Graph and download economic data for Percent of Employment in Agriculture in the United States (DISCONTINUED) (USAPEMANA) from 1970 to 2012 about agriculture, percent, employment, and USA.

In South Korea in 2023, approximately 2.4 million people were working in the agricultural industry. The majority of these were farmers and families, with people in forestry and fishery numbering slightly over 290 thousand. There were just little less than one million farming households.

Shrinking number of farmers

The development of manufacturing and high-tech industries in Korea meant the number of people involved in agriculture, forestry, and fishing has been dropping steadily for years. The mainstay of the economy became automobiles, ships, semiconductors, petroleum products, and so on. People in agriculture accounted for approximately five percent of the total population. This rate saw a steady decrease and is expected to fall further. The aging of current farmers and the industry’s unpopularity among younger generations is also having a negative effect. Youths prefer better paying and less physical strenuous occupations in companies or government service, among others. Around half of all farmers are more than 65 years old; this is the typical retirement age in Korea.

Possibilities for growth of Korean agriculture

In recent years, an increased focus on healthy eating and the introduction of technology into agriculture have created the potential for further industry growth. Koreans today are showing greater concern with the food they consume, whether they be grains, vegetables, or meat products. Organic farming and so-called ‘wellbeing’ foods are popular, despite the higher price tags associated with such products, leading to a growing market for organic agriculture and food products.

Employment in agriculture (% of total employment) (modeled ILO estimate) in United States was reported at 1.5696 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Employment in agriculture (% of total employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

In 2023, the employment in the agricultural sector as share of total employment in Argentina decreased by 0.04 percentage points (-6.15 percent) compared to 2022. Employment in agriculture is the share of individuals working in agriculture, hunting, forestry, and fishing in order to produce a good or service for profit or pay from the total employed. The data covers people working in a certain period, or not working as a result of being temporarily absent from a job, or in a working-time arrangement.Find more key insights for the employment in the agricultural sector as share of total employment in countries like Chile and Paraguay.

Employment in agriculture (% of total employment) (modeled ILO estimate) in France was reported at 2.5054 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. France - Employment in agriculture (% of total employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Data Series: Percentage distribution of employed population in agricultural sector, by sex Indicator: I.6 - Percentage distribution of employed population by sector, each sex (Sectors here refer to Agriculture; Industry; Services) Source year: 2023 This dataset is part of the Minimum Gender Dataset compiled by the United Nations Statistics Division. Domain: Economic structures, participation in productive activities and access to resources

Abstract

The Census of Agriculture & Fisheries (AGC 2011) is a national government operation geared towards the collection and compilation of statistics in the agriculture sector of the country. The collected data will constitute the bases from which policymakers and planners will formulate plans for the country's development.

The first Census of Agriculture (CoA) in the Cook Islands was conducted in 1988 and the second in 2000. Both censuses were supported technically by FAO. The Cook Islands also has a long history of population census taking at 5-yearly intervals in years ending in 1 and 6. Traditionally the Census of Population and Dwellings (CoPD) has included questions on agricultural activity at the household level, types of crops grown, livestock numbers, farm machinery and involvement in fishing and pearl farming activities. Section 3 of this report looks at data collected in the CoPD 2011 related to agricultural, fishing and pearl farming activities

Geographic coverage

National coverage.

Analysis unit

Household; Holding; Parcel; Individual.

Universe

The census covered all households, agricultural operators, agricultural establishments, fishing operators and pearl farmers.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The census of population and dwellings had 4 categories of agricultural activity, namely: subsistence only, commercial only, subsistence and commercial and no agriculture. For those engaged in agricultural activity a further breakdown was collected, namely: vegetables, fruit, flowers and other. The census of agriculture also had 4 categories but for crop growing only, namely, non-agricultural, minor agricultural, subsistence and commercial. The differences in these classifications and the types of agriculture included make comparisons difficult, however, it is useful to evaluate the two sets of data and draw conclusions as to the extent of agricultural activity in the cook islands from these two sources.

The questionnaires used for the census of agriculture 2000 and the census of population and dwellings 2006, related to agriculture, were reviewed and efforts made to avoid duplication. In particular, the question on the numbers of livestock kept by the household was dropped from the census of population and dwellings as this data was being collected in the census of agriculture. Likewise, information on machinery and equipment was dropped from the census of agriculture as this was being collected in the census of population and dwelling. Questions on the extent of involvement in agricultural activity at the household level were maintained in both censuses as was the extent of involvement in fishing and pearl farming. This provided a useful coverage check for the census of agriculture, in particular, although it was noted that there were definitional differences between the two censuses especially related to flower cultivation which was considered an agricultural activity in the census of population and dwellings but not in the census of agriculture. At the individual level, data on labour inputs was recorded in the census of agriculture by age and sex but other data at the individual level has then to be obtained through linkages to the census of population and dwellings through the person and household number.

