The share of the global population with access to electricity in 2022 was roughly 91 percent, up from 71.4 percent in 1990. South Sudan was the least electrified country worldwide, followed by Burundi.

Between 2018 and 2022, people in households in the ‘other’, Asian and black ethnic groups were the most likely to be in persistent low income, both before and after housing costs, out of all ethnic groups.

Looking back 45 years or so, progress against poverty in India has been highly uneven over time and space. It took 20 years for the national poverty rate to fall below—and stay below—its value in the early 1950s. And trend rates of poverty reduction have differed appreciably between states. This research project aimed to understand what influence economy-wide and sectoral factors have played in the evolution of poverty measures for India since the 1950s, and to draw lessons for the future. This database contains detailed statistics on a wide range of topics in India. The data are presented at the state level and at the all-India level separately. The database uses published information to construct comprehensive series in six subject blocks. Period coverage is roughly from 1950 to 1994. The database contains 30 spreadsheets and 89 text files (ASCII) that are grouped into the six subject blocks. The formats and sizes of the 30 spreadsheets vary considerably. The list of variables included: . Expenditures (distribution) . National Accounts . Prices Wages . Population . Rainfall

According to a forecast from May 2025, the unemployment rate in Italy could reach 5.9 percent by the end of the year, 3.5 percentage points less than in 2021, when the COVID-19 outbreak had a disastrous impact on the labor market. The rate is then expected to remain stable in 2026. Weak employment situation Unemployment in Italy started increasing after the 2008 financial crisis and peaked at 12.7 percent in 2014. It mostly affected the young population. Similarly, the youth unemployment rate also increased significantly during the same period, reaching over 40 percent in 2014. Even if the figures decreased in the following years, in 2022 the rates were still particularly high in the southern regions. Indeed, the youth unemployment rate in the regions of Sicily and Campania stood at around 43 percent. COVID-19 impact on the economy The coronavirus (COVID-19) outbreak had a serious impact on Italy’s economy. In June 2020, most Italian respondents declared that the coronavirus pandemic had impacted or would impact their personal incomes in the future. In addition, the fear of losing the job due to the pandemic has been increasing in the country, with more than half of respondents worrying about this in July 2020.

The EU Statistics on Income and Living Conditions (EU-SILC) aim to collect timely and comparable cross-sectional and longitudinal data on income, poverty, social exclusion and living conditions.

The EU-SILC project was launched in 2003 between 6 Member States (Belgium, Denmark, Greece, Ireland, Luxembourg and Austria) and Norway. The legal basis entered into force in 2004 and now covers all EU countries plus Iceland, Norway and Switzerland.

EU-SILC provides two types of data:

• Cross-sectional data over a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions
• Longitudinal data on individual-level changes over time, observed periodically over a 4-year period.

Information on social exclusion and housing conditions is collected mainly at household level, while labour, education and health information is obtained from individuals aged 16 and over. Income variables at detailed component level are also mainly collected from individuals.

In Austria, approximately 6,000 households are surveyed every year.

Between April 2008 and March 2024, households from the Pakistani and Bangladeshi ethnic groups were the most likely to live in low income out of all ethnic groups, before and after housing costs.

In 2019, people from most ethnic minority groups were more likely than White British people to live in the most deprived neighbourhoods.

The Children in low-income families' local area statistics (CiLIF), provides information on the number and proportion of children living in Absolute low income by local area across the United Kingdom.The summary Statistical Release and tables which also show the proportions of children living in low income families are available here: Children in low income families: local area statistics - GOV.UK (www.gov.uk)Statistics on the number of children in low income families by financial year are published on Stat-Xplore. Figures are calibrated to the Households Below Average Income (HBAI) survey regional estimates of children in low income but provide more granular local area information not available from the HBAI, for example by Local Authority, Westminster Parliamentary Constituency and Ward.Absolute low-income is defined as a family in low income Before Housing Costs (BHC) in the reference year in comparison with incomes in 2010/11. A family must have claimed Child Benefit and at least one other household benefit (Universal Credit, tax credits, or Housing Benefit) at any point in the year to be classed as low income in these statistics. Gross income measure is Before Housing Costs (BHC) and includes contributions from earnings, state support and pensions.

