| Language | Approved | Translated | Unfinished | Unfinished words | Unfinished characters | Untranslated | Checks | Suggestions | Comments | |
|---|---|---|---|---|---|---|---|---|---|---|
| English MIT | 0 | 0 | 0 | 0 | 6 | 0 | 0 | |||
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| Arabic MIT | 0% | 3% | 31 | 13,228 | 230,531 | 31 | 1 | 0 | 0 | |
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| French MIT | 0% | 40% | 19 | 12,108 | 223,023 | 4 | 7 | 0 | 0 | |
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| Portuguese MIT | 0% | 0% | 32 | 13,237 | 230,585 | 32 | 0 | 0 | 0 | |
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| Russian MIT | 0% | 0% | 32 | 13,237 | 230,585 | 32 | 0 | 0 | 0 | |
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| Spanish MIT | 0% | 0% | 32 | 13,237 | 230,585 | 32 | 0 | 0 | 0 | |
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Overview
| Project website | github.com/worldbank/sdg-metadata |
|---|---|
| Instructions for translators | This project is limited to Russian translation only, for now. More detailed instructions to come. |
| Project maintainers |
 brockfanning
|
| Translation license | MIT License |
| Translation process |
|
| Source code repository |
https://github.com/worldbank/sdg-metadata
|
| Repository branch | master |
| Last remote commit |
Merge pull request #542 from weblate/weblate-sdg-metadata-1-1-1a
85d59a4a6e9
brockfanning authored 2 weeks ago |
| Last commit in Weblate |
Added translation using Weblate (French)
45fdb83e15c
Weblate authored 8 months ago |
| Weblate repository |
https://hosted.weblate.org/git/sdg-metadata/1-1-1a/
|
| File mask | translations-metadata/*/1-2-2.yml |
| Monolingual base language file | translations-metadata/en/1-2-2.yml |
yesterday
String statistics
| Strings percent | Hosted strings | Words percent | Hosted words | Characters percent | Hosted characters | |
|---|---|---|---|---|---|---|
| Total | 192 | 79,422 | 1,383,510 | |||
| Source | 32 | 13,237 | 230,585 | |||
| Approved | 0% | 0 | 0% | 0 | 0% | 0 |
| Waiting for review | 7% | 14 | 1% | 1,138 | 1% | 7,616 |
| Translated | 23% | 46 | 18% | 14,375 | 17% | 238,201 |
| Needs editing | 7% | 15 | 15% | 12,069 | 16% | 222,746 |
| Read-only | 16% | 32 | 16% | 13,237 | 16% | 230,585 |
| Failing checks | 7% | 14 | 28% | 22,460 | 31% | 433,688 |
| Strings with suggestions | 0% | 0 | 0% | 0 | 0% | 0 |
| Untranslated strings | 68% | 131 | 66% | 52,978 | 66% | 922,563 |
Quick numbers
and previous 30 days
Trends of last 30 days
—
Hosted words
+100%
—
Hosted strings
+100%
—
Translated
+23%
—
Contributors
—
 None
Resource updated |
|
|
None
String updated in the repository |
<table>
<tbody> <tr> <td> <p><strong>Country</strong></p> </td> <td> <p>Reference</p> </td> </tr> <tr> <td> <p><strong>Afghanistan</strong></p> </td> <td> <p>(2016)</p> <p>Official publication: <a href="https://www.mppn.org/wp-content/uploads/2019/03/AFG_2019_vs9_online.pdf">Afghanistan Multidimensional Poverty Index 2016-2017</a> </p> <p>(2019) </p> <p>Official publication: <a href="https://microdatalib.worldbank.org/index.php/catalog/12377/related-materials">Income and Expenditure & Labor Force Survey 2019-2020</a> </p> </td> </tr> <tr> <td> <p><strong>Albania</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a> </p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Angola</strong></p> </td> <td> <p>Official publication: <a href="https://www.unicef.org/esa/sites/unicef.org.esa/files/2019-01/UNICEF-Angola-2018-A-Multidimensional-Analysis-of-Child-Poverty.pdf">Childhood in Angola - A Multidimensional Analysis of Child Poverty</a>/ </p> <p><a href="https://ophi.org.uk/wp-content/uploads/Angola_PM_2020.pdf">Pobreza Multidimensional em Angola</a> </p> </td> </tr> <tr> <td> <p><strong>Armenia</strong></p> </td> <td> <p>(2010-2017)<br>Official publication: <a href="https://www.armstat.am/en/?nid=82&id=2095">Social Snapshot and Poverty in Armenia: Statistical and analytical report, 2018</a> </p> <p>Methodological documentation: <a href="http://documents.worldbank.org/curated/en/111701504028830403/The-many-faces-of-deprivation-a-multidimensional-approach-to-poverty-in-Armenia">The Many Faces of Deprivation: A Multidimensional Approach to Poverty in Armenia</a> </p> <p><br>(2018)<br>Official publication: <a href="https://armstat.am/am/?nid=82&id=2217">Social Snapshot and Poverty in Armenia, 2019</a> </p> <p>Methodological documentation: <a href="http://documents.worldbank.org/curated/en/111701504028830403/The-many-faces-of-deprivation-a-multidimensional-approach-to-poverty-in-Armenia">The Many Faces of Deprivation: A Multidimensional Approach to Poverty in Armenia</a> </p> <p>(2019)</p> <p>Official publication: <a href="https://armstat.am/en/?nid=82&id=2438">Social Snapshot and Poverty in Armenia, 2021</a></p> <p>Methodological documentation: <a href="http://documents.worldbank.org/curated/en/111701504028830403/The-many-faces-of-deprivation-a-multidimensional-approach-to-poverty-in-Armenia">The Many Faces of Deprivation: A Multidimensional Approach to Poverty in Armenia</a> </p> </td> </tr> <tr> <td> <p><strong>Austria</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a> </p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Belgium</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a> </p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Bhutan</strong></p> </td> <td> <p>(2010)</p> <p>Official publication: CHILD POVERTY IN BHUTAN: Insights from Multidimensional Child Poverty Index and Qualitative Interviews with Poor Children</p> <p>(2012, 2017)</p> <p>Official publication: <a href="https://ophi.org.uk/wp-content/uploads/Bhutan_2017_vs5_23Dec_online.pdf">Bhutan Multidimensional Poverty Index 2017</a> </p> </td> </tr> <tr> <td> <p><strong>Bulgaria</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a> </p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Burundi</strong></p> </td> <td> <p>Official publication: </p> <p><a href="https://www.ilo.org/surveyLib/index.php/catalog/2153/download/18083">Rapport de l’enquête modulaire sur les conditions de vie des ménages 2013/2014</a> /</p> <p><a href="https://www.unicef.org/esa/sites/unicef.org.esa/files/2018-09/UNICEF-Burundi-2017-Child-Poverty.pdf">La Pauvreté des Enfants au Burundi</a></p> </td> </tr> <tr> <td> <p><strong>Chile</strong></p> </td> <td> <p>(2011 and 2013)<br>Official publication: <a href="http://www.desarrollosocialyfamilia.gob.cl/pdf/upload/Libro_IDS_2015_final.pdf">Informe de desarrollo social 2015</a><br>(2015 and 2017)<br>Official publication: <a href="http://observatorio.ministeriodesarrollosocial.gob.cl/storage/docs/casen/2017/Resultados_pobreza_Casen_2017.pdf">http://observatorio.ministeriodesarrollosocial.gob.cl/storage/docs/casen/2017/Resultados_pobreza_Casen_2017.pdf</a> </p> </td> </tr> <tr> <td> <p><strong>Colombia</strong></p> </td> <td> <p>(2010)</p> <p>Official publication: <a href="https://www.dane.gov.co/files/investigaciones/condiciones_vida/pobreza/2018/bt_pobreza_multidimensional_18.pdf">Pobreza multidimensional en Colombia</a></p> <p>(2011-2020)</p> <p>Official publication: <a href="https://www.dane.gov.co/files/investigaciones/condiciones_vida/pobreza/2020/presentacion-rueda-de-prensa-pobreza-multidimensional-20.pdf">Pobreza Multidimensional</a></p> </td> </tr> <tr> <td> <p><strong>Costa Rica</strong></p> </td> <td> <p>Official publication: <a href="https://admin.inec.cr/sites/default/files/2022-10/reenaho2022.pdf">Encuesta Nacional de Hogares Julio 2022 RESULTADOS GENERALES</a> <br><br></p> </td> </tr> <tr> <td> <p><strong>Croatia</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a> </p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a> </p> </td> </tr> <tr> <td> <p><strong>Cyprus</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a> </p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Czechia</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a> </p> </td> </tr> <tr> <td> <p><strong>Denmark</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Dominican Republic</strong></p> </td> <td> <p>(2010-2016)</p> <p>Official publication: <a href="https://mepyd.gob.do/wp-content/uploads/drive/UAAES/Publicaciones/El%20indice%20de%20Pobreza%20Multidimensional%20para%20America%20Latina.pdf">The <br>Multidimensional Poverty Index for Latin America (MPI-LA): an application for the Dominican Republic 2000-2016</a>. </p> <p>(2017-2019)</p> <p>Official publication: <a href="https://mepyd.gob.do/sisdom">Sistema de Indicadores Sociales de la República Dominicana SISDOM 19</a></p> </td> </tr> <tr> <td> <p><strong>Ecuador</strong></p> </td> <td> <p>Official publication: <a href="https://www.ecuadorencifras.gob.ec/documentos/web-inec/POBREZA/2019/Diciembre-2019/Tabulados%20IPM-dic%2019.xlsx">National Employment, Underemployment and Unemployment Survey (ENEMDU) 2019</a> </p> </td> </tr> <tr> <td> <p><strong>Egypt</strong></p> </td> <td> <p>Official publication: <a href="https://www.unicef.org/egypt/reports/understanding-child-multidimensional-poverty-egypt">Understanding Multidimensional Poverty in Egypt</a> </p> </td> </tr> <tr> <td> <p><strong>El