The Great Recession was a period of economic contraction which came in the wake of the Global Financial Crisis of 2007-2008. The recession was triggered by the collapse of the U.S. housing market and subsequent bankruptcies among Wall Street financial institutions, the most significant of which being the bankruptcy of Lehman Brothers in September 2008, the largest bankruptcy in U.S. history. These economic convulsions caused consumer confidence, measured by the Consumer Confidence Index (CCI), to drop sharply in 2007 and the beginning of 2008. How does the Consumer Confidence Index work? The CCI measures household's expectation of their future economic situation and, consequently, their likely future spending and savings decisions. A score of 100 in the index would indicate a neutral economic outlook, with consumers neither being optimistic nor pessimistic about the near future. Scores below 100 are then more pessimistic, while scores above 100 indicate optimism about the economy. Consumer confidence can have a self-fulfilling effect on the economy, as when consumers are pessimistic about the economy, they tend to save and postpone spending, contracting aggregate demand and causing the economy to slow down. Conversely, when consumers are optimistic and willing to spend, this can have a reinforcing effect as wages and employment may rise when consumers spend more. CCI and the Great Recession As the reality of the trouble which the U.S. financial sector was in set in over 2007, consumer confidence dropped sharply from being slightly positive, to being deeply pessimistic by the Summer of 2008. While confidence began to slowly rebound up until September 2008, with the panic caused by Lehman's bankruptcy and the freezing of new credit creation, the CCI plummeted once more, reaching its lowest point during the recession in February 2008. The U.S. government stepped in to prevent the bankruptcy of AIG in 2008, promising to do the same for any future possible failures in the financial system. This 'backstopping' policy, whereby the government assured that the economy would not be allowed to fall further into crisis, along with the Federal Reserve's unconventional monetary policies used to restart the economy, contributed to a rebound in consumer confidence in 2009 and 2010. In spite of this, consumers still remained pessimistic about the economy.

In spring 2023, more than half of surveyed consumers in the United States said they could live without buying apparel for a little while if they entered a recession in the next six months. Ranking second, many also said they could put a hold on buying home improvement items during times of economic uncertainty.

The survey asked respondents to compare their expenditure and consumer behaviour (concerning e.g. food, housing, leisure activities, alcohol, travel) to those of the average consumer. The respondents were asked which things and household items they considered necessary and what they would do if they had more money. The survey carried a set of attitudinal statements about consumption and lifestyle (e.g. "I like to drink wine when eating" or "Quality is more important to me than price"). Some questions covered on what grounds respondents made decisions on economical, family or work matters. The extent to which the deep recession of the early 1990s had affected the household was examined. One theme pertained to community identification: whether the respondents felt they were part of their family, workplace, community, Finnish society, and how much their way of spending or borrowing money, etc. was similar to that of other people. The respondents were asked to define different generations and to assess whether there was any conflict between them. They rated the importance of various things (e.g. self-respect, world peace, prosperity, independence) to themselves and the safety of their own life, community, society and the world. Views were probed on how much insecurity e.g. pollution, cuts to certain public services and increasing the national debt would cause. Some questions covered personal feelings of insecurity concerning e.g. livelihood, finances, relationships. The respondents evaluated risks in the present-day society and rated the risk involved in different actions (e.g. contracting a loan, travelling, speeding, flying, using drugs, casual sex). The survey contained questions about the income, expenditure, savings and debts of the respondents and the household. Credit card use, defaults on payments/debts and the resulting bad credit were charted. The respondents were asked what their methods of coping were when short of money, that is, whether they would borrow, reduce expenditure, gamble, etc. Background variables included respondents' sex, tenure, marital status, household size, number of children, basic and vocational education, economic activity, occupation of the respondent, the spouse and parents, experiences of unemployment, financial circumstances, social class, voting in elections and party preference.

This survey was undertaken to assess consumer sentiment and buying plans. Open-ended questions were asked concerning evaluations and expectations about personal finances, employment, recession, price changes, and the national business situation. Additional variables probe respondents' buying intentions for a house, automobiles, appliances, and other consumer durables, and respondents' appraisals of present market conditions for purchasing houses and other durables. Other variables probe respondents' assessments of their financial status relative to the previous year, as well as their opinions of Gerald Ford and Jimmy Carter in comparative terms as to who would do a better job and improve the economy if elected as president, and of fuel economy cars. Information is also provided on respondents' investments in stocks or bonds, debts owed, car owned and the plans to buy a new vehicle, towns lived in five years ago, type of house lived in currently and for what length of time in the year, and the number of telephones owned. Demographic variables provide information on respondents' age, sex, race, marital status, education, occupation, employment status, and family income.