Statistical disclosure control has been applied with Stat-Xplore, which guards against the identification of an individual claimant.

description: The World Bank regularly has conducted Living Standards Measurement Surveys (LSMS) for 39 developing and transition countries. The surveys collect household level data on a wide range of poverty-related information, including household demographics, consumption, living conditions, income and assets. Specialized surveys including integrated surveys on agriculture (LSMS-ISA) that provide additional information on household s agricultural operations.; abstract: The World Bank regularly has conducted Living Standards Measurement Surveys (LSMS) for 39 developing and transition countries. The surveys collect household level data on a wide range of poverty-related information, including household demographics, consumption, living conditions, income and assets. Specialized surveys including integrated surveys on agriculture (LSMS-ISA) that provide additional information on household s agricultural operations.

The English Housing Survey (EHS ) Fuel Poverty Datasets are comprised of fuel poverty variables derived from the EHS, and a number of EHS variables commonly used in fuel poverty reporting. The EHS is a continuous national survey commissioned by the Ministry of Housing, Community and Local Government (MHCLG) that collects information about people's housing circumstances and the condition and energy efficiency of housing in England.

End User Licence and Special Licence Versions
Similar to the main EHS, two versions of the Fuel Poverty dataset are available from 2014 onwards. The Special Licence version contains additional, more detailed, variables, and is therefore subject to more restrictive access conditions. Users should check the End User Licence version first to see whether it meeds their needs, before making an application for the Special Licence version.

Fuel Poverty Dataset

The fuel poverty dataset is comprised of fuel poverty variables derived from the English Housing Survey (EHS), and a number of EHS variables commonly used in fuel poverty reporting. The fieldwork for the EHS is carried out each financial year (between April and March). The fuel poverty datasets combine data from two consecutive financial years. Full information on the EHS survey is available at the DLUHC EHS website and further information on Fuel Poverty and the EHS can be sought from FuelPoverty@beis.gov.uk and ehs@communities.gov.uk respectively. Guidance on use of EHS data provided by DLUHC should also be applied to the fuel poverty dataset.

Further information may be found in the Annual Fuel Poverty Statistics Report: 2021 (2019 Data) on the gov.uk website.

Abstract

The main purpose of the Household Income Expenditure Survey (HIES) 2016 was to offer high quality and nationwide representative household data that provided information on incomes and expenditure in order to update the Consumer Price Index (CPI), improve National Accounts statistics, provide agricultural data and measure poverty as well as other socio-economic indicators. These statistics were urgently required for evidence-based policy making and monitoring of implementation results supported by the Poverty Reduction Strategy (I & II), the AfT and the Liberia National Vision 2030. The survey was implemented by the Liberia Institute of Statistics and Geo-Information Services (LISGIS) over a 12-month period, starting from January 2016 and was completed in January 2017. LISGIS completed a total of 8,350 interviews, thus providing sufficient observations to make the data statistically significant at the county level. The data captured the effects of seasonality, making it the first of its kind in Liberia. Support for the survey was offered by the Government of Liberia, the World Bank, the European Union, the Swedish International Development Corporation Agency, the United States Agency for International Development and the African Development Bank. The objectives of the 2016 HIES were:

1. Update the Consumer Price Index (CPI): To obtain a new set of weights for the basket of goods and services that upgrade the Monrovia Consumer Price Index (MCPI) and the National Consumer Price Index (NCPI) and to revise the CPI basket of goods and services in Liberia to reflect the current consumption pattern of residence.
2. Improve National Accounts Statistics: To get information on annual household expenditure patterns in order to update the household component of the National Accounts.
3. Measure Poverty: To prepare robust poverty indices that enable the understanding of poverty dynamics across the country and of the factors influencing them.
4. Improve Agricultural Statistics: To obtain nationally representative and policy relevant agricultural statistics in order to undertake in-depth analysis of agricultural households.
5. Capture Socio-economic Impact of Ebola Virus Disease (EVD): To obtain a post-EVD dataset which allows for an in-depth analysis of the socioeconomic impact of EVD on households.
6. Benchmark Agenda for Transformation Indicators: To provide an update on selected socioeconomic indicators used to benchmark the government’s policies embedded within the Agenda for Transformation.
7. Develop Statistical Capacity: Emphasize capacity building and development of sustainable statistical systems through every stage of the project to produce accurate and timely information about Liberia.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The original sample design for the HIES exploited two-phased clustered sampling methods, encompassing a nationally representative sample of households in every quarter and was obtained using the 2008 National Housing and Population Census sampling frame. The procedures used for each sampling stage are as follows:
i. First stage
Selection of sample EAs. The sample EAs for the 2016 HIES were selected within each stratum systematically with Probability Proportional to Size from the ordered list of EAs in the sampling frame. They are selected separately for each county by urban/rural stratum. The measure of size for each EA was based on the number of households from the sampling frame of EAs based on the 2008 Liberia Census. Within each stratum the EAs were ordered geographically by district, clan and EA codes. This provided implicit geographic stratification of the sampling frame.