Salvador</strong></p> </td> <td> <p>Official publication: <a href="https://cepei.org/wp-content/uploads/2020/01/Informe_ODS-1.pdf">INFORME EL SALVADOR 2019</a></p> <p>Methodological documentation: <a href="https://www.cepal.org/sites/default/files/presentations/08-10-el_salvador-medicion-multidimencional-pobreza.pdf">EHMP 2016 El Salvador</a>/ <a href="https://www.transparencia.gob.sv/institutions/capres/documents/292084/download#:~:text=La%20intensidad%20de%20la%20pobreza,saneamiento%20(83.8%20%25)%20y%20las">Informe MMP 2017</a>. </p> </td> </tr> <tr> <td> <p><strong>Estonia</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Finland</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a> </p> </td> </tr> <tr> <td> <p><strong>France</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a> </p> </td> </tr> <tr> <td> <p><strong>Germany</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Ghana</strong></p> </td> <td> <p>(2010)</p> <p>Official publication: <a href="https://www.undp.org/content/dam/ghana/docs/Doc/Inclgro/Non-Monetary%20Poverty%20in%20Ghana%20(24-10-13).pdf">Non-Monetary Poverty in Ghana</a> </p> <p>(2011, 2016, 2018)</p> <p>Official publication: <a href="https://www.gh.undp.org/content/dam/ghana/docs/Reports/UNDP_GH_MPI_Report_2020.pdf">Ghana Multidimensional Poverty Index (MPI) report 2020</a></p> <p>(2017)</p> <p>Official publication: <a href="https://www.unicef.org/ghana/media/2676/file/Multi-Dimensional%20Child%20Poverty%20Report.pdf">Multi-Dimensional Child Poverty in Ghana</a> </p> </td> </tr> <tr> <td> <p><strong>Greece</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Guatemala</strong></p> </td> <td> <p>Official publication: </p> <p><a href="https://www.mintrabajo.gob.gt/images/Servicios/DGT/ComisionNacionalSalario/InformacionGeneral/MIDES/Estad%C3%ADsticas_Indic%C3%A9_de_Pobreza_Multidimensional_2014.xlsx">https://www.mintrabajo.gob.gt/images/Servicios/DGT/ComisionNacionalSalario/InformacionGeneral/MIDES/Estad%C3%ADsticas_Indic%C3%A9_de_Pobreza_Multidimensional_2014.xlsx</a></p> </td> </tr> <tr> <td> <p><strong>Guinea</strong></p> </td> <td> <p>Official publication : </p> <p><a href="http://www.stat-guinee.org/images/Documents/Publications/INS/rapports_enquetes/RGPH3/RGPH3_rapport_pauvrete.pdf">RECENSEMENT GENERAL DE LA POPULATION ET DE L’HABITATION</a> </p> </td> </tr> <tr> <td> <p><strong>Guinea Bissau</strong></p> </td> <td> <p>(2010, 2014)</p> <p>Official publication: <a href="https://uprdoc.ohchr.org/uprweb/downloadfile.aspx?filename=7579&file=Annexe7https://uprdoc.ohchr.org/uprweb/downloadfile.aspx?filename=7579&file=Annexe7">PAUVRETE MULTIDIMENSIONNELLE ET PRIVATIONS MULTIPLES DES ENFANTS EN GUINEE-BISSAU</a><br></p> </td> </tr> <tr> <td> <p><strong>Honduras</strong></p> </td> <td> <p>Official publication : </p> <p><a href="https://mppn.org/wp-content/uploads/2019/10/IPM_SINTESIS_SERIE_12_16_Final.pdf">Multidimensional Poverty Index 2012- 2016</a></p> </td> </tr> <tr> <td> <p><strong>Hungary</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Iceland</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Ireland</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Italy</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Kosovo</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Latvia</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Lesotho</strong></p> </td> <td> <p>Official publication:</p> <p><a href="https://lesotho.un.org/sites/default/files/2019-10/Lesotho%20child%20poverty_main%20report_4%20Oct.pdf">Child Poverty in Lesotho: Understanding the Extent of Multiple Overlapping Deprivation</a></p> </td> </tr> <tr> <td> <p><strong>Lithuania</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Luxembourg</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Malawi</strong></p> </td> <td> <p>(2013)</p> <p>Official publication: <a href="https://www.unicef.org/esa/sites/unicef.org.esa/files/2018-09/UNICEF-Malawi-2016-Child-Poverty.PDF">Child Poverty in Malawi</a></p> <p>(2016)</p> <p>Official publication: <a href="https://www.unicef.org/esa/sites/unicef.org.esa/files/2019-01/UNICEF-Malawi-2018-Child-Poverty-in-Malawi.pdf">Child Poverty in Malawi</a></p> </td> </tr> <tr> <td> <p><strong>Malaysia</strong></p> </td> <td> <p>(2014, 2016)</p> <p>Official publication: <a href="https://www.talentcorp.com.my/clients/TalentCorp_2016_7A6571AE-D9D0-4175-B35D-99EC514F2D24/contentms/img/publication/Mid-Term%20Review%20of%2011th%20Malaysia%20Plan.pdf">Mid-term Review of the Eleventh Malaysia Plan, 2016–2020: New Priorities and Emphases:</a> </p> <p>(2019)</p> <p>Official publication: </p> <p><a href="https://newss.statistics.gov.my/newss-portalx/ep/epFreeDownloadContentSearch.seam?cid=158397">https://newss.statistics.gov.my/newss-portalx/ep/epFreeDownloadContentSearch.seam?cid=158397</a></p> </td> </tr> <tr> <td> <p><strong>Maldives</strong></p> </td> <td> <p>Official publication: <a href="http://statisticsmaldives.gov.mv/nbs/wp-content/uploads/2020/06/Multidimensional-Poverty-in-Maldives-2020_4th-june.pdf">National Multidimensional Poverty in Maldives 2020</a> </p> </td> </tr> <tr> <td> <p><strong>Mali</strong></p> </td> <td> <p>(2015)</p> <p>Official publication : <a href="https://www.unicef.org/mali/rapports/privation-multidimensionnelle-et-pauvret%C3%A9-des-enfants-au-mali">Privation multidimensionnelle et pauvreté des enfants au Mali</a></p> <p>(2016)</p> <p>Official publication : <a href="https://www.instat-mali.org/laravel-filemanager/files/shares/eq/rap-ind16-17_eq.pdf">La pauvreté à plusieurs dimensions au Mali</a></p> </td> </tr> <tr> <td> <p><strong>Malta</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Mexico</strong></p> </td> <td> <p>(2010, 2012, 2014)<br>Official publication: <a href="https://www.coneval.org.mx/Medicion/MP/Paginas/Pobreza-2018.aspx">https://www.coneval.org.mx/Medicion/MP/Paginas/Pobreza-2018.aspx</a></p> <p>Methodological documentation: https://www.coneval.org.mx/Informes/Coordinacion/Publicaciones%20oficiales/MEDICION_MULTIDIMENSIONAL_SEGUNDA_EDICION.pdf<br><br>(2016, 2018, 2020)<br>Official publication: <a href="https://www.coneval.org.mx/Medicion/MP/Paginas/Pobreza_2020.aspx">https://www.coneval.org.mx/Medicion/MP/Paginas/Pobreza_2020.aspx</a></p> <p>Methodological documentation: <a href="https://www.coneval.org.mx/InformesPublicaciones/InformesPublicaciones/Documents/Metodologia-medicion-multidimensional-3er-edicion.pdf">https://www.coneval.org.mx/InformesPublicaciones/InformesPublicaciones/Documents/Metodologia-medicion-multidimensional-3er-edicion.pdf</a></p> </td> </tr> <tr> <td> <p><strong>Montenegro</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Morocco</strong></p> </td> <td> <p>(2011)<br>Official publication: <a href="https://www.hcp.ma/Les-Cahiers-du-Plan-N-43-Mars-Avril-2013_a1248.html">Principaux résultats de l’Enquête nationale sur l’anthropométrie 2011</a><strong> </strong><br>(2014)<br>Official publication: <a href="https://www.hcp.ma/file/198688/">Principaux résultats de la cartographie de la pauvreté multidimensionnelle 2004 - 2014 : Paysage territorial et dynamique</a></p> </td> </tr> <tr> <td> <p><strong>Mozambique</strong></p> </td> <td> <p>Official publication: <a href="https://www.wider.unu.edu/sites/default/files/Final_QUARTA%20AVALIA%C3%87AO%20NACIONAL%20DA%20POBREZA_2016-10-26_2.pdf">Poverty and Well-being in Mozambique: Fourth National Poverty Assessment (IOF 2014/2015) </a> </p> </td> </tr> <tr> <td> <p><strong>Namibia</strong></p> </td> <td> <p>Official publication: <a href="https://www.unicef.org/esa/media/9041/file/UNICEF-Namibia-Multidimensional-Poverty-Index-2021.pd">Namibia Multidimensional Poverty Index (MPI) report 2021</a></p> </td> </tr> <tr> <td> <p><strong>Nepal</strong></p> </td> <td> <p>(2011)</p> <p>Official publication: <a href="https://www.npc.gov.np/images/category/Nepal_MPI.pdf">Nepal Multidimensional Poverty Index 2018</a> <br>(2014,2019)</p> <p>Official publication: <a href="https://mppn.org/wp-content/uploads/2021/08/MPI_Report_2021_for_web.pdf">Nepal Multidimensional Poverty Index </a> </p> </td> </tr> <tr> <td> <p><strong>Netherlands</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Nigeria</strong></p> </td> <td> <p>(2017)</p> <p>Official publication: <a href="https://hdr.undp.org/content/national-human-development-report-2018-nigeria">National Human Development Report 2018</a></p> <p>(2021)</p> <p>Official publication: <a href="https://nigerianstat.gov.ng/elibrary/read/1241254"><strong> </strong>Nigeria Multidimensional Poverty Index</a></p> </td> </tr> <tr> <td> <p><strong>North Macedonia</strong></p> </td> <td> <p>(2010)</p> <p>Official publication: <a href="http://www.stat.gov.mk/Publikacii/2.4.15.01.pdf">Survey on Income and Living Conditions, 2012</a></p> <p>(2011-2013)<br>Official publication: <a href="http://www.stat.gov.mk/Publikacii/2.4.15.13.pdf)">Survey on Income and Living Conditions, 2013</a><br>(2014-2016)<br>Official publication: <a href="http://www.stat.gov.mk/Publikacii/2.4.17.13.pdf">Survey on Income and Living Conditions, 2016</a><br>(2017)<br>Official publication: <a href="http://www.stat.gov.mk/Publikacii/2.4.18.13.pdf">Survey on Income and Living Conditions, 2017</a> <br>(2018)<br>Official publication: <a href="http://makstat.stat.gov.mk/PXWeb/pxweb/en/MakStat/MakStat__ZivotenStandard__LaekenIndikatorSiromastija/425_ZivStd_Mk_LaekenAROPE_ml.px/?rxid=46ee0f64-2992-4b45-a2d9-c">http://makstat.stat.gov.mk/PXWeb/pxweb/en/MakStat/MakStat__ZivotenStandard__LaekenIndikatorSiromastija/425_ZivStd_Mk_LaekenAROPE_ml.px/?rxid=46ee0f64-2992-4b45-a2d9-c</a></p> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p>Methodological documentation: <a href="http://www.stat.gov.mk/MetodoloskiObjasSoop_en.aspx?id=115&rbrObl=13">Laeken Poverty Indicators</a></p> </td> </tr> <tr> <td> <p><strong>Norway</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a> </p> </td> </tr> <tr> <td> <p><strong>Pakistan</strong></p> </td> <td> <p>Official publication: <a href="https://www.undp.org/content/dam/pakistan/docs/MPI/Multidimensional%20Poverty%20in%20Pakistan.pdf">Multidimensional Poverty in Pakistan</a></p> </td> </tr> <tr> <td> <p><strong>Palestine</strong></p> </td> <td> <p>Official publication: <a href="https://mppn.org/wp-content/uploads/2020/06/book2524-Palestine-28-48.pdf">Multi-dimensional Poverty Profile in Palestine, 2017</a> </p> </td> </tr> <tr> <td> <p><strong>Panama</strong></p> </td> <td> <p>(2017)</p> <p>Official publication: <a href="https://www.mides.gob.pa/wp-content/uploads/2017/06/Informe-del-%c3%8dndice-de-Pobreza-Multidimensional-de-Panam%c3%a1-2017.pdf">Panama Multidimensional Poverty Index</a></p> <p>(2018)</p> <p>Official publication: <a href="https://www.mides.gob.pa/wp-content/uploads/2018/09/MEF_DAES-Informe-del-IPM-de-ni%c3%b1os-ni%c3%b1as-y-adolescentes-a%c3%b1o-2018.pdf">Multidimensional Poverty Index of Boys, Girlsand Adolescents in Panama - IPM-NNA</a></p> </td> </tr> <tr> <td> <p><strong>Paraguay</strong></p> </td> <td> <p>Official publication: <a href="https://www.ine.gov.py/Publicaciones/Biblioteca/documento/8e39_BOLETIN_TECNICO_IPM_2020.pdf">Multidimensional poverty index</a></p> </td> </tr> <tr> <td> <p><strong>Philippines</strong></p> </td> <td> <p>Official document: <a href="https://psa.gov.ph/poverty-press-releases/nid/136930">Philippine Statistics Authority press release</a> </p> <p>Methodological documentation: <a href="https://psa.gov.ph/sites/default/files/mpi%20technical%20notes.pdf">Technical notes on the estimation of the MPI based on the initial methodology</a> </p> </td> </tr> <tr> <td> <p><strong>Poland</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a> </p> </td> </tr> <tr> <td> <p><strong>Romania</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a> </p> </td> </tr> <tr> <td> <p><strong>Rwanda</strong></p> </td> <td> <p>Official publication: <a href="https://www.mppn.org/wp-content/uploads/2018/12/EICV5_Thematic-Report_Multidimensional-Poverty-Index_MPI.pdf">Rwanda Multidimensional Poverty Index Report, 2018</a> </p> </td> </tr> <tr> <td> <p><strong>Saint Lucia</strong></p> </td> <td> <p>Official publication: <a href="https://www.stats.gov.lc/wp-content/uploads/2019/01/Saint-Lucia-National-Report-of-Living-Conditions-2016-Final_December-2018.pdf">Saint Lucia National Report of Living Conditions 2016</a></p> </td> </tr> <tr> <td> <p><strong>São Tomé and Príncipe</strong></p> </td> <td> <p>Official publication: </p> <p><a href="https://www.academia.edu/24458392/Analyse_de_la_situation_des_enfants_et_des_femmes_%C3%A0_S%C3%A3o_Tom%C3%A9-et-Principe_en_2015">Analyse de la situation des enfants et des femmes à São Tomé-et-Principe en 2015</a> </p> </td> </tr> <tr> <td> <p><strong>Serbia</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a> </p> </td> </tr> <tr> <td> <p><strong>Seychelles</strong></p> </td> <td> <p>Official publication: <a href="https://www.nbs.gov.sc/downloads/social-statistics/multidimensional-poverty-index/2018">https://www.nbs.gov.sc/downloads/social-statistics/multidimensional-poverty-index/2018</a></p> </td> </tr> <tr> <td> <p><strong>Slovakia</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a> </p> </td> </tr> <tr> <td> <p><strong>Slovenia</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a> </p> </td> </tr> <tr> <td> <p><strong>South Africa</strong></p> </td> <td> <p>(2011)</p> <p>Official publication: <a href="http://www.statssa.gov.za/publications/Report-03-10-08/Report-03-10-082014.pdf">The South African MPI</a> </p> <p>(2016)</p> <p>Official publication: <a href="http://documents.worldbank.org/curated/en/530481521735906534/pdf/124521-REV-OUO-South-Africa-Poverty-and-Inequality-Assessment-Report-2018-FINAL-WEB.pdf">Overcoming Poverty and Inequality in South Africa</a> </p> </td> </tr> <tr> <td> <p><strong>Spain</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a> </p> </td> </tr> <tr> <td> <p><strong>Sri Lanka</strong></p> </td> <td> <p>(2016)</p> <p>Official publication: <a href="http://www.statistics.gov.lk/Resource/en/Poverty/GMPI_Bulletin2019.pdf">Global Multidimensional Poverty for Sri Lanka</a></p> <p>(2019)</p> <p>Official publication: <a href="http://www.statistics.gov.lk/Poverty/StaticalInformation/MultidimensionalPovertyinSriLanka-2019">Multidimensional Poverty in Sri Lanka</a> </p> </td> </tr> <tr> <td> <p><strong>Sweden</strong></p> </td> <td> <p>Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a> </p> </td> </tr> <tr> <td> <p><strong>Thailand</strong></p> </td> <td> <p>(2015)</p> <p>Official publication: <a href="https://www.unicef.org/thailand/sites/unicef.org.thailand/files/2019-09/unicef%20Thailand%20Child%20MPI%20Report-TH-for%20web%20v02_0.pdf">Thailand Child Poverty Report</a></p> <p>(2017)</p> <p>Official publication: <a href="http://social.nesdc.go.th/social/Portals/0/Documents/%e0%b8%a3%e0%b8%a7%e0%b8%a1%20NMPI%2007102019%20(1630)_2305.pdf">http://social.nesdc.go.th/social/Portals/0/Documents/%e0%b8%a3%e0%b8%a7%e0%b8%a1%20NMPI%2007102019%20(1630)_2305.pdf</a></p> <p>Methodological documentation:</p> <p><a href="http://www.nso.go.th/sites/2014en/Pages/survey/Social/Household/The-2017-Household-Socio-Economic-Survey.aspx">http://www.nso.go.th/sites/2014en/Pages/survey/Social/Household/The-2017-Household-Socio-Economic-Survey.aspx</a></p> </td> </tr> <tr> <td> <p><strong>Turkey</strong></p> </td> <td> <p> Official publication: <a href="https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en">People at risk of poverty or social exclusion by age and sex – EU 2030 target </a></p> <p><a href="https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf">Sustainable development in the European Union </a></p> </td> </tr> <tr> <td> <p><strong>Uganda</strong></p> </td> <td> <p>Official publication: <a href="https://www.ubos.org/release-of-the-multi-poverty-dimensional-index-report-2022">Multidimensional Poverty Index Report</a> </p> </td> </tr> <tr> <td> <p><strong>Vietnam</strong></p> </td> <td> <p>Official publication: <a href="https://www.gso.gov.vn/en/px-web/?pxid=E1144&theme=Health%2C%20Culture%2C%20Sport%20and%20Living%20standard">https://www.gso.gov.vn/en/px-web/?pxid=E1144&theme=Health%2C%20Culture%2C%20Sport%20and%20Living%20standard</a></p> </td> </tr> <tr> <td> <p><strong>Zambia</strong></p> </td> <td> <p>Official publication: <a href="https://www.unicef.org/zambia/reports/child-poverty-zambia-report-2018">Child Poverty in Zambia</a></p> </td> </tr> <tr> <td> <p><strong>Zimbabwe</strong></p> </td> <td> <p>Official publication: <a href="https://www.unicef.org/esa/media/10241/file/UNICEF-Zimbabwe-MODA-Child-Poverty-Report-2021.pdf">Child Poverty in Zimbabwe</a></p> </td> </tr> </tbody> </table> <p><strong>References:</strong></p> <p>Alkire, Sabina and James Foster (2007): “Counting and multidimensional poverty measurement”, Working Paper Nº 7 and No 32 (revised), Oxford Poverty and Human Development Initiative.</p> <p>Alkire, S., Roche, J. M., Ballon, P., Foster, J., Santos, M. E., & Seth, S. (2015). <em>Multidimensional poverty measurement and analysis</em>. Oxford University Press, USA.</p> <p>Beccaria, L. and Minujín, A. (1985) “Alternative methods for measuring the evolution of poverty” Proceedings of the 45th Session, ISI</p> <p>CONEVAL (2010). <em>Methodology for Multidimensional Poverty Measurement in Mexico</em>. Consejo Nacional de Evaluación de la Política de Desarrollo Social, Mexico City.</p> <p>Datt, G. (2017) “Distribution-sensitive multidimensional poverty measures with an application to India”, Monash Business School, Department of Economics, Discussion Paper number 6.</p> <p>Decancq, K. and M. A. Lugo. (2013). “Weights in multidimensional Indices of well-being: an overview”. <em>Econometric Reviews 32</em> (1): 7-34.</p> <p>Dixon, R., and M. Nussbaum (2012) “Children’s rights and a capabilities approach: The question of special priority”, 97 Cornell Law Review. Volume 97, number 37: 549-593.</p> <p>Erikson, R (1989) ‘Descriptions of Inequality: The Swedish Approach to Welfare Research’, UNU WIDER Working Paper 67</p> <p>Feres, J. C., & Mancero, X. (2001). <em>El método de las necesidades básicas insatisfechas (NBI) y sus aplicaciones en América Latina</em>. Cepal.