The household questionnaire was administered in each household, which collected various information on levels of agricultural activity, holdings detail (including name of operator, total area, number of separate parcels, location), crops currently growing and/or harvested (including crops currently growing, total area, number of plants,crops planted and/or harvested, total area, number of plants), proportion of income from agriculture, loans for agriculture purposes, fertilizers, agricultural chemicals, improved varieties, other selected activities during the last 12 months (including bee keeping, hydroponic, floriculture, handicrafts), traditional methods on food storage and planting, travelling with locally grown food, water usage

In addition to a household questionnaire, questions were administered in each household for holding which collected various information on holding iidentification, parcel details during the lasts 12 months (including location, area, land tenure, land use, months used), scattered plants/trees (including number of plants), labour input for persons 15 years and over working during the last month (including sex, age, status, type, average hours worked per week, wages per month, benefits and other paid job)

In addition to a holding questionnaire, questions were administered for parcels which collected various information (during the last 12 months) on plot details (including proportion to parcel area, crops grown, method of planting, number of plants and proportion for sale), crops planted and harvested (including area harvested, number of plants and proportion for sale)

In addition to a household questionnaire, questions were administered in each household for livestock which collected various information on type and number of livestock, type of operation, nature of disposal during the last 12 months (including kind of livestock, number disposed (including home use, feast/gifts, sold, slaughtered, live)

In addition to a household questionnaire, questions were administered in each household for fishing which collected various information on household members engaged, main purpose of fishing activity, household members (including average hours spent per week), details of fishing activities (including forms of fishing, number of people fishing, location, average number of fishing trips, average hours per fishing trip), boat details (including type of boat, length, engine), proportion of fish caught/collected and sold, proportion consumed

In addition to a household questionnaire, questions were administered in each household for pearl farming which collected various information (during the last 12 months) on farming details (including farm lines, spat collector lines, spat details, number of farm shells, labour input (including person number, sex, age, status, type, average hours worked per week, wages per month, benefits received, other paid job) , boat operation (including times used per week), type of equipment and facility, number of times per week, number owned, hired, borrowed), shelling details, proportion of income, loan details

The questionnnaires, that were developed in English, contain was divided into 5 forms: -Household Form: Levels of agricultural activity, List of agricultural holdings, Crops, Income from agricultural activities, Loans, Fertilizers, Other relevant questions. -Holding Form: Parcel details, Scattered plants/trees, Labour inputs. -Parcel Form: Number of sepearate plots, Plot details, Crops. -Livestock Form: Livestock details, Type of operation, Nature of disposal. -Fishing & Pearl Farming Form: Fisheries activities details, Pearl farm information, Labour inputs, Boats and other equipment used, Other relevant information.

Cleaning operations

The length and complexity of the census of agriculture forms made the exercise much more time consuming and virtually all records had to be edited. The data capture and data cleaning exercise for the census of agriculture took the best part of 12 months, including the adjustments following the re-enumeration of Aitutaki. Tabulation also proved to be challenging because of the need for considerable internal computation of areas and numbers of plants. The final database was then split up into a number of smaller databases designed for each set of tables. The tabulation was done using Microsoft EXCEL and ACCESS

In interpreting the results of the census of agriculture, account needs to be taken of the fact that households classified as having no agricultural or fishing activities in the census of population and dwellings were excluded from the census of agriculture, especially on Rarotonga. Other definitional differences between the two censuses should also be noted. The census of population and dwellings defined agricultural activity as crops, livestock and floriculture whereas the ensus of agriculture definition was primarily crops. Livestock and poultry raising was treated separately in the census of agriculture and flower growing was only included in the census of agriculture if it was a commercial activity or was carried out in conjunction with food crop activities.

In 2023, the employment in the agricultural sector as share of total employment in Brazil decreased by 0.5 percentage points (-5.73 percent) compared to 2022. The share thereby reached its lowest value in recent years. Employment in agriculture is the share of individuals working in agriculture, hunting, forestry, and fishing in order to produce a good or service for profit or pay from the total employed. The data covers people working in a certain period, or not working as a result of being temporarily absent from a job, or in a working-time arrangement.Find more statistics on other topics about Brazil with key insights such as share of value added by the agriculture, forestry and fishing sector to the gross domestic product.

