North Macedonia

  • Updated 27 July 2022 | Dataset date: January 01, 1960-December 31, 2021
    Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. For the 70 percent of the world's poor who live in rural areas, agriculture is the main source of income and employment. But depletion and degradation of land and water pose serious challenges to producing enough food and other agricultural products to sustain livelihoods here and meet the needs of urban populations. Data presented here include measures of agricultural inputs, outputs, and productivity compiled by the UN's Food and Agriculture Organization.
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    This dataset updates: Every month
  • Updated 27 July 2022 | Dataset date: January 01, 2011-December 31, 2021
    Data used to update country toplines in HDX. Contains data from World Bank's data portal.
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    This dataset updates: Every month
  • Updated 23 July 2022 | Dataset date: January 01, 2016-December 31, 2021
    The Safeguarding Health in Conflict Coalition (SHCC) is made up of 40 health provider organizations, humanitarian groups, human rights organizations, NGOs, and academic programs to take action to protect health workers and end attacks against them. This page is managed by SHCC member Insecurity Insight.
    8400+ Downloads
    This dataset updates: As needed
  • Data collated by UNHCR, containing information about forcibly displaced populations and stateless persons, spanning across more than 70 years of statistical activities. The data includes the countries / territories of asylum and origin. Specific resources are available for end-year population totals, demographics, asylum applications, decisions, and solutions availed by refugees and IDPs (resettlement, naturalisation or returns).
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    This dataset updates: Every six months
  • Updated 16 July 2022 | Dataset date: January 01, 2019-June 30, 2022
    This dataset includes incidents affecting the affecting the protection of IDPs and refugees. The data contains incidents identified in open sources. Categorized by country and with links to relevant Monthly News Brief.
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    This dataset updates: As needed
  • Updated 30 June 2022 | Dataset date: June 30, 2022-June 30, 2022
    Republic of North Macedonia population density for 400m H3 hexagons. Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution. Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand (LINZ Data Service) NZ Building Outlines and OpenStreetMap data. Gobal version of population dataset: Kontur Population: Global Population Density for 400m H3 Hexagons
    This dataset updates: As needed
  • Updated 28 June 2022 | Dataset date: February 17, 2021-March 03, 2021
    In partnership with Yale, Meta launched a climate change opinion survey that explores public climate change knowledge, attitudes, policy preferences, and behaviors. The 2022 survey includes respondents from nearly 200 countries and territories. We are sharing country level data from this survey, providing policymakers, research institutions, and nonprofits with an international view of public climate change opinion. For more information please see https://dataforgood.facebook.com/dfg/tools/climate-change-opinion-survey If you're interested in becoming a research partner and accessing record level data, please email dataforgood@fb.com.
    600+ Downloads
    This dataset updates: Every year
  • Updated Live | Dataset date: February 08, 2021-August 16, 2022
    Number of children 6-59 months admitted for TREATMENT OF SEVERE ACUTE MALNUTRITION (SAM) by country
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    This dataset updates: Live
  • Updated 28 June 2022 | Dataset date: August 02, 2021-August 16, 2022
    This dataset contains the number of confirmed cases, recoveries and deaths by country and subnational region due to the Coronavirus pandemic in Europe. Since the outbreak of the COVID-19 crisis, the Joint Research Centre (JRC) has been supporting the European Commission in multidisciplinary areas to understand the COVID-19 emergency, anticipate its impacts, and support contingency planning. This data provides an overview of the monitoring in the area of the 34 UCPM Participating States plus Switzerland related to sub-national data (admin level 1) on numbers of contagious and fatalities by COVID-19, collected directly from the National Authoritative sources (National monitoring websites, when available). The sub-national granularity of the data allows to have a fit-for-purpose model to early capture the local spread and response to the COVID-19 outbreak. The data is maintained on the JRC COVID-19 Github Repository
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    This dataset updates: As needed
  • Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. "People Displaced" refers to the number of people living in displacement as of the end of each year. "New Displacement" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once. Contains data from IDMC's Global Internal Displacement Database.
