Labor Force Participation Rate in Mexico increased to 59.40 percent in January from 59.30 percent in December of 2024. This dataset provides - Mexico Labor Force Participation Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Labor Force Participation Rate in Sri Lanka decreased to 46.90 percent in the third quarter of 2024 from 47.80 percent in the second quarter of 2024. This dataset provides - Sri Lanka Labor Force Participation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Infra-Annual Labor Statistics: Inactivity Rate Total: From 15 to 64 Years for United States (LRIN64TTUSQ156S) from Q1 1977 to Q4 2024 about 15 to 64 years, participation, labor force, labor, rate, and USA.

In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

The unadjusted unemployment rate in the United States stood at 3.9 percent in October 2024. This data is not seasonally adjusted. The adjusted monthly unemployment rate can be found here and the monthly civilian labor force participation rate here.

Graph and download economic data for Noncyclical Rate of Unemployment (NROU) from Q1 1949 to Q4 2034 about NAIRU, long-term, projection, unemployment, rate, and USA.

The seasonally-adjusted national unemployment rate is measured on a monthly basis in the United States. In October 2024, the national unemployment rate was at 4.1 percent. Seasonal adjustment is a statistical method of removing the seasonal component of a time series that is used when analyzing non-seasonal trends. U.S. monthly unemployment rate According to the Bureau of Labor Statistics - the principle fact-finding agency for the U.S. Federal Government in labor economics and statistics - unemployment decreased dramatically between 2010 and 2019. This trend of decreasing unemployment followed after a high in 2010 resulting from the 2008 financial crisis. However, after a smaller financial crisis due to the COVID-19 pandemic, unemployment reached 8.1 percent in 2020. As the economy recovered, the unemployment rate fell to 5.3 in 2021, and fell even further in 2022. Additional statistics from the BLS paint an interesting picture of unemployment in the United States. In November 2023, the states with the highest (seasonally adjusted) unemployment rate were the Nevada and the District of Columbia. Unemployment was the lowest in Maryland, at 1.8 percent. Workers in the agricultural and related industries suffered the highest unemployment rate of any industry at seven percent in December 2023.

Unemployment Rate in Indonesia decreased to 4.82 percent in the first quarter of 2024 from 5.32 percent in the third quarter of 2023. This dataset provides the latest reported value for - Indonesia Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In the third quarter of 2023, the share of employment in the informal sector in Mexico was 54.8 percent. Throughout the whole period under consideration, the fourth quarter of 2015 accounts with the highest share for informal employment with a 57.9 percent. In the contrast, the second quarter of 2020 recorded the lowest share in this sector with around 51 percent.

Labor Market overview In 2022, the labor force participation rate in Mexico has remained stable at 65 percent. Nearly 58.5 million people are employed and 1.8 million are unemployed, encompassing over 60 million with those who are fully employed, partially employed, and actively seeking employment. Furthermore, the majority of the workforce was concentrated in the services sector, accounting for nearly two-thirds of the total labor market.

Unemployment

Mexico has experienced a consistent decrease in the unemployment rate over the last three years, reaching a rate of 3.31 percent in 2022. The youth population has been particularly affected by this trend, and their level of educational attainment has not been sufficient to mitigate these effects. The primary reason for unemployment has been the termination of contracts, and the lack of job opportunities has resulted in nearly 920,000 people with higher education degrees being unemployed as of 2023.

Key information about Sweden Labour Productivity Growth

• Sweden Labour Productivity improved by 2.56 % YoY in Dec 2024, compared with a growth of 2.14 % in the previous quarter
• Sweden Labour Productivity Growth data is updated quarterly, available from Mar 1994 to Dec 2024, averaging at 1.37 %
• The data reached an all-time high of 10.70 % in Jun 2021 and a record low of -5.73 % in Dec 2008

CEIC calculates Labour Productivity Growth from quarterly Real GDP and monthly Employment. Statistics Sweden provides Real GDP in local currency, at chain linked 2023 prices and Employment. Labour Productivity Growth prior to Q1 2002 is calculated from Employment sourced from Eurostat.


