Non Farm Payrolls in the United States increased by 73 thousand in July 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.

Nonfarm Payrolls present the number of new jobs created during the given month, in all non-agricultural sectors of the U.S. The indicator growth can have a positive effect on dollar quotes

In October 2024, employment in private education and health services increased by roughly 57,000 in the United States from September 2024. The data are seasonally adjusted. According to the BLS, the data is derived from the Current Employment Statistics (CES) program which surveys about 140,000 businesses and government agencies each month, representing approximately 440,000 individual worksites, in order to provide detailed industry data on employment.

United States Employment: sa: Non Agriculture: Wage & Salary Workers (WS) data was reported at 144,524.000 Person th in Jun 2018. This records an increase from the previous number of 144,124.000 Person th for May 2018. United States Employment: sa: Non Agriculture: Wage & Salary Workers (WS) data is updated monthly, averaging 89,797.000 Person th from Jan 1948 (Median) to Jun 2018, with 846 observations. The data reached an all-time high of 144,524.000 Person th in Jun 2018 and a record low of 42,690.000 Person th in Jul 1949. United States Employment: sa: Non Agriculture: Wage & Salary Workers (WS) data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G014: Current Population Survey: Employment: Seasonally Adjusted.

Graph and download economic data for All Employees: Total Nonfarm in California (CANA) from Jan 1990 to Jun 2025 about payrolls, nonfarm, CA, employment, and USA.

The Quarterly Census of Employment and Wages (QCEW) program (also known as ES-202) collects employment and wage data from employers covered by New York State's Unemployment Insurance (UI) Law. This program is a cooperative program with the U.S. Bureau of Labor Statistics. QCEW data encompass approximately 97 percent of New York's nonfarm employment, providing a virtual census of employees and their wages as well as the most complete universe of employment and wage data, by industry, at the State, regional and county levels. "Covered" employment refers broadly to both private-sector employees as well as state, county, and municipal government employees insured under the New York State Unemployment Insurance (UI) Act. Federal employees are insured under separate laws, but are considered covered for the purposes of the program. Employee categories not covered by UI include some agricultural workers, railroad workers, private household workers, student workers, the self-employed, and unpaid family workers. QCEW data are similar to monthly Current Employment Statistics (CES) data in that they reflect jobs by place of work; therefore, if a person holds two jobs, he or she is counted twice. However, since the QCEW program, by definition, only measures employment covered by unemployment insurance laws, its totals will not be the same as CES employment totals due to the employee categories excluded by UI.

Graph and download economic data for Employment Level - Agriculture and Related Industries, Wage and Salary Workers (LNU02032184) from Jan 1948 to Jun 2025 about agriculture, salaries, workers, 16 years +, wages, household survey, employment, industry, and USA.

The Quarterly Census of Employment and Wages (QCEW) program (also known as ES-202) collects employment and wage data from employers covered by New York State's Unemployment Insurance (UI) Law. This program is a cooperative program with the U.S. Bureau of Labor Statistics. QCEW data encompass approximately 97 percent of New York's nonfarm employment, providing a virtual census of employees and their wages as well as the most complete universe of employment and wage data, by industry, at the State, regional and county levels. "Covered" employment refers broadly to both private-sector employees as well as state, county, and municipal government employees insured under the New York State Unemployment Insurance (UI) Act. Federal employees are insured under separate laws, but are considered covered for the purposes of the program. Employee categories not covered by UI include some agricultural workers, railroad workers, private household workers, student workers, the self-employed, and unpaid family workers. QCEW data are similar to monthly Current Employment Statistics (CES) data in that they reflect jobs by place of work; therefore, if a person holds two jobs, he or she is counted twice. However, since the QCEW program, by definition, only measures employment covered by unemployment insurance laws, its totals will not be the same as CES employment totals due to the employee categories excluded by UI.