In the United States, consumer spending on media was estimated to grow by *** percent in 2022. According to the forecast scenarios, the expenditure would decrease by **** or ***** percent in the following year.

Scenario A: According to this first scenario, the recession would only have a short-term impact on consumers' media spending. At the height of the recession in 2023, consumers are expected to spend less on entertainment to offset rising energy and consumer product prices. The economy should begin to recover from the recession by 2024 and should be fully mended by 2027, while spending on media will be back to pre-pandemic levels.

Scenario B: The second scenario predicts a long-term impact of the recession on media consumption behavior. Ad-supported options will replace subscription-based offers, whereas on-and-off subscribing will increase, driven by special offers and consumers unsubscribing after those offers expire. The inflation will hit harder in 2023 than according to the first scenario and behavior changes will stick even after 2027 when the economy has fully recovered.

This survey was undertaken to assess consumer sentiment and buying plans. Open-ended questions were asked concerning evaluations and expectations about personal finances, employment, recession, price changes, and the national business situation. Additional variables probe respondents' buying intentions for a house, automobiles, appliances, and other consumer durables, and the respondents' appraisals of present market conditions for purchasing houses and other durables. Other variables probe respondents' opinions of government price controls, government spending, especially spending on welfare, income tax filing and returns, small foreign cars as compared to small American cars, and their financial status relative to the previous year. Information is also provided on respondents' car ownership and the make and use of it, and spending plans for their income tax refunds. Demographic variables provide information on respondents' age, sex, race, marital status, occupation, employment status, and family income.

We estimate across-county spending flows between firms and consumers for every county in the United States, providing a new consumption link that has not been studied previously. We highlight the importance of this link by estimating the effect of changes in local housing wealth on consumption and employment from 2001 to 2019. We generally find that the effect from changes in housing wealth crosses borders to affect consumption and employment in a pattern consistent with our spending flows. However, we find potential consumers who reside outside the local commuting zone disproportionately affect local spending and employment during the Great Recession.

Consumer Confidence in the United States increased to 61.80 points in July from 60.70 points in June of 2025. This dataset provides the latest reported value for - United States Consumer Sentiment - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

We examine the effect of the 2008 economic recession on consumers’ observed expenditures for eco-labelled grocery products. Traditional price theory predicts that consumers change their spending during an economic downturn and we would expect the sales share of eco-labelled products to fall since these are relatively more expensive than non-labelled products. We use supermarket loyalty card data from the UK and show that the recession had widely different effects on the expenditure share of different eco-labelled grocery products. We confirm, empirically, that expenditure shares on organic products declined over the time period under study but the expenditures share for fair-trade products increased over the same period. We evaluate alternative models of decision making to explain our results, viz., a salience model and a model of reputation signalling. We find that both of these models give a plausible explanation of our empirical results.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

NGT:TSX Stock: Are We Headed for a Recession?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This survey of 1,004 adult residents includes questions from earlier Orange County Annual Surveys. It also includes key indicators from the PPIC Statewide Survey for comparisons with the state and regions of California. The study also considers racial/ethnic, income, and political differences. The following issues are explored in this Orange County Survey: Orange County issues, state issues and national issues. Orange County Issues include such questions as: What are the trends over time in ratings of life in Orange County? How satisfied are residents with their finances, local public services, local government, the economy, and with the quality of life in Orange County? Compared to other regions of the state, how much of a problem are issues such as traffic congestion, the economy, growth, and housing in Orange County? What are residents preferences for transportation plans and local transportation taxes?
Online data analysis & additional documentation in Link below.

In the United States, consumer spending on media was estimated to amount to about *** billion U.S. dollars in 2022. According to the forecast scenarios, that annual value would surpass *** billion or stand just below *** billion dollars by 2027. What do the scenarios mean? In scenario A, the recession would only have a short-term impact on consumer media spending. At the height of the recession in 2023, consumers are expected to spend less on entertainment to offset rising energy and consumer product prices. The economy should begin to recover by 2024 and should be fully mended by 2027, with spending on media back to pre-pandemic levels.