Further information about Sweden Labour Productivity Growth

• In the latest reports, Sweden Population reached 10.38 Person mn in Dec 2020
• Its Unemployment Rate dropped to 7.19 % in May 2023
• Monthly Earnings of Sweden stood at 4,518.89 USD in Dec 2024
• The country's Labour Force Participation Rate increased to 74.83 % in Jan 2025

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

In any society, the human element represents the basis of the work force which exercises all the service and production activities. Therefore, it is a mandate to produce labor force statistics and studies, that is related to the growth and distribution of manpower and labor force distribution by different types and characteristics.

In this context, the Central Agency for Public Mobilization and Statistics conducts "Quarterly Labor Force Survey" which includes data on the size of manpower and labor force (employed and unemployed) and their geographical distribution by their characteristics.

By the end of each year, CAPMAS issues the annual aggregated labor force bulletin publication that includes the results of the quarterly survey rounds that represent the manpower and labor force characteristics during the year.

----> Historical Review of the Labor Force Survey:

1- The First Labor Force survey was undertaken in 1957. The first round was conducted in November of that year, the survey continued to be conducted in successive rounds (quarterly, bi-annually, or annually) till now.

2- Starting the October 2006 round, the fieldwork of the labor force survey was developed to focus on the following two points: a. The importance of using the panel sample that is part of the survey sample, to monitor the dynamic changes of the labor market. b. Improving the used questionnaire to include more questions, that help in better defining of relationship to labor force of each household member (employed, unemployed, out of labor force ...etc.). In addition to re-order of some of the already existing questions in much logical way.

3- Starting the January 2008 round, the used methodology was developed to collect more representative sample during the survey year. this is done through distributing the sample of each governorate into five groups, the questionnaires are collected from each of them separately every 15 days for 3 months (in the middle and the end of the month)

----> The survey aims at covering the following topics:

1- Measuring the size of the Egyptian labor force among civilians (for all governorates of the republic) by their different characteristics. 2- Measuring the employment rate at national level and different geographical areas. 3- Measuring the distribution of employed people by the following characteristics: gender, age, educational status, occupation, economic activity, and sector. 4- Measuring unemployment rate at different geographic areas. 5- Measuring the distribution of unemployed people by the following characteristics: gender, age, educational status, unemployment type "ever employed/never employed", occupation, economic activity, and sector for people who have ever worked.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

----> Sample Design and Selection

The sample of the LFS 2006 survey is a simple systematic random sample.

----> Sample Size

The sample size varied in each quarter (it is Q1=19429, Q2=19419, Q3=19119 and Q4=18835) households with a total number of 76802 households annually. These households are distributed on the governorate level (urban/rural).

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among external resources in Arabic.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire design follows the latest International Labor Organization (ILO) concepts and definitions of labor force, employment, and unemployment.

The questionnaire comprises 3 tables in addition to the identification and geographic data of household on the cover page.

----> Table 1- Demographic and employment characteristics and basic data for all household individuals

Including: gender, age, educational status, marital status, residence mobility and current work status

----> Table 2- Employment characteristics table

This table is filled by employed individuals at the time of the survey or those who were engaged to work during the reference week, and provided information on: - Relationship to employer: employer, self-employed, waged worker, and unpaid family worker - Economic activity - Sector - Occupation - Effective working hours - Work place - Average monthly wage

----> Table 3- Unemployment characteristics table

This table is filled by all unemployed individuals who satisfied the unemployment criteria, and provided information on: - Type of unemployment (unemployed, unemployed ever worked) - Economic activity and occupation in the last held job before being unemployed - Last unemployment duration in months - Main reason for unemployment

Cleaning operations

----> Raw Data

Office editing is one of the main stages of the survey. It started once the questionnaires were received from the field and accomplished by the selected work groups. It includes: a-Editing of coverage and completeness b-Editing of consistency

----> Harmonized Data

• The STATA is used to clean and SPSS is used harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Graph and download economic data for Employment Cost Index: Wages and Salaries: Private Industry Workers (ECIWAG) from Q1 2001 to Q4 2024 about cost, ECI, salaries, workers, private industries, wages, private, employment, industry, inflation, indexes, and USA.

