An Improved Measure of Global Poverty
Prepared for the Millennium Challenge Corporation
By
Tahira Chaudary
Michael Christopherson
Erik Dolson
Ian Peterson
Diana Rosales-Mitte
Christopher Russell
Kimberly Wild
Workshop in International Public Affairs
Spring 2015
©2015 Board of Regents of the University of Wisconsin System
All rights reserved.
For an online copy, see
www.lafollette.wisc.edu/research-public-service/workshops-in-public-affairs
publications@lafollette.wisc.edu
The Robert M. La Follette School of Public Affairs is a teaching and research department
of the University of Wisconsin–Madison. The school takes no stand on policy issues;
opinions expressed in these pages reflect the views of the authors.
The University of Wisconsin–Madison is an equal opportunity and affirmative-action educator and employer.
We promote excellence through diversity in all programs.
Table of Contents
List of Tables and Figure ............................................................................................................... iv Foreword ......................................................................................................................................... v Acknowledgments.......................................................................................................................... vi Executive Summary ...................................................................................................................... vii Introduction ..................................................................................................................................... 1 Section I. Literature Review ........................................................................................................... 2 A. Global and Sub-National Distributions of Poverty ............................................................. 2 B. Poverty Measurement Techniques ...................................................................................... 5 C. Summary of Literature Review ......................................................................................... 12 Section II. The Status Quo and Policy Alternatives ..................................................................... 13 A. Status Quo: GNI Per Capita as a Poverty Measure........................................................... 13 B. Adjusted Status Quo: $2-a-Day Combined Poverty Line-Poverty Gap Measure............. 13 C. Adjusted Status Quo: MPI ................................................................................................ 14 Section III. Criteria for Analysis................................................................................................... 17 A. Comprehensiveness........................................................................................................... 17 B. Data Quality ...................................................................................................................... 18 C. Data Availability ............................................................................................................... 18 D. Selection Flexibility .......................................................................................................... 19 Section IV. Analysis of Alternatives ............................................................................................ 20 A. Status Quo: GNI Per Capita .............................................................................................. 21 B. Adjusted Status Quo: $2-a-Day Combined Poverty Line-Poverty Gap Measure............. 22 C. Adjusted Status Quo: MPI ................................................................................................ 24 Section V. Recommendation ........................................................................................................ 27 Appendices .................................................................................................................................... 29 Appendix A: Status Quo, $2-a-day, MPI, and Alternatives Analytical Matrix ........................ 29 Appendix B: UMIC Countries Included in Adjusted Status Quos: $2-a-Day and $2-a-Day
Poverty Ratio-Poverty Gap Combined ..................................................................................... 30 Appendix C: LIC, LMIC, and UMIC Countries Included by World Bank GNI Per Capita
and $2-a-day Measures and Excluded in Global MPI .............................................................. 31 Appendix D: UMIC Countries Included in the MPI Adjusted Status Quo .............................. 32 Appendix E: Excluded High Income and Upper Middle Income Countries ............................ 33 References ..................................................................................................................................... 35 iii
List of Tables and Figure
Table 1: Countries with High Poverty Levels................................................................................. 2 Table 2: UMICs with Significant Poor Populations ....................................................................... 5 Figure 1: Country GNIs and Income Cutoffs ................................................................................. 6 Table 3: MPI Deprivation Indicators ............................................................................................ 11 Table 4: Select Country Combined Metric Data........................................................................... 14 Table 5: Select Global MPI Sub-National Results Sorted by Headcount Ratio ........................... 16 Table 6: Status Quo and Alternative Analytical Matrix ............................................................... 20 Table 7: Destitution Indicators...................................................................................................... 25 Table A1: Alternative Analytical Matrix ...................................................................................... 29 Table B1: UMICs Included with Combined Poverty Ratio-Poverty Gap Metric ......................... 30 Table B2: UMICs Included with $2-a-Day Poverty Ratio Only .................................................. 30 Table C1: LIC, LMIC, and UMICs Excluded in the Global MPI ................................................ 31 Table D1: UMIC Countries Included in the MPI Adjusted Status Quo ....................................... 32 Table E1: High Income Country Exclusions Due to Outdated Data ............................................ 33 Table E2: UMIC Country Exclusions Due to Outdated Data ....................................................... 34 iv
Foreword
The La Follette School of Public Affairs at the University of WisconsinMadison offers a twoyear graduate program leading to a Master of Public Affairs or a Master of International Public
Affairs degree. In both programs, students develop analytical tools with which to assess policy
responses to issues, evaluate implications of policies for efficiency and equity, and interpret and
present data relevant to policy considerations.
Students in the Master of International Public Affairs program produced this report for the
Millennium Challenge Corporation. The students are enrolled in the Workshop in International
Public Affairs, the capstone course in their graduate program. The workshop challenges the
students to improve their analytical skills by applying them to an issue with a substantial
international component and to contribute useful knowledge and recommendations to their client.
It provides them with practical experience applying the tools of analysis acquired during three
semesters of prior coursework to actual problems clients face in the public, non-governmental,
and private sectors. Students work in teams to produce carefully crafted policy reports that meet
high professional standards. The reports are research-based, analytical, evaluative, and (where
relevant) prescriptive responses for real-world clients. This culminating experience is the ideal
equivalent of the thesis for the La Follette School degrees in public affairs. While the acquisition
of a set of analytical skills is important, it is no substitute for learning by doing.
The opinions and judgments presented in the report do not represent the views, official or
unofficial, of the La Follette School or of the client for which the report was prepared.
Melanie Frances Manion
Vilas-Jordan Professor of Public Affairs and Political Science
May 2015
v
Acknowledgments
We would like to extend our thanks to all of the faculty and staff at the La Follette School of
Public Affairs, for without their academic knowledge, technical help, and moral support, the
production of this report would not have been possible. First and foremost we are grateful to
Professor Melanie Manion for her guidance throughout the semester. We would also like to
thank Karen Faster for her editorial assistance. Finally, we would like to thank Farah Ereiqat and
the Millennium Challenge Corporation for providing us with the opportunity to work on a project
that aspires to help combat global poverty.
vi
Executive Summary
At the request of the Millennium Challenge Corporation (MCC), this report discusses the
distribution of global poverty and the extent to which global measures adequately represent and
capture poverty. The MCC uses the World Bank’s gross national income per capita metric for
this purpose. Countries with incomes below $1,045 are low-income countries while those with
incomes between $1,046 and $4,125 are considered lower-middle Income countries. Both groups
are eligible for MCC assistance, but a country that has an income above $4,125 is ineligible.
However, a substantial portion of the world’s poor do not live in countries in either of the two
groups and are therefore ineligible for MCC assistance. Therefore the MCC seeks to broaden and
improve its definition of poverty. We recommend that the MCC adopt a new metric that
combines the status quo and an alternative poverty metric, the Global Multidimensional Poverty
Index (MPI), which we term the MPI adjusted status quo.
In our literature review, we find that a growing number of countries that have graduated into the
middle-income category still contain a substantial number of poor people. Seventy-two percent
of the world’s poor were concentrated in countries not classified as low-income in 2011. These
countries are excluded from receiving assistance. In addition, we found that poverty tends to be
concentrated in particular regions within a country. Because the status quo poverty metric gives
no indication of regional income differences, it is not a useful tool for analyzing within-country
poverty differences.
We describe two methods for analyzing poverty: income poverty line metrics and
multidimensional metrics. Income poverty line metrics establish a poverty line, which is the
amount of income necessary to purchase basic needs, including food, shelter and clothing. One
prominent example of this type of metric is the World Bank’s $2-a-day line, which serves as an
international poverty line. Multidimensional metrics aggregate data on a variety of poverty
indicators, such as lack of education, healthcare, and other necessities, and then combine this
data into one index. The leading example of a multidimensional metric is the Global
Multidimensional Poverty Index (MPI), developed by the Oxford Poverty and Human
Development Initiative and the United Nations Development Programme.
We analyze two alternative metrics that provide a more accurate picture of global poverty. Both
alternatives are modifications of the status quo, the World Bank’s gross national income per
capita. And we find that both alternatives offer a more feasible option for the MCC because they
requires less organizational change. The two alternatives we analyze are (1) the status quo
combined with the $2-a-day poverty line, termed the $2-a-day adjusted status quo, and (2) the
status quo combined with the MPI, termed the MPI adjusted status quo.
We find that both metrics offer substantial improvements over the status quo because they
broaden the scope of poverty, increasing the number of poor that the MCC can serve. However,
we recommend that the MCC adopt the MPI adjusted status quo because it is the most accurate
metric in terms of comprehensiveness. It provides data on various poverty manifestations while
also providing data at sub-national levels. Furthermore, the MPI adjusted status quo expands
MCC eligibility to more countries than the $2-a-day adjusted status quo. In sum, the MPI
adjusted status quo provides a more complete understanding of global poverty while broadening
the pool of potential MCC sub-national partners.
vii
Introduction
The Millennium Challenge Corporation (MCC) measures poverty using the World Bank’s
definition of Gross National Income (GNI) per capita, which is the national sum of all of the
wealth created within a country during a given year, divided by the mid-year population of the
country. The MCC mandate requires the use of the World Bank GNI per capita country
classifications for its country poverty analysis. Only countries the World Bank classifies as lowincome countries (LIC) or lower-middle-income countries (LMIC) are eligible for partnerships
with the MCC. The caps are defined by the World Bank and change yearly; in 2015, LICs have a
GNI per capita under $1,045and LMICs have a GNI per capita of $1,046 to $4,125.1 Hence, an
upper-middle-income country (UMIC) with a GNI greater than $4,125, is ineligible for an MCC
partnership. GNI is used for country classification by the World Bank because it is easily
measurable, accessible, updated frequently, and correlates closely with higher standards of living
(World Bank 2015a).
