Multidimensional Poverty in the Voluntary National Reviews
From 2016 to 2020 many countries have reported progress on multidimensional poverty reduction in their Voluntary National Reviews (VNR) presented to the UN. Below we highlight excerpts of these documents and link to the full reports for reference.
Armenia (2020) “The assessment of multidimensional poverty complements the analysis on monetary poverty. The share of multi-dimensionally poor population, that is, the share deprived in at least one dimension, in 2018 was 23.6%, with most deprivations visible in housing and labor (Armstat 2019). 61.6 percent of children are deprived in two or more dimensions. The number was as high as 74.9 percent in rural areas, while it is 52 percent in urban settings. Children are mostly deprived in utilities, housing, and leisure.” (page 13)
Bangladesh (2020) provides details of the Bangladesh Multidimensional Poverty Index (MPI) “Bangladesh has constructed Multidimensional Poverty Index (MPI) to measure the deprivation of the population in three dimensions, viz. health, education, and living standard. Information from Table 1.5 reveals that the incidence of multidimensional poverty in Bangladesh is 37.5 per cent. On the other hand, the average intensity of poverty, which reflects the share of deprivations each poor person experiences on average, is 46.9 per cent. That is, each poor person is, on average, deprived in 47 per cent of the weighted indicators. The MPI, which is the product of H and A, ascends to 0.176. This means that multidimensional poor people in Bangladesh experience 17.6 per cent of the total deprivations that would be experienced if all people were deprived of all indicators.” (page 34)
(Bangladesh 2017) writes of plans “for introducing a Multidimensional Poverty Index (MPI) measurement in future.” (page 13)
Belize (2017) writes that “the enhanced methodology due to be employed in the Caribbean Development Bank (COB) sponsored country poverty assessment will allow Belize to measure poverty using a national multidimensional definition, which is currently being finalised after a draft was developed as part of the Comprehensive Review of the Social Protection System in Belize conducted last year. This follows the methodology of the Oxford Poverty and Human Development Initiative (OPHI).” (page 20)
Bhutan (2018) mentioned its national MPI and the Child MPI as tools to help eradicate poverty (SDG1): “While multidimensional poverty has reduced significantly rural poverty is much higher than urban poverty: 8.1 per cent compared to 1.2 per cent and children between 0-9 years of age are found to be poorest age group in Bhutan.” (page 28)
Burundi (2020) “Child poverty analysis reveals more children suffers monetary poverty (69%) and multidimensional poverty (78.2%) (MODA 2017).” (page 129)
Chile (2017) shared that their national MPI identifies 20.9% of the population as poor. Chile’s MPI is disaggregated by subnational regions, rural-urban, gender, age cohort, indigenous status. And policy reflects not just the level but also the composition of poverty in terms of education, health, work, housing and environment, and social networks. In Chile’s consultations preparing for the VNR, they found that one of the most relevant challenges was Poverty in all its dimensions – monetary, and multidimensional.
Colombia (2016) confirmed poverty reduction in the country is experienced in all its dimensions. Non-monetary reductions were tracked and accelerated using a multidimensional measure that implemented the Alkire-Foster method. In 2010, 30.4% of the population were MPI poor where as in 2015 it was 20.2%. Colombia’s MPI is disaggregated by gender, age, urban/rural, and regions.
Costa Rica (2020) shared details of the Bridge to Development strategy which makes use of MPI, and its work with the private sector including a partnership with Horizonte Positivo which launched a business MPI currently in use at 61 companies to monitor the socioeconomic relaity of more than 27,000 households to inform strategies to address deprivations. “In the case of multidimensional poverty, since 2015, it has been reduced 2.7 pp, from 21.8% to
19.1% in the country between 2015 – 2018.” (page 128)
Costa Rica (2017) also uses the MPI based on Alkire-Foster method, since it sees the challenge of Poverty reduction should be approached in a multidimensional form, as it is national strategy outlines. Since October 2015, Costa Rica has used its MPI to coordinate policies and implement novel initiatives – especially those that address traditionally excluded populations like those with disability, indigenous persons, women, and migrants.
Democratic Republic of the Congo (2020) reported “Definitely, the social and human development in the DRC remains precarious with a multidimensional poverty index (MPI) of 0.185. Over 40% of Congolese live in multidimensional poverty with several deprivations.” (page 67)
Dominican Republic (2018) indicates in its VNR that the government, with the United Nations System support, identified five policy areas (accelerators) that would help to speed up the achievement of the SDGs, one of them is multidimensional poverty reduction.
