Contains data from World Bank's data portal covering various economic and social indicators (one per resource).

Comprehensive statistical dataset for Papua New Guinea including demographic, economic, and social indicators for the year 2025.

Papua New Guinea Prevalence of Severe Food Insecurity in the Population: % of population data was reported at 27.000 % in 2022. This stayed constant from the previous number of 27.000 % for 2021. Papua New Guinea Prevalence of Severe Food Insecurity in the Population: % of population data is updated yearly, averaging 27.000 % from Dec 2017 (Median) to 2022, with 6 observations. The data reached an all-time high of 27.000 % in 2022 and a record low of 27.000 % in 2022. Papua New Guinea Prevalence of Severe Food Insecurity in the Population: % of population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Papua New Guinea – Table PG.World Bank.WDI: Social: Health Statistics. The percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to several of the most severe experiences described in the FIES questions, such as to have been forced to reduce the quantity of the food, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources.;Food and Agriculture Organization of the United Nations (FAO);;

The Nexus Project is a collaboration between IFPRI and its partners, including national statistical agencies and research institutions. Our aim is to improve the quality of social accounting matrices (SAMs) used for computable general equilibrium (CGE) modeling. The Nexus Project develops toolkits and establishes common data standards, procedures, and classification systems for constructing and updating national SAMs. The 2019 Papua New Guinea SAM follows the Standard Nexus Structure. The open access version of the Papua New Guinea SAM separates domestic production into 42 activities. Factors are disaggregated into labor, agricultural land, and capital. Labor is further disaggregated across three education categories. Representative households are disaggregated by rural and urban areas and by per capita expenditure quintile. The remaining accounts include enterprises, government, taxes, savings-and-investment, and the rest of the word.

Papua New Guinea PG: Coverage: Social Protection & Labour Programs: % of Population data was reported at 4.244 % in 2009. Papua New Guinea PG: Coverage: Social Protection & Labour Programs: % of Population data is updated yearly, averaging 4.244 % from Dec 2009 (Median) to 2009, with 1 observations. Papua New Guinea PG: Coverage: Social Protection & Labour Programs: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Papua New Guinea – Table PG.World Bank: Social Protection. Coverage of social protection and labor programs (SPL) shows the percentage of population participating in social insurance, social safety net, and unemployment benefits and active labor market programs. Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

The urban indicators data available here are analyzed, compiled and published by UN-Habitat’s Global Urban Observatory which supports governments, local authorities and civil society organizations to develop urban indicators, data and statistics. Urban statistics are collected through household surveys and censuses conducted by national statistics authorities. Global Urban Observatory team analyses and compiles urban indicators statistics from surveys and censuses. Additionally, Local urban observatories collect, compile and analyze urban data for national policy development. Population statistics are produced by the United Nations Department of Economic and Social Affairs, World Urbanization Prospects.

Papua New Guinea PG: Coverage: Social Insurance Programs: % of Population data was reported at 1.005 % in 2009. Papua New Guinea PG: Coverage: Social Insurance Programs: % of Population data is updated yearly, averaging 1.005 % from Dec 2009 (Median) to 2009, with 1 observations. Papua New Guinea PG: Coverage: Social Insurance Programs: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Papua New Guinea – Table PG.World Bank: Social Protection. Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Papua New Guinea PG: Coverage: Social Safety Net Programs: % of Population data was reported at 3.355 % in 2009. Papua New Guinea PG: Coverage: Social Safety Net Programs: % of Population data is updated yearly, averaging 3.355 % from Dec 2009 (Median) to 2009, with 1 observations. Papua New Guinea PG: Coverage: Social Safety Net Programs: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Papua New Guinea – Table PG.World Bank: Social Protection. Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Papua New Guinea PG: Coverage: Social Safety Net Programs: Poorest Quintile: % of Population data was reported at 1.920 % in 2009. Papua New Guinea PG: Coverage: Social Safety Net Programs: Poorest Quintile: % of Population data is updated yearly, averaging 1.920 % from Dec 2009 (Median) to 2009, with 1 observations. Papua New Guinea PG: Coverage: Social Safety Net Programs: Poorest Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Papua New Guinea – Table PG.World Bank: Social Protection. Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11723377%2F59bc70fb3d13d9f954e317aacbfd2bd6%2FPopulation.png?generation=1681981140865261&alt=media" alt="">