However, poverty trends are changing, and the World Bank’s income classifications are no
longer an accurate reflection of poverty. The world has grown richer, with 28 of 63 countries
graduating from LIC to LMIC status during 2000–2011 (World Bank 2015a). In addition, critics
view the LIC and LMIC country classifications as arbitrary. As Angus Deaton, professor of
economics at Princeton University, writes, “decades of research into people’s spending patterns
and income levels has always failed to find any… point at which behavior suddenly changes, and
which we might use as the cutoff for a poverty line” (Deaton 2004, 9). Countries that fall above
LIC or LMIC status may have poverty-related challenges similar to (or even more pronounced
than) countries with lower GNIs. However, because GNI levels determine which countries are
eligible for MCC partnerships, such countries may not receive aid. In short, a country’s
classification may unjustifiably prevent impoverished citizens from receiving aid. This issue
underlines the need for a poverty measurement that better captures within-country poverty
dynamics and better informs policymakers of the location of the world’s poor.
This report examines the debate, presents evidence for change, discusses alternative poverty
measures, and evaluates them against one another. It is organized into five sections. Section I
discusses the distribution of poverty in the world. It presents evidence that the MCC’s current
poverty metric is missing a large number of the world’s poorest people. It also presents the
theoretical underpinnings of monetary and multi-dimensional methods of measuring poverty.
Section II discusses the status quo method of measuring poverty. It then presents two policy
alternatives: the $2-a-day adjusted status quo and an adjusted status quo which incorporates the
Global Multidimensional Poverty Index, which we term, the multidimensional poverty index
adjusted status quo. Section III describes the criteria our team used to evaluate the status quo and
alternative policies while in Section IV, we analyze the policies against each of the criteria.
Lastly, Section V provides our recommendation, that the MCC adopt either aforementioned
alternative, but specifically the multidimensional poverty index adjusted status quo.
1
In this report, all monetary figures are expressed in United States Dollars.
1
Section I. Literature Review
A. Global and Sub-National Distributions of Poverty
To inform our poverty metric analysis, we review the characteristics and distribution of poverty.
A growing number of countries that have graduated into the middle-income country (MIC) status
still have a substantial number of poor people (Sumner 2013). This distribution changes the
landscape of poverty, which was traditionally viewed as a low-income country issue. Seventytwo percent of the world’s poor were concentrated in countries not classified as low-income in
2011(Kanbur and Sumner 2011, 2).
The new distribution of poverty, measured using the $1.25-a-day metric, is a result of income
growth experienced in some countries since the 1990s.2 As Kanbur and Sumner point out, these
countries became LMICs because they “got richer in average per capita terms” (2011, 3). As
Table 1 shows, India, Nigeria, Pakistan, and Indonesia are now considered LMICs, while China
is classified as an upper-middle-income country; yet, all these countries still have significant
poverty (World Bank 2015c). The new geography of global poverty has various components
such as regional, sub-national, and urban and rural, which we examine in detail below.
Table 1: Countries with High Poverty Levels
Survey
Year
Classification
in 2010
Bangladesh
2010
LIC
China (rural)
Democratic
Republic of
the Congo
Ethiopia
2010
UMIC
2005
Country
Population
Percentage
Poor ($1.25-aday)3
Number of
Poor People
(in millions)
151,125,475
43.26
65
1,337,705,000
17.59
235
LIC
54,028,003
87.72
47
2010
LIC
87,095,281
36.79
32
India
2009
LMIC
1,190,138,069
32.64
388
Indonesia
2010
LMIC
240,676,485
18.33
44
Nigeria
2009
LMIC
155,381,020
62.03
96
Pakistan
2010
LMIC
173,149,306
12.74
22
Philippines
2012
LMIC
93,444,322
18.96
18
Tanzania
2011
LIC
44,973,330
43.48
20
Source: Authors based on World Bank 2015c.
2
The distribution of poverty is generally described using an international income-based metric: the $1.25-a-day
poverty line. This measure is a headcount ratio of the percentage of a population that lives at or below $1.25 a day
which is the World Bank’s definition of extreme poverty. This measure is determined using purchasing power
parities, multilateral price indexes summarizing price levels in a group of countries, to equate the value of a United
States Dollar across countries. In 2011, 17 percent of people in the developing world lived at or below $1.25 (World
Bank 2014). The $2 poverty line is the median poverty line for all developing countries; 2.2 billion people, 31
percent of the world’s population, lived on less than $2 a day in 2011 (World Bank 2014).
3
The $1.25-a-day measurements are adjusted in terms of purchasing power parity (PPP).
2
Regional Poverty
Poverty is regionally concentrated, with substantially more poverty in South Asia and SubSaharan Africa than in the rest of the world. Eastern Europe and Central Asia have the lowest
income poverty among developing countries, followed by the Middle East and North Africa, and
Latin America and the Caribbean (Alvaredo and Gasparini 2013, 62). A 2011 study conducted
by the Brookings Institution notes that 75 percent of the poor were concentrated in eight
countries in 2005, compared to 15 countries in 2015 (Chandy and Gertz 2011, 9).
Since 1985, more than 90 percent of the world’s poor resided in three regions: East Asia, South
Asia, and Sub-Saharan Africa. However, with Asia’s economic rise (predominantly in East and
Central Asia), its share of global poverty is falling sharply, leaving Africa with the largest
proportion of the world’s poor. From 2005 to 2015, Asia’s share of global poverty fell from twothirds to one-third, while Africa’s share was projected to double from 28 to 60 percent (Chandy
and Gertz 2011, 8).
Economic growth since 1998 was instrumental in reducing poverty in South Asia. However,
poverty rates have not fallen fast enough to reduce the total number of poor people. The number
of people living on less than $1.25 a day increased from 549 million in 1981 to 595 million in
2005. In India, where three-fourths of the world’s poor live, the number increased from 420
million in 1981 to 455 million in 2005 (Alvaredo and Gasparini 2013, 76). Aside from India, a
considerable number of poor people live in Pakistan and Bangladesh, signifying that poverty is
an issue for the entire region.
Poverty in Africa is most evident in rural areas, where 70 percent of the continent’s poor reside.
In 2010, the mean GNI per capita in Sub-Saharan African countries was $1,798, compared to the
developing countries’ mean of $4,291 (Alvaredo and Gasparini 2013, 5). However, inconsistent
data in Sub-Saharan Africa makes it difficult to determine income inequality and other economic
changes in the region. According to the World Bank, household income and consumption
surveys need to be updated in the African region where “on average, the most recent survey is
from 2009.” This lag in time makes it difficult to target groups to implement poverty reduction
programs (World Bank 2015e, 202).
According to Alvaredo and Gasparini (2013), few countries have enough reliable data to
determine the change in inequality, although evidence indicates inequality is high on average.
They argue Sub-Saharan Africa is “one of the most unequal regions in the world, and that
disparities have remained persistent over time” (Alvaredo and Gasparini 2013, 39). Nigeria
contains a significant percentage of the world’s poor. Chandy and Gertz (2011) predict almost 55
percent of Nigeria’s population of 173.6 million will live on less than $1.25 a day by the end of
2015. Nigeria is classified as a LMIC, despite its high degree of poverty. The World Bank
estimates that people living on less than $1.25-a-day (2005 purchasing power parity) in SubSaharan Africa constitute 41 percent of Africa’s population, or 403 million people. If poverty
trends continue, the World Bank projects South Asia and Sub-Saharan Africa will have 632
million people living below the poverty line, 91 percent of the world’s poor, by 2020 (World
Bank 2015e, 19).
3
Sub-National Concentrations of Poverty
Poverty also has a sub-national component. Limited data and policy constraints make it difficult
to measure and assess both poverty incidence and the efficiency of targeting groups within a
country for aid. Under the status quo, the MCC is unable to work on a sub-national level,
meaning a considerable number of the world’s poor are excluded from the benefits of U.S. aid.
Urban vs. Rural Poverty within Countries
One characteristic of sub-national poverty is the clear distinction between urban and rural
poverty, especially in countries that have recently graduated to LMIC or UMIC status but
continue to have large numbers of poor people. In the case of UMIC-classified China, the
income gap between rural and urban groups is widening. According to the International Fund for
Agricultural Development, 50 percent of the Chinese population lives in rural areas where the
income levels are three times lower than urban areas (International Fund for Development 2014).
However, given the increasing trend of urbanization, poverty rates vary depending on the
country and that the poor may not necessarily be concentrated exclusively in urban or rural areas.
For example, Kenya and Vietnam have different poverty line measures for urban and rural areas
(CIESIN 2006, 33). A comparison of poverty rates in 2006 shows poverty rates are lower in
Vietnam’s urban areas, while rates are lower in Kenya’s rural areas (CIESIN 2006, 35).
Poverty in UMICs
As mentioned above, the status quo metric may exclude a large number of the world’s poor from
receiving MCC assistance. Table 2 highlights this fact, showing UMICs with a high degree of
poverty, along with the number of poor in that country. This table provides evidence that the
status quo disqualifies some high-poverty nations from receiving MCC assistance. The most
notable country in the list is China, with more than 250 million people in poverty. Despite this
large concentration of poverty, China is not eligible to receive assistance. South Africa and
Brazil also have significant numbers of poor people, yet their UMIC status excludes them from
receiving aid. South Africa has a particularly high percentage of its population in poverty, with
more than 26 percent of people living on less than $2 a day. Several Latin American countries—
Colombia, Peru, and Ecuador, for example—have high percentages of their population in
poverty, but the absolute number of poor people in these areas is smaller than that of China.
The large number of poor people in UMICs who are excluded from MCC assistance reinforces
the need to reevaluate the status quo poverty metric.