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    This dataset updates: Every year
  • Updated 31 May 2022 | Dataset date: March 01, 2020-May 22, 2022
    NOTE: We plan to no longer update this dataset after May 22 2022. These data sets are intended to inform researchers and public health experts about how populations are responding to physical distancing measures. In particular, there are two metrics, Change in Movement and Stay Put, that provide a slightly different perspective on movement trends. Change in Movement looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures, while Stay Put looks at the fraction of the population that appear to stay within a small area during an entire day. Full details, including the privacy protections in this data, are available here: https://research.fb.com/blog/2020/06/protecting-privacy-in-facebook-mobility-data-during-the-covid-19-response/
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    This dataset updates: As needed
  • Updated 24 May 2022 | Dataset date: September 01, 2016-April 01, 2018
    More than 200 million businesses use Facebook. Meta partners with various academic and international organizations to survey these businesses throughout the year to learn about their perspectives, challenges and opportunities. With more businesses leveraging online tools each day, these surveys provide a lens into a new mobilized and digital economy. The Future of Business Survey (FoBS) is a collaboration between Meta, the OECD and the World Bank to provide timely insights on the perceptions, challenges, and outlook of online Small and Medium Enterprises or Businesses (SMEs/SMBs). The target population consists of SMEs that have an active Facebook business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. The Future of Business Survey was first launched as a monthly survey in 17 countries in February 2016 and expanded to 42 countries in 2018. In 2019, the Future of Business survey increased coverage to 97 countries and moved to a bi-annual cadence. In 2020, the Future of Business Survey shifted to do additional monthly waves with questions focused on business challenges related to COVID-19. Meta also shares information from other SMB surveys, such as those that feed into the Global State of Small Business Reports, which are fielded to active Facebook Page Administrators and the general population of Facebook users. These surveys are conducted in approximately 30 countries across the globe and are also conducted on a roughly bi-annual basis. Survey questions for all surveys cover a range of topics depending on the survey wave such as business characteristics, challenges, financials and strategy in addition to custom modules related to regulation, gender inequity, access to finance, digital technologies, reduction in revenues, business closures, reduction of employees and challenges/needs of the business. Aggregated country level data is available to the public here for each wave and controlled access microdata is available to Data for Good partners. To request access to survey microdata or to see published Meta reports, please visit: futureofbusinesssurvey.org Update 12/12/21: We have transitioned all small business survey datasets to have survey weights applied. Files with weighted estimates have now replaced the previously posted unweighted estimates. Update 10/4/21: Aggregate data in files [gsosb_2021julyaugust_data_aggregate_weighted.csv] and [gsosb_2021february_data_aggregate_weighted.csv] have been updated to provide weighted results and to correct an error found in those two files. These files were discovered to present results for large businesses rather than small and medium businesses and have been updated to provide the information for the latter group. Downloads of these two files that took place between April 1 and September 30 2021 were affected and analysis using downloads from this period may need to be amended. No analyses or reports published by Facebook were affected. Please contact dataforgood@fb.com for any questions.
