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.

In 1990, the unemployment rate of the United States stood at 5.6 percent. Since then there have been many significant fluctuations to this number - the 2008 financial crisis left millions of people without work, as did the COVID-19 pandemic. By the end of 2022 and throughout 2023, the unemployment rate came to 3.6 percent, the lowest rate seen for decades. However, 2024 saw an increase up to four percent. For monthly updates on unemployment in the United States visit either the monthly national unemployment rate here, or the monthly state unemployment rate here. Both are seasonally adjusted. UnemploymentUnemployment is defined as a situation when an employed person is laid off, fired or quits his work and is still actively looking for a job. Unemployment can be found even in the healthiest economies, and many economists consider an unemployment rate at or below five percent to mean there is 'full employment' within an economy. If former employed persons go back to school or leave the job to take care of children they are no longer part of the active labor force and therefore not counted among the unemployed. Unemployment can also be the effect of events that are not part of the normal dynamics of an economy. Layoffs can be the result of technological progress, for example when robots replace workers in automobile production. Sometimes unemployment is caused by job outsourcing, due to the fact that employers often search for cheap labor around the globe and not only domestically. In 2022, the tech sector in the U.S. experienced significant lay-offs amid growing economic uncertainty. In the fourth quarter of 2022, more than 70,000 workers were laid off, despite low unemployment nationwide. The unemployment rate in the United States varies from state to state. In 2021, California had the highest number of unemployed persons with 1.38 million out of work.

The statistic shows the distribution of the workforce across economic sectors in the United States from 2013 to 2023. In 2023, 1.57 percent of the workforce in the US was employed in agriculture, 19.34 percent in industry and 79.09 percent in services. See U.S. GDP per capita for more information. American workforce A significant majority of the American labor force is employed in the services sector, while the other sectors, industry and agriculture, account for less than 20 percent of the US economy. However, the United States is among the top exporters of agricultural goods – the total value of US agricultural exports has more than doubled since 2000. A severe plunge in the employment rate in the US since 1990 shows that the American economy is still in turmoil after the economic crisis of 2008. Unemployment is still significantly higher than it was before the crisis, and most of those unemployed and looking for a job are younger than 25; youth unemployment is a severe problem for the United States, many college or university graduates struggle to find a job right away. Still, the number of employees in the US since 1990 has been increasing slowly, with a slight setback during and after the recession. Both the number of full-time and of part-time workers have increased during the same period. When looking at the distribution of jobs among men and women, both project the general downward trend. A comparison of the employment rate of men in the US since 1990 and the employment rate of women since 1990 shows that more men tend to be employed than women.

In February 2025, the agriculture and related private wage and salary workers industry had the highest unemployment rate in the United States, at eight percent. In comparison, self-employed workers, unincorporated, and unpaid family workers had the lowest unemployment rate, at 4.3 percent. The average for all industries was 4.5 percent. U.S. unemployment There are several factors that impact unemployment, as it fluctuates with the state of the economy. Unfortunately, the forecasted unemployment rate in the United States is expected to increase as we head into the latter half of the decade. Those with a bachelor’s degree or higher saw the lowest unemployment rate from 1992 to 2022 in the United States, which is attributed to the fact that higher levels of education are seen as more desirable in the workforce. Nevada unemployment Nevada is one of the states with the highest unemployment rates in the country and Vermont typically has one of the lowest unemployment rates. These are seasonally adjusted rates, which means that seasonal factors such as holiday periods and weather events that influence employment periods are removed. Nevada's economy consists of industries that are currently suffering high unemployment rates such as tourism. As of May 2023, about 5.4 percent of Nevada's population was unemployed, possibly due to the lingering impact of the coronavirus pandemic.

Unemployment Rate in India decreased to 8.20 percent in January from 8.30 percent in December of 2024. This dataset provides - India Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Regional unemployment rates used by the Employment Insurance program, by effective date, current month.