4
Table 2: UMICs with Significant Poor Populations
Country
Brazil
China
Colombia
Dominican
Republic
Ecuador
Panama
Peru
Survey
Year
2012
2011
2012
Number of
Percentage People in
Population
on $2 a Day
Poverty
198,656,019
6.79
13,488,744
1,344,130,000
18.61
250,142,593
47,704,427
12
5,724,531
2011
10,147,598
8.52
864,575
2012
2012
15,492,264
3,802,281
8.44
8.87
1,307,547
337,262
2011
29,614,887
8.71
2,579,457
South Africa
2011
51,553,479
26.19
Source: Authors calculations based on World Bank 2015d.
13,501,856
B. Poverty Measurement Techniques
Here we discuss two of the more popular methods of measuring and analyzing poverty—income
measures and multidimensional measures—noting the positive and negative aspects of each and
their implications for the MCC. This section provides a background for the policy alternatives
considered in the next section.
Income-based Metrics
Income is the most common form of poverty metric. Income metrics are preferred because they
are relatively easy to calculate and compare across individuals, families, and countries. Using an
income metric, poverty is typically defined as a shortfall in income below a certain threshold.
For the World Bank, poverty lines are the GNI per capita thresholds for LIC, LMIC, UMIC, and
high-income countries. The $1.25-a-day and $2-a-day poverty lines are also common income
based descriptors of poverty. In this sub-section, also address the advantages and disadvantages
of income based measures of poverty.
1. GNI Per Capita
The MCC measures poverty using the World Bank’s definition of GNI per capita, a national
income indicator. This metric presents a national sum of all of the wealth created within a
country during a given year. According to the World Bank definition: “GNI is the sum of value
added by all resident producers plus any product taxes (less subsidies) not included in the
valuation of output plus net receipts of primary income (compensation of employees and
property income) from abroad” (World Bank 2015a). The World Bank uses GNI because it is
easily accessible and updated frequently, and it correlates closely with higher standards of living
(World Bank 2015a).
To compare nations, GNI is converted to United States Dollars using the Atlas method that
smooths income rate fluctuations among countries (World Bank 2015b) by averaging the prior
three years’ exchange rates and adjusting them for country-specific inflation, while providing a
method for estimating exchange rates when data are unreliable (World Bank 2015b). The World
Bank considered using PPP but does not because of the concerns about fluctuations among the
5
currencies, data, and time constraints mentioned above. GNI is divided by mid-year population
to generate GNI per capita (World Bank 2015b).
We determine from the evidence presented that the MCC’s poverty metric, based on GNI per
capita, disqualifies some of the most densely poor countries from receiving assistance. For this
reason, it should be updated. Chandy and Gertz (2011) present the “new geography of global
poverty,” which creates a need to identify and target the poor, though they live in non-poor
countries. The capability to work with impoverished populations at a sub-national level could
help address these new challenges.
Furthermore, critics challenge the World Bank’s finding of a close link between GNI per capita
and broad-based development by arguing that fixed GNI thresholds are arbitrary and classifying
a country as LIC or LMIC says little about differences across countries. The cutoffs do not group
countries into certain categories; they merely indicate which countries are or are not “poor.” As
Charles Kenny argues (2014), if meaningful distinctions exist among LICs, LMICs, and highincome countries, then their categories should feature natural clusters of those respective groups.
Graduating from LIC to LMIC means little when the cutoffs do not account for an individual
country’s status, Kenny says, and countries generally do not naturally cluster in income
categories. Figure 1 shows that country incomes (the blue dotted line) are essentially a
continuous line (the model yellow line) showing no breaks or clustering that would support
splitting countries into different income groups.
Figure 1: Country GNIs and Income Cutoffs
Source: Kenny, 2014.
6
2. Poverty Lines: $1.25 and $2-a-day
Another method for calculating poverty is to develop an income poverty line, which is based on
the income deemed necessary to fulfill basic needs. This method indicates that anyone living
below the poverty line is considered poor. In this sense, an income poverty line is a money
metric for the underlying concept of well-being (Chen and Ravallion 2012).
Determining the poverty line is an important step in developing an income poverty metric which
involves computing the value of a basket of goods necessary to fulfill a person’s basic needs.
Researchers often use the amount of money needed to buy a diet sufficient to meet nutritional
needs, in addition to an allowance for other essentials (Chen and Ravallion 2012), although this
varies by country. The extreme heterogeneity of diets across countries makes cross-national
comparisons difficult, complicating the task of establishing an international poverty line.
Once a poverty line is determined, the next step is calculating the severity of poverty in a
country. Calculating the severity can be achieved in a number of ways, including doing a
headcount, developing a headcount ratio, calculating the average shortfall in income below the
poverty line, or by combining the three methods. Calculating a headcount measure of poverty
consists of counting the people who fall below the established poverty line; doing so determines
the absolute number of people in poverty, but does not explain how far below the poverty line
the poor fall. Dividing headcount by total population determines a headcount ratio, which is
useful for making cross-country comparisons. The severity of poverty can be calculated by
determining how far each poor person is from the poverty line. Dividing the severity figure by
the poverty line gives the percent distance of each person from the poverty line. Averaging all
the percent distances from poverty line for a community reflects the relative severity of poverty
in that community.
The World Bank’s $1.25-a-day and $2-a-day measures are the most widely used poverty line
metrics. These poverty lines attempt to establish an international poverty metric. The World
Bank considers anyone living on less than $1.25 a day to be in extreme poverty. Those living on
less than $2 a day are considered poor. The $2-a-day metric is very close to the median poverty
line for developing countries, while the $1.25-a-day line is the average poverty line of the 15
poorest countries (Alvaredo and Gasparini 2013).
To construct the $1.25-a-day and $2-a-day measures, researchers collected data on the poverty
lines used within individual countries and converted them into international dollars using a
purchasing power parities conversion. They analyzed these data to determine the $1.25 average
for the poorest countries and $2 average for all developing countries (Chen and Ravallion 2012;
Alvaredo-Gasparini 2011).
The purchasing power parities calculations are undertaken infrequently because of the resources
required to collect and analyze comparable price data across countries; the World Bank still
relies on purchasing power parities calculations from 2005 for its currency conversion
calculations. After determining the purchasing power parities’ exchange rates, household surveys
are undertaken in each country to determine the percentage of the population living at or below
these lines (Chen and Ravallion 2012). We are left with an aggregate poverty figure for each
country.
7
Using the $1.25 and $2-a-day metric allows us to calculate the percentage of the population that
is poor for any given country. These figures can then be compared across countries to gain an
international picture of poverty. For example, in 2012, India had 26 percent of its population
living on less than $1.25 a day and 59 percent on less than $2 a day, while 6 percent of China’s
population lived on less than $1.25 a day and 19 percent of its population on less than $2 a day
(World Bank 2014). The metrics indicate that the scope of poverty is greater in India than in
China.
3. Advantages and Disadvantages of Income-based Poverty Line Metrics
The main advantage of the $2-a-day poverty line metric is that it provides a relatively accurate
indication of how many people are poor. Hence, the $2-a-day is a marked improvement upon the
World Bank’s GNI per capita measure, which classifies an entire country as poor and says
nothing about the severity nor the scope of poverty in a country. In addition, when combined
with a poverty gap calculation, income poverty line metrics also indicate the severity of poverty
in a country.
Despite their extensive use, the $1.25 and $2-a-day measures have attracted significant criticism.
The two major critiques focus around the ideas that: utilizing purchasing power parities
calculations to convert countries’ poverty lines into international terms leads to an inaccurate
definition of poverty in many countries, and the $1.25 and $2-a-day cutoffs are arbitrary because,
by definition, half of the relevant countries will have poverty lines above this level.
The former critique arises because the bundle of goods used to derive purchasing power parities
calculations may not accurately reflect the consumption patterns of the poor. The calculations are
based on the average bundle of goods consumed within a country, not necessarily the goods poor
people consume. Because the poor often consume different goods from the rich and both groups
consume different goods in different countries, the purchasing-power-parities-adjusted incomes
for the rich and poor might move in different directions when the prices of various commodities
change (Basu 2014).
In addition to these critiques, another drawback of this metric is infrequency of data collection.
The household surveys needed to obtain the information for this metric are rarely undertaken
yearly in developing countries. Many countries classified as LIC or LMIC, like Afghanistan,
Algeria, Eritrea, Guyana, Lebanon, and Mongolia, simply have no data at all for this metric
(World Bank 2014).
The World Bank acknowledges that relying on purchasing power parities for computing poverty
is dangerous because their calculation is a complex process. Moreover, while changes in
purchasing power parities may affect the incidence of poverty, this is not necessarily the case
(Basu 2014).
Multidimensional Metrics
Coinciding with the creation of the Millennium Development Goals in 2000, development
economists became interested in producing multidimensional poverty estimates to capture
welfare characteristics of poverty that are not represented in unidimensional poverty measures.
This interest in multidimensional poverty methodologies is due to the widespread belief that
poverty consists of more than just low-income status. Poverty also encompasses qualities such as
8
living standards, access to goods and services, and quality of life. Additionally, evidence
suggests individuals who are income-poor and individuals who are multidimensional are not
always the same (Alkire and Sumner 2013). However, the multidimensional nature of poverty
presents numerous challenges for those interested in understanding the incidence and distribution
of global poverty. Foremost is the theory and execution of identifying and measuring the
variables that determine whether an individual is poor.
There is little disagreement throughout the literature that poverty is multidimensional. However,
a great debate exists over how to measure multidimensional poverty (Ferreira and Lugo 2013).
