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Characteristics of Poverty in
Upper Middle Income Countries
Prepared for Millennium Challenge Corporation
By
Drew Buys
Hyunseok Kim
Matthew Smalley
Christie Stassel
Julianna Stohs
Soong Kit Wong
Workshop in International Public Affairs
Spring 2017
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©2017 Board of Regents of the University of Wisconsin System All rights reserved.
For an online copy, see http://www.lafollette.wisc.edu/outreach-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.
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Table of Contents
Table of Contents ..................................................................................................................................................................... ii
List of Tables & Figures ........................................................................................................................................................ iv
Foreword ..................................................................................................................................................................................... v
Acknowledgements ............................................................................................................................................................... vi
List of Abbreviations ............................................................................................................................................................ vii
Glossary .................................................................................................................................................................................... viii
Executive Summary ............................................................................................................................................................... ix
Introduction ............................................................................................................................................................................... 1
Poverty in UMICs ..................................................................................................................................................................... 1
Median Income Justification ............................................................................................................................................... 3
Separating “Poor UMICs” & “Rich UMICs” ..................................................................................................................... 4
Methodology .............................................................................................................................................................................. 7
Linear Interpolation & Nearest Available Year ...................................................................................................... 7
Principal Components Analysis .................................................................................................................................... 8
Linear Probability Model ............................................................................................................................................... 10
Results ........................................................................................................................................................................................ 10
Analysis of Shared Characteristics ................................................................................................................................. 11
Component 1: Life Expectancy, Incidence of TB, Linguistic & Religious Fractionalization ............... 12
Component 13: Refugee Population by Country of Asylum ............................................................................ 14
Component 15: Net Migration, Gender Ratio in the Labor Force, Natural Resource Protection ..... 15
Component 7: Mobile Cellular Subscriptions, International Migrant Stock, High-Technology
Exports .................................................................................................................................................................................. 17
Component 3: Elderly with Non-elderly Co-residence Rate, Death Rate .................................................. 19
Limitations ............................................................................................................................................................................... 21
Conclusion ................................................................................................................................................................................ 23
Appendix A: Current MCC Selection Process.............................................................................................................. 25
Criteria for Selection ....................................................................................................................................................... 25
Appendix B: Poverty Measure Selection ...................................................................................................................... 26
Comparison of Median Income to Poverty Headcount Ratios in UMICs .................................................... 29
Appendix C: Omitted UMICs .............................................................................................................................................. 30
Appendix D: Variable Selection ....................................................................................................................................... 31
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Appendix E: Variable List ................................................................................................................................................... 33
Appendix F: Principal Component Analysis ............................................................................................................... 41
Mathematical approach.................................................................................................................................................. 42
Appendix G: Principal Component Summaries ......................................................................................................... 45
Appendix H: Regression Tables ....................................................................................................................................... 48
References ................................................................................................................................................................................ 51
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List of Tables & Figures Figure 1: Trends in Country Classification by Income Level, 1985–2015 ....................................................... 2
Figure 2: Impoverished Populations by Country Income Level in Millions of People, 2013 ................... 3
Figure 3: UMICs with Median Income below $10 ...................................................................................................... 5
Figure 4: Life Expectancy at Birth................................................................................................................................... 12
Figure 5: TB Cases per 100,000 people ........................................................................................................................ 14
Figure 6: Gender Ratio in the Labor Force .................................................................................................................. 16
Figure 7: Net Migration ....................................................................................................................................................... 16
Figure 8: Mobile Cellphone Subscriptions .................................................................................................................. 18
Figure 9: Elderly with Non-elderly Co-residence Rate .......................................................................................... 20
Figure 10: Mortality Rate ................................................................................................................................................... 21
Figure 11: MCC Annual Selection Timeline ................................................................................................................. 25
Figure 12: Poverty Measure Comparison .................................................................................................................... 27
Figure 13: UMICs at the Median Poverty Headcount Ratio, Proportion of Population ............................ 29
Figure 14: Relationship between Political Rights and Civil Liberties .............................................................. 41
Figure 15: Relationship between Political Rights and Civil Liberties, Component 1 ................................ 42
Figure 16: Variable Loadings on Principal Components ....................................................................................... 44
Table 1: GNI per Capita, Median Income, and Poverty Headcount for UMICs* ............................................. 6
Table 2: Principal Components* ........................................................................................................................................ 9
Table 3: Regression Results .............................................................................................................................................. 11
Table 4: Poverty Measure Decision Matrix ................................................................................................................. 27
Table 5: Omitted UMICs ...................................................................................................................................................... 30
Table 6: MCC Selection Indicators, FY17 ..................................................................................................................... 31
Table 7: Variable List ........................................................................................................................................................... 33
Table 8: Principal Component Variances..................................................................................................................... 43
Table 9: Regression Results on Dependent Variable, Poor ≤ $10 Median Income ..................................... 48
Table 10: Regressions Results on Dependent Variable, Poor ≤ $5 Median Income ................................... 49
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Foreword This report is the result of collaboration between the La Follette School of Public Affairs at the University of Wisconsin–Madison and the Millennium Challenge Corporation (MCC), a U.S. foreign-aid agency. The objective is to provide graduate students at the La Follette School with the opportunity to improve their policy analysis skills while providing the client an analysis of policies and practices for improving MCC assistance to middle income countries that have significant poverty population.
The La Follette School offers a two-year graduate program leading to a Master’s degree in International Public Affairs (MIPA). Students study policy analysis and public management, and they can choose to pursue a concentration in a policy focus area. They spend the first year and a half of the program taking courses in which they develop the expertise needed to analyze public policies. The authors of this report all are in their final semester of their degree program and are enrolled in Public Affairs 860, Workshop in International Public Affairs. Although acquiring a set of policy analysis skills is important, there is no substitute for actually doing policy analysis as a means of experiential learning. Public Affairs 860 gives graduate students that opportunity.
This year, workshop students in the MIPA program were divided into two teams. The other team performed an analysis of schooling approaches that promote best outcomes for disadvantaged children in a set of nations chosen in conjunction with the client at the request of the United Nations Children’s Fund (UNICEF).
MCC seeks to reduce global poverty through targeted economic initiatives aimed at low and lower middle income countries. The MCC is currently restricted from funding countries that exceed the World Bank’s established upper middle income county (UMIC) average income threshold, despite the fact that because of growing inequality within these countries, many of world’s poorest people are found in UMICs. MCC has asked the MIPA team to help it understand the most salient characteristics of poverty in UMICs in order to better target aid initiatives.
The report finds that general health conditions strongly predict poverty for UMICs in their sample, a relationship that grows stronger in the case of the most poverty-prone UMICs. In addition, there are strong correlations between poverty and the proportion of refugees and migrants within a country. A third important predictor of high poverty in a UMIC is the lack of a high-technology economy, which includes information and communication technologies.
Recognizing that poverty and development need are heterogeneous, the team encourages MCC to continue to investigate regional- and country-specific contexts to ensure that future compacts reach targeted aid recipients and prevent the most vulnerable populations in UMICs from sliding further into poverty.
Timothy M. Smeeding Lee Rainwater Distinguished Professor
of Public Affairs and Economics May 2017
Madison, Wisconsin
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Acknowledgements We would like to thank the Millennium Challenge Corporation for offering us the opportunity to
delve into such an interesting project. In particular, Daniel Barnes and Christopher Maloney
provided clarity on numerous topics of relevance to the Millennium Challenge Corporation’s
internal policies and strategic direction.
We appreciate the guidance we received on our methodology from Drs. Jason Fletcher, Christopher
McKelvey, Rourke O’Brien, and Emilia Tjernström of the La Follette School of Public Affairs, Dr.
Wei-Yin Loh of the UW-Madison Department of Statistics, and Juwon Hwang of the UW-Madison
School of Journalism & Mass Communication. Lastly, many thanks are due to our advisor, Dr.
Timothy Smeeding, for the advice and direction he provided over the course of this project.
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List of Abbreviations FDI Foreign direct investment
FY Fiscal year
GDP Gross domestic product
GNI Gross national income
HIC High income country
IMF International Monetary Fund
LIC Low income country
LMIC Lower middle income country
MCC Millennium Challenge Corporation
MIC Middle income country
PCA Principal component analysis
PPP Purchasing power parity
TB Tuberculosis
UMIC Upper middle income country
WHO World Health Organization
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Glossary Compact
Grant agreement from Millennium Challenge Corporation
Country classifications by income level
High income country
A country with a GNI per capita of $12,476 or more (FY17)
Low income country
A country with a GNI per capita of $1,025 or less (FY17)
Lower middle income country
A country with a GNI per capita of $1,026–$4,035 (FY17)
Middle income country
A country with a GNI per capita of $1,026–$12,475 (FY17)
Upper middle income country
A country with a GNI per capita of $4,036–$12,475 (FY17)
Gross national income (GNI)
The sum of a nation’s gross domestic product (GDP) plus net income received from overseas
Nonparametric
Not involving any assumptions as to the form or parameters of a frequency distribution
Per capita
For each person; average per person
Principal component analysis
A statistical technique that reduces correlated independent variables into uncorrelated indices
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Executive Summary The Millennium Challenge Corporation (MCC) is a U.S. foreign-aid agency that seeks to reduce
global poverty through targeted economic initiatives aimed at low and lower middle income
countries. Under current statute, MCC is restricted from funding countries that exceed the World
Bank’s established upper middle income threshold, $4,035 gross national income (GNI) per capita
in 2017. Due to rapid global economic growth, MCC faces a decreasing candidate pool, with a 35
percent reduction in the number of countries classified as low and lower middle income in the last
two decades. While some of this progress can be attributed to global efforts to combat poverty, it is
undermined by the fact that many of the world’s poorest people are not living in the world’s
poorest countries. MCC has acknowledged the limitations of using strict GNI cutoff thresholds and is
interested in understanding the most salient characteristics of poverty in upper middle income
countries (UMICs) in order to better target their aid initiatives.
Because the largest proportion of the world’s poor live in middle income countries, we argue that
GNI per capita is not an appropriate sole measure for development need. We used median income
as an alternative poverty measure to capture distributional inequity. We then separated UMICs into
“rich” and “poor” categories using a $10 median income threshold, as this is the threshold below
which a household is more vulnerable to fall into poverty.
We then investigated potential shared characteristics of rich and poor UMICs beginning with
indicators used in the MCC selection process. We also included variables that have been shown to
be particularly important for growth in middle income economies. We reduced 63 variables into 15
principal components that adequately represent the data. The regression of these components on
our “poor” UMIC dummy variable established which components best predict median incomes of
$10 and below. We conducted the same analysis using $5 as the median income threshold to
determine if the same trends hold true for the poorest UMICs.
Our analysis suggests that there are significantly different development characteristics between
poor and rich UMICs. Poor healthcare capacity is a strong indicator of lower median incomes, a
relationship that grows stronger as we evaluate the shared characteristics of the poorest UMICs. In
addition, there are strong correlations between median incomes and the number of refugees and
migrants. We also note that countries with higher levels of religious and linguistic fractionalization
are more likely to have lower median incomes, a relationship that is especially prevalent among the
poorest UMICs. However, this is not to suggest that homogeneity is necessary for economic success,
but rather that fractionalization may be an indicator of greater development need. Lastly, the
absence of a high-technology economy is a strong predictor of low median income. This is
particularly related to mobile technology access as well as high-tech exports.
These results come with some limitations. The nature of this analysis allows us to describe shared
characteristics of a subset of countries within the sample of UMICs, not to determine the causal
factors that are uniformly predictive of median incomes. Therefore, it is not surprising that many of
these components are not statistically significant because poverty and development are
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heterogeneous and will differ across regions and countries. More research should be devoted to
investigating regional variation before policies and interventions are developed. Additionally, we
were constrained from identifying trends across time because our dependent variable, median
income, was only collected at a single point in time for many of the countries in our sample.
Continued collection of median income data can inform future research in order to account for
growth rates and control for time-invariant trends. In general, data availability affected not only
which countries could be included in the sample of UMICs, but also the variables that were included
as potential predictors in the analysis. As a result, we were forced to omit some potential
explanatory variables as well as countries with considerable amounts of missing data. Some
countries are missing data due to conflict and would likely change some of the results of this
analysis.
