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.

By April 2026, it is projected that there is a probability of ***** percent that the United States will fall into another economic recession. This reflects a significant decrease from the projection of the preceding month.

The Long Depression was, by a large margin, the longest-lasting recession in U.S. history. It began in the U.S. with the Panic of 1873, and lasted for over five years. This depression was the largest in a series of recessions at the turn of the 20th century, which proved to be a period of overall stagnation as the U.S. financial markets failed to keep pace with industrialization and changes in monetary policy. Great Depression The Great Depression, however, is widely considered to have been the most severe recession in U.S. history. Following the Wall Street Crash in 1929, the country's economy collapsed, wages fell and a quarter of the workforce was unemployed. It would take almost four years for recovery to begin. Additionally, U.S. expansion and integration in international markets allowed the depression to become a global event, which became a major catalyst in the build up to the Second World War. Decreasing severity When comparing recessions before and after the Great Depression, they have generally become shorter and less frequent over time. Only three recessions in the latter period have lasted more than one year. Additionally, while there were 12 recessions between 1880 and 1920, there were only six recessions between 1980 and 2020. The most severe recession in recent years was the financial crisis of 2007 (known as the Great Recession), where irresponsible lending policies and lack of government regulation allowed for a property bubble to develop and become detached from the economy over time, this eventually became untenable and the bubble burst. Although the causes of both the Great Depression and Great Recession were similar in many aspects, economists have been able to use historical evidence to try and predict, prevent, or limit the impact of future recessions.

Abstract

This research was conducted in Latvia in February-March 2010 as part of the second round of The Financial Crisis Survey. Data from 221 establishments from private nonagricultural formal sector was analyzed to quantify the effect of the 2008 global financial crisis on companies in Latvia.

Researchers revisited establishments interviewed in Latvia 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

271 establishments that participated in Latvia 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 Latvia 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. For the core industries sample sizes were inflated by about 2% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel.

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 6 regions. These regions are Riga, Pieriga, Vidzeme, Kurzeme, Zemgale, and Latgale.

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.

The source of the sample frame was the January 2008 version of the Business Register of the Central Statistical Bureau of Latvia. 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 26.32% (195 out of 741 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

This data package includes the underlying data files to replicate the data and charts presented in Egypt’s 2023-24 economic crisis: Will this time be different? by Ruchir Agarwal and Adnan Mazarei, PIIE Policy Brief 24-6.

If you use the data, please cite as: Agarwal, Ruchir, and Adnan Mazarei. 2024. Egypt’s 2023-24 economic crisis: Will this time be different?. PIIE Policy Brief 24-6. Washington, DC: Peterson Institute for International Economics.

From the onset of the Global Financial Crisis in the Summer of 2007, the world economy experienced an almost unprecedented period of turmoil in which millions of people were made unemployed, businesses declared bankruptcy en masse, and structurally critical financial institutions failed. The crisis was triggered by the collapse of the U.S. housing market and subsequent losses by investment banks such as Bear Stearns, Lehman Brothers, and Merrill Lynch. These institutions, which had become over-leveraged with complex financial securities known as derivatives, were tied to each other through a web of financial contracts, meaning that the collapse of one investment bank could trigger the collapse of several others. As Lehman Brothers failed on September 15. 2008, becoming the largest bankruptcy in U.S. history, shockwaves were felt throughout the global financial system. The sudden stop of flows of credit worldwide caused a financial panic and sent most of the world's largest economies into a deep recession, later known as the Great Recession. The World Economy in recession
More than any other period in history, the world economy had become highly interconnected and interdependent over the period from the 1970s to 2007. As governments liberalized financial flows, banks and other financial institutions could take money in one country and invest it in another part of the globe. Financial institutions and other non-financial companies became multinational, meaning that they had subsidiaries and partners in many regions. All this meant that when Wall Street, the center of global finance in New York City, was shaken by bankruptcies and credit freezes in late 2007, other advanced economies did not need to wait long to feel the tremors. All of the G7 countries, the seven most economically advanced western-aligned countries, entered recession in 2008, before experiencing an even deeper trough in 2009. While all returned to growth by 2010, this was less stable in the countries of the Eurozone (Germany, France, Italy) over the following years due to the Eurozone crisis, as well as in Japan, which has had issues with low growth since the mid-1990s.