There is also skepticism that any aggregate measure of multidimensional poverty can be viable
for policymakers. The nexus of the debate stems from the question of whether the
multidimensional aspects of poverty can be aggregated to develop a scalar index from which
variables and geographic distribution can be ranked. In the forefront of the camp supporting a
single index aggregation is the methodology proposed by Alkire and Foster (2011), known as the
“AF framework.” It provides a practical framework for identifying and measuring aggregate
multidimensional poverty. The AF framework is an example of deprivation aggregation, a
system in which welfare variables are weighted and averaged into a single index. The AF
framework is the basis for the Global Multidimensional Poverty Index (MPI), a scalar index
inaugurated in 2010 by the Oxford Poverty and Human Development Initiative and the United
Nations Development Programme.
Skeptics argue that deprivation aggregation methodologies are subject to the same pitfalls found
in unidimensional poverty measures. An alternative approach is to establish a “credible set of
‘multiple indices’” rather than a single “multidimensional index” (Ravallion 2011, 17). Having
multiple indices allows users to attach importance to different variables of multidimensional
poverty depending on the user’s preferences. Ravallion (2011) also argues that attainment
aggregation is a more suitable method than deprivation aggregation, due to the lack of
consideration given to markets and prices in deprivation aggregation methodologies. Attainment
aggregation focuses on the use of prices to determine individual welfare whereas deprivation
aggregation uses weights set by economists. However, to date, no methodology supporting
multiple indices or attainment aggregation has been put into practical use.
We discuss two examples of multidimensional poverty indicators in this section: the Human
Development Index and the MPI. These are the only widely used multidimensional poverty
indices.
1. Human Development Index
A brief review of the HDI, developed by Amartya Sen and Mahbub ul Haq in 1990, is worth
noting. The newest interpretation, the Inequality-Adjusted Human Development Index is widely
discussed by policymakers around the world. The Index is a multidimensional scalar index based
on indicators in health, education, and income. While representing some of the multidimensional
aspects of poverty, it is a welfare index “that combines the aggregate dimensional achievements
of all people (not just the poor) into one overall score” (Alkire and Foster 2011, 15).
Furthermore, this index is not published annually and statistical studies question its validity. For
example, up to 34 percent of countries may be misclassified and key estimated parameters vary
by up to 100 percent due to data error (Wolff, Chong, and Auffhammer 2011).
9
2. Global Multidimensional Poverty Index (MPI)
The most widely used multidimensional poverty aggregation methodology in practice is the MPI.
The Oxford Poverty and Human Development Initiative and the United Nations Development
Programme collect MPI data from across the globe and then compile the scores into one index,
the Global MPI. The Oxford Poverty and Human Development Initiative maintains that MPI data
allow a more effective allocation of resources, improved policy design, the identification of
interconnections among deprivations4, and the ability to monitor the efficacy of policies over a
period of time (OPHI 2015b). The MPI consists of 10 weighted indicators of poverty. Table 3
shows which indicators are included in the index and their weights: If a person is deprived in
one-third or more of the indicators, they are considered poor (Alkire and Robles Aguilar 2015,
1). An Oxford Poverty and Human Development Initiative report explains, “the MPI is
calculated by multiplying the incidence of poverty by the average intensity of poverty across the
poor; as a result, it reflects both the share of people in poverty and the degree to which they are
deprived” (Alkire et al. 2014, 1). Despite the theoretical judgments made regarding indicator
equivalence, preliminary research suggests that the Global MPI is accurate across a variety of
weights given to the indicators (Alkire et al. 2010).
The Oxford Poverty and Human Development Initiative and United Nations Development
Programme update the Global MPI twice per year; the most recent update at the time of writing
was January 2015. This updated index includes data for 110 countries, or 78 percent of the
world’s population (OPHI 2015a).
4
Under the MPI, a deprivation refers to a shortcoming in one particular multidimensional indicator of poverty.
Deprivations are discussed in more detail below.
10
Table 3: MPI Deprivation Indicators
Dimension Indicator
Years of
Schooling
Education
Child School
Attendance
Health
Living
Standard
Deprived If…
No one in the household has completed five years
of schooling
Relative
Weight
1/6
Any school-aged child is not attending school up
to grade 8.
1/6
Child Mortality
Any child has died in the family.
1/6
Nutrition
Any adult or child for whom there is nutritional
information is malnourished.
1/6
Electricity
The household has no electricity
1/18
Improved
Sanitation
The household’s sanitation facility is not improved
(according to MDG guidelines), or it is improved
but shared with other households.
1/18
Safe Drinking
Water
The household does not have access to improved
drinking water (according to MDG guidelines) or
safe drinking water is more than a 30-minute walk
from home, round trip.
1/18
Flooring
The household has a dirt, sand, or dung floor
1/18
Cooking Fuel
The household cooks with dung, wood or
charcoal.
1/18
Assets
The household does not own more than one
radio, TV, telephone, bike, motorbike or
refrigerator and does not own a car or truck.
1/18
Source: Alkire et al. 2014.
3. Advantages and Disadvantages of the MPI
The MPI is useful for showing a sub-national distribution of poverty, as its data cover 803 subnational regions. Of the 110 countries included in the MPI, 71 countries are represented in the
sub-national regions (OPHI 2015a). By the MPI measure of poverty, 71 percent of poor people
live in middle-income countries (Alkire et al. 2014, 1). The MPI can identify those who are most
in need of assistance by measuring the intensity of poverty. Those suffering from multiple
aspects of poverty, such as poor health, lack of access to clean drinking water, or lack of
education or skills, generally are more in need of assistance than those suffering from a single
aspect of poverty. Duncan Green of Oxfam explains this idea when he writes, “being poor and
sick is very different from being poor and healthy” (Green 2014).
Critics of the MPI assert certain aspects of the index fall short. Some point out the indicator
categories are arbitrarily chosen and weighted to produce a single index, and the methodology
must make difficult judgments on the equivalence of the 10 indicators (Ravallion 2011, 16).
David Satterthwaite (2014) of the International Institute for the Environment and Development,
for example, argues the MPI’s use of a single indicator regarding housing (whether floors are
made of dirt, sand, or dung) underestimates poverty in urban areas, where housing may be
inadequate but not captured by this indicator. Others critique the weighting of the indicators.
Martin Ravallion, director of the Development Research Group at the World Bank writes, “it is
far from clear that ‘education poverty’ should have the same weight as ‘health poverty’”
11
(Ravallion 2011, 5). Still others maintain the MPI does not account for important aspects of
poverty, such as “conflict, personal security, domestic and social violence, issues of
power/empowerment” and “intra-household dynamics” (Green 2014). Another notable criticism
of the MPI is the data constraints that arise in cross-country comparisons. The MPI occasionally
uses survey data from different years and some countries do not have data for various indicator
categories.
C. Summary of Literature Review
Given the evidence above, the World Bank’s GNI poverty measurement methodology falls short
in reflecting the distribution, scope, and seriousness of poverty within a country. Countries that
fall outside the LIC or LMIC cutoffs may have poverty-related challenges that are more
pronounced than countries with lower GNIs. However, because GNI levels determine whether
the MCC can provide aid, the potential for a mismatch between income and country needs
increases.
12
Section II. The Status Quo and Policy Alternatives
In this section, we present the status quo and two policy alternatives. The two alternatives are
adjusted status quos in that they incorporating the MCC’s GNI method of measuring poverty.
The first combines a poverty line metric with the GNI and the second combines the MPI with the
GNI.
A. Status Quo: GNI Per Capita as a Poverty Measure
To be a candidate for MCC program assistance under the status quo, countries must conform to
two criteria: not to exceed certain annual per capita, income levels and not to be subject to any
number of U.S. or international sanctions. The MCC uses the World Bank’s GNI per capita
income data (Atlas method) and the historical ceiling for eligibility determined by the World
Bank’s International Development Association, which divides countries into four income
categories: LICs, LMICs, UMICs, HICs. LICs have a per capita income less than or equal to
$1,045, and LMIC candidates have a per capita income of $1,046 to $4,125. The pool of possible
LIC and LMIC candidates was 83 in 2015 (Tarnoff 2015).
The GNI metric presents a national sum of all of the wealth created within a country during a
given year. GNI is the sum of value added by all resident producers plus any product taxes (less
subsidies) not included in the valuation of output plus net receipts of primary income
(compensation of employees and property income) from abroad. It is used for country
classification because it is easily measurable, accessible, updated frequently, and correlates
closely with higher standards of living (World Bank 2015a).
B. Adjusted Status Quo: $2-a-Day Combined Poverty Line-Poverty Gap Measure
The MCC can enhance its GNI metric by combining it with the $2-a-day measure, which we
term the $2-a-day adjusted status quo.
The $1.25 and $2 metrics attempt to describe two different types of poverty. The $1.25-a-day
line is considered to be the threshold for absolute poverty. That is, anyone living on less than this
amount lives in extreme poverty, making barely enough (or sometimes not enough) to satisfy
their nutritional needs. As the $2-a-day measure of the poverty line is the mean of the poverty
lines of all developing countries, it reflects a more general conception of poverty in the
developing world. Therefore, we used a $2-a-day metric because it provides access to a greater
proportion of the world’s poor. For the rest of our paper we consider only the $2-a-day metric.
Using the $2-a-day metric, the MCC could work with all LIC and LMIC categories and expand
eligibility to those countries with 8 percent or more of their population living on less than $2-aday. We arrived at an 8 percent cutoff line by taking the median level of $2-a-day poverty in all
countries in the sample and lowering it slightly so as not to cut out countries close to the median.
The cutoff line would need to be re-calculated each year as data are updated.
We also calculated a combined poverty-headcount gap measure that captures the percentage of
poor people in a nation and their average income shortfall. We combined the poverty headcount
ratio and poverty gap by multiplying them and then taking the square root of the product. We
labelled this figure the combined metric poverty score – it produces a poverty measurement
13
ranging from 0 to 100, with 100 representing the poorest and 0 the least poor. The number
increases when: (1) a country has more of its population living on less than $2 a day, or (2) the
average income shortfall of the poor increases. The combined metric score thereby captures the
intensity of poverty. We chose the median figure, seven, for this metric as the cutoff point.