In September 2024, the District of Columbia had the highest unemployment rate in the United States, with an unemployment rate of 5.7. The unemployment rate was also high in Nevada, with an unemployment rate of 5.6 percent in February. Unemployment in the U.S. A person is considered unemployed if they have no job and are currently looking for a job and available to work. The unemployment rate in the United States varies across states. Nation-wide unemployment was 3.4 percent as of April 2023. Unemployment can be affected by various factors including economic conditions and global competition. During economic prosperity, unemployment rates generally decrease and during times of recession, rates increase. The seasons can also have an impact on the unemployment rate, especially during winter, when there is lower demand for construction workers or other professionals who typically work outdoors. The retail sector also experiences fluctuating demand for workers, particularly during the holiday-shopping season, when demand for workers increases. For this reason, labor statistics are usually presented as being either seasonally adjusted or unadjusted. The data presented in this statistic have been seasonally adjusted, but the monthly unadjusted unemployment rate can be accessed here.

Number of employees by North American Industry Classification System (NAICS) and data type (seasonally adjusted, trend-cycle and unadjusted), last 5 months. Data are also available for the standard error of the estimate, the standard error of the month-to-month change and the standard error of the year-over-year change.

Abstract

The 61st round of the Nationbal Sample Survey was conducted during July, 2004 to June, 2005. The survey was spread over 7,999 villages and 4,602 urban blocks covering 1,24,680 households (79,306 in rural areas and 45,374 in urban areas) and enumerating 6,02,833 persons (3,98,025 in rural areas and 2,04,808 in urban areas). Employment and unemployment were measured with three different approaches, viz. usual status with a reference period of one year, current weekly status with one week reference period and current daily status based on the daily activity pursued during each day of the reference week. Unless otherwise stated, ‘all’ usual status workers will mean all workers taking into consideration the usual principal and subsidiary status taken together.

Geographic coverage

The survey covered the whole of the Indian Union except (i) Leh (Ladakh) and Kargil districts of Jammu & Kashmir, (ii) interior villages of Nagaland situated beyond five kilometres of the bus route and (iii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year.

Analysis unit

Household, individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Outline of sample design: A stratified multi-stage design has been adopted for the 61st round survey. The first stage units (FSU) are the 2001 census villages in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. The ultimate stage units (USU) are households in both the sectors. In the case of large villages/blocks requiring hamlet-group (hg)/sub-block (sb) formation, one intermediate stage is the selection of two hgs/sbs from each FSU.

Sampling Frame for First Stage Units: For the rural sector, the list of 2001 census villages (panchayat wards for Kerala) constitutes the sampling frame. For the urban sector, the list of latest available Urban Frame Survey (UFS) blocks has been considered as the sampling frame.

Stratification: Within each district of a State/UT, two basic strata have been formed: i) rural stratum comprising of all rural areas of the district and (ii) urban stratum comprising of all the urban areas of the district. However, if there are one or more towns with population 10 lakhs or more as per population census 2001 in a district, each of them will also form a separate basic stratum and the remaining urban areas of the district will be considered as another basic stratum. There are 27 towns with population 10 lakhs or more at all-India level as per census 2001.

Sub-stratification:

• Rural sector: If 'r' be the sample size allocated for a rural stratum, the number of sub-strata formed is 'r/2'. The villages within a district as per frame have been first arranged in ascending order of population. Then sub-strata 1 to 'r/2' have been demarcated in such a way that each sub-stratum comprises a group of villages of the arranged frame and has more or less equal population.

• Urban sector: If 'u' be the sample size for a urban stratum, 'u/2' number of sub-strata have been formed. The towns within a district, except those with population 10 lakhs or more, have been first arranged in ascending order of population. Next, UFS blocks of each town have been arranged by IV unit no. × block no. in ascending order. From this arranged frame of UFS blocks of all the towns, 'u/2' number of sub-strata has been formed in such a way that each sub-stratum has more or less equal number of UFS blocks.

For towns with population 10 lakhs or more, the urban blocks have been first arranged by IV unit no. × block no. in ascending order. Then 'u/2' number of sub-strata has been formed in such a way that each sub-stratum has more or less equal number of blocks.

Total sample size (FSUs): 12784 FSUs have been allocated at all-India level on the basis of investigator strength in different States/UTs for central sample and 14992 for state sample.

