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  • Valanciunas climbs to fourth in EuroBasket rebounding charts

    Valanciunas climbs to fourth in EuroBasket rebounding charts

    The official EuroBasket app

    TAMPERE (Finland) – Lithuania’s Jonas Valanciunas rose to fourth in the FIBA EuroBasket rebounding charts, overtaking Spain’s Marc Gasol on the opening day of the 2025 edition.

    The 33-year-old needed just eight rebounds to pass his Spanish rival on the list of rebounds at the tournament, since the data was recorded in 1995.

    Valanciunas did so after pulling down nine rebounds – along with a team-high 18 points – in Lithuania’s 94-70 win over Great Britain.

    “If we don’t get the win, the individual records mean nothing,” said Valanciunas post-game. “But it’s nice to overtake Marc and hopefully I can keep climbing.”

    Check out the game report

    Lithuania open FIBA EuroBasket 2025 with convincing win

    Since making his debut at FIBA EuroBasket 2011, Valanciunas has averaged 7.7 rebounds along with 12 points before the start of this summer’s edition, where Lithuania are playing in Group B alongside Germany, Montenegro, Great Britain, Sweden and hosts Finland.

    Rank

    Player

    Country

    Total Rebounds

    1

    Pau Gasol

    Spain

    475

    2

    Dirk Nowitzki

    Germany

    364

    3

    Boris Diaw

    France

    314

    4

    Jonas Valanciunas

    Lithuania

    309*

    5

    Marc Gasol

    Spain

    307

    6

    Andrei Kirilenko

    Russia

    284

    7

    Ioannis Bourousis

    Greece

    257

    8

    Felipe Reyes

    Spain

    252

    9

    Mirsad Turkcan

    Türkiye

    247

    10

    Florent Pietrus

    France

    232

    * as of August 27, 2025.

    FIBA

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  • Aker Solutions selected for major hydropower upgrade in Norway

    Aker Solutions will deliver the turbine and main mechanical systems for the Blåfalli Fjellhaugen hydropower project in Kvinnherad, developed by Sunnhordland Kraftlag (SKL), a regional hydropower company in Western Norway. The project will deliver one of the largest new hydropower plants constructed in Norway during the last 20 years.

     Blåfalli Fjellhaugen will add 185 MW of regulated hydropower and generate an additional 70 GWh annually. Located within the existing Blådalsvassdraget system, the plant will increase total installed capacity in the watercourse to around 550 MW, with annual production reaching approximately 1.7 TWh – equivalent to the electricity use of more than 100,000 households.

    Construction starts in September, with LNS responsible for tunnelling and building the underground powerhouse. Konecranes will deliver the crane system, Lysaker & Thorrud the mechanical waterway components, and Hitachi the transformer and related systems.

    Andritz Hydro supplies control and instrumentation systems for operation, monitoring, and power supply, as well as high-voltage systems at generator voltage level. Norconsult provides consultancy services for the planning and design of the power plant.

    “We are excited to contribute to one of the largest new hydropower developments in Norway this decade. On the shoulders of 170 years of turbine innovation, and coupled with solid project execution, we will deliver reliable equipment designed for tomorrow’s power system.” says Simen Vogt-Svendsen, SVP Hydropower at Aker Solutions.

    For SKL, the agreements represent a major milestone.

    “This is a big and important day for SKL. We have worked consistently on upgrading and developing hydropower in the Blådalsvassdraget system over the past 25 years. We look forward to starting construction of the Blåfalli Fjellhaugen hydropower plant together with these experienced companies,” says John Martin Mjånes, CEO of SKL.

    Project manager Kjetil Heimvik underlines the long preparation for the project:

    “Blåfalli Fjellhaugen is a project we have been working on for well over ten years. We are pleased to have skilled and experienced partners on board to realize this plant. We have had good cooperation during the preparations and look forward to getting started.”

    The overall construction time is expected to be about four years.

     

     

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  • Why Salmonella Dublin poses a food safety threat in beef and dairy

    Why Salmonella Dublin poses a food safety threat in beef and dairy

    A nationwide genomic study shows that while Salmonella Dublin looks genetically uniform, it hides powerful resistance traits that threaten cattle, people, and the food supply alike.