</p> <p>Foster, James, Joel Greer and Erik Thorbecke (1984), “A class of decomposable poverty measures”, Econometrica, vol. 52, Nº 3</p> <p>Gordon, D. (2006). The concept and measurement of poverty. <em>Poverty and Social Exclusion in Britain. The Millennium Survey, Policy Press, Bristol</em>, 29-69.</p> <p>ILO (1976) Employment, Growth and Basic Needs: A One-World Problem, Geneva.</p> <p>Minujin, A. (1995) “Squeezed: the middle class in Latin America” Environment and Urbanization, Vol. 7, No. 2</p> <p>Morris, Morris D. (1978). ‘A physical quality of life index”. Urban Ecology, 3(3): 225–240.</p> <p>Narayan, D. (2000). <em>Voices of the poor: Can anyone hear us?</em>. World Bank.</p> <p>Streeten, Paul, Shahid Javed Burki, Mahbub Ul Haq, Norman Hicks and Frances Stewart (1981). First Things First: Meeting Basic Human Needs in the Developing Countries. World Bank.</p> <p>The Child Poverty Unit (2014). Child Poverty Act 2010, http://www.legislation.gov.uk/ukpga/2010/9/contents,</p> <p>UNICEF (2019) Measuring and monitoring child poverty: Position paper https://data.unicef.org/resources/measuring-and-monitoring-child-poverty/</p> <p>United Nations Economic Commission for Europe (2020) Poverty measurement: Guide to data disaggregation, ECE/CES/2020/9: Conference of European Statisticians: Geneva.</p> <p>World Bank (2017). <em>Monitoring Global Poverty: Report of the Commission on Global Poverty</em>. Washington, DC: World Bank. </p> <p>World Bank.2018. <em>Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle</em>. Washington, D.C: World Bank Group.</p> <p>World Bank, UNDP and UNICEF 2021. A Roadmap for Countries Measuring Multidimensional Poverty. Washington, DC: World Bank. License: Creative Commons Attribution CC BY 3.0 IGO.</p> |
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<p><strong>Comparability:</strong></p>
<p>As it was mentioned in section 4, the compiled data of SDG 1.2.2 are not intended to be comparable across countries due to national definitions. It is quite common that countries use a different number of dimensions and a variety of indicators depending on the country context. As SDG 1.2.2 explicitly says multidimensional poverty should be estimated in each country according to national definitions, this lack of comparability is not an issue.</p> <p><strong>Sources of discrepancies:</strong></p> <p>Given there is no custodian agency to estimate internationally comparable levels of multidimensional poverty, there are no, <em>stricto sensu</em>, challenges in terms of discrepancies. Nevertheless, sometimes agencies do calculate multidimensional poverty, using common and comparable dimensions, indicators, and thresholds for different types of reports or analyses. In these cases, it has to be remembered that these are not official (i.e. government sanctioned and approved) estimates. Most importantly, they should not be used to replace nationally owned estimates.</p> |
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<p><strong>Level of disaggregation:</strong></p>
<p>Official multidimensional poverty headcount (% population) is disaggregated by sex and age. The age band for official multidimensional poverty headcount for children is mostly 0-17, but some countries have different age definition for children, such as 0-15 in El Salvador. Geographically it is disaggregated by urban and rural areas.</p> <p><strong>Years of Reporting:</strong></p> <p>Years of reporting in the SDG 1.2.2 indicators are those when the source survey has been conducted except for the AROPE. When the survey year is split into two years, the first year has been reported. In AROPE, the reference period for all dimensions along with the indicators is disseminated as well as variables related to the materially deprived items in question in the survey year, except for age, income, variables on arrears, work intensity of the household, country of birth. As far as age is concerned, depending on the EU-SILC question, age can refer to two different moments in time: (i) age at the end of the income reference period; (ii) age at the date of interview. The age at the end of the income reference period is considered as the main age (e.g. it is used to define the statistical population, sample person, etc.). For income, the income reference period is a fixed 12-month period (such as the previous calendar or tax year). Variables on arrears refer to the last 12 months, while work intensity of the household refers to the number of months that all working age household members have been working during the income reference year. </p> <p><strong>Data Availability:</strong></p> <p>So far, 78 countries' multidimensional poverty measurements were reported and confirmed by SDG focal points. However, the availability of the multidimensional poverty indicator over time differs greatly from country to country. The following table 5 shows the years in which data is available for a country (the coloured boxes). The star mark indicates that data on multidimensional deprivation for children is available. </p> <p>Table 5: Headcount data availability for countries</p> <table> <tbody> <tr> <td> <p><strong>Country</strong></p> </td> <td> <p><strong>2011</strong></p> </td> <td> <p><strong>2012</strong></p> </td> <td> <p><strong>2013</strong></p> </td> <td> <p><strong>2014</strong></p> </td> <td> <p><strong>2015</strong></p> </td> <td> <p><strong>2016</strong></p> </td> <td> <p><strong>2017</strong></p> </td> <td> <p><strong>2018</strong></p> </td> <td> <p><strong>2019</strong></p> </td> <td> <p><strong>2020</strong></p> </td> <td> <p><strong>2021</strong></p> </td> <td> <p><strong>2022</strong></p> </td> </tr> <tr> <td> <p>Afghanistan</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p> PH</p> </td> <td></td> <td></td> <td></td> <td> <p> PH</p> </td> <td></td> <td></td> </tr> <tr> <td> <p>Albania</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p> PH</p> </td> <td> <p> PH</p> </td> <td> <p> PH</p> </td> <td> <p> PH</p> </td> <td></td> <td></td> </tr> <tr> <td> <p>Angola</p> </td> <td></td> <td></td> <td></td> <td></td> <td> <p> PH *</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Armenia</p> </td> <td> <p> PH</p> </td> <td> <p> PH</p> </td> <td> <p>PH *</p> </td> <td> <p>PH</p> </td> <td> <p>PH *</p> </td> <td> <p>PH *</p> </td> <td> <p>PH *</p> </td> <td> <p>PH</p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> </tr> <tr> <td> <p>Austria</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Belgium</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH </p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Bhutan</p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Bulgaria</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> </tr> <tr> <td> <p>Burundi</p> </td> <td></td> <td></td> <td> <p>PH *</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Chile</p> </td> <td> <p>PH</p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Colombia</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Costa Rica</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> </tr> <tr> <td> <p>Croatia</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Cyprus</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Czechia</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Denmark</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Dominican Republic</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Ecuador</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> </tr> <tr> <td> <p>Egypt</p> </td> <td></td> <td></td> <td></td> <td> <p>*</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>El Salvador</p> </td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Estonia</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Finland</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>France</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Germany</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Ghana</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td> <p>*</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Greece</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Guatemala</p> </td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Guinea</p> </td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Guinea Bissau</p> </td> <td></td> <td></td> <td></td> <td> <p>PH *</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Honduras</p> </td> <td></td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Hungary</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Iceland</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Ireland</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Italy</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Kosovo</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Latvia</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Lesotho</p> </td> <td></td> <td></td> <td></td> <td> <p>*</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Lithuania</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Luxembourg</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Malawi</p> </td> <td></td> <td></td> <td> <p>*</p> </td> <td></td> <td></td> <td> <p>*</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Malaysia</p> </td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Maldives</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Mali</p> </td> <td></td> <td></td> <td></td> <td></td> <td> <p>*</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Malta</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Mexico</p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> </tr> <tr> <td> <p>Montenegro</p> </td> <td></td> <td></td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> </tr> <tr> <td> <p>Morocco</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Mozambique</p> </td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Namibia</p> </td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Nepal</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Netherlands</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> </tr> <tr> <td> <p>Nigeria</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>North Macedonia</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> </tr> <tr> <td> <p>Norway</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> </tr> <tr> <td> <p>Pakistan</p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Palestine</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Panama</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Paraguay</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> </tr> <tr> <td> <p>Philippines</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Poland</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Romania</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Rwanda</p> </td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Saint Lucia</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>São Tomé and Príncipe</p> </td> <td></td> <td></td> <td></td> <td> <p>*</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Serbia</p> </td> <td></td> <td></td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> </tr> <tr> <td> <p>Seychelles</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Slovakia</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Slovenia</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>South Africa</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Spain</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Sri Lanka</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Sweden</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> </tr> <tr> <td> <p>Thailand</p> </td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH </p> </td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Turkey</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> </tr> <tr> <td> <p>Uganda</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td> <p>PH</p> </td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Vietnam</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td> <p>PH</p> </td> <td></td> <td></td> </tr> <tr> <td> <p>Zambia</p> </td> <td></td> <td></td> <td></td> <td></td> <td> <p>*</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td> <p>Zimbabwe</p> </td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td> <p>*</p> </td> <td></td> <td></td> <td></td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p> PH</p> </td> <td> <p>Poverty headcount data available</p> </td> </tr> <tr> <td> <p>*</p> </td> <td> <p>Multidimensional deprivation for children available</p> </td> </tr> </tbody> </table> |
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<p>As the custodians of the data are countries, the partner agencies do not conduct any quality assessment on the data itself other than ensuring that the data corresponds to those numbers officially published.</p>
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<p>Since the data for indicator 1.2.2 are based on the national definitions of poverty – and consequently the indicators and thresholds used to produce them are different, as described in the “comments and limitations” section, data are not comparable across countries. Thus, regional and global aggregates are not produced.</p>
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<p>The data has been validated by a three-stage approach to ensure its accuracy. First, the data is entered by World Bank staff assigned to each country, typically in consultation with the country NSO and/or country official documents. That data is sent to UNICEF and UNDP country officers for the validation. After integrating inputs from these three agencies, the data is sent to the SDGs focal point for each country for their final approval. For countries where the World Bank does not have any country offices, such as for OECD and EU countries, the World Bank collected the information based on data source available online, and sent it directly to the official counterparts of each country for verification.</p>
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<p>The measurement of poverty involves two crucial steps: (1) identification – identifying who is poor, and (2) aggregation – compiling the individual’s information into a summary measure. There are different ways to perform these two steps. All measures currently being estimated by countries or multilateral organizations use the counting approach. Therefore, what follows relates only to counting approaches, even if other non-counting methodologies have been developed by experts. </p>
<p>The identification and aggregation of the multidimensionally poor involves the following steps:</p> <ol> <li>Define the set of relevant dimensions of poverty, and for each of these define a set of indicators.</li> <li>For each dimension, determine the criteria to assess deprivation based on the indicators.</li> <li>For each indicator, define a satisfaction threshold, such that a person (or household) with an achievement below the threshold will be identified as deprived in that indicator.</li> <li>For each indicator, compare each person’s (or household’s) achievement with the satisfaction threshold and create a variable that assumes, for example, the value 1 if the person is deprived in that indicator and 0 otherwise, and then classify them as either deprived or not in that indicator. </li> <li>For each individual (or household), sum up the number of deprivations. In the summation, each indicator can be weighted differently or equally. Typically, if there are more indicators in one dimension than in others, indicator weights are adjusted to ensure equal weights across dimensions, but this need not be the case.</li> <li>Define a poverty cut-off, such that a person exceeding the cut-off will be identified and counted (aggregated) as poor. </li> <li>Aggregate up across individuals (or households) to obtain a measurement of multidimensional poverty for the country or region of interest. </li> </ol> <p>To illustrate this method, suppose a hypothetical society with five people, where multidimensional poverty is measured based on four indicators: per capita household income, years of schooling, access to sanitation, and access to source of water. The deprivation thresholds for these indicators are, respectively: 400 monetary units (e.g. dollars, pesos, shillings), 5 years of schooling for adults, having access to improved sanitation, and having access to improved sources of water. In this example, the four indicators are weighted equally<sup><a href="#footnote-3" id="footnote-ref-3">[2]</a></sup>, and the multidimensional poverty cut-off is two out of the four indicators. That is, the person would be considered poor if she is deprived in at least two out of the four indicators. Table 2 presents the individuals’ achievements in each of the four relevant indicators, and the deprivation cut-offs are shown in the bottom row. The achievements falling below the deprivation thresholds are highlighted in red. Table 3 shows the deprivation status of all individuals in the four indicators. Column (5) shows the sum of deprivations. Comparing this sum with the poverty cut-off (as mentioned above, two out of four) the individuals can be classified as poor and non-poor, as shown in column (6). </p> <p><strong>Table 2. Individual achievements in the variables selected to define multidimensional poverty</strong></p> <table> <tbody> <tr> <td> <p><strong>Individual</strong></p> </td> <td> <p><strong>Income</strong></p> <p><strong>(in dollars)</strong></p> </td> <td> <p><strong>Schooling</strong></p> <p><strong>(in years of education)</strong></p> </td> <td> <p><strong>Improved Sanitation </strong></p> </td> <td> <p><strong>Improved Water</strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>100</p> </td> <td> <p>3</p> </td> <td> <p>No</p> </td> <td> <p>No</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>200</p> </td> <td> <p>2</p> </td> <td> <p>No </p> </td> <td> <p>Yes </p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>350</p> </td> <td> <p>5</p> </td> <td> <p>Yes </p> </td> <td> <p>Yes </p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>500</p> </td> <td> <p>4</p> </td> <td> <p>Yes</p> </td> <td> <p>No</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>600</p> </td> <td> <p>6</p> </td> <td> <p>Yes</p> </td> <td> <p>Yes </p> </td> </tr> <tr> <td> <p>Deprivation cut-offs</p> </td> <td> <p>400</p> </td> <td> <p>5</p> </td> <td> <p>Yes</p> </td> <td> <p>Yes </p> </td> </tr> </tbody> </table> <p>Note: Please note that the water and sanitation indicators are binary variables where a value of 1 corresponds to having access to an improved sanitation or water source, and is 0 otherwise.