The Federal National Mortgage Association, commonly known as Fannie Mae, was created by the U.S. congress in 1938, in order to maintain liquidity and stability in the domestic mortgage market. The company is a government-sponsored enterprise (GSE), meaning that while it was a publicly traded company for most of its history, it was still supported by the federal government. While there is no legally binding guarantee of shares in GSEs or their securities, it is generally acknowledged that the U.S. government is highly unlikely to let these enterprises fail. Due to these implicit guarantees, GSEs are able to access financing at a reduced cost of interest. Fannie Mae's main activity is the purchasing of mortgage loans from their originators (banks, mortgage brokers etc.) and packaging them into mortgage-backed securities (MBS) in order to ease the access of U.S. homebuyers to housing credit. The early 2000s U.S. mortgage finance boom During the early 2000s, Fannie Mae was swept up in the U.S. housing boom which eventually led to the financial crisis of 2007-2008. The association's stated goal of increasing access of lower income families to housing finance coalesced with the interests of private mortgage lenders and Wall Street investment banks, who had become heavily reliant on the housing market to drive profits. Private lenders had begun to offer riskier mortgage loans in the early 2000s due to low interest rates in the wake of the "Dot Com" crash and their need to maintain profits through increasing the volume of loans on their books. The securitized products created by these private lenders did not maintain the standards which had traditionally been upheld by GSEs. Due to their market share being eaten into by private firms, however, the GSEs involved in the mortgage markets began to also lower their standards, resulting in a 'race to the bottom'. The fall of Fannie Mae The lowering of lending standards was a key factor in creating the housing bubble, as mortgages were now being offered to borrowers with little or no ability to repay the loans. Combined with fraudulent practices from credit ratings agencies, who rated the junk securities created from these mortgage loans as being of the highest standard, this led directly to the financial panic that erupted on Wall Street beginning in 2007. As the U.S. economy slowed down in 2006, mortgage delinquency rates began to spike. Fannie Mae's losses in the mortgage security market in 2006 and 2007, along with the losses of the related GSE 'Freddie Mac', had caused its share value to plummet, stoking fears that it may collapse. On September 7th 2008, Fannie Mae was taken into government conservatorship along with Freddie Mac, with their stocks being delisted from stock exchanges in 2010. This act was seen as an unprecedented direct intervention into the economy by the U.S. government, and a symbol of how far the U.S. housing market had fallen.

The Global Financial Crisis of 2008-09 was a period of severe macroeconomic instability for the United States and the global economy more generally. The crisis was precipitated by the collapse of a number of financial institutions who were deeply involved in the U.S. mortgage market and associated credit markets. Beginning in the Summer of 2007, a number of banks began to report issues with increasing mortgage delinquencies and the problem of not being able to accurately price derivatives contracts which were based on bundles of these U.S. residential mortgages. By the end of 2008, U.S. financial institutions had begun to fail due to their exposure to the housing market, leading to one of the deepest recessions in the history of the United States and to extensive government bailouts of the financial sector.

Subprime and the collapse of the U.S. mortgage market

The early 2000s had seen explosive growth in the U.S. mortgage market, as credit became cheaper due to the Federal Reserve's decision to lower interest rates in the aftermath of the 2001 'Dot Com' Crash, as well as because of the increasing globalization of financial flows which directed funds into U.S. financial markets. Lower mortgage rates gave incentive to financial institutions to begin lending to riskier borrowers, using so-called 'subprime' loans. These were loans to borrowers with poor credit scores, who would not have met the requirements for a conventional mortgage loan. In order to hedge against the risk of these riskier loans, financial institutions began to use complex financial instruments known as derivatives, which bundled mortgage loans together and allowed the risk of default to be sold on to willing investors. This practice was supposed to remove the risk from these loans, by effectively allowing credit institutions to buy insurance against delinquencies. Due to the fraudulent practices of credit ratings agencies, however, the price of these contacts did not reflect the real risk of the loans involved. As the reality of the inability of the borrowers to repay began to kick in during 2007, the financial markets which traded these derivatives came under increasing stress and eventually led to a 'sudden stop' in trading and credit intermediation during 2008.

