As a result of the coronavirus (COVID-19) crisis, many people worldwide faced job insecurity and income disruption. For mortgage borrowers in the United States, this means increased risk of delayed loan repayment, default and foreclosure.

In April 2020, the share of single-family housing mortgages owned by Freddie Mac that were in forbearance and delinquent for ** days spiked to ** percent. One year later, as of April 2021, approximately ** percent of the mortgage loans in forbearance were delinquent for over *** days.

In the fourth quarter of 2024, ***** billion U.S. dollars worth of student loans were in forbearance in the United States. This reflects the effects of the coronavirus (COVID-19) pandemic, where the government temporarily paused student loan payments and froze the accumulation of interest. Federal student loan repayments resumed in October 2023, with *** billion U.S. dollars worth of student loans in repayment as of ** 2024. During this time period, outstanding student loan debt in the U.S. totaled over **** trillion U.S. dollars.

We are releasing data that compares the HHS Provider Relief Fund and the CMS Accelerated and Advance Payments by State and provider as of May 15, 2020. This data is already available on other websites, but this chart brings the information together into one view for comparison. You can find additional information on the Accelerated and Advance Payments at the following links: Fact Sheet: https://www.cms.gov/files/document/Accelerated-and-Advanced-Payments-Fac... Zip file on providers in each state: https://www.cms.gov/files/zip/accelerated-payment-provider-details-state... Medicare Accelerated and Advance Payments State-by-State information and by Provider Type: https://www.cms.gov/files/document/covid-accelerated-and-advance-payment.... This file was assembled by HHS via CMS, HRSA and reviewed by leadership and compares the HHS Provider Relief Fund and the CMS Accelerated and Advance Payments by State and provider as of December 4, 2020. HHS Provider Relief Fund President Trump is providing support to healthcare providers fighting the coronavirus disease 2019 (COVID-19) pandemic through the bipartisan Coronavirus Aid, Relief, & Economic Security Act and the Paycheck Protection Program and Health Care Enhancement Act, which provide a total of $175 billion for relief funds to hospitals and other healthcare providers on the front lines of the COVID-19 response. This funding supports healthcare-related expenses or lost revenue attributable to COVID-19 and ensures uninsured Americans can get treatment for COVID-19. HHS is distributing this Provider Relief Fund money and these payments do not need to be repaid. The Department allocated $50 billion of the Provider Relief Fund for general distribution to Medicare facilities and providers impacted by COVID-19, based on eligible providers' net reimbursement. It allocated another $22 billion to providers in areas particularly impacted by the COVID-19 outbreak, rural providers, and providers who serve low-income populations and uninsured Americans. HHS will be allocating the remaining funds in the near future. As part of the Provider Relief Fund distribution, all providers have 45 days to attest that they meet certain criteria to keep the funding they received, including public disclosure. As of May 15, 2020, there has been a total of $34 billion in attested payments. The chart only includes those providers that have attested to the payments by that date. We will continue to update this information and add the additional providers and payments once their attestation is complete. CMS Accelerated and Advance Payments Program On March 28, 2020, to increase cash flow to providers of services and suppliers impacted by the coronavirus disease 2019 (COVID-19) pandemic, the Centers for Medicare & Medicaid Services (CMS) expanded the Accelerated and Advance Payment Program to a broader group of Medicare Part A providers and Part B suppliers. Beginning on April 26, 2020, CMS stopped accepting new applications for the Advance Payment Program, and CMS began reevaluating all pending and new applications for Accelerated Payments in light of the availability of direct payments made through HHS’s Provider Relief Fund. Since expanding the AAP program on March 28, 2020, CMS approved over 21,000 applications totaling $59.6 billion in payments to Part A providers, which includes hospitals, through May 18, 2020. For Part B suppliers—including doctors, non-physician practitioners and durable medical equipment suppliers— during the same time period, CMS approved almost 24,000 applications advancing $40.4 billion in payments. The AAP program is not a grant, and providers and suppliers are required to repay the loan. Provider Relief Fund Data - https://data.cdc.gov/Administrative/Provider-Relief-Fund-COVID-19-High-I...

