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 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

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.

What is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).This updated dataset contains 116 jobs attributes including the 10 most likely jobs to be impacted for each county, the total employment and employment by sector. An attribute list is included below.An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.Attribute ListFIPSCounty NameStateTotal JobsWhite Collar JobsBlue Collar JobsService JobsWhite Collar %Blue Collar %Service %Government JobsGovernment %Primarily Self-Employed JobsPrimarily Self-Employed %Job Change, Last Ten YearsIndustry 1 NameIndustry 1 EmplIndustry 1 %Industry 2 NameIndustry 2 EmplIndustry 2 %Industry 3 NameIndustry 3 EmplIndustry 3 %Industry 4 NameIndustry 4 EmplIndustry 4 %Industry 5 NameIndustry 5 EmplIndustry 5 %Industry 6 NameIndustry 6 EmplIndustry 6 %Industry 7 NameIndustry 7 EmplIndustry 7 %Industry 8 NameIndustry 8 EmplIndustry 8 %Industry 9 NameIndustry 9 EmplIndustry 9 %Industry 10 NameIndustry 10 EmplIndustry 10 %All Other IndustriesAll Other Industries EmplAll Other Industies %Agriculture, Food & Natural Resources EmplArchitecture and Construction EmplArts, A/V Technology & Communications EmplBusiness, Management & Administration EmplEducation & Training EmplFinance EmplGovernment & Public Administration EmplHealth Science EmplHospitality & Tourism EmplHuman Services EmplInformation Technology EmplLaw, Public Safety, Corrections & Security EmplManufacturing EmplMarketing, Sales & Service EmplScience, Technology, Engineering & Mathematics EmplTransportation, Distribution & Logistics EmplAgriculture, Food & Natural Resources %Architecture and Construction %Arts, A/V Technology & Communications %Business, Management & Administration %Education & Training %Finance %Government & Public Administration %Health Science %Hospitality & Tourism %Human Services %Information Technology %Law, Public Safety, Corrections & Security %Manufacturing %Marketing, Sales & Service %Science, Technology, Engineering & Mathematics %Transportation, Distribution & Logistics %COVID-19 Vulnerability IndexAverage Wages per WorkerAvg Wages Growth, Last Ten YearsUnemployment RateUnderemployment RatePrime-Age Labor Force Participation RateSkilled Career 1Skilled Career 1 EmplSkilled Career 1 Avg Ann WagesSkilled Career 2Skilled Career 2 EmplSkilled Career 2 Avg Ann WagesSkilled Career 3Skilled Career 3 EmplSkilled Career 3 Avg Ann WagesSkilled Career 4Skilled Career 4 EmplSkilled Career 4 Avg Ann WagesSkilled Career 5Skilled Career 5 EmplSkilled Career 5 Avg Ann WagesSkilled Career 6Skilled Career 6 EmplSkilled Career 6 Avg Ann WagesSkilled Career 7Skilled Career 7 EmplSkilled Career 7 Avg Ann WagesSkilled Career 8Skilled Career 8 EmplSkilled Career 8 Avg Ann WagesSkilled Career 9Skilled Career 9 EmplSkilled Career 9 Avg Ann WagesSkilled Career 10Skilled Career 10 EmplSkilled Career 10 Avg Ann Wages