Abstract

The cleaned and harmonized version of the survey data produced and published by the Economic Research Forum represents 100% of the original survey data collected by the Central Agency for Public Mobilization and Statistics (CAPMAS)

In any society, the human element represents the basis of the work force which exercises all the service and production activities. Therefore, it is a mandate to produce labor force statistics and studies, that is related to the growth and distribution of manpower and labor force distribution by different types and characteristics.

In this context, the Central Agency for Public Mobilization and Statistics conducts "Quarterly Labor Force Survey" which includes data on the size of manpower and labor force (employed and unemployed) and their geographical distribution by their characteristics.

By the end of each year, CAPMAS issues the annual aggregated labor force bulletin publication that includes the results of the quarterly survey rounds that represent the manpower and labor force characteristics during the year.

----> Historical Review of the Labor Force Survey:

1- The First Labor Force survey was undertaken in 1957. The first round was conducted in November of that year, the survey continued to be conducted in successive rounds (quarterly, bi-annually, or annually) till now.

2- Starting the October 2006 round, the fieldwork of the labor force survey was developed to focus on the following two points: a. The importance of using the panel sample that is part of the survey sample, to monitor the dynamic changes of the labor market. b. Improving the used questionnaire to include more questions, that help in better defining of relationship to labor force of each household member (employed, unemployed, out of labor force ...etc.). In addition to re-order of some of the already existing questions in much logical way.

3- Starting the January 2008 round, the used methodology was developed to collect more representative sample during the survey year. this is done through distributing the sample of each governorate into five groups, the questionnaires are collected from each of them separately every 15 days for 3 months (in the middle and the end of the month)

----> The survey aims at covering the following topics:

1- Measuring the size of the Egyptian labor force among civilians (for all governorates of the republic) by their different characteristics. 2- Measuring the employment rate at national level and different geographical areas. 3- Measuring the distribution of employed people by the following characteristics: gender, age, educational status, occupation, economic activity, and sector. 4- Measuring unemployment rate at different geographic areas. 5- Measuring the distribution of unemployed people by the following characteristics: gender, age, educational status, unemployment type "ever employed/never employed", occupation, economic activity, and sector for people who have ever worked.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The cleaned and harmonized version of the survey data produced and published by the Economic Research Forum represents 100% of the original survey data collected by the Central Agency for Public Mobilization and Statistics (CAPMAS)

Sample Design and Selection

The sample of the LFS 2006 survey is a simple systematic random sample.

Sample Size

The sample size varied in each quarter (it is Q1=19429, Q2=19419, Q3=19119 and Q4=18835) households with a total number of 76802 households annually. These households are distributed on the governorate level (urban/rural).

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among external resources in Arabic.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire design follows the latest International Labor Organization (ILO) concepts and definitions of labor force, employment, and unemployment.

The questionnaire comprises 3 tables in addition to the identification and geographic data of household on the cover page.

----> Table 1- Demographic and employment characteristics and basic data for all household individuals

Including: gender, age, educational status, marital status, residence mobility and current work status

----> Table 2- Employment characteristics table

This table is filled by employed individuals at the time of the survey or those who were engaged to work during the reference week, and provided information on: - Relationship to employer: employer, self-employed, waged worker, and unpaid family worker - Economic activity - Sector - Occupation - Effective working hours - Work place - Average monthly wage

----> Table 3- Unemployment characteristics table

This table is filled by all unemployed individuals who satisfied the unemployment criteria, and provided information on: - Type of unemployment (unemployed, unemployed ever worked) - Economic activity and occupation in the last held job before being unemployed - Last unemployment duration in months - Main reason for unemployment

Cleaning operations

----> Raw Data

Office editing is one of the main stages of the survey. It started once the questionnaires were received from the field and accomplished by the selected work groups. It includes: a-Editing of coverage and completeness b-Editing of consistency