This paper examines the aftermath of postwar financial crises in advanced countries. We construct a new semiannual series on financial distress in 24 OECD countries for the period 1967-2012. The series is based on assessments of the health of countries' financial systems from a consistent, real-time narrative source, and classifies financial distress on a relatively fine scale. We find that the average decline in output following a financial crisis is statistically significant and persistent, but only moderate in size. More important, we find that the average decline is sensitive to the specification and sample, and that the aftermath of crises is highly variable across major episodes. A simple forecasting exercise suggests that one important driver of the variation is the severity and persistence of financial distress itself. At the same time, we find little evidence of nonlinearities in the relationship between financial distress and the aftermaths of crises.

Abstract

This research was conducted in Romania in February-March 2010 as part of the second round of The Financial Crisis Survey. Data from 304 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

536 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.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in International Coordination of Economic Policies in the Global Financial Crisis: Successes, Failures, and Consequences, PIIE Working Paper 19-11.

If you use the data, please cite as: Truman, Edwin M. (2019). International Coordination of Economic Policies in the Global Financial Crisis: Successes, Failures, and Consequences. PIIE Working Paper 19-11. Peterson Institute for International Economics.

The Global Financial Crisis (2007-2008), which began due to the collapse of the U.S. housing market, had a negative effect in many regions across the globe. The global recession which followed the crisis in 2008 and 2009 showed how interdependent and synchronized many of the world's economies had become, with the largest advanced economies showing very similar patterns of negative GDP growth during the crisis. Among the largest emerging economies (commonly referred to as the 'E7'), however, a different pattern emerged, with some countries avoiding a recession altogether. Some commentators have particularly pointed to 2008-2009 as the moment in which China emerged on the world stage as an economic superpower and a key driver of global economic growth. The Great Recession in the developing world While some countries, such as Russia, Mexico, and Turkey, experienced severe recessions due to their connections to the United States and Europe, others such as China, India, and Indonesia managed to record significant economic growth during the period. This can be partly explained by the decoupling from western financial systems which these countries undertook following the Asian financial crises of 1997, making many Asian nations more wary of opening their countries to 'hot money' from other countries. Other likely explanations of this trend are that these countries have large domestic economies which are not entirely reliant on the advanced economies, that their export sectors produce goods which are inelastic (meaning they are still bought during recessions), and that the Chinese economic stimulus worth almost 600 billion U.S. dollars in 2008/2009 increased growth in the region.

Graph and download economic data for Dates of U.S. recessions as inferred by GDP-based recession indicator (JHDUSRGDPBR) from Q4 1967 to Q1 2025 about recession indicators, GDP, and USA.

Abstract

This research was conducted in Turkey in February-March 2010 as part of the second round of The Financial Crisis Survey. Data from 606 establishments from private nonagricultural formal sector was analyzed to quantify the effect of the 2008 global financial crisis on companies in Turkey.

Researchers revisited establishments interviewed in Turkey Enterprise Survey 2008. 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

1152 establishments that participated in Turkey Enterprise Survey 2008 were contacted for The Financial Crisis Survey. The implementing contractor received directions that the final achieved sample should include at least 650 establishments.

Stratified random sampling was used in Turkey Enterprise Survey 2008. Three levels of stratification were implemented: industry, establishment size, and oblast (region).

For industry stratification, the universe was divided into 5 manufacturing industries, 1 services industry -retail -, and two residual sectors. Each manufacturing industry had a target of 160 interviews. The services industry and the two residual sectors had a target of 120 interviews. For the manufacturing industries sample sizes were inflated by about 33% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel.

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 5 regions. These regions are Marmara, Aegean, South, Central Anatolia and Black Sea-Eastern.

The Turkey sample contains panel data. The wave 1 panel "Investment Climate Private Enterprise Survey implemented in Turkey" consisted of 1325 establishments interviewed in 2005. A total of 425 establishments have been re-interviewed.

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.

The source of the sample frame was twofold. Universe estimates were taken from the TOBB database which contains a full list of establishments in manufacturing sectors. TOBB refers to the Union of Chambers and Commodity Exchanges of Turkey. Universe estimates for service sectors were taken from the Statistical Institute of Statistics (SIS) with additional information based on SIC code from the Turkish Studies Institute (TSI). Comparisons were made between estimates in TOBB and SIS to establish that the two sources are comparable and hence can be used side by side.

