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@@ -2806,3 +2806,4 @@ The Causal Effects of Income on Political Attitudes and Behavior: A Randomized F
 From Novelty to Norm: Uncovering the Drivers of Virtual Tour Effectiveness in Real Estate Sales,Miremad Soleymanian & Yi Qian,"This study examines the effectiveness of virtual tours and digital marketing strategies in enhancing real estate sales using a unique dataset combining MLS data, government-assessed property values, and agents’ marketing activities. While virtual tours are often perceived as a powerful tool to boost sales, their impact is context-dependent. Using classical econometric models and causal machine learning techniques, we find that virtual tours increase property sale prices by an average of 1%. However, the effect has declined over time, particularly post-COVID, indicating a shift from being a novel feature to a standard practice. Further analysis using causal random forests reveals significant heterogeneity in their effectiveness across property attributes, market conditions, and agent characteristics. Virtual tours are less impactful for highly differentiated properties but more beneficial in competitive markets and for less experienced agents who lack familiarity with the local market. These results suggest that real estate agents may benefit from considering property features, market dynamics, and their own experience when deciding how to use virtual tours. Our findings offer valuable insights for practitioners looking to optimize digital marketing strategies and enhance sales performance.",https://www.nber.org/papers/w33204,33204,December 2024,NBER,12/02/2024
 Whither inflation targeting as a global monetary standard?, Claudio Borio,"From its tentative beginnings, inflation targeting has spread to become the de facto global monetary standard. Historically, only the Gold Standard has had a longer lifespan...",https://www.bis.org/publ/work1230.htm,1230,2024-12-02,BIS,12/03/2024
 Predicting College Closures and Financial Distress,"Robert Kelchen, Dubravka Ritter, Douglas Webber","The authors assemble the most comprehensive data set to date on the characteristics of colleges and universities, including dates of operation, institutional setting, student body, staff, and finance data from 2002–2023.  Using these data, they develop a series of predictive models of financial distress among colleges and universities, utilizing factors like operational revenue/expense patterns, sources of revenue, metrics of liquidity and leverage, enrollment/staff patterns, and prior signs of significant financial strain. The authors benchmark their models against existing financial accountability metrics used by the federal government, showing that modern machine learning techniques combined with richer data are far more effective at predicting college closures than linear probability models or these existing federal metrics. The preferred model, which combines an off-the-shelf machine learning algorithm with the richest set of explanatory variables, can significantly improve predictive accuracy even for institutions with complete data, but is particularly helpful for predicting instances of financial distress for institutions with spotty data. The authors then conduct simulations using their estimates to contemplate likely increases in future closures, showing that enrollment challenges resulting from an impending demographic cliff are likely to significantly increase annual college closures for reasonable scenarios.",https://www.philadelphiafed.org/consumer-finance/education-finance/predicting-college-closures-and-financial-distress,24-20,December 2024,FED-PHILADELPHIA,12/03/2024
+Monetary policy pass-through to consumer prices: evidence from granular price data,"Anastasia Allayioti, Lucyna Gόrnicka, Sarah Holton, Catalina Martínez Hernández","We document that about 33% of the core inflation basket in the euro area is sensitive to monetary policy shocks. We assess potential theoretical mechanisms driving the sensitivity. Our results suggest that items of a discretionary nature, as reflected in a higher share in the consumption baskets of richer households, and those with larger role of credit in financing their purchase, tend to be more sensitive.Non-sensitive items are more frequently subject to administered prices and include non-discretionary items such as rents and medical services. Energy intensity does not seem to drive our results and the sensitive items are not dominated by durable goods, but are relatively evenly split between goods and services. Estimations over different samples show that the impact of monetary policy shocks on sensitive core inflation has become larger recently.",https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp3003~78aa7cc112.en.pdf?419ba7aaad637a2efb80aa503bfe652b,3003,3 December 2024,ECB,12/04/2024