Identifying the Complex Causes of Civil War: A Machine Learning Approach 3030819922, 9783030819927

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Identifying the Complex Causes of Civil War A Machine Learning Approach Atin Basuchoudhary James T. Bang John David Tinni Sen

Identifying the Complex Causes of Civil War

Atin Basuchoudhary · James T. Bang · John David · Tinni Sen

Identifying the Complex Causes of Civil War A Machine Learning Approach

Atin Basuchoudhary Department Economics and Business Virginia Military Institute Lexington, VA, USA John David Applied Mathematics Virginia Military Institute Lexington, VA, USA

James T. Bang Finance, Economics, and Decision Science Saint Ambrose University Davenport, IA, USA Tinni Sen Department Economics and Business Virginia Military Institute Lexington, VA, USA

ISBN 978-3-030-81992-7 ISBN 978-3-030-81993-4 (eBook) https://doi.org/10.1007/978-3-030-81993-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: © Harvey Loake This Palgrave Pivot imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1

Prologue: Why This Book?

1

2

Data Description and Preliminary Processing

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3

Methodological Workflow

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4

Constitutional Changes and Civil War

49

5

Infant Mortality, State Capacity, Rents, and Civil War

61

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Foreign Aid and Civil Conflict

75

7

Demonstrations, Grievance, and Civil Conflict

89

8

Epilogue

101

Appendices

105

Index

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v

List of Figures

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

1.1 1.2 2.1 3.1 3.2 3.3 3.4 3.5 4.1 5.1 6.1 6.2

Fig. 7.1

The rise, fall, and rise of modern civil war Civil conflict trends RFE prediction errors Backdoor criterion Recursive feature elimination accuracy plot Eliminating feedback variables Eliminating intermediate variables Methodological workflow Constitutional changes cause conflict Partial dependence plot for government expenditures Net Secondary Income, causal pattern Causal effect of official development assistance on civil conflict Peaceful demonstrations increase the risk of conflict

5 6 16 33 37 40 41 45 53 67 79 83 95

vii

List of Tables

Table Table Table Table Table Table Table Table

2.1 2.2 4.1 4.2 5.1 5.2 6.1 6.2

Table 7.1 Table 7.2

65 optimal variables, by rank Alphabetical list of the 65 optimal variables Alternate treatments? A constitutional compact model Alternative treatments for infant mortality Variables eliminated from the model Potential alternate treatments Variables that mediate the causal effect of net secondary income on civil conflict Alternate treatments for demonstrations Variables intervening between demonstrations and civil conflict

17 22 51 57 64 66 77 78 91 92

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CHAPTER 1

Prologue: Why This Book?

Abstract This chapter lays out how this book adds to the methodological literature investigating empirical causality. At the same time, it highlights why understanding the complex causes of civil conflict is a worthy endeavor. Keywords Civil war · Causality · Empirically informed covariate selection · Partial dependence plots · Theory agnostic

The study of civil war is complex and challenging. A scientific approach to understanding civil war requires a clear sense of the causes. Causality, though, is a fraught concept. For something to be a cause for an event, its presence must lead to an event. At the same time, its absence should not lead to the event. However, few things in the social sciences are monocausal. These other potential causes need to be disentangled from the causal link of interest. This book is one of the first that lays out a methodology which systematically analyzes the complex causes of civil conflict. First, we present a systematic way of identifying the right causal variables in a modeling approach reminiscent of the dosage/treatment methodology common in the epidemiological healthcare literature (Velentgas et al., 2013). This process, which we call Empirically Informed Covariate Selection (EICS), highlights types of variables that should or should not © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Basuchoudhary et al., Identifying the Complex Causes of Civil War, https://doi.org/10.1007/978-3-030-81993-4_1

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be included in a model to determine whether a variable is causal. Once we show this process, we choose several variables to test the nature of their causal link to civil conflict using partial dependence plots (PDP’s). We suggest that knowing the nature of the causal links through the EICS lens elicits meaningful policy responses to end civil conflict. We have not chosen these variables to suggest we have a comprehensive answer for the question of how to end civil war. Rather, we have chosen these variables to showcase methodological issues that come up when we investigate the causes of civil war using our systematic approach. A randomized controlled trial (RCT) can achieve this effect. However, at least in the social sciences and in the study of civil war, RCTs have two problems. The first problem is that it is impossible to ethically and even practically do a randomized control trial to elicit the causes of civil war. Suppose one believes that the presence of an autocratic regime is a cause of civil war. For an RCT to work, populations of people have to be randomly assigned to two groups and force one of these groups to be subjected to autocracy. This sort of assignment is practically impossible and ethically wrong. Therefore, it seems the only conclusion is to admit that understanding civil war is not amenable to science. Social scientists continue to try to do science, though. In some cases, this is limited to identifying the correlates of civil conflict. That progresses science only partially because it does not identify causes. However, it is definitely a step forward because there is a plausible case to be made that the causes for civil war may lie among the correlates. This approach is also anecdotal since a correlate in one situation may not correlate in another. Nevertheless, anecdotes can be helpful. If a series of anecdotes tell similar stories, then maybe these anecdotes, taken together, approximate the truth. It would be heroic indeed to devise peaceful coexistence out of stories. Should one believe and act on tales when lives are at stake? The myriad problems of human existence, civil war is one of them, require sterner solutions. Econometrics provides many of these solutions. Using instrumental variables and other related methods, for example, can elicit causal links if the underlying assumptions are met. However, most studies provide very little information on whether these assumptions are met (Kraay, 2015). In some cases, it may be impossible even to know. Consequently, whether something is a cause of civil war or not depends on what other variables are included in a model estimating causality or not (Blattman & Miguel, 2010). One way to address this problem is to look at

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micro or subnational conflicts to tease out the causal links. Nevertheless, then it is not clear whether these micro-level studies can be generalized. This state of things is less than ideal. RCTs have a similar generalizability problem. Humans are complex cultural beings. We do not respond to the same things in the same way. Thus, RCTs, even when practical and able to pass muster with institutional review boards, may have very limited generalizability. Just because something is a cause in one situation does not mean it is a cause in others. For example, many RCTs (human subject experiments with trust games and the like must be included in this rubric) have very local interpretations. It is at best unclear whether the results of these RCTs in one population can be generalized to others. One problem with many of these results is that the studied populations are mainly western and mainly consist of undergraduate students. It is unclear whether these results can be generalized (Muthukrishna et al., 2020) to, say, older women in Botswana. On the other hand, purely generalized models of human behavior may be too general. The rational choice actor has been the workhorse model for economists. It has its advantages because it is not susceptible to the charge of assuming people are fundamentally different. Science has often trailed off into pseudoscience and outright racist oppression when people believe that people are fundamentally different. Experiments with ultimatum games, for example, suggest that people behave in culturally determined ways (Henrich et al., 2001). Subjects in Papua New Guinea rejected fair offers just as predicted by pure rational choice models, while most other cultures made fair offers in violation of pure rational choice models, even when they knew that there would be no opportunity to reciprocate. We learn that the pure rational choice model is not quite general, but then neither is the sense that fairness is a social value for everyone. Culture mattered for these ultimatum games. Therefore, current attempts to understand civil war seem to be foundering because they offer fragmented yet tantalizing glimpses into the causal structure of conflict. RCTs, on the other hand, the gold standard of science, even when practically doable and ethical, suggest that local, cultural results may not translate into global, human insights. We suggest a melding of empirical approaches. We look at large global data set collated from many sources to discern if there are generalized patterns to civil war. Usual econometric approaches often fail when confronted with high-dimensional problems that are common in

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the social sciences. We develop a systematic way, Empirically Informed Covariate Selection (EICS), to identify variables that should be included or excluded from a model in this high-dimensional environment. We use well-known statistical theory to justify why our model selection process allows predictive technologies to be interpreted causally. We then report the results of the most accurate of these predictive technologies. In the end, we have causally interpretable predictions. Because our data is global and our results are accurate, our results are both causal and generalizable. Why should we care? Armitage (2017), among others, has noted an upswelling in intrastate war relative to other kinds of war. Figure 1.1 illustrates this fact. A hiatus of intrastate conflict that began at the end of the Cold War seems to be ending. The number of intrastate conflicts seems to have caught up with a peak around 1989. But measurement matters. Our data (see Chapter 2 for a complete description) suggests a waning of different types of conflicts that have the flavor of non-traditional warfare, and generally falls under the rubric of civil conflict. This data is more in line with some other stylized facts about civil conflict (Fig. 1.2). Direct intrastate war-related deaths may have waned as well (Gleditsch et al., 2016). This waning of war violence argument is consistent with Pinker’s (2012) argument that humanity has become psychologically disinclined toward war. However, the costs of war remain high. Civil wars are particularly pernicious because non-combatants are often killed more indiscriminately and indirectly than interstate wars governed by at least some semblance of convention (Macmillan, 2020). Ghobarah et al. (2003) find that more people die from causes attributable to civil war than actual bullets. Other studies confirm this dismal death toll (Plumper & Neumayer, 2006). Plumper and Neumayer (2006) also find that women are disproportionately victimized by war even as most combatants and combatant deaths are male. Gates et al. (2012) further convincingly show that civil war has long-term developmental consequences. We should care about civil war even as war wanes because it is devastating for those it affects. We cannot turn away from the suffering (Luke 10: 25–37, New International Version of the Holy Bible). We divide this book into two parts. The first part deals with developing the methodological framework in Chapters 2 and 3. We describe the data and preliminary data processing in Chapter 2. Chapter 3 is the keystone for this book. This chapter describes our methodology to make a case for why our results can be interpreted causally. We also

1 This graph was publicly available at the UCDP website (Pettersson et al., 2021).

Fig. 1.1 The rise, fall, and rise of modern civil war1

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Fig. 1.2 Civil conflict trends

describe the intuition of our technology, directing the reader to the more formal aspects through the citation index. We do not develop any new technology or statistical method. Our innovation is to bring together well-known technologies together to create a methodological workflow. This new workflow provides a framework for forging robust causal links and is the book’s main contribution. We have provided a detailed description of our data, including what we use and what we do not in Chapter 2. The variables in the final dataset for choosing the model are described in Appendix 1. We present the code for all aspects of this book, from data preprocessing to the causal analysis, in Appendix 2. The technology we use also provides a more nuanced understanding of the causes of civil conflict. Rather than report parametric point estimates and reports p values, we report partial dependence plots. These are graphs that show how the risk of civil war changes as the variable of interest takes on different values. Often these results are interestingly nonlinear. This approach has two benefits. First, the nonlinearities emanate from

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the data instead of being theoretically imposed on the model. Second, the data is empirically validated by predictive accuracy. There is no test of significance. Both these factors reduce researcher biases while providing nuanced insights.2 Partial dependence plots are perhaps more common in the health fields than in the social sciences. In health fields, they are called dose response functions. We use the terms “dose response functions” and “partial dependence plot” interchangeably. Chapters 4, 5, 6, and 7, focus on how a particular variable of interest affects the likelihood of civil war. Each chapter adds new insights into why civil war happens. A small caveat may be in order here. A substantial part of the literature is devoted to differentiating between civil war onset and duration. We do not make this distinction because that is not our purpose. We aim to identify causal predictors of civil war. Our methodology is entirely agnostic, though. It can also be used to understand the causes of civil war onset and duration as well. Nevertheless, the remarkable accuracy of our modeling approach suggests that perhaps the distinction may not matter as much. In any case, predicting whether some aspect of war will happen sometime five years from now is very different from the duration/onset debate. We find accurate and causal variables for different aspects of war five years ahead of when it happens. Causal predictions are one of our key contributions. These four chapters, each, also provide insights into the scope of our methodology. We have chosen these variables precisely to highlight these methodological insights. So, our choice of variables in each of these four chapters is not random. We leave the reader with one last thought before the journey begins. The social sciences use theory to motivate hypotheses. Sometimes researchers test these hypotheses. We suggest a data-driven approach reminiscent of the patient observations of Copernicus to let patterns in the data motivate theory. The theory can then generate more testable hypotheses in an iterative process. We suggest that this iterative combination of deductive and inductive reasoning can help us understand the complexities of human behavior and particularly war. The epilogue summarizes our main results.

2 For a detailed technical description of everything in this paragraph, see Basuchoudhary et al. (2018).

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References Armitage, D. (2017). Civil wars: A history in ideas. Yale University Press. Basuchoudhary, A., Bang, J. T., Sen, T., & David, J. (2018). Predicting hotspots: Using machine learning to understand civil conflict. Rowman & Littlefield. Blattman, C., & Miguel, E. (2010). Civil war. Journal of Economic Literature, 48(1), 3–57. Gates, S., Hegre, H., Nygård, H. M., & Strand, H. (2012). Development consequences of armed conflict. World Development, 40(9), 1713–1722. Ghobarah, H. A., Huth, P., & Russett, B. (2003). Civil wars kill and maim people-long after the shooting stops. American Political Science Review, 97 , 189–202. Gleditsch, N. P., Melander, E., Urdal, H., & Mason, D. (2016). Introduction– patterns of armed conflict since 1945. In D. Mason & S. McLaughlin Mitchell (Eds.),What do we know about civil war (pp.15–32). Rowman & Littlefield. Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., & McElreath, R. (2001). In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review, 91(2), 73–78. Kraay, A., 2015. Weak instruments in growth regressions: Implications for recent cross-country evidence on inequality and growth (World Bank Policy Research Working Paper No. 7494). MacMillan, M. (2020). War: How conflict shaped us. Random House. Muthukrishna, M., Bell, A. V., Henrich, J., Curtin, C. M., Gedranovich, A., McInerney, J., & Thue, B. (2020). Beyond Western, Educated, Industrial, Rich, and Democratic (WEIRD) psychology: Measuring and mapping scales of cultural and psychological distance. Psychological Science, 31(6), 678–701. Pettersson, T., Davis, S., Deniz, A., Engström, G., Hawach, N., Högbladh, S., Sollenberg, M., & Öberg, M. (2021). Organized violence 1989–2020, with a special emphasis on Syria. Journal of Peace Research, 58(4). Pinker, S. (2012). The better angels of our nature: Why violence has declined. Penguin Group USA. Plümper, T., & Neumayer, E. (2006). The unequal burden of war: The effect of armed conflict on the gender gap in life expectancy. International Organization, 60, 723–754. Velentgas, P., Dreyer, N. A., Nourjah, P., Smith, S. R., & Torchia, M. M. (Eds.). (2013, January). Developing a Protocol for observational comparative effectiveness research: A user’s guide. AHRQ Publication No. 12(13)-EHC099. Rockville, MD: Agency for Healthcare Research and Quality. www.effective healthcare.ahrq.gov/Methods-OCER.cfm

CHAPTER 2

Data Description and Preliminary Processing

Abstract This chapter describes the data and the initial data cleaning process that precedes actual analysis. First, we describe how we eliminated variables with insufficient data and then explain the process of imputing missing values in the remaining data set. Finally, we describe the process of Recursive Feature Elimination that results in the optimal number of variables that predict civil conflict. Keywords Data sources · Imputation of missing data · Recursive Feature Elimination · Optimal number of predictors

We collect and combine data from nine separate sources and capture a variety of commonly cited economic, demographic, and institutional correlates of civil war, as well as a slew of not-so-commonly cited correlates. We then impute missing values using random forest imputation methods and select the most relevant predictors of conflict using a recursive feature extraction method. This chapter describes the sources of our data, our variables, and the methods we use for preparing the data.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Basuchoudhary et al., Identifying the Complex Causes of Civil War, https://doi.org/10.1007/978-3-030-81993-4_2

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2.1

Data Sources

Our outcome variable, civil conflict, is a binary variable based on a combination of four indicators of conflict collected by the Political Instability Task Force (PITF) (PITF, 2021) at the Center for Systematic Peace (CSP). These four indicators are: revolutionary civil war, ethnic conflict, adverse regime change, and genocide or politicide. The PITF considers a country to have been in civil conflict if it experienced any of these four kinds of conflicts in any given year or if it experienced several instances of any one kind of conflict in a year. We define a country to be in civil conflict in a given year if it experienced at least one incident of any one of these four kinds of conflict. We define a country to not be in civil conflict if it did not experience any of the four in a year. Our economic and demographic data are from the Penn World Tables (PWT), the Standardized World Income Inequality Database (SWIID), and the World Development Indicators (WDI). PWT (Feenstra et al., 2015) measures the key components of expenditure—consumption, investment, government, and trade—in prices adjusted for purchasing power parity (PPP). It also reports the PPPadjusted internal price indices for each component and the PPP-implied exchange rate. SWIID produces measures of income inequality for most countries. However, data on some years are missing and there are multiple estimates for others. Moreover, some of these measures are based on income and others on consumption. To address these issues, we use the Solt (2016) database. Frederick Solt (2016) combined and standardized the SWIID data using multiple imputations to produce a relatively complete panel of measures of market income inequality, income redistribution, and net income inequality after redistribution for 100–170 countries since the mid-1980s. Before the mid-1980s, however, the data are much sparser. The largest of our datasets is the WDI (World Development Indicators, 2021). The data package that comes with the full WDI download (currently) includes 1435 variables that come from the original WDI as well as from other World Bank-sponsored sources such as: The International Monetary Fund, the International Labour Organization, the Organization for Economic Cooperation and Development, the United Nations Population Division, the World Health Organization, and other World Bank data initiatives.