Countries with a score higher than seven would qualify for MCC assistance.
A headcount ratio measure remains a viable standalone option the MCC can use, but for the
remainder of our paper we focus on the combined headcount-poverty gap metric because it
presents the most precise measurement of poverty using a poverty line. To illustrate this metric,
see Table 4, which shows the headcount poverty measure, the poverty gap measure, and the
combined measure for a select group of countries. Notice that the medium-poor countries in the
table are all UMIC, yet they are included in the eligible countries because their combined
poverty headcount-gap measure is above the cutoff of seven.
Table 4: Select Country Combined Metric Data
Relative
Poverty
Levels
Poverty
Gap
Combined
Metric
Poverty
Score
Data Year
Country
Country
Classification
Poverty
Headcount
Ratio
Most
poor
Madagascar
Zambia
Malawi
LIC
LMIC
LIC
95.13
86.56
88.14
64.91
56.64
52.1
78.58
70.02
67.76
2010
2010
2010
Medium
poor
South Africa
China
Iraq
Colombia
UMIC
UMIC
UMIC
UMIC
26.19
18.61
21.17
12.00
7.65
5.46
4.69
4.69
14.15
10.08
9.96
7.50
2011
2011
2012
2012
0.11
0
0.03
0.02
0
0
0.05
0
0
2011
2011
2010
Poland
HICa
Belarus
UMIC
Ukraine
LMIC
a
A “HIC” is a high-income country.
Source: Adapted from World Bank 2015d.
Least
poor
This metric has the benefit of including many countries with the largest number of the world’s
poor and would be easier to implement than an entirely new measurement system. Using this
method would involve two phases: first, the MCC would undertake its normal eligibility
selection procedure using the World Bank’s GNI per capita cutoffs. Then, the MCC would look
at countries excluded by this criterion. Any country with a score of seven or higher in the
combined metric poverty score would be eligible for MCC assistance, even if not an LIC or
LMIC.
C. Adjusted Status Quo: MPI
Combining the Global MPI with the existing GNI measure allows the MCC to identify countries
facing significant poverty which fall outside the LIC and LMIC classifications. We term this new
measure the MPI adjusted status quo.
14
Because the Global MPI only measures poverty in countries identified as developing countries
with some degree of poverty, the prerequisite argument for MCC intervention is justifiable in all
of the included countries.5 However, while the Global MPI is updated twice yearly, the survey
data for some countries are outdated. For example, China uses survey data from 2002. We
choose a cutoff date of 2010 for the survey data. Hence, countries with data collected prior to
2010 will be excluded because outdated data might overstate the poverty in those countries. This
cutoff date requires yearly monitoring during which country information is updated and a
determination is made as to a practical cutoff date will be for each year.
Adopting the MPI as a poverty measurement is challenging because it restricts the partnership
selection pool to countries measured by the Global MPI. Additionally, some criticize the MPI’s
lack of a market or price indicator. Martin Ravallion of the World Bank writes, “it is one thing to
recognize that markets and prices are missing or imperfect, and quite another to ignore them in
welfare and poverty measurement” (Ravallion 2011, 18). Proponents of the MPI maintain that it
provides a more accurate picture of poverty because it specifically measures deprivation.
Supporters point to the MPI’s value as an “analytical tool to identify the most vulnerable people,
show aspects in which they are deprived and help to reveal the interconnections among
deprivations” (CROP 2010).
Because of these drawbacks, we think the Global MPI is best used together with an income
poverty metric like the status quo. The selection pool of eligible countries for the MPI adjusted
status quo includes the 83 countries classified by the World Bank as LIC or LMIC in fiscal year
2015. To determine eligible but currently excluded countries using the Global MPI, we focus at
or above the UMIC classification. The latest Global MPI identifies 28 UMICs as impoverished
in some capacity. Of these 28 countries, 15 are identified using survey data from 2010 or after.
Acting on this alternative, on the basis of survey data, the MCC would be able to form
partnerships with an additional 15 UMICs, all of which are identified as poor by the Global MPI,
fall outside the status quo measure of MCC eligibility, and have relatively current survey data
(See Appendix D). Another 10 countries that the Global MPI identifies as poor are high-income
countries, but the survey data for them is severely outdated. (See Appendix E for a list of
countries excluded from MCC eligibility).
Additionally, the Global MPI measures poverty in 803 sub-national regions, including 13
UMICs. Of these 13, seven have survey data from 2010 or later. These seven countries comprise
111 sub-national regions. These 111 sub-national regions provide another avenue for MCC
intervention in areas excluded by the status quo measure. The cumulative sub-national data in the
Global MPI also allows the MCC to consider poverty concentration in countries its status quo
measure includes. Of the 72 LICs and LMICs included in the Global MPI, 57 have sub-national
data, encompassing 601 sub-national regions. Of these 601 sub-national regions, 477 from 44
countries have survey data from 2010 or later. These 477 regions can provide greater focus for
MCC intervention in already eligible countries. Table 5 lists examples of Global MPI subnational results from LICs, LMICs, and UMICs. It demonstrates that the severity of poverty
differs across regions within the same country.
5
For a list of LIC, LMIC, and UMICs that are not included in the Global MPI see Appendix C.
15
Table 5: Select Global MPI Sub-National Results Sorted by Headcount Ratio
GNI
Headcount
Category
Ratio
MPI Poor
2010 Total
Population Population
Vulnerable
(In
(In
Intensity to Poverty Thousands) Thousands)
Region
Country
Bolivar Sur,
Sucre,
Cordoba
Colombia
UMIC
14.7
41.4
16.1
455
3,086
Medellin
A.M.
Colombia
UMIC
1.4
35.4
0.7
51
3,776
Équateur
Democratic
Republic of the
Congo
LIC
87.5
54.8
10.2
7,408
8,464
Kinshasa
Democratic
Republic of the
Congo
LIC
23.4
43.6
32.0
1,257
5,381
Bouenza
Democratic
Republic of the
Congo
LMIC
56.2
48.7
21.8
260
464
Democratic
Brassaville Republic of the
Congo
LMIC
24.7
41.8
24.4
347
1,404
Lempira
Honduras
LMIC
41.9
48.0
27.9
128
305
Cortés
Honduras
LMIC
6.0
42.5
9.9
81
1,354
Missan
Iraq
UMIC
24.1
40.6
6.8
230
954
Suleimaniya
Iraq
UMIC
2.7
34.9
3.6
50
1,831
Jalal-Abad
Kyrgyzstan
LMIC
2.0
37.0
14.6
19
934
Osh Oblast
Kyrgyzstan
LMIC
1.9
36.4
10.9
21
1,118
Central
Nepal
LIC
46.2
50.4
15.6
4,074
8,823
Western
Nepal
LIC
33.4
46.9
15.5
1,865
5,589
Loreto
Peru
UMIC
32.5
45.2
24.9
328
1,008
Arequipa
Peru
UMIC
2.3
38.0
6.5
25
1,086
Khatlon
Tajikistan
LIC
20.5
41.7
27.0
553
2,696
41.2
20.1
180
2,218
Sughd
Tajikistan
LIC
8.1
Source: Authors calculations based on OPHI 2015a.
16
Section III. Criteria for Analysis
We determined four criteria on which to base our analysis of the status quo and alternatives
described above: poverty comprehensiveness, data quality, data availability, and selection
flexibility. Each criterion consists of two to four impact categories, or subcomponents of the
criterion. We selected the criteria based on the MCC’s requirements and goals for a poverty
metric. Below, we describe the criteria in detail.
A. Comprehensiveness
The comprehensiveness criterion describes how poverty measures capture the extent and severity
of poverty.
Number of the World’s Poor Included
The impact category for evaluating comprehensiveness tallies the number of the world’s poor
captured by each metric. This impact category provides indicates whether the metric helps the
MCC further its mission of reducing poverty. We measure this impact category by calculating
the number of the world’s poor included with each metric.
To do so, we used the $2-a-day poverty line and Global MPI datasets to estimate the number of
people in poverty, as they are the best poverty metrics we identified. For all countries classified
as LIC or LMIC, we multiplied the headcount ratios by the country’s population in the year the
survey was taken, providing an estimate of the number of people in poverty. We summed the
populations captured by both metrics to obtain a poverty baseline. Both datasets are missing
information on LIC and LMICs due to a lack of data or outdated datasets, so the number of poor
is inaccurate, but it is a fair estimate in line with our research.
Severity of Poverty
An ideal poverty metric should indicate the severity and provide a nuanced picture of poverty
within a country. For example, consider two countries that each has 50 percent of the population
in poverty using a certain poverty line; in one country, the average income of the poor is just
below the poverty line, but in the other country, the average income of the poor is 90 percent
below the poverty line. If the poverty metric does not indicate the severity of poverty, these
countries will be viewed similarly, although the poor in the latter country clearly are much
poorer than those in the former. We evaluate whether a metric describes the severity of poverty
by indicating whether it includes some measure of the degree of poverty in a country. For
example, a poverty metric may indicate that 60 percent of a country’s population is poor and that
the average income of the poor falls short of the poverty line by 50 percent.
Inequality
Inequality often affects the growth and reduction of poverty in a given region. A metric including
inequality gives a picture of the distribution of income within a country, rather than conveying an
income average. Economists argue that high inequality can lend itself to poverty reduction
depending on the level of economic growth. However, high initial inequality reduces the
effectiveness of economic growth on the reduction of poverty (Fosu 2011). Given these
convoluted relationships among inequality, economic growth, and poverty reduction, it is
imperative to understand the level of inequality within a country, region, or sub-national area to
17
develop a poverty reduction strategy. A successful poverty reduction strategy must take initial
economic conditions into account prior to implementation (Thorbecke 2013). Given that any
indication of inequality can be beneficial when developing a development strategy, we determine
whether a metric includes an inequality measure and evaluate the extent to which inequality is
captured.