    14000+ Downloads
    This dataset updates: As needed
  • Updated 14 April 2022 | Dataset date: April 07, 2022-April 07, 2022
    North Macedonia administrative division with aggregated population. Built from Kontur Population: Global Population Density for 400m H3 Hexagons on top of OpenStreetMap administrative boundaries data. Enriched with HASC codes for regions taken from Wikidata. Gobal version of boundaries dataset: Kontur Boundaries: Global administrative division with aggregated population
    This dataset updates: As needed
  • Updated 10 March 2022 | Dataset date: December 31, 2021-December 31, 2021
    The INFORM subnational model for South East Europe for 3 pilot countries: Albania, Montenegro and North Macedonia, was initiated by UNDRR Regional Office for Europe and Central Asia (ROECA) and the Secretariat of Disaster Preparedness and Prevention Initiative for South East Europe (DPPI SEE) upon prior consent of all DPPI SEE Member States. The INFORM model has been developed under supervision of the EC's JRC INFORM team and financial support from USAID BHA. The INFORM risk index results were produced in close collaboration with national and regional organization/data providers followed by regular meetings of the Regional Working Group (RWG) on INFORM. The RWG members are 2-3 representatives from each Government in the pilot countries (represented by their national civil protection authorities), including the Sendai Monitor National Focal Point, DPPI SEE Secretariat and UNDRR ROECA. Partners hope to use the model to improve cooperation between humanitarian and development actors in managing risk and building resilience across the region. INFORM identifies areas at a high risk of humanitarian crisis that are more likely to require international assistance. The INFORM model is based on risk concepts published in scientific literature and envisages three dimensions of risk: Hazards & Exposure, Vulnerability and Lack of Coping Capacity. The INFORM model is split into different levels to provide a quick overview of the underlying factors leading to humanitarian risk. The INFORM subnational model for South East Europe is developed at the first administrative level (corresponding to the subnational regions, capitals and municipalities) of Albania, Montenegro and North Macedonia. The INFORM risk index was calculated for 44 administrative units. The INFORM index supports a proactive disaster risk management framework. It will be helpful for an objective allocation of resources for disaster risk reduction and management as well as for coordinated actions focused on anticipating, mitigating, and preparing for humanitarian emergencies. It also identifies areas for improvement in national disaster data availability and compliance with implementation of Sendai Framework for DRR, SDGs and other global initiatives.
    30+ Downloads
    This dataset updates: Every year
  • Updated 11 February 2022 | Dataset date: April 01, 2021-August 16, 2022
    The Relative Wealth Index predicts the relative standard of living within countries using de-identified connectivity data, satellite imagery and other nontraditional data sources. The data is provided for 93 low and middle-income countries at 2.4km resolution. Please cite / attribute any use of this dataset using the following: Microestimates of wealth for all low- and middle-income countries Guanghua Chi, Han Fang, Sourav Chatterjee, Joshua E. Blumenstock Proceedings of the National Academy of Sciences Jan 2022, 119 (3) e2113658119; DOI: 10.1073/pnas.2113658119 More details are available here: https://dataforgood.fb.com/tools/relative-wealth-index/ Research publication for the Relative Wealth Index is available here: https://www.pnas.org/content/119/3/e2113658119 Press coverage of the release of the Relative Wealth Index here: https://www.fastcompany.com/90625436/these-new-poverty-maps-could-reshape-how-we-deliver-humanitarian-aid An interactive map of the Relative Wealth Index is available here: http://beta.povertymaps.net/
    17000+ Downloads
    This dataset updates: As needed
  • Updated 14 January 2022 | Dataset date: September 21, 2020-September 21, 2020
    This data contains aggregated weighted statistics at the regional level by gender for the 2020 Survey on Gender Equality At Home as well as the country and regional level for the 2021 wave. The Survey on Gender Equality at Home generates a global snapshot of women and men’s access to resources, their time spent on unpaid care work, and their attitudes about equality. Researchers and nonprofits interested in access to survey microdata can apply at: https://dataforgood.facebook.com/dfg/tools/survey-on-gender-equality-at-home
    3500+ Downloads
    This dataset updates: Every year
  • Updated 15 December 2021 | Dataset date: October 13, 2021-October 13, 2021
    We use an anonymized snapshot of all active Facebook users and their friendship networks to measure the intensity of connectedness between locations. The Social Connectedness Index (SCI) is a measure of the social connectedness between different geographies. Specifically, it measures the relative probability that two individuals across two locations are friends with each other on Facebook. Details on the underlying data and the construction of the index are provided in the “Facebook Social Connectedness Index - Data Notes.pdf” file. Please also see https://dataforgood.fb.com/ as well as the associated research paper “Social Connectedness: Measurement, Determinants and Effects,” published in the Journal of Economic Perspectives (https://www.aeaweb.org/articles?id=10.1257/jep.32.3.259). Region identifiers are taken from GADM v2.8 https://gadm.org/download_country_v2.html. Future versions will update IDs to be compatible with the newest GADM version.