As of March 2021, the Japan Housing Finance Agency (JHF) had approved more than ** thousand home loan payment modifications due to the coronavirus pandemic. Various payment modification options have been available since March 2020.

Status of repayment of loan from the Canada Emergency Business Account (CEBA) and if the business or organization anticipates having the liquidity available or access to credit to repay the CEBA loan by December 31, 2026, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, fourth quarter of 2023.

Covid-19 has severely impacted employment opportunity and earning potential for workers in Cambodia's garment and footwear sector. Manifesting initially as an economic crisis, the impacts of manufacturing shutdowns and consumer lockdowns around the world slowed the garment sector's output. This led to employment suspensions and terminations affecting hundreds of thousands of workers in Cambodia alone.

For two years, the ReFashion study has uniquely tracked the impacts of the pandemic on a cohort of 200 female workers in Cambodia, from January 2020 through to December 2021. The study combines a quantitative survey of female workers to measure monthly trends in employment, household finances, and wellbeing, with qualitative interviews to explore emergent themes in greater depth. Each of these components is repeated with the same cohort of participants at strategic intervals. The research methods are designed to capture an in-depth and long-term understanding of women workers' lives through the pandemic.

Our findings indicate that the COVID-19 pandemic has exacerbated a crisis of over-indebtedness for workers in the garment industry, with severe consequences for the short and long-term health and wellbeing of workers and their families. Over-indebtedness is reached when a credit borrower 'is continuously struggling to meet repayment deadlines and has to make unduly high sacrifices related to his or her loan obligations'.

Even before the COVID-19 pandemic, credit borrowing had become commonplace among low-income households in Cambodia, which has one of the largest microfinance industries in the world in terms of borrowers per capita. This widescale borrowing enables households to temporarily deal with the lack of social protection and public services in the country, allowing them to meet costs of health care and invest in housing in times of urgent need. High levels of borrowing by garment workers specifically, as evidenced in the ReFashion study, indicate that flagship efforts to foster 'Decent Work' in the garment sector in Cambodia have not precluded the need for some workers to take on significant loans to supplement their low wages and fill the gaps in social protection provision.

During the COVID-19 pandemic, garment workers' 'financial inclusion' became even more vital to their ability to cope with the economic emergency they faced, by using access to credit to smooth short terms gaps in income caused by employment suspensions. Yet at the same time, reduced earning capacity hindered workers' ability to make existing loan repayments. To meet outstanding commitments, many resorted to reducing daily expenditure on necessities including food. Most workers reported their household food intake as inadequate and many reported experiencing hunger during the pandemic. Such unduly high sacrifices are neither just nor sustainable.

The COVID-19 pandemic is having significant repercussions on the global garment industry, of huge importance not only to Cambodia's economy, but also to its 1 million workers, 80% of whom are women. Many garment factories are interrupting production with the effect that 1/4 of workers have been dismissed or temporarily suspended. Formal social protection in the sector, though improving due to multi-stakeholder efforts, is weak and fragile. Mixed-method longitudinal research will track and amplify the experiences and coping mechanisms of 200 women workers as they navigate the financial repercussions of the COVID-19 pandemic. The project's interdisciplinary team from human geography, political economy, and organisation studies will generate new knowledge on underlying and differentiating determinants of risk and resilience arising from formal and informal social protections.

The ambitious study will focus its policy attention on learning to 'Build Back Better' social protection to prevent and mitigate longer-term impacts of the pandemic and future risk events. Our approach centres women's representation in planning and decision-making as critical to 'stitching back better' just and resilient garment supply chains to make progress towards gender equality (SDG5), inclusive economic growth and decent work (SDG8). The project's impact, within its 18-month lifetime, will be compelled by its partnerships with, and pro-active convening together, of government (Cambodian Ministry of Labor, British Embassy), regulators (ILO, Better Factories Cambodia), industry (Garment Manufacturers Association in Cambodia, H&M), think tanks (ODI), workers' organisations (CATU, the only female-led union in Cambodia), and women's media (Women's Media Center and the Messenger Band).