    Study: Genomic evolution of Salmonella Dublin in cattle and humans in the United States. Image credit: Parilov/Shutterstock.com 

    The foodborne pathogen Salmonella Dublin shows increasing antimicrobial resistance (AMR). It also spreads in the American food chain, compromising food safety and threatening food security. A recent paper published in Applied and Environmental Microbiology explored biosurveillance data for S. Dublin to understand how it changes in various human and non-human hosts.

    Introduction

    S. Dublin is a zoonotic microbe that has adapted well to cattle. It contaminates the human food chain and can cause severe disease in cattle and animals. Its scientific name is Salmonella enterica subsp. enterica serovar Dublin (S. Dublin). It is the most common strain obtained from clinical cattle case submissions, and the second-most common from non-clinical cattle submissions, rather than all clinical infections.

    S. Dublin contaminates other cattle or humans primarily via the feco-oral route but can also be carried through saliva or milk. Infected calves become severely ill. A high proportion develop septicemia and respiratory disease, leading to death. Survivors may become healthy carriers and shed the microbe at intervals later in life.

    Mature cattle develop gastroenteritis following infection, decreasing lactational milk production. Again, they often become healthy carriers, with shedding rates varying with physiological, environmental, and disease-related factors. Their calves are more likely to be infected and develop septicemia.

    The USA is the world’s largest beef producer and among the top three for major dairy products. Thus, S. Dublin has a documented impact on dairy and beef production.

    Raw milk, soft cheeses, and contaminated beef are the primary sources of foodborne S. Dublin outbreaks and contact with infected cattle. It forms an occupational hazard for veterinarians and cattle workers. Humans suffer more severely and require hospitalization more often with S. Dublin infection than from other serovars, and it is more likely to cause illnesses requiring hospitalization.

    Antimicrobial resistance (AMR) and multi-drug resistance (MDR) enhance the severity of S. Dublin infections, conferring resistance to fluoroquinolones like ciprofloxacin, or cephalosporins like ceftriaxone. The National Antimicrobial Resistance Monitoring System (NARMS) monitors AMR in this and other pathogens known to infect humans across the USA, especially using whole-genome sequence (WGS) techniques.

    This has produced a large amount of publicly available data, which has provided insights into the prevalence or rise of AMR by species, regions, or time periods, singly or in combination. The current study focused on S. Dublin AMR and virulence using public genomic data within the One Health framework.

    About the study

    The study included data on 2,150 strains of S. Dublin collected from various parts of the USA between 2002 and 2023. About 580 were from infected cattle, 664 from infected humans, but most from environmental sources. The researchers then analyzed the genomes, especially the plasmids, virulence factors, and AMR genes. They assessed phylogenetic relationships using single-nucleotide polymorphism (SNP) differences to compare the genomes.

    Study findings

    Bovine, human, and environmental samples displayed distinctive features, displaying variations in the AMR potential and the genomic constitution. There were 116 genes that determined various aspects of bacterial virulence. Most of these genes (99) were present in 99% or more strains.

    Antibiotic resistance genes were present in 1-10 per strain, out of 49 unique genes identified. Most conferred resistance to specific drugs, a few to metals like copper or gold, and two to biocides. Two genes were common to almost all strains.

    AMR prevalence

    Interestingly, unlike the global picture, S. Dublin shows a higher AMR potential in the USA, especially in bovine samples collected from clinically infected cattle.

    S. Dublin strains from overtly infected cattle had the highest prevalence of AMR genes specific to various drugs. This includes resistance to a critical cephalosporin class used to treat invasive and severe diseases in children and calves. These samples also often showed resistance to florfenicol, which is used to treat calf pneumonia.

    Quinolone resistance was most prevalent in environmental strains, likely linked to food supply chain contamination and indirect selective pressures, but not directly attributed to adult human drug use.

    Multidrug resistance

    Strains from infected cattle also had the highest levels of multidrug resistance plasmid IncA/C2, and the most significant diversity of genes. The fitness cost of acquiring AMR point mutations is less than that of plasmids. Moreover, plasmid acquisition mandates exposure to diverse plasmid-harboring environments and microbial communities. Thus, a less diverse environmental exposure could explain these differences between bovine and other sources.

    Genomic stability

    This is supported by environmental sources having a higher proportion of core genes and fewer accessory (shell and cloud) genes than clinical strains. Environmental, human, and bovine strains had distinctive features, such as a high proportion of core genes for the environmental vs the least proportion for the bovine strains. The reverse was true of cloud and shell genes. Most functional genes were shared across all strains, irrespective of the source. 

