This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

Inflation occurs when there is a sustained increase in the general price level of goods and services in an economy over time. It impacts various aspects of the economy, including purchasing power, consumer behaviour, savings, and investment. Moderate inflation is typically a sign of a healthy, growing economy, as it encourages spending and investment. However, high or unpredictable inflation can erode the value of money, disrupt financial planning, and lead to economic uncertainty.

To analyze the impact of inflation, we need to compare it with other economic indicators. So, to analyze the impact of inflation on the economy, we will compare it with the exchange rates over time. This comparison is important because exchange rates are influenced by inflation differentials between countries, such that higher inflation in a country generally leads to a weaker currency relative to countries with lower inflation.

This data package includes the underlying data files to replicate the data and charts presented in What caused the US pandemic-era inflation? PIIE Working Paper 23-4.

If you use the data, please cite as: Bernanke, Ben, and Olivier Blanchard. 2023. What caused the US pandemic-era inflation? PIIE Working Paper 23-4. Washington, DC: Peterson Institute for International Economics.

Inflation Rate in Nigeria decreased to 16.05 percent in October from 18.02 percent in September of 2025. This dataset provides - Nigeria Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Monthly and long-term Mexico Inflation data: historical series and analyst forecasts curated by FocusEconomics.

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

Inflation Rate in Russia decreased to 7.70 percent in October from 8 percent in September of 2025. This dataset provides - Russia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

US Turkey Production

US Turkey Production: Value, Pounds, and Turkey Numbers

By Throwback Thursday [source]

About this dataset

The dataset titled Turkey Production in the US: 1984-2016 provides comprehensive information on the value of turkey production, pounds produced, and the number of turkeys raised in the United States. This dataset draws data from the United States Department of Agriculture Economic Research Service (USDA ERS) and covers a period spanning from 1984 to 2016.

This dataset includes multiple columns that offer crucial insights into turkey production trends over time. Firstly, there are columns dedicated to capturing the value of turkey production both in raw monetary terms and after adjusting for inflation. These values are reported in US dollars and serve as indicators of the economic significance and growth within this sector.

Additionally, this dataset presents data on pounds produced, which measures the total weight of turkeys produced within a given year. This information is essential for assessing production levels and fluctuations over time.

Moreover, another key column provides details on turkeys raised annually. This metric represents the total number of turkeys bred or developed during each specific year. By tracking changes in these figures across different years, it becomes possible to discern patterns related to turkey farming practices or industry demand.

In summary, this extensive dataset offers rich insights into various aspects relating to turkey production in the United States between 1984 and 2016. It covers significant variables such as value of production (adjusted for inflation), pounds produced, and number of turkeys raised throughout these years. With such detailed data available within this dataset, researchers can delve into analyzing historical trends while policymakers can make well-informed decisions based on an understanding of past developments in this crucial industry sector

How to use the dataset

This dataset provides information on the value of turkey production, pounds produced, and the number of turkeys raised in the United States from 1984 to 2016. It can be used for analysis or research purposes related to turkey production trends and patterns over this period.

Here's a guide on how to effectively utilize this dataset:

• Understanding the Columns:

  • Year: This column represents the year in which the data was recorded.
  • Value of Production: This column shows the total value of turkey production in dollars for a given year.
  • Value of Production - Inflation Adjusted: This column provides an adjusted value of turkey production, taking inflation into account.
  • Pounds Produced: This column displays the total weight of turkeys produced in pounds for a given year.
  • Turkeys Raised: This column indicates the total number of turkeys raised for a given year.

• Analyzing Turkey Production Trends: You can analyze how turkey production has changed over time by examining each variable individually or comparing them with each other. For example:

  • Plotting Year against Value of Production will give you an overview of how turkey production's value has evolved over the years.
  • Analyzing Pounds Produced and Turkeys Raised together could provide insights into productivity per bird.

• Identifying Factors Affecting Turkey Production: Use this dataset to investigate factors that may have influenced changes in turkey production from 1984-2016. Consider exploring these questions:

  • Are there any notable spikes or declines in value, pounds produced, or turkeys raised? What could be causing these patterns?
  • How does inflation-adjusted value differ from nominal value? Can you identify any trends related to economic conditions?

• Comparing Data Across Years: By grouping data by specific years or sets of years, you can make comparisons and identify trends. Some potential questions to explore include:

  • How has turkey production changed before and after significant events, such as economic recessions or disease outbreaks?
  • Have there been any notable shifts in turkey production methods or technology that may have affected the industry's performance?

• Potential Applications:

  • Researchers: This dataset can be valuable for researchers studying the economics and market dynamics of turkey production in the United States. You can use these data points to analyze long-term trends, identify influential factors, and develop predictive models.
  • Investors: Investors interested in the agriculture

Research Ideas

• Analyzing Trends: This dataset can be used to an...