COVID-19 Relief Restaurant Revitalization Fund Recipients

The restaurants that received revitalization fundings during the peak of the Covid-19 pandemic

By State of New York [source]

About this dataset

The American Rescue Plan Act established the Restaurant Revitalization Fund (RRF) to provide funding to help restaurants and other eligible businesses keep their doors open. This program provided restaurants with funding equal to their pandemic-related revenue loss up to $10 million per business and no more than $5 million per physical location. Recipients are not required to repay the funding as long as funds are used for eligible uses no later than March 11, 2023.

This dataset details New York State recipients of RRF funds, including the loan number, approval date, business name, address, city, state, zip code, grant amount, franchise name (if applicable), rural/urban indicator, HUBZone indicator, Congressional District (CD), and indicators of whether the grant was used for outdoor seating, a covered supplier expense, debt relief or refinancing, food expenses related to on-site consumption or delivery/catering services ,indoor maintenance expenses such as rent or mortgage payments ,or operations expenditures such as employee salaries

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How to use the dataset

Research Ideas

• Identify restaurant trends during the COVID-19 pandemic.
• Identify areas of the country that have been most affected by the pandemic.
• Which businesses are most likely to receive funding from the government

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

See the dataset description for more information.

Columns

File: us-sba-covid-19-relief-to-nys-business-restaurant-revitalization-fund-1.csv | Column name | Description | |:-------------------------------------|:-----------------------------------------------------------------------------------| | LoanNumber | The loan number for the recipient. (Integer) | | ApprovalDate_Year | The year the loan was approved. (Integer) | | ApprovalDate_Month | The month the loan was approved. (Integer) | | ApprovalDate_Day | The day the loan was approved. (Integer) | | BusinessName | The name of the business that received the loan. (String) | | BusinessAddress | The address of the business that received the loan. (String) | | BusinessCity | The city of the business that received the loan. (String) | | BusinessState | The state of the business that received the loan. (String) | | BusinessZip | The zip code of the business that received the loan. (String) | | GrantAmount | The amount of the grant received by the business. (Float) | | FranchiseName | The name of the franchise, if applicable. (String) | | RuralUrbanIndicator | An indicator of whether the business is located in a rural or urban area. (String) | | HubzoneIndicator | An indicator of whether the business is located in a HUBZone. (String) | | CD | The congressional district in which the business is located. (String) | | grant_purp_cons_outdoor_seating | An indicator of whether the grant was used for outdoor seating. (String) | | grant_purpose_covered_supplier | An indicator of whether the grant was used for a covered supplier. (String) | | grant_purpose_debt | An indicator of whether the grant was used for debt relief. (String) | | grant_purpose_food | An indicator of whether the grant was used for food purposes. (String) | | grant_purpose_maintenance_indoor | An indicator of whether the grant was used for indoor maintenance. (String) | | grant_purpose_operations | An indicator of whether the grant was used for operations. (String) |

...

Abstract

The recent global economic slowdown, caused by the COVID-19 pandemic, created an urgent need for timely data to monitor the socioeconomic impacts of the pandemic. Tanzania is among other countries in the world which are affected by the recent global economic slowdown, caused by the COVID-19 pandemic. Therefore, there is an urgent need for timely data to monitor and mitigate the socio-economic impacts of the crisis in the country. Responding to this need, the National Bureau of Statistics (NBS) and the Office of the Chief Government Statistician (OCGS), Zanzibar in collaboration with the World Bank and Research on Poverty Alleviation (REPOA) implemented a rapid household telephone survey called the Tanzania High-Frequency Welfare Monitoring Survey (HFWMS).

Thus, the main objective of the survey is to obtain timely data that is critical for evidence-based decision making aimed at mitigating the socio-economic impact of the downturn caused by COVID-19 pandemic by filling critical gaps of information that can be used by the government and stakeholders to help design policies to mitigate the negative impacts on its population.

Geographic coverage

National

Analysis unit

Households Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary sample for this activity was drawn from the 2014/15 NPS and 2017/18 HBS. Target sample completion each month is estimated at 3000 households. The 2014/15 NPS acted as the primary sample frame, complimented by the 2017/18 HBS.

The sample for the HFWMPS was drawn from the 2014/15 NPS and 2017/18 HBS. Both surveys were conducted over a 12-month period and are nationally representative. During the implementation of the surveys, phone numbers are collected from interviewed households and reference persons who are in close contact with the household in order to assist in locating and interviewing households who may have moved in subsequent waves of the survey. This comprehensive set of phone numbers as well as the already well-established relationship between NBS and these households made this an ideal frame from which to conduct the HFWMS in Tanzania.