ii. Second stage
Selection of sample households within a sample EA. A random systematic sample of 10 households were selected from the listing for each sample EA. Using this type of table, the supervisor only has to look up the total number of households listed, and a specific systematic sample of households is identified in the corresponding row of the table.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were three questionnaires administered for this survey: 1. Household and Individual Questionnaire 2. Market Price Questionnaire 3. Agricultural Recall Questionnaire

Cleaning operations

The data entry clerk for each team, using data entry software called CSPro, entered data for each household in the field. For each household, an error report was generated on-site, which identified key problems with the data collected (outliers, incorrect entries, inconsistencies with skip patterns, basic filters for age and gender specific questions etc.). The Supervisor along with the Data Entry Clerk and the Enumerator that collected the data reviewed these errors. Callbacks were made to households if necessary to verify information and rectify the errors while in that EA.

Once the data were collected in each EA, they were sent to LISGIS headquarters for further processing along with EA reports for each area visited. The HIES Technical committee converted the data into STATA and ran several consistency checks to manage overall data quality and prepared reports to identify key problems with the data set and called the field teams to update them about the same. Monthly reports were prepared by summarizing observations from data received from the field alongside statistics on data collection status to share with the field teams and LISGIS Management.

The English Housing Survey (EHS ) Fuel Poverty Datasets are comprised of fuel poverty variables derived from the EHS, and a number of EHS variables commonly used in fuel poverty reporting. The EHS is a continuous national survey commissioned by the Ministry of Housing, Community and Local Government (MHCLG) that collects information about people's housing circumstances and the condition and energy efficiency of housing in England.

End User Licence and Special Licence Versions
Similar to the main EHS, two versions of the Fuel Poverty dataset are available from 2014 onwards. The Special Licence version contains additional, more detailed, variables, and is therefore subject to more restrictive access conditions. Users should check the End User Licence version first to see whether it meeds their needs, before making an application for the Special Licence version.

Fuel Poverty Dataset

The fuel poverty dataset is comprised of fuel poverty variables derived from the English Housing Survey (EHS), and a number of EHS variables commonly used in fuel poverty reporting. The fieldwork for the EHS is carried out each financial year (between April and March). The fuel poverty datasets combine data from two consecutive financial years. Full information on the EHS survey is available at the DLUHC EHS website and further information on Fuel Poverty and the EHS can be sought from FuelPoverty@beis.gov.uk and ehs@communities.gov.uk respectively. Guidance on use of EHS data provided by MHCLG should also be applied to the fuel poverty dataset.

Further information may be found in the Annual Fuel Poverty Statistics Report: 2021 (2019 Data) on the gov.uk website.

As of 2024, around **** million people in South Africa are living in extreme poverty, with the poverty threshold at **** U.S. dollars daily. This means that ******* more people were pushed into poverty compared to 2023. Moreover, the headcount was forecast to increase in the coming years. By 2030, over **** million South Africans will live on a maximum of **** U.S. dollars per day. Who is considered poor domestically? Poverty is measured using several matrices. For example, local authorities tend to rely on the national poverty line, assessed based on consumer price indices (CPI) of a basket of goods of food and non-food components. In 2023, the domestic poverty line in South Africa stood at ***** South African rand per month (around ***** U.S. dollars per month). According to a survey, social inequality and poverty worried a significant share of the South African respondents. As of September 2024, some ** percent of the respondents reported that they were worried about the state of poverty and unequal income distribution in the country.   Eastern Cape residents received more grants South Africa’s labor market has struggled to absorb the country’s population. In 2023, almost a third of the economically active population was unemployed. Local authorities employ relief assistance and social grants in an attempt to reduce poverty and assist poor individuals. In 2023, almost ** percent of South African households received state support, with the majority share benefiting in the Eastern Cape.