The population data from the United Nations is a dataset that contains information on the estimated population of each country in the world for various years between 1980 and 2050. The dataset includes the following columns:

• place: Name of the country or region
• pop1980: Estimated population for the year 1980
• pop2000: Estimated population for the year 2000
• pop2010: Estimated population for the year 2010
• pop2022: Estimated population for the year 2022
• pop2023: Estimated population for the year 2023
• pop2030: Estimated population for the year 2030
• pop2050: Estimated population for the year 2050
• country: ISO 3166-1 alpha-3 code of the country
• area: Total land and water area of the country (in square kilometers)
• landAreaKm: Land area of the country (in square kilometers)
• cca2: ISO 3166-1 alpha-2 code of the country
• cca3: ISO 3166-1 alpha-3 code of the country
• netChange: Annual net change in population (in thousands)
• growthRate: Annual population growth rate (as a percentage)
• worldPercentage: Percentage of world population
• density: Population density (in persons per square kilometer)
• densityMi: Population density (in persons per square mile)
• rank: Rank of the country by population

The dataset provides a comprehensive overview of the population of each country over time and can be used to analyze population trends, make population projections, and compare the population of different countries. The dataset can also be used in combination with other data sources to explore correlations between population and various social and economic indicators.

Time series data for the statistic Paying taxes: Time (hours per year) and country Papua New Guinea. Indicator Definition:The time to comply with tax laws measures the time taken to prepare, ?le and pay three major types of taxes and contributions: the corporate income tax, value added or sales tax and labor taxes, including payroll taxes and social contributions.The indicator "Paying taxes: Time (hours per year)" stands at 207.00 as of 12/31/2019. Regarding the One-Year-Change of the series, the current value constitutes an increase of 1.97 percent compared to the value the year prior.The 1 year change in percent is 1.97.The 3 year change in percent is -0.9569.The 5 year change in percent is -0.9569.The 10 year change in percent is 6.98.The Serie's long term average value is 202.77. It's latest available value, on 12/31/2019, is 2.09 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2008, to it's latest available value, on 12/31/2019, is +6.98%.The Serie's change in percent from it's maximum value, on 12/31/2014, to it's latest available value, on 12/31/2019, is -0.957%.

In 2024, the budget balance in relation to the gross domestic product (GDP) in Papua New Guinea was estimated at -3.23 percent. Between 1983 and 2024, the figure dropped by 0.21 percentage points, though the decline followed an uneven course rather than a steady trajectory. The forecast shows the budget balance will steadily grow by 3.63 percentage points from 2024 to 2030.The indicator describes the general government net lending / borrowing, which is calculated as revenue minus total expenditure. The International Monetary Fund defines the general government expenditure as consisting of total expenses and the net acquisition of nonfinancial assets. The general government revenue consists of the revenue from taxes, social contributions, grants receivable, and other revenue.

Abstract

The primary objective of the 2006 DHS is to provide to the Department of Health (DOH), Department of National Planning and Monitoring (DNPM) and other relevant institutions and users with updated and reliable data on infant and child mortality, fertility preferences, family planning behavior, maternal mortality, utilization of maternal and child health services, knowledge of HIV/AIDS and behavior, sexually risk behavior and information on the general household amenities. This information contributes to policy planning, monitoring, and program evaluation for development at all levels of government particularly at the national and provincial levels. The information will also be used to assess the performance of government development interventions aimed at addressing the targets set out under the MDG and MTDS. The long-term objective of the survey is to technically strengthen the capacity of the NSO in conducting and analyzing the results of future surveys.

The successful conduct and completion of this survey is a result of the combined effort of individuals and institutions particularly in their participation and cooperation in the Users Advisory Committee (UAC) and the National Steering Committee (NSC) in the different phases of the survey.

The survey was conducted by the Population and Social Statistics Division of the National Statistical Office of PNG. The 2006 DHS was jointly funded by the Government of PNG and Donor Partners through ADB while technical assistance was provided by International Consultants and NSO Philippines.

