This data package includes the underlying data to replicate the charts, tables, and calculations presented in Labor market tightness and inflation before and after the COVID-19 pandemic, PIIE Working Paper 24-23.

If you use the data, please cite as:

Bloesch, Justin. 2024. Labor market tightness and inflation before and after the COVID-19 pandemic. PIIE Working Paper 24-23. Washington: Peterson Institute for International Economics.

This statistic reflects the perception of the Spanish population regarding the severity of the economic and labor consequences caused by the coronavirus (COVID-19). In April 2020, more than three quarters of those surveyed, specifically 80 percent, thought that the consequences of the crisis caused by this pandemic could be very serious.

The data collection from jobseekers and firms is done as part of the enrollment and operations for the job search platform called “Job Talash”. As part of the operations for the platform, vacancies are listed from enrolled firms on the platform and match candidates who meet the requirements of the vacancy. Candidates are then invited to apply for the vacancies that they have been matched to. Firm Survey Dataset The Firm survey dataset consists of all the attempts made to employers to enlist vacancies on the platform. The dataset also has the ads listing data which specifies the requirements of firms for vacancies that are listed on the platform, Job Talash. All registered firms on the platform receive a call every 3 months, asking them if they’d like to list a vacancy on the platform. If they decide to list a vacancy, the information about the requirements for the vacancy is collected so that relevant candidates can be matched to those jobs. The Jobseeker Dataset The Jobseeker dataset is based on the job matches generated for the jobseekers periodically based on their profile that includes, work experience, gender, education level and job interest. These job matches are communicated to the jobseeker via text message and phone call. A screening instrument is used by the field team while making phone calls for giving job updates to the jobseekers and recording their interest in the available positions. Along with the application interest, also information is collected about whether they have been employed to earn an income in the last 14 or 30 days (randomized recall period for each jobseeker).

♾️ Mekong Open Development

abstract Based on the analysis of IBGE data on the labor market before and during the pandemic, as well as on relevant literature, this article contemplates three dimensions: (i) a brief overview of the context of the economic and employment crisis, of the changes that resulted in the 2017 labor reform and of labor market indicators in the period prior to the health crisis; (ii) labor market indicators in the context of the pandemic, which signal impacts on labor; (iii) the challenges imposed on labor unions resulting from the intensification of the neoliberal agenda of the last four years. The article shows that the health crisis caused by Sars-CoV-2 has increased the fragility of the labor market, which had already been in a process of deterioration in the last four years in Brazil. It hit the working class of various economic sectors in striking and diverse manners, and unevenly in the different regions of Brazil.

The labor markets in the US and Mexico are closely linked through migrant workers and remittances and the changes in remittance flow may alter labor allocations in the origin households. In this paper, we investigate how the prevalence of the Covid-19 epidemic in the US affected the local labor market in Mexico. We construct a Mexican municipality-level measure of the exposure to Covid-19 in the US using data on Covid-19 prevalence across US states and data on migrants' destinations across the US states. We find a positive effect of Covid-19 exposure in the US on the hours worked among workers in Mexico yet no significant effects were found for the local wages. We also find that the effect varies across subgroups which indicates that the responses in hours worked depend on the household dynamics, the nature of the occupation-specific tasks, and the migration intensity. Finally, we document the potential mechanism behind the effect on the hours worked, which is through the changes in remittances sent to the origin municipalities in Mexico.

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on Morocco and assess the policy responses in a rapidly changing context, reliable data is imperative, and the need to resort to a dynamic data collection tool at a time when countries in the region are in a state of flux cannot be overstated. The COVID-19 MENA Monitor Survey was led by the Economic Research Forum (ERF) to provide data for researchers and policy makers on the socio-economic and labor market impact of the global COVID-19 pandemic on households. The ERF COVID-19 MENA Monitor Survey is constructed using a series of short panel phone surveys that are conducted approximately every two months, covering topics such as demographic and household characteristics, education and children, labor market status, income, social safety net, employment and unemployment detection, employment characteristics, and social distancing. In addition to the survey's panel design, which will permit the study of various phenomena over time, the survey also takes into account the key demographic and socio-economic characteristics of each country in the questionnaires' design to understand the different distributional consequences of the impact of COVID-19 and responses to it. This design allows further study of the effect of the pandemic on different vulnerable groups including women, informal and irregular workers, low skilled workers, and youth. The ERF COVID-19 MENA Monitor Survey is a wide-ranging, nationally representative panel survey.The baseline wave of this dataset was collected in October-November 2020 and harmonized by the Economic Research Forum (ERF). This dataset was collected in June 2021, harmonized by the Economic Research Forum (ERF) and is featured as the fourth wave for Morocco in the COVID-19 MENA Monitor Surveys.

The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between other Arab countries (Egypt, Tunisia, Jordan, and Sudan). All the COVID-19 MENA Monitor surveys incorporate similar survey designs, with data on households and individuals within those households.