Despite these limitations, we are confident in our finding that UMICs with median incomes of $10
and below share characteristics that are significantly different from wealthier UMICs. GNI per
capita does not fully capture poverty and development need in UMICs. We recommend that MCC
consider adopting an additional metric, such as median income, in order to consider these
differences when determining aid eligibility. We also recognize that poverty and development need
are heterogeneous, so we encourage MCC to continue to investigate regional- and country-specific
contexts to ensure that future aid agreements reach their targeted recipients and prevent the most
vulnerable populations in UMICs from sliding further into poverty.
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Introduction The Millennium Challenge Corporation’s (MCC) authorizing legislation, the Millennium Challenge
Act of 2003, established the framework for MCC funding, referred to as compacts. Per this statute,
the line between lower middle income countries (LMICs) and upper middle income countries
(UMICs) matches the World Bank’s established UMIC threshold, which was set at $4,035 gross
national income (GNI) per capita in fiscal year (FY) 2017 (World Bank 2017b). Under current
statute, countries that exceed this GNI level are ineligible for MCC assistance, though ones that
make the transition while in the compact implementation phase are unaffected (Millennium
Challenge Corporation 2016a). Countries with GNI per capita below the UMIC threshold are
assessed using MCC’s scorecards, which take into account 20 indicators from third-party sources
(Millennium Challenge Corporation 2017a). In FY17, only 40 percent—33 of the 82 eligible
countries—passed the scorecard, leaving an increasingly limited applicant pool (Millennium
Challenge Corporation 2016c). MCC also faces a dwindling applicant pool as countries graduate
from LMIC to UMIC status. The decrease in the number of low income countries (LICs) and LMICs
and the commensurate rise in the number of UMICs and high income countries (HICs) is illustrated
in figure 1. More information on MCC’s selection process can be found in appendix A.
Using GNI as a strict eligibility cutoff restricts MCC from providing assistance to developing UMICs
that increasingly represent larger proportions of the world’s poor. MCC has acknowledged this
limitation and seeks to understand the most salient characteristics these countries share in hopes
of identifying the drivers of poverty in UMICs (Millennium Challenge Corporation 2017b). The
primary objective of this analysis is to identify indicators that distinguish poor UMICs from rich
UMICs to contribute to MCC’s understanding of poverty in middle income countries (MICs).
Poverty in UMICs The number of countries classified as LICs and LMICs has dropped by 35 percent in the last two
decades, as seen in figure 1. While this progress can be attributed to global efforts to combat
poverty, it also underscores an observation made by Kanbur and Sumner—many of the world’s
poorest people are not living in the world’s poorest countries (2012). As large countries like China,
India, and Nigeria climbed out of the low-income bracket, persistent and growing in-country
inequality resulted in larger proportions of the world’s poor living in MICs (Kanbur and Sumner
2012). In 2013, 65 percent of individuals living on less than $1.901 per day lived in MICs. While
most of these people live in countries categorized as LMICs, there is still substantial poverty within
UMICs, as shown in figure 2.
1 $1.90 per day is the World Bank’s updated metric for $1 per day, its prior indicator for extreme poverty. $3.10 is the updated equivalent to the World Bank’s $2 per day metric, which is often used in richer countries (including UMICs) instead of $1 per day.
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Figure 1: Trends in Country Classification by Income Level, 1985–2015
Source: World Bank
If development trends persist and countries continue to surpass upper middle income GNI per
capita thresholds, larger proportions of the world’s poor will graduate from MCC development
assistance. Namibia exhibits this trend. Within the last decade, Namibia’s GNI per capita grew
enough to place it in the UMIC category. Despite this, almost 25 percent of Namibia’s population
remains in extreme poverty—almost 550,000 people combined (World Bank 2017b). Namibia was
consistently an LMIC between 1989 and 2008, when it finally jumped to UMIC status (World Bank
2017b). In this case, Namibia achieved gross domestic product (GDP) growth on a unique trajectory
and decreased the relative proportion of its population living in poverty. Nevertheless, the number
of people requiring assistance in UMICs remains substantial, as illustrated by figure 2.
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Figure 2: Impoverished Populations by Country Income Level in Millions of People, 2013
Source: World Bank, Development Research Group
The development community has recognized that rigid GNI per capita thresholds do not adequately
portray development need (Ravallion 2001; Sumner 2010; Alonso et al. 2014a; Rose, Birdsall, and
Diofasi 2016). While World Bank economic classifications are created each year for analytical
convenience, they were never intended to be the sole informer of operational budgets for
development agencies (Badiee 2012). Despite this, many aid agencies primarily use GNI per capita
to determine aid eligibility (Alonso et al. 2014b).
Median Income Justification Although national account-based thresholds such as GNI per capita typically serve as the
preliminary determinants for international development, they do little to depict the true well-being
of the population. Conversely, consumption- and income-related measures succeed in conveying
development need at the individual level (Birdsall and Meyer 2015). Specifically, the median
household consumption/income per capita, hereafter referred to as median income, blends aspects
of a country’s poverty rate and poverty gap into a single easily understood, comparable metric
(Birdsall and Meyer 2015).
Median income can more accurately depict well-being because it:
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1) eliminates public and private expenditures that do not contribute to household income
(Rose, Birdsall, and Diofasi 2016),
2) corrects for unequal income distributions within countries with high mean income, but low
median income, revealing that the lower half of the population is still financially vulnerable
(Birdsall and Meyer 2015),
3) is drawn from household surveys that can prevent government interference in national
statistics because household surveys generally involve more international technical
assistance and oversight (Rose, Birdsall, and Diofasi 2016),
4) can depict changes in well-being over time, both within and between countries (Birdsall and
Meyer 2015),
5) is easily accessible on the World Bank’s PovcalNet,
6) is available for 144 countries, and
7) was recently updated in 2016 using 2011 purchasing power parity (PPP) (Rose, Birdsall,
and Diofasi 2016).
Consult appendix B for more information on the selection of median income as our poverty metric
of choice.
Using survey-based data presents both advantages and disadvantages for a poverty metric. As
mentioned previously, it better insulates the metric from interference by national institutions.
However, because household surveys lack an international standard, survey-based metrics can
potentially suffer from inconsistency, poor data quality, and incompatibility across countries. This
can be significant because these flaws are indicative of deficient institutions, which are often
present in countries that have the greatest development need (Rose, Birdsall, and Diofasi 2016).
Despite the inherent shortcomings of survey data, these metrics are “a more convincing indicator of
well-being at the household and individual level than any national accounts measures,” (Birdsall
and Meyer 2015).
We use median income to identify UMICs above the GNI per capita threshold who still have
substantial poverty. Countries where growth has been more concentrated among the wealthier
segments of the population will tend to have a lower median income. Thus, by using median income
to separate UMICs into two groups, we identify countries where equitable growth has not been the
norm.
Separating “Poor UMICs” & “Rich UMICs” We separate UMICs into “poor UMICs” and “rich UMICs” based on median income data provided by
the Center for Global Development (2016). To identify a consumption threshold that would
delineate between poor and rich UMICs, we consulted the literature to better understand the role of
the middle class in developing countries. Banerjee and Duflo refer to the middle class as households
that have daily per capita expenditures between $6 and $10 (2008). Building on this definition,
Birdsall explains that households feel economically secure at “around $10 a day per person” and
can “save for the future” at that income level (2010). Similarly, Lopez-Calva and Ortiz-Juarez
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classify households with a daily household per capita income below $10 as the “vulnerable class,”
which is a term for those at risk of slipping into poverty in the future due to external shocks (2011).
To strengthen prior analyses, Ferreira et al. adopt a measurement of the middle class based on a 10
percent probability of falling into poverty over a five-year time horizon (2012). This exercise, based
on data collected in Latin America, suggests a lower-bound threshold for the middle class of $10 in
daily household per capita income.
The general consensus in the literature leads us to define a poor UMIC as any country with a GNI
per capita between $4,036 and $12,475 with a median income less than or equal to $10 in 2011
PPP. This aligns with the recommendation of a recent Center for Global Development report, which
stated this would add an additional 28 countries to MCC’s candidate pool (Rose, Birdsall, and
Diofasi 2016). Figure 3 illustrates the income classifications using 2017 GNI per capita data. The
bottom right quadrant displays the poor UMIC group, which covers 25 countries in 2017. Table 1
lists the UMICs included in our analysis.
Figure 3: UMICs with Median Income below $10
Source: Millennium Challenge Corporation, World Bank
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GNI per capita (Current USD)
Median Income (2011 PPP$)
Poverty Headcount Ratio at $3.10/day
(2011 PPP$) (% of population)
Poverty Headcount Ratio at $1.90/day
(2011 PPP$) (% of population)
Po
or
UM
IC
Angola 4,180 2.9 54.5 30.13
Namibia 5,190 3.4 45.7 22.6
Botswana 6,460 4.5 35.7 18.24
South Africa 6,080 4.6 34.7 16.56
Georgia 4,160 4.65 25.27 9.77
Gabon 9,200 4.9 24.4 7.97
Albania 4,280 6.5 6.79 1.06
Azerbaijan 6,560 7.6 2.51 0.49
Mexico 9,710 7.7 10.95 3.04
Ecuador 6,030 7.8 10.22 3.82
Dominican Republic
6,240 8.1 9.12 2.32
Macedonia, FYR 5,140 8.15 8.71 1.33
Romania 9,500 8.35 11.6 6.11
Colombia 7,140 8.4 13.2 5.68
Venezuela, RB 11,780 8.4 14.9 9.24
Mauritius 9,780 9.05 2.96 0.53
China 7,930 9.1 11.1 1.85
Peru 6,130 10 9.01 3.13
Ric
h U
MIC
Kazakhstan 11,390 10.45 0.26 0.04
Thailand 5,720 10.75 0.92 0.04
Serbia 5,540 11.1 1.33 0.19
Paraguay 4,190 11.25 6.99 2.77
Brazil 9,850 11.4 7.56 3.66
Turkey 9,950 12.2 2.62 0.33
Bulgaria 7,480 13 4.7 2.03
Panama 11,880 13 8.37 3.77
Costa Rica 10,400 13.9 3.93 1.61
Malaysia 10,570 14 2.71 0.28
Bosnia and Herzegovina
4,670 16.7 0.45 0.07
Belarus 6,460 17.1 0.07 0.03
Russia 11,450 18.5 0.48 0.04 *We excluded some countries from our analysis. A list of those countries can be found in appendix C.
† Median income is reported separately for urban and rural China. This analysis uses the urban China data. Source: World Bank (GNI and poverty headcount data) and the Center for Global Development (median income
data)
Table 1: GNI per Capita, Median Income, and Poverty Headcount for UMICs*
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Methodology After selecting the $10 median income threshold as the method of dividing UMICs into “poor” and
“rich” groups, we gathered data that could explain why countries fall into one group or the other.
These variables were identified as potential shared characteristics of poverty in UMICs. We first
identified indicators used in the MCC selection process. These variables, while used as proxies for
good governance, also reflect investments in human capital and other correlates of growth. After
consulting the available literature on growth in UMICs, we also included variables that have been
shown to be particularly important in the case of UMICs. For example, our variable on high-
technology exports takes into account a country’s ability to produce goods that require extensive
research and development. Our variable selection process is documented in more detail in
appendix D, while the list of variables used in the analysis is in appendix E. After addressing missing
data in the dataset, we used statistical methods to reduce our variable list so we could perform
regression analysis with decreased multicollinearity, and thus more precision, in our calculations.
Ultimately, the analysis identifies correlates of poverty in UMICs, not causal relationships between
poverty and the shared characteristics.
Linear Interpolation & Nearest Available Year
Missing data is an inherent problem in our dataset given the wide variety of factors we attempted
to include in the analysis. If a country did not have any reported data for a given variable, we were
unable to reliably fill in those data points. We were, however, able to impute data that was missing
for some years, including for the year in which we had median income data. We refer to these
missing data points for particular observations as missingness.
We took two approaches to deal with missingness in our data. For years that lacked data between
years with available data, we used linear interpolation to fill in the gaps. It uses the points
immediately before and after a stretch of missingness to calculate a line between those two points.
For example, if a variable’s value was 15 in 2007 and 18 in 2010, but missing in 2008 and 2009
linear interpolation replaces those missing values with 16 and 17 respectively. To make this
operation more accurate, whenever possible we collected data for a longer time range than the one
we intended to use to regress on median income (2004–2015).