Scenario B predicts a long-term impact of the recession on media consumption behavior. Ad-supported options will replace subscription-based offers, whereas on-and-off subscribing will increase, driven by special offers and consumers unsubscribing after those offers expire. Behavior changes will stick even after 2027 when the economy has fully recovered. Media usage today Media usage in the United States has already changed within just one year. Recent data from the beginning of 2023 shows that consumers opt for free entertainment choices. More people indicate watching free-on-demand TV, more of them also listen to the radio. Podcasts also gained in popularity, compared to the first quarter of 2022. Also fewer people say they don’t watch live TV, which is a potential sign of the growing popularity of free-ad-supported-TV (FAST) services as well.

This survey was undertaken to assess consumer sentiment and buying plans, as well as unemployment, travel, long-distance telephone calls, and attitudes toward proposed anti-recession measures. Open-ended questions were asked concerning evaluations and expectations about price changes, employment, recession, and the national business situation. Information was elicited on the number of people then working who had been laid off intermittently in the past year or who were working shorter hours or who had lost their jobs. Questions were also asked about how families whose income had been reduced by unemployment or by shorter hours had managed financially, and what might stimulate business and reduce unemployment. Respondents were also asked about their plans for future travels, travel experiences, and overseas travel preferences, and their reactions to the introduction of jet planes for commercial use. Additional variables probe respondents' telephone usage and the effects of the recession on their use of telephones for long-distance calls. Data are also provided on respondents buying intentions for a house, automobiles, appliances, and other consumer durables, as well as their appraisals of present market conditions for purchasing these items. Demographic variables provide information on age, race, sex, marital status, education, occupation, religion, and family income. A supplementary sample of 122 respondents, consisting of a specially selected Detroit unemployment sample, is available upon request only.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Understanding the Dynamics and Implications of a Housing Market Recession

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

LON:ETX Stock: Are We Headed for a Recession?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Abstract (en): This survey was undertaken to assess consumer sentiment and buying plans, respondents' satisfaction with the appliances owned, and their opinions about the Cold War between the former Soviet Union and the West and its perceived effect on taxes and the economy, as well as their assessment of the possibility of an outbreak of a major world war in the near future. Open-ended questions were asked concerning evaluations and expectations about price changes, employment, tax reduction, recession, and the national business situation. Additional variables probe respondents' buying intentions for a house, automobiles, appliances, and other consumer durables, as well as their appraisals of present market conditions for purchasing these items. Other variables probe respondents' satisfaction with their location, neighborhood, and living space, and their assessment of their financial status relative to the previous year. Information is also provided on savings. Demographic variables provide information on age, sex, race, marital status, education, occupation, and family income. All families living in continental United States dwelling units, exclusive of those on military reservations. One respondent from each family unit in the dwellings sampled, usually the head of the family, or the wife. The dwelling units were selected by area probability sampling from 48 primary sampling units. For each dwelling unit in the sample, an interview was sought with a respondent from the primary family and from each secondary family (if any). The head of the family (usually the husband) was the preferred respondent, but the wife could substitute if the head was not readily available. The codebook is provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.

Barsky and Sims (2012, AER) demonstrated, via indirect inference, that confidence innovations can be viewed as noisy signals about medium-term economic growth. They highlighted that the connection between confidence and subsequent activity, such as consumption and output, is primarily driven by news shocks about the future. We expand upon their research by incorporating the Great Recession and ZLB episodes, during which animal spirits have a greater potential to influence economic activity. Nevertheless, we confirm the main finding of Barsky and Sims (2012) that this relationship is predominantly driven by news about the future rather than animal spirits.

The global consumer spending on media amounted to nearly *** billion U.S. dollars in 2021. According to the forecast scenarios, that annual value would stand just below *** billion or just above ************ dollars by 2027.Media spending - potential scenarios Scenario A: According to this first scenario, the recession would only have a short-term impact on consumers' media spending. At the height of the recession in 2023, consumers are expected to spend less on entertainment to offset rising energy and consumer product prices. The economy should begin to recover from the recession by 2024 and should be fully mended by 2027, while spending on media will be back to pre-pandemic levels.

Scenario B: The second scenario predicts a long-term impact of the recession on media consumption behavior. Ad-supported options will replace subscription-based offers, whereas on-and-off subscribing will increase, driven by special offers and consumers unsubscribing after those offers expire. The inflation will hit harder in 2023 than according to the first scenario and behavior changes will stick even after 2027 when the economy has fully recovered.