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Our data on the institutional correlates of conflict are from the CrossNational Time-Series (CNTS) data (Banks & Wilson, 2021) archives, the Database of Political Institutions (DPI, Scartascini, et al., 2018), The International Country Risk Guide (ICRG, PRS Group, 2018) from the Political Risk Service Group, and the Polity Project (Polity) from the Center for Systemic Peace. Although the CNTS includes both institutional, economic, and demographic data, for our purposes, its measure of institutions and institutional stability, are most useful. CNTS data on variables such as assassinations, government crises, strikes, and anti-government demonstrations are ones that might predict conflict. Data on the different kinds of executive bodies, and the ways in which these bodies are selected, major cabinet changes, legislative coalitions and competitiveness, and constitutional changes, are also relevant. CNTS data on economic and demographic variables such as GDP, energy use, telephone and media use, population, and school enrollment, are often missing. Thus, we gather data on these variables from other sources, such as the WDI, where the missing values are interpolated or imputed. Our data on variables that capture the structure of government, and the composition of the actors within the government, are from the DPI. This includes variables on the numbers of seats held by the executive and the various parties in the legislature, their ideological alignment, and how the parties identify along nationalistic, regional, religious, or rural bases. It also includes variables that tell us whether there were elections in a given year, whether there were allegations of fraud in those elections, the competitiveness of the elections, and the number of checks on power. The ICRG from the Political Risk Services (PRS) Group is a proprietary database of indices covering measures of democracy, corruption, bureaucratic quality, property rights, and various measures of institutional and social stability. These data can be predictive of conflict, but since they are proprietary, the methodology for how the indices are constructed is not fully explained. The data also do not begin until 1984. Polity data (Marshall & Gurr, 2020) measures the democratic accountability of institutions of a country. It includes various measures of constraints on executive and legislative power, the competitiveness of selection processes, regime durability, and regime change. Finally, studies cite cultural heterogeneity as an important factor in fomenting conflict (Easterly & Levine, 1997; Montalvo & Reynal-Querol, 2002). To account for these factors, we include data on ethnic and

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religious fractionalization and polarization from Montalvo and ReynalQuerol (2002).

2.2

Data Preparation

We start with more than 1500 variables from nine different sources. Combining this data presented a few problems. In this section, we explain and describe our methods to address these problems. Different data sources often use different country names (and codes). We assign each country a uniform country name so that the datasets all share the same unique IDs for country and year.1 We also exclude (mainly from the WDI data) any location names that identified a region, e.g. “East Asia and Pacific” or “Middle East and North Africa,” or an economic group of countries, e.g. “Heavily Indebted Poor Countries” or “Lowand Middle-Income Countries.” Our data sources also use different codes to identify (what boils down to) unavailable data. In these cases, we substituted NA values for these specialized codes. Some examples of these substitutions include: 1. In the DPI, numerical −999, string “−999,” string “NA,” and blank values all indicate that there is not any information available for that data point. 2. In Polity, numerical −66, −77, and −88 indicate different types of missingness. One problem with Polity is that their coding rules for the sub-indices for democracy and autocracy involve the adverse regime change variable from the PITF dataset. This is our outcome variable. To address this, we employ the methods proposed by Vreeland (2008) and Plümper and Neumayer (2010) and create a “de-conflicted” “X-Polity” version of the variables in the Polity dataset,2 and purge this explicit measure of conflict from the construction of the Polity index (and its sub-indices) before considering it in our analysis. 1 We used the package “countrycode” to assist with this matching. However, in each dataset a handful of countries did not contain information that matched properly, and we did make some manual substitutions. 2 The R package “recodepolity” (Haass, 2017) includes syntax for recoding the Polity variables.

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The fall of the Iron Curtain, which began in 1989 with the reunification of Germany, and culminated in 1991 with the collapse of the Soviet Union, drastically changed the map of countries in our analysis. To address this problem, we narrow our data set to the years after 1990. As a practical matter, many variables that studies have considered to be correlates of civil war do not have strong data coverage before about 1980. After addressing these data issues, we merge the data from the nine datasets by matching them on country and year. We keep only the country–year dyads whose ids appear in the WDI dataset, which covers most of the variables in our final data. We construct our outcome variable, civil conflict, by assigning it a value of one to the country–year dyad if PITF records at least one incident, and a value of zero otherwise. We further prune the set of variables. If more than 50% of the observations for a variable were missing, we drop the variable. Further, if two variables from different datasets measure identical features, we drop one, keeping data from more widely used sources like WDI. Finally, if the variable correlates with another variable or group of variables, we keep only one of the correlated variables, trying to pick the broader or more common measure. We manually inspect the matrix of pairwise correlations and eliminate variables that are correlated at an absolute correlation coefficient value of about 0.85 or more. This leaves us with a list of 353 possible variables.3

2.3

Data Imputation

The first step of any data analysis is to clean and preprocess data to build a representative sample. This requires a strategy for dealing with gaps in the data. The most common strategy has been to ignore these observations entirely. However, this means that we either exclude countries that experience the highest rates of civil conflict because of missing data, or not examine correlates that might have somewhat sparser data available, or both. 3 In fact, some variables in this list potentially capture similar levels or channels through which conflict might emerge or be prevented. However, our goal is not to eliminate every potential duplicate; it is merely to reduce the list to something our feature extraction algorithm can handle in a reasonable timeframe.

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We avoid these problems by using random forest imputation to fill in the missing values.4 This method predicts the missing values of each of the predictors from a random forest model using all of the other covariates for conflict.5 The imputed values come from predicting each explanatory variable in a random forest model over many trees. In addition, since each tree used to calculate this average considers multiple alternative prior splits on many other explanatory variables, random forest imputation exploits many possible sources of nonlinearity and interactions among the other determinants. Averaging over all of the trees, then iterating the process using the imputed values from the previous iteration until the imputations converge,6 we obtain imputed values for missing data points that more information about the conditional distribution than linear imputation methods. An important advantage to the random forest approach to imputing missing values and variable selection lies in its flexibility. It has the advantage of creating imputed values that do not presuppose any prior functional form on the missing variables, that are quite stable, and that are naturally bounded by the domains of the observed data. By contrast, parametric methods like linear multiple imputation estimate parameters based on an assumed distribution and functional form for the model of the variables with missing values. Depending on the sensitivity of the parameters and the distributions of the covariates these imputation methods may lead to imputed values outside of the logical bounds for the variable (Shah et al., 2014; Wulff & Jeppesen, 2017). After imputing the missing values in our dataset, we calculate fiveyear moving averages of the numerical variables in our dataset, and then take the five-year lags of all of the variables (of the moving averages for numerical variables, and of the current-year values for the factor variables).

4 We use R version 4.0.2 for all machine learning analysis (R Core Team, 2020); we use the missForest package, version 1.4 for imputations (Stekhoven & Buehlmann, 2012). For an application of random forest imputation methods to data analyzing terrorism see Spangler and White (2020). 5 We impute each variable based on a random forest prediction of the missing values of that variable that includes 100 trees. In each of the forests consider just 4 variables at each node of every tree to prevent a single variable or small number of variables from dominating the imputation. 6 We set the maximum number of iterations at 5 to limit computation size and time.

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Finally, we inspect the variables and eliminate those for which more than 30 percent of the original data was missing. This leaves 261 variables to consider in our feature extraction (see Appendix 1 for the entire list).

2.4 Recursive Feature Elimination with Random Forests We select the features that we consider as potential causes of conflict using a random forest Recursive Feature Elimination (RFE), which we implement using the rfe function available in the caret package (version 6.0-86) in R. Recursive feature elimination eliminates least important features a few at a time based on the cross-validated variable importance calculated from multiple random forests specified with the same variables. In our case, we consider specification sizes (numbers of variables included in the model) in increments of five variables starting with 5 and going up to 350, as well as the full 353 variables. We cross-validate the forests for each specification size 8 times based on a random subsample of 80% of the observations in our learning sample. The remainder of this section will briefly describe this process and present the results of the RFE. RFE starts with the entire set of variables (in our case the 353 that remained after culling). We then build eight forests—corresponding to the number of cross validations we chose—with 500 trees each using a randomly selected 80% sample of the observations in the learning using randomForest. For this “kitchen sink” model, we then calculate the (crossvalidated) average accuracy of the eight forests based on the 20% of the observations that each forest randomly omitted. To determine which variables to eliminate, the RFE calculates the importance of each variables based on the accuracy dropout loss that occurs when we randomly assign the observed values of that variable to the observations in the sample. In the first round of our RFE drops the 3 variables with the least loss of accuracy from randomly assigning the values to bring the total number of variables in the second round to 350, the nearest multiple of five. RFE repeats these steps sequentially, reducing the number of variables in the model by 5 at each round according to which variables have the lowest importance based on accuracy dropout loss. It calculates the crossvalidated accuracy of each model size (5, 10, … 350, 353 variables) and selects the “optimal” number of variables as the number of variables that achieves the highest overall (cross-validated) accuracy).

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We present a graphical representation of the results of the RFE in Fig. 2.1.

Fig. 2.1 RFE prediction errors

The model with the top 65 most important variables achieves the highest degree of prediction accuracy after which there is a slight drop, but overall a fairly flat relationship between model size and accuracy. We list the 65 variables by their rank, in Table 2.1, and alphabetically, in Table 2.2

Description

Annual Net secondary income (current US$)

Infant Mortality Rate

Neonatal Mortality Rate

Under-five Mortality Rate

Rural population

Merchandise Exports to Arab World (% of Exports) Grants, Excluding Technical Cooperation

Net Official Assistance and Aid Received (Constant USD

Anti-Government Demonstrations

1

2

3

4

5

6 7

8

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65 optimal variables, by rank

Rank

Table 2.1

CNTS

WDI

WDI WDI

WDI

WDI

WDI

WDI

WDI

Source

(continued)

Transfers made in cash, goods, or services for which no repayment is required. Data excludes technical cooperation grant. Data are in current U.S. dollars Made to countries and territories on the Development Assistance Countries list of aid recipients, net of repayments. Data are in constant 2018 U.S. dollars Any peaceful public gathering of at least 100 people for the primary purpose of displaying opposition to government policies, excluding demonstrations of a distinctly anti-foreign nature

Transfers are recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current Number of infants dying before reaching one year of age, annual Number of neonates dying before reaching 28 days of age, annual Number of children dying before reaching age five, annual People living in rural areas as defined by national statistical offices. Calculated as the difference between total population and urban population, annual

Notes

2 DATA DESCRIPTION AND PRELIMINARY PROCESSING.

17

Description

Merchandise Exports to Low- and Middle-Income MENA Economies (% of Exports)

Total Population

Female Population

Share of Gross Capital Formation at PPP Population of Largest City (Total)

Average Depreciation Rate of Capital Stock Number of Assassinations

Agricultural Land (% of land)

Merchandise Imports to Low- and Middle-Income MENA Economies (% of Imports)

Inflation Rate (Consumer Price Index) Arable Land (% of land)

10

11

12

13 14

15 16

17

18

19 20

(continued)

Rank

Table 2.1

WDI WDI

WDI

WDI

PWT CNTS

PWT WDI

WDI

WDI

WDI

Source

Number of times there is an attempt to murder, or an actual murder of, any important government official or politician Land abandoned as a result of shifting cultivation is excluded Sum of merchandise imports by the reporting economy from low- and middle-income economies in the East Asia and Pacific

Urban population living in the country’s largest metropolitan area

Sum of merchandise exports from the reporting economy to low- and middle-income economies in the East Asia and Pacific region according to World Bank classification of economies Counts all residents regardless of legal status or citizenship. The values shown are mid-year estimates Counts all female residents regardless of legal status or citizenship

Notes

18 A. BASUCHOUDHARY ET AL.

Major Constitutional Changes

Primary Enrollment Rate

Rural Population (%)

Net Primary Income (from Abroad, Current LCU)

Permanent Cropland (%) Official Exchange Rate Arable Land (Hectares per Person) Female Primary Enrollment

Major Government Crises

Phones, Excluding Cellular per Capita Urban Population (% of total pop) Merchandise Exports to Low- and Middle-Income Sub-Saharan African Economies (% of Exports) External Balance on Goods and Services (Current LCU) Primary + Secondary Enrollment per Capita

21

22

23

24

25 26 27 28

29

30 31 32

34

33

Description

Rank

CNTS

WDI

CNTS WDI WDI

CNTS

WDI CNTS WDI WDI

WDI

WDI

WDI

CNTS

Source

(continued)

exports of goods and services minus imports of goods and services

As a percentage of total students at primary level, includes enrollments in public and private schools Any rapidly developing situation that threatens to bring the downfall of the present regime—excluding situations of revolt aimed at such overthrow

Number of basic alterations in a state’s constitutional structure. Extreme case—adoption of a new constitution that significantly alters the prerogatives of the various branches of government, such as substitution of presidential for parliamentary government or the replacement of monarchical by republican rule Ratio of total enrollment to the population of the primary education Calculated as the difference between total population and urban population. (% of total population) Includes the net labor income and net property and entrepreneurial income. Data are in current local currency Excludes land under trees grown for wood or timber LC per U.S. dollar

Notes

2 DATA DESCRIPTION AND PRELIMINARY PROCESSING.

19

Population in Largest City (% of Urban Population)

Adjusted Savings: Education Expenditure (% of GNI)

Agricultural Methane Emissions (% of Total)

Merchandise Imports from Arab World (% of Imports) Merchandise Imports from Low- and Middle-Income Europe/Central Asian Economies (% of Imports) Merchandise Imports by Reporting Economy, Residual (% of Imports) Agricultural NO2 Emissions (% of Total)

Numerical country index from DPI Other Greenhouse Gas Emissions (KT)

Share of Government Consumption at PPP CO2 Emissions (Metric Tons per Capita) GDP Deflator (Base Year Varies) Population Density

35

36

37

38 39

41

42 43

44 45 46 47

40

Description

(continued)

Rank

Table 2.1

PWT WDI WDI CNTS

DPI WDI

WDI

WDI

WDI WDI

WDI

WDI

WDI

Source

By-product emissions of hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. Thousand metric tons of CO2 equivalent

Total merchandise imports less the sum of imports from high-, low-, and middle-income economies Emissions produced through fertilizer use (synthetic and animal manure), animal waste management, agricultural waste burning (nonenergy, on-site), and savanna burning

percentage of a country’s urban population living in that country’s largest metropolitan area Current operating expenditures in education, including wages and salaries and excluding capital investments in buildings and equipment Emissions from animals, animal waste, rice production, agricultural waste burning (non-energy, onsite), and savanna burning

Notes

20 A. BASUCHOUDHARY ET AL.

WDI WDI

Total Land Area (km2 ) Agriculture, Forestry, and Fishing Value Added (% of GDP) Primary School Enrollment per Capita Gross Capital Formation (% of GDP)

Agricultural Methane Emissions (KT CO2 Equivalent) Life Expectancy at Birth

NO2 Emissions in Energy Sector (% of Total)

59 60

63 64

65

61 62

WDI PWT WDI CNTS WDI CNTS WDI WDI CNTS

Official Exchange Rate Share of Household Consumption at PPP Adolescent Fertility Rate All School Enrollment per Capita GDP per Capita (Constant LCU) All Phones per Capita Male Population (%) Female Population (%) Number of Riots

50 51 52 53 54 55 56 57 58

WDI

WDI WDI

CNTS WDI

WDI WDI

Inflation Rate (GDP Deflator) Population Density

48 49

Source

Description

Rank

Number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life

Outlays on additions to the fixed assets of the economy plus net changes in the level of inventories

Any violent demonstration or clash of more than 100 citizens involving the use of physical force

Number of births per 1000 women ages 15–19

Midyear population divided by land area in square kilometers Annual average

Notes

2 DATA DESCRIPTION AND PRELIMINARY PROCESSING.

21

Adjusted Savings: Education Expenditure (% of GNI)

Adolescent Fertility Rate Agricultural Land (% of land)

Agricultural Methane Emissions (% of Total)

Agricultural Methane Emissions (KT CO2 Equivalent) Agricultural NO2 Emissions (% of Total)

Agriculture, Forestry, and Fishing Value Added (% of GDP) All Phones per Capita All School Enrollment per Capita Annual Net secondary income (current US$)

Anti-Government Demonstrations

36

52 17

37

63 41

60

9

55 53 1

Description

Alphabetical list of the 65 optimal variables

Rank

Table 2.2

CNTS

CNTS CNTS WDI

WDI

WDI WDI

WDI

WDI WDI

WDI

Source

Transfers are recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current Any peaceful public gathering of at least 100 people for the primary purpose of displaying opposition to government policies, excluding demonstrations of a distinctly anti-foreign nature

Emissions produced through fertilizer use (synthetic and animal manure), animal waste management, agricultural waste burning (nonenergy, on-site), and savanna burning

Current operating expenditures in education, including wages and salaries and excluding capital investments in buildings and equipment Number of births per 1000 women ages 15–19 Land abandoned as a result of shifting cultivation is excluded Emissions from animals, animal waste, rice production, agricultural waste burning (non-energy, onsite), and savanna burning

Notes

22 A. BASUCHOUDHARY ET AL.

Arable Land (% of land) Arable Land (Hectares per Person) Assassinations

Average Depreciation Rate of Capital Stock CO2 Emissions (Metric Tons per Capita) External Balance on Goods and Services (Current LCU) Female Population

Female Population (%) Female Primary Enrollment

GDP Deflator (Base Year Varies) GDP per Capita (Constant LCU) Grants, Excluding Technical Cooperation

Gross Capital Formation (% of GDP)

Infant Mortality Rate

Inflation Rate (Consumer Price Index) Inflation Rate (GDP Deflator)

20 27 16

15 45 33

57 28

46 54 7

62

2

19 48

12

Description

Rank

WDI WDI

WDI

WDI

WDI WDI WDI

WDI WDI

WDI

PWT WDI WDI

WDI WDI CNTS

Source

(continued)

Transfers made in cash, goods, or services for which no repayment is required. Data excludes technical cooperation grant. Data are in current U.S. dollars Outlays on additions to the fixed assets of the economy plus net changes in the level of inventories Number of infants dying before reaching one year of age, annual

As a percentage of total students at primary level, includes enrollments in public and private schools

exports of goods and services minus imports of goods and services Counts all female residents regardless of legal status or citizenship

Number of times there is an attempt to murder, or an actual murder of, any important government official or politician

Notes

2 DATA DESCRIPTION AND PRELIMINARY PROCESSING.

23

Life Expectancy at Birth

Major Constitutional Changes

Major Government Crisis

Male Population (%) Merchandise Exports to Arab World (% of Exports) Merchandise Exports to Low and Middle Income Sub-Saharan African Economies (% of Exports) Merchandise Exports to Low- and Middle-Income MENA Economies (% of Exports)

Merchandise Imports by Reporting Economy, Residual (% of Imports) Merchandise Imports from Arab World (% of Imports)

64

21

29

56 6 32

40

38

10

Description

(continued)

Rank

Table 2.2

WDI

WDI

WDI

WDI WDI WDI

CNTS

CNTS

WDI

Source

Sum of merchandise exports from the reporting economy to low- and middle-income economies in the East Asia and Pacific region according to World Bank classification of economies Total merchandise imports less the sum of imports from high-, low-, and middle-income economies

Number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life Number of basic alterations in a state’s constitutional structure. Extreme case—adoption of a new constitution that significantly alters the prerogatives of the various branches of government, such as substitution of presidential for parliamentary government or the replacement of monarchical by republican rule Any rapidly developing situation that threatens to bring the downfall of the present regime—excluding situations of revolt aimed at such overthrow

Notes

24 A. BASUCHOUDHARY ET AL.

Merchandise Imports from Low- and Middle-Income Europe/Central Asian Economies (% of Imports) Merchandise Imports to Low- and Middle-Income MENA Economies (% of Imports)

Neonatal Mortality Rate

Net Official Assistance and Aid Received (Constant USD

Net Primary Income (from Abroad, Current LCU)

NO2 Emissions in Energy Sector (% of Total) Numerical country index from DPI Official Exchange Rate Official Exchange Rate Other Greenhouse Gas Emissions (KT)

Permanent Cropland (%) Phones, Excluding Cellular per Capita Population Density Population Density

Population in Largest City (% of Urban Population)

39

3

8

24

65 42 26 50 43

25 30 47 49

35

18

Description

Rank

WDI

WDI CNTS CNTS WDI

WDI DPI CNTS WDI WDI

WDI

WDI

WDI

WDI

WDI

Source

DATA DESCRIPTION AND PRELIMINARY PROCESSING.

