Multidimensional poverty measures complement monetary poverty statistics by offering a wider perspective on poverty. 

They identify people who may be overlooked without a multidimensional perspective. While monetary and multidimensional poverty measures overlap, they also form distinct groups.  Research has shown that multidimensional poverty levels are not tightly linked to economic growth. If monetary and multidimensional poverty measures use the same data source, the overlaps and mismatches between both can be analysed to see which people are poor according to one measure but not the other. 

The MPI can be broken down or disaggregated in several different ways to see how populations are affected by poverty, typically across a country or among specific communities. In contrast, monetary data does not often allow for this type of disaggregation. 

• The Ghana MPI published in 2020 found that 26.34% of the population were multidimensionally poor, but not monetarily poor. This population were at risk of being left behind without a multidimensional measure.  

Since its launch in 2019, the national MPI in Thailand has been used as empirical evidence to push forward policies in dimensions with high contributions to MPI, such as pension reform and the expansion of the free internet services programme.

Multidimensional poverty measures reinforce the notion that tackling poverty requires multisectoral efforts across a range of government departments/ministries. The MPI is a weighted index, the different indicators are given agreed weights following consultation with a range of stakeholders. Analysing the contributions of indicators to the overall MPI helps identify which deprivations are driving poverty in an area or subgroup. 

The MPI provides a tool to break down government silos. The MPI can be used to help coordinate policymaking and implementation across the various ministries affected and stakeholders charged with poverty reduction. The MPI offers a joint goal to help governments more easily persuade departments and ministries to work together. It integrates many different aspects of poverty into a single measure, reflecting interconnections among deprivations and helping to identify poverty traps and the poorest households.  

• Colombia’s President Santos championed a policy roundtable in 2011 with key ministers so that joined-up coordi­nated policies could meet ambitious poverty reduction targets. 
• The India National MPI was developed in part to coordinate state and federal government-organised interventions to make a meaningful impact on poverty. 

By integrating MPI information into the budget allocation process, duplications in investment, or gaps, can be identified, resources shared out rationally and transparently, and efficient progress can be made against poverty.  

• In 2015, the Costa Rica MPI identified large mismatches between spending on social development policies and the objectives of the MPI. In response the then-President Solis issued a Presidential decree that the MPI must be part of budget allocation in the future.  MPI reduction accelerated as a result.  
• According to Mozambique’s 2020 Voluntary National Review of Agenda 2030 for Sustainable Development, Mozambique was basing 30% of its provincial budget allocation on the MPI.  

MPI data can be used to track the progress of poverty reduction policies in national and local development plans and related strategies, and, where planned, can be incorporated into the monitoring and evaluation frameworks for specific policies.  

The MPI can more quickly reflect the effects of changes in policies than income alone. For example, if a new social programme aimed at improving education is introduced to an area, it will be multiple years before the benefits from education are reflected in an income measure. In contrast, a multidimensional poverty measure that includes child school attendance and achievement in education could reveal the results of policies geared towards improving education and measure education’s contribution to overall multidimensional poverty. MPI can be used with impact evaluation methodologies to assess the performance of social policies provided due attention is paid to the assessment framework from the outset. It is useful to note that impact evaluation techniques traditionally assess the impact of a single variable at a time, but the impact on multidimensional variables pose methodological complexity.  

• Colombia's roundtable made use of a traffic light system within a dashboard for monitoring progress in different indicators the impacts of policies. A red dot attached to an indicator indicated a 0%–10% advance towards the quarterly or yearly goal, a yellow dot represented a 10%–25% advance, and a green dot represented 25% or more. (See also: Zavaleta, D. and Angulo, R. (2017). ‘National Roundtable and Dashboard for Poverty Reduction in Colombia’, OPHI Briefing 45, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford).  
• The MPI can also be used to evaluate the performance of programmes as has been demonstrated by OPHI’s work with the World Food Programme evaluating the humanitarian cash transfer programmes aimed at refugees in Turkey.