Geographic coverage

National level Regional level Urban and Rural

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure household members (usual residents), all women and men aged 15-50 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary focus of the 2006 DHS is to provide estimates of key population and health indicators at the national level. A secondary but important priority is to also provide estimates at the regional level, and for urban and rural areas respectively. The 2006 DHS employed the same survey methodology used in the 1996 DHS. The 2006 DHS sample was a two stage self-weighting systematic cluster sample of regions with the first stage being at the census unit level and the second stage at the household level. The 2000 Census frame comprised of a list of census units was used to select the sample of 10,000 households for the 2006 DHS.

A total of 667 clusters were selected from the four regions. All census units were listed in a geographic order within their districts, and districts within each province and the sample was selected accordingly through the use of appropriate sampling fraction. The distribution of households according to urban-rural sectors was as follows:

8,000 households were allocated to the rural areas of PNG. The proportional allocation was used to allocate the first 4,000 households to regions based on projected citizen household population in 2006. The other 4,000 households were allocated equally across all four regions to ensure that each region have sufficient sample for regional level analysis.

2,000 households were allocated to the urban areas of PNG using proportional allocation based on the 2006 projected urban citizen population. This allocation was to ensure that the most accurate estimates for urban areas are obtained at the national level.

All households in the selected census units were listed in a separate field operation from June to July 2006. From the list of households, 16 households were selected in the rural census units and 12 in the urban census units using systematic sampling. All women and men age 15-50 years who were either usual residents of the selected households or visitors present in the household on the night before the survey were eligible to be interviewed. Further information on the survey design is contained in Appendix A of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2006 DHS namely; the Household Questionnaire (HHQ), the Female Individual Questionnaire (FIQ) and the Male Individual Questionnaire (MIQ). The planning and development of these questionnaires involved close consultation with the UAC members comprising of the following line departments and agencies namely; Department of Health (DOH), Department of Education (DOE), Department of National Planning and Monitoring (DNPM), National Aids Council Secretariat (NACS), Department of Agriculture and Livestock (DAL), Department of Labour and Employment (DLE), University of Papua New Guinea (UPNG), National Research Institute (NRI) and representatives from Development partners.

The HHQ was designed to collect background information for all members of the selected households. This information was used to identify eligible female and male respondents for the respective individual questionnaires. Additional information on household amenities and services, and malaria prevention was also collected.

The FIQ contains questions on respondents background, including marriage and polygyny; birth history, maternal and child health, knowledge and use of contraception, fertility preferences, HIV/AIDS including new modules on sexual risk behaviour and attitudes to issues of well being. All females age 15-50 years identified from the HHQ were eligible for interview using this questionnaire.

The MIQ collected almost the same information as in the FIQ except for birth history. All males age 15-50 years identified from the HHQ were eligible to be interviewed using the MIQ.

Two pre-tests were carried out aimed at testing the flow of the existing and new questions and the administering of the MIQ between March and April 2006. The final questionnaires contained all the modules used in the 1996 DHS including new modules on malaria prevention, sexual risk behaviour and attitudes to issues of well being.

Cleaning operations

All questionnaires from the field were sent to the NSO headquarters in Port Moresby in February 2007 for editing and coding, data entry and data cleaning. Editing was done in 3 stages to enable the creation of clean data files for each province from which the tabulations were generated. Data entry and processing were done using the CSPro software and was completed by October 2008.

Response rate

Table A.2 of the survey report provides a summary of the sample implementation of the 2006 DHS. Despite the recency of the household listing, approximately 7 per cent of households could not be contacted due to prolonged absence or because their dwellings were vacant or had been destroyed. Among the households contacted, a response rate of 97 per cent was achieved. Within the 9,017 households successfully interviewed, a total of 11, 456 women and 11, 463 of men age 15-49 years were eligible to be interviewed. Successful interviews were conducted with 90 per cent of eligible women (10, 353) and 88 per cent of eligible men (10,077). The most common cause of non-response was absence (5 per cent). Among the regions, the rate of success among women was highest in all the regions (92 per cent each) except for Momase region at 86 per cent. The rate of success among men was highest in Highlands and Islands region and lowest in Momase region. The overall response rate, calculated as the product of the household and female individual response rate (.97*.90) was 87 per cent.