(continued)

Midyear population divided by land area in square kilometers percentage of a country’s urban population living in that country’s largest metropolitan area

LC per US dollar Annual average By-product emissions of hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. Thousand metric tons of CO2 equivalent Excludes land under trees grown for wood or timber

Sum of merchandise imports by the reporting economy from low- and middle-income economies in the East Asia and Pacific Number of neonates dying before reaching 28 days of age, annual Made to countries and territories on the Development Assistance Countries list of aid recipients, net of repayments. Data are in constant 2018 U.S. dollars Includes the net labor income and net property and entrepreneurial income. Data are in current local currency

Notes

2

25

CNTS WDI

Primary + Secondary Enrollment per Capita Primary Enrollment Rate

Primary School Enrollment per Capita Riots

Rural population

Rural Population (%)

Share of Government Consumption at PPP Share of Gross Capital Formation at PPP Share of Household Consumption at PPP Total Land Area (km2 ) Total Population

Under-five Mortality Rate

Urban Population (% of total pop)

34 22

61 58

5

23

44 13 51 59 11

4

31

WDI

WDI

PWT PWT PWT WDI WDI

WDI

WDI

CNTS CNTS

WDI

Population of Largest City (Total)

14

Source

Description

(continued)

Rank

Table 2.2

Counts all residents regardless of legal status or citizenship. The values shown are midyear estimates Number of children dying before reaching age five, annual

Any violent demonstration or clash of more than 100 citizens involving the use of physical force People living in rural areas as defined by national statistical offices. Calculated as the difference between total population and urban population, annual Calculated as the difference between total population and urban population. (% of total population)

Ratio of total enrollment to the population of the primary education

Urban population living in the country’s largest metropolitan area

Notes

26 A. BASUCHOUDHARY ET AL.

2

DATA DESCRIPTION AND PRELIMINARY PROCESSING.

27

References Banks, A. S., & Wilson, K. A. (2021). Cross-national time-series data archive. Jerusalem, Israel: Databanks International. https://www.cntsdata.com/. Last accessed 14 October 2021. Easterly, W., & Levine, R. (1997). Africa’s growth tragedy: Policies and ethnic divisions. The Quarterly Journal of Economics, 1203–1250. Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015). The next generation of the Penn World Table. American Economic Review, 105(10), 3150–3182. Haass, F. (2017). Recodepolity Package. https://github.com/felixhaass/recode polity. Last accessed 14 October 2021. Marshall, M. G., & Gurr, T. R. (2020). Political regime characteristics and transitions, 1800–2018. Center for Systemic Peace. Montalvo, J. G., & Reynal-Querol, M. (2002). Why ethnic fractionalization? Polarization, ethnic conflict and growth (No. 660). Political Instability Task Force (PITF). (2021). State Failure Problem Set 1955–2018. https://www.systemicpeace.org/inscrdata.html. Last accessed 14 October 2021. PRS Group. (2018). Researchers dataset table 3B. Political. Team. R. C. (2020). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-pro ject.org/. Scartascini, C., Cruz, C., & Keefer, P. (2018). The database of political institutions 2017 (DPI2017). Washington, Banco Interamericano de Desarrollo. Shah, A. D., Bartlett, J. W., Carpenter, J., Nicholas, O., & Hemingway, H. (2014). Comparison of random forest and parametric imputation models for imputing missing data using MICE: A CALIBER study. American Journal of Epidemiology, 179(6), 764–774. Solt, F. (2016). The standardized world income inequality database. Social Science Quarterly, 97 (5), 1267–1281. Spangler, E., & White, D. (2020). Terrorist attack attribution with machine learning based multiple imputation. Available at SSRN 3648711. Stekhoven, D. J., & Bühlmann, P. (2012). MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112–118. World Development Indicators. (2021). The World Bank Group, https://dat abank.worldbank.org/source/worlddevelopment-indicators. Last accessed 14 October 2021. Wulff, J. N., & Jeppesen, L. E. (2017). Multiple imputation by chained equations in praxis: Guidelines and review. Electronic Journal of Business Research Methods, 15(1), 41–56.

CHAPTER 3

Methodological Workflow

Abstract This chapter describes a formal methodological sequence of events that allows us to interpret hitherto “black-box” machine learning algorithmic outcomes as the effect of some cause. While we apply this methodological workflow to understanding the causes of civil conflict, it can help answer many questions in economics and political science. We use the backdoor criteria to help determine causal impacts and introduce the idea of empirically informed covariate selection to guide the researcher to fulfill these criteria reasonably. Last we suggest that predictive accuracy is a critical element in highlighting the nature of the causal impact. Keyword Empirically Informed Covariate Selection (EICS) · Recursive Feature Elimination (RFE) · Risk variables · Confounder variables · Collider variables · Intermediate variables · Partial Dependence Plots (PDP) · Bayesian Additive Regression Tree (BART)

A randomized control trial (RCT) is a gold standard for identifying causality. This experimental approach assigns subjects randomly into two groups where one group gets a certain dosage of a potentially causal variable while the other does not. The randomization ensures that both groups have a similar distribution of all possible “other” causal variables

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Basuchoudhary et al., Identifying the Complex Causes of Civil War, https://doi.org/10.1007/978-3-030-81993-4_3

29

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while only the treatment group is dosed with the potentially causal variable of interest. Suppose we see that this potentially causal variable has an effect while there is no such effect in the other non-treatment or “control” group. In that case, we can assign causality because we can discern both facets of causality (Pearl & Mackenzie, 2018). The treatment group highlights that the effect happens when the dosage happens. The control group (where on average, apart from the dosage, is exactly the same as the treatment group) shows that the effect does not happen without the dosage. In this book, we are fundamentally interested in the question of “which treatments can a government or policymaker implement that will cause the likelihood of future conflict to decrease?” Though the gold standard for determining causality, RCT’s are both impossible and unethical in this context. Nevertheless, we cannot give up on the question if we ever intend to end conflict. So far, some social scientists have gotten around this vexing question by adroitly sidestepping the question of causality, preferring to use scientific euphemisms instead (Hernán, 2018). Others, Abadie and Gardeazabal (2003) for example, have used synthetic control mechanisms quite effectively to isolate causality.1 Yet, causality is what we are and should be interested in if we intend to end conflict. In Genesis, God asks Adam if he ate the apple, a yes or no question. Adam’s simple answer should have been yes; he ate the apple. Instead, Adam promptly assigned a cause for his action instead of answering the question—apparently, Eve made him do it. God, instead of responding like any sage grandmother by noting whether Adam would have jumped off a cliff if Eve had told him to, instead asks Eve the same yes or no question. Eve, again promptly responds that the serpent made her do it. Humankind is quite familiar with causative thinking, even if scientists do have a reasonable fear of overstating causative results (Hernán & Robins, 2020). Hernán and Robins (2020) further notes: As a human being, you are already innately familiar with causal inference’s fundamental concepts. Through sheer existence, you know what a causal effect is, understand the difference between association and causation, and you have used this knowledge consistently throughout your life. Had you not, you’d be dead. Without basic causal concepts, you would not have survived long enough to read this chapter, let alone learn to read. As a 1 Abadie et al. (2011) have developed an R package for this purpose.

3

METHODOLOGICAL WORKFLOW

31

toddler you would have jumped right into the swimming pool after seeing others reach the jam jar. As a teenager, you would have skied down the most dangerous slopes after seeing those who did won the next ski race. As a parent, you would have refused to give antibiotics to your sick child after observing that those children who took their medicines were not at the park the next day.

So how do we scientifically address the issue of causality when RCT’s are not feasible? In this chapter, we describe a systematic methodological workflow to address this question. To our knowledge this sort of systematic approach is innovative for the social sciences.

3.1

The Idea

Doing causal inference on observational data involves two key steps. The first step is to make a decision about the feasibility of a causal link between some treatment T and a variable Y . Such decisions, by necessity, are based on domain knowledge and data availability. If such a causal link is feasible, then the next step is to estimate this relationship. A causal link between T and Y depends on other variables, X. In an RCT this dependency is modeled with the random assignment of the treatment , effectively controlling for all the possible “other” variables that may matter for establishing a link between T and Y . For observational data this process has to be more explicit. This can be accomplished in two ways. The first is to assume some level of unconfoundedness/ignorabilty in the problem. Assuming unconfoundedness means that the treatment assignment mechanism does not depend on the potential outcomes after conditioning upon the covariates X (Imbens, 2015). There are stronger more technical versions of this assumption that we will not go into here. Intuitively think of T as a medicine, X as pretreatment health measurements, and Y being a health outcome. Unconfoundedness says that once we adjust for pretreatment health being assigned the treatment is independent of the potential outcomes. This is an untestable assumption. Thankfully Pearl (1993) notes that the strongly ignorable treatment assignment (SITA) assumption is equivalent to satisfying something he calls the backdoor criterion on causal directed acyclic graphs (DAGs). We will describe what this means in more detail in the next section. Pearl (1993) then states “Reducing the SITA conditions to the graphical backdoor criterion facilitates the search for an optimal conditioning set S (X in

32

A. BASUCHOUDHARY ET AL.

our notation) and significantly simplifies the judgments required for ratifying the validity of such conditions in practical situations.2 ” Satisfying the backdoor criterion is still an assumption. However, it provides a way to be more systematic about how we can use empirical evidence in conjunction with theoretical judgment to intentionally satisfy the backdoor criteria. Our empirically informed covariate selection (EICS) process is a humanassisted algorithm is just such a system. We describe EICS in the next section. We estimate the average dose response function E[Y (t )] where t ∈ T after identifying a causal relationship between T and Y . Zhao and Hastie (2021) relates the back door criteria to the calculation of E[Y (t )] and shows how it is the integral of the expected value of Y given both T and X integrated over X. This work also discusses how E[Y (t )] is related to the partial dependency plot (PDP), a common model analysis technique in machine learning. Thus a PDP estimated after using EICS expresses a causal pattern. There are a large number of techniques for estimating E[Y (t )] for both discrete and continuous variables (Galagate, 2016). Dorie et al. (2019) studies a large number of these methods, compares their performances on several tests, and discusses situations that affect algorithm performance.

3.2 Empirically Informed Covariate Selection (EICS) Our target variable (Y ) is whether one of four types of conflict happened in any given country in any given year. We have a pool of variables described elsewhere that are potentially related to conflict. We will delineate a process that identifies the causes of conflict from within this pool of variables. This process has several steps. The first step is to identify potential causal variables. We use the RFE technique to identify consistent predictors of conflict (X ). We postulate that predictors are not necessarily causal. However, a theory-guided search for a causal variable (T ) should begin within these predictive variables. We postulate that non-predictive variables cannot directly cause conflict (Basuchoudhary et al., 2018). At the very least, though, the top predictors of conflict should be controls for any causal analysis. But what if there are other variables not in X (X elim ) 2 For some intuitive examples see Velentgas et al. (2013, Chapter 7).

3

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33

that affect variables in X and T ? Or, what if there are variables in X that affect T , and T affects the variable as well. The backdoor criterion must be satisfied to tie down a real causal link between T and Y , given a sufficient set of variables X (Pearl, 2009; Zhao & Hastie, 2019). Say we want to evaluate T ’s effect on Y . X is a sufficient set of variables that specifies a causal model. The backdoor criteria states (see Fig. 3.1): A set X is admissible (or “sufficient”) for estimating the causal effect of T on Y if two conditions hold: 1. No element of X is a descendant of T. 2. The elements of X “block” all “backdoor” paths from T to Y —namely, all paths that end with an arrow pointing to T.

Note first that the backdoor criteria allow us to rule out systematic assignment patterns in the treatment on the way toward estimating a causal effect. The sufficient set X is the required relevant controls to evaluate T ’s causal impact on Y . The backdoor criteria also eliminate the endogeneity problems rife in the conflict literature. In econometrics, instrumental variables can solve some of these problems if the right instruments exist. But econometric attempts at teasing out the causes of conflict appear to be very sensitive to model specification. Our methodology

Fig. 3.1 Backdoor criterion

34

A. BASUCHOUDHARY ET AL.

addresses both the model specification problem and the IV problem. For the former, we have an algorithmic approach to identify proper model specification. As for the latter, we have a methodology that does not require a search for an IV. In either situation, we can reduce our reliance on the slings and arrows of fate. Before we formalize our approach, it is necessary to define variable types that should not be included in the sufficient set. The first step is to identify variables, the risk factors, that potentially influence Y . We also need to identify the class of variables, confounders, that influence the treatment T and Y . Risk factors, should be included because of efficiency gains in estimation even though it is unnecessary to include them to reduce bias (Velentgas et al., 2013). Further, confounder variables must also be included to control bias. The backdoor criterion requires all possible backdoor paths between T and Y to be blocked by an adjustment variable. We use the RFE algorithm (described below) to identify these risk factors and confounders. We argue that risk factors and confounders should, at the very least, predict the outcome. In other words, variables that robustly improve predictive accuracy should be included in a model specification even if they are not necessarily causal. High predictive accuracy further minimizes the likelihood we are leaving important confounders or risk factors out. But the nature of the interrelationships within these predictive variables, the non-predictive variables, T , and Y , requires further scrutiny. T may have a causal link to an “intermediate” variable, which, in turn, has a causal link to Y . Intermediate variables should be eliminated because they may artificially “dilute” a causal pathway’s strength. Collider variables are variables caused by both T and Y . Velentgas et al. (2013) does an excellent job describing this in the context of rain, wet grass, and sprinklers. Say we observe rain falling and think it causes sprinklers to come on. In reality, there is, of course, no causal link. But now, let’s say we observe wet grass when it rains and when the sprinklers are on. Therefore, we surmise these events are linked and include wet grass in our causal model as a control. Since both sprinklers turning on and rain makes grass wet, including wet grass in the model creates a spurious correlation between rainfall and sprinklers. We should eliminate colliders from the sufficient set. Indeed, collider variable inclusion may be why the civil conflict literature is replete with unstable causal links susceptible to variations in model specification.

3

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There may be a further complication if an important predictor variable is highly correlated with T . Leaving such a variable in the model as a control is clearly problematic since we will never know if the effect/or lack thereof was a consequence of the treatment; the high correlation effectively implies that both the treatment and the control group received the treatment. The correlated variable needs to be eliminated. From a theoretical perspective, the correlation may hint at a deeper connection between these two variables, perhaps measuring the same idea in two different ways. Therefore, theory and reason should guide is in deciding which variable to keep. We have highlighted the sort of variables necessary for a complete model specification guided by the the backdoor criteria. This concept suggests that we can infer a causal link between T and Y only if we include risk and confounder variables while leaving out intermediate variables, collider variables, and highly correlated variables with the treatment. We have developed an R package that simplifies the process of applying EICS , i.e. the steps that follow. The package is available at https://github.com/bangecon/eics. The base code for everything we do is also available in the appendix of this book. 3.2.1

Identifying Risk and Confounder Variables

We described our basic data preparation process in Chapter 2. This first step in the process left us with 261 variables from an initial list of about 1500 variables.3 We further reduce the number of causal variable candidates using recursive feature elimination (a.k.a. backward selection) based on the random forest algorithm.4 The second step of Empirically Informed Covariate Selection (EICS) starts with the full list of 261 covariates. It then trains a random forest to the covariates to find (a) the cross-validated5 prediction accuracy (or error) of that size of the model and (b) the cross-validated importance of each of the variables in the model. The algorithm then eliminates the k least important variables. It repeats the process until the size of the model

3 See Appendix 1 for a complete listing of the 261 variables. 4 Specifically, we use the “rfe” command from the caret package (Kuhn et al., 2020). 5 See Chapter 2 for a more detailed description of this process. Appendix 2 has the

code.

