Originally published on May 9, 2014
Last week the International Comparison Program published Purchasing Power Parity (PPP) numbers for 2011. Based on surveys of prices in almost 200 countries, these numbers aim to effectively create a set of exchange rates that equalize what each country’s currency can purchase. PPP influences several economic outcomes of interest. For example, as highlighted by The Economist, the new PPP numbers mean that by the end of this year China’s economy, measured in Gross Domestic Product PPP, will likely be larger than the U.S.’s economy.
The new PPP values also have a significant effect on poverty rates. Initial calculations by analysts at the Brookings Institution and the Center for Global Development (see below) indicate that applying the new PPP reduces the number of people in extreme poverty by hundreds of millions of people. Because relative prices in many countries are lower than previously estimated, millions of individuals in India, Nigeria, Bangladesh, Kenya, and many other countries whose incomes were previously counted as below the $1.25 per day cutoff for extreme poverty will now be categorized as above the extreme poverty cutoff, according to these calculations.
The World Bank is expected to release new poverty numbers based on the PPP soon, but in the meantime other analysts have already estimated the dramatic changes in poverty numbers that the new PPP values are expected to generate. The Brookings Institution’s Laurence Chandy and Homi Kharas wrote an illuminating blog post calculating the changes in global and national poverty rates. The Center for Global Development’s Sarah Dykstra, Charles Kenny and Justin Sandefur wrote a blog post with their calculations and analysis, engagingly titled Global Absolute Poverty Fell by Almost Half on Tuesday. Chandy and Kharas point out that not only does the PPP revision reduce the number of people whose incomes are below the $1.25 per day cutoff, but it will also change the cutoff itself, which is based on poverty lines in the world’s 15 poorest countries.
Understanding and accurately measuring poverty are critically important. Eradicating extreme poverty by 2030 is becoming a central goal of the World Bank, the U.S. Agency for International Development, the High-level Panel on the Post-2015 Development Agenda, and other global development actors. Incorporating the latest price data into measures of extreme poverty is necessary to accurately assess the scale and distribution of the problem and to gauge progress in combating it.
However, the startling impact that the PPP revision can have on poverty rates also points to limitations of income-based poverty measures that by their nature rely on relative price patterns across countries. As Dykstra, Kenny and Sandefur point out, nothing changed in the lives or opportunities of the hundreds of millions of people who had been classified as living in extreme poverty but will suddenly no longer be, given the PPP revisions. The authors conclude, “In fact, if the new PPP numbers suggest anything, it is that the quality of health or education or access to services associated with a given income has just gone down.”
Their statement highlights the reality that the challenges poor individuals face are not just lack of income – which is what the $1.25 measure captures – but also lack of quality health care, education, nutrition, sanitation, housing, and other basic needs.
The limitations of income-based poverty measures highlighted by the PPP revisions point to the value that can be derived from multidimensional poverty measures. The most widely used such measure, the Multidimensional Poverty Index (MPI), was developed by the Oxford Poverty and Human Development Initiative (OPHI) and UNDP and is used in over 100 countries to measure deprivations at the household level in health, education, housing, sanitation, water, energy, and assets.
Unlike income-based poverty measures, the MPI is not dependent on the PPP, inflation, or other macroeconomic variables. Rather, the MPI directly measures deprivations in critical aspects of poor individuals’ lives, such as child mortality, nutrition, sanitation, school attendance, and access to water. The MPI uses the Alkire-Foster measurement method and is decomposable by dimension, household, and sub-national region. Several national governments have adapted the measure to create national poverty measures and a Multidimensional Poverty Peer Network is working to strengthen country applications.
Income-based poverty measures play a critical role in assessing poverty, designing poverty reduction strategies, and evaluating impacts. Multidimensional poverty measures do not replace income-based poverty measures. Multidimensional measures complement income-based measures by providing a comprehensive picture of the breadth of deprivations that poor household experience and – importantly – the extent to which households experience multiple deprivations.
Is measuring both overkill? Interesting to researchers but not relevant to the targeting or implementation of poverty reduction activities? Sabina Alkire, Jose Manuel Roche, and Andy Sumner find that patterns of multidimensional poverty differ from patterns of income poverty across countries; and examining India, Alkire and Suman Seth find that patterns of income poverty reduction within the country differ from patterns of multidimensional poverty reduction. This suggests that using only one type of poverty measure to target program efforts may miss large numbers of poor households. Furthermore, understanding the specific deprivations and combinations of deprivations that households in different communities and regions face can help inform programs to focus on the sectors of greatest need and on households with multiple deprivations.
To eradicate extreme poverty, we need to apply all the tools at our disposal. When it comes to metrics, this means measuring both income poverty and multidimensional poverty. Especially when statistical earthquakes like a revised PPP strike.