What is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).This updated dataset contains 116 jobs attributes including the 10 most likely jobs to be impacted for each county, the total employment and employment by sector. An attribute list is included below.An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.Attribute ListFIPSCounty NameStateTotal JobsWhite Collar JobsBlue Collar JobsService JobsWhite Collar %Blue Collar %Service %Government JobsGovernment %Primarily Self-Employed JobsPrimarily Self-Employed %Job Change, Last Ten YearsIndustry 1 NameIndustry 1 EmplIndustry 1 %Industry 2 NameIndustry 2 EmplIndustry 2 %Industry 3 NameIndustry 3 EmplIndustry 3 %Industry 4 NameIndustry 4 EmplIndustry 4 %Industry 5 NameIndustry 5 EmplIndustry 5 %Industry 6 NameIndustry 6 EmplIndustry 6 %Industry 7 NameIndustry 7 EmplIndustry 7 %Industry 8 NameIndustry 8 EmplIndustry 8 %Industry 9 NameIndustry 9 EmplIndustry 9 %Industry 10 NameIndustry 10 EmplIndustry 10 %All Other IndustriesAll Other Industries EmplAll Other Industies %Agriculture, Food & Natural Resources EmplArchitecture and Construction EmplArts, A/V Technology & Communications EmplBusiness, Management & Administration EmplEducation & Training EmplFinance EmplGovernment & Public Administration EmplHealth Science EmplHospitality & Tourism EmplHuman Services EmplInformation Technology EmplLaw, Public Safety, Corrections & Security EmplManufacturing EmplMarketing, Sales & Service EmplScience, Technology, Engineering & Mathematics EmplTransportation, Distribution & Logistics EmplAgriculture, Food & Natural Resources %Architecture and Construction %Arts, A/V Technology & Communications %Business, Management & Administration %Education & Training %Finance %Government & Public Administration %Health Science %Hospitality & Tourism %Human Services %Information Technology %Law, Public Safety, Corrections & Security %Manufacturing %Marketing, Sales & Service %Science, Technology, Engineering & Mathematics %Transportation, Distribution & Logistics %COVID-19 Vulnerability IndexAverage Wages per WorkerAvg Wages Growth, Last Ten YearsUnemployment RateUnderemployment RatePrime-Age Labor Force Participation RateSkilled Career 1Skilled Career 1 EmplSkilled Career 1 Avg Ann WagesSkilled Career 2Skilled Career 2 EmplSkilled Career 2 Avg Ann WagesSkilled Career 3Skilled Career 3 EmplSkilled Career 3 Avg Ann WagesSkilled Career 4Skilled Career 4 EmplSkilled Career 4 Avg Ann WagesSkilled Career 5Skilled Career 5 EmplSkilled Career 5 Avg Ann WagesSkilled Career 6Skilled Career 6 EmplSkilled Career 6 Avg Ann WagesSkilled Career 7Skilled Career 7 EmplSkilled Career 7 Avg Ann WagesSkilled Career 8Skilled Career 8 EmplSkilled Career 8 Avg Ann WagesSkilled Career 9Skilled Career 9 EmplSkilled Career 9 Avg Ann WagesSkilled Career 10Skilled Career 10 EmplSkilled Career 10 Avg Ann Wages

Introducing the Indeed Job Postings Index

Hiring Lab's Job Postings Tracker is being re-released as the Indeed Job Postings Index. By Chris Glynn

Indeed Hiring Lab is re-releasing our Job Postings Tracker as the Indeed Job Postings Index, a daily measure of labor market activity that is updated and will continue to be released weekly. Covering seven national markets in the US, Canada, United Kingdom, Ireland, France, Germany, and Australia, the Indeed Job Postings Index meets one of Hiring Lab’s primary goals: produce high quality and high frequency labor market metrics using Indeed’s proprietary data.

The primary difference between the Indeed Job Postings Index and the legacy Job Postings Tracker is the level. The Indeed Job Postings Index is set to 100 on February 1, 2020, and this effectively provides a uniform level shift of 100 to the existing Job Postings Tracker across all time points.The Job Postings Tracker measured the percent change in postings from February 1st, 2020. For example, if the Job Postings Tracker were 40%, the corresponding Indeed Job Postings Index on the same date would be 140. Additionally, we are now including year-over-year and month-over-month percent changes in the Indeed Job Postings Index as part of our data portal on hiringlab.org/data and on our GitHub page. Month-over-month changes are calculated as 28 day (4 week) differences to control for day of week.

As Covid-19 fades from the global labor market discussion, moving to an index better reflects current economic conditions. The Indeed Job Postings Index allows us to compare job postings more naturally across flexible date ranges as opposed to comparing to the pre-pandemic baseline. It also places Indeed’s job postings metric in a broader class of macroeconomic indexes such as the Case Shiller Index that measures house price appreciation and the Consumer Price Index that measures inflation.

Data Schema Each market covered by a Hiring Lab economist has a folder in this repo. Each folder contains the following files:

aggregate_job_postings_{country_code}.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings and new jobs postings (on Indeed for 7 days or fewer) for that market, as well as non-seasonally adjusted postings since February 1, 2020 for total job postings.

job_postings_by_sector_{country_code}.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for occupational sectors for that market. We do not share sectoral data for Ireland.