Allocation of total sample to States and UTs: The total number of sample FSUs is allocated to the States and UTs in proportion to population as per census 2001 subject to the availability of investigators ensuring more or less uniform work-load.

Allocation of State/UT level sample to rural and urban sectors: State/UT level sample size is allocated between two sectors in proportion to population as per census 2001 with 1.5 weightage to urban sector subject to the restriction that urban sample size for bigger states like Maharashtra, Tamil Nadu etc. should not exceed the rural sample size. A minimum of 8 FSUs has been allocated to each state/UT separately for rural and urban areas.

Allocation to strata: Within each sector of a State/UT, the respective sample size is allocated to the different strata in proportion to the stratum population as per census 2001. Allocations at stratum level have been adjusted to a multiple of 4 with a minimum sample size of 4.

Selection of FSUs: Two FSUs have been selected from each sub-stratum of a district of rural sector with Probability Proportional to Size With Replacement (PPSWR), size being the population as per Population Census 2001. For urban sector, two FSUs have been selected from each sub-stratum by using Simple Random Sampling Without Replacement (SRSWOR). Within each sub-stratum, samples have been drawn in the form of two independent sub-samples in both the rural and urban sectors.

Selection of hamlet-groups/sub-blocks/households - important steps

Criterion for hamlet-group/sub-block formation: Large villages/blocks having approximate present population of 1200 or more will be divided into a suitable number (say, D) of 'hamlet-groups' in the rural sector and 'sub-blocks' in the urban sector as stated below.

approximate present population of the sample village/block / no. of hgs/sbs to be formed (D)

less than 1200 (no hamlet-groups/sub-blocks): 1
1200 to 1799: 3 1800 to 2399: 4 2400 to 2999: 5 3000 to 3599: 6 …..and so on

For rural areas of Himachal Pradesh, Sikkim and Poonch, Rajouri, Udhampur, Doda districts of Jammu and Kashmir and Idukki district of Kerala, the number of hamlet-groups formed is as follows.

approximate present population of the sample village / no. of hgs to be formed

less than 600 (no hamlet-groups): 1
600 to 899: 3
900 to 1199: 4
1200 to 1499: 5 …..and so on

Two hamlet-groups/sub-blocks are selected from a large village/UFS block wherever hamlet-groups/sub-blocks have been formed, by SRSWOR. Listing and selection of the households are done independently in the two selected hamlet-groups/sub-blocks. In case hamlet-groups/sub-blocks are to be formed in the sample FSU, the same would be done by more or less equalizing population.

Formation of Second Stage Strata and allocation of households

For both Schedule 1.0 and Schedule 10, households listed in the selected village/block/ hamlet-groups/sub-blocks are stratified into three second stage strata (SSS) as given below.

Rural: The three second-stage-strata (SSS) in the rural sector are formed in the following order:

SSS 1: relatively affluent households
SSS 2: from the remaining households, households having principal earning from non- agricultural activity
SSS 3: other households

Urban: In the urban sector, the three second-stage strata (SSS) are formed as under:

Two cut-off points, say 'A' and 'B', based on MPCE of NSS 55th round, have been determined at NSS Region level in such a way that top 10% of households have MPCE more than 'A' and bottom 30% have MPCE less than 'B'. Then three second-stage-strata (SSS) are formed in the urban sector in the following order:

SSS 1: households with MPCE more than A (i.e. MPCE > A)
SSS 2: households with MPCE equal to or less than A but equal to or more than B ( i.e. B = MPCE = A)
SSS 3: households with MPCE less than B (i.e. MPCE < B)

The number of households to be surveyed in each FSU is 10 for each of the schedules 1.0 and 10. C

Selection of households for Schedules 1.0 and 10: From each SSS the sample households for both the schedules are selected by SRSWOR. If a household is selected both for schedule 1.0 and schedule 10, only schedule 1.0 would be canvassed in that household and the sample household for schedule 10 would be replaced by next household in the frame for schedule 10.

