Initial Jobless Claims in the United States decreased to 216 thousand in the week ending November 22 of 2025 from 222 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The employment and unemployment indicator shows several data points. The first figure is the number of people in the labor force, which includes the number of people who are either working or looking for work. The second two figures, the number of people who are employed and the number of people who are unemployed, are the two subcategories of the labor force. The unemployment rate is a calculation of the number of people who are in the labor force and unemployed as a percentage of the total number of people in the labor force.

The unemployment rate does not include people who are not employed and not in the labor force. This includes adults who are neither working nor looking for work. For example, full-time students may choose not to seek any employment during their college career, and are thus not considered in the unemployment rate. Stay-at-home parents and other caregivers are also considered outside of the labor force, and therefore outside the scope of the unemployment rate.

The unemployment rate is a key economic indicator, and is illustrative of economic conditions in the county at the individual scale.

There are additional considerations to the unemployment rate. Because it does not count those who are outside the labor force, it can exclude individuals who were looking for a job previously, but have since given up. The impact of this on the overall unemployment rate is difficult to quantify, but it is important to note because it shows that no statistic is perfect.

The unemployment rates for Champaign County, the City of Champaign, and the City of Urbana are extremely similar between 2000 and 2023.

All three areas saw a dramatic increase in the unemployment rate between 2006 and 2009. The unemployment rates for all three areas decreased overall between 2010 and 2019. However, the unemployment rate in all three areas rose sharply in 2020 due to the effects of the COVID-19 pandemic. The unemployment rate in all three areas dropped again in 2021 as pandemic restrictions were removed, and were almost back to 2019 rates in 2022. However, the unemployment rate in all three areas rose slightly from 2022 to 2023.

This data is sourced from the Illinois Department of Employment Security’s Local Area Unemployment Statistics (LAUS), and from the U.S. Bureau of Labor Statistics.

Sources: Illinois Department of Employment Security, Local Area Unemployment Statistics (LAUS); U.S. Bureau of Labor Statistics.

Unemployment Rate in Germany remained unchanged at 6.30 percent in November. This dataset provides the latest reported value for - Germany Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Statistical open data on LAU regions of Slovakia, Czech Republic, Poland, Hungary (and other countries in the future). LAU1 regions are called counties, okres, okresy, powiat, járás, járási, NUTS4, LAU, Local Administrative Units, ... and there are 733 of them in this V4 dataset. Overall, we cover 733 regions which are described by 137.828 observations (panel data rows) and more than 1.760.229 data points.

This LAU dataset contains panel data on population, on age structure of inhabitants, on number and on structure of registered unemployed. Dataset prepared by Michal Páleník. Output files are in json, shapefiles, xls, ods, json, topojson or CSV formats. Downloadable at zenodo.org.

This dataset consists of:

data on unemployment (by gender, education and duration of unemployment),

data on vacancies,

open data on population in Visegrad counties (by age and gender),

data on unemployment share.

Combined latest dataset

dataset of the latest available data on unemployment, vacancies and population

dataset includes map contours (shp, topojson or geojson format), relation id in OpenStreetMap, wikidata entry code,

it also includes NUTS4 code, LAU1 code used by national statistical office and abbreviation of the region (usually license plate),

source of map contours is OpenStreetMap, licensed under ODbL

no time series, only most recent data on population and unemployment combined in one output file

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies, pop_period, TOTAL, Y15-64, Y15-64-females, local_lau, osm_id, abbr, wikidata, population_density, area_square_km, way

Slovakia – SK: 79 LAU1 regions, data for 2024-10-01, 1.659 data,

Czech Republic – CZ: 77 LAU1 regions, data for 2024-10-01, 1.617 data,

Poland – PL: 380 LAU1 regions, data for 2024-09-01, 6.840 data,

Hungary – HU: 197 LAU1 regions, data for 2024-10-01, 2.955 data,

13.071 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 79 77 380 197

lau LAU code of the region 79 77 380 197

name name of the region in local language 79 77 380 197

registered_unemployed number of unemployed registered at labour offices 79 77 380 197

registered_unemployed_females number of unemployed women 79 77 380 197

disponible_unemployed unemployed able to accept job offer 79 77 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 79 77 380 197

long_term unemployed for longer than 1 year 79 77 380 0

unemployment_inflow inflow into unemployment 79 77 0 0

unemployment_outflow outflow from unemployment 79 77 0 0

below_25 number of unemployed below 25 years of age 79 77 380 197

over_55 unemployed older than 55 years 79 77 380 197

vacancies number of vacancies reported by labour offices 79 77 380 0

pop_period date of population data 79 77 380 197

TOTAL total population 79 77 380 197

Y15-64 number of people between 15 and 64 years of age, population in economically active age 79 77 380 197