For certain markets, we also share subnational job postings trends. In the United States, we provide:

metro_job_postings_us.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in US metropolitan areas with a population of at least 500,000 people.

state_job_postings_us.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in the US states and the District of Columbia.

In Canada, we provide:

provincial_postings_ca.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in each Canadian provinces. In the United Kingdom, we provide:

regional_postings_gb.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in each region in the UK.

city_postings_gb.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in each city in the UK.

Github link: https://github.com/hiring-lab/job_postings_tracker#data-schema Hiring Lab Link: https://www.hiringlab.org/2022/12/15/introducing-the-indeed-job-postings-index/

https://static01.nyt.com/images/2020/11/18/nyregion/00nyblind1/merlin_179220645_b77f46ff-a503-40b6-bf2b-4922a676e61b-superJumbo.jpg" alt=""> This dataset offers a comprehensive insight into the economic trajectories of nine major economies from the onset of the COVID-19 pandemic through the beginning of 2024. It encompasses crucial economic indicators and financial market data, covering aspects such as manufacturing and services performance, consumer sentiment, monetary policies, inflation rates, unemployment rates, and overall economic output. Additionally, it includes price data for each economy, with values compared against the dollar for clarity. With data spanning this period, the dataset provides valuable insights for analysts, researchers, and stakeholders into the impact of the pandemic and other significant events on these economies, facilitating an assessment of their resilience, challenges, and opportunities.

Countries included : Australia / Canada / China / Europe / Japan / New Zealand / Switzerland / United Kingdom / United States

Column Descriptions:

• Country : The name of the country.
• Date : The date format (e.g., YYYY-MM-DD).
• Manufacturing PMI : Purchasing Managers' Index (PMI) for the manufacturing sector, indicating the economic health and activity level of the manufacturing industry.
• Services PMI : Purchasing Managers' Index (PMI) for the services sector, indicating the economic health and activity level of the services industry.
• Consumer Confidence : A measure of consumer sentiment or confidence in the economy, indicating consumers' optimism or pessimism about their financial situation and the overall state of the economy.
• Interest Rates : The prevailing interest rates set by the central bank or monetary authority, which influence borrowing costs and investment decisions.
• CPI YoY : Consumer Price Index (CPI) Year-over-Year change, indicating the percentage change in the average price level of a basket of consumer goods and services over the previous year.
• Core CPI : Core Consumer Price Index (CPI), which excludes volatile items such as food and energy prices, providing a measure of underlying inflation trends.
• Unemployment Rate : The percentage of the labor force that is unemployed and actively seeking employment, indicating the health of the labor market.
• GDP YoY : Gross Domestic Product (GDP) Year-over-Year change, indicating the percentage change in the total value of goods and services produced by a country's economy.
• Ticker: Ticker symbol for the corresponding financial asset or index.
• Open: The opening price of the financial asset or index on the specified date.
• High: The highest price of the financial asset or index during the specified date.
• Low: The lowest price of the financial asset or index during the specified date.
• Close: The closing price of the financial asset or index on the specified date.

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.

Unemployment numbers and rates for those aged 16 or over. The unemployed population consists of those people out of work, who are actively looking for work and are available to start immediately.

Unemployed numbers and rates also shown for equalities groups, by age, sex, ethnic group, and disability.

The data are taken from the Labour Force Survey and Annual Population Survey, produced by the Office for National Statistics.

The data are produced monthly on a rolling quarterly basis. The month shown is the month the quarter ends on.

The International Labour Organization defines unemployed people as: without a job, want a job, have actively sought work in the last 4 weeks and are available to start work in the next 2 weeks, or, out of work, have found a job and are waiting to start it in the next 2 weeks.

The figures in this dataset are adjusted to compensate for seasonal variations in employment (seasonally adjusted).

Data by equalities groups has a longer time lag and is only available quarterly from the Annual Population Survey, which is not seasonally adjusted.

Useful links

Click here for Regional labour market statistics from the Office for National Statistics.

Click here for Labour market statistics from the Office for National Statistics.

See here for GLA Economics' Labour Market Analysis.

See here for Economic Inactivity statistics.

See here for Employment rates.

This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

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 wave 3 of this dataset was collected in April 2021, harmonized by the Economic Research Forum (ERF) and is featured as data for Household/Individual. The survey is in the process of further expansion to include other waves.