Geographic coverage

National

Analysis unit

Household and Individuals

Universe

The survey covered a national random sample of mobile phone users aged 18-64.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample universe for the household survey was mobile phone users aged 18-64. Random digit dialing (RDD), within the range of valid numbers, was used, with up to three attempts if a phone number was not picked up/answered, was disconnected or busy, or picked up but could not complete the interview at that time. Samples were stratified by country-specific market shares of mobile operators. The sample is designed to cover at least 2000 unique households and individuals. A question is included in the survey for the number of phone numbers within the household to weight appropriately. Further weighting of the household and individual samples was done to reflect the demographic composition of the population as obtained by the most recent publicly available data with individual phone ownership and relevant demographic and labour market characteristics. In the individual interview, respondents who are employers or self-employed were asked to respond to either the household enterprise or farmer modules. For follow-up waves, previous wave respondents were recontacted if they consented to follow-up in the previous wave. Up to three attempts were used, including contacting second and family/friend numbers, if provided in wave one, on the third call. If the individual could not be reached or refused, a refresher individual was added to the sample in their place, randomly selected as with base wave respondents. All the respondents who consented to follow up in the prior wave were contacted in order to include them in the subsequent wave. Households are be followed up every two months up to a total of four interviews. Interviews are conducted by experienced survey research or polling firms in each country using computer-assisted telephone interviewing (CATI) techniques.

Mode of data collection

Computer Assisted Telephone Interview [cati]

In this project, rich administrative data on search and recruitment from a low-wage online job portal are used to study the labor market impacts of COVID-19 in India. The data from the job portal includes information on vacancies and job seekers across 2019 and 2020. It covers all users that either posted a vacancy or applied to a job on the portal across the two years. The following datasets are available: Aggregate data State level data Each dataset reports the following details: Vacancies: Number of vacancies; number of full-time vacancies; average minimum salary for full-time vacancies; number of full-time vacancies above minimum salary offer of Rs. 15,000; average minimum experience for full-time vacancies Job seekers: Number of job seekers; Number of job seekers by gender, age and education

Due to the coronavirus pandemic, more than half of the respondents felt the negative effects of the crisis on the labor market in Poland. The most frequent result was deterioration of work and employment conditions in 2020. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Replication Data for: The Effects of COVID-19 on Labor Force Nonparticipation in the Short Run: Racial and Ethnic Disparities under the Review of Social Economy.

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.

This dashboard is part of SDGs Today. Please see sdgstoday.orgThe world of work has been severely impacted by COVID-19. In particular, lockdown measures have resulted in significant losses in working hours and income. The International Labour Organisation (ILO) is tracking these impacts and the seventh edition of its ILO Monitor indicates that 93% of the world’s population reside in countries with workplace closure measures in place.Despite a greater than expected rebound in the later half of 2020, work loss remained high for the year. The ILO estimates that 8.8% of global working hours (approximately 255 million full-time jobs) were lost in 2020, relative to the fourth quarter of 2019. These losses were particularly high in Latin America and the Caribbean , Southern Europe and Southern Asia.Working-hour losses are an aggregate indicator of the impact of the COVID-19 crisis on the labour market. Estimates are made using the ILO’s ‘nowcasting’ model, which is a data-driven statistical prediction model that provides a real-time measure of the state of the labour market, drawing on real-time economic and labour market data.For more information, ILO’s contact information is available here.

BackgroundLong-term health conditions can affect labour market outcomes. COVID-19 may have increased labour market inequalities, e.g. due to restricted opportunities for clinically vulnerable people. Evaluating COVID-19’s impact could help target support.AimTo quantify the effect of several long-term conditions on UK labour market outcomes during the COVID-19 pandemic and compare them to pre-pandemic outcomes.MethodsThe Understanding Society COVID-19 survey collected responses from around 20,000 UK residents in nine waves from April 2020-September 2021. Participants employed in January/February 2020 with a variety of long-term conditions were matched with people without the condition but with similar baseline characteristics. Models estimated probability of employment, hours worked and earnings. We compared these results with results from a two-year pre-pandemic period. We also modelled probability of furlough and home-working frequency during COVID-19.ResultsMost conditions (asthma, arthritis, emotional/nervous/psychiatric problems, vascular/pulmonary/liver conditions, epilepsy) were associated with reduced employment probability and/or hours worked during COVID-19, but not pre-pandemic. Furlough was more likely for people with pulmonary conditions. People with arthritis and cancer were slower to return to in-person working. Few effects were seen for earnings.ConclusionCOVID-19 had a disproportionate impact on people with long-term conditions’ labour market outcomes.

