Graph and download economic data for Unemployment Rate in Chicago-Naperville-Elgin, IL-IN-WI (MSA) (CHIC917URN) from Jan 1990 to Apr 2025 about Chicago, IL, IN, WI, unemployment, rate, and USA.

Graph and download economic data for Unemployment Rate in Cook County, IL (ILCOOK1URN) from Jan 1990 to May 2025 about Cook County, IL; Chicago; IL; unemployment; rate; and USA.

In 2023, the unemployment rate in Illinois was at 4.5 percent. This is a decrease from the previous year, when the unemployment rate stood at 4.6 percent, and is down from a high of 10.5 percent in 2010.

Unemployment Rate in Illinois was 4.80% in April of 2025, according to the United States Federal Reserve. Historically, Unemployment Rate in Illinois reached a record high of 18.30 in April of 2020 and a record low of 3.60 in November of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in Illinois - last updated from the United States Federal Reserve on June of 2025.

Unemployment Rate in Chicago-Naperville-Elgin, IL-IN-WI (MSA) was 5.40% in December of 2024, according to the United States Federal Reserve. Historically, Unemployment Rate in Chicago-Naperville-Elgin, IL-IN-WI (MSA) reached a record high of 18.70 in April of 2020 and a record low of 3.50 in December of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in Chicago-Naperville-Elgin, IL-IN-WI (MSA) - last updated from the United States Federal Reserve on June of 2025.

Unemployment Rate in Chicago-Naperville-Elgin, IL-IN-WI (MSA) was 4.80% in April of 2025, according to the United States Federal Reserve. Historically, Unemployment Rate in Chicago-Naperville-Elgin, IL-IN-WI (MSA) reached a record high of 18.60 in April of 2020 and a record low of 3.20 in November of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in Chicago-Naperville-Elgin, IL-IN-WI (MSA) - last updated from the United States Federal Reserve on June of 2025.

Graph and download economic data for Unemployment Rate in Lake County, IN (INLAKE9URN) from Jan 1990 to Apr 2025 about Lake County, IN; Chicago; IN; unemployment; rate; and USA.

This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2008 – 2012. The indicators are the percent of occupied housing units with more than one person per room (i.e., crowded housing); the percent of households living below the federal poverty level; the percent of persons in the labor force over the age of 16 years that are unemployed; the percent of persons over the age of 25 years without a high school diploma; the percent of the population under 18 or over 64 years of age (i.e., dependency); and per capita income. Indicators for Chicago as a whole are provided in the final row of the table. See the full dataset description for more information at: https://data.cityofchicago.org/api/views/fwb8-6aw5/files/A5KBlegGR2nWI1jgP6pjJl32CTPwPbkl9KU3FxlZk-A?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\ECONOMIC_INDICATORS\Dataset_Description_socioeconomic_indicators_2012_FOR_PORTAL_ONLY.pdf

This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2007 – 2011. The indicators are the percent of occupied housing units with more than one person per room (i.e., crowded housing); the percent of households living below the federal poverty level; the percent of persons in the labor force over the age of 16 years that are unemployed; the percent of persons over the age of 25 years without a high school diploma; the percent of the population under 18 or over 64 years of age (i.e., dependency); and per capita income. Indicators for Chicago as a whole are provided in the final row of the table. See the full dataset description for more information at https://data.cityofchicago.org/api/assets/8D10B9D1-CCA3-4E7E-92C7-5125E9AB46E9.

Areas of Chicago, based on census tracts, that are the most socioeconomically disadvantaged, for the purpose of promoting equitable hiring within areas of economic need. Qualifying areas were identified using three criteria, based on data from the 2014 American Community Survey: household income, poverty rate, and unemployment rate.

These area designations are used for workforce bid incentives for City contracts administered by the Department of Procurement Services. They will also be used for workforce requirements for construction at the temporary casino facility, as agreed to in the Host Community Agreement between Bally’s and the City of Chicago.

The designations are made under Section 2-92-390 of the City of Chicago code.

This dataset is in a forma​​t for spatial datasets that is inherently tabular but allows for a map as a derived view. Please click the indicated link below for such a map.

To export the data in either tabular or geographic format, please use the Export button on this dataset.

Graph and download economic data for Unemployment Rate in DuPage County, IL (ILDUPA0URN) from Jan 1990 to Apr 2025 about Du Page County, IL; Chicago; IL; unemployment; rate; and USA.

In the December 2024 ranking of the unemployment rates in the United States' larger metropolitan areas, the Minneapolis-St. Paul-Bloomington, Minnesota metro area had the lowest rate, at 2.5 percent. In the same period, the unemployment rate was highest in the Las Vegas-Henderson-Paradise, Nevada metro area at 5.9 percent.