The "State Fact Sheets" provide the most recently available farm and rural data compiled by the Economic Research Service, USDA. It provide 2 pages of facts for each state and also a summary fact sheet for the United States. Included are data on population, jobs, income and poverty, and farms. The data comes from a variety of sources including the Bureau of Labor Statistics, Bureau of Economic Analysis, the Bureau of the Census, and ERS.

Collection Organization: Economic Research Service.

Collection Methodology: The data come from a variety of sources including the Bureau of Labor Statistics, Bureau of Economic Analysis, the Bureau of the Census, and ERS.

Collection Frequency: Varies by data source.

Update Characteristics: Selective updates 2 times a year.

STATISTICAL INFORMATION:

The data reside in 52 ASCII text files. LANGUAGE:

English ACCESS/AVAILABILITY:

Data Center: USDA Economic Research Service Dissemination Media: Diskette, Internet gopher, Internet home page File Format: ASCII, Lotus/dBase Access Instructions: Call NASS at 1-800-999-6779 for historical series data available on diskette. For historical series data available online, connect to the Internet home page at Cornell University.

URL: 'http://usda.mannlib.cornell.edu/usda'

Access to the data or reports may be achieved through the ERS-NASS information system:

WWW: 'http://usda.mannlib.cornell.edu/usda' Gopher client: 'gopher://gopher.mannlib.cornell.edu:70/'

For subscription direct to an e-mail address, send an e-mail message to:

usda-reports@usda.mannlib.cornell.edu

Type the word "lists" (without quotes) in the body of the message.

HIES collects a wealth of information on HH income and expenditure, such as source of income by industry, HH expenditure on goods and services, and income and expenditure associated with subsistence production and consumption. In addition to this, HIES collects information on sectoral and thematic areas, such as education, health, labour force, primary activities, transport, information and communication, transfers and remittances, food expenditure (acquisition) and gender. The Pacific Islands regionally standardized HIES instruments and procedures were adopted by NSO for the 2015/2016 HIES. These standards, were designed to feed high-quality data to HIES data end users for: • deriving expenditure weights and other useful data for the revision of the CPI; • supplementing the data available for use in compiling official estimates of various components in the System of NA; • supplementing the data available for production of the balance of payments; and • gathering information on poverty lines and the incidence of poverty in Niue.

The data allow for the production of useful indicators and information on the industries covered in the survey, including providing data to inform indicators under the United Nations Sustainable Development Goals (SDGs). This report, the above listed outputs, and additional thematic analyses, collectively provide information to assist with multisector planning and policy formation. The 2015/2016 HIES was conducted to update the 2002 HIES data and aimed to estimate the total amount HH spent and earnt over the past 12 months at the national level (total expenditure and income).

V01: Cleaned, labelled and anonymized version of the Master file.

HOUSEHOLD: Housing characteristics, tenure characteristics, utilities and communication, goods and assets, vehicles and accessories, loans, expenditure, income.

INDIVIDUAL: Demographic profile, economic activities, health, communication.

• Collection start: 2015
• Collection end: 2016

This dataset focuses on the social protection policy responses to poverty in Nigeria. It provides comprehensive information on various aspects of living conditions, demographics, and socio-economic factors of 204 respondents from the Bwari, Kuje, and Gwagwalada Area Councils in Abuja, Nigeria. The data was collected and analysed using SPSS, and descriptive statistics were used to explore the variables of interest. The dataset has been extrapolated to Excel for easy accessibility. The dataset includes descriptive results on several key aspects. It covers the education level of the respondents, the distribution of household heads among them, the types of dwellings they live in, the health conditions within their households, access to medical care, accommodation types, and waste distribution. The dataset also provides key variables of insights into the poverty levels and perceptions among the respondents. The "MPI" (Multidimensional Poverty Index) measures multidimensional poverty, while "povertylevel" indicates the poverty level of the respondents. In addition to the key variables, the dataset includes additional rows that highlight different combinations of variables related to living conditions. These combinations include dwelling types, sources of tap water, sanitation facilities, lighting sources, access to radio, television, and telephone, as well as information regarding meal skipping, healthcare access, and employment status. The dataset also includes socio-demographic characteristics that were considered in the study. These characteristics include sex, age, education level, employment income, household head, type of dwelling, waste distribution, and source of energy.