Market Panic and The Great Recession

As borrowers failed to make repayments, this had a knock-on effect among financial institutions who were highly leveraged with financial instruments based on the mortgage market. Lehman Brothers, one of the world's largest investment banks, failed on September 15th 2008, causing widespread panic in financial markets. Due to the fear of an unprecedented collapse in the financial sector which would have untold consequences for the wider economy, the U.S. government and central bank, The Fed, intervened the following day to bailout the United States' largest insurance company, AIG, and to backstop financial markets. The crisis prompted a deep recession, known colloquially as The Great Recession, drawing parallels between this period and The Great Depression. The collapse of credit intermediation in the economy lead to further issues in the real economy, as business were increasingly unable to pay back loans and were forced to lay off staff, driving unemployment to a high of almost 10 percent in 2010. While there has been criticism of the U.S. government's actions to bailout the financial institutions involved, the actions of the government and the Fed are seen by many as having prevented the crisis from spiraling into a depression of the magnitude of The Great Depression.

Formaat: AVI
Omvang: 22,7
Duur: 2:42

Online beschikbaar: [01-12-2014]
Uploaded on Oct 6, 2008

If you're confused as to why the U.S. economy is going down the drain, this should clear things up for you.
Written by Doug Moe - http://www.dougmoe.net/
Directed by Phelps Harmon - http://www.celebritydarwinism.com
Cast
Dad.........................Doug Moe
Son..........................Jeremy Rosenblum

Abstract

This research was conducted in Hungary in February-March 2010 as part of the second round of The Financial Crisis Survey. Data from 152 establishments from private nonagricultural formal sector was analyzed to quantify the effect of the 2008 global financial crisis on companies in Hungary.

Researchers revisited establishments interviewed in Hungary Enterprise Survey 2009. Efforts were made to contact all respondents of the baseline survey to determine which of the companies were still operating and which were not. From the information collected during telephone interviews, indicators were computed to measure the effects of the financial crisis on key elements of the private economy: sales, employment, finances, and expectations of the future.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study was the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The manufacturing and services sectors were the primary business sectors of interest. This corresponded to firms classified with International Standard Industrial Classification of All Economic Activities (ISIC) codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies were targeted for interviews. Services firms included construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government ownership were excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

291 establishments that participated in Hungary Enterprise Survey 2009 were contacted for The Financial Crisis Survey. The implementing contractor received directions that the final achieved sample should include at least 150 establishments.

For Hungary Enterprise Survey 2009, the sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into 23 manufacturing industries, 2 services industries -retail and IT-, and one residual sector. Each sector had a target of 90 interviews.

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in three regions. These regions are Central Hungary, West Hungary and East Hungary.

Given the stratified design, sample frames containing a complete and updated list of establishments for the selected regions were required. Great efforts were made to obtain the best source for these listings. However, the quality of the sample frames was not optimal and, therefore, some adjustments were needed to correct for the presence of ineligible units. These adjustments are reflected in the weights computation.

For most countries covered in 2008-2009 BEEPS, two sample frames were used. The first was supplied by the World Bank and consisted of enterprises interviewed in BEEPS 2005. The World Bank required that attempts should be made to re-interview establishments responding to the BEEPS 2005 survey where they were within the selected geographical regions and met eligibility criteria. That sample is referred to as the Panel. The second frame for Hungary was the Dun & Bradstreet database, which was considered the most reliable for the country. That frame was sent to the TNS statistical team in London to select the establishments for interviews.

The quality of the frame was assessed at the onset of the project. The frame proved to be useful though it showed positive rates of non-eligibility, repetition, non-existent units, etc. These problems are typical of establishment surveys, but given the impact these inaccuracies may have on the results, adjustments were needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of contacts to complete the survey was 4.6% (29 out of 630 establishments).

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The following survey instrument is available: - Financial Crisis Survey Questionnaire

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks and callbacks.