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]

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on Sudan 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 April 2021. Wave 2 of this dataset was collected in August 2021, harmonized by the Economic Research Forum (ERF) and is featured as data for Household/Individual.

The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between other Arab countries (Egypt, Tunisia, Jordan, and Morocco). 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]

Introduction These analyses are based on online job postings data provided by Lightcast. This near real-time data can be used to track and assess the demand for labour in London, though there are some caveats to keep in mind: * Comparisons with publicly available online job postings data indicate that the Lightcast data may be more sensitive to short-term volatility in the labour market. * The composition of job postings by occupational groups has not changed significantly over time, with postings for managerial and professional occupations accounting for more than half of total job postings in London over the past five years. * Online job postings do not appear to reflect the overall composition of vacancies in the London labour market, as the number of vacancies is more evenly distributed across occupational groups in other data sources. See the briefing on Understanding online job postings data . ### Job postings analyses New analyses on job postings data will be released on a quarterly basis. Experimental data on green jobs and skills postings can be found here. ### Labour market analyses For other analyses, see our separate Datastore page.

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on Tunisia 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 November 2020 and harmonized by the Economic Research Forum (ERF) and is featured as wave 1 for Household/Individual data.This dataset was collected in February 2021, harmonized by the Economic Research Forum (ERF) and is featured as the second wave for Egypt in the COVID-19 MENA Monitor Surveys The survey is in the process of further expansion to include other waves

The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between other Arab countries (Egypt, Morocco, 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. The follow-up occurred for the second wave and 64.7% (1,294 of 2,000) Nov. 2020 respondents in Tunisia were successfully tracked on Feb. 2021.

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]

The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

LFS response to COVID-19

From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2024 Reweighting

In February 2024, reweighted person-level data from July-September 2022 onwards were released. Up to July-September 2023, only the person weight was updated (PWT23); the income weight remains at 2022 (PIWT22). The 2023 income weight (PIWT23) was included from the October-December 2023 quarter. Users are encouraged to read the ONS methodological note of 5 February, Impact of reweighting on Labour Force Survey key indicators: 2024, which includes important information on the 2024 reweighting exercise.

End User Licence and Secure Access QLFS data

Two versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).

The Secure Access version contains more detailed variables relating to:

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• finer detail geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories;
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address
• other additional detailed variables may also be included.

The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Introduction

As a part of the Google Data Analytics Professional Certificate Program, this case study serves as a data analytics adventure and a way to dive into something personal. While many face the difficulty of finding employment out of college, it became especially tedious to do so due to the COVID-19 pandemic. As such, this case study revolves around unemployment trends from 2021 using data sourced from the United States Bureau of Labor Statistics. I used datasets surrounding unemployment and employment trends in 2021 to answer the following:

Questions

1. What methods for job searching were the most prevalent across age ranges? Across gender/race/Hispanic-Latino ethnicity?
2. What trends exist between and within the most prevalent venues for job searching among the unemployed?
3. What job sector(s) does the majority of the population comprise? What trends exist within and between the most popular job sector and the least popular job sector? What relationship do these factors have with race/gender/Hispanic-Latino ethnicity?
4. How does information about prevalent job searching influence the job market and the applicants in the job search phase?

Insights (see the data section below for charts, graphs, and the .Rmd file I utilized)

• In 2021, the unemployed, with ages ranging from 16-65, preferred resumes and applications as their method for seeking out jobs. This method was especially prevalent in the age range 16-34, where, the highest bracket of job seekers were 24-35 years old. A close second was contacting an employer directly, primarily used by 45-64-year-olds. When considering gender/ethnicity/race, however, compared to their male counterparts, white women and women of color were the highest users of the resumes and applications method. However, white males and men of color were the highest users of the contacting employers directly method.
• Among the unemployed resumes were overall the most prevalent method of applying for jobs in 2021, where, people aged 16--34 and women regardless of ethnicity/race were the most likely to utilize this method to search for jobs.
• The majority of the population resides in the "Management, Professional, and related occupations" job sector, with the least popular form of occupations being in the "Farming, Fishing, and Forestry" sector. This sentiment can be found almost across all genders/races/ethnicities, though, some other job sectors like "Production, transportation, and material moving occupations" and "Natural resources, construction, and maintenance occupations" were more prevalent concerning the Black/African American men, Hispanic/Latino women, and Hispanic/Latino men respectively.
• This information is highly useful for job industries, specifically, those in the "Management, Professional, and related occupations" sector. With this, industries in this job sector can project what their incoming job applicant pool may look like and how to prepare for making the application process more accessible. This information can also serve to reinforce fairness and inclusivity in the job application process and in the work environment.