Mode of data collection

Face-to-face [f2f]

Research instrument

In the present round, Schedule 10 on employment-unemployment consists of 16 blocks.

The first three blocks, viz. Blocks 0, 1 and 2, are used to record identification of sample households and particulars of field operations, as is the common practice in usual NSS rounds. Similarly, the last two blocks, viz., Blocks 10 & 11, are again the usual blocks to record the remarks of investigator and comments by supervisory officer(s), respectively. Block 3 will be for recording the household characteristics like household size, religion, social group, land possessed and cultivated, monthly per capita consumer expenditure, etc., and Block 3.1 for recording particulars of indebtedness of rural labour households.

Block 4 is used for recording the demographic particulars and attendance in educational institutions of all the household members. Particulars of vocational training receiving/received by the household members will also be collected in block 4.

In Block 5.1, particulars of usual principal activity of all the household members will be recorded along with some particulars of the enterprises in which the usual status workers (excluding those in crop and plantation activities) are engaged. Information on informal employment will also be collected in block 5.1. Similarly, the particulars of one subsidiary economic activity of the household members along with some

The statistic shows the unemployment rate in Canada from 2019 to 2023, with projections up until 2029. In 2023, the unemployment rate in Canada was at around 5.41 percent. Canada’s economy Three-quarter of Canada’s workforce is employed in the services sector, with the other two sectors, agriculture and industry, accounting for the rest of Canada’s employment. The country’s main export and import partner is the United States. Although both export and import figures have increased over the last few years, the trade balance of goods in Canada – i.e. the value of Canada’s exports minus the value of its imports – has slumped dramatically since the economic crisis hit in 2008. In 2009, for the first time in a decade, Canada reported a trade deficit, and the figures are still struggling to recover. Additionally, Canada’s public debt has been increasing since the crisis. Although a few key figures are still not back to the usual level, Canada and its economy seem to have more or less bounced back from the crisis; as can be seen above, the unemployment rate is gradually decreasing, for example, and gross domestic product / GDP in Canada has been increasing steadily. Canada is thus among the countries with the largest proportion of global gross domestic product / GDP based on Purchasing Power Parity. Canada is among the leading trading nations worldwide, and an important part of its economy is the export of oil. The country hosts significant oil resources, in fact, its capacity is the third-largest after those of Saudi Arabia and Venezuela.

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.

Snapshot img

US Residential Construction Market Size 2025-2029

The residential construction market size in the US is forecast to increase by USD 242.9 million at a CAGR of 4.5% between 2024 and 2029.

The residential construction market is experiencing significant growth, driven by several key factors. Firstly, the increasing household formation rates in the US continue to fuel demand for new housing units. Secondly, there is a rising focus on sustainability in residential construction projects, with homebuilders increasingly adopting energy-efficient and eco-friendly building materials and practices.
However, the market also faces challenges, including a shortage of skilled labor for large-scale residential real estate projects, which can impact project timelines and budgets. These trends and challenges are shaping the future of the residential construction industry in the US.

What will be the US Residential Construction Market Size During the Forecast Period?

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The residential construction market is experiencing a significant shift as the affordable housing trend gains momentum. The Federal Reserve's decision to keep the federal funds rate low has contributed to a decrease in mortgage rates, making it an opportune time for home buyers to enter the market. However, the housing supply remains a concern, with construction spending in the residential investment sector showing only modest growth. The labor market's current state is another factor influencing the residential construction industry. With a low unemployment rate, there is a high demand for labor, leading to increased wages and, in turn, higher construction costs.
Inflation also poses a challenge, as it erodes the purchasing power of home buyers and builders alike. The economy's overall health plays a crucial role in the residential construction market's dynamics. A strong economy typically leads to increased demand for new homes, as evidenced by the double-digit growth in housing starts and building permits for single-family homes. However, a recession can lead to a significant decrease in construction activity, as seen in the cancellation rate of housing projects. The Federal Reserve's interest rate decisions, inflation, and the economy's health all impact the residential construction market. Affordable housing programs, such as housing choice vouchers and fair housing programs, play a vital role in ensuring access to housing for a broader population. The construction sectors must navigate these market dynamics to remain competitive and meet the demand for new homes.
The US residential construction market is seeing significant shifts, driven by various housing market trends. Sustainable homebuilding practices are gaining momentum, with a focus on energy-efficient homes and green building materials. Modular construction and prefab housing are becoming increasingly popular for their cost-effective and timely solutions. Urban redevelopment projects are revitalizing city areas, while suburban expansion is fueling demand for new homes. Affordable housing projects are crucial in addressing housing shortages, and real estate investment continues to thrive in these sectors. Smart home integration is also on the rise, with luxury home construction embracing high-tech features. The impact of mortgage rates, coupled with multifamily housing growth and home renovation demand, adds complexity to the market's dynamics.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Apartments and condominiums
  Villas
  Other types


