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

For an online copy, see http://www.lafollette.wisc.edu/outreach-public-service/workshops-in-public-affairs [email protected]

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.

i | P a g e 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

ii | P a g e 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

iii | P a g e 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

iv | P a g e 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

v | P a g e 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

ix | P a g e 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

4 | P a g e 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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Table 1: GNI per Capita, Median Income, and Poverty Headcount for UMICs* Poverty Headcount Poverty Headcount GNI per capita Median Income Ratio at $3.10/day Ratio at $1.90/day (Current USD) (2011 PPP$) (2011 PPP$) (2011 PPP$) (% of population) (% of population) 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

Poor UMIC Poor 6,240 8.1 9.12 2.32 Republic 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 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

Rich UMIC Costa Rica 10,400 13.9 3.93 1.61 Malaysia 10,570 14 2.71 0.28 Bosnia and 4,670 16.7 0.45 0.07 Herzegovina 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)

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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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Table 2: Principal Components* Principal Data Explanatory Variables Component Variability ↓ Life Expectancy ↑ Incidence of ↑ Linguistic ↑ Religious 1 24.37% Tuberculosis Fractionalization Fractionalization

↓ Inflation Rate ↑ Distance to Frontier: 2 14.15% Trading Across Borders

↓ Elderly with Non- ↑ Death Rate 3 8.53% Elderly Co-Residence Rate ↑ Personal ↑ Agriculture, Value ↑ Rural Population ↑ Level of Free Trade 4 6.94% Remittances Received Added (% of GDP) (% of GDP) ↑ Quality of Port ↑ Distance to Frontier: 5 5.41% Infrastructure Enforcing Contracts

↑ Distance to Frontier: 6 4.53% Resolving Insolvency

↑ Mobile Cellular ↑ International Migrant ↑ High-Technology Exports 7 4.26% Subscriptions Stock (% of manufactured exports) ↓ Refugee Population ↑ Distance to Frontier: 8 3.55% (Country of Origin, % of Construction Permits Population) ↓ Manufacturing, ↓ FDI ↓ Financial Resources 9 3.18% Value Added (% of Provided to the Private GDP) Sector (% of GDP) ↑ Exports of 10 3.05% Goods/Services (% of GDP) ↑ Government Net 11 2.78% Lending (% of GDP)

↑ Internally Displaced ↑ Number of Secure ↑ Rule of Law 12 2.62% Persons (% of Internet Servers Population) ↑ Refugee Population 13 2.40% (Country of Asylum, % of Population) ↑ Conflict ↓ Religious 14 1.96% Fractionalization

↓ Net Migration ↑ Natural Resource ↑ Ratio of Female to Male 15 1.86% Protection Labor Participation Rate

*More detail on each component is available in appendix G. Source: Authors’ calculations

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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.

푝표표푟푖 = 훽0 + 훽1푃퐶1 + 훽2푃퐶2+. . . + 훽15푃퐶15 + 훾1푅1 + 훾2푅2 + 훾3푅3 + 훾5푙푛퐺푁퐼 + 푒푖 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

14 | P a g e 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

17 | P a g e 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

18 | P a g e 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

21 | P a g e 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

22 | 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

By October December • Create • Country candidate • Publish scorecards • Final country list selection available determination methodology of eligibility Late Summer Late Fall

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.

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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 Median Index Household Income Cross Country Measure Absolute Measure Relative Measure Country Coverage Current

Positive Moderate Negative

Source: Authors’ calculations

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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.

Table 5: Omitted UMICs

Median Income Population < 1 Insufficient Data Not Available million 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)

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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 Control of Corruption Completion Rate Fiscal Policy Girls' Secondary Education Freedom of Information Enrollment Ratio Gender in the Economy Health Expenditures Government Effectiveness Inflation Immunization Rates Political Rights Land Rights and Access Natural Resource Protection Rule of Law Regulatory Quality Primary Education Expenditures Trade Policy

Source: Millennium Challenge Corporation

Each indicator also includes multiple underlying indicators. For example, the Access to Credit indicator is based on two International Finance Corporation indicators: the depth of credit information index and the strength of legal rights of borrowers and lenders. In other cases, an MCC indicator is based on datasets that aggregate many sources. For example, the Rule of Law indicator leverages the Worldwide Governance indicators, which combine “up to 23 different assessments and surveys” to generate six governance scores (Millennium Challenge Corporation 2017a). Because our goal was to achieve specificity in our results, we chose to use the components of each MCC indicator separately rather than composite measures. For the same reason, we included several of the MCC’s supplemental indicators. The most important of these were related to business conditions.