BackgroundWomen's empowerment is one critical pathway through which agriculture can impact women's nutrition; however, empirical evidence is still limited. We evaluated the associations of women's participation, input, and decision-making in key agricultural and household activities with women's diet quality.MethodsWe analyzed data from a cross-sectional study of 870 women engaged in homestead agriculture. We used food frequency questionnaires to assess women's diets and computed women's diet quality using the Prime Diet Quality Score (PDQS) (range 0–42), which captures healthy and unhealthy foods. We evaluated women's decision-making in 8 activities, food crop farming, cash crop farming, livestock raising, non-farm economic activities, wage/salary employment, fishing, major household expenditures, and minor household expenditures. Generalized estimating equations (GEE) linear models were used to evaluate associations between (a) women's participation, (b) decision-making, (c) adequate input, (d) adequate extent of independence in decision-making in agriculture, and (e) adequate input in use of agricultural income with their PDQS. Adequate input was defined as input into some, most or all decisions compared to input into few decisions or none. Adequate extent of independence was defined as input to a medium or high extent compared to input to a small extent or none.FindingsMedian PDQS was 19 (IQR: 16–21). Women's adequate input in decision-making on wage and salary employment (estimate: 4.19, 95% CI: 2.80, 5.57) and minor expenditures were associated with higher PDQS vs. inadequate input. Women with independence in decision-making on livestock production (estimate: 0.97, 95% CI: 0.05, 1.90) and minor household expenditures, and women with adequate decision-making in the use of income from wages/salaries (estimate: 3.16, 95% CI: 2.44, 3.87) had higher PDQS. Participation in agricultural activities was positively associated with PDQS.ConclusionsWomen's participation and input in decision-making in wage and salary employment, livestock production, and minor household expenditures were strongly associated with the consumption of better-quality diets. Women participating in multiple farm activities were also likely to have better diet quality. This study adds to the growing evidence on the pathways through which women's empowerment may influence women's nutrition in rural Tanzania.

The US crop services industry is currently navigating a period of growth in response to several key market dynamics, particularly within the agricultural sector. The rising demand for organic crops, driven by consumers seeking sustainable, chemical-free food options, is increasing revenue for service providers offering specialized support for organic farming practices. Meanwhile, in the broader crop market, there are mixed impacts. Wheat prices have seen an upward trend due to reduced yields in the EU and export restrictions from Russia, prompting wheat growers to increase investment in soil preparation and crop spraying services, thereby boosting demand. Conversely, the crop markets for corn and soybeans have faced pressure from increased production in Brazil, pressuring prices and encouraging growers to save on costs, tempering otherwise solid service revenue growth. Overall, industry revenue has increased at a CAGR of 0.1% in the current period, reaching $36.0 billion after a drop of 2.1% in 2025. Labor costs significantly influence the crop services industry, as agricultural wages have outpaced those in non-farm sectors due to a shortage of skilled workers. This increase in labor expenses, compounded by restrictive immigration policies, poses a challenge to maintaining profitability. Although revenue has risen, profit has declined as many service providers find it difficult to transfer rising wages and high purchase costs to their clients, who are themselves contending with reduced crop receipts. The pressure of keeping service prices competitive amid rising operational costs is forcing providers to implement cost-control measures such as mechanization and worker training programs to sustain profitability and continue delivering essential services to the agricultural sector. Looking ahead, the crop services industry is bracing for a period of revenue declines amid challenges in sustaining profit. With record-level crop yields forecasted through 2025, there will be increased opportunities for agricultural services to enhance harvesting efficiency and optimize yields. However, these production gains will also push crop prices downwards due to heightened global stock levels, greatly constraining farmers' spending on industry services and leading to declining revenues. Beyond 2025, planted acreage is expected to taper off, though crop prices will remain low as well, depressed by increasing international competition. Additionally, climate change and sustainability initiatives are expected to play critical roles in providing new sources of demand for adaptive and resilient farming solutions. Service providers focusing on innovation and aligning with these emerging needs—particularly within sustainable practices—can position themselves as essential partners and better weather the negative effects that dropping crop prices will have. Industry revenue is estimated to decrease at a CAGR of 1.6% to reach $33.3 billion in 2030.

EnhancedHousingMarketData.csv is an auxiliary dataset for the "Housing Prices" competition, containing key economic and demographic indicators vital for real estate market analysis. It includes data on non-farm employment, housing price index, per capita income, total quarterly wages, quantitative indexes of real GDP, total GDP, real GDP, stable population, employed individuals, and the average weekly wage in the private sector, along with the unemployment rate. This dataset aids in better understanding the factors influencing housing prices and allows for a more in-depth analysis of the real estate market.

"**TotalNonfarmEmployees**" - reflects the total number of employees working outside the agricultural sector. This figure includes workers in industries such as manufacturing, construction, trade, transportation, education, healthcare, and other non-agricultural sectors, making it a key indicator of economic activity and employment in the region.

"**HousingPriceIndex**" - represents a housing price index, reflecting changes in real estate prices in a specific region for a given month. This index can be used to analyze trends in the real estate market and assess the overall economic conditions.

