Unemployment in King County resulting from strategies to slow the spread of COVID-19

This study, integrating sentiment, emotion, discourse, and timeline analyses together, conducts a corpus-based sentiment analysis of the news articles on unemployment from the New York Times in 2020, to capture the emotional dynamics conveyed by the newspaper as the pandemic-induced unemployment developed in the US. The results reveal that positive sentiment in the news articles on unemployment is significantly higher than negative sentiment. In emotion analysis, “trust” and “anticipation”rank the first and second among the eight emotions, while “fear”and “sadness” top the negative emotions. Complemented with a discourse analysis approach, the study reveals that the change of the sentiments and emotions over time is linked with the evolution of the pandemic and unemployment, the policy response as well as the protests against ethnic inequalities. This study highlights the important role mainstream news media play in information dissemination and solution-focused reportage at the time of severe crisis.This dataset contains 14 documents for the data of 2 sentiments and 8 emotions, generated by Python. It includes NRC lexicon categories for the sentiments and emotions in the study (data1-10), top 10 high-frequency words associated to the sentiments and emotions in each of the 12 subcorpora (data11-12), and monthly values of the sentiments and emotions in 2020 (data 13-14).

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.

This archive provides the code and data to replicate the analysis in "Unemployment Insurance Recipiency During the Covid-19 Pandemic" published in the National Tax Journal in 2023.

Full edition for scientific use. The study "Causes and Consequences of Unemployment in the COVID-19 pandemic" addresses the financial and psychological consequences of unemployment for those affected in the second year of the Corona pandemic. The study is based on an Austria-wide standardised telephone survey of 1844 people aged 15 to 64. The interviews took place between 29 May and 11 July 2021. 1215 interviewees were unemployed at the time of the interview, 332 of them long-term unemployed, 629 interviewees were employed.

This dataset includes county-level COVID-19 cases and fatalities for all 50 U.S. states between January 21, 2020 and January 30th, 2021 as cumulative totals and by epi week. Standardized cases and fatalities are also calculated per 100,000 population. Data also includes county urban-rural designations, social vulnerability index (SoVI) values, community resilience values, unemployment change percentages, and coded county/state level COVID-19 mitigation value assignments. For more information on data manipulations or calculations, please reach out to corresponding author (Sarah L. Jackson - SJ36@email.sc.edu).

Coronavirus-19 Cases (Hourly Update) vs. Median Household Income (ACS)See Detailed graphs and tables describing the COVID-19 crisis in New Mexico, updated daily (includes some county level data not found elsewhere) - https://sites.google.com/view/new-mexico-covid19-tracking/homeCDC's Description of the Social Vulnerability Index (takes into account 15 different selected indicators):https://svi.cdc.gov/

This layer contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values. The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic. Most current month: August 2025 (preliminary values at the state and county level) The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor force Data obtained from the U.S. Bureau of Labor Statistics. Data downloaded: October 1, 2025Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and CountyNation Data Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS"s county release schedule. BLS releases their county statistics roughly 2 months after-the-fact. The data is joined to 2023 TIGER boundaries from the U.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.As of the January 2022 estimates released on March 18th, 2022, BLS is reporting new data for the two new census areas in Alaska - Copper River and Chugach - and historical data for the previous census area - Valdez Cordova.As of the March 17th, 2025 release, BLS now reports data for 9 planning regions in Connecticut rather than the 8 previous counties. To better understand the different labor force statistics included in this map, see the diagram below from BLS:

This graph shows the unemployment rate forecasts following the outbreak of the coronavirus (COVID-19) in France from the first quarter of 2020, when the unemployment rate stood at around ***** percent, to the fourth quarter of 2026. OECD predictions estimated that unemployment will increase gradually in each quarter of 2022 and 2023, before a decrease in 2024.

The COVID-19 pandemic caused regressive income declines, but also led to progressive policy responses. Using administrative U.S. tax data, which are a near-universal panel dataset that can track income changes over time, we consider the distribution of annual income declines during the COVID-19 pandemic relative to the Great Recession. We then show how the unprecedented policy response to the pandemic, through enhanced unemployment insurance benefits and stimulus checks, affected the distribution of these declines

Continued Claims for UI released by the CT Department of Labor. Continued Claims are total number of individuals being paid benefits in any particular week. Claims data can be access directly from CT DOL here: https://www1.ctdol.state.ct.us/lmi/claimsdata.asp

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

For data on continued claims at the town level, see the dataset "Continued Claims for Unemployment Benefits by Town" here: https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-by-Town/r83t-9bjm

For data on initial claims see the following two datasets:

"Initial Claims for Unemployment Benefits in Connecticut," https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits/j3yj-ek9y

"Initial Claims for Unemployment Benefits by Town," https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits-by-Town/twvc-s7wy

Ireland DE: HU: Change in Employer SIC Revenue data was reported at -730.000 EUR mn in Dec 2021. This records an increase from the previous number of -736.000 EUR mn for Sep 2021. Ireland DE: HU: Change in Employer SIC Revenue data is updated quarterly, averaging -736.000 EUR mn from Mar 2021 (Median) to Dec 2021, with 4 observations. The data reached an all-time high of -730.000 EUR mn in Dec 2021 and a record low of -736.000 EUR mn in Sep 2021. Ireland DE: HU: Change in Employer SIC Revenue data remains active status in CEIC and is reported by Economic and Social Research Institute. The data is categorized under Global Database’s Ireland – Table IE.F013: Potential Costs and Distributional Effect: COVID-19 Related Unemployment. [COVID-19-IMPACT]