Following the drastic increase directly after the COVID-19 pandemic, the delinquency rate started to gradually decline, falling below *** percent in the second quarter of 2023. In the second half of 2023, the delinquency rate picked up, but remained stable throughout 2024. In the first quarter of 2025, **** percent of mortgage loans were delinquent. That was significantly lower than the **** percent during the onset of the COVID-19 pandemic in 2020 or the peak of *** percent during the subprime mortgage crisis of 2007-2010. What does the mortgage delinquency rate tell us? The mortgage delinquency rate is the share of the total number of mortgaged home loans in the U.S. where payment is overdue by 30 days or more. Many borrowers eventually manage to service their loan, though, as indicated by the markedly lower foreclosure rates. Total home mortgage debt in the U.S. stood at almost ** trillion U.S. dollars in 2024. Not all mortgage loans are made equal ‘Subprime’ loans, being targeted at high-risk borrowers and generally coupled with higher interest rates to compensate for the risk. These loans have far higher delinquency rates than conventional loans. Defaulting on such loans was one of the triggers for the 2007-2010 financial crisis, with subprime delinquency rates reaching almost ** percent around this time. These higher delinquency rates translate into higher foreclosure rates, which peaked at just under ** percent of all subprime mortgages in 2011.

Abstract

This research was conducted in Lithuania in June-July 2010 as part of the third round of The Financial Crisis Survey. Data from 217 establishments from private nonagricultural formal sector was analyzed to quantify the effect of the 2008 global financial crisis on companies in Lithuania.

Researchers revisited establishments interviewed in Lithuania Enterprise Survey 2009. Efforts were made to contact all respondents of the baseline survey to determine which of the companies were still operating and which were not. From the information collected during telephone interviews, indicators were computed to measure the effects of the financial crisis on key elements of the private economy: sales, employment, finances, and expectations of the future.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study was the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The manufacturing and services sectors were the primary business sectors of interest. This corresponded to firms classified with International Standard Industrial Classification of All Economic Activities (ISIC) codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies were targeted for interviews. Services firms included construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government ownership were excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

276 establishments that participated in Lithuania Enterprise Survey 2009 were contacted for The Financial Crisis Survey. The implementing contractor received directions that the final achieved sample should include at least 120 establishments.

For Lithuania Enterprise Survey 2009, the sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector. Each industry had a target of 90 interviews.

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in 4 regions. These regions are Coast and West, North East, South West and Vilniaus.

Given the stratified design, sample frames containing a complete and updated list of establishments for the selected regions were required. Great efforts were made to obtain the best source for these listings. However, the quality of the sample frames was not optimal and, therefore, some adjustments were needed to correct for the presence of ineligible units. These adjustments are reflected in the weights computation.

For most countries covered in 2008-2009 BEEPS two sample frames were used. The first source of the sample frame was Creditreform Lietuva - 2008- Organization database. A copy of that frame was sent to the statistical team in London to select the establishments for interview. The second frame, supplied by the World Bank/EBRD, consisted of enterprises interviewed in BEEPS 2005. The clients required that the attempts should be made to re-interview establishments responding to the BEEPS 2005 survey where they were within the selected geographical regions and met eligibility criteria. That sample is referred to as the Panel.

The quality of the frame was assessed at the onset of the project. The frame proved to be useful though it showed positive rates of non-eligibility, repetition, non-existent units, etc. These problems are typical of establishment surveys, but given the impact these inaccuracies may have on the results, adjustments were needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of contacts to complete the survey was 25.1% (446 out of 1777 establishments).

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The following survey instrument is available: - Financial Crisis Survey Questionnaire

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks and callbacks.

Abstract

This research was conducted in Lithuania in June-July 2009 as part of the first round of The Financial Crisis Survey. Data from 239 establishments from private nonagricultural formal sector was analyzed to quantify the effect of the 2008 global financial crisis on companies in Lithuania.

Researchers revisited establishments interviewed in Lithuania Enterprise Survey 2009. Efforts were made to contact all respondents of the baseline survey to determine which of the companies were still operating and which were not. From the information collected during telephone interviews, indicators were computed to measure the effects of the financial crisis on key elements of the private economy: sales, employment, finances, and expectations of the future.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study was the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The manufacturing and services sectors were the primary business sectors of interest. This corresponded to firms classified with International Standard Industrial Classification of All Economic Activities (ISIC) codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies were targeted for interviews. Services firms included construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government ownership were excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

276 establishments that participated in Lithuania Enterprise Survey 2009 were contacted for The Financial Crisis Survey. The implementing contractor received directions that the final achieved sample should include at least 120 establishments.