Most national MPIs cannot be used for household-level targeting because of data constraints, but they can be used alongside registry data and adapted to help with targeting poor individuals for enrollment on public service programmes or conditional cash transfers.  

• A version of an AF-method based index, a Multidimensional Vulnerability Index, was used to provide electronic vouchers for food, medicines and biosafety equipment targeted to independent workers and self-employed persons in Honduras in 2020. 
• Viet Nam has used a Poverty Census to identify people living in multidimensional poverty. This is updated annually at the local level using information from registers, survey questionnaires, and community monitoring. This is used (along with monetary poverty) to identify priority groups of eligible beneficiaries for poverty reduction programmes. 

The poorest tend to be hit hardest by crises. Poverty measures in these contexts are more important than ever to monitor setbacks to poverty reduction and to guard against relapses into poverty.  

• The World Bank has used the Pakistan MPI to identify the poorest 15 districts of Sindh for flood relief in 2021. 
• During the COVID-19 pandemic, MPIs were adapted to create Multidimensional Vulnerability Indices in Pakistan and Bhutan using the AF method. The purpose of these measures was to identify where people might live who were either already multidimensionally poor and who might be more vulnerable to the disease according to relevant indicators, such as overcrowding. 
• OPHI created maps to show the number and proportion of people that were at high risk to COVID-19 across 40 countries and their subnational regions in sub-Saharan Africa. ‘High risk’ was defined as the experience of overlapping deprivations in three poverty indicators from the global MPI, namely: nutrition, drinking water and cooking fuel. The maps visualized clusters of high risk that spanned national boundaries. 
• OPHI undertook MPI analysis to simulate the short-term impacts of the pandemic on multidimensional poverty given the absence of new data during lockdowns. See Alkire, S., Nogales, R., Quinn, N. N. and Suppa, N. (2021). ‘Global multidimensional poverty and COVID-19: A decade of progress at risk?’, OPHI Research in Progress 61a, Oxford Poverty and Human Development Initiative, University of Oxford. A later, enhanced, version of this paper is published in the Social Science and Medicine vol. 291 (December 2021), paper No. 114457.
• OPHI also worked on projecting future impacts of COVID-19 on global multidimensional poverty trends. This work was featured in the global MPI report with UNDP HDRO in 2020 and 2022.   
• To address the impacts of climate change on poverty, there is ongoing research exploring the relationship between multidimensional poverty and the environment.   

MPIs can help policymakers measure progress towards national and international targets for poverty reduction. The MPI sets a baseline and establishes current levels of multidimensional poverty and tracks progress towards national and international goals, such as national development plans, or the SDGs. Keeping track over time provides information on the trends in poverty reduction efforts, both in terms of overall rates of poverty, the severity of poverty and those close to the poverty line. Through reporting on targets, it is possible to share the best practice of what may have worked or did not. 

• The MPI can guide National Development Plans. Nigeria incorporated the Nigeria MPI 2022 into its National Development Plans including Mid-Term Development Plans, the National Poverty Reduction with Growth Strategy (NPRGS), the Food & Nutrition (NMPAFN 2021-25), the National Action Plan for revitalization of WASH, and Social Protection Policy.
• As of 2021, 41 countries have reported on MPI in the Voluntary National Reviews at the High-Level Political Forum at the UN.
• Countries are invited to report national (or where data doesn't permit global) MPI results for SDG indicator 1.2.2 in the Global SDG Indicators Database. 
• For more information on MPI and the SDGs.

National MPIs which are published can be used by a wide range of actors including NGOs, private sector businesses and journalists all with the aim of focusing attention on the poorest members of society. 

• Businesses in Costa Rica have worked on adapting MPIs to their businesses to target interventions to alleviate poverty among employees. 
• The 'social business' MSI Reproductive Choices is another example. It uses MPI data to assess where to reach the poorest people for their programmes.
• Journalists have used the global MPI and national MPIs as an evidence base for their work covering poverty, politics and humanitarian news. See OPHI in the media.