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 43% (2811 out of 6458 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.

Using a novel dataset, which merges good-level prices underlying the PPI with the respondents' balance sheets, we show that liquidity constrained firms increased prices in 2008, while their unconstrained counterparts cut prices. We develop a model in which firms face financial frictions while setting prices in customer markets. Financial distortions create an incentive for firms to raise prices in response to adverse financial or demand shocks. This reaction reflects the firms' decisions to preserve internal liquidity and avoid accessing external finance, factors that strengthen the countercyclical behavior of markups and attenuate the response of inflation to fluctuations in output.

Abstract

This research was conducted in Lithuania in February-March 2010 as part of the second round of The Financial Crisis Survey. Data from 224 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.

The project ‘Truth, Accountability or Impunity? Transitional Justice and the Economic Crisis’ completed a repository of policies of accountability in response to the post-2008 Great Recession in six European countries (Ireland, Iceland, Greece, Cyprus, Portugal & Spain). The repository included recorded prosecutions of bank executives, office holders and politicians on charges related to white collar crimes and/or corruption in the lead up to the economic crisis. It also includes fact finding commissions (i.e. independent commissions of inquiry and/or parliamentary commissions of inquiry) designed to document patterns of policy and institutional failures that led to the economic meltdown, in the period between 2010-2018. The rationale for developing the repository was, first, to map the range of policies deployed and, second, to investigate potential variations in the national policies. In parallel with the development of the repository, the project included the conduct of approximately 133 confidential semi-structured interviews in Ireland, Iceland, Greece, Cyprus, Portugal, Spain, Washington D.C. (IMF) and Brussels (EU). These included interviews with prosecutors, judges, elected officials (e.g. former Prime Ministers, Ministers, MPs), unelected officials (e.g. policymakers at central banks, relevant ministries, EU bodies, senior IMF executives etc), NGO members, journalists, academics, defense lawyers and other informed stakeholders to understand the rationale and their attitudes towards policies of accountability. There is little emphasis in the extant literature on the role and impact of different mechanisms of accountability in post-crisis settings, so these interviews were expected to shed useful analytical light. Finally, with regards to the case selection six European countries with similar background conditions and exposure to the crisis but different policy responses, each representing a different approach to accountability.The comparative project applies concepts of transitional justice, namely, 'dealing with the past', to investigate how six European societies (Spain, Portugal, Greece, Ireland, Cyprus, Iceland) have come to terms with the origins and consequences of the post-2008 financial crisis. The economic aspects of the crash are well discussed elsewhere; the proposed project argues significant political and legal lessons can be learned from the crisis, but these are missed by viewing it only through an economic lens. Simply stated, transitional justice, a framework developed over the past forty years, considers how national political elites balance popular calls for truth and justice with the pragmatic need for stability in the aftermath of crisis. Prosecutions, truth recovery and amnesties or impunity are much studied mechanisms. Notably, these mechanisms have been deployed in the cases under consideration. Spain and Portugal took only minimal steps to address the causes of the crisis, in effect, pursuing a policy of immunity. Iceland and Cyprus set up ad hoc truth commissions to document the causes of the crisis. Ireland and Greece have prosecuted and convicted a number of bankers and politicians deemed responsible. The project seeks to explain why, despite similar background conditions, societies have formulated different policy responses and to identify the strengths and limitations of each response. This is important. Examining the comparative experience of societies who experiment with policy mechanisms will contribute to the design of better policy responses in times of crisis, decreasing the level of social upheaval, boosting political legitimacy and paving the way for meaningful institutional reform. This project is explicitly about the intersection of politics and law; it focuses on issues of political and institutional failure and the role of law in promoting accountability, responsibility and political learning from economic crises.

This dataset captures multi-market financial indicators that can be used to study financial crises, market stress, and economic stability. It integrates simulated data from stock, bond, and foreign exchange (forex) markets, along with volatility metrics and a binary crisis label.

The dataset provides a comprehensive view of cross-market behavior and is suitable for tasks such as crisis detection, financial risk analysis, and market interdependence studies.