36

A. BASUCHOUDHARY ET AL.

is less than or equal to the number that would be eliminated in the next iteration. Therefore, the algorithm determines the “optimal” model yielding the highest overall predictive accuracy. However, in cases where the “optimal” model involves a large number of the original variables (or especially a high proportion) of the variables, the algorithm also produces a plot (or table) of the predictive accuracy of the alternative model sizes. This plot allows one to find an “elbow” or model size beyond which there is no clear or consistent gain to adding more variables. From our list of 261 variables, the random forest recursive feature elimination selected 65 variables as the “optimal” size.6 Notice that the accuracy peak of 98% is at around 65 variables. This also corresponded to roughly the point after which there was no clear or consistent gain to adding additional variables. We present the plot of cross-validated accuracies against model size in Figs. 2.1 and 3.2 (they are the same figure; we just include it here as well for reading ease). The list of variables chosen by the RFE is reported in Chapter 2 as well. While predictors are not necessarily causal, non-predictive variables are unlikely to be proximate causes for civil conflict. Thus, the search for causal variables should start from among the more predictive variables. We choose variables from among these RFE chosen 65 to perform our causal analysis. Further, The almost 98% predictive accuracy (Fig. 3.2) noted here suggests that omitting less important variables does not affect the overall predictive accuracy. We may think of this result in a slightly different way. If the omitted variables do not improve predictive accuracy when included, they are also unlikely to be sources of omitted variable biases. In short, the RFE methodology dramatically reduces the risk of omitted variable bias though other complications exist. We will address these complications in the next subsection. That said, this list of 65 most important predictors of conflict is a vital part of our workflow toward estimating a causal effect. Consider the basic principles of an RCT. Subjects are distributed randomly with the expectation that all variables that may affect the outcome, except for the causal variable of interest, cancel each other out. Causal variables may predict. But non-predictive variables ought not to be causal. Thus, the top 65 6 See Appendix 1 for a list of the 261 variables and Chapter 2 for the list of the RFE chosen 65.

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METHODOLOGICAL WORKFLOW

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Fig. 3.2 Recursive feature elimination accuracy plot

variables identified here are most likely to be causal while the remaining, culled, variables are not. To be sure, not all the variables in these top 65 may be causal, but at least the proximate causes for civil conflict should revolve around these top 65. Thus, we must account for these variables as possible causes for civil conflict. We will choose one of these variables as the dosage or cause variable. Then we must assure ourselves that our estimation sample is “balanced” among the remaining 64.7 It should be evident that the most important predictors of conflict— the few, the proud, the 65—must at least be risk factors or confounders in addition to being, potentially at least, causal. Nevertheless, some of these 7 There are existing algorithms (e.g., CausalDRF) that do this and then measure the dosage effect. That is our final step. But we still have some work to do before we get there.

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variables may also be colliders and intermediate variables. We describe our system for identifying colliders and intermediate variables in the next subsection. Further, while we have an algorithm that helps us identify intermediate variables and alternative candidates for causal treatments, such decisions cannot be devoid of theory. We will gain further clarity on these points in the following chapters, where we describe the variables we chose to identify the causes of conflict. 3.2.2

Eliminating Colliders and Intermediate Variables

We noted that our RFE methodology gave us a parsimonious list of 64 variables. The omitted variables were unlikely to be risk factors or confounders since any such variable should at the very least be able to predict conflict. Thus, the RFE process helps identify the widest possible array of variables that may matter for conflict. The RFE does not select some variables. One of these variables may be a better treatment as a matter of theory and policy. For example, infant mortality may be an important predictor. However, infant mortality is also likely to be symptomatic of something else. This “something else” may not be chosen by the RFE because the RFE is focused on predictive accuracy rather than distinguishing between cause and symptom. Nevertheless, the “other” variable, total government consumption expenditures, for example, may be a more theoretically plausible treatment. It seems quite unlikely that infant mortality causes civil conflict, while variations in government consumption expenditures may have a clearer theoretical link to civil conflict. Our algorithm highlights just such a role for consumption expenditures. Our approach, therefore, provides an avenue for man–machine cooperation in a way that can garner genuine insight. Of course, even if there was a solid theoretical reason that infant mortality causes civil conflict, that finding by itself would be irrelevant for policy. Such a finding would merely kick the causal can upstream— the question then would be what causes infant mortality? That is, our algorithm lays out different potential causal pathways. In step 3 we calculate the permuted predictor importance (Molnar, 2020, Matlab or R documentation) of the eliminated variables, X elim , on the treatment, T . Basically, X elim includes variables not chosen by the RFE. Step 3 determines if any of these eliminated variables are highly predictive of the treatment and based on the subject matter expertise may make sense as a more reasonable proxy to the treatment of interest.

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The “Empirically Informed” idea of the method is that while X elimi (the ith variable in X elim ) may be predictive of T , it may or may not make more sense as a treatment. This is just a suggestion to the subject matter expert. If a more reasonable treatment is found, the algorithm is restarted with this as the proposed treatment. While the subject matter expert could do this purely based on domain knowledge, in our case, this would require them to study 261 potential variables that may be better proxies of treatment. It does not replace domain knowledge. It simply allows it to focus on a few variables highly predictive of T , thereby saving time. It also allows the subject matter expert to further explore causal links from the non-RFE variables to the treatment, potentially outlining important causal chains. Though, this process too requires human judgment. A completely algorithmic approach can keep extending the causal chain or indeed have the researcher go around in circles. Some variables within the RFE may be highly correlated with the treatment T . Theory comes into play here again. The two correlated variables may be measuring very similar things. In such a case, a scientist should pick one and not the other. Which variable should be picked? This, too, is a theoretical question of construct validity. Two measures may be correlated, but the theoretically justified measure should be chosen.8 Once again, this is an opportunity for human–machine cooperation to identify a better model specification. We, therefore, need to examine the potential relationships between T and each X RFEi (the ith variable in the RFE identified group), again using the permuted predictor importance. Let TX = [T X RFE ] be the matrix combining T and X RFE . Let P ij be the matrix of the predictor importance of each predictor j on the variable i in matrix TX. Note each row of P, P i is normalized by taking the z-score so that the rows are comparable. This matrix is used to determine several potential relationships between T and X i . Step four determines if P ij and P ji are both above a threshold, zthresh, which would suggest that T is an important predictor of X RFEi and vice versa, as illustrated in Fig. 3.3. If this is the case then it may be that T and X RFEi are essentially measuring the same thing or there is some kind of feedback loop between the variables. If so then the analyst may want to 8 Construct validity refers to ‘a particular measure relates to other measures consistent with theoretically derived hypotheses concerning the concepts (or constructs) that are being measured’ (Carmines & Zeller, 1979: 23).

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Fig. 3.3 Eliminating feedback variables

choose the more appropriate treatment between T and X RFEi . Again this is just a suggestion from the subject matter expert. However, it does help them more quickly understand the potential structure of the underlying causal diagram. The value for zthresh is user-determined. As the values of P ij are standard normal. A value close to three would require the user to make a small number of decisions, while a value close to zero would evoke questions on nearly half the variables. We may also have a situation where the treatment predicts another variable chosen by the RFE. This variable predicts civil conflict well since the RFE has chosen it. This suggests a causal pathway from the chosen treatment to civil conflict that operates through this variable. For example, consider the birth weight paradox. Say we hypothesize a causal relationship between smoking T and infant mortality. Here birth weight would be an intermediate variable that should not be included in the final model. Our human-led algorithmic method would show smoking as a strong predictor of birth weight then leave it to the subject matter expert to not include birth weight in the final model based on domain knowledge. In Step five, we determine if P ij is larger than some userdetermined z threshold. If so, then X RFEi is potentially an intermediate variable between T and Y (Velentgas et al., 2013) (Fig. 3.4). We have eliminated the collider variable problem by making future conflict the variable we wish to explain. Recall that both the treatment

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Fig. 3.4 Eliminating intermediate variables

T and the target Y predict a collider X. But in our context, the target Y is future civil conflict. It would be Terminator-style science fiction if future conflict could predict something in the past. This may be something we have to come back to in future work where T and X are, e.g., contemporaneous. Now finally, we have a dataset that is correctly specified for causal analysis because it has: • Step 2. Identified confounders and risk factors. • Step 3. Identified potential alternate treatments from non-RFE variables. • Step 4. Identified RFE variables correlated with the treatment. • Step 5. Discovered any potential intermediate variable bias. At the same time, this algorithmic approach reduces the number of subject matter decisions that need to be made, thereby reducing SME biases. Most saliently, this process leads to variables choices that, by satisfying the backdoor criteria, let us interpret our next step as a causal pattern.

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Causal Results

3.3

We use Bayesian Additive Regression Trees (BART) to complete our causal apparatus (Schafer & Kang, 2008). To make causal inferences, however, the model specification has to meet three preconditions. First, we need the model to be an accurate depiction of the relationship between our treatments, T , and confounders, X , and output Y (future conflict). It is unlikely that the relationship between economic/political factors and future conflict can be accurately modeled with a linear or logistic relationship. Second and somewhat related to the first issue, we need the relationship between the confounders and conflict to be the same for those experiencing conflict and those not. While this issue may vary by treatment, it is likely a stronger assumption than one should make. Finally, we need the distributions of covariates between the treated and untreated groups to overlap sufficiently. EICS does this, if not perfectly, then at least systematically. These three factors, described in the EICS section above, are what makes our next step causal by ensuring that our model satisfies the backdoor criterion (Pearl, 2009). We use EICS identified variables to specify our models and then run algorithms to detect patterns in the data. 3.3.1

Partial Dependence Plots

Partial dependence plots map the possible values of the input variable of interest onto the observed incidence of civil conflict. In econometric terms they display the marginal effect of variable x k conditional on the observed values of all of the other variables, (x 1,-k , x 2,-k , … x n,-k ) Specifically, it plots the graph of the function: n  1  fˆ(x) = f xk , xi,−k , n i=1

 where the summand, f xk , xi,−k , is defined by: 

f (x) = ln[ p1 (x)] −

C 1  ln[ ps (x)] C s=1

for classification models and by the value of the target variable for regression models. In the equation, s indexes the classes of the target variable and C is the total number of classes. So, if an increase in a given

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factor, x k , increases the probability of recession (s = 1), then the value of the function plotted by the PDP also increases. Thus, partial dependence plots merely find data patterns that show how a variable of interest affects civil conflict. These are not causal patterns by themselves. It is the EICS process of model specification that, by systematically attempting to satisfy the backdoor criteria, allows us to interpret them as causal. 3.3.2

The Most Accurate Partial Dependence Plot

BART has become a prevalent method for causal inference on observational data (see Chipman et al., 2010 for details) primarily because BART-based approaches are more accurate when competing against other prediction modeling approaches (Dorie et al., 2019).9 BART is more accurate for many reasons. Algorithm parameters (tree depth, shrinkage factors) are unconstrained. However, these parameters are influenced both by the prior distributions, which can be set to values that work well for many problems and learned from the data in Markov Chain Monte Carlo (MCMC) process. Further, BART allows uncertainty calculations in a machine learning framework. BART’s tree models are often highly accurate, allowing for better confounder control than many parametric approaches. Finally, shallow trees avoid the issue of over modeling interaction factors that deeper trees can have. We show other technologies, for example, Logit, Inverse Probability Treatment Weighting (Robins et al., 2000), and BART (Hill, 2011), to compare our results using BART with propensity scores (Galagate, 2016). All these technologies use our EICS approach to model specification. Thus, all these technologies are “causal” because the model specification satisfies the backdoor criteria. Nevertheless, these causal algorithms may vary quite widely in identifying marginal effects. Thus the question of accuracy conferred by BART is quite essential. There are many well-validated R packages for this approach; we use the bart_est function in the causaldrf package, version 0.3. The “bart_est” function simply calls the “bart” function from the “BayesTree” package 9 Dorie uses 5 BART-only algorithms: BART-regular, BART with cross-validated priors, Bart with multiple MCMC starting points (at percentile intervals), and another Bart with multiple MCMC starting points (at symmetric intervals), and Bart with a propensity score included.

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and constructs a pdp from the predicted values. Specifically, bart_est estimates a BART model with the following options: 1. The learning sample matrix of predictor variables (treatment and controls), “x.train,” is the (n by k) matrix of observed values for the predictors in the outcome model. 2. The learning sample vector of outcomes, “y.train,” is the (n by 1) vector of observed values for the outcome (conflict). 3. The test sample matrix of predictor variables, “x.test,” is built by from the following steps: • Create a sequence of s hypothetical values for the treatment, T i = {T 1 , …, T s } • For each i, create a matrix X i = [T i ; X -T ], where the first column repeats the ith value of the treatment in the sequence for all n observations. • Stack the matrices of test predictors for the different values of the treatment. The bart_est function estimates the BART model using the learning data in (1) and (2). It then predicts the outcome (probability of conflict) using the stacked test sample for different values of T . Finally, for each hypothetical value of T , the function averages the predictions of the model over all of the observations, holding the values of the controls at their observed values. We then plot these average predictions. Unlike other estimators, BART does not estimate the dosage level but instead makes adjustments to the priors over the whole dataset to fit an unbiased prediction for the response (Chipman et al., 2010). For continuous treatments, the BART fits the response by comparing the outcomes at their actual treatment levels to their outcomes if the dosage had been zero (Hill, 2011). In the rest of the book, we focus on the BART model with the propensity score included as an additional covariate (Dorie et al., 2019).10 This estimator is computationally intensive and therefore takes some time to estimate. However, given recent advances in computational hardware, this is not a serious constraint for datasets social scientists use. 10 Having estimated the IPTW model, we extract the weights from this model. These weights equal the inverse of the propensity score. To get the propensity score itself, we merely take the reciprocal of this and include it in the BART algorithm’s model formula.

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The Complete Methodological Workflow

Schafer and Kang (2008) has suggested that. A popular strategy for ruling out alternatives is to measure as many confounders—pretreatment variables that may be related to both the treatment and the response—as possible and then estimate what the difference in average response between treated and untreated persons would be if the average values of the confounders in both groups were equal. This idea, which underlies classical analysis of covariance (ANCOVA) and regression, still prevails in many areas of social and behavioral science.

However, to tie down a causal link, ideally, we should have a series of parallel universes where we could expose each country to a different treatment and measure whether they experienced conflict in the future. We would then be able to measure the average causal effect (ACE) given differences in the outcomes in the two universes. That is causal inferences can be made only if the same individuals respond differently to a treatment (Rubin, 2004). Schafer and Kang (2008) suggests that they are related concepts that only under special conditions may produce identical values for a population causal effect. This chapter outlines (see Fig. 3.5) a human-guided machine-learned algorithmic approach to finding these special conditions. Our innovation is to delineate a systematic way, EICS, for interpreting a partial dependence plot causally.

Data PreparaƟon

RFE

Alternate Treatments

Fig. 3.5 Methodological workflow

Treatment Correlates

Intermediate Variables

BART ParƟal Dependence Plot

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3.4

Takeaways

1. EICS is a systematic way to justify whether a model specification satisfies the Backdoor Criteria. 2. BART and other ways to derive partial dependence plots can only be interpreted causally if the backdoor criteria are satisfied. 3. BART is the most accurate way to identify the marginal effect of a variable on some target like Civil Conflict.

References Abadie, A., Diamond, A., & Hainmueller, J. (2011). Synth: An R package for synthetic control methods in comparative case studies. Journal of Statistical Software, 42(13). Abadie, A., & Gardeazabal, J. (2003). The economic costs of conflict: A case study of the Basque Country. American Economic Review, 93(1), 113–132. https://doi.org/10.1257/000282803321455188. Basuchoudhary, A., et al. (2018). Predicting hotspots: Using machine learning to understand civil conflict. Rowman & Littlefield. Carmines, E. G., & Zeller, R. A. (1979). Reliability and validity assessment. Sage. Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). BART: Bayesian additive regression trees. The Annals of Applied Statistics, 4(1), 266–298. Dorie, V., et al. (2019). Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition. Statistical Science, 34(1), 43–68. Galagate, D. (2016). Causal inference with a continuous treatment and outcome: Alternative estimators for parametric dose-response functions with applications. Doctoral dissertation, University of Maryland, College Park. Hernán, M. A. (2018). The C-word: Scientific euphemisms do not improve causal inference from observational data. American Journal of Public Health, 108(5), 616–619. Hernán, M. A., & Robins, J. M. (2020). Causal inference: What if . Chapman & Hill/CRC. Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1), 217–240. Imbens, G. W. (2015). Matching methods in practice: Three examples. Journal of Human Resources, 50(2), 373–419. Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., ... & Kenkel, B. (2020). caret: Classification and regression training. R package

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version 6.0–86. Available at: https://cran.r-project.org/web/packages/caret/ caret.Pdf. Accessed 20 March, 2020. Molnar, C. (2020). Interpretable machine learning. Lulu.com. Pearl, J. (1993). [Bayesian analysis in expert systems]: Comment: Graphical models, causality and intervention. Statistical Science, 8(3), 266–269. Pearl, J. (2009). Causality. Cambridge University Press. Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Basic Books. Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550–560. Rubin, D. B. (2004). Direct and indirect causal effects via potential outcomes. Scandinavian Journal of Statistics, 31(2), 161–170. Schafer, J. L., & Kang, J. (2008). Average causal effects from nonrandomized studies: A practical guide and simulated example. Psychological Methods, 13(4), 279. Velentgas, P., et al. (Eds.). (2013). Developing a protocol for observational comparative effectiveness research: A user’s guide. Government Printing Office. Zhao, Q., & Hastie, T. (2019). Causal interpretations of black-box models. Journal of Business and Economic Statistics, 39(1), 1–10. Zhao, Q., & Hastie, T. (2021). Causal interpretations of black-box models. Journal of Business & Economic Statistics, 39(1), 272–281.

CHAPTER 4

Constitutional Changes and Civil War

Abstract In this chapter, we show that the process of constitutionmaking can cause civil conflict, i.e., constitution-making itself can derail the peaceful intent of a constitution. Further, we show that the causal relationship between constitutional changes and civil conflict is non-linear in meaningful ways. Last, we offer that algorithm accuracy matters for policy success. Keywords Civil conflict · Major constitutional changes · Illustrating Empirically Informed Model Selection · Illustrating Partial Dependence Plots · Causal interpretations

Major constitutional changes is ranked 21st on our list of 65. Why choose this one? It is the first political variable to appear in the ranking—the previous twenty being social, economic, or demographic variables. That alone justifies a deeper causal look at the variable. At the same time, constitutions are at the very heart of what unifies polities—the opposite of civil conflict. That writing constitutions destined to promote peace should be a predictor of conflict appears to be both reasonable and strange, deserving consideration. Further recall that these 65 variables risk and confounder variables and potentially a starting point for the sufficient set we need to determine causality as described in Chapter 3. However, we © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Basuchoudhary et al., Identifying the Complex Causes of Civil War, https://doi.org/10.1007/978-3-030-81993-4_4

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do need to explore whether there may be alternative treatments that proxy constitutional changes in a more theoretically plausible way and eliminate highly correlated and intermediate variables. Last, we will make some important methodological points; our technology reveals the nature of this variable’s nonlinear causal effect on civil conflict without any a priori assumptions, data drives theory, and the choice of accurate predictive technology really matters.