Inflation Rate in Turkey decreased to 32.87 percent in October from 33.29 percent in September of 2025. This dataset provides the latest reported value for - Turkey Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Monthly and long-term Chile Inflation data: historical series and analyst forecasts curated by FocusEconomics.

Inflation Rate in Brazil decreased to 4.68 percent in October from 5.17 percent in September of 2025. This dataset provides - Brazil Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Weather Disaster Costs and Deaths

Costs and Deaths of Billion Dollar Weather Disasters in the US

By Throwback Thursday [source]

About this dataset

The Billion Dollar Weather Disasters in the US dataset is a valuable resource containing comprehensive historical data on weather events in the United States that have caused billions of dollars in damages and resulted in loss of lives. It provides insights into various types and categories of weather disasters, such as hurricanes, tornadoes, floods, wildfires, and more.

The dataset includes essential information about each weather disaster event, starting with its name or title referred to as Disaster. A brief summary or description of each event is provided under the column Description, giving readers an understanding of its impact and extent. Furthermore, the dataset categorizes each disaster based on its type under the column Disaster Type. This classification helps researchers and analysts to identify patterns or common characteristics among similar types of weather disasters.

One crucial aspect covered by this dataset is the economic impact of these severe weather events. The total cost incurred due to each catastrophic occurrence has been meticulously recorded in millions of dollars. To ensure accuracy across different time periods, these costs are adjusted for inflation using the Consumer Price Index (CPI), providing a standardized measure that enables meaningful comparisons between different events.

A significant measure reflecting the severity of these weather disasters is the number of deaths they have caused. This dataset presents this valuable statistic under the column Deaths, allowing researchers to assess not only economic implications but also human impacts associated with each disaster event.

Obtained from NOAA National Centers for Environmental Information (NCEI) U.S., this data serves as a reliable source for understanding past weather calamities within US borders. Its wide range includes devastating storms, destructive wildfires, deadly heatwaves, crippling droughts; all contributing to one overarching objective – better preparedness for future climate-related challenges.

By analyzing this comprehensive dataset, researchers can gain insights into trends over time while identifying regions most vulnerable to specific types of extreme weather events. These findings allow policymakers and emergency response planners to make informed decisions regarding resource allocation, risk mitigation strategies, and community resilience-building initiatives

How to use the dataset

1. Understanding the Columns

The dataset contains several columns that provide important information about each weather disaster event. Let's understand what each column represents:

• Disaster: The name or title of the weather disaster event.
• Disaster Type: The type or category of the weather disaster event.
• Total CPI-Adjusted Cost (Millions of Dollars): The total cost of the weather disaster event in millions of dollars, adjusted for inflation using the Consumer Price Index (CPI).
• Deaths: The number of deaths caused by the weather disaster event.
• Description: A brief description or summary of the weather disaster event.

2. Exploring Total Cost and Deaths

One key aspect to explore is how much damage was caused by each weather disaster event, as well as its human impact in terms of fatalities. By analyzing these factors, you can gain insights into which types of disasters are more costly and have a higher mortality rate.

You can start by visualizing the Total CPI-Adjusted Cost (Millions of Dollars) column to identify which disasters have been more financially devastating over time. Additionally, you can analyze the Deaths column to gauge which types of disasters have had a greater impact on human lives.

3. Comparing Disasters

Another interesting analysis would involve comparing different disasters based on their characteristics such as type, cost, and fatalities. You can group similar types together and compare their costs or death tolls across different time periods.

For example, you could examine whether hurricanes tend to cause higher financial losses compared to floods or wildfires. Or, you could analyze if certain types of disasters have been more deadly than others.

4. Analyzing Descriptions

The Description column provides a brief summary of each weather disaster event. Analyzing the descriptions can give you valuable insights into the specific circumstances surrounding each event. By understanding the context and conditions, you can get a better understanding of why some events resulted i...

Billion Dollar Weather Disasters

Financial and human impact of significant weather disasters in the United States

By Throwback Thursday [source]

About this dataset

Introduction:

Dataset Details: This dataset presents comprehensive information related to billion-dollar weather disasters that occurred in the United States. Each entry includes specific details about a particular disaster event:

1. Disaster: This column contains the name or title associated with each weather disaster.

2. Disaster Type: This column categorizes each disaster into specific types or categories such as hurricanes, floods, heatwaves, tornadoes, wildfires.

3. Beginning Date: The starting date when a particular weather disaster occurred.

4. Ending Date: The end date marking the conclusion of a given weather disaster.

5. Total CPI-Adjusted Cost (Millions of Dollars): This column provides an accurate representation of the total cost incurred by each disaster in millions of dollars while being adjusted for inflation using the Consumer Price Index (CPI).