Type

  New construction
  Renovation


Application

  Single family
  Multi-family


Geography

  US

By Product Insights

The apartments and condominiums segment is estimated to witness significant growth during the forecast period.

The residential construction market in the US is experiencing growth in the apartment and condominium sectors, driven by shifting preferences and lifestyle choices. Urbanization is a significant factor fueling this trend, as more individuals opt for the conveniences and amenities offered in urban areas. As a result, developers are constructing modern, sustainable, and community-focused living spaces in the form of high-rise apartment buildings and condominium complexes. These structures cater to various demographics, including intergenerational groups and younger generations, reflecting diverse living circumstances. The labor economy and vaccination rates have also contributed to the continued activity in the residential sector, allowing for steady progress in construction projects. While the non-residential sector has faced challenges, the residential sector remains a vi

Abstract

Statistical information on all aspects of the population is vital for the design, implementation, monitoring and evaluation of economic and social development plan and policy issues. Labour force survey is among the important sources of data to assess the participation of the population in the economic and social development process of the country. It is useful to indicate the extent of available and unutilized human resources that must be absorbed by the national economy to ensure full employment and economic wellbeing of the population.

The general objective of the 2013 National Labor Force Survey was designed to provide statistical data on the size, distribution and characteristics of the economically active and the distribution in the various sectors of the economy in both urban and rural areas. The data will be useful for policy makers, planners, researchers, and other institutions and individuals engaged in the design, implementation and monitoring of human resource development plans, programs and projects. The specific objectives of this survey are: • Generate data on the size of the potential work force that is available to participate in production process; • Determine the activity status and rate of economic participation of different sub-groups of the population; • Identify those who are actually contributing to the economic development (i.e., employed) and those who are out of the sphere of productive activities; • Identify the size, distribution and characteristics of employed population by occupation and Industry, status in employment, sector of employment and earnings from employment...etc. • Provide data on the size, distribution and characteristics of unemployed population and rate of unemployment; • Assess the situation of women's employment or the participation of women in the labour force; • Provide time series data to trace changes over time.

Geographic coverage

The survey covered all rural and urban parts of the country except the non-sedentary areas of six zones of Somali region.

Analysis unit

• Households
• Individuals (household members aged 5 years and above)

Kind of data

Sample survey data [ssd]

Frequency of data collection

Every five years

Sampling procedure

Sampling Frame The list of Sampling Frame obtained from the 2007 Population and Housing Census is used to select EAs. A fresh list of households from each EA was prepared at the beginning of the survey period. The list was then used as a frame for selecting sample households of each EAs.

Sample Design For the purpose of the survey the country was divided into three broad categories, rural (Category I), major urban center (Category II) and other urban center categories (Category III).

Sample Size and Selection Scheme Category I: Totally 842 EAs and 25260 households were selected from this category. Sample EAs of each reporting level was selected using Probability Proportional to Size (PPS) systematic sampling technique; size being number of household obtained from the 2007 Population and Housing Census. From the fresh list of households prepared at the beginning of the survey 30 households per EA were systematically selected and surveyed. For the distribution of planned and covered number of samples from each domain see

Category II: In this category 817 EAs and 24510 households were selected. Sample EAs from each reporting level in this category were also selected using probability proportional to size (PPS) systematic sampling; size being number of households obtained from the 2007 Population and Housing Census is used to select EAs. From the fresh list of households prepared at the beginning of the survey 30 households per EA were systematically selected and covered by the study. The table below (Summary Table 2.2) shows planned and covered EAs and households in each domain.