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    This dataset updates: As needed
  • Updated 14 December 2021 | Dataset date: October 07, 2020-October 15, 2022
    This table contains subnational multidimensional poverty data from the data tables published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the severe deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. The global MPI 2021 methodology is detailed in Alkire, Kanagaratnam & Suppa (2021).
    50+ Downloads
    This dataset updates: Every year
  • Live list of active aid activities for The former Yugoslav Republic of Macedonia shared via the International Aid Transparency Initiative (IATI). Includes both humanitarian and development activities. More information on each activity (including financial data) is available from http://www.d-portal.org
    This dataset updates: Live
  • Updated 4 August 2021 | Dataset date: January 01, 1970-December 31, 2019
    Education indicators for Republic of North Macedonia. Contains data from the UNESCO Institute for Statistics bulk data service covering the following categories: National Monitoring (made 2021 March), SDG 4 Global and Thematic (made 2021 March), Demographic and Socio-economic (made 2021 March)
    1000+ Downloads
    This dataset updates: Every three months
  • Updated 18 July 2021 | Dataset date: January 01, 2000-December 31, 2020
    Food Security Indicators for Republic of North Macedonia. Contains data from the FAOSTAT bulk data service.
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    This dataset updates: Every year
  • Updated 4 May 2021 | Dataset date: January 01, 1990-December 31, 2030
    The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities. The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.
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    This dataset updates: Every year
  • Under the leadership of UNDP and DCO, an inter-agency task team developed the UN framework for the immediate socio-economic response to COVID-19 (adopted in April 2020) to govern its response over 12 to 18 months. To measure the UN’s support to the socio-economic response and recovery, UN entities developed a simple monitoring framework with 18 programmatic indicators (endorsed by the UNSDG in July 2020). Lead entities – based on their mandate and comparative advantage – were nominated to lead the development of methodological notes for each indicator and lead the collection of data at the country level. These lead entities reported through the Office of the Resident Coordinators the collective UN results on a quarterly basis through UN Info. All 2020 data was reported by March 2021. This is the UN development system’s first comprehensive attempt at measuring its collective programming contribution and results. These programmatic indicators enabled the UN system to monitor the progress and achievements of UNCT’s collective actions in socio-economic response. In support of the Secretary-General’s call for a "… single, consolidated dashboard to provide up-to-date visibility on [COVID-19] activities and progress across all pillars” all data was published in real time on the COVID-19 data portal, hosted by DCO. The data is disaggregated by geography (rural/urban), sex, age group and at-risk populations -- to measure system-wide results on the socio-economic response to the pandemic, in order to ensure UNDS accountability and transparency for results.
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    This dataset updates: Every three months
  • Updated 24 November 2020 | Dataset date: January 01, 2000-December 31, 2020
    WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator) -Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method. -Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674
    10+ Downloads
    This dataset updates: Every year
  • Updated 24 November 2020 | Dataset date: January 01, 2000-December 31, 2020
    WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below. These are 100m resolution gridded population estimates using customized methods ("bottom-up" and/or "top-down") developed for the latest data available from each country. They can also be visualised and explored through the woprVision App. The remaining datasets in the links below are produced using the "top-down" method, with either the unconstrained or constrained top-down disaggregation method used. Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively): - Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020. - Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020. - Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019) -Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019). -Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the "Unconstrained individual countries 2000-2020" datasets. -Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020. -Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national population estimates (UN 2019). Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing "top-down" methods and time periods are still available for download here: Individual countries and Whole Continent. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645
    10+ Downloads
    This dataset updates: Every year