    This suggests that clinical disease-causing strains are more adaptable. They cross environmental and host barriers, face immune defenses, and must incorporate into different microbial communities.

    Conversely, less adaptable environmental strains from apparently healthy cattle may more easily contaminate post-harvest food supply chains. The food supply chain is also a more closely controlled environment, reducing the need for microbial adaptation.

    Human strains cover the midpoint between clinical bovine and environmental strains, perhaps representing a mix. This could indicate adequate S. Dublin adaptation to cause invasive human disease after non-human transmission. Alternatively, they could represent the strains more likely to cause symptomatic illness, as others may have escaped recognition.

    Phylogenetic resemblance

    Surprisingly, 72% of the strains closely resembled one or more other strains with less than 20 SNP differences, irrespective of the source, period, or geographic location. The phylogenetic tree showed that most were on the same trunk.

    Thus, the microbe shows high cross-reservoir genomic similarity, but the study does not establish direct transmission directionally between humans, cattle, and environmental reservoirs. The outcome is the widespread distribution of S. Dublin over the USA through closely related strains over a vast area and over a long period of time.

    This “underscores the importance of considering strain source when assessing and monitoring antimicrobial resistance.” The study also highlights the need to improve publicly available databases for better phylogenetic and functional analysis.

    Conclusions

    “Our analyses of Salmonella Dublin reveal a striking degree of genomic similarity among strains circulating in U.S. cattle, human, and environmental reservoirs. However, this apparent homogeneity masks differences in genomic stability and antimicrobial resistance elements, highlighting distinct evolutionary trajectories within each reservoir.”

    Antimicrobial stewardship policies must be uniformly applied to human and animal health practices. Ensuring food safety and biosecurity remains an important field of public health.

    Download your PDF copy now!

    Journal reference:

    • Kenney, S. M., M’ikanatha, N. M., Ganda, E., et al. (2025). Genomic evolution of Salmonella Dublin in cattle and humans in the United States. Applied and Environmental Microbiology. doi: https://doi.org/10.1128/aem.00689-25. https://journals.asm.org/doi/10.1128/aem.00689-25

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  • Evaluating the impact of field epidemiology training programs: a descriptive review of the published literature | Human Resources for Health

    Evaluating the impact of field epidemiology training programs: a descriptive review of the published literature | Human Resources for Health

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  • Windows 11 now has better Bluetooth quality for game chat and voice calls

    Windows 11 now has better Bluetooth quality for game chat and voice calls

    Microsoft is addressing muffled audio quality on Bluetooth headsets by introducing a new LE Audio feature for Windows 11. Built on top of the Bluetooth Low Energy radio spec, LE Audio uses a new compression algorithm that results in higher quality audio that Microsoft says will “drastically” improve the audio experience in games and calls on Windows 11.

    “When using an LE Audio device with a Windows 11 PC that supports super wideband stereo, the switch into game chat no longer causes an abrupt drop in audio quality,” explains Mike Ajax, a principal program manager lead at Microsoft. Game audio will remain in stereo and stream at super wideband quality, instead of the limited experience of Bluetooth Classic.

    LE Audio uses a 32kHz sample rate while using voice apps like Teams or Discord, and replaces the Advanced Audio Distribution Profile (A2DP) and Hands-Free Profile (HFP) of Bluetooth Classic. HFP used a 8kHz sample rate and resulted in muffled audio, but most Bluetooth headsets now support improve audio compression and a better sample rate for “wideband” or “super wideband” voice.

    The LE Audio support will also improve calls in apps like Microsoft Teams. While Teams uses Spatial Audio for wired headsets, it depends on stereo audio and hasn’t previously been supported on Bluetooth headsets. The super wideband stereo support over Bluetooth LE Audio enables Spatial Audio in Teams, and can be toggled on in the audio settings inside the Teams client on Windows.

    All you need is a Bluetooth headset that supports Bluetooth LE Audio, as well as a Windows 11 PC that also supports LE Audio and has the latest drivers and Windows 11 24H2 update. Existing PCs should get the necessary driver updates later this year, and Microsoft expects “most new mobile PCs that launch starting in late 2025 will have support from the factory.”