Category III: 127 urban centers, 296 EAs and 8,880 households were selected in this category. Urban centers from each domain and EAs from each urban center were selected using probability proportional to size systematic selection method; size being number of households obtained from the 2007 Population and Housing Census is used to select EAs. From the fresh listing of each EA 30 households were systematically selected and the study carried out on the 30 households ultimately selected. Summary Table 2.3 below shows the number of planned and sampled EAs and households by domain.

For details on sampling design, see: Ethiopian Central Statistical Agency. Analytica Report on The 2013 National Labour Force Survey

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey is mainly aimed at providing information on the economic characteristics of the population aged 10 years and above, i.e., their activity status, employment, and unemployment situation during the last seven days prior to the survey date. It has also covered detailed socio-demographic background variables such as age, sex, relationship to the head of household, migration, disability, literacy status, educational level, training and marital status. The survey has used a structured questionnaire to produce the required data. Before taking its final shape, the draft questionnaire was commented by CSA senior staff member from different directorate as well as top management. Based on the comment given by professionals, the content, layout and presentation of the questionnaire were amended.

The questionnaire was organized in to six sections; Section 1: Area identification of the selected household: this section dealt with area identification of the respondents such as region, zone, wereda, etc. Section 2: Socio- demographic characteristics of households: it consisted of the general socio-demographic characteristics of the population such as age, sex, education, status and type of migration, disability, literacy status, educational Attainment, types of training and marital status. Section 3: Economic activities during the last seven days: this section dealt with a range of questions which helps to see the status and characteristics of employed persons in a current status approach such as hours of work in productive activities, occupation, industry, status in employment, earnings from employment, job mobility, service year for paid employees employment in the formal and informal sector and time related under employment. Section 4: Unemployment and characteristics of unemployed persons: this section focused on the size, rate and characteristics of the unemployed population. Section 5: Economic activities during the last twelve months: this section consists of the usual economic activity status refereeing to the long reference period i.e. engaged in productive activities during most of the last twelve months, reason for not being active, status in employment, main occupation and industry with two digit codes. Section 6: Economic activities of children aged 5-17 years: this section comprises information on the participation of children aged 5-17 years in the economic activities, whether attending education, reason for not attending education, whether they were working during the last seven days, reason for working, for whom they are working, types of injury at work place, whether using protective wear while working and frequency of working periods, and orphan hood status.

The questionnaire used in the field for data collection was prepared in Amharic language. Most questions have pre-coded answers. A copy of the questionnaire translated to English is attached as an external resource.

Cleaning operations

The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the field supervisors and the heads of branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry by the subject matter experts.

Using the computer edit specifications prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing or data cleaning was made by referring back to the filled-in questionnaire. This is an important part of data processing operation in attaining the required level of data quality. Consistency checks and re-checks were also made based on frequency and tabulation results. This was done by senior programmers using CSPro software in collaboration with the senior subject experts from Labour Statistics Team of the CSA.

Response rate

• For the rural domains, the response rate was 99.60%
• For the major urban centers domains, the response rate was 99.51%
• For the other urban centers domains, the response rate was 99.62%

The tech industry had a rough start to 2024. Technology companies worldwide saw a significant reduction in their workforce in the first quarter of 2024, with over 57 thousand employees being laid off. By the second quarter, layoffs impacted more than 43 thousand tech employees. In the final quarter of the year around 12 thousand employees were 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 167.6 thousand 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 263 thousand laid off employees in the global tech sector by trhe 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.

The statistic shows the distribution of the workforce across economic sectors in China from 2013 to 2023. In 2023, around 22.8 percent of the workforce were employed in the agricultural sector, 29.1 percent in the industrial sector and 48.1 percent in the service sector. This year, the share of agriculture increased for the first time in more than two decades, which highlights the difficult situation of the labor market due to the pandemic and economic downturn at the end of the year.