Multidimensionality
Poverty is more than a simple income or expenditure shortfall, but rather a multidimensional
condition (Ferreira and Lugo 2013). Common examples of indicators outside of income status
include health, education, living conditions, economic mobility, and civil and human rights
conditions. An ideal metric captures some or all of the supplemental characteristics that reflect a
more complete understanding of poverty. We determine whether a metric includes characteristics
of poverty other than income or expenditure and evaluate the validity and comprehensiveness of
the measures.
B. Data Quality
The data quality criterion includes two impact categories: whether the data is third-party verified
and whether it has sub-national capabilities.
Third-Party Verified
The MCC relies on independently produced, third-party data to drive the annual process of
selecting country partners for large-scale grants. Third-party data provides an objective
comparison of how LIC and LMICs performed in the previous year. The organization considers
transparency and data quality that is third-party verifiable to be an extremely important goal.
Thus, this impact category indicates whether an alternative has data that have been verified by a
reputable third party.
Sub-National Capability
As discussed above, poverty may be concentrated in certain sub-national regions within a
country. As such, the capability to identify the poorest regions at a sub-national level helps the
MCC determine if it is serving the greatest number of people in poverty. A metric that captures
sub-national poverty gives the MCC more precise information about the location of the most
impoverished populations. This impact category indicates whether each metric has sub-national
capabilities.
C. Data Availability
Data availability includes two impact categories: the frequency with which data are collected and
the number of countries included.
Collection Frequency
Under the Millennium Challenge Corporation Act of 2003, the MCC is required to determine the
eligibility of each country on an annual basis. It also favors indicators that “have appropriate
consistency in results from year to year” (MCC 2014b). As the MCC needs consistent
information about poverty metrics across countries, this impact category indicates how often data
are collected or adjusted.
18
Number of Countries Surveyed
This impact category describes the number of countries for which a respective form of data is
available. The more countries surveyed, the better the metric for this evaluation. The number of
countries surveyed is worthy of consideration in an analysis of poverty metrics because to create
a metric, a country, or third party, must perform labor-intensive data gathering or surveying.
Ideally, the data necessary to create this metric are available for all countries, but in reality many
barriers exist, such as lack of resources, extreme conditions of poverty, political instability, and
geographic or demographic challenges. Hence, the number of countries surveyed describes the
feasibility in adopting a particular poverty metric.
D. Selection Flexibility
This criterion evaluates the flexibility of each alternative in terms of how many partnership
opportunities are made available to the MCC. Expanding partnership opportunities gives the
MCC more flexibility in choosing compact countries because there will be a larger pool of
candidates.
Number of Eligible Countries
This impact category is a tally of the number of eligible countries in the candidate pool of the
alternative. The more countries included in this category, the more opportunities the MCC has to
enter into partnerships.
Partnership Opportunities
Going forward, the MCC could expand on its ability to only form national partnerships by
working at a sub-national or regional level. This impact category identifies the capability of each
metric to provide for national, regional, or sub-national partnership opportunities. Although this
analysis does not provide a framework for sub-national or regional partnerships, a metric’s
ability to capture poverty at those levels could benefit the MCC should it choose to explore those
types of partnerships.
19
Section IV. Analysis of Alternatives
In this section we use our criteria and impact categories to assess the status quo and the
alternative poverty metrics proposed in the previous section. Table 6 below, summarizes our
findings.6
Table 6: Status Quo and Alternative Analytical Matrix
Criteria
Impact Categories
Status Quo
Alternative 1: $2a-day Adjusted
Status Quo
Alternative 2: MPI
Adjusted Status
Quo
Number of Global
Poor
1.1 – 1.4 billion
1.8 billion
1.6 billion
Severity of Poverty
None
Yes: represented
by poverty gap
Yes:
represented by
intensity of
deprivation
Inequality
Not represented
Not represented
Yes: Represented
by inequality among
the poor
MultiDimensionality
No
No
Yes: income,
education, health,
living standards
Poverty
Comprehensive
-ness
Third-Party Verified Yes: World Bank Yes: World Bank
Data Quality
Sub-national
Capability
None
None
Yes: regional and
sub-national
Collection
Frequency
Annual
1-5 years,
updated annually
1-15 years, updated
twice yearly
Number of
Countries Surveyed
214
214
Average yearly
update: 52
GNI: 214
MPI: 110
Average Yearly
Update:9
Number of
Candidate
Countries
83
87
98
Partnership
Opportunities
83 national level
87 national level
98 national level,
588 sub-national
regions
Data Availability
Selection
Flexibility
Yes: World Bank,
OPHI & UNDP
Note: UNDP = United Nations Development Programme; OPHI = Oxford Poverty and Human
Development Initiative
Sources: Authors calculations based on World Bank 2015a and 2015c, OPHI 2015a.
6
Additionally, see Appendix A, providing an extended analysis of both the $2-a-day and MPI as standalone
measures of poverty, not combined with the status quo.
20
A. Status Quo: GNI Per Capita
The GNI per capita poverty metric is limited because it measures the degree to which a country
is poor, as opposed to the number of poor people within a country. It does not describe the scale
and scope of poverty within a country because it relies on a single country aggregate metric.
Relying on this measure obfuscates income and poverty differences within a country. It does not
inform the MCC about sub-national poverty dynamics, such as geographic concentrations of
poverty, it does not describe which groups are poor, and hence, it does not go very far in
improving or informing specific development and assistance policies.
Comprehensiveness
1. Number of World’s Poor Included
A significant share of the world’s poor are not identified as such because they live in a country
with a GNI per capita too high to be classified as an LIC or LMIC. Use of the GNI per capita
metric excludes more than two-thirds of the world’s poor from receiving aid (Kanbur and
Sumner 2011). Therefore, the status quo does not adequately describe global distributional
realities of poverty. While the status quo does well enough in identifying poor countries, it is
limited in its ability to identify, and label as poor, an enormous percentage of the poor that live in
“non-poor” countries.
2. Severity of Poverty
The GNI per capita method is limited in its ability to describe the severity of poverty on an
individual basis and, to a lesser extent, on a national level. By using the average income of a
country’s citizens, the status quo communicates the extent to which a country, on average, is
poor. Comparing GNIs is a fair method for examining countries’ global disposition and a good
first step in understanding living conditions. The status quo provides for reasonable inferences
with respect to a country’s capacity for growth or poverty intervention. However, the status quo
does not communicate the percentage of people within a country who are living at very poor
levels or the number of people in those groups. Thus, the status quo does not precisely
communicate the severity of poverty experienced at an individual level.
3. Inequality
The GNI does not reflect inequalities in income distribution.
4. Multidimensionality
The status quo GNI measure provides no multidimensional description of poverty. It does not
use any indicators of poverty, such as access to water, electricity, or education; rather, it relies
solely on income to measure poverty. In this sense, the status quo is limited in its ability to
comprehensively capture how poverty manifests itself and how the extremely poor experience
poverty. Although the MCC uses country scorecards to determine which countries may be
eligible for assistance, it ultimately defines the poorest countries through the income-based GNI
method.
21
Data Quality
1. Third-Party Verified
World Bank currently collects and provides poverty data. It then applies an Atlas conversion
method to adjust GNI per capita calculations into United States Dollars for country comparisons.
In terms of its GNI poverty data collection and analysis, the World Bank is an objective and
independent third party whose data are available to the public.
2. Sub-National Capability
The current MCC poverty measurement technique does not consider the poorest regions on a
sub-national level.
Data Availability
1. Collection Frequency
The GNI, the current poverty metric used by the MCC, is annually updated.
2. Number of Countries Surveyed
The MCC does not have a cap on the absolute number of countries it can consider; the number of
countries surveyed is 214.
Selection Flexibility
1. Number of Countries
The MCC’s current model limits it to working with LICs and LMICs. For the 2015 financial
year, 83 countries were classified as such.
2. Partnership Opportunities
The status quo allows for partnerships in 83 countries and on a national level only. Because the
status quo is limited to country partnerships, MCC is limited in its ability to form sub-national or
regional partnerships.
B. Adjusted Status Quo: $2-a-Day Combined Poverty Line-Poverty Gap Measure
Comprehensiveness
1. Number of World’s Poor Included
The adjusted status quo with the $2-a-day combined metric increases the number of the world’s
poor included relative to the status quo. Using this metric, four additional countries become
eligible for MCC assistance (see Appendix B). Although the metric does not add a large number
of countries, several of these have a large number of poor people. For example, South Africa, a
country with a high proportion of its population in poverty, is included using this metric.
22
2. Severity of Poverty
This metric gives a good indication of the degree of poverty within a country because it includes
a poverty gap measure. It measures the average percent shortfall of the poor from the $2-a-day
line. In this way, the metric indicates how poor the poor are, in addition to how many people are
poor, using the $2-a-day line.
3. Inequality
A country with a high GNI per capita, but a large share of the population living on less than $2a-day, must have relatively high levels of inequality. Using the $2-a-day adjusted status quo
allows the most unequal countries, like South Africa, to be included in the candidate pool, even
if they have a high GNI per capita. The adjusted status quo is therefore an improvement on the
status quo because the status quo gives no indication of inequality within a country.
4. Multidimensionality
The $2-a-day adjusted status quo lacks multidimensionality. The $2-a-day line is an incomebased metric and does not include any factors that indicate the depth or severity of poverty.