Employment in agriculture (% of total employment) (modeled ILO estimate) in Japan was reported at 3.0051 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Japan - Employment in agriculture (% of total employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Employment in agriculture (% of total employment) (modeled ILO estimate) in China was reported at 22.33 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. China - Employment in agriculture (% of total employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Employment in agriculture (% of total employment) (modeled ILO estimate) in Bangladesh was reported at 35.27 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Bangladesh - Employment in agriculture (% of total employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on May of 2025.

Employment in agriculture (% of total employment) (modeled ILO estimate) in Thailand was reported at 30.11 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Thailand - Employment in agriculture (% of total employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Data Series: Percentage distribution of employed population in agricultural sector, by sex Indicator: I.8 - Percentage distribution of employed population by sector, each sex (Sectors here refer to Agriculture; Industry; Services) Source year: 2022 This dataset is part of the Minimum Gender Dataset compiled by the United Nations Statistics Division. Domain: Economic structures, participation in productive activities and access to resources

Abstract

The 2009 Agricultural Census was undertaken by the Samoa Bureau of Statistics in collaboration with the Ministry of Agriculture and Fisheries. The Census collected a large volume of information pertaining to the agricultural activities of households. Enumeration was carried out for 5 weeks in November/December 2009 by enumerators selected from the villages through interview and a basic test. The test included basic mathematical skills, knowledge of agricultural practices and map reading. This was to ensure that the enumerators are of high quality. The officers of the Samoa Bureau of Statistics and the Ministry of Agriculture and Fisheries were allocated to specified areas as supervisors.

Geographic coverage

National

Analysis unit

Households (Agricultural and non-Agricultural) Agricultural Holdings

Kind of data

Census/enumeration data [cen]

Sampling procedure

For any census to be successfully carried out, good household lists and enumeration area maps are pre-requisites. A list of households in respect of each enumeration block in the country was prepared in 2005 for the 2006 Population Census. The updated household list from the 2006 Population Census was used as a frame for the Agricultural Census.

Mode of data collection

Face-to-face [f2f]

Research instrument

The methodology for carrying out the census of Agriculture in Samoa was a combination of complete count and sample survey. Thus the census was basically two part operation. The first part involved all households who were required to complete the Household Form. The households identified as agriculturally active from the Household Forms (Subsistence, Subsistence and Cash and Commercial) were required to complete the Holding Form for every holding operated.

The second part of the questionnaire was designed to cover 25 percent of all agricultural holdings as identified in the first part, with selection made on systematic sample basis (every fourth holding selected). Thus while the Household Form was canvassed in respect of all households, the Holding Form was to be completed by agriculturally active Households only and the Parcel Form was completed in respect of 25 percent of the agricultural holdings.

Printing of Questionnaires and Instruction Manuals In all there were three questionnaires and two instruction manuals one in Samoan and one in English. The three questionnaires were printed on different coloured paper for ease of identification. All census documents were printed and distributed well in advance of the start of the field work.

Cleaning operations

The Secretariat of Pacific community (SPC) provided technical assistance for data processing. The TA was delivered in two separate missions, first to implement data entry, and the second mission was to perform data editing and generate final tabulation for final report. Prior to the start of data entry, Siaumau Misela of Samoa Bureau of Statistics was invited to SPC in December 2009 for a two weeks attachment. Misela worked closely with the SPC data processing specialist in developing the data entry system using CSPro (Census and Survey Processing System). The first mission of the data processing specialist in January 2010 was to finalize and implement data entry. The second mission in October 2010 concentrated mainly on data editing, data recode and generating final tables. The data processing (manual and computer) was done in the Data Processing Section of the Samoa Bureau of Statistics. To facilitate the manual and machine processing of the forms, questionnaires from the same enumeration area were bound together in a batch / folio and assigned a batch id. This id consists of the District, Village and the enumeration area codes. These forms were subjected to manual data scrutiny and corrections. The data entry was implemented using ENTRY of CSPro, and BATCH EDIT for the validation of encoded data items. Data entry was run through a network, which link all data entry work station to a server. A team of 6 staff (1 permanent and 5 temporary) were assigned to do the data processing.

Data appraisal

Fifty percent key verification was done on all the batches, and questionnaires with key verification error rate higher than the tolerance limit was subjected to 100 percent key verification. Additional checks were added in the validation program. Detected errors and inconsistencies were corrected in the batch files.