4.1 Empirically Informed Covariate Selection Results These results follow the methodological pathway we introduced in Chapter 2. The reader will recall that the first step was to identify the most predictive variables. We identified 65 variables in Chapter 3. We now need to identify a sufficient set of variables and controls from among the 65 to eliminate intermediate and highly correlated variables. But before that, it is important to think whether constitutional changes are a plausible choice for a causal variable. In Chapter 2, we noted that the RFE process chose the most predictive variables. We suggested that the search for causality should begin within this group; after all, causal variables should be predictive even if predictive variables are not necessarily causal. Nevertheless, we noted that some variables not chosen by the RFE might predict one of the chosen variables. In which case, it may be plausible to think of the non-RFE variable as the real cause. Of course, from an algorithmic perspective, one might conclude that if the non-RFE variable was an important enough predictor, it would have been included in the RFE. Yet, it is important to check if there is a theoretically plausible reason to choose a different variable as an alternate. Our algorithm identifies these alternate choices.1 They are listed in Table 4.1. The first six variables do not appear to have any theoretical connection with constitutional changes. Regime Durability, Electoral Competitiveness, Purges, General Strikes, Coups, and Major Cabinet Changes may be plausible substitutes. Yet they have two strikes against them. The most obvious issue is that the algorithm did not choose them as an important predictor. Further, there is no obvious causal theoretical path

1 See Chapter 2 for a description of this process.

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Table 4.1 Alternate treatments? Variable name

Variable description

Lag.AG.PRD.LVSK.XD.5 Lag.EN.ATM.CO2E.KD.GD.5 Lag.FM.AST.NFRG.CN.5 Lag.SP.POP.2529.MA.5Y.5

Livestock production index (2004–2006 = 100) CO2 emissions (kg per 2010 US$ of GDP) Net foreign assets (current LCU) Population ages 25–29, male (% of male population) Age dependency ratio, old (% of working-age population) Agricultural raw materials exports (% of merchandise exports) Regime Durability Share of residual trade and GDP statistical discrepancy at current PPPs Price level of capital formation, price level of USA GDPo in 2011 = 1 Price level of imports (price level of USA GDPo in 2011 = 1 legislative index of electoral competitiveness Executive index of electoral competitiveness General Strikes Purges Number of Coups d’Etat

Lag.SP.POP.DPND.OL.5 Lag.TX.VAL.MRCH.R2.ZS.5 Lag.durable.5 Lag.csh_r.5 Lag.pl_i.5 Lag.pl_m.5 Lag.liec.5 Lag.eiec.5 Lag.domestic2.5 Lag.domestic5.5 Lag.polit03.5

from these variables to major constitutional changes. Of course, they do predict major constitutional changes. It is, therefore, reasonable to pursue possible connections with major constitutional changes. In fact, our algorithmic approach brings forward possible hypotheses that can be tested in the future. Nevertheless, without a clear theoretical connection nor predictive value, following up on these connections seems unreasonable for this first pass at identifying the causes of conflict. The previous paragraph makes an important point. The reader will note that our machine learning algorithm is human-assisted. Human reason and theory suggest that we should stick with constitutional changes rather than use one of the alternates indicated by the algorithm. Other researchers may indeed choose one of the alternates if they feel there is a plausible and robust theoretical reason. We do not use the word “strong” loosely in the previous sentence since eliminating the alternate variables from the RFE suggests that only a theoretically solid causal link should be sufficient to overcome these variables’ weak predictive value. It does

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not seem meaningful, for example, to suggest that electoral competitiveness or the dependency ratio can substitute for the process of constitution writing. On the other hand, the algorithm provides a valuable check—as we will note in the next chapter. The reader will notice this pattern in the remaining chapters. Algorithms have not yet replaced human judgment. This judgment is also vital because there is some value for not falling into the temptation of thinking of algorithms as perpetual recursive machines. If predictive variables have predictive variables, how far back do we go? theory and judgment must be the only answer at this time. The algorithm does not identify any intermediate or correlated variables in this case. Consequently, we can use the 65 variables identified by the RFE as a sufficient set of controls to test whether major constitutional changes cause civil conflict.

4.2

Do Constitutional Changes Cause Civil War?

The short answer is yes. Figure 4.1 shows the causal relationship between constitutional changes and civil conflict because the partial dependence plot is an artifact of the careful variable selection process we have developed, thereby fulfilling the backdoor criteria. While we show this relationship for other methodologies, we will focus on Bayesian Additive Regression Trees (BART) for all the reasons we highlight in Chapter 3. The reader will notice that most of the data we have is clustered between 0.0 and 0.6. These numbers represent a 5-year average. That is, 0.2 corresponds to one constitutional change in 5 years. Between 0.2 and 0.6, there is a gradual increase in the probability of conflict from about 20 to 40%; i.e. the likelihood of civil conflict doubles. Beyond 0.6 though, the likelihood of civil conflict stabilizes. But in this range, there are a lot less data as well. We will focus our attention on the regions where data is dense. Recall that Dorie’s (2019)simulation showed that BART with propensity scores is the most accurate way to model response surfaces. If so, then Logit models consistently overpredict the likelihood of conflict while the “inverse probability with treatment weight algorithm” actually suggests (alone among the techniques alluded to here) that the likelihood of civil conflict falls as the number of constitutional changes rise. Thus, the choice of algorithm clearly matters. Second, and with some trepidation

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Fig. 4.1 Constitutional changes cause conflict

about fewer data points, it seems more than 0.6 levels of constitutional changes don’t seem to affect conflict much. A more careful perusal of the BART figure suggests another inflection point at around 0.4. Given the relative thickness of data in this region, we may elevate this inflection point to make two points over the one at 0.6. First, that the causal relationship between major constitutional changes and civil war is nonlinear. Second, there may be tipping points in this relationship; the one at 0.4 and possibly 0.6. This section has identified a causal relationship between the number of major constitutional changes and future civil war. Recall that one of the differences in our approach to social science requires the evidence to do the talking. This empirically driven approach is not without theory; after all, our initial choice of variables are social, political, economic, demographic, and cultural variables.

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These are broad categories of variables that often explain human behaviors in many different contexts. That is to say, we are not looking at say tidal influences to explain civil conflict. Nevertheless, beyond this broad level of choice, we are letting the algorithm find variables that may cause conflict. We are then showcasing a consistent methodology to test for causality. The point here is not to claim that major constitutional changes are remarkable among the 65 variables that predict conflict. We chose that variable because it was the highest-ranked political variable among the 65 risk factors. Our algorithms are assisted by human intent. Does this result highlight certain aspects of the existing theory linking major constitutional changes to civil conflict? We turn to this matter in the next section.

4.3 Data-Driven Theory. How Might Constitutional Changes Cause a Civil War? Constitutional compacts are supposed to resolve conflict. In Hobbesian terms, constitutions are Leviathan. Ideally, though, and unlike Hobbes’s Leviathan, constitutional compacts are voluntary and constrained (pp. 16, Fukuyama, 2011). This combination should be a guarantor of peaceful coexistence. Yet, empirically, very little is known about the actual process of forming these constitutional compacts. In the previous section, we presented evidence that constitutional changes cause conflict. But why? In this section, we formulate a theoretical basis for our findings. Hobbes was responding to the English civil war in Leviathan. Primed by his context, he thought conflict was the natural state. Since no one wants to live in this state, he thought Leviathan was a rational—and more importantly—legitimate response. Locke makes a different case for a constitutional compact. He notes that no compact is legitimate unless entered into at a time of peace without violating any natural rights. Locke was not buying Leviathan, preferring some constrained and responsive monarchy instead. Kant and Rousseau were relatively unconcerned about how social contracts like constitutions were formed. Instead, they viewed the social contract as a way of judging existing laws on whether they achieved principles of justice. Rawls generalizes this position and represents the social contract as a way to systematically apply principles of justice that go beyond political constitutions to, for example, the socioeconomic institutions for wealth creation and distribution. In short, to all these thinkers, a social contract was a device, a thought experiment, to

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discover whether existing social institutions were just or not (Freeman, 2019). Rawls was not satisfied with Locke’s version of this thought experiment. Locke required people in the original state of nature to have full knowledge of self (to include things like their ethnicity, gender, social status, etc.). Locke assumes that people, given this self-knowledge and understanding of others, would abrogate their natural rights (in Locke’s case to a constitutional monarch aided by an elected assembly). Rawls points out the practical flaw in this conclusion. Developing criteria for judge whether a set of rules are just with such prior knowledge seems likely to result in criteria that justify an existing social system. This criticism has practical import, and we will return to it. Rawls tried to extricate his argument from Locke’s judgmental morass with his “veil of ignorance.” A Rawlsian person is to view any new rule without knowing her personal situation, e.g., gender or race but does know general things like how gravity works or the idea of supply and demand. This sort of person, therefore, is not tempted to bend the social contract in her favor. Nevertheless, even the Rawlsian approach remains a way to judge whether a constitutional contract is just or not—once one is proposed or already exists. Generally speaking, the bulk of traditional constitutional political economy theory focuses on constitutions as an end product. Rawls and then others like Binmore (1994) spend a considerable amount of additional effort thinking through the process of compliance with the social contract after it has been established. After all, the purpose of a constitution is to ensure people live in a just and, therefore, stable conflict-free society where individuals have a clear incentive to adhere to a constitutional contract’s broad principles. Rawls does have a reasonable way to come up with a just constitution that, in theory, solves Lockes problem with agents who know the nature of their status and, therefore, would choose to retain privilege while calling it justice. Yet reality looks more like Locke’s “original” state—thereby militating against both Locke and Rawls’s expectations that a just constitution is a rational outcome. When people do know their own status and relative power, they may want more or keep what they have. This reality makes either constitution writing or changing fraught with the taint of conflict. But the process of constitution building, or indeed any political process, is a “contest between interests” (Buchanan & Tullock, 1965).

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In Buchanan and Tullock’s work, future uncertainty is a binding force. I may have power today, but who knows about tomorrow; thus, I have an incentive to create a coordinated system that protects me if I lose power tomorrow. Nevertheless, since coordination is also costly, the process of coordination can lead to prisoners’ dilemmas while crafting the social contract. Now future uncertainty can lead to the coordinating process breaking down. Early in 2021, as a pandemic raged across the world, protests broke out in Haiti (and continue at the time of writing). The proximate cause for the protests was a constitutional change (NPR, 2021) that allowed the President two consecutive terms. Haiti has a history of “President for life.” For many Haitians giving a President two successive terms was a runway to a power grab. President Moise, perhaps unsurprisingly, supports this change. Others feel that people currently in power have far too much say in drafting the new constitution while excluding others. In short, constitutional changes are often viewed as, and indeed often are, redistributions of power. Thus whatever criterion, Rawlsian or anything else, one might use to justify a just constitution, any such judgment is a thought experiment regarding a document that already exists at least in someone’s imagination or in reality. This judgment is ex-post. But the process of actually developing a constitution does not happen behind a Rawlsian veil, people of flesh and blood with things to lose and gain write constitutions. A New York Times article (DePalma, 1997) points out the inherent dichotomy in the social contracts embodied in constitutions. Constitutions are supposed to embody consensus—after all, it is the expression of how free people choose to govern their social relationships. Yet, the process of writing constitutions is controversial. However, this dichotomy fades if one views constitution writing from the lens of a contest for power rather than a “documentation of outcomes” (Duchacek, 1973; Hart, 2001). From this point of view, it is perhaps unsurprising to find that constitutional changes can cause conflict. Further, constitutional changes to electoral rules and forms of government affect the political economy of growth and redistribution (Persson & Tabellini, 2004). Thus, constitutional changes have very real redistributive effects on people’s lives and well-being, who may therefore resent these changes enough to fight. Old constitutions sometimes lose relevance and need to be renegotiated. Or, new constitutions need to be written. Either way, entrenched procedures and relationships among people or peoples underpin the process of constitution writing. As we have noted above, this process

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Table 4.2 A constitutional compact model

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Cooperate Defect

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Cooperate

Defect

A, a B, 0

0, b D, d

redistributes power and resources because, for example, it generates questions about who is a citizen and even the meaning of citizenship (for a detailed discussion of such issues, see Finn, 2018). Whatever the reason, it seems plausible that people afraid of losing power and resources, and people wanting to gain power and resources might come into conflict with each other (Hart, 2001). Such selfish behavior may trump the kind of cooperation expected in a constitutional compact and help determine a theoretical pathway showing how constitutional changes can trigger civil conflict. Consider the following game structure. Two people, Row, and Column can cooperate and benefit from, say, a social contract. Or they may go their own selfish ways and defect into conflict (Table 4.2). Cooperation is beneficial—presumably, constitutions are beneficial; otherwise, why would anyone even think of it as a possibility! Now let us consider two cases here. Say, A > B > D > 0 and a > b > d > 0 for row and column players respectively. This payoff structure gives an assurance game. Both {Cooperate, Cooperate} and {Defect, Defect} are Nash Equilibria. The former is payoff dominant while the latter is risk dominant. Even if cooperation gives the better payoff and is a Nash Equilibrium, defection may be a stable outcome if Row and Column want to avoid the risk of getting a 0, a risk that becomes much more salient if they do not trust each other. In constitution writing, then there has to be a sense of trust between people. Yet, the very process of rewriting a constitution or coming up with a new one may seed distrust. For example, a constitutional change perceived to be harmful to a minority ethnic group could reduce trust between members of that group and, say, a majority group. Thus starting points, history matters for constitutional compacts. A Rawlsian original state behind a veil of ignorance may be a great thought experiment to judge a constitution. Still, it may be unrealistic when actual resources and even lives are at stake. If for some reason, trust has broken down, any past agreements may well end. In game-theoretic terms, there may be an equilibrium shift from Cooperate to Defect.

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A slight change in the payoff structure in Table 4.1 has a different pathway for how constitutional changes may cause civil conflict. Now say, B > A > D > 0 and b > a > d > 0. This is a classic prisoner’s dilemma. Cooperation is not even a possibility unless the game is repeated with some uncertainty. Suppose the game is repeated. Then cooperation can be maintained if participants sufficiently fear the likelihood of defection in the future. Of course, if the future is uncertain, so is the likelihood of the loss from future defection. This increases the likelihood that people will defect today and enter into conflict. In this case, rather than mistrust generated by the constitutional change process, future uncertainty leads to conflict. It should not be surprising that a constitution writing process involving people with different statuses can be fraught with uncertainty. Once again, constitution writing can cause civil conflict. Our results appear to fit the sort of theoretical model we have described above. More constitutional changes can either increase uncertainty or reduce trust among people if these changes are perceived as power grabs. In any case, an observer should expect to see an inflection point in the relationship between constitutional changes and civil conflict. The sheer number of constitutional changes generate uncertainty and distrust, thus causing conflict. Our finding, further, can elevate certain aspects of theory over others. Basuchoudhary et al. (2018) note that machine learning algorithms can distinguish between competing theories of civil conflict. Suppose variables salient for one theory are more predictive than variables pertaining to another theory. In that case, the theory with greater predictive accuracy is more likely to be better. Yet, they note too that prediction accuracy is not the same thing as causality. Here we have an additional, causal way of sifting among competing theories. Notice that the RFE process has elevated major constitutional changes as a better predictor of civil conflict over structural political variables encoded in, say, the Polity data set.2 This result suggests political structures matter less than the political process, a finding consistent with Basuchoudhary et al. (2018). Additionally, Kasfir (2010) suggests, for example, that it is hard to distinguish between the causes of civil conflict empirically. He highlights 2 The Polity project from the Center for Systematic Peace seeks to measure the democratic accountability of institutions of a country. It includes various measures of constraints on executive and legislative power, competitiveness of selection processes. It also includes measures of regime durability and change.

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that civil conflict may be an expression of sheer predation or a security dilemma. Our result presented in this section suggests that security dilemmas expressed in the constitution writing process are indeed a cause of conflict. A few concluding thoughts are relevant for this section. The reader will note that we have tried to articulate a theoretical justification for our result; constitutional changes cause civil conflict. This section, however, follows mostly theoretically agnostic empirical results. We are suggesting a process of inductive reasoning for developing robust theory. Second, to the best of our knowledge, our results are the first to empirically highlight the causal relationship between the process of constitution making and civil conflict. These results, in turn, reflect the deep impracticality of Rawlsian and other such thought experiments to judge whether a constitution is just. The sausage-making might make the taste of the result irrelevant. After all, if the purpose of a constitution is to live in a peaceful, just society, we should at least acknowledge that the act of writing a constitution, even if the end result is just in a Rawlsian sense, can lead to war.

4.4

Takeaways

1. The process of constitution-making can cause civil conflict. 2. The process of constitution can derail the peaceful intent of a constitution. Thus, the process of constitution-making influences whether the development of a peaceful and just constitution is even possible. 3. The causal relationship between constitutional changes and civil conflict is nonlinear. 4. Inflection points characterize the nonlinear relationship between civil conflict and constitutional changes. 5. Our approach develops theory from data, thus reducing bias. 6. Choosing BART matters since algorithmically derived relationships can vary quite widely.

References Basuchoudhary, A., Bang, J. T., Sen, T., & David, J. (2018). Predicting hotspots: Using machine learning to understand civil conflict. Rowman & Littlefield. Binmore, K. G. (1994). Game theory and the social contract: Just playing (Vol. 2). MIT Press.