Data Quality
This alternative has some drawbacks in terms of data quality. Although its data is verified, this
information is not available for all 214 countries on a yearly basis. Furthermore, this metric is not
useful at the sub-national level because estimates are at the national level.
1. Third-Party Verified
The data for this metric are third-party verified. The World Bank collects the data on income,
GNI per capita, and the $2-a-day poverty line, including the $2-a-day poverty gap measure. The
headcount of people living below the poverty line and the poverty gap measure are available
through the World Bank’s interactive tool, PovcalNet, which allows us to replicate its
calculations to estimate the poor in countries and regions (World Bank 2015c).
2. Sub-National Capability
This metric presents only one measure for an entire country. Therefore, GNI per capita with the
$2-a-day combined metric does not reflect where poverty is concentrated within a country.
Data Availability
1. Collection Frequency
The limited amount of data and the infrequency with which they are collected is a major
drawback of the $2-a-day adjusted status quo. Almost no countries collect $2-a-day data
annually because this information is very costly to collect, as it must be done through household
surveys. The lack of updated data limits the applicability and appropriateness of this metric for
the MCC’s purposes.
2. Number of Countries Surveyed
The number of countries surveyed for the $2-a-day adjusted status quo varies significantly from
year to year. The $2-a-day metric has updated, on average, 52 countries a year since 1995; with a
23
high of 77 in 2010 and a low of 26 in 1997. We took observations between the years 2010 and
2012 to determine the median percentage of people living on less than $2 a day.
Selection Flexibility
The $2-a-day adjusted status quo expands the pool of candidate countries where the MCC could
work and the partnership opportunities at the national level.
1. Number of Countries
By using this alternative, the MCC would have information on the poor from 87 countries that
would represent the candidate pool. This pool incorporates four additional countries in the status
quo: South Africa, Iraq, Colombia, and China. Table 4, above, demonstrates the combined metric
poverty score for those four countries and Appendix B, Table B2, provides the percentage of
poor in the nine countries that fell above the poverty headcount threshold of 8 percent.
2. Partnership Opportunities
This alternative offers access to 92 partnership opportunities at the national level. However, the
$2-a-day poverty line does not provide any additional sub-national identification of the poor.
Therefore, the MCC could not use this metric to identify potential partners in cities or regions.
C. Adjusted Status Quo: MPI
Comprehensiveness
1. Number of World’s Poor Included
The number of the world’s poor to be assisted by the MCC would increase using an MPI
adjusted status quo. In the 15 UMICs that are newly included for MCC eligibility with the
Global MPI, 19.2 million people are considered poor through use of the MPI adjusted status quo.
Among the 111 sub-national regions within the newly included UMICs, 12.75 million people are
considered poor (see Appendix D). These additional countries and sub-national regions include
poor people within UMICs. However, this increase is greatly diminished due to exclusions of
UMICs because of outdated survey data. We believe data prior to 2010 do not accurately reflect
the distribution of poverty today. For this reason, our formulation of this alternative rejects
UMICs that are included in the MPI and have data gathered prior to 2010. As new data become
available, more countries will be eligible for MCC partnership. See Appendix E for a table of the
UMICs excluded due to outdated data.
2. Severity of Poverty
The Global MPI devotes considerable attention to the varying ways in which poverty is
manifested by measuring the “intensity” of poverty across 10 indicator categories in health,
education, and living standards. A deprivation in one or more of the indicator categories can be
compared geographically and over time. Intensity of MPI poverty varies by the number of
indicator categories that a person is deprived of. The share of the population that is deprived in
three or more indicator categories is considered MPI-poor. The share of the population that is
deprived of two indicator categories, falling short of the three-indicator threshold, is considered
vulnerable to poverty. Additionally, more deprivations are shown in the intensity measurements.
The MPI measures destitution in 49 countries thus far. Destitution is considered to be a more
24
severe deprivation in any indicator category (see Table 7). Overall, the MPI adds a great
contribution for understanding severity of poverty.
3. Inequality
The latest publication of the Global MPI uses a positive measure of variance from 0 to 1 as a
separate measure of inequality that attempts to address the differences that exist among the
world’s poor. This value represents the amount of inequality that exists among people who are
already considered MPI-poor. If everyone within a population has the same number of
deprivations, the value is 0. This value allows for a better understanding of inequality among the
poor, but not of the overall population. This value is advantageous for policy formulation
targeting poor populations, but is limited due to lack of consideration of overall inequality within
a population of poor and non-poor people.
Table 7: Destitution Indicators
Dimension
Education
Health
Living
Standards
Indicator
Deprived if…
Relative
Weight
Years of
Schooling
No one in the household has completed at least
one year of schooling
1/6
Child School
Attendance
No child is attending school up to the age at which
they should finish class 6
1/6
Child Mortality
2 or more children have died in the household
1/6
Nutrition
Severe undernourishment of any adult or child
1/6
Electricity
The household has no electricity (no change)
1/18
Improved
Sanitation
There is no facility (open defecation)
1/18
Safe Drinking
Water
The household does not have access to safe
drinking water, or safe water is more than a 45minute walk (round trip)
1/18
Flooring
The household has a dirt, sand, or dung floor (no
change)
1/18
Cooking Fuel
The household cooks with dung or wood
(coal/lignite/charcoal are now non-deprived)
1/18
Assets
The household has no assets (radio, mobile
phone, etc.) and no car.
1/18
Source: Alkire, 2014.
4. Multidimensionality
The most notable characteristic of the Global MPI is multidimensionality. The Global MPI is not
an income measurement, but rather a series of 10 deprivation indicators in health, education, and
living standards. The Global MPI is the first international measurement tool for measuring
poverty across multiple dimensions. The multidimensional nature of the MPI is a great strength
in that it generates a more complete understanding of poverty than a simple income
measurement. However, as noted in a previous section, some criticize the indicator categories as
arbitrarily chosen and weighted.
25
Data Quality
1. Third-Party Verified
The data used for this adjusted status quo are third-party verified. The Global MPI was
developed by the Oxford Poverty and Human Development Initiative and the United Nations
Development Programme in 2010 and is included as part of the latter’s Human Development
Report each year. The reliability and credibility of its producers and publishers is high.
2. Sub-National Capability
The MPI is distinguishable from other poverty metrics by its sub-national measures. The MPI
maintains data for 803 sub-national regions within 71 countries. Of the 803 sub-national regions,
588 regions come from 51 countries having survey data from 2010 or later, identifying the
poorest regions within these countries. Although the MPI does not provide sub-national data for
each country, the data available can provide valuable insight. One of the major omissions due to
outdated data is India. The Global MPI results estimate that India had more than 614 million
people who were MPI-poor at the time of the survey in 2005.
Data Availability
1. Collection Frequency
The MPI is updated twice a year. However, some MPI country data are outdated. While the MPI
uses “the most recent and reliable data since 2002,” there is clearly the potential for drastic
changes in developing countries over such a lengthy period (OPHI 2015c). For example, data
from Turkey was collected in 2003. From 2003 to 2014, Turkey experienced a number of
significant changes, including a GDP increase of over $500 billion dollars (World Bank 2015b).
The MPI discloses the year in which data were collected for each country.
2. Number of Countries Surveyed
As of January 2015, the MPI collected data for 110 countries, representing 78 percent of the world’s
population (OPHI 2015c); the MPI is updated with new data, on average, for nine countries per year.
Due to the relative infancy of the Global MPI, we expect this number to grow. Forty countries that
fall under LIC, LMIC, and UMIC classification by GNI per capita are not included in the Global
MPI due to lack of survey data. Because the Global MPI continually updates results based on survey
data availability, these countries could be introduced going forward.
Selection Flexibility
1. Number of Countries
Using the status quo in conjunction with the MPI allows for 98 eligible countries based on our
analysis. In comparison with the status quo using fiscal year 2015 data, this option results in an
increase of 15 eligible UMICs. This number is expected to grow with updated data collection.
2. Partnership Opportunities
This alternative allows for partnerships with 98 countries, 477 sub-national regions within LICs
and LMICs, and 111 sub-national regions within UMICs.
26
Section V. Recommendation
We recommend the MCC adopt the MPI Adjusted Status Quo. While both adjusted status quo
metrics are an improvement over the current GNI-based approach, particularly in their ability to
provide a more comprehensive analysis of poverty, the MPI adjusted status quo outperforms the
$2-a-day alternative in many criteria. The MPI adjusted status quo also provides for more
selection flexibility and better allows the MCC to create sub-national partnerships in the poorest
regions of the world. We conclude the MPI adjusted status quo improves on the current method
of measuring poverty for the reasons below.
With respect to measuring poverty comprehensiveness, both the MPI and $2-a-day alternatives
offer improvements over the status quo. They both perform better than the status quo in
describing the severity of poverty within a country. The MPI adjusted status quo describes incountry poverty severity through its intensity of deprivation, while the $2-a-day adjusted status
quo uses a poverty gap calculation, to describe poverty severity in terms of an average income
shortfall. The MPI fares better than both of the other metrics in terms of comprehensiveness
because it is the only one to capture multidimensional poverty, defining poverty in terms of
quality of life deprivations. The MPI adjusted status quo offers improvement over the status quo
in capturing inequality.
All of our data are verified or compiled by reliable third parties. The GNI and $2-a-day datasets
used in our analysis are developed by the World Bank (World Bank 2015a). The Global MPI is
compiled and maintained by the United Nations Development Programme and the Oxford
Poverty and Human Development Initiative. However, the surveys used by the MPI and the $2a-day alternatives are not conducted yearly, and the number of countries with recent data
available is limited. For the impact category of sub-national capability, the only alternative that
contains any sub-national indicators is the MPI. It has information on 803 sub-national regions.