</p> <p><strong>Table 3: Deprivation status, deprivation score and poverty status</strong></p> <table> <tbody> <tr> <td rowspan="2"> <p><strong>Individual</strong></p> </td> <td colspan="4"> <p><strong>Deprived in…</strong></p> </td> <td rowspan="2"> <p><strong>Sum of Deprivations </strong></p> </td> <td rowspan="2"> <p><strong>Poor (at least two out of four)</strong></p> </td> </tr> <tr> <td> <p>Income</p> </td> <td> <p>Schooling</p> </td> <td> <p>Sanitation</p> </td> <td> <p>Water</p> </td> </tr> <tr> <td></td> <td> <p>(1)</p> </td> <td> <p>(2)</p> </td> <td> <p>(3)</p> </td> <td> <p>(4)</p> </td> <td> <p>(5)</p> </td> <td> <p>(6)</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>4</p> </td> <td> <p>Yes</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>0</p> </td> <td> <p>3</p> </td> <td> <p>Yes</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>1</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>1</p> </td> <td> <p>No</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>0</p> </td> <td> <p>1</p> </td> <td> <p>0</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>Yes</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>No</p> </td> </tr> </tbody> </table> <p>The last step involves aggregating the information across individuals. The most common summary measure is the headcount ratio or incidence of poverty. The headcount ratio is the proportion of the total population classed as poor. In the example above, the incidence of multidimensional poverty is 60 percent (<math xmlns="http://www.w3.org/1998/Math/MathML"> <mo>=</mo> <mfrac> <mrow> <mn>3</mn> </mrow> <mrow> <mn>5</mn> </mrow> </mfrac> <mo>×</mo> <mn>100</mn> </math>). All empirical examples discussed in this section use the headcount ratio as the core measure of multidimensional poverty. On one hand, this measure is very intuitive and can be disaggregated by population sub-groups. On the other hand, it cannot be broken down by the contributions of each different indicator and it is not sensitive to the number of deprivations experienced by the poor. Because of these limitations, some methodologies propose other summary measures in addition to the headcount ratio. For the purpose of reporting on SDG Indicator 1.2.2, countries only need to compute the headcount ratio. </p> <ol> <li>Unmet Basic Needs</li> </ol> <p>The measures of Unmet Basic Needs (UBN), which proliferated in Latin America in the 1980s, are a direct application of the counting approach.<sup><a href="#footnote-4" id="footnote-ref-4">[3]</a></sup> These measures often use census data to produce detailed maps of poverty and can also be estimated using household surveys. They identify the poor using the counting approach as described above, following all the steps mentioned, and aggregate the information across households and people using incidence ratios. Most generally, the share of households or individuals with unmet basic needs is presented for different poverty cut-offs – that is, the proportion of households and people with one or more unmet basic need, the proportion of households and people with two or more unmet basic needs, and so on. The basic needs considered in these measures usually include (Feres and Mancero, 2001): access to housing that meets minimum housing standards, access to basic services that guarantee minimum sanitary conditions, access to basic education, and economic capacity to achieve minimum consumption levels. When these measures are estimated using census data, they can be highly disaggregated geographically, which makes it possible to construct detailed maps of poverty at district, municipality and even census ratio levels. Because of this property, maps of unmet basic needs have sometimes been used to allocate resources across areas. </p> <ol> <li>Multidimensional Poverty Measurement in Mexico</li> </ol> <p>The counting approach has been used to assess the number of people that are deprived simultaneously in income and in some non-monetary dimensions.<sup><a href="#footnote-5" id="footnote-ref-5">[4]</a></sup> Early applications can be found in Ireland, and more recently, in the United Kingdom for measuring child poverty.<sup><a href="#footnote-6" id="footnote-ref-6">[5]</a></sup> But the first country to develop an official and permanent measure of multidimensional poverty in the developing world was Mexico. The National Council for Evaluation of Social Development Policy (CONEVAL) led that process. In Mexico, multidimensional poverty is measured in the space of economic well-being and social rights, at the individual level:</p> <p>“A person is considered to be multidimensionally poor when the exercise of at least one of her social rights is not guaranteed and if she also has an income that is insufficient to buy the goods and services required to fully satisfy her needs.” (<a href="https://www.coneval.org.mx/rw/resource/coneval/med_pobreza/MPMMPingles100903.pdf">CONEVAL, 2010</a>) </p> <p><strong>Table 4: Dimensions and indicators of the measure of multidimensional poverty of Mexico</strong></p> <table> <tbody> <tr> <td> <p><strong>Type of Dimension</strong></p> </td> <td> <p><strong>Dimension</strong></p> </td> <td> <p><strong>Indicator</strong></p> </td> </tr> <tr> <td> <p>Economic well-being</p> </td> <td> <p>Economic well-being</p> </td> <td> <p>Income per capita</p> </td> </tr> <tr> <td rowspan="6"> <p>Social rights</p> </td> <td> <p>Education</p> </td> <td> <p>Educational gap (meeting a minimum level of education for their age cohort)</p> </td> </tr> <tr> <td> <p>Health</p> </td> <td> <p>Enrolled in the Social Health Protection System</p> </td> </tr> <tr> <td> <p>Social security</p> </td> <td> <p>Access to social security</p> </td> </tr> <tr> <td> <p>Housing </p> </td> <td> <p>Quality and spaces of dwelling (floor, roof, walls, and overcrowding)</p> </td> </tr> <tr> <td> <p>Services in the dwelling</p> </td> <td> <p>Access to basic services in dwelling (water, drainage, electricity, cooking fuel)</p> </td> </tr> <tr> <td> <p>Food</p> </td> <td> <p>Food security</p> </td> </tr> </tbody> </table> <p>All persons whose income per capita is insufficient to cover necessary goods and services are considered deprived in economic well-being. For social rights, each of the six indicators in Table is generated as a binary variable, with 1 representing deprivation, and 0 otherwise. In the cases in which there is more than one indicator, that is, for housing and access to services in the dwelling, the individual is classified as deprived if she fails to meet the threshold for any single indicator within the dimension. The social deprivation index is then defined as the sum of these six indicators associated with social deprivation. The six dimensions are equally weighted, as all human rights are considered equally important. The social deprivation index thus takes a value between zero (the person is not deprived in any of the six social rights indicators) and six (the individual is deprived in all of them).</p> <p>The classification of the population according to this method is illustrated in Figure 1. The vertical axis represents the space of economic well-being, measured by per capita household income. The horizontal axis represents the space of social rights. In this axis, individuals at the origin have a social deprivation index of six, individuals placed more to the right have fewer deprivations. The deprivation cutoff in the space of social rights is one, and individuals to the left of this threshold or on this threshold are considered to be deprived in social rights. People are divided into four groups (CONEVAL 2010, p. 32):</p> <ol> <li><em>Multidimensionally poor</em>. People with an income below the economic well-being threshold and with one or more unfulfilled social rights.</li> <li><em>Vulnerable due to social deprivation</em>. Socially deprived people with an income higher than the economic well-being threshold.</li> <li><em>Vulnerable due to income</em>. Population with no social deprivations and with an income below the economic well-being threshold.</li> <li><em>Not multidimensionally poor and not vulnerable</em>. Population with an income higher than the economic well-being threshold and with no social deprivations. </li> </ol> <p><strong> </strong><strong>Figure 1: Identification of the multidimensionally poor in Mexico</strong></p> <p><img src="data:image/png;base64,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"></p> <p>Source: Adapted of CONEVAL (2010).