Ecuador (2020) reported ‘policies have been designed to face multidimensional poverty, a condition that is measured in the country through the national Multidimensional Poverty Rate (MPR), which identifies the existence of multiple deprivations in the areas of health, education, work, and habitat. This indicator went from 37.5% to 35.2%, between 2014 and 2016, with a variation of 2.3 percentage points. Despite this, the current conditions influenced the indicator to reach 38, 1% in 2019.” (page 45)
Ecuador (2018) described the multidimensional poverty index as part of its strategy for the ODS1.
Egypt (2016) drew attention to its reduction of the global MPI published by UNDP and Oxford University, which is a recommended indicator for SDG Target 1.2
El Salvador (2019) uses a National MPI as an official measure of poverty to track SDG indicator 1.2.2.
Guatemala (2017) uses the Global MPI as measure of poverty to track SDG indicator 1.2.2 as a provisional basis. Nowadays, Guatemala is in the first phase of a national MPI construction process, which would imply to estimate this index in an official way at country level for the first time, in order to complement the income poverty measure.
Honduras (2020) confirmed a “comprehensive policy strategy has been developed” with SEDISS, CENISS and the United Nations Population Fund (UNFPA) which prioritizes “22 municipalities in the departments of Colón, Cortés, Francisco Morazán, Gracias a Dios, Intibucá, La Paz, Lempira, Olancho, Valle and Yoro, due to the high prevalence of teenage pregnancy, the incidence of multidimensional poverty, and access to social services.” (page 73)
Honduras (2017) uses a national MPI as an official measure of poverty to track SDG1. It observed that its MPI helps it to advance not only in SDG1 but also in other SDGs such as food security (SDG2), Education (SDG4), Water and sanitation (SDG6) and others.
India (2020) reported that “In its war against poverty, India with its focus on economic growth and social inclusion, has halved the incidence of multidimensional poverty by lifting 271 million from the most vulnerable sections of society out of poverty, while reducing extreme income poverty from 21.2 per cent in 2011 to 13.4 per cent in 2015. Deprivations have significantly reduced across nutrition, child mortality, education, sanitation and drinking water,
electricity and housing, and other basic services.” (page 18)
“The incidence of multidimensional poverty, as measured by the Multidimensional Poverty Index of OPHI and UNDP, reduced by half to 27.5 per cent between 2005-06 and 2015-16, implying that over 271 million people escaped poverty. Deprivations significantly reduced in all 10 indicators – nutrition, child mortality, years of schooling, school attendance, cooking fuel, sanitation, drinking water, electricity, housing and assets” (page 45)
India (2017) made a significant move towards addressing multidimensional poverty by using a multidimensional targeting approach, in order to leave no one behind.
Indonesia (2017) flagged multidimensional Poverty as an ‘emerging issue’ that is related to education, health, living standards. Indonesia is working “to ascertain multidimensional poverty in order to improve poverty alleviation programs to be effective in identifying the roots causes of poverty, which are different in each region.” (page 19 and 20)
Jamaica (2019) indicates that “A critical next step for Jamaica is the development of a tailored multidimensional poverty index for the country. The approach being considered departs from the conventional, as it seeks to engage community participation in determining the indicators most relevant to the country.” (page 25)
Jordan (2015) described that it is developing “a multi-dimensional poverty index specific to Jordan (still in progress), based on the right to a decent and dignified life as opposed to the fulfilment of basic-needs approach.” (page 40)
Kenya (2020) reported “A sizeable proportion of Kenya’s population continues to suffer multidimensional poverty and exclusion from basic social and economic benefits and opportunities for sustainable livelihoods.” (page 34)
“…during the 2017-2019 period, the multidimensional poverty among men, women and children was 38.9 per cent while geographically, the multidimensional poverty was 20.3 percent for urban areas and 48.4 per cent for rural areas.” (page 43)
Kyrgyzstan (2020) reported “The Multidimensional Poverty Index has been used in the Kyrgyz Republic since 2016, with the measurement methodology for assessing multidimensional poverty approved in 2020.” (page 11) “National data shows that there are substantial in-country geographic variations in the development level, with concentration of prevailing multidimensional poverty ‘hotspots’ in rural and remote areas in the Kyrgyz Republic.” (page 32)
“… on the basis of the global methodology for measuring the Multidimensional Poverty Index, a National Multidimensional Poverty Index (NMPI) has been developed, which includes eleven indicators in five areas of measurement: health, monetary poverty, housing conditions, food security and education” (page 139)
Malawi (2020) shared “To ensure data disaggregation and in-depth understanding of service delivery challenges and effectiveness, Malawi has rolled out the efforts of producing the Multidimensional Poverty Index Reports, which is being support by the UN.” (page 94)
“In 2018, the National Statistical Office and the Ministry of Finance’s Economic Planning and Development with support from UNICEF conducted the second multidimensional child poverty assessment in Malawi… The analysis showed that for a multidimensional poverty threshold of 2 deprivations or more, 60.5% of children in Malawi aged 0-17 years are multi-dimensionally poor, a slight decline from 63% in 2011.” (page 38)
Mexico (2018) described the poverty situation of Mexican people.