When data was missing either because a variable was not collected before a certain year or because
more recent data had not been collected and released yet, we took the data from the nearest
available year to fill in the missingness. In most cases, this was within one to two years of the
desired year based on when median income was collected. We used data from the nearest available
year because linear extrapolation caused some of our variable values to exceed the range of their
scales. For example, if a country’s score on the political rights indicator had moved from a 6 out of 7
to a 7 out of 7 before a period of open-ended missingness, linear extrapolation would fill the next
year with an 8 out of 7, then a 9 out of 7, and so on, which would “break” the scale.
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Principal Components Analysis
Principal components analysis (PCA) is one of the oldest and most popular multivariate statistical
techniques, having been introduced by Karl Pearson in 1901 and further developed by Harold
Hotelling in 1933. PCA is a nonparametric method that allows analysts to reduce large numbers of
potentially correlated variables into a smaller number of uncorrelated indices or “components” that
maximize the variance within the data. This allowed us to distill a substantial amount of
information presented in the data into a finite number of components rather than attempting an
analysis of all 63 independent variables.
PCA is particularly useful for large multivariate datasets that are highly correlated. Our data
contains 31 UMIC countries with median income data and 63 predictor variables for analysis. Using
PCA, we reduced the dimension of our data to 15 principal components that represent 89.58
percent of the variance in the data. Because our principal components reasonably represent the
initial data, we could use these components in lieu of the individual variables to proceed with our
analysis. A more in-depth explanation of PCA is available in appendix F.
Table 2 summarizes the 15 components used in our analysis. The data variability represents the
amount of the variance in the data explained by each component. Upward-pointing arrows indicate
a positive loading on the component, while downward-pointing arrows indicate a negative loading.
When the arrows on explanatory variables within a component point in the same direction, those
variables positively correlate with each other. When the arrows on a component’s explanatory
variable point in opposite directions, they are negatively correlated. For example, in the first
component, incidence of tuberculosis (TB), linguistic fractionalization, and religious
fractionalization are all positively correlated and have the same effect on a country’s likelihood to
be poor. Life expectancy has the opposite effect. When there is only one variable in a component, it
means that component is poorly defined.
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Principal Component
Data Variability
Explanatory Variables
1 24.37% ↓ Life Expectancy ↑ Incidence of
Tuberculosis ↑ Linguistic Fractionalization
↑ Religious Fractionalization
2 14.15% ↓ Inflation Rate ↑ Distance to Frontier:
Trading Across Borders
3 8.53% ↓ Elderly with Non-Elderly Co-Residence Rate
↑ Death Rate
4 6.94% ↑ Personal Remittances Received (% of GDP)
↑ Agriculture, Value Added (% of GDP)
↑ Rural Population ↑ Level of Free Trade
5 5.41% ↑ Quality of Port Infrastructure
↑ Distance to Frontier: Enforcing Contracts
6 4.53% ↑ Distance to Frontier: Resolving Insolvency
7 4.26% ↑ Mobile Cellular Subscriptions
↑ International Migrant Stock
↑ High-Technology Exports (% of manufactured exports)
8 3.55% ↓ Refugee Population (Country of Origin, % of Population)
↑ Distance to Frontier: Construction Permits
9 3.18% ↓ Manufacturing, Value Added (% of GDP)
↓ FDI ↓ Financial Resources Provided to the Private Sector (% of GDP)
10 3.05% ↑ Exports of Goods/Services (% of GDP)
11 2.78% ↑ Government Net Lending (% of GDP)
12 2.62% ↑ Internally Displaced Persons (% of Population)
↑ Number of Secure Internet Servers
↑ Rule of Law
13 2.40% ↑ Refugee Population (Country of Asylum, % of Population)
14 1.96% ↑ Conflict ↓ Religious
Fractionalization
15 1.86% ↓ Net Migration ↑ Natural Resource
Protection ↑ Ratio of Female to Male Labor Participation Rate
*More detail on each component is available in appendix G. Source: Authors’ calculations
Table 2: Principal Components*
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Linear Probability Model
After reducing our data into 15 principal components, we used a linear probability model to
determine if any components were statistically significant in predicting whether a country is a rich
or poor UMIC. We chose the linear probability model over a logit model because it doesn’t assume
identical variance across groups, including the world regions we controlled for in this analysis.
We ran two sets of our model, one where the dependent variable poor indicated a median income of
less than or equal to $10 and one where the dependent variable poor indicated a median income of
less than or equal to $5. The $5 dependent variable model was selected as a proxy for looking at
Sub-Saharan Africa. Of the six countries in our sample with a median income at or below $5, five are
in Sub-Saharan Africa; there are six total Sub-Saharan African countries in our sample, so most of
the variance in the subset is captured in this version of our dependent variable. We started with a
basic model that regressed our dependent variable on only the principal components. We then ran
regressions controlling for log GNI per capita, a country’s region of the world (e.g. Latin America
and the Caribbean), and for both at once. Controlling for log GNI per capita allowed us to take into
account the difference between being a poor UMIC at GNI levels ranging from $4,160 to $11,880.
We controlled for regions at $10 to account for regional differences in poverty. We were unable to
control for regions at $5 due to sample size constraints.
𝑝𝑜𝑜𝑟𝑖 = 𝛽0 + 𝛽1𝑃𝐶1 + 𝛽2𝑃𝐶2+. . . + 𝛽15𝑃𝐶15 + 𝛾1𝑅1 + 𝛾2𝑅2 + 𝛾3𝑅3 + 𝛾5𝑙𝑛𝐺𝑁𝐼 + 𝑒𝑖
where
poori is the dependent variable (rich/poor UMIC) for country i,
β1 to β15 are the regression model coefficients determined in the analysis,
PC1 to PC15 are the independent variables (principal components) for country i,
γ1 to γ5 are the regression model coefficients on the control variables,
R1 to R3 are the controls for regions of the world,
lnGNI is the control for the natural log of GNI,
ei is the residual error or difference between the observed and estimated dependent
variable for country i.
Results As illustrated by table 3, our results confirm that there are significant differences between poorer
and richer UMICs. Principal components 1, 3, 4, 7, 8, 10, 12, 13, and 15 were significant in at least
one version of our model. Principal components 1, 7, and 13 were significant at the 5 percent level
for our most basic model with $10 median income as the dependent variable and no controls.
However, as controls are added we lost significance in principal components 1 and 7, and principal
component 13 became significant only at the 10 percent level. Principal component 3 became
significant at the 5 percent level once we controlled for region.
When $5 was used as the dependent variable, principal component 1 was significant at the 5
percent level throughout. In the basic model, principal components 10, 13, and 15 were significant.
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When we added controls for GNI, component 4 showed significance for the first time. Components 1
and 15 were significant at the 5 percent level and components 4, 10, 12 and 13 were significant at
the 10 percent level. For further information on the regression results, see appendix H.
Table 3: Regression Results
Legend
• Significant at 5% • Significant at 10%
+ Positive Coefficient - Negative Coefficient
Source: Authors’ calculations
Analysis of Shared Characteristics Our analysis found that health conditions are still important for UMICs, even if the diseases of
concern are different from those for LICs or LMICs. The movement of both migrants and refugees
also plays a pivotal role. The third major theme across many of our significant components is the
high-technology economy. Fractionalization of a country’s population on linguistic and religious
lines also contributed to differences between rich and poor UMICs.
Principal components are listed below in order of statistical significance. It is important to note that
variables within the components are merely collinear, and a meaningful correlation beyond that
cannot be extracted. As part of the same statistically significant component, each variable loads
independently on the component’s linear model. Thus, discussion of specific variables within the
component is intended to be descriptive.
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Component 1: Life Expectancy, Incidence of TB, Linguistic &
Religious Fractionalization
The first principal component, comprised of two health variables and two fractionalization
variables, is the strongest predictor of poverty when a poor UMIC was defined as a country with
less than or equal to $5 median income. Here, we found a relationship with component 1 at the
significance threshold of 0.05, which remained true even when we controlled for GNI. Component 1
was also significant when the median income threshold was $10. However, in this case the
relationship had greater significance in the base model (p<.05) than when we controlled for GNI
(p<.10) and did not meet significance thresholds when we controlled for region.
The first half of the component is comprised of two health variables: life expectancy and TB
incidence. In both instances, these health metrics are not only proxies for health policy and the
quality of healthcare, but also the quality of governance. Life expectancy is especially important
because it is the ultimate assessment of these attributes, whereas a measure such as TB incidence is
much narrower. The link between poverty and health outcomes is well-established, whether it is a
relationship to an increased likelihood of premature death (Bell et al. 2016), greater incidence of
diabetes and cardiovascular diseases (Stringhini et al. 2010), increased mental health issues and
decreased physical activity (Pampel, Krueger, and Denney 2010), or as a predictor of mortality (Van
Raalte et al. 2011). Governments can also affect longevity inequality in their population through
policies that explicitly target income inequality, such as market regulation and investment in
education and infrastructure (Neumayer and Plümper 2016). Thus, it is unsurprising that we found
a significant relationship between poverty in UMICs and poor health outcomes. As life expectancy
decreases or the number of TB cases increases, the likelihood that a UMIC in our sample was
defined as poor also increased. Figure 4 illustrates the stark difference between poor and rich
UMICs at the $5 threshold.
Figure 4: Life Expectancy at Birth
Source: World Bank
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As noted previously, public health scientists have shown a clear link between life expectancy and
poverty. In our analysis, the incidence of TB was strongly negatively correlated with life expectancy.
Higher TB incidence is also an indicator of poverty. In absolute numbers, South Africa has one of the
highest TB populations in the world. Moreover, Sub-Saharan Africa as a region has the highest rate
of TB incidence in the world as shown in figure 5. Apart from Mauritius, every Sub-Saharan African
country in our sample had a TB incidence of greater than 300 cases per 100,000 people per year,
more than three times the incidence in China (World Health Organization [WHO] 2016). TB
incidence represents several health concerns: vaccine distribution, availability of preventive
healthcare, and access to long-term treatment. For example, access to the BCG vaccine, a common
vaccine against TB, is encouraged by WHO in countries with an increased risk of TB, yet roughly
half of the countries in Sub-Saharan Africa immunize less than 90 percent of the target population
(WHO 2016a). Latent TB is also treatable but requires preventive screening because symptoms will
not present themselves. Preventive screening requires either a skin or blood test. Although the
patient is not yet ill and cannot transmit TB in this form, treatment of latent TB is key to prevent
future illness (WHO 2017a). Finally, TB can be cured in most cases. However, it requires a strict,
six-month supervised drug intervention. Furthermore, if the intervention is abandoned or not
strictly followed, drug resistance can be created and transmitted. Treatment of drug-resistant TB
requires 9 to 12 months of treatment, entails increased costs, and has the potential for more
harmful side effects (WHO 2017b). Regardless of whether it is drug-resistant, intervention requires
long-term treatment that is only possible with improved healthcare infrastructure. Thus, a higher
incidence of TB directly reflects the country’s ability to cope with preventable and curable illnesses.
Vaccines, tests, and treatment for TB are relatively inexpensive, but require initiative, follow-
through, and supervision. The barrier to decreasing the incidence of TB is not monetary per se, but
rather a lack of access to healthcare and low-quality healthcare infrastructure. This variable is a
good proxy for these country characteristics. It is noteworthy that other variables for child
mortality and vaccination rates did not explain enough variability in the data to be included in a
principal component.
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Figure 5: TB Cases per 100,000 people
Source: World Bank
The second half of the component is comprised of measures for linguistic and religious
fractionalization in the country. The fractionalization indices range from 0, most homogenous, to 1,
most heterogeneous, based on the number of languages and religious groups in the country. We
observed that a country in our sample was more likely to be a poor UMIC if the country had greater
linguistic and religious fractionalization. The authors of these indices also created a third measure
for ethnic fractionalization. Their original analysis concluded that linguistic and ethnic
fractionalization were “determinants of economic success,” but that religious fractionalization was
less important (Alesina et al. 2002). However, their analysis included 190 countries across all
income levels, whereas our research was specific to the 31 UMICs in our sample. For these UMICs,
linguistic and religious fractionalization were more correlated. Other research has continued to test
the relationship between fractionalization and economic success. Not only is the correlation
verified, but fractionalization has also been shown to be a barrier to income redistribution (Haan
2015).