6. Deaths: This numeric column records the number of deaths caused by each specific weather event.

7. Description: A brief yet informative summary describing key characteristics or impacts associated with a particular weather disaster.

By utilizing this rich dataset combined with advanced analytical tools and visualizations techniques; researchers can derive meaningful insights to support effective decision-making processes aimed at mitigating future damage caused by such destructive phenomena

How to use the dataset

Understanding the Columns

Before we delve into analyzing and visualizing the data, it's important to understand the meaning of each column:

• Disaster: The name or title of the weather disaster.
• Disaster Type: The type or category of the weather disaster.
• Total CPI-Adjusted Cost (Millions of Dollars): The total cost of each weather disaster in millions of dollars adjusted for inflation using the Consumer Price Index (CPI).
• Deaths: The number of deaths caused by each weather disaster.
• Description: A brief description or summary detailing each weather disaster.

Exploring Data Analysis Opportunities

Now that we have a clear understanding of what each column represents let's explore how you can use this dataset for analyzing billion-dollar weather disasters in more depth:

• Analyzing Financial Impact

  Utilize the Total CPI-Adjusted Cost column to analyze and compare the financial impact caused by different types or categoriesof billion-dollar disasters. You can plot graphs, compute averages, identify outliers or trends over time.

• Assessing HumanImpact

  Use data from Deaths column todeterminehow different typesorcategoriesofweatherdisastersvaryin theirhumanimpact.Visualizeandcomparethedeath tolls associated with various catastrophic events.

• Identifying Frequent Disaster Types

  Observe which types or categoriesofweatherdisastersoccurmore frequently than othersbyanalyzingtheDisaster Typecolumn.PlotagraptoshowthedistributionandfrequencyofthedisastertypesintheUnitedStates.

• Exploring Disaster Descriptions

  Dive deeper into the unique aspects of each weather disaster by studying the Description column. This will provide additional context and insight into the specific events.

Making Data Visualizations

Data visualizations can help you represent, summarize, and communicate patterns or insights hidden within the dataset. Here are a few ideas for creating impactful visualizations:

• Create a bar chart depicting the financial cost (Total CPI-Adjusted Cost) of different disaster types.

• Develop a line graph showing how deaths have varied over time for various weather disasters.

• Design a pie chart

Research Ideas

• Analyzing the financial impact of different types of weather disasters: This dataset provides information on the total cost of billion-dollar weather disasters, adjusted for inflation. By analyzing this data, one can gain insights into which types of weather events have the highest financial impact, helping to prioritize preparedness and mitigation efforts.
• Examining trends in weather disasters over time: With information on the beginning and ending dates of each event, this dataset can be used to analyze trends in the frequency and duration of billion-dollar weather disasters in the United States. This analysis could help identify if certain types of ...

About this dataset

Context

The dataset tabulates the median household income in Economy. It can be utilized to understand the trend in median household income and to analyze the income distribution in Economy by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Economy, PA Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Economy, PA: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Economy, PA
• Economy, PA households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Economy median household income. You can refer the same here

Inflation Rate in Morocco decreased to 0.10 percent in October from 0.40 percent in September of 2025. This dataset provides - Morocco Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Experimental evolution studies can be used to explore genomic response to artificial and natural selection. In such studies, loci that display larger allele frequency change than expected by genetic drift alone are assumed to be directly or indirectly associated with traits under selection. However, such studies report surprisingly many loci under selection, suggesting that current tests for allele frequency change may be subject to p-value inflation and hence be anti-conservative. One factor known from genome wide association (GWA) studies to cause p-value inflation is population stratification, such as relatedness among individuals. Here we suggest that by treating presence of an individual in a population after selection as a binary response variable, existing GWA methods can be used to account for relatedness when estimating allele frequency change. We show that accounting for relatedness like this effectively reduces false positives in tests for allele frequency change in simulated data with varying levels of population structure. However, once relatedness has been accounted for, the power to detect causal loci under selection is low. Finally, we demonstrate the presence of p-value inflation in allele frequency change in empirical data spanning multiple generations from an artificial selection experiment on tarsus length in two wild populations of house sparrow, and correct for this using genomic control. Our results indicate that since allele frequencies in large parts of the genome may change when selection acts on a heritable trait, such selection is likely to have considerable and immediate consequences for the eco-evolutionary dynamics of the affected populations.

About this dataset

Context

The dataset tabulates the median household income in Cedar County. It can be utilized to understand the trend in median household income and to analyze the income distribution in Cedar County by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Cedar County, MO Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Cedar County, MO: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Cedar County, MO
• Cedar County, MO households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Cedar County median household income. You can refer the same here

As Table 1 but for system 2.

Conditioning on the third variable is implied. Significant causal effects are denoted in bold.

Inflation Rate in Germany remained unchanged at 2.30 percent in November. This dataset provides the latest reported value for - Germany Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.