Y15-64-females number of women between 15 and 64 years of age 79 77 380 197

local_lau region's code used by local labour offices 79 77 380 197

osm_id relation id in OpenStreetMap database 79 77 380 197

abbr abbreviation used for this region 79 77 380 0

wikidata wikidata identification code 79 77 380 197

population_density population density 79 77 380 197

area_square_km area of the region in square kilometres 79 77 380 197

way geometry, polygon of given region 79 77 380 197

Unemployment dataset

time series of unemployment data in Visegrad regions

by gender, duration of unemployment, education level, age groups, vacancies,

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies

Slovakia – SK: 79 LAU1 regions, data for 334 periods (1997-01-01 ... 2024-10-01), 202.082 data,

Czech Republic – CZ: 77 LAU1 regions, data for 244 periods (2004-07-01 ... 2024-10-01), 147.528 data,

Poland – PL: 380 LAU1 regions, data for 189 periods (2005-03-01 ... 2024-09-01), 314.100 data,

Hungary – HU: 197 LAU1 regions, data for 106 periods (2016-01-01 ... 2024-10-01), 104.408 data,

768.118 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 26 386 18 788 71 772 20 882

lau LAU code of the region 26 386 18 788 71 772 20 882

name name of the region in local language 26 386 18 788 71 772 20 882

registered_unemployed number of unemployed registered at labour offices 26 386 18 788 71 772 20 882

registered_unemployed_females number of unemployed women 26 386 18 788 62 676 20 882

disponible_unemployed unemployed able to accept job offer 25 438 18 788 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 11 771 9855 41 388 20 881

long_term unemployed for longer than 1 year 24 253 9855 41 388 0

unemployment_inflow inflow into unemployment 26 149 16 478 0 0

unemployment_outflow outflow from unemployment 26 149 16 478 0 0

below_25 number of unemployed below 25 years of age 11 929 9855 17 100 20 881

over_55 unemployed older than 55 years 11 929 9855 17 100 20 882

vacancies number of vacancies reported by labour offices 11 692 18 788 62 676 0

Population dataset

time series on population by gender and 5 year age groups in V4 counties

columns: period, lau, name, gender, TOTAL, Y00-04, Y05-09, Y10-14, Y15-19, Y20-24, Y25-29, Y30-34, Y35-39, Y40-44, Y45-49, Y50-54, Y55-59, Y60-64, Y65-69, Y70-74, Y75-79, Y80-84, Y85-89, Y90-94, Y_GE95, Y15-64

Slovakia – SK: 79 LAU1 regions, data for 28 periods (1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 152.628 data,

Czech Republic – CZ: 78 LAU1 regions, data for 24 periods (2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 125.862 data,

Poland – PL: 382 LAU1 regions, data for 29 periods (1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 626.941 data,

Hungary – HU: 197 LAU1 regions, data for 11 periods (2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 86.680 data,

992.111 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 6636 5574 32 883 4334

lau LAU code of the region 6636 5574 32 883 4334

name name of the region in local language 6636 5574 32 883 4334

gender gender (male or female) 6636 5574 32 883 4334

TOTAL total population 6636 5574 32 503 4334

Y00-04 inhabitants between 00 to 04 years inclusive 6636 5574 32 503 4334

Y05-09 number of inhabitants between 05 to 09 years of age 6636 5574 32 503 4334

Y10-14 number of people between 10 to 14 years inclusive 6636 5574 32 503 4334

Y15-19 number of inhabitants between 15 to 19 years of age 6636 5574 32 503 4334

Y20-24 number of people between 20 to 24 years inclusive 6636 5574 32 503 4334

Y25-29 number of inhabitants between 25 to 29 years of age 6636 5574 32 503 4334

Y30-34 inhabitants between 30 to 34 years inclusive 6636 5574 32 503 4334

Y35-39 number of inhabitants between 35 to 39 years of age 6636 5574 32 503 4334

Y40-44 inhabitants between 40 to 44 years inclusive 6636 5574 32 503 4334

Y45-49 number of inhabitants younger than 49 and older than 45 years 6636 5574 32 503 4334

Y50-54 inhabitants between 50 to 54 years inclusive 6636 5574 32 503 4334

Y55-59 number of inhabitants between 55 to 59 years of age 6636 5574 32 503 4334

Y60-64 inhabitants between 60 to 64 years inclusive 6636 5574 32 503 4334

Y65-69 number of inhabitants younger than 69 and older than 65 years 6636 5574 32 503 4334