For Lithuania Enterprise Survey 2009, the sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector. Each industry had a target of 90 interviews.

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in 4 regions. These regions are Coast and West, North East, South West and Vilniaus.

Given the stratified design, sample frames containing a complete and updated list of establishments for the selected regions were required. Great efforts were made to obtain the best source for these listings. However, the quality of the sample frames was not optimal and, therefore, some adjustments were needed to correct for the presence of ineligible units. These adjustments are reflected in the weights computation.

For most countries covered in 2008-2009 BEEPS two sample frames were used. The first source of the sample frame was Creditreform Lietuva - 2008- Organization database. A copy of that frame was sent to the statistical team in London to select the establishments for interview. The second frame, supplied by the World Bank/EBRD, consisted of enterprises interviewed in BEEPS 2005. The clients required that the attempts should be made to re-interview establishments responding to the BEEPS 2005 survey where they were within the selected geographical regions and met eligibility criteria. That sample is referred to as the Panel.

The quality of the frame was assessed at the onset of the project. The frame proved to be useful though it showed positive rates of non-eligibility, repetition, non-existent units, etc. These problems are typical of establishment surveys, but given the impact these inaccuracies may have on the results, adjustments were needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of contacts to complete the survey was 25.1% (446 out of 1777 establishments).

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The following survey instrument is available: - Financial Crisis Survey Questionnaire

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks and callbacks.

This file contains the data and code for the publication "The Federal Reserve's Response to the Global Financial Crisis and Its Long-Term Impact: An Interrupted Time-Series Natural Experimental Analysis" by A. C. Kamkoum, 2023.

During the Global Financial Crisis of 2007-2008, a number of systemically important financial institutions in the United States declared bankruptcy, sought takeovers to prevent financial failure, or turned to the U.S. government for bailouts. Two of these institutions, Fannie Mae and Freddie Mac, were government-sponsored enterprises (GSEs), meaning that they were set up by the federal government in order to steer credit towards lower income homebuyers through interventions in the secondary mortgage market. While both were chartered by the government, they were also publicly traded companies, with a majority of shares owned by private investors. The fall of Fannie Mae and Freddie Mac These GSEs' business model was based on buying mortgages from their originators (banks, mortgage brokers, etc.) and then packaging groups of these mortgages together as mortgage-backed securities (MBS), before selling these on again to private investors. While this allowed the expansion of mortgage credit, meaning that many Americans were able to buy houses who would not have in other cases, this also contributed to the growing speculation in the housing market and related financial derivatives, such as MBS. The lowering of mortgage lending standards by originators in the early 2000s, as well as the need for GSEs to compete with their private sector rivals, meant that Fannie Mae and Freddie Mac became caught up in the financial mania associated with the early 2000s U.S. housing bubble. As their losses mounted due to the bursting of the bubble in 2007, both companies came under increasing financial stress, finally being brought into government conservatorship in September 2008. Fannie Mae and Freddie Mac were eventually unlisted from stock exchanges in 2010.

Formaat: MP4
Omvang: 47,2 Mb
27 February 2008

Online beschikbaar: [01-12-2014]
Standard Youtube License
Uploaded on Jun 11, 2008
Video summary of the ALDE workshop "The International Financial Crisis: Its causes and what to do about it?"

Event date: 27/02/08 14:00 to 18:00
Location: Room ASP 5G2, European Parliament, Brussels
This workshop will bring together Members of the European Parliament, economists, academics and journalists as well as representatives of the European Commission to discuss the lessons that have to be drawn from the recent financial crisis caused by the US sub-prime mortgage market.

With the view of the informal ECOFIN meeting in April which will look at the financial sector supervision and crisis management mechanisms, this workshop aims at debating a wide range of topics including:
- how to improve the existing supervisory framework,
- how to combat the opacity of financial markets and improve transparency requirements,
- how to address the rating agencies' performance and conflict of interest,
- what regulatory lessons are to be learnt in order to avoid a repetition of the sub-prime and the resulting credit crunch.

