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Non Farm Payrolls in the United States increased by 147 thousand in June of 2025. This dataset provides the latest reported value for - United States Non Farm Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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Private businesses in the United States fired -33 thousand workers in June of 2025 compared to 29 thousand in May of 2025. This dataset provides the latest reported value for - United States ADP Employment Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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Non Farm Payrolls in Canada increased by 18215 thousand in April of 2025. This dataset provides - Canada Non Farm Payrolls- actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Graph and download economic data for Job Openings: Total Nonfarm (JTSJOL) from Dec 2000 to May 2025 about job openings, vacancy, nonfarm, and USA.
In October 2024, the average working week for all employees on private nonfarm payrolls in the United States was at 34.3 hours. This includes part-time workers. The data have been seasonally adjusted. Employed persons consist of all employees on private nonfarm payrolls. U.S. working week As in most industrialized countries, the standard work week in the United States begins on Monday and ends on Friday. According to data released by the Bureau of Labor Statistics, the average workweek for all employees (including part-time) working in private industries in the United States amounted to about 34.5 hours in 2022. Over the course of one month, the U.S. workforce works about 3.9 billion hours in total.The average work week can differ heavily from industry to industry. An employee in the mining and logging industry worked about 45.5 hours a week in April 2023, while employees in private education and health services worked for an average of 33.4 hours per week.
In October 2024, the average hourly earnings for all employees on private nonfarm payrolls in the United States stood at 35.46 U.S. dollars. The data have been seasonally adjusted. Employed persons are employees on nonfarm payrolls and consist of: persons who did any work for pay or profit during the survey reference week; persons who did at least 15 hours of unpaid work in a family-operated enterprise; and persons who were temporarily absent from their regular jobs because of illness, vacation, bad weather, industrial dispute, or various personal reasons.
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Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CES0500000003) from Mar 2006 to Jun 2025 about earnings, average, establishment survey, hours, wages, private, employment, and USA.
China Living Standards Survey (CLSS) consists of one household survey and one community (village) survey, conducted in Hebei and Liaoning Provinces (northern and northeast China) in July 1995 and July 1997 respectively. Five villages from each three sample counties of each province were selected (six were selected in Liaoyang County of Liaoning Province because of administrative area change). About 880 farm households were selected from total thirty-one sample villages for the household survey. The same thirty-one villages formed the samples of community survey. This document provides information on the content of different questionnaires, the survey design and implementation, data processing activities, and the different available data sets.
The China Living Standards Survey (CLSS) was conducted only in Hebei and Liaoning Provinces (northern and northeast China).
Sample survey data [ssd]
The CLSS sample is not a rigorous random sample drawn from a well-defined population. Instead it is only a rough approximation of the rural population in Hebei and Liaoning provinces in Northeastern China. The reason for this is that part of the motivation for the survey was to compare the current conditions with conditions that existed in Hebei and Liaoning in the 1930’s. Because of this, three counties in Hebei and three counties in Liaoning were selected as "primary sampling units" because data had been collected from those six counties by the Japanese occupation government in the 1930’s. Within each of these six counties (xian) five villages (cun) were selected, for an overall total of 30 villages (in fact, an administrative change in one village led to 31 villages being selected). In each county a "main village" was selected that was in fact a village that had been surveyed in the 1930s. Because of the interest in these villages 50 households were selected from each of these six villages (one for each of the six counties). In addition, four other villages were selected in each county. These other villages were not drawn randomly but were selected so as to "represent" variation within the county. Within each of these villages 20 households were selected for interviews. Thus the intended sample size was 780 households, 130 from each county.
Unlike county and village selection, the selection of households within each village was done according to standard sample selection procedures. In each village, a list of all households in the village was obtained from village leaders. An "interval" was calculated as the number of the households in the village divided by the number of households desired for the sample (50 for main villages and 20 for other villages). For the list of households, a random number was drawn between 1 and the interval number. This was used as a starting point. The interval was then added to this number to get a second number, then the interval was added to this second number to get a third number, and so on. The set of numbers produced were the numbers used to select the households, in terms of their order on the list.