----> Harmonized Data

• STATA is used to clean and SPSS is used harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Unemployment Rate in Iran decreased to 7.20 percent in the fourth quarter of 2024 from 7.50 percent in the third quarter of 2024. This dataset provides - Iran Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2023, the rate of surveyed unemployment in urban areas of China amounted to approximately 5.2 percent. The unemployment rate is expected to decrease slightly to 5.1 percent in 2024 and the following years. Monthly unemployment ranged at a level of around 5.2 percent in the third quarter of 2024. Unemployment rate in China In 2017, the National Statistics Bureau of China introduced surveyed unemployment as a new indicator of unemployment in the country. It is based on monthly surveys among the labor force in urban areas of China. Surveyed unemployment replaced registered unemployment figures, which were often criticized for missing out large parts of the urban labor force and thereby not presenting a true picture of urban unemployment levels. However, current unemployment figures still do not include rural areas.A main concern in China’s current state of employment lies within the large regional differences. As of 2021, the unemployment rate in northeastern regions of China was notably higher than in China’s southern parts. In Beijing, China’s political and cultural center, registered unemployment ranged at around 3.2 percent for 2021. Indicators of economic activities Apart from the unemployment rate, most commonly used indicators to measure economic activities of a country are GDP growth and inflation rate. According to an IMF forecast, GDP growth in China will decrease to about 4.8 percent in 2024, after 5.2 percent in 2023, depicting a decrease of six percentage points from 10.6 percent in 2010. Quarterly growth data published by the National Bureau of Statistics indicated 4.6 percent GDP growth for the third quarter of 2024.

Unemployment Rate in Spain decreased to 10.61 percent in the fourth quarter of 2024 from 11.21 percent in the third quarter of 2024. This dataset provides the latest reported value for - Spain Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Unemployment Rate - Black or African American (LNS14000006) from Jan 1972 to Feb 2025 about African-American, 16 years +, household survey, unemployment, rate, and USA.

Unemployment refers to the share of the labor force that is without work but available for and seeking employment.

Overview: Each quarter, the Temporary Foreign Worker Program (TFWP) publishes Labour Market Impact Assessment (LMIA) statistics on Open Government Data Portal, including quarterly and annual LMIA data related to, but not limited to, requested and approved TFW positions, employment location, employment occupations, sectors, TFWP stream and temporary foreign workers by country of origin. The TFWP does not collect data on the number of TFWs who are hired by an employer and have arrived in Canada. The decision to issue a work permit rests with Immigration, Refugees and Citizenship Canada (IRCC) and not all positions on a positive LMIA result in a work permit. For these reasons, data provided in the LMIA statistics cannot be used to calculate the number of TFWs that have entered or will enter Canada. IRCC publishes annual statistics on the number of foreign workers who are issued a work permit: https://open.canada.ca/data/en/dataset/360024f2-17e9-4558-bfc1-3616485d65b9. Please note that all quarterly tables have been updated to NOC 2021 (5 digit and training, education, experience and responsibilities (TEER) based). As such, Table 5, 8, 17, and 24 will no longer be updated but will remain as archived tables. Frequency of Publication: Quarterly LMIA statistics cover data for the four quarters of the previous calendar year and the quarter(s) of the current calendar year. Quarterly data is released within two to three months of the most recent quarter. The release dates for quarterly data are as follows: Q1 (January to March) will be published by early June of the current year; Q2 (April to June) will be published by early September of the current year; Q3 (July to September) will be published by early December of the current year; and Q4 (October to December) will be published by early March of the next year. Annual statistics cover eight consecutive years of LMIA data and are scheduled to be released in March of the next year. Published Data: As part of the quarterly release, the TFWP updates LMIA data for 28 tables broken down by: TFW positions: Tables 1 to 10, 12, 13, and 22 to 24; LMIA applications: Tables 14 to 18; Employers: Tables 11, and 19 to 21; and Seasonal Agricultural Worker Program (SAWP): Tables 25 to 28. In addition, the TFWP publishes 2 lists of employers who were issued a positive or negative LMIA: Employers who were issued a positive LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/90fed587-1364-4f33-a9ee-208181dc0b97/resource/b369ae20-0c7e-4d10-93ca-07c86c91e6fe); and Employers who were issued a negative LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/f82f66f2-a22b-4511-bccf-e1d74db39ae5/resource/94a0dbee-e9d9-4492-ab52-07f0f0fb255b). Things to Remember: 1. When data are presented on positive or negative LMIAs, the decision date is used to allocate which quarter the data falls into. However, when data are presented on when LMIAs are requested, it is based on the date when the LMIA is received by ESDC. 2. As of the publication of 2022Q1- 2023Q4 data (published in April 2024) and going forward, all LMIAs in support of 'Permanent Residence (PR) Only' are included in TFWP statistics, unless indicated otherwise. All quarterly data in this report includes PR Only LMIAs. Dual-intent LMIAs and corresponding positions are included under their respective TFWP stream (e.g., low-wage, high-wage, etc.) This may impact program reporting over time. 3. Attention should be given for data that are presented by ‘Unique Employers’ when it comes to manipulating the data within that specific table. One employer could be counted towards multiple groups if they have multiple positive LMIAs across categories such as program stream, province or territory, or economic region. For example, an employer could request TFWs for two different business locations, and this employer would be counted in the statistics of both economic regions. As such, the sum of the rows within these ‘Unique Employer’ tables will not add up to the aggregate total.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population of South Africa with one exception. The only institutional subpopulation included in the QLFS sample are individuals in worker's hostels. Persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Quarterly Labour Force Survey (QLFS) uses a master sample frame which has been developed as a general-purpose household survey frame that can be used by all other Stats SA household surveys that have reasonably compatible design requirement as the QLFS. The 2013 master sample is based on information collected during the 2011 population Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the master sample since they covered the entire country and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the master sample with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current master sample (3 324) reflects an 8,0% increase in the size of the master sample compared to the previous (2007) master sample (which had 3 080 PSUs). The larger master sample of PSUs was selected to improve the precision (smaller CVs) of the QLFS estimates.