According to Cognitive Market Research, the global AI Sensor Market with Recession Market size is USD 2.8 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 38.6% from 2024 to 2031. Market Dynamics of AI Sensor Market with Recession Market

Key Drivers for AI Sensor Market with Recession Market

Advancements in AI and Machine Learning: Rapid advances in artificial intelligence and machine learning are boosting the use of Al sensors. Algorithms are getting increasingly sophisticated and capable of handling complicated data from sensors, enabling real-time decision-making and predictive analytics. These developments allow Al sensors to detect patterns, anomalies, and trends in data streams, making them useful in applications such as picture recognition, natural language processing, and predictive maintenance. For instance, in manufacturing, Al sensors may detect faults in real time, improving quality control and lowering waste. Al sensors also improve the capability of autonomous systems and robots. They can perceive their surroundings, adjust to changing circumstances, and make sound decisions. This is especially crucial in industries like agriculture, where autonomous drones equipped with Al sensors can check crop health, detect pest infestations, and optimize pesticide use. Security and Surveillance applications

Key Restraints for AI Sensor Market with Recession Market

Capital Spending Delays in Price-Sensitive Sectors: Businesses in a variety of sectors, including retail, consumer electronics, and the automobile industry, frequently postpone or abandon capital-intensive initiatives and technological advancements during recessions. This has a direct impact on the use of AI sensors in consumer electronics, smart factories, and new goods, momentarily reducing market expansion.

Semiconductor shortages and supply chain disruptions: Complex semiconductor components are necessary for AI sensors, and supply chain bottlenecks are frequently made worse by global economic downturns. Delays in shipping, reduced manufacturing capacity, and geopolitical unrest can all affect sensor production and lengthen lead times, making it more difficult for industries to deploy sensors on time.

Key Trends for AI Sensor Market with Recession Market

Transition to Low-Cost Advanced AI Sensors: Industries are turning to edge AI sensors that analyze data locally in order to deal with financial restrictions. This eliminates the need for expensive cloud infrastructure and latency problems. Due to their simplicity of deployment and reduced total cost of ownership, small, energy-efficient sensors with on-chip AI are becoming more and more popular. Growing Utilization in Energy Efficiency and Predictive Maintenance: Operational efficiency is a top priority for financially stressed organizations, and AI sensors are essential for energy optimization and predictive maintenance. Industrial equipment with sensors built in can anticipate malfunctions, prolong the life of machinery, and use less electricity, all of which can result in quantifiable cost savings during recessions. Introduction of the AI Sensor Market with Recession Market

Al sensors are also improving the capabilities of autonomous systems and robots. They can perceive their surroundings, adjust to changing conditions, and make sound decisions. This is especially crucial in industries like agriculture, where autonomous drones equipped with Al sensors can check crop health, detect pest infestations, and optimize pesticide use. Also, increased demand for life-saving healthcare equipment and self-driving capabilities in new electric vehicles are expected to fuel growth. The global shift towards digitization is expected to boost growth even further.

Context

This dataset was built using the Philadelphia Federal Reserve's State Coincident Indices and the Bry-Boschan Method for business cycle dating. In the tradition of Owyang, Piger, et al. business cycles are calculated on the state level which provides interesting analysis opportunities for looking at recession timing for different regions or sectors present in different states. The MSA level data utilizes the Economic Coincident Indices available on the St. Louis FRED website and uses a variant of the non-parametric algorithm described in Metro Business Cycles (Arias et al. 2016) to date MSA level recessions.

Content

This data is from 1982 through 2018 and includes whether the economy is in a recession or not, with forward looking and backward looking data available for observations as well. Additionally, various FRED St. Louis series were joined, like the University of Michigan Consumer Sentiment Index and the Global Price of Brent Crude. The 2012 value added as a percent for different NAICS groups is included as well for sectoral analysis, although better data over time for this would prove beneficial. The industries file attempts to correct this, but has fewer years available.

Acknowledgements

Special thanks to the researchers at the Federal Reserve Banks of Philadelphia and St. Louis for collecting and making available much of the data that went into this dataset.

Inspiration

I was inspired by researchers that have attempted to take business cycle dating to the state and MSA level. Local business cycle dating methodologies allow for a more robust understanding of what goes into a recession and how sectoral composition can affect a state or MSA's "resilience" to recessions. This could have applications for weighting business cycle risk for companies based on geographic dispersion of customers, as well as local policymakers if local forecasting could be done successfully.