Macedonia (2020) “Based on the assessment of the most pronounced disparities and key factors of discrimination such as identity (e.g., age, sex, ethnicity, religion, and disability), geographical location, vulnerability to shocks, adverse governance effects and specific socio-economic status (facing multidimensional poverty and inequality), most vulnerable groups in North Macedonia include: Youth who are Not in Education Employment or Training (NEET); Women and Girls; Roma Community; Children; People with Disabilities; Refugees / Migrants / Asylum Seekers / Internally Displaced Persons / Stateless Persons; LGBTI; People Living in Rural Areas / Small Farmers; Elderly Persons.” (pages 14-15)
Morocco (2020) reported a “downward trend has been observed for multidimensional poverty. Its rate has indeed decreased between 2004 and 2014, from 25% to 8.2% at the national level, from 9% to 2% in the middle urban and from 45% to 18% in rural areas. The number of poor people according to the 11 criteria of multidimensional poverty reached, in 2014, about 2.8 million of which 85% of them are rural”. (page 31)
“The analysis, by dimensions, of multidimensional poverty, shows that the educational deprivations of adults and children contribute to it with more than half, the deprivation of access to basic infrastructure with 20%, the housing conditions with 14% and health services with 11%. At the regional level, it was found that the poorest regions are the ones that experienced between 2004 and 2014 the largest decline in multidimensional poverty, especially the regions of Marrakech-Safi (from 34.0% to 11.3%), Tangier-Tetouan-Al Hoceima (from 30.3% to 9.5%) and Béni Mellal-Kénifra (from 31.0% to 13.4 %).” (page 32)
“In addition, multidimensional poverty varied in 2014 between 13.4% recorded in the region of
Béni Mellal-Khénifra and 4.1% in the Casablanca-Settat region and in the southern regions. The
education that represents its main source of deprivation, fluctuates between 48.2% in the region of Béni Mellal-Khénifra and 63.5% in the region of Casablanca-Settat. “(page 122)
Mozambique (2020) “Poverty still affects almost half the population, around 46% of children aged 0-17 are multidimensional poor while 49% are monetary poor” (page 3)
“The State Budget adopted criteria for resource allocation at provincial and district level that take into consideration the multidimensional index of Poverty, the population and the territory, as a way of compensating for inequalities between the Provinces within the country. ” (page 33)
“The multidimensional poverty of children (0 – 17 years), which includes eight dimensions -family, nutrition, education, work, health, water, sanitation and hygiene (WASH), participation and housing – is 46%.” (page 33)
“Currently, the allocation of resources by provinces follows two criteria, namely population (70%) and multidimensional poverty index (30%), with consumption – 30%, water and sanitation – 30%, health – 20% and education – 20%)103. The district has as indicators, the population, surface area, district own revenues and the multidimensional poverty index.” (page 85)
Nepal (2020) reported “Nepal has been able to maintain a high economic growth rate of around 7 per cent in the last few years, and the absolute poverty and multidimensional poverty levels have been gradually reducing every year. ” (page 26)
“A number of projects have been initiated and coordination needs to be further enhanced to
ensure their effectiveness. With more detail mapping and disaggregated data on multidimensional poverty and targeted and comprehensive interventions, these programs will be further consolidated.” (page 30)
“Despite consistent efforts, low capital formation, low income, persistence of multidimensional
poverty, difficult geographic terrain and high cost of infrastructure require huge resources for
investment.” (page 59)
Nepal (2017) stated: “Multi-dimensional poverty reduced from 64.7 percent in 2006 to 44.2 percent in 2015 (OPHI 2016) dropping by an average of two percentage points per year. These achievements were largely due to improved health and education and increased remittances incomes.” … “The government aims to bring down the percentage of people living below the poverty line to 4.9 percent and to reduce multi-dimensional poverty to 10 percent by 2030. The NPC has set targets of an annual economic growth rate of 7.2 percent and 4.7 percent annual growth in the agriculture sector during the Fourteenth Plan period (2016/17- 2018/19) and to increase per capita gross national incomes to $2,500 by 2030”.