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MCC uses these indicators to determine a country’s capacity to absorb assistance and to quantify their success implementing previous reforms. However, MCC is one niche in the international development space. Accordingly, we sought potential poverty characteristics outside the MCC evaluation framework. After a survey of related academic works, our final set of potential characteristics focused on middle income countries (both LMICs and UMICs).

Lastly, we checked the resulting list of potential characteristics for suitability. We retained variables that were sufficiently complete for all countries and recently updated. We also removed descriptors that overlapped to reduce the likelihood of collinearity or selection bias. Finally, datasets were required to be objective measures that quantify the given characteristic. For example, scores that merely ranked countries were not suitable because they provide a relative measure of the characteristic.

The resulting list included more than 150 potential common characteristics of poor UMICs. Although this was a considerable number, it ensured that all possibilities were under consideration. However, to conduct statistical analysis we needed to address the missing data present within the dataset. Additionally, the data already included the full population of UMICs, and therefore a new sample could not be taken. We removed variables that were not sufficiently complete to impute, such as particularly weak sets, a metric’s subcomponents, and overlapping characteristics.

The reduction produced a list of 63 potential characteristics with data able to be imputed by linear interpolation and nearest value. The complete list of variables is included in appendix E.

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Appendix E: Variable List Table 7: Variable List

Variable Definition Source Percentage of population with access to improved water sources. Access to Improved drinking water sources include piped water on

Improved premises and other improved drinking water sources. A higher World Bank Water Sources percentage indicates that there is better improved water coverage. Age Ratio of dependents (age 0-14, >65) to the working-age

Dependency population (age 15-64). A larger ratio translates to more World Bank Ratio dependents relative to the working-age population. Total value added by agriculture as a percentage of GDP. Agriculture, Agriculture includes forestry, hunting, fishing, cultivation of crops,

World Bank Value Added and livestock production. A higher percentage indicates that a larger proportion of the economy is agriculture-based. Number of ATMs per 100,000 people. A higher number indicates

ATMs World Bank that there is greater access to ATMS. 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, Freedom Civil Liberties

associational and organizational rights, rule of law, and personal House autonomy and individual rights. A lower score indicates greater civil liberties. 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 Uppsala conflict is a contested incompatibility that concerns the Conflict Conflict government and/or territory where the use of armed force Data

between two parties, of which at least one is the government, Program results in at least 25 battle-related deaths in one calendar year. 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, Control of

including both petty and grand forms of corruption as well as World Bank Corruption "capture" of the state by elites and private interests. A higher index score indicates that corruption is perceived to be better controlled.

Country Country name World Bank Country Year Year of the observation

Number of deaths per 1,000 people. A higher number indicates a

Death Rate World Bank higher rate of death in the population.

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Score between 0 and 100 that shows the distance in performance Distance to on access to credit to the "frontier" country. The measure includes World Frontier: Access to the strength of legal rights relating to credit, and the depth of

Bank Credit information pertaining to credit. A score of 0 represents the lowest performance possible and 100 represents the frontier. Score between 0 and 100 that shows the distance in performance Distance to on obtaining construction permits to the "frontier" country. The Frontier: measure includes the number of procedures, time taken, and World

Construction monetary cost involved with getting a construction permit as well Bank Permits as new building quality control. A score of 0 represents the lowest performance possible and 100 represents the frontier. Score between 0 and 100 that shows the distance in performance Distance to on enforcing contracts to the "frontier" country. The measure World Frontier: Enforcing includes the time taken, monetary cost, and quality of judicial

Bank Contracts processes, in relation to contracts. A score of 0 represents the lowest performance possible and 100 represents the frontier. Score between 0 and 100 that shows the distance in performance on getting electricity to the "frontier" country. The measure Distance to includes the number of procedures, time taken, and monetary cost World Frontier: Getting involved with getting connected to the electricity grid as well as the

Bank Electricity reliability of electricity supply, and the transparency of electricity tariffs. A score of 0 represents the lowest performance possible and 100 represents the frontier. Score between 0 and 100 that shows the distance in performance Distance to on the tax environment to the "frontier" country. The measure World Frontier: Paying includes the number of payments per year, time taken to file, tax

