatically expanded spending on public education after World War I, with the average urban school district increasing per pupil expenditures by over 70 percent by 1924. We provide the first evaluation of these unprecedented investments in public education using a new dataset and plausibly exogenous growth in school spending generated by anti-German sentiment. We find that school resources significantly increased educational attainment and wages later in life, particularly for less advantaged children. Increases in expenditures can explain about 40 percent of the sizable increase in educational attainment of cohorts born between 1895 and 1913.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/102897/original</guid>
<dc:creator>Schmick, Ethan; Shertzer, Allison</dc:creator>
<dc:date>2026-03-16</dc:date>
<rdau:hasExtent>66</rdau:hasExtent>
<dc:subject>school spending; returns to educational resources</dc:subject>
<swpo:hasNumber>26-15</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.15</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>On the Wisdom of Crowds (of Economists)</title>
<link>https://fedinprint.org/item/fedpwp/102886/original</link>
<description>
<![CDATA[We study the properties of macroeconomic survey forecast response averages as the number of survey respondents grows. Such averages are “portfolios” of forecasts. We characterize the speed and pattern of the gains from diversification as a function of portfolio size (the number of survey respondents) in both (1) the key real-world data-based environment of the U.S. Survey of Professional Forecasters, and (2) the theoretical model-based environment of equicorrelated forecast errors. We proceed by proposing and comparing various direct and model-based “crowd size signature plots,” which summarize the forecasting performance of k-average forecasts as a function of k, where k is the number of forecasts in the average. We then estimate the equicorrelation model for growth and inflation forecast errors by choosing model parameters to minimize the divergence between direct and model-based signature plots. The results indicate near-perfect equicorrelation model fit for both growth and inflation, which we explicate by showing analytically that, under very weak conditions, the direct and fitted equicorrelation model-based signature plots are identical at a particular model parameter configuration. That parameter configuration immediately suggests an analytic closed-form estimator for the direct signature plot, so that equicorrelation ultimately emerges as a device for convenient calculation of direct signature plots, rather than a separate “model” producing separate signature plots. Finally, we find that the gains from survey diversification are greater for inflation forecasts than for growth forecasts, and that they are largely exhausted with inclusion of 5–10 representative forecasters.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/102886/original</guid>
<dc:creator>Mora, Aaron; Diebold, Francis X.; Shin, Minchul</dc:creator>
<dc:date>2026-03-09</dc:date>
<rdau:hasExtent>36</rdau:hasExtent>
<dc:subject>Survey of professional forecasters; forecast combination; model averaging; equicorrelation</dc:subject>
<swpo:hasNumber>26-14</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.14</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>Discussion Private Canaries: The Value of Private-Sector  Data for U.S. Monetary Policy Making (Presentation Slides)</title>
<link>https://fedinprint.org/item/fedpsp/102884</link>
<description>
<![CDATA[At the 2026 U.S. Monetary Policy Forum, Philadelphia Fed President and CEO Anna Paulson engaged in a discussion with San Francisco Fed President and CEO Mary C. Daly following the presentation of the 2026 report, "Private Canaries: The Value of Private-Sector Data For U.S. Monetary Policy Making."]]>
</description>
<guid>https://fedinprint.org/item/fedpsp/102884</guid>
<dc:creator>Paulson, Anna L.</dc:creator>
<dc:date>2026-03-06</dc:date>
<rdau:hasExtent>10</rdau:hasExtent>
<bibo:series>Speech</bibo:series>
</item>
<item>
<title>Discussion Points on “Private Canaries: The Value of Private Sector Data for U.S. Monetary Policy Making”</title>
<link>https://fedinprint.org/item/fedpsp/102883</link>
<description>
<![CDATA[At the 2026 U.S. Monetary Policy Forum, Philadelphia Fed President and CEO Anna Paulson engaged in a discussion with San Francisco Fed President and CEO Mary C. Daly following the presentation of the 2026 report, "Private Canaries: The Value of Private-Sector Data For U.S. Monetary Policy Making."]]>
</description>
<guid>https://fedinprint.org/item/fedpsp/102883</guid>
<dc:creator>Paulson, Anna L.</dc:creator>
<dc:date>2026-03-06</dc:date>
<rdau:hasExtent>3</rdau:hasExtent>
<bibo:series>Speech</bibo:series>
</item>
<item>
<title>Polarized Contributions but Convergent Agendas</title>
<link>https://fedinprint.org/item/fedpwp/102846/original</link>
<description>
<![CDATA[In a canonical model of policy formation, campaign contributions, and electoral competition, we show that, despite donor polarization, candidates’ agendas converge. If purely office-motivated candidates move away from the centrist agenda, they increase their opponents’ contributions more than their own. An extension that introduces a “job ladder” for the candidates leads to candidates caring about absolute levels of campaign contributions and generates divergence of political agendas in equilibrium. We provide empirical evidence of campaign contributions affecting candidates’ chances of “promotion,” and characterize key comparative statics of the extended model. In the model, caps on campaign contributions lower polarization in equilibrium.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/102846/original</guid>
<dc:creator>Livshits, Igor; Drautzburg, Thorsten; Wright, Mark L. J.</dc:creator>
<dc:date>2026-03-03</dc:date>
<rdau:hasExtent>50</rdau:hasExtent>
<dc:subject>Polarization; Campaign Contributions; Agendas</dc:subject>
<swpo:hasNumber>26-05</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.05</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>Demand-Based Asset Pricing in General Equilibrium</title>
<link>https://fedinprint.org/item/fedpwp/102820/original</link>
<description>