When limited to the MPI adjusted status quo datasets from 2010, data on 588 regions are
available. The alternative that best meets our criteria for data quality is the MPI adjusted status
quo. It has data on sub-national poverty, and its components are verified by the World Bank, the
United Nations Development Programme, and Oxford Poverty and Human Development
Initiative.
When evaluating data availability, we see few differences among the status quo and policy
alternatives. Neither adjusted status quo improves the frequency of collection or number of
countries surveyed. The MPI adjusted status quo would continue to give the MCC access to
annually updated data from the World Bank, in addition to the Global MPI database released
twice a year. Even though the Oxford Poverty and Human Development Initiative publishes the
MPI report more frequently, not all countries’ data are updated. This limitation makes the Global
MPI unreliable as the only poverty metric. Similarly, the $2-a-day adjusted status quo would also
continue to use the GNI World Bank data together with $2-a-day data, on account the limited
frequency of collection. Not all countries have a $2-a-day headcount measure for every year. In
terms of the number of countries surveyed, all three alternatives provide data for 214 countries.
However, the MPI adjusted status incorporates an additional database, specific to
multidimensional poverty, for 110 of the 214 countries.
27
In terms of selection flexibility, the MPI adjusted status quo allows for the largest number of
candidate countries. These 98 countries are determined to be eligible according to their status as
LIC or LMIC, or as an excluded country with significant poverty identified by the Global MPI.
This alternative continues the existing selection process and supplements it with the Global MPI
to identify additional impoverished populations. For 2015, this alternative increases the number
of eligible countries by 15, using the status quo as a baseline. The $2-a-day adjusted status quo
adds four countries excluded by the status quo, increasing the selection pool to 87 countries.
Additionally, it does not offer any other partnership opportunities as it only measures poverty at
the national level. The MPI adjusted status quo allows for the largest number of partnership
opportunities by offering the opportunity for sub-national partnerships. For 2015, there are 588
potential partnerships for sub-national regions. Of these, 477 are within LICs or LMICs, and 111
are within UMICs.
Given the analysis above, we recommend the MCC adopt the MPI adjusted status quo as its
poverty metric because this metric outperforms the status quo on virtually all dimensions we
identify as important criteria. It performs better than the $2-a-day adjusted status quo in its
comprehensiveness. Most notably, this metric allows the MCC to see poverty at a sub-national
level. For this reason, the MPI adjusted status quo is the best available poverty metric for the
MCC to use when selecting partners.
28
Appendices
Appendix A: Status Quo, $2-a-day, MPI, and Alternatives Analytical Matrix
The table below expands Table 6 to include the $2-a-day and MPI metrics for comparison.
Table A1: Alternative Analytical Matrix
Criteria
Impact
Categories
Status
Quo
$2 Poverty
Line
MPI
Number of Global
Poor
1.1–1.4
billion
2.2 billion
1.6 billion
Adjusted Status Quo
Poverty
MPI
Line
1.8 billion
1.6 billion
Yes:
Yes:
Yes:
Yes:
represented
represented represented represented
None
by intensity
Severity of Poverty
by poverty by Intensity of by poverty
of
ratio
deprivation
gap
deprivation
Yes:
Yes:
CompreYes:
Yes:
represented
represented
Not
hensiveness
represented
represented
by inequality
by inequality
represent
Inequality
by poverty
by poverty
amongst the
amongst the
ed
gap
gap
poor
poor
Yes:
Yes:
income,
education,
No
No
No
education,
Multidimensional
health, Living
health, living
standards
standards
Yes: World
Yes:
Yes: World Yes: OPHI & Yes: World
Bank, OPHI
World
3rd Party Verified
Bank
UNDP
Bank
& UNDP
Bank
Data Quality
Yes: regional
Yes:
Sub-national
None
None
and subNone
regional and
Capability
national
sub-national
3-5 years,
1-5 years, 1-15 years,
Collection
Annual
updated
5-15 years
updated
updated
Frequency
annually
annually
twice yearly
GNI: 214
Data Availability
214
110
214
MPI: 110
Number of
average
average
average
average
214
Countries
yearly
yearly
yearly
yearly
Surveyed
update: 52
Update: 9
update: 52
update: 9
Number of
83
46
88
87
98
Candidate
Countries
Selection
62 national
98 national
Flexibility
83
588 sub588 subPartnership
46 national
87 national
national
national
national
Opportunities
regions
regions
Note: UNDP refers to United Nations Development Programme; OPHI refers to Oxford Poverty and
Human Development Initiative.
Sources: World Bank 2015a and 2015c, OPHI 2015a.
29
Appendix B: UMIC Countries Included in Adjusted Status Quos: $2-a-Day and
$2-a-Day Poverty Ratio-Poverty Gap Combined
Table B1 lists countries that would be included using our recommended GNI and combined
poverty-gap metric described on Table 5.
Table B1: UMICs Included with Combined Poverty Ratio-Poverty Gap Metric
Country
Percentage Below $2-a-day
Poverty Gap at $2-a-day
Combined Metric
South Africa**
26.19
7.65
14.15
China**
18.61
5.46
10.08
Iraq***
21.17
4.69
9.96
Colombia***
12.00
4.69
7.50
$2-a-day combined metric cutoff score of 7 or greater
Source: Authors calculations based on World Bank 2015c.
** Data from 2011; *** Data from 2012.
Table B2 shows the countries that would be included if the MCC combined the GNI and the $2a-day poverty ratio metrics.
Table B2: UMICs Included with $2-a-Day Poverty Ratio Only
Percentage at or Below $2a-day Poverty Line
Country
China **
18.61
Columbia ***
12.00
Dominican Republic ***
8.76
Ecuador ***
8.44
Iraq ***
21.17
Namibia *
43.15
Panama ***
8.87
Peru ***
7.99
South Africa **
26.19
$2-a-day cutoff >≈8 percent of population under $2-a-day
Source: Authors calculations based on World Bank 2015c.
* Data from 2010; ** Data from 2011; *** Data from 2012.
30
Appendix C: LIC, LMIC, and UMIC Countries Included by World Bank GNI Per
Capita and $2-a-day Measures and Excluded in Global MPI
The following table is the list of countries that are excluded by the Global MPI but included in
the GNI per capita and $2-a-day measures.
Table C1: LIC, LMIC, and UMICs Excluded in the Global MPI
LIC
LMIC
Eritrea
Cabo Verde
Algeria
Libya
North Korea
El Salvador
American Samoa
Malaysia
Myanmar
Kiribati
Angola
Marshall Islands
Kosovo
Botswana
Mauritius
Federated States of
Micronesia
Bulgaria
Palau
Papau New Guinea
Costa Rica
Panama
Samoa
Cuba
Romania
Solomon Islands
Dominica
Seychelles
Total: 3
UMIC
South Sudan
Fiji
St. Lucia
Sudan
Grenada
St. Vincent and the Grenadines
Iran
Tonga
Jamaica
Turkmenistan
Lebanon
Tuvalu
Total: 10
Venezuela
Total: 27
Source: Authors calculations based on OPHI 2015a.
31
Appendix D: UMIC Countries Included in the MPI Adjusted Status Quo
The table below lists the countries that are included with the recommended MPI adjusted status
quo.
Table D1: UMIC Countries Included in the MPI Adjusted Status Quo
Country
Population in Poverty
Headcount Ratio Poverty Intensity
(in thousands)*
Belize
15
4.6
39.6
Bosnia and Herzegovina
20
0.5
37.3
Colombia
2,500
5.4
40.9
Gabon
269
16.5
42.5
Iraq
3,706
11.6
38.5
Jordan
119
1.7
35
Kazakhstan
28
0.2
36.2
Macedonia
14
0.7
35.7
Mexico
3,383
2.8
38.8
Peru
3,149
10.5
41
St. Lucia
2
1.0
35.4
Serbia
10
0.1
40.2
South Africa
5,815
11.1
39.5
Suriname
31
5.9
40.8
Tunisia
126
1.2
38.5
Total: 15
19,187
Countries have MPI data collected since 2010
Source: Authors calculations based on OPHI 2015a. Data taken from year of survey.
32
Appendix E: Excluded High Income and Upper Middle Income Countries
The tables below are the high income and UMICs excluded from consideration from the MPI
adjusted status quo due to old data.
Table E1: High Income Country Exclusions Due to Outdated Data
Year of
Survey
Data
Headcount
Ratio
Intensity
MPI Poor Population - Year
of Survey (In Thousands)
Croatia
2003
4.4
36.3
193
Czech Republic
2002
3.1
33.4
319
Estonia
2003
7.2
36.5
97
Country
Latvia
2003
1.6
37.9
37
Russia
2003
1.3
38.9
1,832
Slovakia
2003
0
0
0
Slovenia
2003
0
0
0
Trinidad and Tobago
2006
5.6
35.1
73
UAE
2003
0.6
Uruguay
2002
1.7
Source: Authors calculations based on OPHI 2015a.
33
35.3
19
34.7
56
Table E2: UMIC Country Exclusions Due to Outdated Data
Country
Year of
Survey
Data
Headcount
Ratio
Intensity
MPI-Poor Population - Year
of Survey (In Thousands)
Albania
2002
1.4
37.7
43
Argentina
2005
2.9
37.6
1,106
Azerbaijan
2006
5.3
39.4
461
Belarus
2005
0
35.1
2
China
2002
12.5
44.9
161,573
Dominican Republic
2007
4.6
39.4
442
Ecuador
2003
2.2
41.6
294
Hungary
2003
4.6
34.3
466
Maldives
2009
5.2
35.6
17
Montenegro
2005
1.5
41.6
9
Namibia
2006
39.6
47.2
823
Thailand
2005
1.6
38.5
1,087
42
4,320
Turkey
2003
6.6
Source: Authors calculations based on OPHI 2015a.
34
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