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  • Bank Liquidity and Financing of Nonbank Mortgage Companies

    Bank Liquidity and Financing of Nonbank Mortgage Companies

    By

    Alessandro Rebucci,
    Alex Sclip,
    Horacio Sapriza and
    Daniel te Kaat



    Economic Brief


    August 2025, No. 25-33



    Key Takeaways

    • Nonbank mortgage companies (NMCs) are directly funded by banks and have significantly expanded their shares of the residential mortgage market.
    • Bank liquidity helps drive the warehouse credit market for NMCs financing.
    • We examine the COVID-19 shock and the subsequent boom in the mortgage market and find that banks with ex-ante higher liquidity expanded less aggressively in supplying credit to NMCs. This lower credit expansion is stronger for smaller NMCs with fewer banking relationships.


    Nonbank mortgage companies (NMCs) have become increasingly important players in the mortgage market. These intermediaries finance their assets — mortgages held for sale (MHFS) and mortgage servicing rights (MSRs) — with warehouse lines of credit. This type of funding provides contingent liquidity during the short period between the origination of a mortgage and the sale or securitization of such an asset in the secondary market.1 The main providers of warehouse lines of credit are a handful of large banks with average assets of $100 billion.

    Some have raised concerns regarding solvency and liquidity risks of NMCs stemming from their close financing ties with the banking sector. In this article, we explore how shocks to the banking system affect NMCs through warehouse lines of credit.

    We focus on the COVID-19 shock, which led to financial market disruptions and a short-lived but deep recession. In turn, this prompted concerns about a potential increase in mortgage defaults, which could also put stress on originators and servicers’ businesses. However, unprecedented fiscal and monetary stimuli instead led to an unexpected boom in the mortgage market. Near-zero interest rates led to a surge in demand for mortgage refinancing, and an increased pace of quantitative easing (QE) flooded banks with liquidity, which was partially diverted to the market for NMC funding. Between January and June 2020, deposits surged from about $13 trillion to $15 trillion (Figure 1), and bank reserves and cash positions also increased (Figure 2) due to QE.

    At the same time, credit lines to NMCs rose significantly from $125 billion at the beginning of 2020 to $200 billion at the end of the year. The expansion of credit lines was accompanied by an unprecedented refinancing boom: Monthly mortgage originations by NMCs rose from $75 billion at the beginning of 2020 to $200 billion by the end. Understanding how liquidity flows were redirected to NMCs and whether financing frictions might be at work is important for understanding the mortgage market boom and any policy lessons that might apply to the market for NMCs’ financing.

    Institutional Background

    The Mortgage Market in 2020-21

    In March 2020, liquidity pressures re-emerged in the mortgage market. The economic downturn and subsequent spike in unemployment raised concerns about potential increases in mortgage defaults. Financial markets were also volatile, and mortgage originators faced up to $5 billion in higher margin calls on to-be-announced hedges.2

    The federal government and the Federal Reserve quickly responded. In terms of fiscal interventions, the Coronavirus Aid Relief and Economic Security (CARES) Act contributed to stabilizing the mortgage market.3 A 2021 paper shows that the CARES Act resulted in a spike in debt forbearance without a commensurate increase in delinquency rates.4 The high uptick in forbearance raised concerns that NMCs would not have the liquidity to advance funds to investors on behalf of borrowers in forbearance. In response, the Federal Housing Finance Agency and Ginnie Mae set up liquidity assistance measures for servicers in GSE and Ginnie Mae pools.

    Also in March 2020, the Fed reduced short-term interest rates to almost zero and increased its purchases of agency mortgage-backed securities (MBS) and Treasury securities. The monthly pace of purchases in the following months was unprecedented, exceeding the QE programs introduced after the global financial crisis. Between March and May, the Fed purchased about $2.1 trillion in Treasurys and MBS. As market conditions improved starting in June 2020, the Fed reduced monthly purchases to $80 billion in Treasurys and $40 billion in MBS per month until October 2021.

    The combination of lower interest rates and higher liquidity injections through QE led to a mortgage boom and a surge in house prices of more than 20 percent. The mortgage boom was mainly driven by higher refinancing activity of high-income and superprime borrowers.5 Despite the historic proportions of the refinancing boom, a 2021 paper documents an incomplete passthrough of lower interest rates to mortgage borrowers due to capacity constraints and operational frictions of credit supply.6 The boom vanished in 2022 after the Fed increased rates and house prices declined progressively.