Sampling error estimates

Appendix B of the survey report describes the general procedure in the computation of sampling errors of the sample survey estimates generated. It basically follows the procedure adopted in most Demographic and Health Surveys.

Data appraisal

Appendix C explains to the data users the quality of the 2006 DHS. Non-sampling errors are those that occur in surveys and censuses through the following causes: a) Failure to locate the selected household b) Mistakes in the way questions were asked c) Misunderstanding by the interviewer or respondent d) Coding errors e) Data entry errors, etc.

Total eradication of non-sampling errors is impossible however great measures were taken to minimize them as much as possible. These measures included: a) Careful questionnaire design b) Pretesting of survey instruments to guarantee their functionality c) A month of interviewers’ and supervisors’ training d) Careful fieldwork supervision including field visits by NSOHQ personnel e) A swift data processing prior to data entry f ) The use of interactive data entry software to minimize errors

Papua New Guinea Percentage of Population Exposure to Wildfires data was reported at 0.120 % in 2020. This records a decrease from the previous number of 0.720 % for 2019. Papua New Guinea Percentage of Population Exposure to Wildfires data is updated yearly, averaging 0.700 % from Dec 2000 (Median) to 2020, with 21 observations. The data reached an all-time high of 9.160 % in 2002 and a record low of 0.100 % in 2012. Papua New Guinea Percentage of Population Exposure to Wildfires data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Papua New Guinea – Table PG.OECD.GGI: Social: Air Quality and Health: Non OECD Member: Annual.

Small island states present a significant challenge in terms of sustainable tourism development. On a small island there are limited resources, economic and social activities tend to be concentrated on the coastal zone, and the interconnectivity between economic, environmental, social, cultural and political spheres is strong and pervasive. Consequently the sustainable development of tourism is more a practical necessity than an optional extra. This paper investigates the question of how to monitor sustainable tourism development (STD) in Samoa, an independent small island state in the South Pacific. It describes some of the methodological considerations and processes involved in the development of STD indicators and particularly highlights the importance of formulating clear objectives before trying to identify indicators, the value of establishing a multi-disciplinary advisory panel, and the necessity of designing an effective and flexible implementation framework for converting indicator results into management action. Available online and also kept in vertical file collection Call Number: VF 6920 [EL] Physical Description: 24 p. ; 29cm

Papua New Guinea PG: Women: % of Total Population data was reported at 48.360 % in 2021. This records an increase from the previous number of 48.310 % for 2020. Papua New Guinea PG: Women: % of Total Population data is updated yearly, averaging 47.875 % from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 48.360 % in 2021 and a record low of 46.930 % in 1990. Papua New Guinea PG: Women: % of Total Population data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Papua New Guinea – Table PG.OECD.GGI: Social: Demography: Non OECD Member: Annual.

Context

The Women Empowerment Index (WEI) is a multifaceted tool designed to assess and track the progress of women's empowerment within societies. Drawing from diverse metrics and indicators, the WEI offers a nuanced understanding of the status of women across various domains. It builds upon the foundation laid by existing indices like the Gender Inequality Index (GII) but focuses specifically on aspects related to women's empowerment. The WEI encompasses several key dimensions, including economic participation, political representation, access to education and healthcare, and social inclusivity. By analyzing these dimensions, the index sheds light on the extent to which women are able to exercise agency, access resources, and participate fully in societal processes.

Content

This dataset provides essential information on gender development indicators, facilitating comprehensive analysis and comparison across countries and regions. Here are the key columns included:

• Country: The name of the country or territory included in the dataset.
• Women's Empowerment Index (WEI) - 2022: Quantifies women's empowerment within each country for the year 2022, offering insights into gender dynamics and opportunities for women.
• Women's Empowerment Group - 2022: Categorizes countries based on their performance and status regarding women's empowerment, facilitating comparative analysis.
• Global Gender Parity Index (GGPI) - 2022: Measures gender parity at a global level for the year 2022, indicating the degree of equality between men and women.
• Gender Parity Group - 2022: Classifies countries into groups based on their level of gender parity, aiding in the identification of trends and disparities.
• Human Development Group - 2021: Classifies countries into specific groups based on their Human Development Index (HDI) scores and development levels for the year 2021.
• Sustainable Development Goal regions: Aligns countries with specific regions as defined by the Sustainable Development Goals (SDGs), providing context for development efforts and initiatives.