Bank Taxes rate, and a post-filing index. A score of 0 represents the lowest performance possible and 100 represents the frontier. Score between 0 and 100 that shows the distance in performance Distance to on protecting minority investors to the "frontier" country. The Frontier: measure includes the extent of disclosures, director liability, World

Protecting shareholder rights, ownership and control, corporate transparency, Bank Minority Investors and the ease of shareholder suits. A score of 0 represents the lowest performance possible and 100 represents the frontier. Score between 0 and 100 that shows the distance in performance Distance to on resolving insolvency to the "frontier" country. The measure World Frontier: Resolving includes the recovery rate and the strength of insolvency

Bank Insolvency framework. A score of 0 represents the lowest performance possible and 100 represents the frontier.

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Score between 0 and 100 that shows the distance in performance on trading across borders to the "frontier" country. The measure Distance to Frontier: includes the time taken and monetary cost to both import and World Trading Across

export, with a lower cost getting a higher score. A score of 0 Bank Borders represents the lowest performance possible and 100 represents the frontier. Percentage of the total population that received the third dose of DTP Immunization

the diphtheria, pertussis, and tetanus (DTP) vaccine. A higher WHO Rate percentage indicates higher DTP immunization rates. Co-residence is defined as elderly people living in a household with non-elderly members (elderly defined as age 60+). This Elderly with Non- indicator is calculated as the number of households where World elderly Co-residence elderly individuals live with non-elderly individuals divided by

Bank Rate the total number of households with elderly individuals in the population. A higher rate indicates that more households have elderly co-residence. Amount of electricity consumed in kWh per capita. Measures the Electric Power production of power, subtracting transmission, distribution, and World

Consumption transformation losses, and own use by the plants. A larger Bank number indicates that more electricity is consumed per capita. 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 World Energy Imports

energy before transformation to other end-use fuels. A higher Bank positive percentage indicates a higher dependence on foreign sources of energy. Number between 0 and 1 that shows the degree of ethnic fractionalization, where 1 represents maximum fractionalization. Ethnic Alesina Measures the degree of ethnic heterogeneity. A higher number Fractionalization et al. indicates that the country is more heterogeneous and fractionalized ethnically. Total exports of goods and services as a percentage of GDP. Exports of Goods/ Measures the value of all goods and other market services World

Services provided to the rest of the world. A higher percentage indicates Bank that a larger portion of the economy is export-based. Number of births per woman. Total fertility rate represents the number of children that would be born to a woman if she were to World Fertility Rate live to the end of her childbearing years and bear children in

Bank accordance with age-specific fertility rates of the specified year. A larger number indicates a higher fertility rate.

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Financial Measure of financial resources provided to the private sector by Resources financial corporations as a percentage of the country’s GDP. A World

Provided to the higher percentage indicates that more credit is being given to the Bank Private Sector private sector. Foreign Direct Net inflow of FDI in current U.S. dollars (unadjusted for inflation). World

Investment (FDI) A higher number indicates greater FDI in the country. Bank Girls’ Secondary Gross enrollment ratio of females to males at the lower secondary

Education education level. A higher ratio indicates that more girls are UNESCO Enrollment enrolled in secondary education. Index between -2.5 and 2.5 assessing government effectiveness. The index score reflects perceptions of the quality of public Government services, quality of the civil service and the degree of its World Effectiveness independence from political pressures, quality of policy

Bank Index formulation and implementation, and credibility of the government's commitment to such policies. A higher score indicates higher perceptions of government effectiveness. 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 Government Net World of transactions in financial assets and liabilities. A negative value

Lending Bank means the government is borrowing more than it is lending. A higher positive percentage indicates that the government is running a greater surplus. Percentage of manufactured exports that are classified as high- High-Technology technology. High-technology products are products with high World

Exports research and development intensity. A higher percentage indicates Bank that more of manufacturing exports are high-technology products. Number of hospital beds per 1,000 people. Includes inpatient beds available in public, private, general, and specialized hospitals and World Hospital Beds

rehabilitation centers. A higher number indicates that there are Bank more hospital beds. Estimated number of new and relapse TB cases per 100,000 Incidence of people arising in a given year. All forms of the disease are World

Tuberculosis (TB) included. A higher number indicates a higher prevalence of TB in Bank the country. Percentage annual growth rate of the GDP implicit deflator that shows the rate of price change in the economy as a whole. The World Inflation Rate GDP implicit deflator is the ratio of GDP in current local currency

Bank to GDP in constant local currency. A higher percentage indicates that prices are rising at a higher rate.