    Bank Liquidity and the Market for Warehouse Credit Lines

    In March 2020, nonfinancial firms experienced cash flow disruptions and drew down unprecedented amounts of credit lines,7 and the banking system was short in its ability to provide liquidity. A pair of recent works show that unused credit line capacity is held by large firms that drew down the majority of credit following the pandemic, putting pressures on bank balance sheets and crowding out lending to smaller firms.8

    Prompt and unprecedented fiscal and monetary policy interventions helped banks satisfy liquidity demands. In two quarters, bank deposits grew by 15.4 percent (or $2 trillion), as seen in Figure 1. This huge deposit inflow is related to several factors, including:

    The Fed implemented QE by purchasing securities from authorized primary dealers by crediting reserve balances to the accounts of banks associated with the dealers’ counterpart.11 If the ultimate seller of these securities was a bank, securities were swapped with reserve balances, and the bank balance sheet did not grow.

    In most cases, however, the ultimate seller of the securities was not a bank. Instead, the Fed credited reserve balances to the accounts of the nonbank’s correspondent bank, which then credited a deposit to the nonbank seller. This translates to an expansion of demand deposits on the liability side, especially uninsured deposits such as checking accounts that depositors can withdraw anytime, as shown in Figure 3. At the same time, reserves mechanically increased on the asset side, indicating that the monetary stimulus increased the aggregate liquidity of the banking sector, as previously seen in Figure 2.

    Banks responded to the large liquidity injection by accommodating the demand for liquidity from the corporate sector and originating off-balance-sheet liabilities in the form of lines of credit. Both demand deposits and lines of credit are claims on bank liquidity and require a stock of liquid assets (that is, reserves and cash) to appropriately manage liquidity risk. Therefore, cash and reserve positions are central for the ability of banks to provide credit lines. Indeed, a 2000 paper shows that the effects of monetary policy changes on lending are stronger for initially liquidity-constrained banks, which has been confirmed by several subsequent papers.12

    In our empirical analysis, we hypothesize that banks that were less liquid before the shock experienced a larger relaxation of their liquidity constraints, allowing them to further increase their lending relative to less liquidity-constrained depository institutions. This should also apply to banks’ loan supply to NMCs. These institutions’ demand for credit is cyclical and negatively correlated with the level of interest rates: Lower interest rates stimulate the mortgage market and the demand for credit from those institutions.

    Market for Warehouse Lines of Credit

    Unlike banks, NMCs cannot access insured deposit funding and are not required to maintain capital and liquidity buffers. Their primary sources of financing are warehouse lines of credit, as they cannot obtain liquidity from the Fed or the Federal Home Loan Banks. Their funding structures expose them to liquidity shocks in the banking market.

    Warehouse lines of credit are short term in nature and are used to finance MHFS. Each originated mortgage is temporarily pledged as collateral to warehouse lenders until it is sold in the secondary market. Warehouse credit lines can be quickly increased or cancelled, and lenders can require additional features on the characteristics of the mortgages pledged as collateral. MSRs can also be pledged as collateral, but the lender might require margin calls, and these instruments may be repriced or even cancelled following interest rate fluctuations.

    The volumes of warehouse credit lines in the system follow the dynamics of the mortgage market. Figure 4 shows the aggregate volumes of warehouse credit lines, while Figure 5 shows the aggregate origination volumes for NMCs and banks. The total amount of warehouse credit committed rose quickly from $120 billion in the third quarter of 2019 to nearly $225 billion at the peak of the mortgage credit market boom in the fourth quarter of 2021.13 On average, the amount used is 60 percent of the total amount committed, and it remained fairly stable throughout the period.

    Despite some concerns about the ability of the banking system to supply contingent liquidity, credit flowed downhill to NMCs. The spike in forbearance granted to mortgage borrowers by the CARES Act had a very limited effect on the market for warehouse financing. The concern rapidly eased after the FHFA announcement that GSEs would purchase mortgages already in forbearance.14 Despite this effect, a few papers document that NMCs facilitated less forbearance than banks.15 To limit the risk of liquidity shortages of servicers, some lenders imposed stricter terms on warehouse credit lines.16

    The sustained mortgage volumes in the second half of 2020 and all of 2021 increased NMC profits and strengthened their balance sheets, and the warehouse credit market expanded with the downstream mortgage origination of NMCs until monetary policy tightened in the first quarter of 2022. In an environment of increasing interest rates, the demand for mortgages decreased, and thus warehouse credit lines began falling.