Component 13: Refugee Population by Country of Asylum
Principal component 13, refugees as a proportion of the population of the country of asylum, is one
of the strongest correlates of poverty when using the $10 median income threshold, remaining
significant at the 5 percent level for the base model as well as when controlling for GNI. When
controlling for region, the component remains significant at the 10 percent level. This indicates that
higher incoming refugee populations are associated with higher median incomes for UMICs. It is
important to note that this is not a causal relationship but could be indicative of a higher capacity
for these countries to absorb refugees. Richer countries may also be perceived as more desirable
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destinations for refugees due to better employment prospects. Turkey is an example of rich UMIC
with a large refugee population as are Serbia and Bosnia and Herzegovina.
Interestingly, the sign on this coefficient changes when the median income threshold is lowered to
$5 per day and is significant (p<.10) for both versions of our model. Choosing a $5 median income
threshold is a close proxy to examine UMICs in Sub-Saharan Africa. This finding indicates that
refugee settlement patterns are systematically different in this region of the world. In this context, a
higher refugee population is a predictor of poverty as measured by a lower median income. These
results are supported by a Pew Research Center analysis, which finds that most refugees flee to
countries in close geographic proximity to their own country (Desilver 2015). This means African
refugees are more likely to move to neighboring countries, which also tend to have low median
incomes. Once again, this cannot be interpreted as a causal relationship, but it does indicate that
geography makes a difference for refugees in fleeing political and economic turmoil.
It should be noted that this finding is limited by the fact that two UMICs with large refugee
populations, Lebanon and Jordan, are excluded from this analysis due to a lack of data. The
inclusion of these outliers may change the results for this principal component.
Component 15: Net Migration, Gender Ratio in the Labor Force,
Natural Resource Protection
Principal component 15, which is comprised of general demographic variables as well as natural
resource protection, is significant for both versions of the model using a $5 threshold for median
income. Controlling for GNI provides a positive coefficient significant at the 5 percent level. While
the relationship between net migration, natural resource protection, and the gender ratio in the
labor force may not be immediately clear, there is an important story to be told.
Lower net migration indicates that larger numbers of individuals are emigrating from a country
along with lower immigration. This can be for economic, political, social, or environmental reasons.
Migrants from Africa are predominantly male (United Nations Department of Economic and Social
Affairs 2016), which aptly describes most countries with median income at or below $5 in figure 7.
As net migration is inversely related to the gender ratio in the labor force, this indicates that as
more people emigrate, women take on more prominent roles in the economy. In figure 6, we see
this to be true in Sub-Saharan Africa, where the gender gap for labor force participation is the
lowest in the world, although it should be noted that many of the work opportunities are believed
to be agricultural or in small-scale enterprises (International Labour Organization 2016).
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Figure 6: Gender Ratio in the Labor Force
Source: World Bank
Figure 7: Net Migration
Source: World Bank
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The indicator for natural resource protection is more puzzling until we consider the sample of
countries that have median incomes of $5 or less. The natural resource protection index captures
the extent to which a country is protecting at least 10 percent of its naturally occurring habitats.
Sub-Saharan Africa is home to large natural wildlife preserves and protected lands. Some of these
are remnants of the colonial era, but substantial amounts of the conserved lands have been
expanded since independence (King 2010). Sub-Saharan Africa has strong eco- and adventure-
tourism sectors, so it makes sense that countries like Namibia, Angola, and Botswana score highly
on natural resource protection. However, some public land expansions have been criticized as
“green grabs,” which occur when the government seizes land under the auspices of conservation,
usually to the detriment of local communities (Blomley 2013). If this were the case, it makes sense
that governments could profit from public lands while household median incomes remain low.
Without more information, however, it’s impossible to assert that higher levels of natural resource
protection causally predict lower median incomes.
When using our original $10 median income threshold, this component has a larger coefficient
when controlling for region (p<.10), which suggests that Sub-Saharan Africa is not completely
driving the results. These results are not significant when we control for GNI, which suggests that a
country’s overall economic strength may influence whether these variables predict a median
income less than $10.
Component 7: Mobile Cellular Subscriptions, International Migrant
Stock, High-Technology Exports
Principal component 7, which is comprised of mobile cellular subscriptions, international migrant
stock, and high-technology exports, is another strong predictor of poverty when using the $10
median income threshold. When a poor UMIC was defined as a country with less than $10 median
income, we found a relationship with component 7 at the significance threshold of 0.05. This
occurred in both the base model and when we controlled for region. The component was also
significant when we controlled for GNI, but at a lower significance threshold (p<.10) and was not
significant when we controlled for both GNI and region. This component was not significant at the
$5 threshold.
The connection between export growth and economic growth is well-founded in both the
theoretical and empirical literature (Cuaresma and Wörz 2005). Along with consumption,
government expenditures, and investment, net exports are a direct contributor to GDP. Increases in
exports will increase national income. Additional trade (or openness) also induces investment in
technology and the cross-border transfer of knowledge. Diversity in export sectors has similar
impacts on investment and growth (Cuaresma and Wörz 2005). Furthermore, investment in
technological specialization will induce trade in other sectors through spillover effects (Laursen
and Meliciani 2000). Greater high-technology exports do correlate to economic growth, which is
more likely to follow from direct investment or increases in domestic productivity (Cuaresma and
Wörz 2005). The results from our analysis coincide with theories that suggest increased exports
and a higher percentage of high-technology exports are more likely to exist in rich UMICs. We saw
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that high-technology exports loaded highly on the seventh principal component, which was
significant at the $10 threshold. This corresponds with the narrative that wealthier UMICs are more
likely to possess the investment-intensive industrial capacity related to high technology.
Secondly, the prevalence of mobile cellular subscriptions is an important enabler of economic
growth, especially in countries with inadequate infrastructure development. People in these
countries can potentially access economic gains that were only previously available through more
costly infrastructure investment. However, despite significant increases in the usage of cellular
phones over the last decade, a sizeable portion of the population still does not have access to this
technology (Aker and Mbiti 2010). In 2016, the World Bank estimated that roughly 7 out of 10
people in the poorest 20 percent of the world’s population own a mobile phone. Even though
mobile phones have become more ubiquitous, a “digital divide” based on income, gender, age, and
geography remains. Thus, it is more difficult for this population to access the economic advantages
that follow the spread of information technology (World Bank 2016). As shown by figure 8, a
country with a higher number of mobile users is more likely to be a rich UMIC.
Figure 8: Mobile Cellphone Subscriptions
Source: World Bank
The final variable associated with this component is the international migrant stock, which is the
percentage of the population that was not born in the country. This statistic also includes refugees.
Similar to the previous discussion regarding refugees by country of asylum, a larger international
migrant stock is more likely to be associated with higher income countries in our sample. We
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cannot determine if the migrant stock is a driver of prosperity or if a country’s greater economic
opportunities attract immigration. However, the role of demographics in economic growth is
important, and increases in population are associated with per capita growth (Boucekkine, Croix,
and Licandro 2002). Thus, if the labor force increases due to migration, we may observe
improvements in the country’s income level. The positive correlation between this variable and
high-tech exports indicates that a thriving high-tech sector may act as an impetus for increased
migration.
Component 3: Elderly with Non-elderly Co-residence Rate, Death
Rate
Principal component 3, which is comprised of the elderly with non-elderly co-residence rate and
the death rate, is also one of the strongest predictors of poverty when using the $10 median income
threshold. When a poor UMIC was defined as a country with less than $10 median income and we
controlled for region as well as region and GNI, we found a relationship with component 3 at the
significance threshold of 0.05. The component was also significant in the base model, but at a lower
significance threshold (p<.10) and was not significant when we only controlled for GNI.
Societal patterns such as education, migration, and demographics all influence the likelihood of co-
residence between elderly and nonelderly populations. Regardless, greater socioeconomic
development leads to a lower incidence of co-residence (Bongaarts and Zimmer 2002). Research in
this area is defined by two main theories. The first states that younger generations are dependent
on the head of the household for housing or employment, while elderly relatives may also rely on
the younger generation for economic support or care. These factors encourage co-residence.
Although it is difficult to determine which generation relies most on the other’s support, it has been
established that co-residence decreases when more economic opportunity is available. Economic
growth will present opportunities for professional advancement and higher wages, which could
either compel the younger generation to abandon the head of household or increase capacity to
care for elderly individuals (Ruggles and Heggeness 2008). Further research has shown that co-
residence patterns vary distinctly across regions, demonstrating that culture heavily influences
living arrangements (Bongaarts and Zimmer 2002). Our analysis of the countries in our sample
confirms that countries with greater economic resources will have less co-residence, as illustrated
by figure 9. Our model falls short of determining causality, but the variable’s negative loading on the
component and the negative coefficient in the linear probability model verify this narrative.
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Figure 9: Elderly with Non-elderly Co-residence Rate
Source: World Bank
In developing countries, life expectancy is highly correlated with per capita GDP. Yet, mortality is
influenced by several key determinants: nutrition, public health, sanitation, vaccination, adequacy
of medical treatments, and pre-natal care. As advances are made across these and other
determinants, mortality rates decrease. These lessons and technologies often originate in high
income countries, but their effects spread globally. Thus, improvements in the mortality rate are
not entirely dependent on a country’s GDP, but rather on the ability of its institutions to implement
proven technologies and policies (Cutler, Deaton, and Lleras-Muney 2006). Regardless, income does
affect the capacity of governments to implement health policies. Surprisingly, our analysis
demonstrates the opposite: as the death rate increases, the likelihood that the country is “rich”
UMIC also increases. This is contrary to the narrative shown by principal component 1, which
showed that improvements in life expectancy and decreases in the incidence of tuberculosis
resulted in an increased likelihood that the country is a “rich” UMIC. However, we believe this is a
characteristic of this specific set of countries in our sample, as seen in figure 10. Among the 10
countries in our sample that had death rates above 10 per 10,000 people, five were classified as
“rich” UMICs: Belarus, Bosnia and Herzegovina, Bulgaria, Russia, and Serbia. Four of these countries
were in the top five. This aligns with the results that show significance at $10 threshold, but not at
$5. When the pool of “rich” UMICs is expanded to included poorer countries, the death rate is no
longer a likely descriptor. Furthermore, this list also shows that high death rates may be a trait of
UMICs in Europe and Central Asia, specifically former Soviet republics.
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Figure 10: Mortality Rate
Source: World Bank
Limitations Of the limitations in our research, the availability of data was the most constraining factor. Data
availability affected the selection of both dependent and independent variables as well as the
decision to drop some countries from the analysis. As discussed in appendix B, we selected the
median income measure as the dependent variable despite its inadequacies because it is a
comparable measure that combines aspects of a country’s poverty rate and poverty gap. Compared
to other poverty metrics, it best meets the needs of this analysis. In terms of the independent
variables, the selection of which is discussed in more detail in appendix D, we were forced to leave
out some metrics because they had too much missing data for the population of UMICs. We were
able to address this in some cases by finding more exhaustive measures of the same characteristic,
but this was not always possible. For example, the Freedom of Information metric and metrics
tracking tertiary education enrollment and completion rates had too many missing values to be
used with PCA. We were unable to find alternative measures that were more complete.
The availability of data also reduced the sample size for several reasons. First, median income
remains a relatively new measure. The metric was updated in 2016 by the Center for Global
Development to cover 144 countries. However, the measure is not available for 15 UMICs (see
appendix C). Complete information on median income in UMICs would provide a population of 50
UMICs, which is still a small sample given the number of independent variables to examine. The
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absence of some median income data reduced the sample size to 40 countries, making it more
difficult to establish a definitive association between poverty in UMICs and a specific characteristic.
Other countries were dropped because of data missingness or population size. Unfortunately, we
had to omit all Middle Eastern and North African countries from the sample for these reasons. This
resulted in a final sample size of 31 UMICs. For the list of omitted UMICs, please see appendix C.
Although a small sample size increases the difficulty of finding statistically significant relationships,
we believe that the final list is still a representative distribution of the population. Since we began
with a population—not a sample—and manually reduced the variable list, we could ensure that we
had a representative sample. Although we reduced the number of countries, the remaining
countries represented relatively more of the poverty in UMICs. Additionally, because we aim to
produce associations, rather than causality, sample size is less of a concern. In future research,
more complete data will not only expand the list of countries, but future collection of the median
income metric will also allow researchers to look at observations over time and increase the sample
size. This additional data will allow researchers to take into account the growth rate of
development trends and control for time-invariant effects.