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

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Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by Freepik

Thumbnail by: Empowerment icons created by BZZRINCANTATION - Flaticon

This dataset and analysis investigate the "Big Pants Gauge" hypothesis proposed by Ralph Lauren, as described by Vanessa Friedman (The New York Times, April 18, 2025). In response to growing societal uncertainty, Lauren suggested that a surge in public interest in baggy pants could function similarly to the traditional "Hemline Index" as a cultural-economic signal.

This project empirically explores Lauren's idea by combining Google search trend data for various loose pant styles with key indicators of market volatility (VIX) and economic policy uncertainty (USEPU). The Python code (BigPants.py) processes the data, performs Newey-West robust regressions, AR(1) modeling, structural break (CUSUM) tests, and Granger causality tests.

Key findings include:

• Significant structural breaks in fashion search trends coinciding with major crises (2008 Global Financial Crisis, COVID-19 pandemic).

• A modest but statistically significant short-term predictive relationship between lagged baggy pants interest and VIX movements.

• No strong evidence of Granger-causal predictive power across multi-month horizons.

The results suggest that fashion search behaviors may serve as informal, early behavioral sensors of collective anxiety, complementing traditional economic indicators. However, they also highlight the complexity of fashion trend dynamics, which can evolve into self-sustaining cultural phenomena independent of macroeconomic conditions.

This upload includes:

• BigPants.py — Python script for data loading, cleaning, modeling, and figure generation (designed for Google Colab or local execution).

• Data files (data-*) — Search trends and macroeconomic indices.

• Output figures (Results_Figure_1.png, Results_Figure_2.png) — Visualization of key results.

• README_BigPantsBigAnxiety.txt — Documentation file explaining structure, usage, and licensing.

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Citation:

Brown, S. (2025). Big Pants, Big Anxiety: Fashion Search Behavior as a Signal of Societal Uncertainty. Zenodo. https://doi.org/10.5281/zenodo.15292232

Context

Income inequality is a global issue reflecting the uneven distribution of wealth within and between countries. Developed nations exhibit varying income levels due to economic policies and labor dynamics, resulting in Gini coefficients of around 0.3 to 0.4. Conversely, developing nations often experience higher income disparities due to limited access to education, healthcare, and jobs, leading to Gini coefficients exceeding 0.4, exacerbating poverty cycles and social tensions. This inequality hampers economic growth, social cohesion, and upward mobility. Addressing it requires comprehensive policies, including progressive taxation and equitable resource distribution, to promote a more just and inclusive society.

Content

This dataset comprises historical information encompassing various indicators concerning Inequality in Income on a global scale. The dataset prominently features: ISO3, Country, Continent, Hemisphere, Human Development Groups, UNDP Developing Regions, HDI Rank (2021), and Inequality in Income from 2010 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Inequality in Income from 2010 to 2021 - Inequality in Income from year 2010 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

https://i.imgur.com/LIrXWPP.png" alt="">

Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by Image by pch.vector on Freepik

Thumbnail by: Image by Salary icons created by Freepik - Flaticon

Papua New Guinea PG: Prevalence of Undernourishment: % of Population data was reported at 27.700 % in 2022. This records an increase from the previous number of 27.000 % for 2021. Papua New Guinea PG: Prevalence of Undernourishment: % of Population data is updated yearly, averaging 27.450 % from Dec 2001 (Median) to 2022, with 22 observations. The data reached an all-time high of 29.200 % in 2010 and a record low of 26.300 % in 2013. Papua New Guinea PG: Prevalence of Undernourishment: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Papua New Guinea – Table PG.World Bank.WDI: Social: Health Statistics. Prevalence of undernourishments is the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. Data showing as 2.5 may signify a prevalence of undernourishment below 2.5%.;Food and Agriculture Organization (http://www.fao.org/faostat/en/#home).;Weighted average;This is the Sustainable Development Goal indicator 2.1.1[https://unstats.un.org/sdgs/metadata/].