The General Household Survey (GHS), ran from 1971-2011 (the UKDS holds data from 1972-2011). It was a continuous annual national survey of people living in private households, conducted by the Office for National Statistics (ONS). The main aim of the survey was to collect data on a range of core topics, covering household, family and individual information. This information was used by government departments and other organisations for planning, policy and monitoring purposes, and to present a picture of households, families and people in Great Britain. In 2008, the GHS became a module of the Integrated Household Survey (IHS). In recognition, the survey was renamed the General Lifestyle Survey (GLF). The GLF closed in January 2012. The 2011 GLF is therefore the last in the series. A limited number of questions previously run on the GLF were subsequently included in the Opinions and Lifestyle Survey (OPN).

Secure Access GHS/GLF
The UKDS holds standard access End User Licence (EUL) data for 1972-2006. A Secure Access version is available, covering the years 2000-2011 - see SN 6716 General Lifestyle Survey, 2000-2011: Secure Access.

History
The GHS was conducted annually until 2011, except for breaks in 1997-1998 when the survey was reviewed, and 1999-2000 when the survey was redeveloped. Further information may be found in the ONS document An overview of 40 years of data (General Lifestyle Survey Overview - a report on the 2011 General Lifestyle Survey) (PDF). Details of changes each year may be found in the individual study documentation.

EU-SILC
In 2005, the European Union (EU) made a legal obligation (EU-SILC) for member states to collect additional statistics on income and living conditions. In addition, the EU-SILC data cover poverty and social exclusion. These statistics are used to help plan and monitor European social policy by comparing poverty indicators and changes over time across the EU. The EU-SILC requirement was integrated into the GHS/GLF in 2005. After the closure of the GLF, EU-SILC was collected via the Family Resources Survey (FRS) until the UK left the EU in 2020.

Reformatted GHS data 1973-1982 - Surrey SPSS Files
SPSS files were created by the University of Surrey for all GHS years from 1973 to 1982 inclusive. The early files were restructured and the case changed from the household to the individual with all of the household information duplicated for each individual. The Surrey SPSS files contain all the original variables as well as some extra derived variables (a few variables were omitted from the data files for 1973-76). In 1973 only, the section on leisure was not included in the Surrey SPSS files. This has subsequently been made available, however, and is now held in a separate study, General Household Survey, 1973: Leisure Questions (SN 3982). Records for the original GHS 1973-1982 ASCII files have been removed from the UK Data Archive catalogue, but the data are still preserved and available upon request.

In 2025, *** percent of Kenya’s population live below **** U.S. dollars per day. This meant that over 8.9 million Kenyans were in extreme poverty, most of whom were in rural areas. Over *** million Kenyans in rural communities lived on less than **** U.S. dollars daily, an amount *** times higher than that recorded in urban regions. Nevertheless, the poverty incidence has declined compared to 2020. That year, businesses closed, unemployment increased, and food prices soared due to the coronavirus (COVID-19) pandemic. Consequently, the country witnessed higher levels of impoverishment, although improvements were already visible in 2021. Overall, the poverty rate in Kenya is expected to decline to ** percent by 2025. Poverty triggers food insecurity Reducing poverty in Kenya puts the country on the way to enhancing food security. As of November 2021, *** million Kenyans lacked sufficient food for consumption. That corresponded to **** percent of the country's population. Also, in 2021, over one-quarter of Kenyan children under five years suffered from chronic malnutrition, a growth failure resulting from a lack of adequate nutrients over a long period. Another *** percent of the children were affected by acute malnutrition, which concerns a rapid deterioration in the nutritional status over a short period. A country where prosperity and poverty walk side by side The poverty incidence in Kenya contrasts with the country's economic development. In 2021, Kenya ranked among the ten highest GDPs in Africa, at almost *** billion U.S. dollars. Moreover, its gross national income per capita has increased to ***** U.S. dollars over the last 10 years, a growth of above**** percent. Generally, while poverty decreased in the country during the same period, Kenya still seems to be far from reaching the United Nation's Sustainable Development Goals (SDGs) to eliminate extreme poverty by 2030.