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Buchanan, J. M., & Tullock, G. (1965). The calculus of consent: Logical foundations of constitutional democracy (Vol. 100). University of Michigan Press. Dorie, V. et al. (2019). Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition. Statistical Science, 34 (1), 43–68. Duchacek, I. (1973). Power maps: Comparative politics of constitutions. ABC Clio. DePalma, A. (1997, November 30). We, the people . . .; Constitutions are the new writers’ market. New York Times. Week in Review, 3. https://www.nyt imes.com/1997/11/30/weekinreview/we-the-people-constitutions-are-thenew-writers-market.html. Last accessed 4 February 2021. Finn, J. E. (2018). Some notes on inclusive constitution-making, citizenship and civic constitutionalism. Edward Elgar Publishing. Freeman, S.(2019). Original position. The Stanford encyclopedia of philosophy (Summer 2019 Edition), Edward N. Zalta (Ed.). https://plato.stanford.edu/ archives/sum2019/entries/original-position/. Fukuyama, F. (2011). The origins of political order: From prehuman times to the French Revolution. Farrar. Hart, V. (2001). Constitution-making and the transformation of conflict. Peace & Change, 26(2), 153–176. Kasfir, N. (2010). Two. Domestic anarchy, security dilemmas, and violent predation: Causes of failure. In When states fail (pp. 53–76). Princeton University Press. NPR (2021, February 3, 3:49 pm EDT). Haiti opens debate on proposed constitutional changes. https://www.pbs.org/newshour/world/haiti-opens-debateon-proposed-constitutional-changes. Accessed 4 February 2021. Persson, T., & Tabellini, G. (2004). Constitutions and economic policy. Journal of Economic Perspectives, 18(1), 75–98.

CHAPTER 5

Infant Mortality, State Capacity, Rents, and Civil War

Abstract This chapter shows how empirically informed covariate selection can help us choose treatments that may be more meaningful than the immediate predictors of conflict. Thus, our algorithmic approach allows interpretation and moves machine learning away from its “black-box” reputation. Further, we suggest that traditional parametric empirical techniques can be misleading and have dangerous policy implications. To be precise, we show that increased government consumption expenditures can increase conflict in some variable ranges, even if the average effect is to reduce conflict. Keywords Infant mortality · General government expenditures · State capacity

The reader will recall that we have structured our results-oriented chapters to illustrate a general point about our methodological approach and noting the causal impact of one of the RFE variables. This chapter focuses on infant mortality because the RFE ranks it as the second most important predictor of civil conflict (Table 2.1). Infant mortality is causally linked with economic growth (Kammerlander & Schulze, 2021). GDP and economic growth are of course one of the famous correlates of civil war. Thus, there are good empirical and theoretical reasons to study infant © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Basuchoudhary et al., Identifying the Complex Causes of Civil War, https://doi.org/10.1007/978-3-030-81993-4_5

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mortality in the context of civil war. In the process we will show how our Empirically Informed Covariate Selection process can point us toward meaningful and nuanced causal analysis beyond the immediate predictors. Specifically, we have chosen this variable not only because of its predictive importance but also because it illustrates pathways toward policy with nuanced but predictable consequences. Moreover, by comparing different predictive technologies, Logit with BART for example, we show that even causal models can be misleading—predictive accuracy matters.

5.1

EICS Results

The RFE identified 65 important predictors of civil conflict. We have consistently argued that while predictors should be interpreted causally, the search for causality should start among these predictors. Causal variables should predict even if all predictors are not causal. Thus, there is a very good reason to look closely at infant mortality. It is the second most important predictor of civil conflict in our robustly validated RFE. Yet, infant mortality is not easily amenable to a theoretically driven understanding of civil conflict, even if its role as a predictive variable seems plausible. Countries about to fall into civil war are probably not paragons of public health provision. But this last sentence is also a theoretical clue. It may be that a country with high infant mortality rates is more likely to fall into a civil war because it lacks the capacity to provide health care; this lack of state capacity may also pique the interest of rebel entrepreneurs. Then, infant mortality becomes symptomatic of weak state capacity. We will justify this notion further below. However, at this time, a couple of methodological notes are necessary. Notice first the process of interpreting our RFE results. The lack of interpretability is a key criticism of machine learning; the oft used “black-box” term comes to mind. Our workflow, however, incorporates human reasoning to make decisions about critical predictors that may be theoretically meaningful, or not. Therefore, we can prioritize theoretical models. This ability to prioritize is a departure from traditional empirics in economics. Typically, economists have tended to use theory to derive model specifications. In the best cases, economists take great care in showing causal links. Nevertheless, as is often the case with civil conflict research, these causal links are unstable because they may appear and disappear as model specifications change (Blattman & Miguel, 2010). Our approach brings

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stability to the process by clearly delineating a model specification process that drives modeling instead of models, by nature incomplete, driving specification. We use this methodology for all our causal examples, though we will explicitly explore this process in this chapter. Our algorithm reveals several treatment alternatives for infant mortality. These are shown in Table 5.1. Recall that these variables are predictors of infant mortality, suggesting they may cause infant mortality. So long as reason indicates that infant mortality is an implausible cause for civil conflict, these predictors of infant mortality can be reasonable alternative treatments. The RFE has not chosen the variables represented in Table 5.1. Thus, picking one of these as a theoretical alternative to infant mortality must pass two strong tests of reason. First, it should be reasonably associated with infant mortality. Second, there should be some reason to believe it could cause conflict. Technical cooperation grants may affect infant mortality and civil conflict, but we look at foreign aid more closely in the next chapter. Pollution certainly can cause infant mortality. Still, it stretches reason to think they will foment civil unrest. Many apocalyptic movies have this theme, but if they did matter in the way represented in the arts, the RFE would have chosen them. Price shocks (Consumer price index) can reasonably cause civil conflict, and indeed there is a long literature on this subject (see, for example, Blair et al., 2021). Therefore it could be a plausible causal alternative treatment. The same case can be made for the trade-related variables (see Blattman & Miguel, 2010 for a slightly dated review of the literature and Ciccone for an updated study focused on sub-Saharan Africa). The resource depletion variables (energy and forest depletion may also cause civil conflict, a notion backed by extensive literature too well-known to require citing.1 Of course, none of these variables were picked by the RFE as robust predictors of conflict, even though large chunks of academic research focuses on these and similar variables as essential factors

1 We should however note that these variables were not chosen by the RFE, somewhat dampening the “greed” argument. This is a topic we will come back to later in the book when we note the salience of demonstrations of grievance.

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Table 5.1 Alternative treatments for infant mortality Alternate variables

Variable description

Lag.BX.GRT.TECH.CD.WD.5 Lag.EN.ATM.CO2E.GF.KT.5

Technical Cooperation grants CO2 emissions from gaseous fuel consumption (kt) Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent) Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent) Consumer price index (2010 = 100) Fixed telephone subscriptions (per 100 people) General government final consumption expenditure (% of GDP) Adjusted savings: net forest depletion (current US$) Adjusted savings: energy depletion (current US$) Net primary income (Net income from abroad) (current US$) Primary school starting age (years) Secondary education, duration (years) Immunization, DPT (% of children ages 12–23 months) Population ages 15–19, male (% of male population) Population ages 25–29, male (% of female population) Population ages 30–34, female (% of male population) Rural population Urban population growth (annual %) Urban population Merchandise exports to low- and middle-income economies in South Asia (% of total merchandise exports) Share of merchandise imports at current PPPs Number of Seats, Largest Party in Legislature Secondary School Enrollment Per Capita (Scaling: 0.0001) Primary/Primary + Secondary School Enrollment (Scaling: 0.01)

Lag.EN.ATM.NOXE.AG.KT.CE.5 Lag.EN.ATM.NOXE.EG.KT.CE.5 Lag.FP.CPI.TOTL.5 Lag.IT.MLT.MAIN.P2.5 Lag.NE.CON.GOVT.ZS.5 Lag.NY.ADJ.DFOR.CD.5 Lag.NY.ADJ.DNGY.GN.ZS.5 Lag.NY.GSR.NFCY.CD.5 Lag.SE.PRM.AGES.5 Lag.SE.SEC.DURS.5 Lag.SH.IMM.IDPT.5 Lag.SP.POP.1519.MA.5Y.5 Lag.SP.POP.2529.MA.5Y.5 Lag.SP.POP.3034.FE.5Y.5 Lag.SP.RUR.TOTL.ZG.5 Lag.SP.URB.GROW.5 Lag.SP.URB.TOTL.5 Lag.TX.VAL.MRCH.R5.ZS.5

Lag.csh_m.5 Lag.legis01.5 Lag.school04.5 Lag.school07.5

(continued)

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Table 5.1 (continued) Alternate variables

Variable description

Lag.trade2.5

Imports Per Capita (Scaling: 0.01)

for explaining civil conflict. This is an interesting avenue to explore. We, however, do not choose to focus on these aspects in this book.2 The demographic variables (population and schooling) can plausibly be connected to conflict, but it seems unlikely they would reasonably be linked with infant mortality except as possible confounders. The lone variable representing political structure in this group of potential treatment alternates also seems to be an implausible replacement for infant mortality. So what is left? General government final consumption expenditures are an interesting alternative to infant mortality as a causal factor for civil conflict because it can represent an aspect of state capacity and affect infant mortality as a public good. We, therefore, choose to explore this variable’s causal link to civil conflict. Let us review our methodological progress to this point. We started with infant mortality because of its remarkable predictive salience. We have ended up choosing general government final consumption expenditures as an alternative treatment through an algorithm-assisted reasoning process. Now it remains to see whether government consumption expenditures can indeed cause conflict. We turn to these results next. Just like infant mortality, government consumption expenditures too may have alternative treatments. We ignore these as being too far afield from the predictive salience of infant mortality. This, too, is a judgment call, justified because neither government consumption expenditures nor its alternate treatments are included in the RFE. But the other two categories of variables, intermediates and correlates of the treatment variable, are relevant because they determine the sufficient set that fits the backdoor criterion for causal analysis.

2 Researchers choose what they may want to focus on all the time, we are merely laying this process out explicitly. We cannot explore every single causal variable in this book. We do intend to follow up these prospects in separate papers. We also invite other researchers on this path, citing us along the way!

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Table 5.2 Variables eliminated from the model Intermediate variables for Govt. consumption expenditures

Description

NE.GDI.TOTL.ZS NV.AGR.TOTL.ZS

Gross capital formation (% of GDP) Agriculture, forestry, and fishing, value added (% of GDP) Adjusted savings: education expenditure (% of GNI) Life expectancy at birth, total (years) Merchandise imports by the reporting economy, residual (% of total merchandise imports) Number of major constitutional changes

NY.ADJ.AEDU.GN.ZS SP.DYN.LE00.IN TM.VAL.MRCH.RS.ZS

polit04

Our algorithm identifies intermediate variables. These variables mediate the treatment’s effect on the target, thereby “diluting” any causal impact. Therefore, we eliminate them from the causal model specification. Nevertheless, some of these variables incite curiosity. That government consumption expenditure has an effect on life expectancy at birth increases our confidence in replacing infant mortality. Further, that consumption expenditures may impact capital formation is a variation on plain old Keynesian analysis. Except for polit04, the effect of consumption expenditures on the remaining variables is obvious. Yet, the impact of consumption expenditures on polit04 is interesting, though we note that our algorithm did not identify government expenditures as an alternative for polit04 in the previous chapter. There may be a rent-seeking story linking government expenditures and major constitutional changes that needs further exploration in future work (Table 5.2). Our algorithm identifies only one variable from the RFE, adjusted savings: education expenditure (% of GNI), that was highly correlated with government consumption expenditures. We eliminate this variable from our model as well. Now, once again, our model is well-specified. It includes the robustly predictive confounders and risk factors to have a sufficient set of controls for causal analysis.

5.2

Causal Analysis

We present the results of the causal BART estimator in Fig. 5.1. The dots

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Fig. 5.1 Partial dependence plot for government expenditures

at zero and one represent the distributions of observed conflicts (at one) and non-conflicts (at zero) along with the observed values of government expenditures. The solid line represents the predicted probabilities of conflict with the distribution of government expenditures, while the dotted line represents the prediction of a naïve, unweighted, univariate regression model. The BART results show that additional expenditures may increase the probability of conflict up to about 15% of GDP. This is a causal effect. Increasing consumption expenditures beyond 15% of GDP has a less perceptible dampening effect on civil conflict. We suggest that this result places the spotlight on state capacity as an explanation for civil conflict. Collier and Hoeffler (2005) have famously pointed out the salience of economic factors that lower the opportunity cost of war as proximate causes of war. Leaving aside the fact that it is quite hard to infer causality in this paper, the correlational precedence of economic factors over other competing explanations of conflict is still remarkable. We, too, find something similar. Our algorithm lends salience to economic “opportunity”

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variables over things like economic inequality, lack of democracy, ethnic and religious polarization. In fact, Collier and Hoeffler find a statistically significant correlation between civil conflict and ethnic dominance. Ethnicity is not a predictor of civil conflict in our robust predictive algorithm. Yet, that government consumption expenditure has a causal impact on civil conflict in a way that corroborates their position. Rebel armies, whatever the motivation for the rebellion, still need to be paid. Arms need to be bought. As Tacitus claimed, money is indeed the sinew of war. So what does a poor rebellion do? Get rents. Jonas Savimbi evolved from Maoism to US-supported freedom fighter to the darling of morally challenged diamond merchants in the space of a couple of decades. Nevertheless, there are many good reasons to be skeptical of a purely Collier style analysis (see, for example, Nathan, 2003). Van der Walle (2004) suggests the more nuanced view, generally accepted in the literature on state capacity and conflict, that states with limited revenue collecting abilities lack the capacity to fight off rebellion. This view generates a few questions. Why does the rebellion want to take over the state if it has a limited capacity to raise revenues? Would the rebellion not be better off controlling some resource-rich region and ignore the state? The Congo is a prime example of just such a stalemate as is the conflict in Southern Nigeria. In fact, if Van der Walle’s argument is correct, then an increase in state expenditures (an expression of state capacity) should see a reduction in conflict. A simple Logit model fitted to our data (the no weighting dotted line in Fig. 5.1) clearly shows how an increase in government expenditures reduces the likelihood of conflict. This is the sort of effect hitherto reported in the literature. It hides a meaningful degree of nuance though—even though we can infer causality based on our model specification. The more accurate BART shows a similar effect, but only after a sharp increase in the likelihood of conflict. Government consumption expenditure is an expression of state capacity, maybe just not entirely in the way normally described in the literature. This finding highlights how our algorithmic approach enables nuanced analysis of even well-accepted variables. Indeed, a purely parametric approach, particularly when used to formulate policy, can be quite misleading. The issue of state capacity and its effect on civil conflict is important. States need to have a capacity to deliver effective services to attain the World Health Organization’s Sustainable Development Goals (World Health Organization, 2016), which among other things, place a heavy

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emphasis on sustainable health financing.3 This delivery is possible only if there is a good administrative capability (Goldfinch et al., 2013). States should also have administrative and military capability to enforce peace deals (De Rouen et al., 2010). In fact, military capability is essential to the Weberian state for maintaining order (Goodwin & Skocpol, 1989). Thus, the idea of state capacity has multiple strands. Our algorithmic causal analysis detangles these strands and emphasizes some strands over others as causal explanations for intrastate conflict. The Weberian approach to state capacity is the crux of a state’s capacity to end rebellion, therefore claiming a centrality in empirical studies that link state capacity to civil conflict. From a purely rational choice perspective military strength increases the opportunity cost of conflict and should therefore reduce the risk of conflict. Our dataset, the one on which we let loose the RFE (though not quite the dogs of war!) includes variables like armed forces personnel (% of total labor force) and military expenditures as a percentage of GDP. Neither were salient enough predictors of civil war to make it into the parsimonious 65 variable lists that predicted conflict the best. This finding, of the dog that did not bark (Conan Doyle, 1893), is in itself remarkable. Our algorithm suggests that the military strand of state capacity does not even rise to the level where it could be considered for a potential causal effect. So, we now must turn to the other strands that form the conceptual basis for understanding state capacity. Fearon and Laitin (2003) suggest that administrative competence, that is a bureaucracy able to manage information, is essential to preventing conflict. Direct, empirical investigation of this idea (De Rouen & Sobek, 2004; Fearon, 2005) indeed suggests this link is justified. De Rouen and Sobek (2004) use ICRG’s index of bureaucratic quality to show that countries with higher quality bureaucracies have less conflict. We have the same variable in our dataset. However, it is not a salient predictor. This highlights another feature of our approach. A robustly validated RFE eliminates mere correlations that are subject to the tyranny of the p value. Better economic and legal institutions are an outcome of an efficient administrative mechanism. In fact, Fearon (2005) uses risk of expropriation and repudiation of government contracts (the ICRG’s investment 3 The Sustainable Development Goals have superseded the Millennium Development Goals which had placed an emphasis on infant mortality. The current goals aim at the same effect but now focus on the centrality of expenditures to achieve such goals. All these merely emphasizes the importance of government consumption spending.