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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 Internally Displaced because of armed conflict, or to avoid the effects of armed

World Bank Persons 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. Percentage of total population that was born in a different country. International migrant stock includes people born International

in a country other than that in which they live, including World Bank Migrant Stock refugees. A higher percentage indicates that the country has a higher proportion of foreign-born residents. Ratio of international tourism receipts to exports of goods and services. International tourism receipts are International

expenditures by international inbound visitors. A higher World Bank Tourism Receipts ratio indicates a greater proportion of exports are from tourism. Number of people out of every 100 that have used the internet in the past three months from any device or

Internet Users World Bank location. A higher number indicates that there are more internet users. Score between 0 and 100 that indicates the level of free trade. It is a composite measure of the trade-weighted Heritage Level of Free Trade average tariff rate and nontariff barriers that affect

Foundation imports and exports of goods and services. A higher score indicates less non-tariff barriers and lower tariff rates. Average number of years a person is expected to live at

Life Expectancy World Bank birth. A larger number means higher life expectancy. Number between 0 and 1 that shows the degree of linguistic fractionalization, where 1 represents maximum Linguistic fractionalization. Measures the degree of linguistic Alesina et al. Fractionalization heterogeneity. A higher number indicates that the dataset country is more heterogeneous and fractionalized linguistically. Total value added by manufacturing as a percentage of Manufacturing, Value

GDP. A higher percentage indicates that more of the World Bank Added economy is based on manufacturing. Percentage of the total population that has received the Measles

first dose of the measles vaccine. A higher percentage WHO Immunization Rate indicates a higher measles immunization rate. Median household income/consumption per capita in Center for Median Income 2011 PPP. A higher median income indicates a higher per Global

capita income for the 50th percentile. Development Number of mobile cellular subscriptions per 100 people. Mobile Cellular

A higher number indicates that more people are World Bank Subscriptions connected to a mobile network.

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Score between 0 and 100 indicating the government’s commitment to habitat preservation and biodiversity Natural Resource protection. This indicator measures the degree to which a Columbia

Protection country achieves the target of protecting at least 17% of University each terrestrial biome within its borders. A higher number indicates a greater commitment to preservation. Total natural resource rents as a percentage of the Natural Resource country’s GDP. A higher percentage indicates that a

World Bank Rents greater proportion of the economy is based on the exploitation of natural resources. Index measuring the relative prices of a country’s imports and exports measured relative to the base year 2000. The Net Barter Terms of barter terms of trade index is calculated as the

World Bank Trade 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. Number of immigrants minus the number of emigrants, including citizens and noncitizens, for the five-year

Net Migration period. A negative number means there is net emigration. World Bank A higher number indicates more migration into the country. Number of secure internet servers per 1 million people. Number of Secure Secure servers use encryption technology in internet

World Bank Internet Servers transactions. A higher number indicates a larger ratio of servers to people. Total personal remittances as a percentage of GDP. Personal Personal remittances comprise personal transfers and

Remittances compensation of employees. A higher percentage World Bank Received indicates that a larger portion of the economy is dependent on remittances. 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, Freedom Political Rights

political pluralism and participation, and functioning of House government. A higher score indicates greater political rights. Ten-year annualized population growth rate. The 10-year annualized rate is the average annualized rate in the past Population Growth 10 years. Population is based on the de facto definition of

World Bank Rate population, which counts all residents regardless of legal status or citizenship. A higher percentage indicates that population is growing at a higher rate. Index between 1 and 7, where 1 represents “extremely underdeveloped” and 7 represents “efficient by Quality of Port international standards.” Measures business executives'

World Bank Infrastructure perceptions of their country's port facilities. A higher index number indicates a more positive perception of port infrastructure.

Ratio of Female to Ratio of female to male labor force participation rate. A World Bank

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Male Labor Force higher ratio indicates a greater female labor participation Participation Rate rate. Fraction of the population that are refugees by country of asylum. Refugees are those recognized as refugees under Refugee Population the conventions of UNHCR and the African Union. Asylum

World Bank (Country of 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. 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 Refugee Population

seekers are excluded. Country of origin refers to the World Bank (Country of Origin) nationality or country of citizenship of a claimant. A higher number indicates that more refugees originate from a country.