Nigeria (2020) “According to the Multidimensional Poverty Index, as reported in the Human Development Report, 2018, the proportion of men, women and children of all ages living in extreme poverty in all its dimensions according to national definitions was measured as 22.5 per cent.” (page 51)
“Nigeria is now home to the largest number of multidimensionally poor in the world. Ten states in north of Nigeria account for 70 per cent of the total who are multidimensionally poor. ” (page 51)
Panama (2020) reported how “for 2017 19.1% of the population was in conditions of multidimensional poverty, for 2018 it went to 19%, obtaining a reduction of 0.1 percentage points. There were also reductions in intensity, that is, in the average deprivations experienced by people in multidimensional poverty, going from 43.5% to 42.4%, a reduction of 1.1 percentage points for the same period. On the other hand, the multidimensional poverty index went from 0.083 points in 2017 to 0.081 points in 2018.” (page 65)
“Panama also has an MPI at the township level (631 townships according to the 2010 political administrative division) that accounts for ten indicators (out of the 17 indicators of the National MPI) that can be calculated using population and housing censuses whose purpose is: to identify from the available evidence, the main deficiencies or non-monetary deprivations that occur simultaneously and directly affect the living conditions of the Panamanian population distributed in the townships of the country, thus for a better geographic targeting of the strategy for reduce poverty in all its dimensions, supporting the development of effective and sustained interventions that guide the design and implementation of public policies and use it as a complement to current national measurements of multidimensional poverty and income.” (page 66)
Panama (2017) extensively described its official MPI as one of the principle instruments to progressively improve public policies. For example, its main messages were: “The multidimensional poverty index was established as a principal instrument for shaping public policy. To that end, dimensions and indicators were selected, the disadvantages and gaps characterising poverty were defined, deprivation was quantified, and poverty was defined in multidimensional terms”.
Philippines (2016) indicated an intention to conduct studies on MPI. The country launched its national MPI in December 2018.
Rwanda (2019) reports the MPI to inform the SDG1.2.2. (page 79)
Seychelles (2020) reported the results from their newly launched MPI “In the third quarter of 2019, the proportion of the population found to be poor according to the MPI (denoted as “H” below) was 11.88 per cent, and the average intensity (average proportion of dimensions in which poor people were deprived, denoted as “A”) was 33.26 per cent. The MPI, which is the
product of H and A (H*A) was 0.040. This means that multidimensionally poor people in Seychelles experience 4 per cent of all the deprivations that would be experienced if all people were deprived in all indicators.” (pages 24-5)
“The four dimensions of the 2019 Seychelles MPI (Living Standards, Health, Education, and Employment) are quantified by a set of 14 indicators. Of these, the largest contributors to multidimensional poverty in the country are deprivations in the highest level of education
attained, meaning that at least one household member has not completed secondary level of
education (24.91 per cent); followed by deprivation in ‘Youth Not in Education, Employment,
or Training (NEET)’ (12.81 per cent), and informal employment (12.40 per cent).” (page 25)
“In addition, those living in the largest households (with 7 or more occupants), appear to be more likely to experience multidimensional poverty (with a headcount ratio of 31.15 per cent),
than those living in the smallest households (with a headcount ratio of 4.89 per cent). In fact, the relationship between household size and multidimensional poverty is quite clear: the poverty rate increases as household size increases.” (page 25)
Sierra Leone (2016), which is reporting MPI as an SDG indicator, indicated an intention to measure multidimensional Poverty, explaining that during its public, regional, and national engagements, one key point that emerged was the “relevance of a multidimensional approach to poverty measurement for the success of the SDGs.” (page 10)
Sri Lanka (2019) indicates that “Multidimensional poverty Index also recorded poverty levels lower than those estimated based on the national (income) poverty line. The multi- dimensional poverty level at the aggregate level was 3.8% in 2012/1343 (compared to 6.7% based on the national poverty line), while that for males and females were 3.9% and 3.7% respectively44 (Target 1.2).” (page 66)
Tajikistan (2017) reported the incidence of MPI in its country, with urban and rural disaggregations. Its strategy to address the urgent issue of Poverty is multidimensional and includes “addressing food security, food quality and safety, energy security, water sector issues, climate change, other SDGs, affecting the standard of living and well-being of the population.” (page 9)
Tonga (2019) “has developed a robust multidimensional poverty measure which is scientifically valid, reliable, additive and contextually appropriate. It has been adopted as the national poverty measure, and is the first of its kind in the Pacific. This is important because recent research has suggested that income poverty measures can underestimate the true extent of poverty. Therefore, effective poverty reduction policies require measures that go beyond income and appropriately reflect the hardship and life experiences of the poor and disadvantaged groups.