Ghana: Employment in agriculture, % of total employment: The latest value from 2022 is 39.74 percent, a decline from 40.34 percent in 2021. In comparison, the world average is 23.00 percent, based on data from 179 countries. Historically, the average for Ghana from 1991 to 2022 is 49.73 percent. The minimum value, 35.18 percent, was reached in 2015 while the maximum of 63.44 percent was recorded in 1991.

Employment in agriculture (% of total employment) (modeled ILO estimate) in India was reported at 43.51 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Employment in agriculture (% of total employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Employment in agriculture (% of total employment) (modeled ILO estimate) in United Kingdom was reported at 0.98647 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. United Kingdom - Employment in agriculture (% of total employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Graph and download economic data for Employment Level - Agriculture and Related Industries (LNU02034560) from Jan 1948 to Apr 2025 about agriculture, 16 years +, household survey, employment, industry, and USA.

The employment in the agricultural sector as share of total employment in Colombia declined to 14.44 percent in 2023. In 2023, the share thereby reached its lowest value in recent years. Employment in agriculture is the share of individuals working in agriculture, hunting, forestry, and fishing in order to produce a good or service for profit or pay from the total employed. The data covers people working in a certain period, or not working as a result of being temporarily absent from a job, or in a working-time arrangement.Find more key insights for the employment in the agricultural sector as share of total employment in countries like Guyana and Suriname.

Abstract

The agricultural sector is the most important sector of the Ugandan economy. Empirical evidence attests to this; for example the share of the agricultural sector to Gross Domestic Product (GDP) is about 21 percent (at the then current prices). According to the Agricultural Module of the 2002 Population and Housing Census, the agricultural sector accounted for 73 percent of the total employment for the persons aged 10 years and above. In addition, 74 percent of the households had an agricultural holding. The long term vision of the Government of Uganda is to eradicate poverty and the strategies for this vision are defined in the then Poverty Eradication Action Plan (PEAP) which has been transformed into the National Development Plan (NDP).

The vision of PMA was to eradicate poverty through transforming subsistence agriculture to commercial agriculture. The whole process of transformation requires accurate and reliable agricultural data to monitor the progress made and inform policy and planning processes

Further, countries are focusing on the need to monitor progress towards the Millennium Development Goals (MDGs) through their National Statistical systems. The World Census of Agriculture (WCA), 2010 was formulated with this in mind and specifically to monitor eradication of extreme poverty and hunger, achievement of Universal Primary Education, Promotion of gender equality and empowerment of women and ensuring environmental sustainability.

Within the framework of the FAO/World Bank Agricultural Statistics Assistance to Uganda, a Data Needs Assessment Study was undertaken in August 1999. One of the major findings was that the Agricultural Statistics System was fragile, vulnerable, un-sustainable and above all, unable to meet the data needs of users. A Census of Agriculture (CA) is major source to meet these demands.

Census taking in Uganda Prior to the conducting of the Uganda Census of Agriculture (UCA), 2008/09 two (2) other censuses had been conducted. The first CA was conducted during 1963/65. The Government of Uganda was assisted by FAO and the then Department for Technical Cooperation of the United Kingdom both of which provided international and census equipment to a varying degree.

The second CA called the National Census of Agriculture and Livestock (NCAL) was conducted during 1990/91. It was funded by United Nations Development Programme (UNDP) and executed by FAO. Therefore the UCA 2008/09 formed the third CA in the history of census taking in Uganda.

Preparatory activities An Agricultural Module was included in the Population and Housing Census 2002, to collect the data that would form a basis for constructing an up-to-date and appropriate sampling frame for a Uganda Census of Agriculture (UCA), 2004/05. A Pre-Test was conducted in 2002 followed by a pilot Census of Agriculture (PCA) which was conducted in 2003.

Lack of financial resources militated against conducting the UCA, 2004/05. During the Financial Year (FY) 2007/08 Government made a budgetary provision for conducting a census of agriculture.

The FY 2007/08 was mainly a preparatory year. As mentioned earlier, the plan had been to conduct a UCA during 2004/05, which did not take place. By 2008/09 (the census reference year), many changes had taken place and needed to be addressed. To this end, another Pre -Test was conducted in May 2008. Based on the findings from the Pre-Test, the UCA instruments had to be revised. Another very important factor for the instruments' revision was an input from the International Consultants (like FAO Statisticians). Other preparatory activities included arrangements to procure census equipment and transport as well as recruiting and training of Field Staff.