    Data

    We assemble a unique dataset covering the bank-NMCs warehouse credit line relationship. Our dataset is the result of manually merging credit-level information from the residential mortgage loan activity segment of mortgage call reports with bank balance sheet data from FDIC call reports. We enrich our dataset with NMC balance sheet information from the financial condition segment of mortgage call reports.

    The average committed credit line is $123.1 million, while the amount used is roughly 60 percent, or $69.7 million.17 The average NMC has 3.7 credit lines, with 15 percent of observations (typically the smaller ones in terms of assets) having only one credit line per NMC. Figure 6 shows the distribution of credit lines across NMCs, where most NMCs have a diversified funding structure with more than three lenders providing credit.18

    Table 1 reports summary statistics at the NMC level, showing that the share of equity capital over total assets is 23 percent for the average NMC, while MSRs are 25 percent of total book equity.19 MHFS is the main asset group in NMCs balance sheets, accounting for an average of 72 percent of total assets.20 These mortgages usually collateralize warehouse credit lines and are sold after origination in the secondary market.

    Table 1: NMC Summary Statistics
    Observations Mean Standard Deviations 25th Percentile 50th Percentile 75th Percentile
    Credit Line Limit (in Millions) 9,779 123.1 351 32.1 139 571
    Credit Line Used (in Millions) 9,779 69.7 98.8 15 45 100
    Number of Credit Lines 9,779 3.644 2.421 2 3 5
    Total Assets (in Billions) 9,779 1.21 6.9 0.15 0.45 1
    Log. Total Assets 9,779 18.52 2.72 17.36 18.77 20.18
    Equity Capital Over Assets 9,577 0.23 0.19 0.12 0.16 0.25
    MSR Over Equity 9,577 0.25 0.39 0 0.05 0.37
    MHFS Over Assets 7,340 0.72 0.19 0.65 0.78 0.84
    Cash Over Assets 9,515 0.11 0.16 0.036 0.06 0.11
    Origination Income Over Assets 9,451 0.12 0.38 0.07 0.02 0.04
    Source: Call reports and authors’ calculations.

    In Table 2, we report summary statistics of the sample of warehouse banks. The average bank has $109 billion in total assets with a higher standard deviation than NMCs, meaning that the sample contains all the largest banks in terms of total assets.

    Table 2: Warehouse Bank Summary Statistics
    Observations Mean Standard Deviations 25th Percentile 50th Percentile 75th Percentile
    Total Assets (in Billions) 822 109 373 3.5 10.5 39.8
    Log. Total Assets 822 16.414 1.862 15.08 16.173 17.5
    Book Equity Over Assets 820 0.115 0.027 0.097 0.112 0.13
    Insured Deposits Over Deposits 798 0.493 0.15 0.395 0.491 0.583
    Uninsured Deposits Over Deposits 798 0.522 0.151 0.426 0.524 0.629
    Cash and Reserves Over Assets 817 0.122 0.117 0.038 0.08 0.165
    Cash Over Assets 815 0.072 0.065 0.027 0.049 0.095
    Reserves Over Assets 817 0.05 0.054 0.01 0.031 0.068
    MBS Over Assets 822 0.047 0.052 0.011 0.031 0.062
    Gov. Securities Over Assets 820 0.052 0.062 0.011 0.037 0.062
    RE Loans Over Assets 822 0.42 0.163 0.315 0.44 0.536
    C&I Loans Over Assets 816 0.142 0.081 0.08 0.135 0.196
    Consumer Loans Over Assets 814 0.038 0.062 0.004 0.014 0.054
    ROA 822 0.026 0.034 0.015 0.028 0.041
    Source: Call reports and authors’ calculations.

    Results

    In this section, we discuss econometric specifications and report results. We first exploit within-NMC variation that enables us to control for loan demand effects and to study the intensive margin of bank credit supply to nonbanks. We then collapse the data at the NMC level and examine whether a bank funding shock also affects NMCs along the extensive margin.

    Within-NMC Analysis: the Intensive Margin

    The setting is a difference-in-difference analysis around the COVID-19 shock. To trace the impact of the shock on the provisioning of credit to NMCs, we exploit variation in ex-ante liquidity positions of banks. Our identification hinges on each NMC borrowing from at least two different banks before and after the shock. This strategy isolates within-NMC variation in the change in credit line supply from banks differently exposed to the shock.