In the spirit of the 2030 Development Agenda, this measure identifies the ‘proportion of men, women and children of all ages living in poverty in all its dimensions according to national definition’. While the impact on an adult falling into poverty temporarily may be felt immediately, for children the effects can last a lifetime as children need access to education and healthcare to gain the best start in life. Child poverty costs society in terms of missed opportunities and wasted potential. This measurement is a new development for Tonga as child poverty is no longer lumped together with general poverty assessments which often focus solely on income levels, and do not consider basic necessities. The measure allows Tonga to fully understand child poverty.
Using this measure, it is estimated that 27% of the population are poor. Furthermore, 14% of the population is estimated to be vulnerable as a result of deprivation, and 22% vulnerable in terms of income measures. Child and adult poverty rates indicate that one in five adults are poor compared to one in three children.” (pages 29-30)
Uganda (2020) recognised the multidimensional nature of poverty: “There have been general improvements in housing conditions, a critical factor in poverty reduction as poverty is multidimensional. Households may be deprived in areas other than income, and household living conditions and access to basic services are key indicators. The proportion of households that used canister wick lamps for lighting declined from 66 percent in 2012/13 to 28 percent in 2016/17, largely attributed to increased access to and use of grid electricity (22 percent) and solar energy (18 percent). Access to safe water has improved from 68 percent in 2013 to 78 percent in 2017, with the highest coverage in Eastern region (89.9 percent), compared with 82.7 percent in Northern, 76.6 percent in Central, and 64.7 percent in Western regions. However, the continued reliance on biomass (above 90 percent of the population) as a main source of cooking energy is continuing to threaten public health and the environment and is indicative of persistent financial and resource inaccessibility of alternative options.” (page 22)
Viet Nam (2018) indicated: “The multidimensional poverty rate was reduced from 9.2 percent in 2016 to less than 7 percent in 2017. However, based on multidimensional poverty criteria, the poverty rate for ethnic minority groups remains relatively high. Social security policies have been implemented nationwide and achieved positive results. By the end of December 2017, more than 13.9 million people had social insurance; by the end of 2015, 100 per-cent of the poor and social protection beneficiaries were provided free insurance cards, and about 81 per-cent of near-poor people had health insurance. The access to basic social services such as electricity, hygienic water has been on a rising trend. Monthly social support is now provided for the social protection beneficiaries in almost 60 provinces/cities.” (page 14)
Zambia (2020) “Recently computed statistics on headcount multidimensional poverty show a reduction from 50 percent in 2016 to 44 percent in 2020. In rural areas, multidimensional poverty declined from 69 percent in 2016 to 59 percent in 2020 while in urban areas, poverty declined from 25 percent to 18 percent respectively. ” (page xii)
“Poverty headcount declined for all provinces except Western Province. In Western Province, poverty headcount rose from 61 percent of the population in 2016 to 67 percent in 2020. North Western Province had the largest decline in poverty headcount, from 60 percent in 2016 to 46 percent in 2020, representing a reduction of 24 percentage points. This means that access to education, living conditions and health services have risen in this province while living conditions have also improved.” (page 19)
Zimbabwe (2019) reports that “Zimbabwe’s multidimensional poverty index (MPI) declined from 0.172 in 2011 to 0.127 in 2014, and the percentage of people who are MPI poor declined significantly from 39.1 per cent in 2011 to 29.7 per cent in 2014.” (page 6). “The MPI is a recommended indicator for SDG Target 1.2 that seeks to ‘reduce at least half the proportion of men, women and children of all ages living in poverty in all its dimensions’ by 2030. These positive developments show Zimbabwe’s progress in tackling extreme poverty and deprivation of its vulnerable populations.” (page 18)