"**AnnualPerCapitaIncome**" - represents the annual per capita income, measured yearly. This indicator reflects the average income per resident in a specific region over a year, serving as an important measure of the population's economic well-being.

"**QuarterlyTotalWages**" - represents the total quarterly wages, measured in dollars and adjusted for seasonal variations. This metric reflects the sum of wages paid by employers insured for unemployment insurance over a calendar quarter. It includes components such as vacation pay, bonuses, and tips.

"**TotalRealGDPChainIndex**" - represents the total annual quantitative index of real GDP, encompassing data from all private sectors and the government. It is based on the Fisher chain-weighted method, tracking changes in production volume or expenditures while eliminating the effects of price changes. This index is useful for comparing the volumes of production or expenditures across different time periods.

"**TotalGDP**" - describes the total Gross Domestic Product (GDP), measured in millions of dollars and calculated annually without seasonal adjustments. This metric encompasses all private sectors and the government, reflecting the market value of all final goods and services produced within an agglomeration. The agglomeration GDP represents the gross output minus intermediate costs, serving as a key indicator of economic activity and production volume.

"**TotalRealGDP**" - represents the total real Gross Domestic Product, measured in millions of chained 2012 dollars and calculated annually without seasonal adjustments. This metric includes data from all private sectors and the government. The real GDP for agglomerations is a measure of the gross product of each agglomeration, adjusted for inflation, and based on national prices for goods and services produced in the agglomeration.

"**StablePopulation**" - reflects the stable population, measured in thousands of people and calculated annually without seasonal adjustments. This metric represents population estimates as of July 1st each year, providing reliable data for analyzing demographic trends and planning purposes.

"**EmployedIndividuals**" - represents the number of employed individuals, measured in persons without seasonal adjustment and updated monthly. The data are derived from the Current Population Survey (CPS). Employed individuals include those who did any paid work, owned a business or farm, worked 15 hours or more as unpaid workers in a family business, or were temporarily absent from their job for various reasons. This metric is important for analyzing employment levels and the economic activity of the population.

"**AverageWeeklyWagePrivate**" - denotes the average weekly wage of private enterprise employees, measured in dollars per week and calculated quarterly without seasonal adjustment. It includes payments made by employers insured against unemployment over the quarter, encompassing vacation pay, bonuses, stock options, tips, and other components. This metric is important for assessing the level of wages in the private sector.

"**UnemploymentRate**" - represents the unemployment rate, measured in percentages and calculated monthly without seasonal adjustments. This metric indicates the proportion of the unemployed within the total labor force, providing key information about the labor market's condition and the population's economic activity.

In the fourth quarter of 2023, paid workers and employees in rural Vietnam earned on average around *** million Vietnamese dong per month. Meanwhile, the average monthly salary for paid workers and employees in urban Vietnam was higher, at approximately *** million Vietnamese dong. Regional income disparity Income inequality in Vietnam is reflected in the noticeable pay gap between urban and rural areas. This urban-rural disparity can be attributed to various determinants, including the salary gap between agricultural and non-farm sectors in Vietnam, as agriculture remains the backbone of the Vietnamese rural economy. Although the country has experienced positive economic growth and decreasing poverty rate within the last few years, the higher poverty rate in rural areas suggests that more can be done to tackle inequality between rural and urban Vietnam. COVID-19 and the Vietnamese labor market Vietnam’s effective response and success in the fight against the coronavirus came at the expense of its economy. In 2021, Vietnam’s unemployment rate reached *** percent, being the highest value recorded within the last decade, with many companies forced to lay off employees during the outbreak. Nevertheless, the labor market in Vietnam is expected to recover, as the Vietnamese economy remains resilient and is expected to grow again post COVID-19.

Graph and download economic data for Employed full time: Wage and salary workers: Farmers, ranchers, and other agricultural managers occupations: 16 years and over: Women (LEU0257853400A) from 2011 to 2024 about farmers, management, occupation, females, full-time, agriculture, salaries, workers, 16 years +, wages, employment, and USA.

