As of October 2020, most employees in Tunisia (approximately ** percent) received a full salary from their employers. On the other hand, over ** percent of the employed individuals were not paid the salary in the same month, as a consequence of the coronavirus (COVID-19) pandemic.

The COVID-19 pandemic hit many industries hard. Lots of people lost their jobs or were forced to reduce their employment radically throughout 2020. As a result, *** million more people globally were classified as poor, meaning that they lived on * U.S. dollars or less daily.

US Covid-19 Cases, Deaths and Mobility by State/County

Analyzing the Impact of the Pandemic on Low-Income Populations

By Liz Friedman [source]

About this dataset

Welcome to the Opportunity Insights Economic Tracker! Our goal is to provide a comprehensive, real-time look into how COVID-19 and stabilization policies are affecting the US economy. To do this, we have compiled a wide array of data points on spending and employment, gathered from several sources.

This dataset includes daily/weekly/monthly information at the state/county/city level for eight types of data: Google Mobility; Low-Income Employment and Earnings; UI Claims; Womply Merchants and Revenue; as well as weekly Math Learning from Zearn. Additionally, three files- Accounting for Geoids-State/County/City provide crosswalks between geographic areas that can be merged with other files having shared geographical levels.

Our goal here is to enable data users around the world to follow economic conditions in the US during this tumultuous period with maximum clarity and precision. We make all our datasets freely available so if you use them we kindly ask you attribute our work by linking or citing both our accompanying paper as well as this Economic Tracker at https://tracktherecoveryorg By doing so you are also agreeing to uphold our privacy & integrity standards which commit us both to individual & business confidentiality without compromising on independent nonpartisan research & policy analysis!

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How to use the dataset

This dataset provides US COVID-19 case and death data, as well as Google Community Mobility Reports, on the state/county level. Here is how to use this dataset:

• Understand the file structure: This dataset consists of three main files: 1) US Cases & Deaths by State/County, 2) Google Community Mobility Reports, and 3) Data from third-parties providing small business openings & revenue information and unemployment insurance claim data (Low Inc Earnings & Employment, UI Claims and Womply Merchants & Revenue).
• Select your Subset: If you are interested in particular types of data (e.g., mobility or employment), select the corresponding files from within each section based on your geographic area of interest – national, state or county level – as indicated in each filename.
• Review metadata variables: Become familiar with the provided variables so that you can select which ones you need to explore further in your analysis. For example, if analyzing mobility trends at a city level look for columns such as ‘Retailer_and_recreation_percent_change’ or ‘Transit Stations Percent Change’; if focusing on employment decline look for columns such pay or emp figures that align with industries of interest to you such as low-income earners (emp_{inclow},pay_{inclow}).
• Unify dateformatting across row values : Convert date formats into one common unit so that all entries have consistent formatting if necessary; for exampe some entries may display dates using YYYY/MM/DD notation while others may use MM//DD//YY format depending on their source datasets; make sure to review column labels carefully before converting units where needed..

• Merge datasets where applicable : Utilize GeoID crosswalks to combine multiple sets with same geographical coverageregionally covering ; example might be combining low income earnings figures with specific county settings by reference geo codes found in related documents like GeoIDs-County .
  6 . Visualise Data : Now that all the different measures have been reviewed can begin generating charts visualize findings . This process may include cleaning up raw figures normalizing across currency formats , mapping geospatial locations others ; once ready create bar graphs line charts maps other visual according aggregate output desired Insightful representations at this stage will help inform concrete policy decisions during outbreak recovery period..

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Research Ideas

• Estimating the Impact of the COVID-19 Pandemic on Small Businesses - By comparing county-level Womply revenue and employment data with pre-COVID data, policymakers can gain an understanding of the economic impact that COVID has had on local small businesses.
• Analyzing Effects of Mobility Restrictions - The Google Mobility data provides insight into geographic areas where...