Germany Labour Force: Unemployment Rate: Total data was reported at 3.700 % in Mar 2025. This records an increase from the previous number of 3.600 % for Feb 2025. Germany Labour Force: Unemployment Rate: Total data is updated monthly, averaging 4.100 % from Jan 2007 (Median) to Mar 2025, with 219 observations. The data reached an all-time high of 9.600 % in Feb 2007 and a record low of 2.800 % in May 2023. Germany Labour Force: Unemployment Rate: Total data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.G008: Labour Force: ILO Concept. [COVID-19-IMPACT]

This is the second survey in the Canadian Perspectives Survey Series. This survey includes information on the impacts of COVID-19 on food security and mental health of individuals, and on their social and employment circumstances.

Due to the impact of the coronavirus (COVID-19) pandemic, it was estimated that the global travel and tourism market had lost roughly 63 million jobs in 2020. While this scenario improved significantly in 2022, the sector still reported around 39 million fewer jobs worldwide compared to 2019. Overall, the Asia-Pacific region recorded the most significant employment loss due to the COVID-19 pandemic, with approximately 28 million fewer travel and tourism jobs in 2022 compared to 2019.

This public use microdata file provides information on the educational, labour market and financial impacts of the COVID-19 pandemic on postsecondary students, and on the students’ concerns about their academic future as a result of the pandemic.

The increase in the extent of working-from-home determined by the COVID-19 health crisis has led to a substantial shift of economic activity across geographical areas; which we refer to as a Zoomshock. When a person works from home rather than at the office, their work-related consumption of goods and services provided by the locally consumed service industries will take place where they live, not where they work. Much of the clientèle of restaurants, coffee bars, pubs, hair stylists, health clubs, taxi providers and the like located near workplaces is transferred to establishment located near where people live. These data are our calculations of the Zoomshock at the MSOA level. They reflect estimats of the change in the number of people working in UK neighbourhoods due to home-working.

The COVID-19 shutdown is not affecting all parts of the UK equally. Economic activity in local consumer service industries (LCSI), such as retail outlets, restaurants, hairdressers, or gardeners has all but stopped; other industries are less affected. These differences among industries and their varying importance across local economies means recovery will be sensitive to local economic conditions and will not be geographically uniform: some neighbourhoods face a higher recovery risk of not being able to return to pre-shutdown levels of economic activity. This recovery risk is the product of two variables. The first is the shock, the effect of the shutdown on local household incomes. The second is the multiplier, the effect on LCSI economic activity following a negative shock to household incomes. In neighbourhoods where many households rely on the LCSI sector as a primary source of income the multiplier may be particularly large, and these neighbourhoods are vulnerable to a vicious circle of reduced spending and reduced incomes. This project will produce data measuring the shock, the multiplier, and the COVID-19 shutdown recovery risk for UK neighbourhoods. These variables will be estimated using individual and firm level information from national surveys and administrative data. The dataset, and corresponding policy report, will be made public and proactively disseminated to guide local and national policy design. Recovery inequality is likely to be substantial: absent intervention, existing regional inequalities may be exacerbated. This research will provide a timely and necessary input into designing appropriate recovery policy.

By exacerbating a pre-existing crisis of childcare in the United States, the COVID- 19 pandemic forced many parents to renegotiate household arrangements. What shapes parents’ preferences over different arrangements? In an online conjoint experiment we assess how childcare availability, work status and earnings, and the intra-household division of labor shape heterosexual American parents’ preferences over different situations. We find that while mothers and fathers equally value outside options for child-care, the lack of such options – a significant feature of the pandemic – does not significantly change their evaluations of other features of household arrangements. Parents’ preferences over employment, earnings, and how to divide up household labor exhibit gendered patterns, which persist regardless of childcare availability. By illustrating the micro-foundations of household decision-making under constraints, our findings help to make sense of women’s retrenchment from the labor market during the pandemic: a pattern which may have long-term economic and political consequences.

This research investigates the impact of the COVID-19 crisis on university graduates' career decision making and planning, their transition into the job market and early career outcomes. The study will further address whether there are inequities across the graduate population on the effects of this crisis in terms of drawing upon resources that may influence their career decision making, including social networks and ties, career planning and resilience. The project utilises a mixed-method research design based on a longitudinal survey and interviews over the course of a year. The outcomes of the project will inform practitioner guidance and policy-making around enhancing graduates' readiness in a challenging labour market, as well as raising policy implications for employer organizations in best-supporting graduates' transition into the economy.

This research investigates the impact of the COVID-19 crisis on university graduates' career decision making and planning, their transition into the job market and early career outcomes. The study will further address whether there are inequities across the graduate population on the effects of this crisis in terms of drawing upon resources that may influence their career decision making, including social networks and ties, career planning and resilience. The project utilises a mixed-method research design based on a longitudinal survey and interviews over the course of a year. The outcomes of the project will inform practitioner guidance and policy-making around enhancing graduates' readiness in a challenging labour market, as well as raising policy implications for employer organizations in best-supporting graduates' transition into the economy.