Gross Value Added: Non Profit Institutions Serving Households: Agriculture: Compensation of Employees: Wages and Salaries在2016达0.000 BRL mn，相较于2015的0.000 BRL mn保持不变。Gross Value Added: Non Profit Institutions Serving Households: Agriculture: Compensation of Employees: Wages and Salaries数据按每年更新，2010至2016期间平均值为0.000 BRL mn，共7份观测结果。该数据的历史最高值出现于2016，达0.000 BRL mn，而历史最低值则出现于2016，为0.000 BRL mn。CEIC提供的Gross Value Added: Non Profit Institutions Serving Households: Agriculture: Compensation of Employees: Wages and Salaries数据处于定期更新的状态，数据来源于Brazilian Institute of Geography and Statistics，数据归类于Brazil Premium Database的National Accounts – Table BR.AC012: SNA 2008: Gross Value Added: by Institutional Sector: Non Profit Institutions。

The footnotes in the table are represented in brackets.Footnotes:1 Earnings or employment income - Refers to total income received by persons aged 15 years and over during calendar year 2010 as wages and salaries, net income from a non-farm unincorporated business and/or professional practice, and/or net farm self-employment income.Wages and salaries - Refers to gross wages and salaries before deductions for such items as income tax, pensions and Employment Insurance. Included in this source are military pay and allowances, tips, commissions and cash bonuses, benefits from wage-loss replacement plans or income-maintenance insurance plans, supplementary unemployment benefits from an employer or union as well as all types of casual earnings during calendar year 2010. Other employment income such as taxable benefits, research grants and royalties are included.Net non-farm income from unincorporated business and/or professional practice - Refers to net income (gross receipts minus expenses of operation such as wages, rents and depreciation) received during calendar year 2010 from the respondent's non-farm unincorporated business or professional practice. In the case of partnerships, only the respondent's share was reported. Also included is net income from persons babysitting in their own homes, persons providing room and board to non-relatives, self-employed fishers, hunters and trappers, operators of direct distributorships such as those selling and delivering cosmetics, as well as freelance activities of artists, writers, music teachers, hairdressers, dressmakers, etc.Net farm income - Refers to net income (gross receipts from farm sales minus depreciation and cost of operation) received during calendar year 2010 from the operation of a farm, either on the respondent's own account or in partnership. In the case of partnerships, only the respondent's share of income was reported. Included with gross receipts are cash advances received in 2010, dividends from cooperatives, rebates and farm-support payments to farmers from federal, provincial and regional agricultural programs (for example, milk subsidies and marketing board payments) and gross insurance proceeds such as payments from the AgriInvest and AgriStability programs. The value of income 'in kind,' such as agricultural products produced and consumed on the farm, is excluded.Median income of individuals - The median income of a specified group of income recipients is that amount which divides their income size distribution, ranked by size of income, into two halves, i.e., the incomes of the first half of individuals are below the median, while those of the second half are above the median. Median income is calculated from the unrounded number of individuals (e.g., males aged 45 to 54) with income in that group.Average income of individuals - Average income of individuals refers to the weighted mean total income of individuals aged 15 years and over who reported income for 2010. Average income is calculated from unrounded data by dividing the aggregate income of a specified group of individuals (e.g., males aged 45 to 54) by the number of individuals with income in that group.The above concept and procedures also apply in the calculation of these statistics for earnings.2 For population with employment income.3 For population with employment income.4 For population with wages and salaries.5 For population with wages and salaries.6 Refers to the first language learned at home in childhood and still understood by the individual on May 10, 2011.

Graph and download economic data for Employed full time: Wage and salary workers: Farming, fishing, and forestry occupations: 16 years and over (LEU0254504100A) from 2000 to 2024 about forestry, fishing, occupation, full-time, agriculture, salaries, workers, 16 years +, wages, employment, and USA.

Graph and download economic data for Employed full time: Wage and salary workers: Farming, fishing, and forestry occupations: 16 years and over (LEU0254504100Q) from Q1 2000 to Q2 2025 about forestry, fishing, occupation, full-time, agriculture, salaries, workers, 16 years +, wages, employment, and USA.

Graph and download economic data for Durable Goods: Agriculture, Construction, and Mining Machinery Manufacturing Payroll Employment in Texas (TX31333100A674FRBDAL) from 1991 to 2024 about payrolls, agriculture, machinery, mining, durable goods, construction, goods, TX, manufacturing, employment, rate, and USA.

Graph and download economic data for Employment Cost Index: Wages and salaries for Private industry workers in Construction, extraction, farming, fishing, and forestry occupations (CIU2020000405000I) from Q1 2001 to Q2 2025 about forestry, fishing, extraction, ECI, occupation, agriculture, salaries, workers, private industries, construction, wages, private, industry, and USA.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Farming, fishing, and forestry occupations: 16 years and over (LEU0254557500A) from 2000 to 2024 about forestry, fishing, second quartile, occupation, full-time, agriculture, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.