PROGRAMME

14:00 - 14:10 Opening remarks: Graham Watson, leader of the of the ALDE Group
14:10 - 14:25 Keynote speech by Charlie McCreevy, Commissioner for the Internal Market and Services, European Commission
14:25 - 14:40 Presentation by Daniel Daianu, MEP (ALDE) of his background paper
14:40 - 15:30 Panel I: Current features of the financial systems and the main causes of the current international crisis.

-John Purvis, MEP EPP
-Eric De Keuleneer, Solvay Business School, Free University of Brussels
-Nigel Phipps, Head of European Regulatory Affairs Moody's
-Wolfgang Munchau, journalist Financial Times
-Robert Priester, European Banking Federation (EBF), Head of Department Banking Supervision and Financial Markets
-Ray Kinsella, Director of the Centre for Insurance Studies University College Dublin
-Servaas Deroose, Director ECFIN.C, Macroeconomy of the euro area and the EU, European Commission
-Leke Van den Burg, MEP PSE
-David Smith, Visiting Professor at Derby Business School

this data base contains african indexes returns , namely MASI , NSE20, INVSAF40 and TUNINDEX before and after the subprime crisis in order toinvestigate the impact of this crisis on linkages among african markets

This file contains the data and code for the publication "The Federal Reserve’s Response to the Global Financial Crisis and its Effects: An Interrupted Time-Series Analysis of the Impact of its Quantitative Easing Programs" by A. C. Kamkoum, 2023.

Abstract

This research was conducted in Romania in June-July 2009 as part of the first round of The Financial Crisis Survey. Data from 370 establishments from private nonagricultural formal sector was analyzed to quantify the effect of the 2008 global financial crisis on companies in Romania.

Researchers revisited establishments interviewed in Romania Enterprise Survey 2009. Efforts were made to contact all respondents of the baseline survey to determine which of the companies were still operating and which were not. From the information collected during telephone interviews, indicators were computed to measure the effects of the financial crisis on key elements of the private economy: sales, employment, finances, and expectations of the future.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study was the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The manufacturing and services sectors were the primary business sectors of interest. This corresponded to firms classified with International Standard Industrial Classification of All Economic Activities (ISIC) codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies were targeted for interviews. Services firms included construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government ownership were excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

541 establishments that participated in Romania Enterprise Survey 2009 were contacted for The Financial Crisis Survey. The implementing contractor received directions that the final achieved sample should include at least 360 establishments.

For Romania Enterprise Survey 2009, the sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into 23 manufacturing industries, 2 services industries -retail and IT-, and one residual sector. Each group of sectors had a target of 180 interviews.

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in eight regions. These regions are Nord-Est, Sud-Est, Sud, Vest, Nord-Vest, Bucuresti, Sud-Vest, and Centru.

Given the stratified design, sample frames containing a complete and updated list of establishments for the selected regions were required. Great efforts were made to obtain the best source for these listings. However, the quality of the sample frames was not optimal and, therefore, some adjustments were needed to correct for the presence of ineligible units. These adjustments are reflected in the weights computation.

For most countries covered in 2008-2009 BEEPS, two sample frames were used. The first was supplied by the World Bank and consisted of enterprises interviewed in BEEPS 2005. The World Bank required that attempts should be made to re-interview establishments responding to the BEEPS 2005 survey where they were within the selected geographical regions and met eligibility criteria. That sample is referred to as the Panel. Some of the establishments in the Panel had less than five employees.

The second frame used in Romania was the Trade Register of Romania. The full frame was not made available. Instead an extract was selected in Romania according to instructions from the TNS statistical team in London.

The quality of the frame was assessed at the onset of the project. The frame proved to be useful though it showed positive rates of non-eligibility, repetition, non-existent units, etc. These problems are typical of establishment surveys, but given the impact these inaccuracies may have on the results, adjustments were needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of contacts to complete the survey was 37% (414 out of 1,115 establishments).

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The following survey instrument is available: - Financial Crisis Survey Questionnaire

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks and callbacks.