As discussed in appendix D, access to complete measures of possible characteristics constrained
what we could assert as a possible shared characteristic among poor UMICs. Because the presence
or omission of a measure could potentially skew the analysis through collinearity or omitted
variable bias, we made every effort to ensure that all potential characteristics were included as
independent variables. Additionally, many metrics were collected at discrete points in time (e.g.
census data), which did not always correspond to the year median income data was collected for
that country. Thus, to retain an independent variable with data that did not line up with the
dependent variable, we employed linear interpolation to approximate its value in the year of
interest. We selected each metric in this analysis because it was the most complete or sole measure
of a specific characteristic. If we did not use interpolation, we would have needed to drop more
variables from the dataset. We relied on the best available data to derive associations between
poverty and UMIC characteristics. Thus, we selected independent variables that were both
comprehensive enough to interpolate and important to the analysis.
Finally, we used PCA to reduce the 63 independent variables to 15 principal components rather
than reducing dimensionality through manual variable selection. The primary concern with PCA is
that it produces “artificially constructed indices” that merely describe correlation between the
variables (Vyas and Kumaranayake 2006). Although the resulting principal components may do
little to describe underlying relationships, it is necessary to eliminate the multicollinearity of our
regressors. PCA does this (Hadi and Ling 1998). Other methods, such as correspondence analysis,
were not applicable to our dataset. Factor analysis evaluates only shared variance rather than all
observed variance (Vyas and Kumaranayake 2006). Yet regressing on principal components can
make it more difficult to interpret the relationship between the explanatory variables and poverty;
any statistical significance found is describing the relationship between the principal component
and poverty. Ultimately, we chose to use PCA instead of opting to disregard a portion of our
explanatory variables. Future research may be able to better target analysis by manually decreasing
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variables when the data measures are more complete and the depth of research into UMIC poverty
increases.
On top of the concerns about interpreting PCA, we should also use caution in extrapolating the
results from our linear probability model. The nature of missingness in our sample could affect the
external validity of our sample. Countries that are currently experiencing severe conflict such as
Libya and Iraq are systematically missing and would likely change results for some principal
components. In addition, our study does not determine the size or causality of effects.
Conclusion Our analysis leads us to determine that there are significantly different development characteristics
between poor and rich UMICs. Poor healthcare capacity is a strong indicator of low median
incomes, a relationship that grows stronger as we evaluate the shared characteristics of the poorest
UMICs. In addition, there are strong correlations between median incomes and the number of
refugees and migrants. However, as noted in the analysis, the direction of this relationship changes
depending on whether we evaluate median incomes at the $10 or $5 thresholds. We also note that
countries with higher levels of religious and linguistic fractionalization are more likely to have
lower median incomes, a relationship that is especially prevalent amongst the poorest UMICs.
However, this is not to suggest that uniformity is necessary for economic success, but rather that
fractionalization may be an indicator of greater development need. The lack of a high-technology
economy is a strong predictor of low median income as well. This is particularly related to
information and communication capacity as well as high-tech exports. It is worth noting that the
shared characteristics among the poorest UMICs (median income of $5 and less) are similar to what
we might expect to see in LMICs, suggesting a higher need for development in these countries.
While some of the shared characteristics are merely demographic indicators, others lend
themselves as potential programs for future MCC compacts.
While we do obtain significant results from our regression analysis, we advise caution in using
these components and variables to make causal predictions. The nature of this analysis allows us to
describe shared characteristics of a subset of countries within the sample of UMICs, not to
determine the causality of median incomes. It is not surprising that many of these components are
not significant in some versions of our model because poverty and development are heterogeneous
and will differ across countries and regions. We should not necessarily expect poor Sub-Saharan
African countries to have the same characteristics as poor Eastern European countries or poor
Latin American countries. More research should be devoted to investigating the regional
characteristics of poverty. While our small sample prevented us from conducting a statistical
analysis of each region, we were able to adapt our model to account for median incomes at or below
$5, which created a crude proxy for the Sub-Saharan African countries in our sample. This showed
us that the poorest UMICs, primarily Sub-Saharan African countries, share different characteristics
than those that are only slightly more prosperous. Understanding this regional variation will allow
MCC to target specific development sectors to build capacity and combat poverty.
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When considering implementation in connection to these results, there is no need to reinvent
MCC’s model. MCC has already done compacts that help address some of the problems afflicting
poor UMICs. For example, Namibia’s compact, which ran from 2009–2014, is a good example of
work MCC has done to target areas with high fractionalization and to encourage equitable growth.
The compact specifically addressed the needs of the Hai//om San, one of Namibia’s most vulnerable
minority groups, by funding infrastructure projects in and around Etosha National Park, where
many Hai//om San live. This occurred in combination with a concession from the Namibian
Ministry of Environment and Tourism giving the Hai//om San exclusive rights to bring tourists into
the park. These initiatives allow the Hai//om San to benefit from the economic opportunity
provided by tourism. Similarly, MCC has several compacts that have included initiatives focused on
building health capacity in LICs and LMICs. Similar projects would be valuable in poor UMICs, which
confront many of the same challenges as LMICs. MCC could use strategies from previous compacts
and apply them to other findings here as well. For example, methods to improve regulations in the
energy sector and attract private capital could be modified for use in high-technology sectors.
Taking this approach in poor UMICs would create opportunities to reduce global poverty.
Because UMICs with median incomes of $10 and below share characteristics that are significantly
different from UMICs with higher median income levels, we argue that GNI per capita is not an
appropriate sole measure for development need. We recommend that MCC adopt an additional
measure such as median income when determining aid eligibility to open up funding to poor UMICs.
This will allow MCC to take into account inequitable distributions of wealth within countries that
leave millions of individuals vulnerable to poverty.
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Appendix A: Current MCC Selection Process
Figure 11: MCC Annual Selection Timeline
Source: Millennium Challenge Corporation
Criteria for Selection2
1. Candidate countries
a. Below GNI per capita UMIC threshold ($4,035 in FY17)
b. Not prohibited from assistance under the Foreign Assistance Act
2. Scorecards
a. “Hard hurdles” a country must pass to be eligible:
i. Democratic rights: either the Civil Liberties or Political Rights indicator
ii. Control of Corruption indicator
b. Passed at least half of the 20 indicators
3. Other considerations
a. “The opportunity to reduce poverty and generate economic growth”: MCC considers
additional factors such as the state of democratic and human rights, economic
growth trends, and the likelihood of a successful partnership to inform its decision-
making and to identify changing trends within the country
b. Availability of funds
c. For returning grantees, additional considerations include:
i. Successful implementation of all prior compacts and constructive
partnership with MCC
ii. Improved scorecard performance
iii. Commitment to further reforms
2 These criteria for selection were taken directly from MCC web content and internal documents.
• Create candidate country list
Late Summer
• Publish selection methodology
By October• Country scorecards available
Late Fall
• Final determination of eligibility
December
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Appendix B: Poverty Measure Selection To determine the characteristics that are shared among poor UMICs, we needed to select a metric of
poverty that encapsulates both absolute (size) and relative (depth) measures of poverty within a
country. Secondly, since this research will seek to evaluate characteristics at the country/region
level, the metric needs to be comparable, available, and current. Specifically, this means that the
chosen metric is set in terms that equate levels of poverty between countries. This also assumes
that respective organizations generated the metric for most countries in the analysis, and that the
data used to produce the metric was recent. Ultimately, the metric is intended to center our
research on the most revealing, accurate measure of poverty currently available.
Of the available measures of poverty currently available, there are roughly seven categories: (1)
quality of life, (2) inequality, (3) national aggregate, (4) headcount, (5) bottom 40, (6) poverty
intensity, and (7) median income. Of these seven categories, five were eliminated from
consideration before analysis. First, quality of life measures that utilize proxies for poverty, such as
the multidimensional poverty index, and inequality measures, such as the Gini coefficient, were
eliminated to permit the evaluation of inequality, health, education, and living standards as
potential characteristics of poverty in UMICs. Secondly, the national aggregate, which includes GNI
per capita, and headcount measures were excluded because they do not illustrate the depth of
poverty. Finally, the bottom 40 category was untenable because it lacks sufficient comparability
across countries. The bottom 40 metric, which analyzes the well-being of each country’s poorest 40
percent, are more suited for evaluating poverty levels within a single country over time.
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Figure 12: Poverty Measure Comparison
Source: Authors’ Calculations
Of the remaining two categories—median income and poverty intensity—a specific metric was
chosen and evaluated. The measure of poverty intensity, the poverty gap index, was selected for its
greater accessibility over other metrics. The second measure is the survey-based median household
consumption/income per capita (referred to as median income).
Table 4: Poverty Measure Decision Matrix
Poverty Gap
Index
Median
Household
Income
Cross Country Measure Absolute Measure Relative Measure Country Coverage Current
Source: Authors’ calculations
Positive Moderate Negative
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The chosen metrics were evaluated based on their comparability across countries, their ability to
measure absolute numbers of poverty as well as the degree of poverty within a country, and the
availability and accuracy of the data. First, comparability assesses the metric’s ability to equate
poverty levels in different environments, which is essential for evaluating characteristics at the
country/region level. This would primarily account for differences in data collection over time and
by country. A metric is considered more favorable if the variance in methodologies between
countries and over time is minimized. The next two criteria – absolute and relative measures –
gauge how well the metric captures both the total number of people in poverty and the degree of
poverty. The metric is considered better if it provides greater insight in the levels of poverty in that
country. Finally, the metric must be available and current. The metric is considered favorable if it is
publicly available, covers a greater number of the UMICs being evaluated, and was updated more
recently.
These two metrics were evaluated based on their comparability across countries, their ability to
measure absolute numbers of poverty as well as the degree of poverty within that country, and the
availability and accuracy of the data. Based on these criteria, the poverty gap index was ineligible
due to the unavailability of consistent data. The other metric, median income, adequately appraises
the level and depth of poverty in each country. Additionally, it produces a single PPP-weighted
value to measure poverty, although it also relies largely on survey data that is inconsistently
measured across countries. Finally, for the purposes of this research, data availability was
considered adequate.
The matrix in table 4 clearly demonstrates that median income is the preferred standard for
evaluating poverty in this analysis. The primary reason is that the median income metric can
correct for skews in income distribution (Rose, Birdsall, and Diofasi 2016) and account for
inequality discrepancies. Thus, median income provides an easily understood, comparable metric
that combines aspects of a country’s poverty rate and poverty gap (Birdsall and Meyer 2015).
It is also worth addressing the downsides to median income. It is a partially inexact comparison in
that it relies on surveys, some of which have quality and periodicity issues. Median income can be
inexact because it reports a mixture of income and consumption data across countries with varied
data-collection methods. Furthermore, median income is surveyed sporadically, which forces the
comparison across discrete and varying points in time. Finally, inaccuracy occurs when an
international standardized survey is not used or if governments interfere with survey results.
Despite these concerns, median income remains the preferred metric because it brings forward the
“best available data” (Rose, Birdsall, and Diofasi 2016). Relying on a non-survey metric would
eliminate the comparability and periodicity issues, but it would increase quality issues and
decrease the meaningfulness of the metric as a measure of poverty. For this analysis, capturing the
depth of poverty was ultimately the most important concern. Going forward, the implementation of
a single ubiquitous household survey by a third party would be the best way to maintain the
significance of the metric and increase its comparability (Birdsall and Meyer 2015).
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Comparison of Median Income to Poverty Headcount Ratios in
UMICs
Although we compared our preferred standard for evaluating poverty, median income, to GNI per
capita in the body of the report, we also wanted to compare it to the commonly used poverty
headcount ratios. This comparison checks how our analysis may have changed had we selected a
different poverty measure. This direct comparison was important given that poverty headcounts
are ubiquitous in development and poverty research.
Figure 13 reflects the change in the classification of poor and rich UMICs had we used the $1.90 or
$3.10 per day headcount ratios. In general, median income lines up with the poverty headcount
ratios of these countries. Most importantly, the poorest UMICs in our sample, those countries below
$5 median income, would not change at all. As expected, there is some variance among countries in
the middle of the distribution. Within the poor UMICs, Albania, Azerbaijan, and Mauritius have
notably lower percentages of people living below these poverty lines than would be expected for
their median income level. On the other side, Paraguay, Brazil, and Panama have higher levels of
people living in poverty than would be expected from the countries we defined as rich UMICs.