The coronavirus (COVID-19) pandemic hit museums worldwide hard, with cultural institutions across the globe forced to close for long periods in 2020 and 2021. According to a March 2021 study in the United States, roughly 61 percent of surveyed museum employees working part-time reported having lost income due to cuts implemented during the pandemic. Reductions in salary, benefits, and/or hours caused part-time museum staff in the United States to lose a median of around eight thousand U.S. dollars since March 2020.

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

Many workers experienced income reduction during the coronavirus disease 2019 (COVID-19) pandemic, which may link to adverse mental health. This study aimed to examine the association of current income and reduction in income during COVID-19 with anxiety and depression levels among non-healthcare workers. This is a multi-city cross-sectional study. We used standardized questionnaires to collect information. We regrouped the current income and income reduction during COVID-19 according to the tertile and median value of each specific city. Depression, Anxiety and Stress Scales-21 item short version (DASS-21) was used to assess anxiety and depression levels. We performed multinomial logistic regression to examine the association of current and reduced income with anxiety and depression. Path models were developed to outline the potential modification/indirect effect of subsidies from government. Large income reduction and low current income were significantly associated with more anxiety/depression symptoms. Path analysis showed that government subsidies could not significantly alleviate the impact of reduced income on anxiety/depression. Our findings showed that large income reduction and low current income were independently associated with anxiety/depression, while these symptoms may not be ameliorated by one-off government funds. This study suggests the need for long-term policies (e.g. developing sustained economic growth policies) to mitigate negative impacts of the COVID-19.

IntroductionOur study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic.MethodsNew York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022.ResultsCOVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes.DiscussionThis study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic.

Context

Covid-19 Data collected from various sources on the internet. This dataset has daily level information on the number of affected cases, deaths, and recovery from the 2019 novel coronavirus. Please note that this is time-series data and so the number of cases on any given day is the cumulative number.

Content

The dataset includes 28 files scrapped from various data sources mainly the John Hopkins GitHub repository, the ministry of health affairs India, worldometer, and Our World in Data website. The details of the files are as follows

• countries-aggregated.csv A simple and cleaned data with 5 columns with self-explanatory names. -covid-19-daily-tests-vs-daily-new-confirmed-cases-per-million.csv A time-series data of daily test conducted v/s daily new confirmed case per million. Entity column represents Country name while code represents ISO code of the country. -covid-contact-tracing.csv Data depicting government policies adopted in case of contact tracing. 0 -> No tracing, 1-> limited tracing, 2-> Comprehensive tracing. -covid-stringency-index.csv The nine metrics used to calculate the Stringency Index are school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stay-at-home requirements; public information campaigns; restrictions on internal movements; and international travel controls. The index on any given day is calculated as the mean score of the nine metrics, each taking a value between 0 and 100. A higher score indicates a stricter response (i.e. 100 = strictest response). -covid-vaccination-doses-per-capita.csv A total number of vaccination doses administered per 100 people in the total population. This is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime (e.g. people receive multiple doses). -covid-vaccine-willingness-and-people-vaccinated-by-country.csv Survey who have not received a COVID vaccine and who are willing vs. unwilling vs. uncertain if they would get a vaccine this week if it was available to them. -covid_india.csv India specific data containing the total number of active cases, recovered and deaths statewide. -cumulative-deaths-and-cases-covid-19.csv A cumulative data containing death and daily confirmed cases in the world. -current-covid-patients-hospital.csv Time series data containing a count of covid patients hospitalized in a country -daily-tests-per-thousand-people-smoothed-7-day.csv Daily test conducted per 1000 people in a running week average. -face-covering-policies-covid.csv Countries are grouped into five categories: 1->No policy 2->Recommended 3->Required in some specified shared/public spaces outside the home with other people present, or some situations when social distancing not possible 4->Required in all shared/public spaces outside the home with other people present or all situations when social distancing not possible 5->Required outside the home at all times regardless of location or presence of other people -full-list-cumulative-total-tests-per-thousand-map.csv Full list of total tests conducted per 1000 people. -income-support-covid.csv Income support captures if the government is covering the salaries or providing direct cash payments, universal basic income, or similar, of people who lose their jobs or cannot work. 0->No income support, 1->covers less than 50% of lost salary, 2-> covers more than 50% of the lost salary. -internal-movement-covid.csv Showing government policies in restricting internal movements. Ranges from 0 to 2 where 2 represents the strictest. -international-travel-covid.csv Showing government policies in restricting international movements. Ranges from 0 to 2 where 2 represents the strictest. -people-fully-vaccinated-covid.csv Contains the count of fully vaccinated people in different countries. -people-vaccinated-covid.csv Contains the total count of vaccinated people in different countries. -positive-rate-daily-smoothed.csv Contains the positivity rate of various countries in a week running average. -public-gathering-rules-covid.csv Restrictions are given based on the size of public gatherings as follows: 0->No restrictions 1 ->Restrictions on very large gatherings (the limit is above 1000 people) 2 -> gatherings between 100-1000 people 3 -> gatherings between 10-100 people 4 -> gatherings of less than 10 people -school-closures-covid.csv School closure during Covid. -share-people-fully-vaccinated-covid.csv Share of people that are fully vaccinated. -stay-at-home-covid.csv Countries are grouped into four categories: 0->No measures 1->Recommended not to leave the house 2->Required to not leave the house with exceptions for daily exercise, grocery shopping, and ‘essent...