    Our strategy nets several key findings with NMCs:

    • Less-liquid banks expand more of their supplies of credit lines to NMCs. An increase in liquidity of one standard deviation leads to a 6.5 percent lower expansion of warehouse credit lines.
    • The effect is higher for small NMCs. An increase in liquidity of one standard deviation is associated with a 7 percent supply-driven decrease in credit expansion.
    • There are no differential effects along NMCs capitalization and quality of mortgages originated.
    • Liquidity’s effect on the supply of credit lines is relevant for small and regional banks but not for large ones.
    • Liquidity interacts with capital: Less-capitalized banks expand credit lines more than well-capitalized counterparts.

    Cross-NMC Analysis: the Extensive Margin

    Although the within-NMC specification allows us to examine whether banks with lower liquidity positions benefited more and expanded credit lines to NMCs, it is not appropriate to assess the overall effect of the shock. This is because our model focuses on existing credit lines, ignoring new and terminated credit line relationships and, hence, the extensive margin.

    Given the importance of the overall effect, we created a second model to estimate the cross-sectional effect of NMCs exposure to the shock. This model yields several more key findings across NMCs:

    • After the shock, there is no decrease in the overall credit expansion for NMCs borrowing from banks more exposed to the shock (low-liquidity banks).
    • The effect is concentrated on small NMCs and triggers a 5 percent lower expansion of credit lines.
    • Small NMCs are less likely to establish new credit relationships.

    Conclusion

    This article discusses how an aggregate bank funding shock affects banks’ credit line supply to nonbanks. We exploit the significant expansion of bank deposits in the first quarter of 2020 in combination with banks’ differential exposure to this shock, which we measure by a bank’s liquid asset ratio before the shock. Our results suggest that more exposed banks increased their credit line supply to NMCs significantly more following the aggregate increase in bank deposits. This effect is present along the intensive and extensive margins.

    Alessandro Rebucci is an economics professor in the Carey Business School at Johns Hopkins University. Alex Sclip is an associate professor in the Department of Management at the University of Verona. Horacio Sapriza is a senior economist and policy advisor in the Research Department at the Federal Reserve Bank of Richmond. Daniel te Kaat is an associate professor of finance at the University of Groningen.

     

    To cite this Economic Brief, please use the following format: Rebucci, Alessandro; Sclip, Alex; Sapriza, Horacio; and te Kaat, Daniel. (August 2025) “Bank Liquidity and Financing of Nonbank Mortgage Companies.” Federal Reserve Bank of Richmond Economic Brief, No. 25-33.

    This article may be photocopied or reprinted in its entirety. Please credit the authors, source, and the Federal Reserve Bank of Richmond and include the italicized statement below.

    Views expressed in this article are those of the authors and not necessarily those of the Federal Reserve Bank of Richmond or the Federal Reserve System.



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  • SpaceX launches 28 Starlink satellites from Florida

    SpaceX launches 28 Starlink satellites from Florida

    Aug. 27 (UPI) — SpaceX launched a Falcon 9 rocket from Cape Canaveral Space Force Station on Wednesday morning, adding another 28 Starlink satellites into low Earth orbit.

    The Starlink mission 10-56 lifted off at 7:10 a.m. with 80% favorable weather. It used the Falcon 9 first-stage booster, tail number 1095, as it flew for the second time.

    The rocket’s first stage launched out at sea at 7:18 a.m. aboard the SpaceX drone ship, “Just Read the Instructions.”

    Unlike a previous flight during which a sonic boom could be heard on the Space Coast, no sonic booms occurred on this mission.

    On Thursday, at the Kennedy Space Center FAA officials will take public comments on Starship-Super Heavy environmental impacts generated by future launches and landing.

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  • Anish Bhanwala wins silver medal in 25m rapid fire pistol

    Anish Bhanwala wins silver medal in 25m rapid fire pistol

    Anish Bhanwala clinched the silver medal in the men’s 25m rapid fire pistol at the Asian Shooting Championship 2025 in Shymkent, Kazakhstan on Wednesday.

    The 22-year-old Indian shooter shot 35 hits in the final to finish just one behind China’s 20-year-old Su Lianbofan, who clinched gold with a junior world record score of 36/40. Republic of Korea’s Lee Jaekyoon, who topped qualification, secured the bronze with 23.

    This was Bhanwala’s second individual medal at the Asian Championships, having won bronze in Changwon in 2023. He won the gold medal at the Commonwealth Games 2018 in Gold Coast and helped India win a team bronze at the 2023 Asian Games in Hangzhou.