Despite the variance within the sample, median income corresponds adequately to other traditional
measures of poverty. We would still expect our findings to be different if we selected an alternate
dependent variable. This is mainly due to our small sample size and the presence of specific
countries that may drive the analysis, not due to vast differences between the poverty measures.
Figure 13: UMICs at the Median Poverty Headcount Ratio, Proportion of Population
Source: World Bank
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Appendix C: Omitted UMICs For a variety of reasons, some countries were not suitable for inclusion in this analysis. Some
countries did not have median income or other data available, largely due to conflict and
insufficient capacity to collect quality data. These countries would not be eligible for MCC assistance
under such conditions. In addition, countries with populations of less than 1 million are not likely
candidates for MCC assistance because such a program has less potential to have far-reaching
impacts. Small nations’ economies typically differ significantly from other economies. For this
reason, the inclusion of these nations in the analysis would likely sway the results and produce
conclusions that do not reflect growth of other economies. Table 5 lists the countries excluded from
this analysis.
Median Income Not Available
Population < 1 million
Insufficient Data
Algeria •
Argentina •
Belize •
Cuba •
Dominica • •
Fiji •
Grenada • •
Guyana • •
Iran, Islamic Republic •
Iraq • •
Jamaica •
Jordan • •
Lebanon • •
Libya • •
Maldives •
Marshall Islands • •
Montenegro •
Palau • •
St. Lucia •
St. Vincent and the Grenadines • •
Suriname •
Tonga • •
Turkmenistan • •
Tuvalu • •
Source: Countries classified as UMIC, FY13-FY17 (World Bank), median income (Center for Global Development), population (World Bank)
Table 5: Omitted UMICs
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Appendix D: Variable Selection Determining the common characteristics of poor UMICs is an open-ended question. Because many
societal or environmental factors could act as a potential correlate with poverty, the pool of
possible characteristics is vast, and omitted variables could bias the analysis. Thus, we first
compiled a comprehensive list of potential independent variables based on a review of previous
work.
We began with the selection indicators that MCC uses to determine a country’s eligibility for
program assistance. We chose these indicators because MCC specifically developed these identifiers
“to be effective in reducing poverty and promoting economic growth” (2016). These indicators are
used to identify countries with the best capacity to implement MCC compacts effectively. They also
reflect investment in human capital and other potential correlates of growth. As shown in table 6
below, MCC lists these indicators under three categories: economic freedom, investing in people,
and ruling justly. Sources for the indicators are varied, including intergovernmental organizations,
think tanks, non-governmental organizations, and universities.
Table 6: MCC Selection Indicators, FY17
Economic Freedom Investing in People Ruling Justly
Access to Credit Child Health Civil Liberties Business Start-Up Girls' Primary Education
Completion Rate Control of Corruption
Fiscal Policy Girls' Secondary Education Enrollment Ratio
Freedom of Information
Gender in the Economy Health Expenditures Government Effectiveness Inflation Immunization Rates Political Rights Land Rights and Access Natural Resource Protection Rule of Law Regulatory Quality Primary Education
Expenditures
Trade Policy
Source: Millennium Challenge Corporation
Each indicator also includes multiple underlying indicators. For example, the Access to Credit
indicator is based on two International Finance Corporation indicators: the depth of credit
information index and the strength of legal rights of borrowers and lenders. In other cases, an MCC
indicator is based on datasets that aggregate many sources. For example, the Rule of Law indicator
leverages the Worldwide Governance indicators, which combine “up to 23 different assessments
and surveys” to generate six governance scores (Millennium Challenge Corporation 2017a).
Because our goal was to achieve specificity in our results, we chose to use the components of each
MCC indicator separately rather than composite measures. For the same reason, we included
several of the MCC’s supplemental indicators. The most important of these were related to business
conditions.
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MCC uses these indicators to determine a country’s capacity to absorb assistance and to quantify
their success implementing previous reforms. However, MCC is one niche in the international
development space. Accordingly, we sought potential poverty characteristics outside the MCC
evaluation framework. After a survey of related academic works, our final set of potential
characteristics focused on middle income countries (both LMICs and UMICs).
Lastly, we checked the resulting list of potential characteristics for suitability. We retained variables
that were sufficiently complete for all countries and recently updated. We also removed descriptors
that overlapped to reduce the likelihood of collinearity or selection bias. Finally, datasets were
required to be objective measures that quantify the given characteristic. For example, scores that
merely ranked countries were not suitable because they provide a relative measure of the
characteristic.
The resulting list included more than 150 potential common characteristics of poor UMICs.
Although this was a considerable number, it ensured that all possibilities were under consideration.
However, to conduct statistical analysis we needed to address the missing data present within the
dataset. Additionally, the data already included the full population of UMICs, and therefore a new
sample could not be taken. We removed variables that were not sufficiently complete to impute,
such as particularly weak sets, a metric’s subcomponents, and overlapping characteristics.
The reduction produced a list of 63 potential characteristics with data able to be imputed by linear
interpolation and nearest value. The complete list of variables is included in appendix E.
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Appendix E: Variable List
Table 7: Variable List
Variable Definition Source
Access to Improved Water Sources
Percentage of population with access to improved water sources. Improved drinking water sources include piped water on premises and other improved drinking water sources. A higher percentage indicates that there is better improved water coverage.
World Bank
Age Dependency Ratio
Ratio of dependents (age 0-14, >65) to the working-age population (age 15-64). A larger ratio translates to more dependents relative to the working-age population.
World Bank
Agriculture, Value Added
Total value added by agriculture as a percentage of GDP. Agriculture includes forestry, hunting, fishing, cultivation of crops, and livestock production. A higher percentage indicates that a larger proportion of the economy is agriculture-based.
World Bank
ATMs Number of ATMs per 100,000 people. A higher number indicates that there is greater access to ATMS.
World Bank
Civil Liberties
Index between 1 and 7 indicating government performance on civil liberties. Performance is measured through 15 civil rights indicators in four subcategories: freedom of expression and belief, associational and organizational rights, rule of law, and personal autonomy and individual rights. A lower score indicates greater civil liberties.
Freedom House
Conflict
Dummy variable that is 1 if there has been armed conflict in the country in the past 5 years, and 0 if there has not. An armed conflict is a contested incompatibility that concerns the government and/or territory where the use of armed force between two parties, of which at least one is the government, results in at least 25 battle-related deaths in one calendar year.
Uppsala Conflict
Data Program
Control of Corruption
Index between -2.5 and 2.5 indicating government performance on controlling corruption. The index score reflects perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption as well as "capture" of the state by elites and private interests. A higher index score indicates that corruption is perceived to be better controlled.
World Bank
Country Country name World Bank
Country Year Year of the observation
Death Rate Number of deaths per 1,000 people. A higher number indicates a higher rate of death in the population.
World Bank
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Distance to Frontier: Access to Credit
Score between 0 and 100 that shows the distance in performance on access to credit to the "frontier" country. The measure includes the strength of legal rights relating to credit, and the depth of information pertaining to credit. A score of 0 represents the lowest performance possible and 100 represents the frontier.
World Bank
Distance to Frontier: Construction Permits
Score between 0 and 100 that shows the distance in performance on obtaining construction permits to the "frontier" country. The measure includes the number of procedures, time taken, and monetary cost involved with getting a construction permit as well as new building quality control. A score of 0 represents the lowest performance possible and 100 represents the frontier.
World Bank
Distance to Frontier: Enforcing Contracts
Score between 0 and 100 that shows the distance in performance on enforcing contracts to the "frontier" country. The measure includes the time taken, monetary cost, and quality of judicial processes, in relation to contracts. A score of 0 represents the lowest performance possible and 100 represents the frontier.
World Bank
Distance to Frontier: Getting Electricity
Score between 0 and 100 that shows the distance in performance on getting electricity to the "frontier" country. The measure includes the number of procedures, time taken, and monetary cost involved with getting connected to the electricity grid as well as the reliability of electricity supply, and the transparency of electricity tariffs. A score of 0 represents the lowest performance possible and 100 represents the frontier.
World Bank
Distance to Frontier: Paying Taxes
Score between 0 and 100 that shows the distance in performance on the tax environment to the "frontier" country. The measure includes the number of payments per year, time taken to file, tax rate, and a post-filing index. A score of 0 represents the lowest performance possible and 100 represents the frontier.
World Bank
Distance to Frontier: Protecting Minority Investors
Score between 0 and 100 that shows the distance in performance on protecting minority investors to the "frontier" country. The measure includes the extent of disclosures, director liability, shareholder rights, ownership and control, corporate transparency, and the ease of shareholder suits. A score of 0 represents the lowest performance possible and 100 represents the frontier.
World Bank
Distance to Frontier: Resolving Insolvency
Score between 0 and 100 that shows the distance in performance on resolving insolvency to the "frontier" country. The measure includes the recovery rate and the strength of insolvency framework. A score of 0 represents the lowest performance possible and 100 represents the frontier.
World Bank
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Distance to Frontier: Trading Across Borders
Score between 0 and 100 that shows the distance in performance on trading across borders to the "frontier" country. The measure includes the time taken and monetary cost to both import and export, with a lower cost getting a higher score. A score of 0 represents the lowest performance possible and 100 represents the frontier.
World Bank
DTP Immunization Rate
Percentage of the total population that received the third dose of the diphtheria, pertussis, and tetanus (DTP) vaccine. A higher percentage indicates higher DTP immunization rates.
WHO
Elderly with Non-elderly Co-residence Rate
Co-residence is defined as elderly people living in a household with non-elderly members (elderly defined as age 60+). This indicator is calculated as the number of households where elderly individuals live with non-elderly individuals divided by the total number of households with elderly individuals in the population. A higher rate indicates that more households have elderly co-residence.
World Bank
Electric Power Consumption
Amount of electricity consumed in kWh per capita. Measures the production of power, subtracting transmission, distribution, and transformation losses, and own use by the plants. A larger number indicates that more electricity is consumed per capita.
World Bank
Energy Imports
Percentage of energy used in the country that is imported, measured in oil equivalents. A negative value indicates that the country is a net exporter. Energy use refers to use of primary energy before transformation to other end-use fuels. A higher positive percentage indicates a higher dependence on foreign sources of energy.
World Bank
Ethnic Fractionalization
Number between 0 and 1 that shows the degree of ethnic fractionalization, where 1 represents maximum fractionalization. Measures the degree of ethnic heterogeneity. A higher number indicates that the country is more heterogeneous and fractionalized ethnically.
Alesina et al.
Exports of Goods/ Services
Total exports of goods and services as a percentage of GDP. Measures the value of all goods and other market services provided to the rest of the world. A higher percentage indicates that a larger portion of the economy is export-based.
World Bank
Fertility Rate
Number of births per woman. Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year. A larger number indicates a higher fertility rate.
World Bank
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Financial Resources Provided to the Private Sector
Measure of financial resources provided to the private sector by financial corporations as a percentage of the country’s GDP. A higher percentage indicates that more credit is being given to the private sector.
World Bank
Foreign Direct Investment (FDI)
Net inflow of FDI in current U.S. dollars (unadjusted for inflation). A higher number indicates greater FDI in the country.
World Bank
Girls’ Secondary Education Enrollment
Gross enrollment ratio of females to males at the lower secondary education level. A higher ratio indicates that more girls are enrolled in secondary education.
UNESCO
Government Effectiveness Index
Index between -2.5 and 2.5 assessing government effectiveness. The index score reflects perceptions of the quality of public services, quality of the civil service and the degree of its independence from political pressures, quality of policy formulation and implementation, and credibility of the government's commitment to such policies. A higher score indicates higher perceptions of government effectiveness.
World Bank
Government Net Lending
Government net lending as a percentage the country’s GDP. The measure equals government revenue minus expense, minus net investment in nonfinancial assets. It is also equal to the net result of transactions in financial assets and liabilities. A negative value means the government is borrowing more than it is lending. A higher positive percentage indicates that the government is running a greater surplus.
World Bank
High-Technology Exports
Percentage of manufactured exports that are classified as high-technology. High-technology products are products with high research and development intensity. A higher percentage indicates that more of manufacturing exports are high-technology products.
World Bank
Hospital Beds
Number of hospital beds per 1,000 people. Includes inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers. A higher number indicates that there are more hospital beds.