In 2023, the GDP of the Chicago-Naperville-Elgin metropolitan area amounted to ****** billion chained 2017 U.S. dollars. The GDP of the United States since 1990 can be accessed here. Economic growth and unemployment in Chicago Economic growth in Chicago, measured by the growth in Gross Domestic Product (GDP), was significant in the years between 2001 and 2022. This growth occurred in a period of growth for cities nationally as seen by growth of other major American cities such as Los Angeles and San Francisco. In contrast to Chicago’s growth, San Francisco’s growth rate demonstrated the effect of a new and booming industry. The influence of technology and internet companies saw San Francisco grow nearly ** percent in comparison to the ** percent growth in GDP achieved by Chicago. As a result, Chicago-Naperville-Elgin ranked third in Gross Metropolitan Product of the United States, by metropolitan area in 2022. The drop in GDP output in 2020 can be attributed to the COVID-19 pandemic.

Graph and download economic data for Unemployment Rate in Kendall County, IL (ILKEND3URN) from Jan 1990 to Apr 2025 about Kendall County, IL; Chicago; IL; unemployment; rate; and USA.

This paper examines the spatial patterns of unemployment in Chicago between 1980 and 1990. We study unemployment clustering with respect to different social and economic distance metrics that reflect the structure of agents' social networks. Specifically, we use physical distance, travel time, and differences in ethnic and occupational distribution between locations. Our goal is to determine whether our estimates of spatial dependence are consistent with models in which agents' employment status is affected by information exchanged locally within their social networks. We present non-parametric estimates of correlation across Census tracts as a function of each distance metric as well as pairs of metrics, both for unemployment rate itself and after conditioning on a set of tract characteristics. Our results indicate that there is a strong positive and statistically significant degree of spatial dependence in the distribution of raw unemployment rates, for all our metrics. However, once we condition on a set of covariates, most of the spatial autocorrelation is eliminated, with the exception of physical and occupational distance. Racial and ethnic composition variables are the single most important factor in explaining the observed correlation patterns.

1918 Pandemic Influenza Mortality, Chicago USAPoint location and week of epidemic of 8,031 influenza and pneumonia deaths recorded during the 1918 Spanish flu pandemic within the city of Chicago. Data was digitized from 1920 City of Chicago Department of Health annual report Date last modified: 25-10-2016. Fields include: ID (FID), indicator of pneumonia (0 or 1, 0 indicates an influenza death, 1 an influenza and pneumonia death), x and y coordinates (with units in meters), and week (sequential week of epidemic). See paper for more details.points.csvFine-scale sociodemographics of Chicago, USA, 1920Socio-demographic data (including population size, illiteracy, unemployment) of 496 census tracts within the City of Chicago. Data was collected from the 1920 national census.tracts.csvShapefile of census tract boundaries in Chicago in 1920Shapefile of census tract boundaries in Chicago in 1920. File included in zip file include IL_tract_a.dbf, IL_tract_a.prj, IL_tract_a.sbn, IL_tract_a.sb...

Graph and download economic data for Unemployment Rate in Lake County, IL (LAUCN170970000000003A) from 1990 to 2024 about Lake County, IL; Chicago; IL; unemployment; rate; and USA.

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.

CVR Chicago Rivet & Machine Co. Common Stock

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.

REFI Chicago Atlantic Real Estate Finance Inc. Common Stock

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 data collection was designed to evaluate the effects of disorderly neighborhood conditions on community decline and residents' reactions toward crime. Data from five previously collected datasets were aggregated and merged to produce this collection: (1) REACTIONS TO CRIME PROJECT, 1977 [CHICAGO, PHILADELPHIA, SAN FRANCISCO]: SURVEY ON FEAR OF CRIME AND CITIZEN BEHAVIOR (ICPSR 8162), (2) CHARACTERISTICS OF HIGH AND LOW CRIME NEIGHBORHOODS IN ATLANTA, 1980 (ICPSR 8951), (3) CRIME FACTORS AND NEIGHBORHOOD DECLINE IN CHICAGO, 1979 (ICPSR 7952), (4) REDUCING FEAR OF CRIME PROGRAM EVALUATION SURVEYS IN NEWARK AND HOUSTON, 1983-1984 (ICPSR 8496), and (5) a survey of citizen participation in crime prevention in six Chicago neighborhoods conducted by Rosenbaum, Lewis, and Grant. Neighborhood-level data cover topics such as disorder, crime, fear, residential satisfaction, and other key factors in community decline. Variables include disorder characteristics such as loitering, drugs, vandalism, noise, and gang activity, demographic characteristics such as race, age, and unemployment rate, and neighborhood crime problems such as burglary, robbery, assault, and rape. Information is also available on crime avoidance behaviors, fear of crime on an aggregated scale, neighborhood satisfaction on an aggregated scale, and cohesion and social interaction.