ABSTRACT Objectives To evaluate the impact of COVID-19 on clinical practice, income, health and lifestyle behavior of Brazilian urologists during the month of April 2020. Materials and Methods A 39-question, web-based survey was sent to all urologist members of the Brazilian Society of Urology. We assessed socio-demographic, professional, health and behavior parameters. The primary goal was to evaluate changes in urologists’ clinical practice and income after two months of COVID-19. We also looked at geographical differences based on the incidence rates of COVID-19 in different states. Results Among 766 urologists who completed the survey, a reduction ≥ 50% of patient visits, elective and emergency surgeries was reported by 83.2%, 89.6% and 54.8%, respectively. An income reduction of ≥ 50% was reported by 54.3%. Measures to reduce costs were implemented by most. Video consultations were performed by 38.7%. Modifications in health and lifestyle included weight gain (32.9%), reduced physical activity (60.0%), increased alcoholic intake (39.9%) and reduced sexual activity (34.9%). Finally, 13.5% of Brazilian urologists were infected with SARS-CoV-2 and about one third required hospitalization. Urologists from the highest COVID-19 incidence states were at a higher risk to have a reduction of patient visits and non-essential surgeries (OR=2.95, 95% CI 1.86 – 4.75; p< 0.0001) and of being infected with SARS-CoV-2 (OR=4.36 95%CI 1.74-10.54, p=0.012). Conclusions COVID-19 produced massive disturbances in Brazilian urologists’ practice, with major reductions in patient visits and surgical procedures. Distressing consequences were also observed on physicians’ income, health and personal lives. These findings are probably applicable to other medical specialties.

COVID-19 vaccination has significantly decreased morbidity, hospitalizations, and death during the pandemic. However, disparities in vaccination uptake threatens to stymie the progress made in safeguarding the health of Americans. Using a nationally representative adult (≥18 years old) sample from the 2021 Medical Expenditure Panel Survey (MEPS), we aimed to explore disparities in COVID-19 vaccine and booster uptake by income levels. To reflect the nature of the survey, a weighted logistic regression analysis was used to explore factors associated with COVID-19 vaccine and booster uptake. A total of 241,645,704 (unweighted n = 21,554) adults were included in the analysis. Average (SD) age of the population was 49 (18) years old, and 51% were female. There were disparities in COVID-19 vaccine and booster uptake by income groups. All other income groups were less likely to receive COVID-19 vaccines and booster shot than those in the high-income group. Those in the poor income group had 55% lower odds of being vaccinated for COVID-19 (aOR = 0.45, p