Key Features Time Series Coverage:

Daily data over ~1,000 days for multiple countries

Stock Market Indicators:

Stock_Index → Simulated stock market index values

Stock_Return → Daily percentage change in stock index

Stock_Volatility → 5-day rolling standard deviation of stock returns

Bond Market Indicators:

Bond_Yield → Simulated 10-year government bond yield

Bond_Yield_Spread → Difference between long-term and short-term yields

Bond_Volatility → Simulated volatility in bond yields

Forex Market Indicators:

FX_Rate → Simulated currency exchange rate

FX_Return → Daily percentage change in exchange rate

FX_Volatility → 5-day rolling standard deviation of forex returns

Global Market Stress Indicator:

VIX → Simulated volatility index representing market stress

Target Variable:

Crisis_Label → Binary flag indicating market condition (0 = Normal, 1 = Crisis)

File Information Format: CSV

Rows: ~3,000 (1,000 days × 3 countries)

Columns: 13 (including target label)

Use Cases:

Financial crisis detection

Market stress and contagion analysis

Cross-market economic studies

This dataset is about book series. It has 1 row and is filtered where the books is Global financial crisis : global impact and solutions. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Fintech Crisis Management Market Outlook

The global Fintech Crisis Management market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 7.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.2% during the forecast period. The strong growth factor for this market is the increasing complexity and frequency of financial crises, which necessitates advanced solutions to manage and mitigate risks effectively.

One of the key growth factors driving the Fintech Crisis Management market is the heightened regulatory pressures faced by financial institutions worldwide. Governments and regulatory bodies are continuously updating and enforcing stringent regulations to ensure financial stability and protect consumer interests. This has led to the adoption of advanced crisis management solutions that can help organizations remain compliant while effectively managing risks. Additionally, the increasing incidences of cyber-attacks and data breaches in the financial sector have further necessitated the implementation of robust crisis management systems to safeguard critical financial data.

Another significant factor contributing to the growth of the Fintech Crisis Management market is the rapid technological advancements such as artificial intelligence, machine learning, and blockchain. These technologies offer innovative approaches to risk assessment, incident detection, and response management. AI and ML algorithms can analyze vast amounts of data to identify potential threats and vulnerabilities in real-time, allowing financial institutions to take proactive measures. Blockchain technology, with its inherent transparency and security features, is being increasingly leveraged for secure transaction processing and data integrity, which are crucial during a financial crisis.

The growing adoption of digital transformation strategies by financial institutions is also fueling the market growth. As banks and other financial service providers increasingly move towards digital platforms, the risk of operational disruptions and technological failures also rises. Fintech crisis management solutions address these challenges by providing comprehensive business continuity planning and incident management capabilities. These solutions ensure that financial institutions can maintain their operations and customer services during unforeseen disruptions, thereby safeguarding their reputation and financial stability.

From a regional perspective, North America is expected to hold the largest market share owing to the presence of major financial institutions and advanced technological infrastructure. The region's stringent regulatory environment further emphasizes the need for effective crisis management solutions. Asia Pacific is anticipated to witness the highest growth rate during the forecast period, driven by the rapid digitization of financial services and increasing regulatory scrutiny in emerging economies such as China and India. Europe also presents significant growth opportunities due to the ongoing digital transformation initiatives and the presence of a robust financial sector.

Component Analysis

The Fintech Crisis Management market is segmented by component into software and services. The software segment includes various applications and platforms designed to manage and mitigate financial crises, while the services segment encompasses consulting, training, and support services that complement the software solutions.

The software segment is expected to dominate the market due to the increasing need for advanced technological solutions that can provide real-time monitoring, risk assessment, and incident management. Financial institutions are investing heavily in software platforms that leverage artificial intelligence and machine learning to predict and mitigate potential crises. These platforms offer comprehensive dashboards, analytics, and reporting tools that enable organizations to respond swiftly and effectively to any disruptions.

On the other hand, the services segment is also projected to witness significant growth. As the complexity of financial regulations and crisis scenarios increases, the demand for expert consulting and support services is on the rise. Financial institutions require specialized knowledge to implement and optimize crisis management solutions, ensure regulatory compliance, and train their staff on best practices. This has led to a growing market for professional services that can provide tailored solutions and ensure the seamless integration of crisis m

Nigerian Journal of Economic and Social Studies, (July).