Distribution of the workforce in China

In 2012, China became the largest exporting country worldwide with an export value of about two trillion U.S. dollars. China’s economic system is largely based on growth and export, with the manufacturing sector being a crucial contributor to the country’s export competitiveness. Economic development was accompanied by a steady rise of labor costs, as well as a significant slowdown in labor force growth. These changes present a serious threat to the era of China as the world’s factory. The share of workforce in agriculture also steadily decreased in China until 2021, while the agricultural gross production value displayed continuous growth, amounting to approximately 7.8 trillion yuan in 2021.

Development of the service sector

Since 2011, the largest share of China’s labor force has been employed in the service sector. However, compared with developed countries, such as Japan or the United States, where 73 and 79 percent of the work force were active in services in 2021 respectively, the proportion of people working in the tertiary sector in China has been relatively low. The Chinese government aims to continue economic reform by moving from an emphasis on investment to consumption, among other measures. This might lead to a stronger service economy. Meanwhile, the size of the urban middle class in China is growing steadily. A growing number of affluent middle class consumers could promote consumption and help China move towards a balanced economy.

In 2023, the education and health services industry employed the largest number of people in the United States. That year, about 36,4 million people were employed in the education and health services industry.

Education and Health Services Industry

Despite being one of the wealthiest nations in the world, the United States has started to fall behind in both education and the health care industry. Although the U.S. spends the most money in both these industries, they do not see their desired results in comparison to other nations. Furthermore, in the education services industry, there was a relatively significant wage gap between men and women. In 2019, men earned about 1,070 U.S. dollars per week on average, while their female counterparts only earned 773 U.S. dollars per week.

Employment in the U.S.

The 2008 financial crisis was a large-scale event that impacted the entire world, especially the United States. The economy started to improve after 2010, and the number of people employed in the United States has been steadily increasing since then. However, the number of people employed in the education sector is expected to slowly decrease until 2026. The overall unemployment rate in the United States has decreased since 2010 as well.

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN FOR ROUND 4 OF THE GLSS A nationally representative sample of households was selected in order to achieve the survey objectives.

Sample Frame For the purposes of this survey the list of the 1984 population census Enumeration Areas (EAs) with population and household information was used as the sampling frame. The primary sampling units were the 1984 EAs with the secondary units being the households in the EAs. This frame, though quite old, was considered inadequate, it being the best available at the time. Indeed, this frame was used for the earlier rounds of the GLSS.

Stratification In order to increase precision and reliability of the estimates, the technique of stratification was employed in the sample design, using geographical factors, ecological zones and location of residence as the main controls. Specifically, the EAs were first stratified according to the three ecological zones namely; Coastal, Forest and Savannah, and then within each zone further stratification was done based on the size of the locality into rural or urban.

SAMPLE SELECTION EAs A two-stage sample was selected for the survey. At the first stage, 300 EAs were selected using systematic sampling with probability proportional to size method (PPS) where the size measure is the 1984 number of households in the EA. This was achieved by ordering the list of EAs with their sizes according to the strata. The size column was then cumulated, and with a random start and a fixed interval the sample EAs were selected.

It was observed that some of the selected EAs had grown in size over time and therefore needed segmentation. In this connection, such EAs were divided into approximately equal parts, each segment constituting about 200 households. Only one segment was then randomly selected for listing of the households.

Households At the second stage, a fixed number of 20 households was systematically selected from each selected EA to give a total of 6,000 households. Additional 5 households were selected as reserve to replace missing households. Equal number of households was selected from each EA in order to reflect the labour force focus of the survey.

NOTE: The above sample selection procedure deviated slightly from that used for the earlier rounds of the GLSS, as such the sample is not self-weighting. This is because, 1. given the long period between 1984 and the GLSS 4 fieldwork the number of households in the various EAs are likely to have grown at different rates. 2. the listing exercise was not properly done as some of the selected EAs were not listed completely. Moreover, it was noted that the segmentation done for larger EAs during the listing was a bit arbitrary.