From the master sample frame, the QLFS takes draws employing a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage. The primary stratification occurred at provincial, metro/non-metro, mining and geography type while the secondary strata were created within the primary strata based on the demographic and socio-economic characteristics of the population. For each quarter of the QLFS, a ¼ of the sampled dwellings is rotated out of the sample. These dwellings are replaced by new dwellings from the same PSU or the next PSU on the list.

For more see the release document that is distributed with the data.

Sampling deviation

It should be noted that the Quarterly Labour Force Survey (QLFS) for Quarter 1 (January to March) of 2020 data collection was disrupted when Stats SA suspended face-to-face data collection for all its surveys on 19 March 2020 as a result of the COVID-19 pandemic and restricted movement. This was to ensure that the field staff and respondents were not exposed to the risk of contracting coronavirus and to contain its spread. As a result, some dwellings (621 or 2,0% of the 30 608 sampled dwelling units) were not interviewed which otherwise would have been interviewed. To compensate for this, Stats SA made use of the fact that the design of the QLFS is such that sampled dwelling units are in the sample for four successive quarters. So, for persons in dwelling units that were not visited as a result of the lockdown, imputations were done where possible using data from the previous quarter. For respondents who were not visited in the first quarter of 2020 but had information from the fourth quarter of 2019, their responses were carried over to the first quarter of 2020.

If the person was shown as unemployed or not economically active in the last quarter of 2019, that was the status assigned to them for the first quarter of 2020. If the person was shown as employed in the fourth quarter of 2019, the imputation was somewhat more complex. This was necessitated by the fact that that there are usually temporary jobs created in the fourth quarter of each year that do not continue into the following year. Accordingly, if the person started the job that he/she held in Q4: 2019 in some previous quarter, it was assumed that the job continued into Q1: 2020. On the other hand, if the job held in Q4: 2019 had only started in that quarter, that person was treated as non-respondent in Q1: 2020.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of five section: Section 1: Biographical information (marital status, language, migration, education, training, literacy, etc.) Section 2: Economic activities for persons aged 15 years and older Section 3: Unemployment and economic inactivity for persons aged 15 years and older Section 4: Main work activities in the last week for persons aged 15 years and older Section 5: Earnings in the main job for employees, employers and own-account workers aged 15 years and older

Data appraisal

COVID 19 Affected data collection for QLFS 2020 Q1. Please see the sampling section for more on this.