** Overall**

Using this information a company can project in 2022-2023 the majority of applicants will either apply to jobs using resumes/applications, the majority of these applicants may be 16-34 years old, and women regardless of ethnicity and race. They can also look out for applicants who are older, 45-64 years old, and applicants who are men regardless of ethnicity and race, being more likely to contact them as an employer directly. If an employer prefers to be directly contacted, they should make sure to consider the difficulties that people of different race/ethnic/and gender identities may have done so, and, either should either make the job positing more welcoming and inclusive to do so or, be sure to include a process of hiring via resumes/applications in order to better represent the unemployed population seeking jobs.

ObjectiveAs a new type of consumer subject in the market that was formerly dominated by multiple person families, single households are driving the change in the buying structure. Food purchase activities have undergone significant changes since the outbreak of the COVID‐19. The objective of this study was to assess and compare variations in food consumption, purchase and handling during the COVID-19 pandemic between single person households (SPH) and multiple person households.MethodA cross-sectional study conducted among 211 individuals in communities in Harris and Waller Counties, Texas. Sociodemographic, food purchase, food consumption and food handling activities during the COVID-19 pandemic were assessed with a validated COVID-19 Nutrition questionnaire.ResultsNon-Hispanic Black participants constituted 42.6%, and 28.4% were Hispanics. Participants were made up of mostly aged 18–24 years (39.3%), 47.9% single household composition, 30.4% in full time employment, and 29.1% partook in food assistance programs. A large proportion of them had never used grocery pickup services, online grocery shopping and a farmers’ market. During the COVID-19 pandemic, majority of the participants used more of large supermarkets, restaurant/fast food, and online grocery but food consumption seemed to remain the same for the majority of participants. For beverages, majority of participants consumed more water, less soda, and no alcohol. There was a significant association between single person household and higher restaurant/fast foods purchase. Many of the participants reported weight gain and less physical activity during the pandemic.ConclusionRestaurant meal purchases was more prevalent in single-person families. The results from the study have the potential to contribute to how public policy officials, food service, and health authorities forecast how different categories of consumers will react in pandemics and may be used to inform area-specific alleviation strategies to minimize the impact of the COVID-19 pandemic and future events.

Dataset Summary: This dataset analyzes layoff trends globally from 1995 to 2024, highlighting the evolution of job sectors and the influence of AI technologies on workforce dynamics. It provides insights into layoffs, reasons behind workforce changes, industry-specific impacts, and future job trends, making it a valuable resource for workforce analytics, AI adoption studies, and economic impact modeling.

Sources and Methodology: This dataset is modeled based on historical events, industry analyses, and logical extrapolations. Key data sources include:

Historical Trends:

Events like the dot-com bubble, global financial crises, and COVID-19.

Reliable sources: U.S. Bureau of Labor Statistics, World Bank, IMF Economic Outlook.

AI Trends and Projections:

Reports from McKinsey & Company, World Economic Forum, and Gartner.

Data on AI job growth and adoption: LinkedIn Economic Graphs, Crunchbase Layoff Tracker.

Skills and Future Jobs:

Reports on emerging skills and workforce trends: Future of Jobs Report 2023, TechCrunch, and Business Insider.

Projections and Logical Assumptions:

Projections for AI adoption, job creation, and displacement are based on publicly available research and extrapolation of trends.

Modeled features like "Future_Job_Trends" and "AI_Job_Percentage" combine factual data with predictive insights.

Potential Use Cases:

Economic Analysis: Study the impact of global events and technological advancements on workforce trends.

AI Adoption Trends: Explore how AI is influencing job creation and displacement across industries.

Policy Planning: Inform government and organizational policies on workforce development and reskilling.

Industry Insights: Gain insights into which industries are most affected by layoffs and which are adopting AI technologies.

Future Workforce Development: Identify emerging skills and prepare for future job market demands.