Abstract (en): This collection, A Longitudinal Study of Public Response, was conducted to understand the trajectory of risk perception amidst an ongoing economic crisis. A nation-wide panel responded to eight surveys beginning in late September 2008 at the peak of the crisis and concluded in August 2011. At least 600 respondents participated in each survey, with 325 completing all eight surveys. The online survey focused on perceptions of risk (savings, investments, retirement, job), negative emotions toward the financial crisis (sadness, anxiety, fear, anger, worry, stress), confidence in national leaders to manage the crisis (President Obama, Congress, Treasury Secretary, business leaders), and belief in one's ability to realize personal objectives despite the crisis. Latent growth curve modeling was conducted to analyze change in risk perception throughout the crisis. Demographic information includes ethnic origin, sex, age, marital status, income, political affiliation and education. This longitudinal panel study was launched on September 29, 2008, the day the Dow experienced its largest one-day point drop. The first of seven waves of data collection was dedicated almost exclusively to public response to the financial crisis. Further data collection followed on October 8, 2008, November 5, 2008, December 6, 2008, March 21, 2009, June 30, 2009, October 6, 2009, and August 9, 2011. The surveys were spaced closer together in the beginning of the study believing that the most change would occur early in the crisis and, of course, not knowing how long the crisis would last. Collecting the first seven waves of data over a year's period allowed time for the public to respond to different phases of the crisis. A panel of over 800 individuals participated in the study. This ongoing Internet panel was developed by Decision Research through word-of-mouth and Internet recruiting (e.g., paying for Google search words). Nonrespondents (panelists invited to participate but who chose not to) did not differ significantly from respondents in terms of age, gender, or education. Surveys were left open for completion for four to six days, although most panelists responded in the first 24 hours. These online panelists were paid at the rate of $15 per hour with a typical payment of $6 and an incentive if they completed all surveys. Any panelist who appeared to rush through the survey was eliminated and not invited to participate again. N/A ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Presence of Common Scales: Lipkus Numeracy Score, Hierarchy-Egalitarianism Scale, Individualism-Communitarianism Response Rates: Wave 1: 81 percent; Wave 2: 89 percent; Wave 2a:80 percent; Wave 3: 87 percent; Wave 4: 85 percent; Wave 5: 91 percent; Wave 6: 76 percent; Wave 7: 74 percent; Wave 8: 79 percent Smallest Geographic Unit: None Convenience sample of Decision Research web-panel participation located throughout the United States. Funding insitution(s): National Science Foundation (SES-0901036). web-based survey

Comprehensive data on Goldman Sachs legal settlements totaling $12+ billion from 2009-2024

Affordable Housing Market Analysis The global affordable housing market is projected to reach $1,983.52 billion by 2033, exhibiting a CAGR of 4.71% from 2025 to 2033. The rising population, urbanization, affordability crisis, and supportive government policies are the primary drivers fueling market growth. The increasing demand for affordable single-family homes, multi-family units, and townhouses, coupled with the adoption of innovative construction methods like prefabrication, 3D printing, and sustainable construction, are key trends shaping the market. The market faces restraints such as escalating land and construction costs, regulatory challenges, and the shortage of skilled labor. Nevertheless, the emergence of crowdfunding platforms and non-profit organizations providing financial assistance, as well as government subsidies and tax incentives, are expected to mitigate these constraints. The market is segmented based on housing type, funding source, construction method, and target demographics. D.R. Horton, Taylor Morrison, PulteGroup, Zillow, Hovnanian Enterprises, and Lennar Corporation are notable companies in the global affordable housing market, with operations in key regions like North America, Europe, and Asia Pacific. Recent developments include: Recent developments in the Affordable Housing Market have highlighted the urgent need for innovative housing solutions as governments and organizations strive to address the growing housing crisis exacerbated by economic challenges and population growth. Various nations are prioritizing policies that encourage public-private partnerships to stimulate investment in affordable housing initiatives. Additionally, the integration of sustainable building practices and smart technologies is gaining traction as stakeholders aim to improve energy efficiency while reducing construction costs. Recent collaborations among international entities and local governments focus on leveraging funding for housing projects, particularly in urban areas where demand is surging. Moreover, rising material costs and labor shortages are prompting stakeholders to explore alternative building materials and methods, including modular construction and 3D printing, to streamline processes. These trends underscore a collective commitment to creating equitable housing opportunities while navigating the complexities of market dynamics, aiming for significant progress by 2032. Overall, this evolving landscape reflects a concerted effort to promote affordability, sustainability, and accessibility in housing worldwide.. Key drivers for this market are: Green building technologies adoption Public-private partnerships expansion Innovative financing solutions development Urban regeneration projects implementation Digital platforms for housing access. Potential restraints include: rising urbanization, government initiatives; increasing housing demand; socioeconomic disparities; affordable financing options.