</p> <p>Among the multidimensionally poor, those in extreme poverty are also identified, by considering a lower economic well-being threshold (the minimum economic well-being threshold)<sup><a href="#footnote-7" id="footnote-ref-7">[6]</a></sup> and a higher deprivation threshold of three of more social deprivations. </p> <p>In terms of aggregation, Mexico produces several categories of summary measures. The core measure is the headcount ratio, that is, the proportion of people who are multidimensionally poor (i.e. the proportion of people in group I in Figure 1). In addition, other headcount measures are also reported, such as the proportion of people deprived in economic well-being, the proportion deprived in each of the social rights, and the proportion showing one or more social deprivations. The depth of poverty is computed separately with respect to economic well-being and social deprivations. The depth of poverty in terms of economic well-being is the average gap between the well-being threshold and the income of poor people.<sup><a href="#footnote-8" id="footnote-ref-8">[7]</a></sup> This measure is reported for groups I and III in Figure 1. The depth of poverty in terms of social deprivations is the average proportion of deprivations among those suffering at least one deprivation. This measure is reported for groups I and II in Figure 1. Finally, the intensity of poverty corresponds to the product of the headcount ratio and the depth of poverty.<sup><a href="#footnote-9" id="footnote-ref-9">[8]</a></sup> This measure is computed for the multidimensionally poor (group I) and the socially deprived (group II).</p> <p>In 2015, Vietnam launched their official multidimensional poverty index, following an approach similar to the one adopted in Mexico but using the household as the unit of analysis. A multidimensionally poor household is a household (1) whose monthly average income per capita is at or below income-based poverty line, OR (2) whose monthly average income per capita is above income-based poverty line but below minimum living standard AND is deprived on at least 3 indices for measuring deprivation of access to basic social services. Ten indicators are included in the list of basic social services. These are (1) adult education, (2) child school attendance, (3) accessibility to health care services, (4) health insurance, (5) quality of house, (6) housing area per capita, (7) drinking water supply, (8) hygienic toilet/latrine, (9) use of telecommunication services, and (10) assets for information accessibility.<sup><sup><a href="#footnote-10" id="footnote-ref-10">[9]</a></sup></sup></p> <ol> <li>At Risk of Poverty or Social Exclusion</li> </ol> <p>The “at-risk-of-poverty or social exclusion” rate, <a href="https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:At_risk_of_poverty_or_social_exclusion_(AROPE)">AROPE</a>, is the main indicator to monitor the EU 2030 target on poverty and social exclusion, aiming at reducing the number of people at risk of poverty or social exclusion by at least 15 million, out of them, at least 5 million should be children. It also was the headline indicator to monitor the <a href="https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:EU_2020_Strategy">EU 2020 Strategy</a> poverty target. It is defined as the proportion of people (or number of persons) that are either at risk of (monetary) poverty, or are living in a household with very low work intensity, or are severely materially and socially deprived. In other words, AROPE considers three dimensions/indicators, and the individual is at risk of poverty or social exclusion if she is deprived in at least one of those components. </p> <p>An individual is <a href="https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:At-risk-of-poverty_rate">at-risk-of-poverty</a> if:</p> <ol> <li>She has an equivalized disposable income (after social transfers) below the at-risk-of-poverty threshold, which is defined as the 60 percent of the national median equivalized disposable income after social transfers. </li> <li>Lives in a household with <a href="https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Persons_living_in_households_with_low_work_intensity">very low work intensity</a>, defined as “people from 0-64 years living in households where the adults (those aged 18-64, but excluding students aged 18-24 and people who are retired according to their self-defined current economic status or who receive any pension (except survivors pension), as well as people in the age bracket 60-64 who are inactive and living in a household where the main income is pensions) worked a working time equal or less than 20% of their total combined work-time potential during the previous year”. </li> <li>Is <a href="https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Severe_material_and_social_deprivation_rate_(SMSD)&stable=0&redirect=no">severely materially and socially deprived</a>, that is if she or her household cannot afford at least seven of the following 13 items<sup><a href="#footnote-11" id="footnote-ref-11">[10]</a></sup>:</li> </ol> <p>List of items at household level:</p> <ul> <li>Capacity to face unexpected expenses</li> <li>Capacity to afford paying for one week annual holiday away from home</li> <li>Capacity to being confronted with payment arrears (on mortgage or rental payments, utility bills, hire purchase instalments or other loan payments)</li> <li>Capacity to afford a meal with meat, chicken, fish or vegetarian equivalent every second day</li> <li>Ability to keep home adequately</li> <li>Have access to a car/van for personal use</li> <li>Replacing worn-out furniture</li> </ul> <p>List of items at individual level:</p> <ul> <li>Having internet connection</li> <li>Replacing worn-out clothes by some new ones</li> <li>Having two pairs of properly fitting shoes (including a pair of all-weather shoes)</li> <li>Spending a small amount of money each week on him/herself</li> <li>Having regular leisure activities</li> <li>Getting together with friends/family for a drink/meal at least once a month</li> </ul> <p>The information on the individuals at risk of poverty and social exclusion is aggregated in the form of an incidence rate, the proportion of individuals in the total population that are identified as being at risk of poverty or social exclusion. People are included only once even if they are in more than one situation (AROPE components mentioned above).</p> <p>The construction of AROPE follows the same steps outlined above that are used in the UBN or mixed (CONEVAL) experiences. In addition, as in the two other highlighted cases, the three dimensions are equally weighted. However, while CONEVAL takes as deprived in social rights as those suffering from at least one deprivation in any indicator within this dimension, AROPE requires that within material and social deprivation at least seven deprivation items out of 13 are needed for establishing severe material and social deprivation.</p> <ol> <li>Alkire-Foster Approach to Multidimensional Poverty </li> </ol> <p>Alkire and Foster presented a family of multidimensional poverty measures based on the counting approach, which has captured global attention and is being widely adopted by countries. The first and most well-known application is the UNDP-OPHI Multidimensional Poverty Index (MPI) at the global level, which has been published since 2011. Since then, many countries have followed their guidance in what is known as “the MPI approach.” </p> <p>The Alkire-Foster family of measures follows the five steps of counting approaches described above and the two stages of identification and aggregation: (1) there is a first cut-off for each deprivation-specific threshold, and (2) there is second cut-off at the aggregation stage to determine whether the person (or household) is multidimensionally poor based on the deprivation score. Differential weights are sometimes used at the aggregation stage, but they are not mandatory. This results in an estimate of the incidence or prevalence of poverty, which is usually referred as H.</p> <p>An innovation introduced by the Alkire-Foster family of measures is that it is possible to account simultaneously for both the incidence of poverty (H), as well as its intensity (A).<sup><a href="#footnote-12" id="footnote-ref-12">[11]</a></sup> The intensity of poverty – also called breadth of poverty – is defined as the average proportion of the relevant multidimensional poverty indicators (weighted or not) in which the poor are deprived. When using categorical variables, it is possible to estimate an adjusted headcount ratio (<math xmlns="http://www.w3.org/1998/Math/MathML"> <msub> <mrow> <mi>M</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </math> or MPI), where </p> <p><math xmlns="http://www.w3.org/1998/Math/MathML"> <msub> <mrow> <mi>M</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mi>H</mi> <mo>×</mo> <mi>A</mi> </math><em>.