Objectives of the UCA.2008/09 While the long-term objective of the UCA, 2008/09 was to have a system of Food and Agriculture Statistics (FAS) in place, the immediate objective was to collect and generate benchmark data needed for monitoring and evaluation of the agricultural sector at all levels, through a nation-wide CA.

Geographic coverage

The Uganda Census of Agriculture 2008/09 covered all the 80 districts in the country as of July 2007.

Analysis unit

Agricultural households, Agricultural holdings

Universe

The Uganda Census of Agriculture 2008/09 was therefore planned to cover all the 80 districts at the time and collect data on various structural characteristics of agricultural holdings. Limited data on livestock variables was planned to be collected because comprehensive livestock data was to be collected in a Livestock Census, 2008.

Kind of data

Census/enumeration data [cen]

Sampling procedure

A stratified two-stage sample design was used for the small and medium-scale household-based agricultural holdings. At the first stage Enumeration Areas (EAs) were selected with Probability Proportional to Size (PPS), and at the second stage, households which were the ultimate sampling units were selected using systematic sampling.

For each of the sampled EAs, listing took place in the field and a number of filter questions (using Listing Module) were administered to determine eligibility (i.e., only the Households with Agricultural Activity would be eligible). Further, the eligible households were stratified into two strata namely, the small/medium holdings stratum and the Private Large-Scale holdings stratum.

On the other hand, district supervisors compiled separate lists of Institutional Farms and Private Large Scale Farms. These were to be covered on a complete enumeration basis.

During sampling, two (2) lists namely for EAs and PLS&IFs were used to identify possibilities of duplication and address them. If a PLS&IF was in both lists, it was deleted from the EA frame. However, if it was found only in the EA frame, it was left as part of the frame from which to sample. In other words, the List was not updated based on the information collected from the EAs sampled from the Area Frame.

The UCA2008/09 estimates were planned to be generated at national, regional and district levels. To achieve this, a sampling scheme of 3,606 EAs and 10 agricultural households in each selected EA, leading to 36,060 households was adopted.

In this design, an optimum number of households to be sampled per EA was determined on the basis of a suitable cost ratio (ratio of the cost per PSU to cost per SSU) and intra-class correlation, calculated from the Agricultural Module data from PHC 2002. For a cost ratio of 40 and intra-class correlation as 0.29, optimum number of households to be selected was obtained as 10.

The required sample size of EAs was selected from each district with probabilities proportional to size (PPS), using the systematic sampling algorithm described in Hansen, Hurwitz, and Madow (1953) while Agricultural Households were selected with equal probability systematic sampling procedure. The measure of Size (MOS) which was used for sample selection was the number of Agricultural Households determined from the 2002 PHC.

Sampling deviation

EAs where there was no enumerations due to insecurity: There were EAs which could not be listed or even enumerated due to insecurity , resistance by residents or nonexistent etc. These were in Moroto, Nakapiririt, Mubende, Kampala etc. Since there were no replicate EAs, the number of sampled EAs in those districts was lowered reducing the estimated number of EAs expected to give good results in those respective districts.

Mode of data collection

Face-to-face [f2f]

Research instrument

The principles of validity, optimization and efficiency which refer to ability for the questionnaires to yield more reliable information per unit cost; measured as a reciprocal of the variance of the estimate and enables objective interpretation of the results was followed. While costs involved man hours and money expended for data collection from sampled units, the design of questionnaires had to collect a minimum set of internationally comparable core data(indices) for Uganda, as enshrined in the pillars of FAO.

Cleaning operations

Data Processing monitored the data quality parameters and data quality team could continuously report to the field operations team who could make feed back to the DSs for improvement. Returned questionnaires were subjected to the following steps Coding, Data capture, Editing, Secondary Editing and Quality control.

Coding This involved making sure that all forms/questionnaires had correct geographical identification information and correct crop codes. The coding team reviewed the sampling of holdings within an enumeration area to see that only eligible/sampled holdings were actually enumerated.

Editing This involved the process of identifying inconsistencies within the data and removing them. At the beginning of UCA data processing, a set of editing rules and guidelines where developed by the data processing team with technical guidance from the subject matter specialists. Many of these were incorporated into the data entry application and others were left for the secondary editing stage.

Secondary Editing Errors that passed the data entry stage were subjected to the editing stage. This stage was meant to find inconsistencies within the data. It brought out problems that required subject matter specialists to resolve. To resolve most of such errors, consultations were made with the national supervisors, district supervisors, UBOS and MAAIF technical teams.

Response rate

The UCA2008/9 had several forms namely; Agricultural Households and holding Characteristics Module; Crop Area Module; Crop Production Module