In fact, the number of households in the sample is 785, as opposed to 780. Most of this difference is due to a village in which 24 households were interviewed, as opposed to the goal of 20 households
Face-to-face [f2f]
Household Questionnaire
The household questionnaire contains sections that collect data on household demographic structure, education, housing conditions, land, agricultural management, household non-agricultural business, household expenditures, gifts, remittances and other income sources, and saving and loans. For some sections (general household information, schooling, housing, gift-exchange, remittance, other income, and credit and savings) the individual designated by the household members as the household head provided responses. For some other sections (farm land, agricultural management, family-run non-farm business, and household consumption expenditure) a member identified as the most knowledgeable provided responses. Identification codes for respondents of different sections indicate who provided the information. In sections where the information collected pertains to individuals (employment), whenever possible, each member of the household was asked to respond for himself or herself, except that parents were allowed to respond for younger children. Therefore, in the case of the employment section it is possible that the information was not provided by the relevant person; variables in this section indicate when this is true.
The household questionnaire was completed in a one-time interview in the summer of 1995. The survey was designed so that more sensitive issues such as credit and savings were discussed near the end. The content of each section is briefly described below.
Section 0 SURVEY INFORMATION
This section mainly summarizes the results of the survey visits. The following information was entered into the computer: whether the survey and the data entry were completed, codes of supervisor’s brief comments on interviewer, data entry operator, and related revising suggestion (e.g., 1. good, 2. revise at office, and 3. re-interview needed). Information about the date of interview, the names of interviewer, supervisor, data enterer, and detail notes of interviewer and supervisor were not entered into the computer.
Section 1 GENERAL HOUSEHOLD INFORMATION
1A HOUSEHOLD STRUCTURE 1B INFORMATION ABOUT THE HOUSEHOLD MEMBERS’ PARENTS 1C INFORMATION ABOUT THE CHILDREN WHO ARE NOT LIVING IN HOME
Section 1A lists the personal id code, sex, relationship to the household head, ethnic group, type of resident permit (agricultural [nongye], non-agricultural [fei nongye], or no resident permit), date of birth, marital status of all people who spent the previous night in that household and for household members who are temporarily away from home. The household head is listed first and receives the personal id code 1. Household members were defined to include “all the people who normally live and eat their meals together in this dwelling.” Those who were absent more than nine of the last twelve months were excluded, except for the head of household. For individuals who are married and whose spouse resides in the household, the personal id number of the spouse is noted. By doing so, information on the spouse can be collected by appropriately merging information from the section 1A and other parts of the survey.
Section 1B collects information on the parents of all household members. For individuals whose parents reside in the household, parents’ personal id numbers are noted, and information can be obtained by appropriately merging information from other parts of the survey. For individuals whose parents do not reside in the household, information is recorded on whether each parent is alive, as well as their schooling and occupation.
Section 1C collects information for children of household members who are not living in home. Children who have died are not included. The information on the name, sex, types of resident permit, age, education level, education cost, reasons not living in home, current living place, and type of job of each such child is recorded.
Section 2 SCHOOLING
In Section 2, information about literacy and numeracy, school attendance, completion, and current enrollment for all household members of preschool age and older. The interpretation of pre-school age appears to have varied, with the result that while education information is available for some children of pre-school age, not all pre-school children were included in this section. But for ages 6 and above information is available for nearly all individuals, so in essence the data on schooling can be said to apply all persons 6 age and above. For those who were enrolled in school at the time of the survey, information was also collected on school attendance, expenses, and scholarships. If applicable, information on serving as an apprentice, technical or professional training was also collected.
Section 3 EMPLOYMENT
3A GENERAL INFORMATION 3B MAJOR NON-FARM JOB IN 1994 3C THE SECOND NON-FARM JOB IN 1994 3D OTHER EMPLOYMENT ACTIVITIES IN 1994 3E SEARCHING FOR NON-FARM JOB 3F PROCESS FOR GETTING MAJOR NON-FARM JOB 3G CORVEE LABOR
All individuals age thirteen and above were asked to respond to the employment activity questions in Section 3. Section 3A collects general information on farm and non-farm employment, such as whether or not the household member worked on household own farm in 1994, when was the last year the member worked on own farm if he/she did not work in 1994, work days and hours during busy season, occupation and sector codes of the major, second, and third non-farm jobs, work days and total income of these non-farm jobs. There is a variable which indicates whether or not the individual responded for himself or herself.
Sections 3B and 3C collect detailed information on the major and the second non-farm job. Information includes number of months worked and which month in 1994 the member worked on these jobs, average works days (or hours) per month (per day), total number of years worked for these jobs by the end of 1994, different components of income, type of employment contracts. Information on employer’s ownership type and location was also collected.