</em></p> <p>The adjusted headcount ratio, just like the other measures described in this note, can be disaggregated by population subgroups (e.g. geographic area, ethnicity), and it can be broken down by dimension or indicator. For more details on the methodology, see Alkire et al. (2015). </p> <p>The Alkire-Foster approach can be seen as a general framework to measure multidimensional poverty that can be tailored to very different contexts. Many of the existing permanent national statistics of multidimensional poverty are based on the global MPI, but with substantial modifications in terms of dimensions, indicators, and thresholds.<sup><a href="#footnote-13" id="footnote-ref-13">[12]</a></sup> Since 2018, the World Bank regularly presents multidimensional poverty measures across countries using the headcount ratio (H), as is done by UNDP-OPHI measure, albeit with differences in the selection of parameters, some of the indicators, and sources of data. In addition to the headcount ratio, the 2018 Poverty and Shared Prosperity report, where the World Bank introduced this multidimensional measure, presents estimates of global poverty using the adjusted headcount ratio of the Alkire-Foster family as well as the distribution-sensitive multidimensional poverty measure, proposed in Datt (2018). </p> <ol> <li>Child Poverty</li> </ol> <p>Children experience and suffer poverty differently than adults (UNICEF, 2019). Their needs are also different, for example in terms of nutrition or education. However, children are often invisible in poverty estimates. That is why the SDG 1.2.2 explicitly mentions children and why countries should establish a child-specific measure of poverty. The European Conference of Statisticians (2020) recommends that countries “develop child-specific and life-cycle adapted multidimensional poverty measures” (Recommendation 29). </p> <p>If child-specific poverty measures are not developed, there is a risk of misinterpreting the evolving situation of children and consequently misinterpreting the impact of policies and external shocks. It is possible that while the situation of children in a given household deteriorates, that household becomes “non-poor” due to indicators that matter only for adults. In such a case, despite the fact that these children are worse-off than they were before, they would no longer be counted as poor. </p> <p>Over 70 low- and middle-income countries which have carried out child poverty analyses based on a child-specific measure of child poverty use the child as the unit of analysis. These countries are in all regions of the developing world, (e.g. Argentina, Armenia, Brazil, Egypt, Ethiopia, Mexico, Sierra Leone, Uganda, and Zambia), as well as in the European Union.</p> <p>Estimating multidimensional child poverty follows the same steps as the other examples mentioned above: the relevant dimensions are identified, criteria to assess deprivation in each dimension are established, and deprived children in each dimension are identified. A threshold is then specified concerning the minimum number of dimensions in which a child must be deprived to be considered poor, and children above or below this threshold are then counted. Moreover, the percentage (and number) of children deprived in exactly one, exactly two, exactly three, et cetera, deprivations are reported and analyzed, as well as the overlaps or simultaneous deprivations. This makes it possible to measure the incidence, the breadth, and the severity of poverty in a simple and integrated way.</p> <p>For child poverty, the selection of dimensions should be based on child rights. However, not all rights constitute child poverty, as explained in the Guidelines on Human Rights and Poverty from the Office of the High Commissioner for Human Rights. According to the Conference of European Statisticians: “Deprivation measures need to be based upon a clear and explicit theory or normative definition of poverty in order to ensure that each indicator is a valid measure, i.e. that <strong>it measures poverty and not some other related (or unrelated) concept such as wellbeing [sic] or happiness</strong>” (Recommendation 28 (a), emphasis added).</p> <p>As in the case of CONEVAL (explicitly) and UBN (implicitly), no differential weights should be applied across dimensions because they are rights. All rights are equally important and cannot be substituted. This is not just emanating from the human rights approach, but it is also the case with capabilities approach, as stated by Dixon and Nussbaum (2012): “A Capabilities Approach is generally committed to the equal protection of rights for all up to a certain threshold. Any trade-off that leaves some people below this threshold will thus be a clear failure of basic justice under a Capabilities Approach” (Children’s Rights and a Capabilities Approach: The Question of Special Priority, p. 554, Public Law and Legal Theory Working Paper No. 384.)</p><div class="footnotes"><div><sup class="footnote-number" id="footnote-3">2</sup><p> Decanq and Lugo (2013) explore and explain various approaches to setting weights. <a href="#footnote-ref-3">↑</a></p></div><div><sup class="footnote-number" id="footnote-4">3</sup><p> This approach was proposed in several publications before being adopted widely in Latin America. See, among others: ILO (1978), Morris (1978) and Streeten et al. (1981). <a href="#footnote-ref-4">↑</a></p></div><div><sup class="footnote-number" id="footnote-5">4</sup><p> Early examples of analyses using this approach include, for instance, Beccaria and Minujín (1985), Minujin, A. (1995), and Erikson, R (1989). <a href="#footnote-ref-5">↑</a></p></div><div><sup class="footnote-number" id="footnote-6">5</sup><p> In Ireland, since 1997 “consistent poverty” is defined as the proportion of people who are both income-poor and cannot afford at least two of the set of items considered essential for a basic standard of living (previously 8, now 11 items are considered as essential). Since 2010, the United Kingdom applies a similar definition for one of its four policy targets on child poverty, combining low income and material deprivation (The Child Poverty Unit, 2014). <a href="#footnote-ref-6">↑</a></p></div><div><sup class="footnote-number" id="footnote-7">6</sup><p> The economic well-being threshold was defined with reference to a basket of basic goods and services. The minimum economic well-being threshold is the minimum required income to acquire enough food to ensure adequate nutrition. <a href="#footnote-ref-7">↑</a></p></div><div><sup class="footnote-number" id="footnote-8">7</sup><p> Foster, Greer and Thorbecke (1976). <a href="#footnote-ref-8">↑</a></p></div><div><sup class="footnote-number" id="footnote-9">8</sup><p> Following Alkire and Foster (2007). <a href="#footnote-ref-9">↑</a></p></div><div><sup class="footnote-number" id="footnote-10">9</sup><p> Vietnam General Statistics Office. <a href="https://www.gso.gov.vn/en/metadata/2019/10/explanation-of-terminology-content-and-methodology-of-some-statistical-indicators-on-living-standard/">https://www.gso.gov.vn/en/metadata/2019/10/explanation-of-terminology-content-and-methodology-of-some-statistical-indicators-on-living-standard/</a> <a href="#footnote-ref-10">↑</a></p></div><div><sup class="footnote-number" id="footnote-11">10</sup><p> In 2021, the AROPE indicator was modified in line with the new EU 2030 target so that the severe material deprivation component includes social deprivation. The low work intensity component was also revised to better account for the social exclusion situation of those in the working age. During 2010-2020, under the EU 2020 target, the households were regarded as severely materially deprived if she can not afford at least four of the following nine items; 1) to pay the rent, mortgage or utility bills, 2) to keep the home adequately warm, 3) to face unexpected expenses, 4) to eat meat or proteins regularly, 5) to go on holiday, 6) a television set, 7) a washing machine, 8) a car, 9) a telephone. <a href="#footnote-ref-11">↑</a></p></div><div><sup class="footnote-number" id="footnote-12">11</sup><p> The formula developed by Datt and featured in the 2018 Poverty and Shared Prosperity report by the World Bank (2018), also allows for a combination of incidence and breadth of poverty. There are several other formulae which allow this combination. <a href="#footnote-ref-12">↑</a></p></div><div><sup class="footnote-number" id="footnote-13">12</sup><p> For information on these measures, visit the website of the Multidimensional Poverty Peer Network (MPPN), <a href="http://www.mppn.org">www.mppn.org</a>. The MPPN was launched in 2013 to provide support to policy makers who are implementing a Multidimensional Poverty Index (MPI) or are exploring the possibility of developing multidimensional measures of poverty. <a href="#footnote-ref-13">↑</a></p></div></div> |
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<p>The compiled data of SDG 1.2.2 is not intended to be comparable across countries due to national definitions. For instance, key parameters to calculate the measure such as the number of indicators, the weight allocated to each indicator etc, are tailored to the country specific context. </p>
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translations-metadata/en/1-2-2.yml” file was changed. 8 months ago