Y70-74 inhabitants between 70 to 74 years inclusive 6636 5574 24 670 4334

Y75-79 number of inhabitants between 75 to 79 years of age 6636 5574 24 670 4334

Y80-84 number of people between 80 to 84 years inclusive 6636 5574 24 670 4334

Y85-89 number of inhabitants younger than 89 and older than 85 years 6636 5574 0 0

Y90-94 inhabitants between 90 to 94 years inclusive 6636 5574 0 0

Y_GE95 number of people 95 years or older 6636 3234 0 0

Y15-64 number of people between 15 and 64 years of age, population in economically active age 6636 5574 32 503 4334

Notes

more examples at www.iz.sk

NUTS4 / LAU1 / LAU codes for HU and PL are created by me, so they can (and will) change in the future; CZ and SK NUTS4 codes are used by local statistical offices, so they should be more stable

NUTS4 codes are consistent with NUTS3 codes used by Eurostat

local_lau variable is an identifier used by local statistical office

abbr is abbreviation of region's name, used for map purposes (usually cars' license plate code; except for Hungary)

wikidata is code used by wikidata

osm_id is region's relation number in the OpenStreetMap database

Example outputs

you can download data in CSV, xml, ods, xlsx, shp, SQL, postgis, topojson, geojson or json format at 📥 doi:10.5281/zenodo.6165135

Counties of Slovakia – unemployment rate in Slovak LAU1 regions

Regions of the Slovak Republic

Unemployment of Czechia and Slovakia – unemployment share in LAU1 regions of Slovakia and Czechia

interactive map on unemployment in Slovakia

Slovakia – SK, Czech Republic – CZ, Hungary – HU, Poland – PL, NUTS3 regions of Slovakia

download at 📥 doi:10.5281/zenodo.6165135

suggested citation: Páleník, M. (2024). LAU1 dataset [Data set]. IZ Bratislava. https://doi.org/10.5281/zenodo.6165135

Unemployment Rate in India remained unchanged at 5.20 percent in October. This dataset provides - India Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Data obtained from ILOSTAT website. Collated various datasets from covid monitoring section. Most of the estimates are from 2020.

Description about columns: 1. country - Name of Country 2. total_weekly_hours_worked(estimates_in_thousands) - Total weekly hours worked of employed persons 3. percentage_of_working_hrs_lost(%) - Percentage of hours lost compared to the baseline (4th quarter of 2019) 4. percent_hours_lost_40hrs_per_week(thousands) - Percentage of hours lost compared to the baseline (4th quarter of 2019) expressed in full-time equivalent employment losses. This measure is constructed by dividing the number of weekly hours lost due to COVID-19 and dividing them by 40. 5. percent_hours_lost_48hrs_per_week(thousands) - Percentage of hours lost compared to the baseline (4th quarter of 2019) expressed in full-time equivalent employment losses. This measure constructed by dividing the number of weekly hours lost due to COVID-19 and dividing them by 48. 6. labour_dependency_ratio - Ratio of dependants (persons aged 0 to 14 + persons aged 15 and above that are either outside the labour force or unemployed) to total employment. 7. employed_female_25+_2019(estimates in thousands) - Employed female in 2019 who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). 8. employed_male_25+_2019(estimates in thousands) - Employed male in 2019 who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). 9. ratio_of_weekly_hours_worked_by_population_age_15-64 - Ratio of total weekly hours worked to population aged 15-64.

Description:

This dataset combines data from three sources to provide a comprehensive overview of county-level socioeconomic indicators, educational attainment, and voting outcomes in the United States. The dataset includes variables such as unemployment rates, median household income, urban influence codes, education levels, and voting percentages for the 2020 U.S. presidential election. By integrating this data, the dataset enables analysis of how factors like income, education, and unemployment correlate with political preferences, offering insights into regional voting behaviors across the country.

References:

The following reference datasets were used to construct this dataset.

[1] Harvard Dataverse, Voting Data Set by County. Available: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi: 10.7910/DVN/VOQCHQ

[2] USDA Economic Research Service, Educational Attainment and Un- employment Data. Available: https://www.ers.usda.gov/data-products/ county-level-data-sets/county-level-data-sets-download-data/

Unemployment Rate in France increased to 7.70 percent in the third quarter of 2025 from 7.60 percent in the second quarter of 2025. This dataset provides the latest reported value for - France Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Unemployment rate, participation rate, and employment rate by educational attainment, gender and age group, annual.

Regional unemployment rates used by the Employment Insurance program, by effective date, current month.

Graph and download economic data for Initial Claims (ICSA) from 1967-01-07 to 2025-11-22 about initial claims, headline figure, and USA.

Unemployment Rate in South Africa decreased to 31.90 percent in the third quarter of 2025 from 33.20 percent in the second quarter of 2025. This dataset provides - South Africa Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The number of people who are unemployed as a percentage of the active labour force (i.e. employed and unemployed).