To obtain a nationally representative sample for the Tanzania HFWMS, a sample size of approximately 3,000 successfully interviewed households was targeted. However, to reach that target, a larger pool of households needed to be selected from the frame due to non-contact and non-response common for telephone surveys. Thus, about 5,750 households were selected to be contacted.

All 5,750 households were contacted in the baseline round of the phone survey. [Error! Reference source not found. ] presents the interview result for the baseline sample. 49.2 percent of sampled households were successfully contacted. Of those contacted, 96 percent or 2,708 households were fully interviewed. These 2,708 households constitute the final successful sample and will be contacted in subsequent rounds of the survey.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Each survey round consists of one questionnaire - a Household Questionnaire administered to all households in the sample.

Baseline The questionnaire gathers information on demographics; employment; education; access to basic services; food security; TASAF; and mental health. The contents of questionnaire are outlined below:

• Cover: Household identifiers and enumerator identifiers.
• Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to.
• Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left.
• Employment: Status and information of income-generating activities (wage work, family business and farming), reason for stopped working, reason for not able to perform activities as usual, and reason for reduced revenue from family business.
• Education: School attendance, type of school attended, learning activities of children at home, return to school, contact with children’s teachers during school closure.
• Access to Basic Services:Household’s access to staple food (maize grain, cassava, rice, and maize flour), medical treatment, and reasons for not being able to access the services.
• Food Security: Household’s food security status during the last 30 days.
• TASAF: Households access to the TASAF money, use of the money received, challenges encountered in accessing the funds.
• Mental Health: Information on 8 items pertaining to measuring mental health.
• Recontact: Data on how the household can be recontacted in the future.
• Interview Results: Result of interview including observation notes by enumerator regarding the interview, respondent and language of interview.

Round 2 The questionnaire gathers information on demographics; employment; non-farm enterprise; tourism; education; access to health services; and TASAF. The contents of questionnaire are outlined below:

• Cover: Household identifiers and enumerator identifiers.
• Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to.
• Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left.
• Employment: Status and information of income-generating activities (wage work, family business and farming), reason for stopped working, and reason for not able to perform activities as usual.
• Non-farm Enterprise: Status and information of non-farm income-generating activities, reason for stopped operating, reason for not able to perform activities as usual, and reason for reduced revenue from family business.
• Tourism: Employment of household members in tourism sector, and who benefits from tourism.
• Education (selected members aged 4-18 years): School attendance, reason for not attending, grade attending, type of school, absence and reason for being absent.
• Access to Health Services: Women’s access to pre-natal/post-natal care, household’s access to preventative care and medical treatment, and reasons for not being able to access the services.
• TASAF: Households access to the TASAF money, use of the money received, challenges encountered in accessing the funds.
• Recontact: Data on how the household can be recontacted in the future.
• Interview Results: Result of interview including observation notes by enumerator regarding the interview, respondent and language of interview

Round 3 The questionnaire gathers information on demographics; employment (respondent and other household members); non-farm enterprise; credit; women savings; and shocks and coping. The contents of questionnaire are outlined below:

• Cover: Household identifiers and enumerator identifiers.
• Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to.
• Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left.
• Employment (respondent): Status and information of income-generating activities (wage work, family business and farming), reason for stopped working, and reason for not able to perform activities as usual.
• Employment (other members): Status in employment (current and 2020), consistency of work in 2020, why currently not working, job search, change in jobs, actual job.
• Non-farm Enterprise: Status and information of non-farm income-generating activities, reason for stopped operating, reason for not able to perform activities as usual, and reason for reduced revenue from family business.
• Credit: Household’s debts status since the beginning of the coronavirus crisis; use of loan, ability to repay loan when their scheduled payment is due.
• Women Savings: Women having bank accounts to financial institutions and changes in their savings since the start of the pandemic.
• Shocks and Coping: Shocks that affected household since the baseline interview and their coping strategies.
• Recontact: Data on how the household can be recontacted in the future.
• Interview Results: Result of interview including observation notes by enumerator regarding the interview, respondent and language of interview.