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profile) to show a similar statistically significant correlation with civil conflict. Yet we find the same variable is not part of the RFE. The machine rejects the predictive value of these correlations. These correlations are not predictive, even after deploying sophisticated econometric causal techniques. What is the value of a cause that cannot predict an effect? The academic literature on civil conflict needs to come to serious terms with such criticism. What about institutions that project state capacity? The Polity index is the operational measure of this theoretical construct of state capacity. Hegre et al. (2001) square the polity 2 variable to show a u-shaped correlation that suggests that consolidated autocracies and democracies tend to be better able to fend of conflict. Fearon and Laitin find a similar result. Gates et al. (2006) delve a little deeper into this idea by formally measuring the “strength” of institutions. Strong democracies are competitive, with high participation levels, and checks on executive authority. Strong autocracies are the opposite. Most countries fall in some intermediate space. They construct the Scalar Index of Politics (SIP) conflict. We did not use the SIP, but CNTS includes the structural variables that have gone into creating SIP. We also include Polity variables in our data. Remarkably not a single political structure variable predicts conflict robustly. The literature on state capacity and conflict uses variables (the ones we have used as well) that possess construct validity (Hendrix, 2010). So the problem here is not measurement error. But it may be that these variables are too disaggregated. The theoretical idea of state capacity may really be a deeply interrelated amalgam of these variables. Hendrix (2010) does a factor analysis to suggest that some of these variables’ “clump” together in theoretically meaningful ways. His first factor, which he calls rational legality, captures the interrelatedness between bureaucratic/administrative capacity, economic development, and political democracy. The second, which he calls rentier autocraticness, consists of the interrelatedness between primary commodity exports, high state capture of economic resources, and low levels of democracy. His third factor, neopatrimoniality, combines the state’s extractive capacity and the extent of reliance on primary commodities and higher military expenditures. We do not use these amalgams, yet our results in this chapter capture the spirit of the idea that neopatrimonial desires to capture rents may be at work. In spite of the extensive literature on the resource curse (see for example Humphreys et al., 2007) we find no evidence that primary

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commodity exports isolated from general trade (for which we do find evidence, as we note in the next chapter) predict civil conflict. But primary commodities are not a perfect proxy for the state’s extractive capacity, Norway has not seen civil conflict in ages. The extractive capacity of the state is essential for any theoretical understanding of state capacity. After all the state needs money to wage war or keep the peace by providing public good. Primary commodities may make mainlining resources easier for states, but they do not seem to predict conflict. Of course, the ability to tax is another way for the state to extract resources. This idea of taxation is also fundamental to the theoretical basis of state formation (Tilly, 1975) just like the Weberian concept of a monopoly on violence. Yet what is the purpose of extraction if not to spend or loot? We have noted that military spending—and obvious destination for such extraction—is not predictively salient. Government consumption expenditures remain as a destination for extracted resources. Government consumption expenditures would cover both public good provision and building large palaces for political leaders. In an ideal world we would be able to disaggregate these destinations for extractive capacity. Nevertheless, the causal impact of government expenditures on civil conflict suggests that looking at the outcome of the state’s extractive capacity, government consumption expenditures, may be instructive. The direction of causality highlights two aspects of Hendrix’s (2010) factor analysis—the rentier autocracy combined with neopatrimonialism. Bratton and Van de Walle (1997) suggest that neopatrimonial dictators use public revenues to buy survival and potentially bypass accountability. International censure may be irrelevant when the people or a close selectorate coterie have bread and circuses.4 However, rents that can be used to buy at least the minimum necessary public support are also an opportunity. This may be why we find our curious result—that an initial increase in government consumption spending increases conflict. As government spending increases initially, i.e., when states are in the process of finding their feet in public spending, this fact alone makes the state a target as contenders realize how the state can conform to satisfy their desire for

4 Muammar Gaddafi’s sons and an inner circle lived pretty extravagantly (https://abc news.go.com/Blotter/inside-moammar-gadhafi-son-mutassim-million-dollar-years/story? id=12984138) but Gaddafi also spent money to unite a relatively fractionalized country under his rule (https://abcnews.go.com/Blotter/inside-moammar-gadhafi-son-mutassimmillion-dollar-years/story?id=12984138).

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power. Subsequently, if states survive this initial assault, further increases in government spending have a small but perceptible effect in lowering the likelihood of conflict. Presumably, in this range, public spending satisfies the social contract somewhat. So, what have we learnt? The lack of state capacity is a much-favored explanation for civil conflict. Yet we find that many of the theoretical aspects of this explanation, ranging from military expenditures to political structures, do not reach their empirical promise. This suggests that much of the empirical literature ignores something critical by ignoring predictions. Causes that cannot predict are suspect, and the less said of correlations the better. Yet one aspect of state capacity, the state’s ability to spend resources, robustly causes and predicts civil conflict, albeit in a nuanced way. To be precise, where a standard econometric approach may show that the average effect of government expenditures on civil conflict is to reduce it, a more predictively accurate machine learning approach like BART suggests nuance. The average effect hides the fact that government expenditures can increase conflict within a certain, low, range of government expenditures even though, beyond that range such expenditures can, also in theoretically meaningful ways, reduce the likelihood of conflict. This last finding is also important for international civil society in trying to end conflict. A policy, for example, increasing foreign aid to help increase consumption expenditure may have the wrong outcome. We therefore turn to explore such issues further in the next chapter.

5.3

Takeaways

1. Our algorithmic approach allows interpretation and moves machine learning away from its “black-box” reputation. 2. Parametric approaches can be misleading. This has dangerous implications for policy. Increased government consumption expenditures can increase conflict in some ranges of the variable even if the average effect is to reduce conflict. 3. The lack of state capacity is a well-known theoretical explanation for civil conflict. Our approach suggests that certain aspects of state capacity matter more for causing civil conflict than others. This nuance adds to the literature on the relationship between state capacity and civil conflict.

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References Blair, G., Christensen, D., & Rudkin, A. (2021). Do commodity price shocks cause armed conflict? A meta-analysis of natural experiments. American Political Science Review, 115(2), 709–716. Blattman, C., & Miguel, E. (2010). Civil war. Journal of Economic Literature, 48(1), 3–57. Bratton, M., & Van de Walle, N. (1997). Democratic experiments in Africa: Regime transitions in comparative perspective. Cambridge University Press. Ciccone, A. (2018). International commodity prices and civil war outbreak: New evidence for Sub-Saharan Africa and beyond (CEPR Discussion Paper No. DP12625). Available at SSRN: https://ssrn.com/abstract=3106829 Collier, P., & Hoeffler, A. (2005). Resource rents, governance, and conflict. Journal of Conflict Resolution, 49(4), 625–633. Conan Doyle, A. (1893). Silver Blaze. Memoirs of Sherlock Holmes. G. Newnes Ltd. De Rouen, K. R., Jr., & Sobek, D. (2004). The dynamics of civil war duration and outcome. Journal of Peace Research, 41(3), 303–320. De Rouen, K., Jr., Ferguson, M. J., Norton, S., Park, Y. H., Lea, J., & StreatBartlett, A. (2010). Civil war peace agreement implementation and state capacity. Journal of Peace Research, 47 (3), 333–346. Fearon, J. D. (2005). Primary commodity exports and civil war. Journal of Conflict Resolution, 49(4), 483–507. Fearon, J. D., & Laitin, D. D. (2003). Ethnicity, insurgency, and civil war. American Political Science Review, 97 (1), 75–90. Gates, S., Hegre, H., Jones, M. P., & Strand, H. (2006). Institutional inconsistency and political instability: Polity duration, 1800–2000. American Journal of Political Science, 50(4), 893–908. Goldfinch, S., De Rouen, K., Jr., & Pospieszna, P. (2013). Flying blind? Evidence for good governance public management reform agendas, implementation and outcomes in low income countries. Public Administration and Development, 33(1), 50–61. Goodwin, J., & Skocpol, T. (1989). Explaining revolutions in the contemporary Third World. Politics & Society, 17 (4), 489–509. Hegre, H., Ellingsen, T., Gates, S., & Gleditsch, N. P. (2001). Toward a democratic civil peace? Democracy, political change, and civil war, 1816–1992. American Political Science Review, 95(1), 33–48. Hendrix, C. S. (2010). Measuring state capacity: Theoretical and empirical implications for the study of civil conflict. Journal of Peace Research, 47 (3), 273–285. Humphreys, M., Sachs, J. D., Stiglitz, J. E., Humphreys, M., & Soros, G. (2007). Escaping the resource curse. Columbia university press.

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Kammerlander, A., & Schulze, G. (2021). Local economic growth and infant mortality (Working Paper). Nathan, L. (2003). The frightful inadequacy of most of the statistics: A critique of Collier and Hoeffler on causes of civil war. Track Two: Constructive Approaches to Community and Political Conflict, 12(5), 5–36. Tilly, C. (1975). Western state-making and theories of political transformation. In C. Tilly (Ed.), The formation of national states in Western Europe (p. 638). Princeton University Press. Van de Walle, N. (2004). The economic correlates of state failure: Taxes, foreign aid, and policies. When states fail: Causes and consequences (pp. 94–115). Princeton University Press. World Health Organization. (2016). Millennium Development Goals. https:// www.who.int/topics/millennium_development_goals/en/. Last accessed 21 Apr 2021.

CHAPTER 6

Foreign Aid and Civil Conflict

Abstract We focus on the effect of foreign aid on civil conflict because this is a well-trodden path in the conflict literature. We find that net secondary income increases conflict risk only for a specific range for that variable. Our finding suggests that there should be greater theoretical scrutiny for why only a small range of values for net secondary income increases the risk of conflict. We believe a game-theoretic approach models the interaction between donors and recipients to identify such equilibrium switches. Moreover, the sort of aid reflected in the current account may matter more than a broader definition of aid. Last we suggest that neither military aid nor foreign direct investment appears to have a causal impact on civil conflict, with important implications for policy. Keywords Net secondary income · Foreign aid · Strategic interactions and equilibrium switches · Foreign aid and peace policy

Net Secondary Income captures current account transfers with no quid pro quo, such as humanitarian aid grants.1 The RFE ranks Annual Net Secondary Income at the top. The RFE’s robustly validated backward elimination of less important variables gives us a certain confidence that 1 See Chapter 2 for a more formal definition.

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such preeminence is empirically justified. There is substantial literature on the relationship between foreign aid and civil conflict. The predictive salience and academic interest in the topic warrant a causal investigation. We will follow the expositional pattern in the last two chapters by discussing EICS (Empirically Informed Covariate Selection) followed by our results and a discussion placing our results in the empirical literature. Specifically, we highlight how machine learning, far from deserving its black-box reputation, provides a more nuanced interpretation of different strands of the civil conflict literature.

6.1

EICS and Specifying a Causal Model

As we noted in Chapter 3, the RFE identifies potential risk factors and confounders as we investigate the causal impact of net secondary income on civil conflict. Therefore, these need to be included in the predictive model as we move toward a causal interpretation of the partial dependence plot generated by BART. Variables in the RFE that may mediate the causal effect of the treatment, net secondary income, on civil conflict, and those that are correlated with the treatment need to be identified and then removed from the model. Finally, we need to look at potential alternate treatments that did not make the RFE cut but may be more theoretically justified candidates as a treatment variable than net secondary income. Of course, this last decision requires full theoretical justification to counter the lack of predictive salience. We turn to these potential alternates next. Net secondary income is a focused measure of international transactions that do not involve an explicit quid pro quo. Table 6.1 lists potential alternative treatments that appear to have no obvious theoretical connection to net secondary income or are broader, less focused categories of expenditure. A third category of alternates is remarkably like net secondary income but has not been chosen by the RFE. For example, in the first category, we have agricultural land and CO2 emissions. Political structure variables like the size of legislature may have plausible theoretical connections to civil conflict. Still, the RFE finds a limited empirical link to civil conflict since that variable is not part of the chosen few. More to the point, there does not appear to be a plausible theoretical link between political structure variables like numul or legis02 and net secondary income plausible enough to suggest replacing it as a treatment. In the second category of broader variables, we have GDP per capita, Gross domestic savings, etc. GDP per capita is a usual correlate of conflict,

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Table 6.1 Potential alternate treatments Variable name

Variable description

Lag.AG.LND.AGRI.K2.5 Lag.BM.GSR.MRCH.CD.5 Lag.BN.CAB.XOKA.CD.5 Lag.BN.GSR.FCTY.CD.5 Lag.BN.KAC.EOMS.CD.5 Lag.BX.GRT.TECH.CD.WD.5 Lag.EN.ATM.CO2E.GF.KT.5

Agricultural land (sq. km) Goods imports (BoP, current US$) Current account balance (% of GDP) Net primary income (BoP, current US$) Net errors and omissions (BoP, current US$) Technical cooperation grants (BoP, current US$) CO2 emissions from gaseous fuel consumption (kt) Methane emissions (kt of CO2 equivalent) Nitrous oxide emissions (thousand metric tons of CO2 equivalent) Aquaculture production (metric tons) Total reserves (includes gold, current US$) Net foreign assets (current LCU) Final consumption expenditure (% of GDP) Adjusted savings: energy depletion (current US$) GDP per capita (constant 2010 US$) Gross domestic savings (% of GDP) Net primary income (Net income from abroad) (current US$) Population ages 30–34, female (% of female population) Urban population (% of total population) Price level of exports, price level of US GDP in 2011 = 1 Opposition party 1 seats Opposition party 2 seats Unaligned parties’ seats Length of Time Country Has been Autocratic or Democratic Size of Legislature (Lower House) Exports Per Capita (Scaling: 0.01) Proportion of World Trade (Scaling: 0.00001)

Lag.EN.ATM.METH.EG.KT.CE.5 Lag.EN.ATM.NOXE.AG.KT.CE.5 Lag.ER.FSH.AQUA.MT.5 Lag.FI.RES.TOTL.CD.5 Lag.FM.AST.NFRG.CN.5 Lag.NE.CON.TOTL.ZS.5 Lag.NY.ADJ.DNGY.CD.5 Lag.NY.GDP.PCAP.KD.5 Lag.NY.GDS.TOTL.ZS.5 Lag.NY.GSR.NFCY.CD.5 Lag.SP.POP.3034.FE.5Y.5 Lag.SP.URB.TOTL.5 Lag.pl_x.5 Lag.opp1seat.5 Lag.opp2seat.5 Lag.numul.5 Lag.tensys_strict.5 Lag.legis02.5 Lag.trade4.5 Lag.trade5.5

standing for such things as state capacity or the opportunity cost of warfare. While GDP per capita may be a reasonable proxy for such things, EICS suggests it is a blunt instrument. This is an important distinction for model specification and leads to a key point. Our approach allows us to be more precise about the kinds of variables we should or should not include in a causal model. Last, variables like net foreign assets are linked to net

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Table 6.2 Variables that mediate the causal effect of net secondary income on civil conflict Intermediate variables

Variable description

Lag.EN.ATM.CO2E.PC.5 Lag.NE.GDI.TOTL.ZS.5 Lag.NV.AGR.TOTL.ZS.5

CO2 emissions (metric tons per capita) Gross capital formation (% of GDP) Agriculture, forestry, and fishing, value added (% of GDP) Merchandise imports from low- and middle-income economies in Europe & Central Asia (% of total merchandise imports) Primary School Enrollment Per Capita (Scaling: 0.0001)

Lag.TM.VAL.MRCH.R2.ZS.5

Lag.School02.5

secondary income, at least in the sense that both are entries in the balance of payment. Yet net secondary income is far more amenable to theoretical interpretation as a cause of civil conflict than net foreign assets. The RFE, by choosing net secondary income over net foreign assets, appears to agree. Thus, overall, we have no reason to think that replacing net secondary income with a non-RFE variable is warranted, even if our EICS algorithm has nudged us to investigate some of these variables. This is a critical contribution of our approach and helps us satisfy the backdoor criteria we emphasize in Chapter 3. Our next step in the EICS process is to eliminate RFE chosen variables that correlate to the treatment. Our EICS algorithm finds no such variable. In Chapter 3, we note that intermediate variables need to be eliminated from the causal model. These are listed in Table 6.2. We have intentionally gone through a process to identify the sufficient set of variables that satisfy the backdoor criteria for inferring a causal link between net secondary income and civil conflict. What is this causal link, and is it theoretically meaningful? We turn to this next.

6.2

Net Secondary Income Causes Civil Conflict?

BART with propensity scores is the most accurate way to represent the average dose response (Dorie et al., 2019). Further, given that we have taken the necessary steps to satisfy the backdoor criteria (and therefore the ignorability conditions) with EICS, Fig. 6.1 represents the causal relationship between net secondary income and civil conflict.

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Fig. 6.1 Net Secondary Income, causal pattern

Figure 6.1 reports three different partial dependence plots derived from three other techniques. All three represent causal relationships between net secondary income and civil conflict because all three utilize a sufficient set of variables to satisfy the backdoor criteria according to our judgment. Nevertheless, accuracy matters. The Logit approach shows a steady increase in the likelihood of conflict as net secondary income increases. This is an artifact of the modeling technique itself and is misleading. The more accurate BART with propensity scores shows a similar average relationship but highlights a clear equilibrium shift. Rather than a steady increase in the likelihood of conflict, it is more accurate to state that net secondary income has no influence on civil conflict except in a very short range. This is a crucial distinction.