This study attempts to explore the impact of external debt ($Debt), foreign reserves ($Reserves), and political stability & absence of violence/terrorism (PS&AVT) on the current financial crisis in Sri Lanka. Using data from 1996 to 2022 obtained from the World Bank (WB) and the Central Bank of Sri Lanka (CBSL), a regression analysis is conducted, with a composite variable named "CRISIS," which accounts for interest rate, inflation, currency devaluation adjusted to GDP growth, as the dependent variable. The findings indicate that, collectively, these predictors significantly contribute to explaining the variance in the financial crisis, although their impact is relatively minor. While the direct influence of PS&AVT on the financial crisis is not statistically significant, it indirectly affects the crisis through its considerable impact on debt and reserves. Granger causality tests showed predictive value for $Debt and $Reserve in relation to CRISIS, but the reverse relationship was not significant. Regression analysis using the error term and scatter plots supports the absence of endogeneity issues in the model. These findings suggest that while external debt and foreign reserves are more directly related to financial crises, political stability and the absence of violence/terrorism can influence the crisis indirectly through their effects on debt accumulation and reserve levels. This study represents a pioneering effort in investigating the impact of external debt, foreign reserves, and political stability on the financial crises in Sri Lanka. By utilizing a comprehensive dataset and applying a regression analysis, it sheds light on the complex interactions between these variables and their influence on the country’s financial stability.

Lehman Brothers, the fourth largest investment bank on Wall Street, declared bankruptcy on the 15th of September 2008, becoming the largest bankruptcy in U.S. history. The investment house, which was founded in the mid-19th century, had become heavily involved in the U.S. housing bubble in the early 2000s, with its large holdings of toxic mortgage-backed securities (MBS) ultimately causing the bank's downfall. The bank had expanded rapidly following the repeal of the Glass-Steagall Act in 1999, which meant that investment banks could also engage in commercial banking activities. Lehman vertically integrated their mortgage business, buying smaller commercial enterprises that originated housing loans, which allowed the bank to expand its MBS holdings. The downfall of Lehman and the crash of '08 As the U.S. housing market began to slow down in 2006, the default rate on housing loans began to spike, triggering losses for Lehman from their MBS portfolio. Lehman's main competitor in mortgage financing, Bear Stearns, was bought by J.P. Morgan Chase in order to prevent bankruptcy in March 2008, leading investors and lenders to become increasingly concerned about the bank's financial health. As the bank relied on short-term funding on money markets in order to meet its obligations, the news of its huge losses in the third-quarter of 2008 further prevented it from funding itself on financial markets. By September, it was clear that without external assistance, the bank would fail. As its losses from credit default swaps mounted due to the deepening crash in the housing market, Lehman was forced to declare bankruptcy on September 15, as no buyer could be found to save the bank. The collapse of Lehman triggered panic in global financial markets, forcing the U.S. government to step in and bail-out the insurance giant AIG the next day on September 16. The effects of this financial crisis hit the non-financial economy hard, causing a global recession in 2009.

"Resolving Failed Banks: Uncertainty, Multiple Bidding, and Auction Design," Jason Allen, Robert Clark, Brent Hickman and Eric Richert, forthcoming, Review of Economic Studies.

DATA AND CODE NEEDED TO REPLICATE ANALYSIS

these are the Replication files for: How Global is the Affordable Housing Crisis? accepted by the International Journal of Housing Markets and Analysis