Round 4 The questionnaire gathers information on demographics; employment; non-farm enterprise; digital technology; and income changes. The contents of questionnaire are outlined below:

• Cover: Household identifiers and enumerator identifiers.
• Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to.
• Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left.
• Employment (respondent): Status and information of

The Russian government allocated nearly 46 billion Russian rubles from the state's reserve fund to stimulating payments to doctors during the coronavirus (COVID-19) pandemic. Furthermore, over 33 billion Russian rubles were distributed to grants for regions to increase the number of hospital beds.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

With government schemes introduced due to the COVID-19 pandemic soon ending, IBISWorld assesses how likely it is that businesses and consumers will turn to P2P lending for finance.

Η παρούσα δημοσίευση παρέχει ενημέρωση σχετικά με τις επιδόσεις των καθεστώτων εγγύησης δανείων της κυβέρνησης για τη νόσο COVID-19, συμπεριλαμβανομένων των εξής: το Σχέδιο Δανείων Διακοπής Επιχειρήσεων του Κορονοϊού (CBILS) το πρόγραμμα δανείων διακοπής μεγάλων επιχειρήσεων του κορονοϊού (CLBILS) το πρόγραμμα αναπήδησης πίσω δανείων (BBLS) Τα δεδομένα προέρχονται από την πύλη της British Business Bank στις 31 Μαρτίου 2022.

According to a survey on personal finance conducted during the fourth quarter of 2024 in the Philippines, ** percent of respondents stated that they expected to be able to pay their current bill or loan payments in full. Nevertheless, ** percent of respondents expected to be unable to spend at least *** of their current bills or loans in full due to financial difficulties.

As a result of the coronavirus (COVID-19) crisis, many people worldwide faced job insecurity and loss of income. For mortgage borrowers in the United States, this means increased default and foreclosure risk. Forbearance is a type of borrower assistance which allows the lender to negotiate a temporary postponement of a mortgage repayment. It allows a payment period relief in lieu of the creditor foreclosing on any property that was used as collateral for the loan.

As of March 2022, New York was one of the states in the United States with highest forbearance rate for Freddie Mac single-family housing loans with approximately **** percent of current loans in forbearance.

In 2021, a survey conducted on small and medium enterprises (SMEs) in Thailand found that **** percent of firms believed that government policies which provided financial aid helped them the most during the COVID-19 pandemic. Financial aid policies included providing loans with low interest, reducing monthly payments on loans for businesses, and pausing debt payments.

In financial year 2024, banks in India advanced over *** trillion Indian rupees in housing loans. This was an increase compared to the previous year. This reflected renewed homebuyer sentiment, as an increasing number of Indians were investing in buying residential property. Growth of home loans market Forty years ago, home loans were an alien concept. People would direct their provident fund savings and retirement benefits toward buying a home. However, three key institutions: HDFC, ICICI Ltd, and the State bank of India with their new lending concepts led to significant changes in the home loan market. Currently different commercial banks, NBFCs, and housing finance companies have flooded the mortgage market, and giving prospective home buyers from diverse strata of society with bargaining power and a chance at affording a home. Inflation and home loans   India is not untouched by global inflation. To address the problem, the Reserve Bank of India hiked the repo rate **** times since April 2022 to *** percent. Consequently, leading banks and housing finance companies raised their lending rates. For a prospective homebuyer, this meant a rise in tenure for home loans. In other words, equivalent monthly payments (EMIs)for homebuyers have lengthened and become more expensive. In financial year 2022, banks in India advanced around *** trillion Indian rupees in housing loans almost reaching pre-COVID levels.

As a result of the coronavirus (COVID-19) crisis, many people worldwide faced job insecurity and loss of income. For mortgage borrowers in the United States, this means increased risk of delayed loan repayment, default and foreclosure.

Between ******** and ********, the value of single-family housing mortgages owned by Freddie Mac in the United States that were over *** days delinquent spiked from approximately *** billion U.S. dollars to over **** billion U.S. dollars. Nevertheless, the total value of delinquent loans fell significantly, from roughly **** billion U.S. dollars to approximately **** billion U.S. dollars. A similar trend can be observed with the number of delinquent loans.