World Bank
Incidence of Tuberculosis (TB)
Estimated number of new and relapse TB cases per 100,000 people arising in a given year. All forms of the disease are included. A higher number indicates a higher prevalence of TB in the country.
World Bank
Inflation Rate
Percentage annual growth rate of the GDP implicit deflator that shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. A higher percentage indicates that prices are rising at a higher rate.
World Bank
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Internally Displaced Persons
High estimate fraction of the population that are internally displaced. Internally displaced persons are people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular because of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters, and who have not crossed an international border. A higher percentage indicates that more people are being displaced.
World Bank
International Migrant Stock
Percentage of total population that was born in a different country. International migrant stock includes people born in a country other than that in which they live, including refugees. A higher percentage indicates that the country has a higher proportion of foreign-born residents.
World Bank
International Tourism Receipts
Ratio of international tourism receipts to exports of goods and services. International tourism receipts are expenditures by international inbound visitors. A higher ratio indicates a greater proportion of exports are from tourism.
World Bank
Internet Users
Number of people out of every 100 that have used the internet in the past three months from any device or location. A higher number indicates that there are more internet users.
World Bank
Level of Free Trade
Score between 0 and 100 that indicates the level of free trade. It is a composite measure of the trade-weighted average tariff rate and nontariff barriers that affect imports and exports of goods and services. A higher score indicates less non-tariff barriers and lower tariff rates.
Heritage Foundation
Life Expectancy Average number of years a person is expected to live at birth. A larger number means higher life expectancy.
World Bank
Linguistic Fractionalization
Number between 0 and 1 that shows the degree of linguistic fractionalization, where 1 represents maximum fractionalization. Measures the degree of linguistic heterogeneity. A higher number indicates that the country is more heterogeneous and fractionalized linguistically.
Alesina et al. dataset
Manufacturing, Value Added
Total value added by manufacturing as a percentage of GDP. A higher percentage indicates that more of the economy is based on manufacturing.
World Bank
Measles Immunization Rate
Percentage of the total population that has received the first dose of the measles vaccine. A higher percentage indicates a higher measles immunization rate.
WHO
Median Income Median household income/consumption per capita in 2011 PPP. A higher median income indicates a higher per capita income for the 50th percentile.
Center for Global
Development
Mobile Cellular Subscriptions
Number of mobile cellular subscriptions per 100 people. A higher number indicates that more people are connected to a mobile network.
World Bank
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Natural Resource Protection
Score between 0 and 100 indicating the government’s commitment to habitat preservation and biodiversity protection. This indicator measures the degree to which a country achieves the target of protecting at least 17% of each terrestrial biome within its borders. A higher number indicates a greater commitment to preservation.
Columbia University
Natural Resource Rents
Total natural resource rents as a percentage of the country’s GDP. A higher percentage indicates that a greater proportion of the economy is based on the exploitation of natural resources.
World Bank
Net Barter Terms of Trade
Index measuring the relative prices of a country’s imports and exports measured relative to the base year 2000. The barter terms of trade index is calculated as the percentage ratio of the export unit value indices to the import unit value indices. A higher index indicates that exports are more expensive and/or imports are cheaper.
World Bank
Net Migration
Number of immigrants minus the number of emigrants, including citizens and noncitizens, for the five-year period. A negative number means there is net emigration. A higher number indicates more migration into the country.
World Bank
Number of Secure Internet Servers
Number of secure internet servers per 1 million people. Secure servers use encryption technology in internet transactions. A higher number indicates a larger ratio of servers to people.
World Bank
Personal Remittances Received
Total personal remittances as a percentage of GDP. Personal remittances comprise personal transfers and compensation of employees. A higher percentage indicates that a larger portion of the economy is dependent on remittances.
World Bank
Political Rights
Index ranging from 0 to 40 that marks the extent of political rights. The measure includes 10 political rights indicators with three subcategories: electoral process, political pluralism and participation, and functioning of government. A higher score indicates greater political rights.
Freedom House
Population Growth Rate
Ten-year annualized population growth rate. The 10-year annualized rate is the average annualized rate in the past 10 years. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. A higher percentage indicates that population is growing at a higher rate.
World Bank
Quality of Port Infrastructure
Index between 1 and 7, where 1 represents “extremely underdeveloped” and 7 represents “efficient by international standards.” Measures business executives' perceptions of their country's port facilities. A higher index number indicates a more positive perception of port infrastructure.
World Bank
Ratio of Female to Ratio of female to male labor force participation rate. A World Bank
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Male Labor Force Participation Rate
higher ratio indicates a greater female labor participation rate.
Refugee Population (Country of Asylum)
Fraction of the population that are refugees by country of asylum. Refugees are those recognized as refugees under the conventions of UNHCR and the African Union. Asylum seekers are excluded. Country of asylum is the country where an asylum claim was filed and granted. A higher percentage indicates that more refugees live in a country.
World Bank
Refugee Population (Country of Origin)
Fraction of the population that are refugees by country of origin. Refugees are those recognized as refugees under the conventions of UNHCR and the African Union. Asylum seekers are excluded. Country of origin refers to the nationality or country of citizenship of a claimant. A higher number indicates that more refugees originate from a country.
World Bank
Region World Bank regional designation World Bank
Regulatory Quality
Index ranging between -2.5 and 2.5 assessing a country’s regulatory quality. The score reflects perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. A higher index number indicates that perceptions of regulatory quality are more positive.
World Bank
Religious Fractionalization
Number between 0 and 1 that shows the degree of religious fractionalization, where 1 represents maximum fractionalization. Measures the degree of religious heterogeneity. A higher number indicates that the country is more religiously heterogeneous.
Alesina et al. dataset
Rule of Law
Index ranging between -2.5 and 2.5 that indicates the extent of the rule of law in the country. The index score reflects perceptions of the extent to which agents have confidence in and abide by the rules of society, the quality of contract enforcement, property rights, the police, and the courts as well as the likelihood of crime and violence. A higher index number indicates that perceptions of the strength of rule of law are more positive.
World Bank
Rural Population Percentage of population that lives in rural areas of the country. A higher percentage indicates that a greater proportion of the population lives in rural areas.
World Bank
Sex Ratio of 0-24 Age Group
Number of males per 100 females in the age group of 0-24. A larger ratio indicates that there are more men relative to women in this age group.
United Nations
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Total Dependency Rate
Total average household dependency rate, computed as the number of dependents in a household divided by the number of working age population in the same household. Dependents are children and elderly, and working-age population is people ages 15 to 60. A higher rate indicates that there are more dependents in the average household in relation to working-age people.
World Bank
Under-five Mortality Rate
Mortality rate of children under 5 years old per 1,000 live births. A higher score indicates a higher mortality rate.
World Bank
Vulnerable Employment
Percentage of total employment that is vulnerably employed. Vulnerable employment comprises unpaid family workers and own-account workers. A higher percentage indicates that more of the country’s workforce is vulnerably employed.
World Bank
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Appendix F: Principal Component Analysis PCA is a statistical procedure that uses an orthogonal transformation to convert a set of
observations of possibly correlated variables into a set of values of linearly uncorrelated variables
called principal components. This allows us to reduce the data to fewer dimensions while using
new basis axes to identify interesting patterns and structures.
To provide a visual example, we will use a simplified dataset that consists of two variables: civil
liberties and political rights. Both indicators are available through Freedom House, which uses an
average of the two to create an overarching Freedom Score. The political rights measure includes a
wide range of rights, including free and fair elections, competitive political parties, and minority
representation in government. The civil liberties measure includes “freedoms of expression,
assembly, association, education, and religion” (Freedom House 2017). This indicator also captures
rule of law, free economic activity, and equality of opportunity.
Though these variables may be highly correlated, they capture different components of governance
and rights. Rather than drop one of these variables from our analysis, we use PCA to reduce this
two-dimensional dataset to a single dimension. We start by graphing a simple correlation
scatterplot in figure 14.
Figure 14: Relationship between Political Rights and Civil Liberties
Source: Authors’ calculations
If we wished to reduce this two-dimensional dataset to a single dimension, we could fit a line to the
scatterplot, as in figure 15. We notice that we lose some accuracy using this method because the
line fails to capture every data point. However, the linear model does present a reliable relationship
between the two variables, and we are confident a single line maximizes the variance between the
two variables. This fitted line becomes the first principal component of this dataset.
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The first principal component, as indicated by the red line, will become our new x-axis. We can then
use a perpendicular line to serve as our y-axis.
Figure 15: Relationship between Political Rights and Civil Liberties, Component 1
Source: Authors’ calculations
To simplify analysis, we rotate the data to fix these new axes. The initial data remains the same; we
are only changing the axis from which we will view this data.
Visualizing 63 dimensions is impossible, so we rely on matrix algebra to calculate our principal
components. To obtain the rotated matrix Y, we multiply the original data matrix X by its
eigenvector matrix Q. An eigenvector is a non-zero vector whose direction does not change when a
linear transformation is applied to it. Each eigenvector has a corresponding eigenvalue λ, a scalar
that indicates the magnitude of the eigenvector. When the covariance among variables in rotated
matrix Y approaches zero, each variable in the rotated matrix has maximized its variance. We could
make a correlation matrix of rotated variables (Cy) as a diagonal matrix, in which the off-diagonal
values are all zero.
Mathematical approach
Cx = 1
𝑛∙XT ∙ X
Cy = 1
𝑛∙YT ∙ Y
= 1
𝑛∙ (XQ)T ∙ XQ
= 1
𝑛∙QTXT ∙ XQ
= QT ∙ Cx ∙ Q
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Cy = QT ∙ Cx ∙ Q is similar to D = Q-1 ∙ A ∙ Q in linear algebra (when using orthonormal eigenvectors to
make QT= Q-1) (Shlens 2014).
We use this mathematical approach to find the diagonal correlation matrix Cy, which consists of
eigenvalues (λ) and the rotation function Q, which consists of eigenvectors corresponding to their
eigenvalues. The columns in the newly rotated matrix Y provide us the principal components such
that 𝑌1(=𝑃𝐶1) 𝑌2 (=𝑃𝐶2) ... 𝑌𝑘 (=𝑃𝐶𝑘).
To aid in the computation, we use the statistical software Stata to conduct our PCA. Table 8
represents principal components and their corresponding eigenvalues. However, not all
components adequately capture the variance within the data. Therefore, we select only components
with eigenvalues greater than 1 as is the standard for PCA.
Table 8: Principal Component Variances
Component Variance(λ) Difference Proportion Cumulative Component 1 15.3514 6.43614 24.37% 24.37% Component 2 8.91523 3.53929 14.15% 38.52% Component 3 5.37594 1.00267 8.53% 47.05% Component 4 4.37327 0.965228 6.94% 53.99% Component 5 3.40805 0.555261 5.41% 59.40% Component 6 2.85278 0.165865 4.53% 63.93% Component 7 2.68692 0.44934 4.26% 68.20% Component 8 2.23758 0.23545 3.55% 71.75% Component 9 2.00213 0.082651 3.18% 74.93% Component 10 1.91948 0.170343 3.05% 77.97% Component 11 1.74914 0.100044 2.78% 80.75% Component 12 1.64909 0.138172 2.62% 83.37% Component 13 1.51092 0.276611 2.40% 85.76% Component 14 1.23431 0.0656207 1.96% 87.72% Component 15 1.16869 0.203297 1.86% 89.58%
Source: Authors’ calculations
We then look to see which variables load highly, or have strong correlations with these
components. These loadings depict how much variation in a variable is explained by the
component. The traditional cut-off point for analysis is greater than the absolute value of 0.30. Our
principal components are linear-weighted functions of our initial independent variables. More generally, we can see that the first component is comprised of a linear combination of all the
original variables, but it is most heavily influenced by the four variables with loadings shown in
figure 16.