<![CDATA[I develop a general equilibrium macro-finance model that integrates demand-based asset pricing. Assets are held by financial intermediaries (“funds”) with investment mandates that induce downward-sloping demand curves. A representative household seeks out profitable investment opportunities by shifting its savings across funds, but it does so only gradually due to frictions in adjusting its portfolio. The aggregate demand for assets in this economy is inelastic and depends on the distribution of net worth across funds. Consequently, shocks to asset supply and unanticipated financial flows have meaningful effects on asset prices. The framework is general enough to accommodate an arbitrary set of intermediaries and assets, so it can be applied to several questions in macro-finance. Analytically, I characterize sufficient statistics to construct counterfactual asset price responses to shocks and show how these statistics relate to estimates of asset demand elasticity in the literature. Quantitatively, I demonstrate that the model can account for the “excess volatility”  in asset prices.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/102820/original</guid>
<dc:creator>Abadi, Joseph</dc:creator>
<dc:date>2026-02-25</dc:date>
<rdau:hasExtent>49</rdau:hasExtent>
<dc:subject>Asset Pricing; Macro-Finance; Financial Intermediation; Slow-Moving Capital</dc:subject>
<swpo:hasNumber>26-12</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.12</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>Strong Spatial Spillovers Determine Neighborhood Shape and Neighborhood Change</title>
<link>https://fedinprint.org/item/fedpwp/102819/original</link>
<description>
<![CDATA[This paper explores how spatial spillovers define neighborhoods and drive neighborhood change through a stylized computable equilibrium model of income-based residential sorting. We find three main results. First, stronger spillovers create larger, more distinct neighborhood clusters even when the spatial scope of externalities is small. Second, spillovers make neighborhoods resistant to small change but also susceptible to rapid shifts between equilibrium states. Third, stronger spillovers concentrate change at cluster boundaries and isolated locations rather than neighborhood interiors. Extending Schelling-like insights to income sorting dynamics, the model treats neighborhood shape as an endogenous outcome determined by decentralized household location choices mediated through housing markets. The framework helps explain persistent urban segregation patterns, neighborhood resilience, and the geography of neighborhood change, offering new approaches for linking spatial spillovers to urban spatial structure and dynamics.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/102819/original</guid>
<dc:creator>Aliprantis, Dionissi; Lin, Jeffrey</dc:creator>
<dc:date>2026-02-23</dc:date>
<rdau:hasExtent>29</rdau:hasExtent>
<dc:subject>Neighborhood dynamics; neighborhood formation; spatial externalities; income sorting; residential segregation; neighborhood morphology</dc:subject>
<swpo:hasNumber>26-11</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.11</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>Does Experience Matter? Past Fraud Experiences, Data Compromises, and Credit Market Behavior</title>
<link>https://fedinprint.org/item/fedpwp/102536/original</link>
<description>
<![CDATA[We study how past experiences with fraud affect individuals’ likelihood of taking precautionary action in credit markets when faced with a new shock that raises their fraud risks. We focus on two kinds of past experiences with fraud: direct experience with fraud and a “near-miss” experience that increased fraud risk but did not directly lead to fraud. Using the 2017 Equifax data breach announcement, we show that individuals with either type of prior experience with fraud were more likely to take a precautionary action — freezing their credit report — than individuals with no prior experience with fraud. We also find that individuals with past direct experience with fraud were more likely to freeze their credit report than individuals who had a past near-miss experience. The individuals who froze their credit report had fewer total accounts and credit inquiries than those who did not, but this reduction in credit did not reduce their credit scores.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/102536/original</guid>
<dc:creator>Blascak, Nathan; Toh, Ying Lei</dc:creator>
<dc:date>2026-02-19</dc:date>
<rdau:hasExtent>54</rdau:hasExtent>
<dc:subject>Equifax data breach; consumer credit; credit freeze</dc:subject>
<swpo:hasNumber>26-10</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.10</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
<item>
<title>Model Risk Under CECL: A Consumer Finance Perspective</title>
<link>https://fedinprint.org/item/fedpwp/102431/original</link>
<description>
<![CDATA[We examine the challenges of economic forecasting and model misspecification errors confronted by financial institutions implementing the novel current expected credit loss (CECL) allowance methodology and its impact on model risk and bias in CECL projections. We document the increased sensitivity to model and macroeconomic forecasting error of the CECL framework with respect to the incurred loss framework that it replaces. An empirical application illustrates how to leverage simple machine learning (ML) strategies and statistical principles in the design of a nimble and flexible CECL modeling framework. We show that, even in consumer loan portfolios with tens of millions of loans, like mortgage, auto, or credit card portfolios, one can develop, estimate, and deploy an array of models quickly and efficiently, and without a forecasting performance penalty. Drawing on more than 20 years of auto loans data and the experience from the Great Recession and the COVID-19 pandemic, we leverage basic econometric principles to identify strategies to deal with biased model projections in times of high economic uncertainty. We advocate for a focus on resiliency and adaptability of models and model infrastructures to novel shocks and uncertain economic conditions.]]>
</description>
<guid>https://fedinprint.org/item/fedpwp/102431/original</guid>
<dc:creator>Canals-Cerda, Jose J.</dc:creator>
<dc:date>2026-02-12</dc:date>
<rdau:hasExtent>55</rdau:hasExtent>
<dc:subject>CECL; Allowance for Loan and Lease Losses; Accounting Regulations; Model Risk</dc:subject>
<swpo:hasNumber>26-09</swpo:hasNumber>
<identifiers:doi>10.21799/frbp.wp.2026.09</identifiers:doi>
<bibo:series>Working Papers</bibo:series>
</item>
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