Region World Bank regional designation World Bank 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

Regulatory Quality sound policies and regulations that permit and promote World Bank private sector development. A higher index number indicates that perceptions of regulatory quality are more positive. Number between 0 and 1 that shows the degree of religious fractionalization, where 1 represents maximum Religious Alesina et al. fractionalization. Measures the degree of religious Fractionalization dataset heterogeneity. A higher number indicates that the country is more religiously heterogeneous. 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

Rule of Law World Bank 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. Percentage of population that lives in rural areas of the

Rural Population country. A higher percentage indicates that a greater World Bank proportion of the population lives in rural areas. Number of males per 100 females in the age group of 0- Sex Ratio of 0-24 Age

24. A larger ratio indicates that there are more men United Nations Group relative to women in this age group.

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Total average household dependency rate, computed as the number of dependents in a household divided by the number of working age Total population in the same household. Dependents are children and elderly, World Dependency

and working-age population is people ages 15 to 60. A higher rate Bank Rate indicates that there are more dependents in the average household in relation to working-age people. Under-five Mortality rate of children under 5 years old per 1,000 live births. A World

Mortality Rate higher score indicates a higher mortality rate. Bank Percentage of total employment that is vulnerably employed. Vulnerable Vulnerable employment comprises unpaid family workers and own- World

Employment account workers. A higher percentage indicates that more of the Bank country’s workforce is vulnerably employed.

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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

1 Cx = ∙XT ∙ X 푛 1 Cy = ∙YT ∙ Y 푛 1 = ∙ (XQ)T ∙ XQ 푛 1 = ∙QTXT ∙ XQ 푛 = 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 푌1(=푃퐶1) 푌2 (=푃퐶2) ... 푌푘 (=푃퐶푘).

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.

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Figure 16: Variable Loadings on Principal Components

Variable Comp1 Comp2 Comp3 Comp4 Comp5 Comp6 Comp7 Comp8 Comp9 Comp10 Comp11 Comp12 Comp13 Comp14 Comp15 Unexplained

idp_per 0.5074 .1795 rpo_per -0.3475 .1367 rpa_per 0.5768 .169 conflict 0.6657 .0791 popgro .03968 epc .05586 itr .1884 iu .1086 iws .09654 mcs 0.3586 .148 prr 0.2992 .1533 qpi 0.5274 .1432 ve .04785 hb .02375 le -0.3297 .0276 tbi 0.4096 .04367 egs 0.5758 .05839 ei .07591 fr .03883 infr -0.3890 .1322 linguistic 0.3162 .0957 ethnic .2019 religion 0.2958 -0.3308 .1559 atm .1963 agri 0.3464 .273 ims 0.4041 .07085 nm -0.4545 .06841 sis 0.3685 .07823 htex 0.4353 .1944 manuf -0.3036 .1148 nbtot .09802 ewnet -0.3440 .04845 deprt .02558 adr .04311 age024 .118 dtp .03267 mcv .07719 GE .07459 FDI -0.5324 .04178 u5mr .02168 rurp 0.3883 .1969 nrr .0663 ctop -0.3634 .09908 dr 0.4431 .06263 cc .08072 gengovlend 0.5636 .09205 gsee .07065 nrp 0.3980 .05761 pr .05715 rq .08172 rol 0.3024 .05526 tp 0.3346 .1344 civilliber~s .05091 c10 0.5533 .09724 c21 .2968 c43 .1401 c50 .1316 c62 .3081 c71 0.3034 .06935 c86 0.4090 .1202 c90 0.5310 .1712 gender 0.5347 .05978 gini .08903

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.

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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

45 | 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

46 | 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.

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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)

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ln GNI -0.0335 -0.462 (0.508) (0.560)

Europe and 1.330 1.360 Central Asia (1.073) (1.089)

Latin America 0.462 0.475 and the (0.885) (0.937) Caribbean

Sub-Saharan 1.433 1.822 Africa (1.105) (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

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(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 0.371 0.367 Central Asia (0.460) (0.479)

Latin America 0.0567 0.0550 and the (0.326) (0.340) Caribbean

Sub-Saharan 0.455 0.404 Africa (0.292) (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

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