The purpose of our dataset is to measure how the Covid-19 pandemic, along health, financial, professional and socio-demographic factors, have affected the behavior of individuals. We are also estimated on repeated measures (life before COVID-19, life now with COVID-19, and life after the COVID-19 pandemic, in terms of future expectation) for a large sample (1746 respondents) from 43 worldwide countries during the period of May 2020 and October 2022. These datasets contain useful information for policymakers to improve the conditions of living in the areas of health and welfare. Is also unique, because: is first survey to investigate the wellbeing in three measurement moments: pre-, during- and post- Covid- 19 pandemic. Second, we discovered a great diversity of factors that influence the behavior of individuals in pandemic context. Third, this dataset permits exploration of levels of happiness and carrying out comparative studies with other countries, because our database contains information about the well-known Subjective Happiness Scale (Lyubomirsky & Lepper, 1999).

During the COVID-19 pandemic, family planning services over the world have been disrupted. There are still uncertainties about the impact on access to contraception, particularly among marginalised populations. This study aimed to assess the effect of COVID-19 on women's access to contraception, focusing on those experiencing loss of income and self-isolation. The International Sexual Health and Reproductive Health (I-SHARE) survey collected data from 5,216 women in 30 countries. Multivariable logistic regression was conducted to assess the association between loss of income during the pandemic, self-isolation and reduced access to contraception. Women experiencing loss of income and those who had self-isolated had reduced access to contraception (respectively aOR 2.3 and 1.7, for both p 

Background: Low-income earners are particularly vulnerable to mental health, consequence of the coronavirus disease 2019 (COVID-19) lockdown restrictions, due to a temporary or permanent loss of income and livelihood, coupled with government-enforced measures of social distancing. This study evaluates the mental health status among low-income earners in southwestern Uganda during the first total COVID-19 lockdown in Uganda.Methods: A cross-sectional descriptive study was undertaken amongst earners whose income falls below the poverty threshold. Two hundred and fifty-three (n = 253) male and female low-income earners between the ages of 18 and 60 years of age were recruited to the study. Modified generalized anxiety disorder (GAD-7), Spielberger's State-Trait Anger Expression Inventory-2 (STAXI-2), and Beck Depression Inventory (BDI) tools as appropriate were used to assess anxiety, anger, and depression respectively among our respondents.Results: Severe anxiety (68.8%) followed by moderate depression (60.5%) and moderate anger (56.9%) were the most common mental health challenges experienced by low-income earners in Bushenyi district. Awareness of mental healthcare increased with the age of respondents in both males and females. A linear relationship was observed with age and depression (r = 0.154, P = 0.014) while positive correlations were observed between anxiety and anger (r = 0.254, P < 0.001); anxiety and depression (r = 0.153, P = 0.015) and anger and depression (r = 0.153, P = 0.015).Conclusion: The study shows the importance of mental health awareness in low resource settings during the current COVID-19 pandemic. Females were identified as persons at risk to mental depression, while anger was highest amongst young males.

In December 2024, the increase in the average gross salary reached *** percent in Poland. On the other hand, there was a decrease in employment in the enterprise sector.

This document contains the raw data from an anonymous, cross-sectional, global online survey that aimed to identify the experiences and operation of research ethics review committees (ERCs) during the COVID-19 pandemic. Respondents were chairs (or their delegates) of ERCs who were involved in the review of COVID-19-related research protocols after March 2020. The 203 participants [130 from high-income countries (HICs) and 73 from low- and middle-income countries (LMICs)] came from diverse entities and organizations from 48 countries (19 HICs and 29 LMICs) in all World Health Organization regions.The survey questionnaire, administered through the Qualtrics Experience Management (XM) online platform, consisted of 50 items, with opportunities for open text responses. This document includes two Excel spreadsheets with the original data from Qualtrics, one for participants from HICs and the other for participants from LMICs.The study received approval from Western University’s Non-Medical Research Ethics Board (Protocol ID 120455). Additionally, it was evaluated by the World Health Organization Research Ethics Review Committee (Protocol ID CERC.0181) and was exempted from further review.