44 | P a g e
Figure 16: Variable Loadings on Principal Components
Source: Authors’ calculations
Not only does PCA allow us to examine interesting patterns and structures along new axes of the
data, but it also removes collinearity among independent variables that will be used in multivariate
regression. By reducing our data to 15 components, we can more easily complete our analysis while
still capturing the variance from our initial variables.
gini .08903
gender 0.5347 .05978
c90 0.5310 .1712
c86 0.4090 .1202
c71 0.3034 .06935
c62 .3081
c50 .1316
c43 .1401
c21 .2968
c10 0.5533 .09724
civilliber~s .05091
tp 0.3346 .1344
rol 0.3024 .05526
rq .08172
pr .05715
nrp 0.3980 .05761
gsee .07065
gengovlend 0.5636 .09205
cc .08072
dr 0.4431 .06263
ctop -0.3634 .09908
nrr .0663
rurp 0.3883 .1969
u5mr .02168
FDI -0.5324 .04178
GE .07459
mcv .07719
dtp .03267
age024 .118
adr .04311
deprt .02558
ewnet -0.3440 .04845
nbtot .09802
manuf -0.3036 .1148
htex 0.4353 .1944
sis 0.3685 .07823
nm -0.4545 .06841
ims 0.4041 .07085
agri 0.3464 .273
atm .1963
religion 0.2958 -0.3308 .1559
ethnic .2019
linguistic 0.3162 .0957
infr -0.3890 .1322
fr .03883
ei .07591
egs 0.5758 .05839
tbi 0.4096 .04367
le -0.3297 .0276
hb .02375
ve .04785
qpi 0.5274 .1432
prr 0.2992 .1533
mcs 0.3586 .148
iws .09654
iu .1086
itr .1884
epc .05586
popgro .03968
conflict 0.6657 .0791
rpa_per 0.5768 .169
rpo_per -0.3475 .1367
idp_per 0.5074 .1795
Variable Comp1 Comp2 Comp3 Comp4 Comp5 Comp6 Comp7 Comp8 Comp9 Comp10 Comp11 Comp12 Comp13 Comp14 Comp15 Unexplained
45 | P a g e
Appendix G: Principal Component Summaries Principal Component 1
The first principal component comprises 24.37 percent of the data variability. The first two
variables, life expectancy and the incidence of TB, are health metrics. Unsurprisingly, these
variables are negatively correlated with one another. The latter two variables, linguistic
fractionalization and religious fractionalization, are demographic metrics that measure the degree
of heterogeneity in language and religion in each country. These variables are positively correlated
with each other. Furthermore, the demographic metrics of linguistic and religious fractionalization
are negatively correlated with life expectancy and positively correlated with the incidence of TB.
Principal Component 2
The second principal component comprises 14.15 percent of the variability in the data. It contains
two explanatory variables, the inflation rate and the distance to frontier for trading across borders.
The relationship between the explanatory variables within this principal component shows that the
inflation rate is negatively correlated with the country’s distance to frontier score on trading across
borders. This indicates that a higher inflation rate coincides with a greater monetary and/or time
cost of international trade, and vice versa.
Principal Component 3
The third principal component comprises 8.53 percent of the data variability. Both the elderly with
non-elderly co-residence rate and death rate are demographic metrics. The elderly with non-elderly
co-residence rate is negatively correlated with the death rate.
Principal Component 4
The fourth principal component comprises 6.94 percent of the variability in the data. The variables
with the highest loading scores include: personal remittances received, value added from
agriculture, rural population, and the level of free trade. All four variables are positively correlated
with one another.
Principal Component 5
The fifth principal component comprises 5.41 percent of the data variability. The first variable, the
quality of port infrastructure, is an infrastructure metric. The second variable, enforcement of
contracts, is a business environment metric. It measures the time and cost for resolving a
commercial dispute through local courts and the quality of the judicial process. The quality of port
infrastructure is positively correlated with enforcement of contracts.
Principal Component 6
The sixth principal component comprises 4.53 percent of the variability in the data. It contains just
one explanatory variable, the distance to frontier for resolving insolvency. It should be noted that
this variable loads positively on the component, meaning that the sign of the component is directly
46 | P a g e
related to the distance to frontier for resolving insolvency. Therefore, if the component were to
have a positive coefficient in the regression, it would indicate that the dependent variable is
positively correlated with the distance to frontier for resolving insolvency, and vice versa.
Principal Component 7
The seventh principal component comprises 4.26 percent of the data variability. The first variable,
mobile cellular subscriptions, is an infrastructure metric. The second variable, international
migrant stock, is a demographic metric. It measures the number of people born in a country other
than that in which they live, including refugees. The third variable, high-technology exports, is
industrialization metric. These three variables are positively correlated with one another.
Principal Component 8
The eighth principal component comprises 3.55 percent of the variability in the data. The variables
with the highest loading scores include the percent of the originating country’s citizens that are
refugees and the distance to frontier for construction permits. The relationship between the
explanatory variables within this principal component shows that the percent of the originating
country’s citizens that are refugees is positively correlated with the ease of getting a construction
permit. This indicates that having a larger diaspora of refugees coincides with a higher distance to
frontier for construction permits score, and vice versa.
Principal Component 9
The ninth principal component comprises 3.18 percent of the data variability. The first variable,
value added by manufacturing, is an industrialization metric. The second variable, FDI, measures
net inflow of foreign direct investment. The third variable is financial resources provided to the
private sector. These three variables are positively correlated with each other.
Principal Component 10
The tenth principal component comprises 3.05 percent of the variability in the data. It contains one
explanatory variable—the value of a country’s exports of goods and services measured in terms of a
percentage of its GDP. It should be noted that this variable loads positively on the component,
meaning that the sign of the component is directly related to the value of a country’s exports of
goods and services. Therefore, if the component were to have a positive coefficient in the
regression, it would indicate that the dependent variable is positively correlated with the value of
the country’s exports, and vice versa.
Principal Component 11
The eleventh principal component comprises 2.78 percent of the data variability. This principal
component consists of only one variable, government net lending, which measures how fiscally
sound the country is. It should be noted that this variable loads positively on the component,
meaning that the sign of the component is directly related to the independent variable’s value.
Therefore, if the component were to have a positive coefficient in the regression, it would indicate
47 | P a g e
that the dependent variable is positively correlated with the value of the government’s net lending,
and vice versa.
Principal Component 12
The twelfth principal component comprises 2.62 percent of the variability in the data. It contains
three explanatory variables: the percentage of the population composed of internally displaced
persons, the number of secure internet servers per million people, and the index score on rule of
law. These indicators all are positively correlated.
Principal Component 13
The thirteenth principal component comprises 2.40 percent of the data variability. This principal
component consists of only one demographic metric, refugee population by country of asylum. It
should be noted that this variable loads positively on the component, meaning that the sign of the
component is directly related to the size of the asylum country’s refugee population. Therefore, if
the component were to have a positive coefficient in the regression, it would indicate that the
dependent variable is positively correlated with the refugee population’s size, and vice versa.
Principal Component 14
The fourteenth principal component comprises 1.96 percent of the variability in the data. The
variables with the highest loading scores include the occurrence of conflict within the past five
years and the degree of religious fractionalization. The level of religious fractionalization is
negatively correlated with the occurrence of a conflict within the last five years. This indicates that
higher religious fractionalization coincides with less conflict, and vice versa.
Principal Component 15
The fifteenth principal component comprises 1.86 percent of the data variability. The first variable,
net migration, is a demographic metric measuring the number of immigrants minus the number of
emigrants. The second variable, natural resource protection, is a governance metric that measures
the government’s commitment to habitat preservation and biodiversity protection. The third
variable is a ratio of female to male labor force participation. Natural resource protection and the
female labor-force participation rate are positively correlated with one another and negatively
correlated with the first variable, net migration.
48 | P a g e
Appendix H: Regression Tables
Table 9: Regression Results on Dependent Variable, Poor ≤ $10 Median Income
Basic Model GNI Control Region Control Both Controls PC 1 0.0644** 0.0629* -0.0464 -0.114 (0.0271) (0.0353) (0.0652) (0.102) PC 2 0.0148 0.0157 0.0799 0.0962 (0.0284) (0.0253) (0.0520) (0.0620) PC 3 -0.0688* -0.0669 -0.174** -0.149** (0.0379) (0.0451) (0.0609) (0.0624) PC 4 -0.0103 -0.0137 -0.0888 -0.137 (0.0460) (0.0566) (0.0591) (0.0832) PC 5 0.0157 0.0145 0.0189 0.00169 (0.0601) (0.0684) (0.0493) (0.0456) PC 6 0.0469 0.0461 0.0786 0.0693 (0.0532) (0.0566) (0.0661) (0.0724) PC 7 -0.130* -0.127* -0.113** -0.0596 (0.0322) (0.0669) (0.0437) (0.0937) PC 8 0.0136 0.0153 0.0582 0.0798 (0.0354) (0.0504) (0.0432) (0.0492) PC 9 -0.00622 -0.00692 -0.139 -0.151 (0.0242) (0.0277) (0.126) (0.134) PC 10 -0.0805 -0.0824 -0.0139 -0.0315 (0.0465) (0.0615) (0.104) (0.101) PC 11 0.0359 0.0358 0.0264 0.0356 (0.0239) (0.0250) (0.0323) (0.0304) PC 12 0.0307 0.0317 -0.0567 -0.0731 (0.0441) (0.0460) (0.0707) (0.0798) PC 13 -0.0975** -0.0984** -0.0965* -0.109* (0.0381) (0.0420) (0.0513) (0.0508) PC 14 -0.0172 -0.0180 -0.0248 -0.0221 (0.0471) (0.0522) (0.0541) (0.0524) PC 15 0.0964 0.0938 0.0999* 0.0611 (0.0589) (0.0793) (0.0538) (0.0720)
49 | P a g e
ln GNI
-0.0335
-0.462
(0.508) (0.560) Europe and Central Asia
1.330 (1.073)
1.360 (1.089)
Latin America and the Caribbean
0.462 (0.885)
0.475 (0.937)
Sub-Saharan Africa
1.433 (1.105)
1.822 (1.362)
constant 0.581** 0.874 -0.360 3.593 (0.0730) (4.466) (0.898) (4.588) N 31 31 31 31 R2 0.671 0.672 0.773 0.791 Standard errors in parentheses ** 95% confidence interval * 90% confidence interval
Source: Authors’ calculations
Table 10: Regressions Results on Dependent Variable, Poor ≤ $5 Median Income
Basic Model GNI Control Region Controls Both Controls PC 1 0.146** 0.153** 0.100** 0.109** (0.0170) (0.0155) (0.0296) (0.0370) PC 2 0.00629 0.00206 0.0251 0.0230 (0.0162) (0.0168) (0.0281) (0.0304) PC 3 0.0147 0.00605 -0.0214 -0.0245 (0.0123) (0.0140) (0.0281) (0.0282) PC 4 0.0404 0.0562* 0.0115 0.0178 (0.0299) (0.0300) (0.0280) (0.0366) PC 5 0.0201 0.0257 0.0203 0.0226 (0.0202) (0.0197) (0.0190) (0.0215) PC 6 -0.0186 -0.0150 -0.0116 -0.0104 (0.0184) (0.0186) (0.0162) (0.0169) PC 7 -0.0134 -0.0289 -0.00967 -0.0167 (0.0199) (0.0234) (0.0241) (0.0324) PC 8 0.0484 0.0406 0.0650* 0.0622
50 | P a g e
(0.0303) (0.0325) (0.0345) (0.0387) PC 9 0.00902 0.0122 -0.0252 -0.0236 (0.0140) (0.0136) (0.0457) (0.0482) PC 10 -0.0532** -0.0442* -0.0383 -0.0360 (0.0194) (0.0238) (0.0267) (0.0297) PC 11 -0.00448 -0.00403 -0.00568 -0.00688 (0.0294) (0.0294) (0.0242) (0.0249) PC 12 -0.0255 -0.0303* -0.0566** -0.0545** (0.0187) (0.0169) (0.0233) (0.0245) PC 13 0.0295* 0.0336* 0.0262* 0.0278* (0.0167) (0.0164) (0.0144) (0.0149) PC 14 0.00943 0.0129 0.00698 0.00663 (0.0303) (0.0300) (0.0284) (0.0292) PC 15 0.0491* 0.0607** 0.0497* 0.0548 (0.0250) (0.0254) (0.0237) (0.0311) ln GNI 0.153 0.0603 (0.129) (0.196) Europe and Central Asia
0.371 (0.460)
0.367 (0.479)
Latin America and the Caribbean
0.0567 (0.326)
0.0550 (0.340)
Sub-Saharan Africa
0.455 (0.292)
0.404 (0.328)
constant 0.194* -1.149 -0.0564 -0.572 (0.0333) (1.138) (0.315) (1.700) N 31 31 31 31 R2 0.893 0.898 0.915 0.915 Standard errors in parentheses ** 95% confidence interval * 90% confidence interval
Source: Authors’ calculations
51 | P a g e
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