This interactive map of Nepal, broken down into five development regions, highlights the Mid-Western and Far-Western regions as the priority area for the Nepal Food Security Enhancement Project (jointly financed by the Nepal Government and GAFSP). The project is being implemented in nineteen hill and mountain districts of these two regions. The interactive map shows sub-national poverty and malnutrition data, as well as information on irrigation in the various regions. The Mid-Western and Far-Western regions are the two regions where poverty and malnutrition are the highest in the country. The Nepal Living Standard Survey (NLSS III, 2010) showed that 37% of the people in the rural hills of these regions fall below the poverty line, compared to the national average of 25.16%. The proportion of underweight children under the age of 5 years in the Mid-Western region is the highest in the country (more than 10%). The project has been designed to enhance food security and nutrition in food insecure communities in these two regions. Data Sources: Nepal Agriculture and Food Security Project (NAFSP) LocationsSource: GAFSP and World Bank Documents. Poverty (Proportion of population below the poverty line) (2010/11): Proportion of the population living on less than Rs 19.261 per year, in average 2010/11 prices.Source: Nepal Central Bureau of Statistics. Poverty in Nepal 2010/11. Nepal Living Standard Survey III 2010/11 (NLSS III). Poverty (Proportion of population below the poverty line at district level) (2011): Proportion of the population living on less than Rs 19.261 per year, in average 2010/11 prices.Source: Nepal Central Bureau of Statistics - World Bank. “Nepal Small Area Estimation of Poverty, 2011 -Estimations based on Living Standards Survey 2010-11, Nepal Census 2011 and GIS information from the Vulnerability Analysis and Mapping Unit of World Food Program Nepal.” Malnutrition (Proportion of underweight children under 5 years) (2011): Prevalence of severely underweight children is the percentage of children aged 0-59 months whose weight for age is less than minus 3 standard deviations below the median weight-for-age of the international reference population.Source: Measure DHS - Nepal Ministry of Health and Population. "2011 Nepal Demographic and Health Survey." Population (Total population) (2011): Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship, except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Source: Nepal Central Bureau of Statistics. “2011 Census Preliminary Results.” Population Density (Persons per 1 square kilometer) (2011): Population divided by land area in square kilometers.Source: Nepal Central Bureau of Statistics. 2011 Census preliminary results. Irrigation (2009/10): Total Irrigated Area in Hectares.Source: Ministry of Agriculture and Co-operatives. Statistical Information on Nepalese Agriculture 2009/10. Irrigation (2011/12): Total irrigated area in hectares.Source: Ministry of Agriculture - Department of Irrigation - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2011/2012." Rice Area (2011-12): Area in hectares of agricultural land used for rice.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2011/2012."

Rice Production (2011-12): Rice harvested expressed in tons.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2011/2012." Rice Productivity (2011-12): Rice yield expressed in kilograms per hectare.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2011/2012." Rice Area (2013-14): Area in hectares of agriculture land used for rice.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2013/2014."

Rice Production (2013-14): Rice harvested expressed in tons.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2013/2014."
Rice Productivity (2013-14): Rice yield expressed in kilograms per hectare.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2013/2014." Wheat Area (2011-12): Area in hectares of agriculture land used for wheat.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2011/2012." Wheat Production (2011-12): Wheat harvested expressed in tons.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2011/2012."
Wheat Productivity (2011-12): Wheat yield expressed in kilograms per hectare.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2011/2012." Wheat Area (2013-14): Area in hectares of agriculture land used for wheat.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2013/2014." Wheat Production (2013-14): Wheat harvested expressed in tons.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2013/2014."
Wheat Productivity (2013-14): Rice yield expressed in kilograms per hectare.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2013/2014." Livestock Inventory (2011-12): Number of cattle, goat, and sheep by district.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2011/2012." Livestock Inventory (2013-14): Number of cattle, goat, and sheep by district.Source: Ministry of Agricultural Development - Agri-Business Promotion and Statistics Division Statistics Section. "Statistical Information on Nepalese Agriculture 2013/2014."

The maps displayed on the GAFSP website are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of GAFSP (and the World Bank Group), any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.