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

23 | P a g e

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.

33 | P a g e

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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