ObjectivesAnalyzing and comparing COVID-19 infection and case-fatality rates across different regions can help improve our response to future pandemics.MethodsWe used public data from the WHO to calculate and compare the COVID-19 infection and case-fatality rates in different continents and income levels from 2019 to 2023.ResultsThe Global prevalence of COVID-19 increased from 0.011 to 0.098, while case fatality rates declined from 0.024 to 0.009. Europe reported the highest cumulative infection rate (0.326), with Africa showing the lowest (0.011). Conversely, Africa experienced the highest cumulative case fatality rates (0.020), with Oceania the lowest (0.002). Infection rates in Asia showed a steady increase in contrast to other continents which observed initial rises followed by decreases. A correlation between economic status and infection rates was identified; high-income countries had the highest cumulative infection rate (0.353) and lowest case fatality rate (0.006). Low-income countries showed low cumulative infection rates (0.006) but the highest case fatality rate (0.016). Initially, high and upper-middle-income countries experienced elevated initial infection and case fatality rates, which subsequently underwent significant reductions.ConclusionsCOVID-19 rates varied significantly by continent and income level. Europe and the Americas faced surges in infections and low case fatality rates. In contrast, Africa experienced low infection rates and higher case fatality rates, with lower- and middle-income nations exceeding case fatality rates in high-income countries over time.

According to a survey conducted towards the end of March 2020, ** percent of respondents residing in G7 countries have already had their personal income impacted by the coronavirus pandemic (COVID-19).

About this dataset

Context

The dataset tabulates the median household income in Sharon town. It can be utilized to understand the trend in median household income and to analyze the income distribution in Sharon town 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).

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

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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.

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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/.

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Uruguay Tax Collection: Income Tax: Additional Social Security Tax COVID-19 Assistance data was reported at 0.000 UYU mn in Dec 2022. This stayed constant from the previous number of 0.000 UYU mn for Nov 2022. Uruguay Tax Collection: Income Tax: Additional Social Security Tax COVID-19 Assistance data is updated monthly, averaging 0.000 UYU mn from Jun 2020 (Median) to Dec 2022, with 31 observations. The data reached an all-time high of 270.000 UYU mn in Jun 2020 and a record low of 0.000 UYU mn in Dec 2022. Uruguay Tax Collection: Income Tax: Additional Social Security Tax COVID-19 Assistance data remains active status in CEIC and is reported by General Tax Directorate. The data is categorized under Global Database’s Uruguay – Table UY.F004: Tax Collection.

With the advancement of marketization, China has achieved rapid economic growth and economic class differentiation. This research analyzes the data from China’s livelihood survey, divides the urban Chinese into five socio-economic classes, and tests their preferences and tendencies for income redistribution. It obtains the general attitude differences in subsidy policy and income inequality during COVID-19. Our conclusion are consistent with the existing literature to a great extent; that is, personal factors (self-interest and belief in fairness) play a crucial role in the attitude of Chinese citizens. In the analysis of situational factors, the results show that the higher the level of marketization, the people are more likely to have stronger negative emotions about subsidy or redistribution policies. Further analysis shows that people with the lowest income are susceptible to the fact that income inequality has become significant and show a strong willingness to support the government’s redistribution policy. In contrast, middle-class people tend to favor the government’s redistribution policy, although they will not benefit much from the redistribution policy. Therefore, they lack the motivation to support the government in vigorously implementing the subsidy policy. Significantly, high-income people are indifferent, as they lack such motivation even more. The difference in redistribution preferences between upper-class and lower-class groups signals polarization in Chinese society, especially income redistribution.