Mode of data collection

Face-to-face [f2f]

The unemployment rate of the United Kingdom was 4.4 percent in January 2025, unchanged from the previous month. Before the arrival of the COVID-19 pandemic, the UK had relatively low levels of unemployment, comparable with the mid-1970s. Between January 2000 and the most recent month, unemployment was highest in November 2011 when the unemployment rate hit 8.5 percent. Will unemployment continue to rise in 2025? Although low by historic standards, there has been a noticeable uptick in the UK's unemployment rate, with other labor market indicators also pointing to further loosening. In December 2024, the number of job vacancies in the UK, fell to its lowest level since May 2021, while payrolled employment declined by 47,000 compared with November. Whether this is a continuation of a broader cooling of the labor market since 2022, or a reaction to more recent economic developments, such as upcoming tax rises for employers, remains to be seen. Forecasts made in late 2024 suggest that the unemployment rate will remain relatively stable in 2025, averaging out at 4.1 percent, and falling again to four percent in 2026.
Demographics of the unemployed As of the third quarter of 2024, the unemployment rate for men was slightly higher than that of women, at 4.4 percent, compared to 4.1 percent. During the financial crisis at the end of the 2000s, the unemployment rate for women peaked at a quarterly rate of 7.7 percent, whereas for men, the rate was 9.1 percent. Unemployment is also heavily associated with age, and young people in general are far more vulnerable to unemployment than older age groups. In late 2011, for example, the unemployment rate for those aged between 16 and 24 reached 22.3 percent, compared with 8.2 percent for people aged 25 to 34, while older age groups had even lower peaks during this time.

In February 2025, the unemployment rate for those aged 16 and over in the United States came to 4.5 percent. Service occupations had an unemployment rate of 6.3 percent in that month. The underemployment rate of the country can be accessed here and the monthly unemployment rate here. Unemployment by occupation in the U.S. The United States Bureau of Labor Statistics publish data on the unemployment situation within certain occupations in the United States on a monthly basis. According to latest data released from May 2023, transportation and material moving occupations experienced the highest level of unemployment that month, with a rate of around 5.6 percent. Second ranked was farming, fishing, and forestry occupations with a rate of 4.9 percent. Total (not seasonally adjusted) unemployment was reported at 3.6 percent in March 2023. Other data on the U.S. unemployment rate by industry and class of worker shows comparable results. It should be noted that the data were not seasonally adjusted to account for normal seasonal fluctuations in unemployment. The monthly unemployment by occupation data can be compared to the seasonally adjusted monthly unemployment rate. In March 2023, the seasonally adjusted unemployment rate was 3.5 percent, which was an increase from the previous month. The annual unemployment rate in 2022 was 3.6 percent, down from a high of 9.6 in 2010. Unemployment in the United States trended downward after the coronavirus pandemic, and is now experiencing consistently low rates - a sign of economic stability. Individuals who opt to leave the workforce and stop looking for employment are not included among the unemployed. The civilian labor force participation rate in the U.S. rose to 62.2 percent in 2022, down from 67.1 percent in 2000, before the financial crisis.

In 2022, 42.86 percent of the workforce in India were employed in agriculture, while the other half was almost evenly distributed among the two other sectors, industry and services. While the share of Indians working in agriculture is declining, it is still the main sector of employment. A BRIC powerhouseTogether with Brazil, Russia, and China, India makes up the four so-called BRIC countries. They are the four fastest-growing emerging countries dubbed BRIC, an acronym, by Jim O’Neill at Goldman Sachs. Being major economies themselves already, these four countries are said to be at a similar economic developmental stage -- on the verge of becoming industrialized countries -- and maybe even dominating the global economy. Together, they are already larger than the rest of the world when it comes to GDP and simple population figures. Among these four, India is ranked second across almost all key indicators, right behind China. Services on the riseWhile most of the Indian workforce is still employed in the agricultural sector, it is the services sector that generates most of the country’s GDP. In fact, when looking at GDP distribution across economic sectors, agriculture lags behind with a mere 15 percent contribution. Some of the leading services industries are telecommunications, software, textiles, and chemicals, and production only seems to increase – currently, the GDP in India is growing, as is employment.