Unemployment Rate in Canada decreased to 6.90 percent in October from 7.10 percent in September of 2025. This dataset provides - Canada Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The IRWM web based EDA mapping tool uses this GIS layer. Created by joining ACS 2019-2023 5 year estimates to the 2020 Census Counties feature class, and the 2023 Unemployment Rate. A Census Place is a location that is incorporated (city or town), unincorporated areas are CDP (Census Designated Place). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The TIGER/Line shapefiles include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The boundaries of all incorporated places are as of April 1, 2020 as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

US County & Zipcode Historical Demographics

Easily lookup US historical demographics by county FIPS or zipcode in seconds with this file containing over 5,901 different columns including:

*Lat/Long *Boundaries *State FIPS *Population from 2010-2019 *Death Rate from 2010-2019 *Unemployment from 2001-2020 *Education from 1970-2019 *Gender and Age Population

Provided by bitrook.com to help Data Scientists clean data faster.

Data Sources

All Data Combined Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Population Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Unemployment Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Zip FIPS Crosswalk Source:

https://data.world/niccolley/us-zipcode-to-county-state

County Boundaries Source:

https://public.opendatasoft.com/explore/dataset/us-county-boundaries/table/?disjunctive.statefp&disjunctive.countyfp&disjunctive.name&disjunctive.namelsad&disjunctive.stusab&disjunctive.state_name

Age Sex Source:

https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/asrh/cc-est2019-agesex-**.csv https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/cc-est2019-agesex.pdf

Races Source:

https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/asrh/cc-est2019-alldata.csv https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/cc-est2019-alldata.pdf

Background

The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.

Secure Access QLFS household data
Up to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. For some quarters, users should note that all missing values in the data are set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. From the 2013 household datasets, the standard -8 and -9 missing categories have been reinstated.

Secure Access household datasets for the QLFS are available from 2002 onwards, and include additional, detailed variables not included in the standard 'End User Licence' (EUL) versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurence of learning difficulty or disability; and benefits.

Prospective users of a Secure Access version of the QLFS will need to fulfil additional requirements, commencing with the completion of an extra application form to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access users must also complete face-to-face training and agree to Secure Access' User Agreement (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access version.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of each volume of the User Guide including the appropriate questionnaires for the years concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance pages before commencing analysis.

The study documentation presented in the Documentation section includes the most recent documentation for the LFS only, due to available space. Documentation for previous years is provided alongside the data for access and is also available upon request.

Review of imputation methods for LFS Household data - changes to missing values
A review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders.

Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.

An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.

Latest Edition Information
For the seventeenth edition (August 2025), one quarterly data file covering the time period July-September, 2024 has been added to the study.

I read a USA Today article from June 2020, where they discuss library usage during the pandemic. Some libraries set up wi-fi networks that extended outside the building, so that people would have access to the Internet even when the library was shutdown. This had me curious about how many people have convenient access to the Internet. There are some companies that rely on web pages instead of phone numbers for customer service. If someone wanted to determine the validity of claims and rumors spread by social media, they either need to have a trusted radio/television new source, or they need convenient access to the Internet to be able to investigate the information (by searching for original articles or unaltered video).

I found a pair of datasets that had information that would let me look at the situation. But while doing data cleaning, I found some problems that required significant effort to diagnose. I figured it would be useful to create a new dataset, and provide it on Kaggle in case others were interested.

I started with the dataset provided by the Institute of Museum and Library Services (IMLS), titled "IMLS Indicators Workbook: Economic Status and Broadband Availability and Adoption". The workbook contained statistics blended from three sources: the U.S. Census Bureau American Community Survey (ACS 5-year 2014-2018 estimates); broadbandnow.com (commercial aggregator of FCC data); and the Bureau of Labor Statistics (local area unemployment statistics).

On December 10, 2020, BroadbandNow.Com (bbn) provided a dataset hosted at GitHub as part of their Open Data Challenge. This had the features I wanted to cross check with the IMLS dataset.

I decided it would be worth it to do a partial clean-up of both sets, and then merge them to create a dataset with fewer problems. However, that still required some choices and compromises, so not problem-free. For example, I retained the 3 BBN features that were present in the original IMLS file, but I plan to use the information saved directly from the BBN file instead.

Unemployment Rate in Egypt increased to 6.40 percent in the third quarter of 2025 from 6.10 percent in the second quarter of 2025. This dataset provides - Egypt Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Technology companies worldwide saw a significant reduction in their workforce in 2025. One of the most recent tech layoffs was by Amazon on October 27, 2025, with ****** employees being laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of ******* employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of ******* laid-off employees in the global tech sector by the end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks, leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.