Disclaimer: This dataset is a combination of historical data, trends, and reasonable projections for future job markets influenced by AI technologies. Projections and estimates should be treated as approximations and not definitive predictions. All efforts have been made to use reliable sources and logical assumptions to ensure accuracy and usefulness for analytical purposes.

Citations:

U.S. Bureau of Labor Statistics (bls.gov)

McKinsey & Company (mckinsey.com)

World Economic Forum (weforum.org)

Gartner Reports (gartner.com)

Crunchbase Layoff Tracker (crunchbase.com)

Future of Jobs Report 2023 (weforum.org/reports)

LinkedIn Economic Graph (economicgraph.linkedin.com)

This datasets presents regional estimates of unemployment of Local Government Area (LGA) regions for each quarter starting December 2010 up to September 2021. The boundaries used for the dataset follow the 2021 edition of the Australian Statistical Geography Standard (ASGS).

Small Area Labour Markets (SALM) presents regional estimates of unemployment and the unemployment rate at two small area levels:

• Approximately 2,200 ABS SA2s, on a State/Territory and Metropolitan/Non-metropolitan basis. Estimates for the Capital City and the Rest of State are provided for the States and the Northern Territory.

• For approximately 540 Australian LGAs.

The SALM Estimates have been smoothed using a four-quarter average to minimise the variability inherent in small area estimates. A description of the methodology used to prepare the estimates in this publication is available on the Explanatory Notes page.

Caution: Highly disaggregated estimates of unemployment and the unemployment rate at the SA2 and LGA level can display significant variability and should be viewed with caution, particularly in regions where the SA4 level unemployment data are showing considerable volatility. As a result, quarter-to-quarter comparisons may not indicate actual movements in the labour market so we recommend using year-on-year comparisons. Even then, large movements in the SA2 and LGA data should be viewed with caution.

The COVID-19 pandemic began to have a significant impact on the Australian labour market from March 2020, when Australia recorded its 100th COVID-19 case and the initial shutdown of non-essential services and trading restrictions took effect. Learn more about the dataset at the LMIP (Labour Market Information Portal).

AURIN has spatially enabled the original data. Smoothed Estimates are not available for all SA2s and LGAs, for more information see the SALM 2016 ASGS Changeover User Guide.

This datasets presents regional estimates of unemployment of Statistical Area Level 2 (SA2) regions for each quarter starting December 2010 up to September 2021. The boundaries used for the dataset follow the 2016 edition of the Australian Statistical Geography Standard (ASGS).

Small Area Labour Markets (SALM) presents regional estimates of unemployment and the unemployment rate at two small area levels:

• Approximately 2,200 ABS SA2s, on a State/Territory and Metropolitan/Non-metropolitan basis. Estimates for the Capital City and the Rest of State are provided for the States and the Northern Territory.

• For approximately 540 Australian LGAs.

The SALM Estimates have been smoothed using a four-quarter average to minimise the variability inherent in small area estimates. A description of the methodology used to prepare the estimates in this publication is available on the Explanatory Notes page.

Caution: Highly disaggregated estimates of unemployment and the unemployment rate at the SA2 and LGA level can display significant variability and should be viewed with caution, particularly in regions where the SA4 level unemployment data are showing considerable volatility. As a result, quarter-to-quarter comparisons may not indicate actual movements in the labour market so we recommend using year-on-year comparisons. Even then, large movements in the SA2 and LGA data should be viewed with caution.

The COVID-19 pandemic began to have a significant impact on the Australian labour market from March 2020, when Australia recorded its 100th COVID-19 case and the initial shutdown of non-essential services and trading restrictions took effect. Learn more about the dataset at the LMIP (Labour Market Information Portal).

AURIN has spatially enabled the original data. Smoothed Estimates are not available for all SA2s and LGAs, for more information see the SALM 2016 ASGS Changeover User Guide.

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on Jordan 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 February 2021. This dataset was collected in June 2021, harmonized by the Economic Research Forum (ERF) and is featured as the second wave for Jordan in the COVID-19 MENA Monitor Surveys. The survey is in the process of further expansion to include other waves.

The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between other Arab countries (Egypt, Tunisia, Morocco, 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 will be designed to cover at least 2,500 unique households and individuals (2000 Jordanians, 500 Syrian Refugees). Attrition is addressed through the addition of refresher households in later waves to maintain that target. 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.

Households will be followed up every two months up to a total of three 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]