Section 3D collects information on average hours spent doing chores and housework at home every day during non-busy and busy season. The chores refer to cooking, laundry, cleaning, shopping, cutting woods, as well as small-scale farm yard animals raising, for example, pigs or chickens. Large-scale animal
In the United States, the average working week for all employees on private nonfarm payrolls was at 34.3 hours in October 2024. The data have been seasonally adjusted. Employed persons consist of all employees on private nonfarm payrolls.
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Graph and download economic data for Average Weekly Hours of All Employees, Total Private (AWHAETP) from Mar 2006 to Jun 2025 about establishment survey, hours, private, employment, and USA.
This statistic shows the annual average of the length of a working week in the United States, for all employees from 2007 to 2023. Employed persons consist of all employees on private nonfarm payrolls. The average working week for all employees on private nonfarm payrolls was at 34.4 hours in 2023.
The recent global economic slowdown, caused by the COVID-19 pandemic, created an urgent need for timely data to monitor the socioeconomic impacts of the pandemic. Tanzania is among other countries in the world which are affected by the recent global economic slowdown, caused by the COVID-19 pandemic. Therefore, there is an urgent need for timely data to monitor and mitigate the socio-economic impacts of the crisis in the country. Responding to this need, the National Bureau of Statistics (NBS) and the Office of the Chief Government Statistician (OCGS), Zanzibar in collaboration with the World Bank and Research on Poverty Alleviation (REPOA) implemented a rapid household telephone survey called the Tanzania High-Frequency Welfare Monitoring Survey (HFWMS).
Thus, the main objective of the survey is to obtain timely data that is critical for evidence-based decision making aimed at mitigating the socio-economic impact of the downturn caused by COVID-19 pandemic by filling critical gaps of information that can be used by the government and stakeholders to help design policies to mitigate the negative impacts on its population.
National
Households Individuals
The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.
Sample survey data [ssd]
Phase one of the Tanzania High Frequency Welfare Monitoring Panel Survey (THFWMPS I) draws its sample from various previous face-to-face surveys, including the Mainland Household Budget Survey (HBS) 2017/18, the Zanzibar HBS 2019/20, and the National Panel Survey (NPS) 2014. The inclusion of telephone numbers from most participants of these surveys provides the foundation for the survey sample.
The target for monthly sample completion is approximately 3,000 households. The NPS serves as the primary sample frame, supplemented by the Mainland and Zanzibar HBS. For THFWMPS Phase II, the sample frame comprises respondents from Phase I who did not explicitly refuse to participate (2,200 households), alongside additional households from the 2021 Booster sample of NPS Wave 5 (NPS 5) households with available phone numbers.
Computer Assisted Personal Interview [capi]
Each survey round consists of one questionnaire - a Household Questionnaire administered to all households in the sample.
Baseline The questionnaire gathers information on demographics; employment; education; access to basic services; food security; TASAF; and mental health. The contents of questionnaire are outlined below:
Round 2 The questionnaire gathers information on demographics; employment; non-farm enterprise; tourism; education; access to health services; and TASAF. The contents of questionnaire are outlined below:
Round 3 The questionnaire gathers information on demographics; employment (respondent and other household members); non-farm enterprise; credit; women savings; and shocks and coping. The contents of questionnaire are outlined below:
Round 4 The questionnaire gathers information on demographics; employment; non-farm enterprise; digital technology; and income changes. The contents of questionnaire are outlined below:
Round 5 The questionnaire gathers information on demographics;
The seasonally-adjusted national unemployment rate is measured on a monthly basis in the United States. In February 2025, 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.
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Average Weekly Hours in the United States decreased to 34.20 Hours in June from 34.30 Hours in May of 2025. This dataset provides - United States Average Weekly Hours - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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The intents of small and secondary occupation farmers regarding development of their companies as well as readiness for retraining in agricultural families.
Topics: Family members involved in non-agricultural employment and classification according to primary and secondary occupational activities; family members helping; intended change of occupation and activity sought; conditions for which one would be ready for retraining; education sought for children, differentiating according to agricultural and non-agricultural occupations; reasons for the occupational goals; company size; readiness to give up the agricultural company or reduce its size on change of occupation; contacts with the employment office regarding personal retraining or search for work and for vocational training of the children; daily working hours; means of transport used.
Demography: age; sex; occupation; employment; size of household.
In 2023, 43.51 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.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Non Farm Payrolls in the United States increased by 147 thousand in June of 2025. This dataset provides the latest reported value for - United States Non Farm Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.