Ireland DE: LU: Change in Employer SIC Revenue data was reported at -379.000 EUR mn in Dec 2021. This records an increase from the previous number of -381.000 EUR mn for Sep 2021. Ireland DE: LU: Change in Employer SIC Revenue data is updated quarterly, averaging -381.000 EUR mn from Mar 2021 (Median) to Dec 2021, with 4 observations. The data reached an all-time high of -379.000 EUR mn in Dec 2021 and a record low of -381.000 EUR mn in Sep 2021. Ireland DE: LU: Change in Employer SIC Revenue data remains active status in CEIC and is reported by Economic and Social Research Institute. The data is categorized under Global Database’s Ireland – Table IE.F013: Potential Costs and Distributional Effect: COVID-19 Related Unemployment. [COVID-19-IMPACT]

The unemployment rate increased by *** percent between March 2020 and May 2020 in Australia. The underemployment rate increased by *** percent in the same period, whereas the participation rate decreased by *** percent. From May 2020 to October 2020, the unemployment rate decreased by *** percent, the underemployment rate decreased by *** percent and the participation rate increased by *** percent.

Dataset

This dataset was created by yashpl11

Contents

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: India's unemployment rate has been a significant concern, with fluctuations over the years. As of my last knowledge update in January 2022, the unemployment rate was around 6-7%.

Rural-Urban Disparities: Unemployment is often higher in rural areas compared to urban areas, where there are more employment opportunities.

Youth Unemployment: India has a significant issue of youth unemployment. A large portion of the population is under the age of 30, and providing employment opportunities for this demographic is a challenge.

Underemployment: Many individuals in India are also affected by underemployment, where they are employed in jobs that are below their skill levels and pay less than their qualifications.

Informal Sector: A substantial portion of India's workforce is engaged in the informal sector, which lacks job security and social benefits.

Gender Disparities: There are notable gender disparities in unemployment rates, with women often facing higher rates of unemployment compared to men.

Education and Unemployment: Higher education levels do not always guarantee employment in India, leading to a mismatch between skills and job opportunities.

Government Initiatives: The Indian government has launched various schemes and initiatives to address unemployment, such as the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) and the Skill India program.

COVID-19 Impact: The COVID-19 pandemic had a significant impact on employment, leading to job losses and economic challenges.

Reason for Unemployment Data

  Current population Survey results showing unemployment data detailing the reason for unemployment. Presented in thousands; seasonally adjusted, quarterly from 2017 to present. About the BLS Unemployment Data including Current Population Survey Demographic Breakdowns: Links to several different datasets, including Current Population Survey results showing seasonally adjusted unemployment data broken out by ethnicity and age, reason for unemployment, and duration of employment prior to unemployment for years including 2017-2019.  Other datasets show over-the-year percent change in the third month's employment level and taxable wages by industry for a given quarter at the County, State, and MSA level yearly from 1990 - present.
  Geography Level: NationalItem Vintage: Not Available
  Update Frequency: N/AAgency: BLSAvailable File Type: Excel 

  Return to Other Federal Agency Datasets Page

Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective
R. Maria del Rio-Chanona, Penny Mealy, Anton Picheler, Francois Lafond, J. Doyne Farmer
contact:
Results
The supply, demand, and total shocks at the industry and occupation level are in files:

industry_variables_and_shock.csv
occupation_variables_and_shock.csv

To reproduce our results we also include
The employment data between industries and occupations
industry_occupation_employment.csv
The classification of work activities
iwa_remotelabor_labels.csv
The essential score of industries at the NAICS 4d level
essential_score_industries_naics_4d_rev.csv

Update.

We have expanded our sample form 660 to 740 occupations

Given the lack of potential vaccines and effective medications, non-pharmaceutical interventions are the major option to curtail the spread of COVID-19. An accurate estimate of the potential impact of different non-pharmaceutical measures on containing, and identify risk factors influencing the spread of COVID-19 is crucial for planning the most effective interventions to curb the spread of COVID-19 and to reduce the deaths. Additive model-based bivariate causal discovery for scalar factors and multivariate Granger causality tests for time series factors are applied to the surveillance data of lab-confirmed Covid-19 cases in the US, University of Maryland Data (UMD) data, and Google mobility data from March 5, 2020 to August 25, 2020 in order to evaluate the contributions of social-biological factors, economics, the Google mobility indexes, and the rate of the virus test to the number of the new cases and number of deaths from COVID-19. We found that active cases/1,000 people, workplaces, tests done/1,000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1,000 people, mobility trends for places of residence (residential), retail and test capacity were the popular significant risk factor for the new cases of COVID-19, and that active cases/1,000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1,000 people, transit stations, mobility trends (transit), tests done/1,000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the popular significant risk factor for the deaths of COVID-19. We observed that no metrics showed significant evidence in mitigating the COVID-19 epidemic in FL and only a few metrics showed evidence in reducing the number of new cases of COVID-19 in AZ, NY and TX. Our results showed that the majority of non-pharmaceutical interventions had a large effect on slowing the transmission and reducing deaths, and that health interventions were still needed to contain COVID-19.