    India had two shooters in the final. Adarsh Singh scored 585-20x to finish second in qualifying, matching Lee’s tally, while Anish also advanced with a fourth-place finish with 583-21x. In the final, Adarsh bowed out in fifth place after shooting 15/25.

    In the men’s 25m rapid fire pistol team standings, India (1738-58x) settled for silver behind South Korea (1748-56x). People’s Republic of China finished third with 1733-64x.

    Alongside Adarsh and Anish, Neeraj Kumar, who finished 18th in the qualifying with a score of 570-17x, completed the Indian line-up for the silver medal.

    India also added a silver medal in the men’s 50m pistol team shooting event. Yogesh Kumar (548-6x), Amanpreet Singh (543-6x) and Ravinder Singh (542-9x) combined for 1633-21x to finish behind Islamic Republic of Iran, who struck gold with 1652-22x. South Korea (1619-18x) claimed the bronze.

    Later in the day, India narrowly missed out on a podium in the trap mixed team event. The pair of Kynan Chenai and Aashima Ahlawat lost to Kazakhstan 38-34 in the bronze medal match. The Indian duo had earlier qualified with a total of 133 to reach the medal playoff.

    The three medals on Wednesday took India’s tally up to 22 medals – nine gold, six silver and seven bronze – in senior events at the 2025 Asian Shooting Championships.

    Manu Bhaker won three bronze medals, with one of them coming in an individual event – the women’s 10m air pistol.

    The Indian senior shooting squad for the Asian competition comprise 35 members competing for medals in 15 events. A total of 129 Indian shooters are also competing in the junior events at the Shymkent meet.

    The Asian Shooting Championship 2025 will conclude on Friday.

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  • The prevalence and associated factors of alcohol consumption among pregnant women in Africa: a systematic review and meta-analysis | BMC Psychiatry

    The prevalence and associated factors of alcohol consumption among pregnant women in Africa: a systematic review and meta-analysis | BMC Psychiatry

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  • Lithuania open FIBA EuroBasket 2025 with convincing win

    Lithuania open FIBA EuroBasket 2025 with convincing win

    The official EuroBasket app

    TAMPERE (Finland) – Lithuania began their FIBA EuroBasket 2025 Group B campaign with a 94-70 win over a gutsy Great Britain side at the Tampere Deck Arena on Wednesday.

    In a nervy curtain-raiser, Lithuania pulled clear late on to create breathing space to get their EuroBasket adventure off to a good start, led by Jonas Valanciunas, who had a near double-double of 18 points and 9 rebounds.

    Turning Point

    Lithuania recovered from a 5-0 deficit early to race away with a 12-0 run in the early stages of the first quarter, which soon became a double-digit cushion within the first possessions of the following period. To Great Britain’s credit, they reduced the deficit late in the second quarter to keep the game interesting going into the break.

    However, that was as good as it got for GB, as Rokas Jokubaitis drilled Lithuania’s first triple, two minutes into the third, which highlighted a 15-2 burst that put the Baltic nation 58-38 ahead, a lead they comfortably held on to.

    TCL Player of the Game

    Jonas Valanciunas led Lithuania with 18 points and 9 rebounds, shooting an impressive 7-for-13 from the floor as Great Britain could not contain him under the basket.

    Jokubaitis supported with 12 points, 6 assists and 5 rebounds, while Azuolas Tubelis had 17 points on 8-of-10 shooting. For GB, Akwasi Yeboah was their standout with a team-high 17 points in defeat.

    Stats Don’t Lie

    Despite having to wait until the early stages of the third quarter to hit their first three-pointer, Lithuania got the job done inside the paint, collecting 13 offensive rebounds and scoring six second-chance points in the first half.

    Lithuania certainly took advantage of its superior height to keep ahead, shooting 50 percent from the field.

    Despite providing highlights, Great Britain’s size hampered them throughout, especially in the absence of big man Gabe Olaseni. They were out-rebounded 57-31.

    Bottom Line

    It wasn’t as convincing as they would have liked, but Lithuania are off to a winning start in Tampere. Great Britain’s overall performance gives them hope for improved performances moving forward. However, they will want to improve their field goal percentage, going shooting 33 percent from the floor.

    They Said

    For more quotes, tune in to the official post-game press conference!

    FIBA

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