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The empirical literature has focused mainly on identifying the correlates of civil conflict. This investigation strategy, while a good beginning, is misleading in many ways. Point parametric estimates of these correlations (or even our best attempts at teasing out causal Logit models) suggest that more conflict happens whenever a covariate changes even a little. This is not true. Many developing countries do not have civil conflict—indeed, most developing countries are not in conflict. Thus, the argument of poverty being a cause of conflict rings hollow (Gartzke, 1999). A more plausible understanding of the relationship between a covariate of civil conflict and conflict is that the relationship is nonlinear and/or there are equilibrium shifts. Here we note just such a relationship. This means that increases in net secondary income will have absolutely no effect on the likelihood of conflict for many countries. For some countries, though, a small increase in net secondary income will drastically increase the possibility of conflict. That is, a mere increase in net secondary income is insufficient to increase the likelihood of conflict, the range where this increase happens matters. BART has identified this range, and in so doing, identified the sorts of countries where aid would increase the risk of conflict and others where it would not. This knowledge is a powerful tool for policymakers. Yet questions remain. Why would net secondary income increase the likelihood of civil conflict? And why would it increase conflict only within a specific range? The answer to the first question has a well-worn raison d’etre. The answer to the second question is unknown but points to a particular benefit of our methodological approach. We will come back to the second question after highlighting how answering the first question freshens and focuses our understanding of civil conflict. Net secondary income has two major current account components (IMF, 2008). Any grant that is not a loan is recorded in this section. Thus, it is a measure of foreign aid, and unlike official development assistance, it includes military grants and humanitarian aid. It also includes remittances from abroad. Thus, as a theoretical construct, net secondary income straddles the literature on the relationship between foreign aid and civil conflict. Even though the formal definition of net secondary income emphasizes that this balance of payment category records current account transactions without any quid pro quo, the fact is aid is non-random. That is, aid is a political or strategic decision. Empirically this means that giving aid to a

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country is an endogenous decision (Findley, 2018). Thus, model specification is critically important to determine any causal links between aid (or net secondary income) and civil conflict. We suggest that an empirically informed covariate selection (EICS) system like ours can resolve some of the controversies regarding the link between aid and civil conflict by specifying a model as correctly as humanly possible. Let us unpack this statement. Does aid increase the risk of civil war? The answer in the literature is unclear. The consensus seems to be it depends. Factors like governance quality, preexisting poverty, inequality, and ethnic heterogeneity matter for the effect of aid on economic outcomes (Burnside & Dollar, 2000; Clemens et al., 2012; Collier, 2007). Since the link between economic outcomes and civil conflict is already well-established, one might conclude that the effect of net secondary income (a measure of aid) on civil conflict is contextual. More direct attempts suggest this contextuality as well. Besley and Persson (2011) find that incoherent institutions make aid more likely to lead to conflict. Strange et al. (2017), on the other hand, find that conflict is less likely when aid is suddenly withdrawn because non-traditional donors like China step in to fill the gap, making aid shocks less likely to increase the risk of conflict. The point here is that the effect of aid on conflict seems to depend quite crucially on how a model is specified. Following our methodology for including all possible variables that might matter and then selecting only variables that are predictive (and therefore likely risk factors or confounders) while eliminating collider, intermediate, and correlated variables formalize the model selection process in a less randomly (and potentially biasedly) fractured way. Consequently, it is possible to understand the actual causal effect better since EICS has accounted for all possible contexts. For example, donor switching will be accounted for in net secondary income since it looks at a country’s balance of payments rather than selectively (and therefore possibly biasedly) chosen donor country data. It also includes private sector donations. Including private donations along with government donations is a more complete measure of total aid. Basically, by ranking net secondary income above official development assistance (see Table 2.1), the algorithm tells us that such private donations matter. It works toward increasing the risk of conflict for some, though not all, countries. Our result contradicts the spirit of Büthe et al. (2012) who suggest

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that private donations through NGO’s are actually done in the recipients best interest. Recall we did not start this process by asking whether aid matters or not—the traditional approach. We started with the algorithm telling us which measure of aid is most predictive after looking at hundreds of variables, quite the opposite. This methodological switch suggests that perhaps the academic literature in this area looked at the wrong “type” of aid. Further, the sensitivity of aid to institutions, for example, is entirely plausible. Yet, the RFE did not choose any of the usual institutional variables as a possible confounder or risk factor. At the very least, this suggests that the sensitivity of aid to weak institutions may be an artifact of a miss-specified model rather than an actual causal connection. Military assistance, potentially included in net secondary income, may have a plausible connection to civil conflict. This, therefore, may be one channel through which an increase in net secondary income can increase the risk of conflict for some countries. For example, military assistance may have increased violence in Colombia (Dube & Naidu, 2015) and terrorism (Bapat, 2011). Institutions here too may mediate the effect of military aid on conflict. For example, Boutton (2016) shows personalist dictatorships are more likely spaces for aid to lead to war. Yet, the role of military assistance alone driving conflict seems unlikely. Net official development assistance is the 8th most predictive variable identified by the RFE. Official development assistance excludes military assistance. We report the causal impact of official development assistance in Fig. 6.2.2 Note that here too, increases in foreign aid cause an increase in the likelihood of conflict. There is a relatively constant and increasing causal relationship between official development assistance and conflict risk. This relationship is very different than the equilibrium shift we see for net secondary income. Figures 6.1 and 6.2, taken together, suggest military aid is not the critical factor driving the causal relationship between aid and civil conflict. Nevertheless, the relationships in Figs. 6.1 and 6.2 indicate that given a thoughtful combination of theoretical considerations and an atheoretically validated model specification, overall, foreign aid increases the likelihood of civil conflict.

2 We follow the same EICS process for ODA but do not report the alternates, intermediates, and correlates, for space concerns. We are happy to share those with interested readers.

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Fig. 6.2 Causal effect of official development assistance on civil conflict

Remittances are another critical component of net secondary income. Unfortunately, remittances are not as well-studied as foreign aid as a potential source of conflict (Findley, 2018). Our algorithms suggest it should be. Remittances are more likely to be countercyclical, flowing into countries when the situation is the darkest. Regan and Frank (2014) indicate that remittances may lower the risk of conflict. Others suggest that remittances increase the risk of terrorist attacks, though Mughal and Anwar (2015) indicate that terrorism may increase the flow of remittances. Bang et al. (2019) note that remittances may make states less likely to respect the physical integrity rights of their citizens. Therefore, remittances may be a buffer that allows states to be more repressive because citizens are less likely to protest if they are flush with foreign cash. On the other hand, such repressive measures enabled by remittances may slowly

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but surely breed resentment among citizens who then might choose to fight. Yet, the literature on civil conflict seems to suggest that resentment and grievances do not cause conflict. We turn to this idea in the next chapter. Before we end this chapter, we need to make one last point about the variable that did not matter. Foreign direct investments (FDI) have often been suggested as a factor that reduces the risk of civil conflict. The argument here is that FDI by promoting economic development reduces the risk of war by increasing the opportunity cost of war. Of course, FDI is a crucial element of economic growth. Barbieri and Reuveny (2005) show that FDI directly reduces the risk of civil war. Collier (2011) notes that FDI may reduce the risk of civil war by promoting growth. Consistent with the literature on civil war in general, controversy abounds. The causality may be reversed—war may plausibly reduce FDI’s (Asiedu, 2006). On the other hand, private businesses may use aid as a signal to increase FDI (Bandyopadhyay et al., 2014). Whatever the various connections between FDI and civil conflict in the literature, our RFE does not include it among the most predictive variables. This is informative. Any variable that matters for civil conflict should be able to predictive. At the very least, any causal variable should be predictive. Thus, it seems unlikely that FDI is in any way causal. Or at least not causal in any way that matters. One question remains through all of this. Why does net secondary income affect civil conflict for only a limited range of values? We do not know. But our results suggest a way forward. Thinking of Fig. 6.1 as an equilibrium switch may be a plausible interpretation. This suggests some strategic interaction between recipient and donor countries that flips a conflict “switch.” Thus, our finding gives us a hint for future theoretical modeling of this relationship. Indeed, as we have noted in an earlier chapter, our approach suggests that a more inductive reasoning process may support the usual deductive reasoning used in the social sciences. We further offer that going from theory to evidence biases the search for evidence toward a particular view and focuses attention away from the truth. Social scientists far too often search for their keys under their favored streetlight. In the end, a few miscellaneous thoughts and caveats may be in order. The reader will have noticed that GDP of any sort is not a predictor of civil conflict among the variables picked through the robustly validated RFE process. Yet, many things that have an impact on the wealth of a

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country do matter. This suggests that the rampant use of GDP as a proxy for things ranging from state capacity to individual happiness (more on this in the next chapter) to the opportunity cost of war is unjustified. GDP is too blunt an instrument. Further, we should also note a significant caveat to our results here. At the beginning of this section, we suggested that foreign aid is nonrandom. That is to say, donors may look to the future likelihood of civil conflict to decide on aid. Thus, our starting position of ignoring collider variables because the future cannot affect the past may well be questionable. Net secondary income may well be just such a collider, suggesting that we should eliminate it from our causal models in some of the other chapters exploring the specific causes of civil conflict. Nevertheless, we do not. There is very little literature on whether the likelihood of future conflict drives current aid. What literature exists on related topics is also all over the place (see Findley, 2018, p. 371 for a summary of such literature). Thus, at this time, we did not feel theoretically bound to believe that the likelihood of future conflict affects current aid decisions in a meaningfully known way. Yet, we might be among the first to honestly point to such caveats because of our unique approach to model specification. Indeed, the literature on the relationship between aid and civil conflict, while noting the possibility that that model specification problems erroneously drive results, has done little to formalize the process of model specification.3 We provide a way out of this morass. Last, Findley (2018) has suggested that aid may activate some intermediate variable, which affects civil war. For example, Beath et al. (2017) suggest that aid helps win hearts and minds, making people more supportive of their government and less likely to rebel (Weintraub, 2016). Our EICS methodology identifies these sorts of variables. We have used our EICS methods to identify a robustly validated set of risk factors, confounders, intermediate variables, and variables correlated to the treatment to create a predictive model that satisfies the backdoor criterion. Consequently, we are more confident of the causal pattern of our results here than may be warranted in the rest of the literature.4 3 Flores and Nooruddin (2009) show that at least given their model specification there is no relationship between aid and conflict, thereby noting the problem with model specifications. 4 We do have a variable tracking hearts and minds – demonstrations against the government. Yet, our algorithm has not identified it as an intermediate variable for net secondary

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6.3

Takeaways

1. Our approach allows us to be more precise about the kinds of variables we should or should not include in a causal model. This may help resolve whether aid increases or decreases the risk of conflict. We find that net secondary income increases conflict risk only for a specific range for that variable. 2. Our finding here suggests that there should be greater theoretical scrutiny for why only a small range of values for net secondary income increases the risk of conflict. We believe a game-theoretic approach models the interaction between donors and recipients to identify such equilibrium switches. 3. The sort of aid reflected in the current account may matter more than a broader definition of aid. EICS helps us find variables that are more precisely linked to civil conflict. 4. Military aid may not be a conflict driver. 5. Foreign direct investment may not matter in helping explain conflict.

References Asiedu, E. (2006). Foreign direct investment in Africa: The role of natural resources, market size, government policy, institutions and political instability. World Economy, 29(1), 63–77. Bandyopadhyay, S., Sandler, T., & Younas, J. (2014). Foreign direct investment, aid, and terrorism. Oxford Economic Papers, 66(1), 25–50. Bang, J. T., Mandal, A., & Mitra, A. (2019). Transnational remittances and state protection of human rights: A case for caution. Economic Notes: Review of Banking, Finance and Monetary Economics, 48(3), e12147. Bapat, N. A. (2011). Transnational terrorism, US military aid, and the incentive to misrepresent. Journal of Peace Research, 48(3), 303–318. Barbieri, K., & Reuveny, R. (2005). Economic globalization and civil war. The Journal of Politics, 67 (4), 1228–1247. Beath, A., Christia, F., & Enikolopov, R. (2017). Can development programs counter insurgencies?: Evidence from a field experiment in Afghanistan (MIT

income. Perhaps if we had a more direct measure of hearts and minds in our dataset Beath et al.’s paper would suggest we leave it out of our model as a matter of theory. We however do not, and our model still retains remarkably low prediction errors. Perhaps hearts and minds matter less than we think.

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Political Science Department Research Paper No. 2011–14). Available at SSRN: https://ssrn.com/abstract=1809677 or https://doi.org/10.2139/ ssrn.1809677 Besley, T., & Persson, T. (2011). The logic of political violence. The Quarterly Journal of Economics, 126(3), 1411–1445. Boutton, A. (2016). Of terrorism and revenue: Why foreign aid exacerbates terrorism in personalist regimes. Conflict Management and Peace Science. Burnside, C., & Dollar, D. (2000). Aid, policies, and growth. American Economic Review, 90(4), 847–868. Büthe, T., Major, S., & De Mello e Souza, A. (2012). The politics of private foreign aid: Humanitarian principles, economic development objectives, and organizational interests in NGO private aid allocation. International Organization, 66(4), 571–607. Clemens, M. A., Radelet, S., Bhavnani, R. R., & Bazzi, S. (2012). Counting chickens when they hatch: Timing and the effects of aid on growth. The Economic Journal, 122(561), 590–617. Collier, P. (2007). Bottom billion. The Blackwell Encyclopedia of Sociology, 23, 1–3. Collier, P. (2011). Wars, guns and votes: Democracy in dangerous places. Random House. Dorie, V., et al. (2019). Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition. Statistical Science, 34(1), 43–68. Dube, O., & Naidu, S. (2015). Bases, bullets, and ballots: The effect of US military aid on political conflict in Colombia. The Journal of Politics, 77 (1), 249–267. Findley, M. G. (2018). Does foreign aid build peace? Annual Review of Political Science, 21, 359–384. Flores, T. E., & Nooruddin, I. (2009). Financing the peace: Evaluating World Bank post-conflict assistance programs. The Review of International Organizations, 4(1), 1–27. Gartzke, E. (1999). War is in the Error Term. International Organization, 567– 587. IMF (2008). SNA, Chapter 8, The Redistribution of Income Accounts. Mughal, M. Y., & Anwar, A. I. (2015). Do migrant remittances react to bouts of terrorism? Defence and Peace Economics, 26(6), 567–582. Regan, P. M., & Frank, R. W. (2014). Migrant remittances and the onset of civil war. Conflict Management and Peace Science, 31(5), 502–520. Strange, A. M., Dreher, A., Fuchs, A., Parks, B., & Tierney, M. J. (2017). Tracking underreported financial flows: China’s development finance and the aid–conflict nexus revisited. Journal of Conflict Resolution, 61(5), 935–963. Weintraub, M. (2016). Do all good things go together? Development assistance and insurgent violence in civil war. The Journal of Politics, 78(4), 989–1002.

CHAPTER 7

Demonstrations, Grievance, and Civil Conflict

Abstract In this chapter, we show that Empirically Informed Covariate Selection can inform future directions of research. Specifically, we have identified how the search for alternate treatments tells us about the possible causes of demonstrations. We find that civil conflict may be part of a continuum of political contention, suggesting that grievance remains a contender as a cause for conflict. Last, as part of a trend with previous chapters, we show that the relative accuracy of different algorithms matters. The less accurate LOGIT, for example, posits a very different causal relationship between demonstrations and civil war than BART. This difference may be why the literature on civil war has not pursued grievance as a cause of conflict. Keywords Grievance matters · Predictive accuracy · Grievance causes conflict

The political economy of civil conflict tilts toward the idea that civil conflict is an economic enterprise. If it is not a direct attempt to loot resources, it is a matter of opportunity (Collier & Hoeffler, 2004; Fearon & Laitin, 2003). Yet, conventional wisdom suggests that emotions matter. A recent article on the civil war in Ethiopia makes this point quite dramatically, noting ethnic differences stoked by unequal participation in that © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Basuchoudhary et al., Identifying the Complex Causes of Civil War, https://doi.org/10.1007/978-3-030-81993-4_7

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nation’s remarkable growth story as a proximate cause (Kirby, 2021). What gives? We suggest that this controversy may well be an artifact of incorrect model specification. After all, a lot of theory suggests that grievances can play a role in fomenting civil conflict (see, for example, Cederman & Girardin, 2007 or Bodea & Elbadawi, 2007). Indeed, there is some direct evidence that grievances can lead to civil war (Chiba & Gleditsch, 2017). Our RFE ranks anti-government demonstrations as the 9th most important predictor of conflict (see Table 2.1). Arguably demonstrations are an expression of grievance. This chapter describes a causal link between peaceful anti-government demonstrations and the likelihood of civil conflict. CNTS measures one particular variable, anti-government demonstrations (domestic8), by collecting data on “Any peaceful public gathering of at least 100 people for the primary purpose of displaying or voicing their opposition to government policies or authority, excluding demonstrations of a distinctly anti-foreign nature.” The key idea here is that these are peaceful demonstrations. Bodea and Elbadawi (2007) have argued that civil war is part of a continuum of violence. Consequently, they code violent demonstrations, coups (successful and unsuccessful), and civil war as the dependent variable in a multinomial Logit model. Our result here suggests that this continuum of violence may be best understood as series of touchpoints— where once anti-government demonstrations start, even if peaceful, they are likely to lead to civil war. As always, we will delve into the EICS process to be systematic about our model specification to satisfy the backdoor criteria. Then we will address our causal result. We have chosen to investigate this variable to highlight a strand of the civil conflict literature that deserves greater scrutiny and interest than it has generated so far. This too is a strength of our methodological approach.

7.1 EICS, Model Specification, Anti-government Demonstrations We will first explore the possibility that there may be alternative treatments for demonstration among the variables not included in the RFE. The reader will recall how we identify alternate treatments from Chapter 3. This table highlights important predictors for anti-government demonstrations. The RFE did not choose the variables in Table 7.1 as

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Table 7.1 Alternate treatments for demonstrations Variables

Variable description

Lag.AG.LND.AGRI.K2.5 Lag.EN.ATM.CO2E.SF.ZS.5

Agricultural land (sq. km) CO2 emissions from solid fuel consumption (% of total) Net foreign assets (current LCU) General government final consumption expenditure (% of GDP) External balance on goods and services (% of GDP) Coal rents (% of GDP) Death rate, crude (per 1,000 people) Population ages 30–34, female (% of female population) Urban population Merchandise imports from high-income economies (% of total merchandise imports) Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports) Regime Durability Exchange rate, national currency/USD (market + estimated) Price level of the capital stock, price level of USA in 2011 = 1 Opposition party other seats General Strikes Purges Size of Legislature/Number of Seats, Largest Party (Scaling: 0.01) Primary/Primary + Secondary School Enrollment (Scaling: 0.01)

Lag.FM.AST.NFRG.CN.5 Lag.NE.CON.GOVT.ZS.5 Lag.NE.RSB.GNFS.ZS.5 Lag.NY.GDP.COAL.RT.ZS.5 Lag.SP.DYN.CDRT.IN.5 Lag.SP.POP.3034.FE.5Y.5 Lag.SP.URB.TOTL.5 Lag.TM.VAL.MRCH.HI.ZS.5 Lag.TM.VAL.MRCH.R5.ZS.5

Lag.durable.5 Lag.xr.5 Lag.pl_n.5 Lag.oppothst.5 Lag.domestic2.5 Lag.domestic5.5 Lag.legis07.5 Lag.school07.5

important predictors of civil conflict, but they do predict anti-government demonstrations. Thus, potentially the variables listed in Table 7.1 could serve as alternatives for demonstrations if there is a meaningful reason to do so that outweighs the lack of predictive salience for civil war. We do not believe this to be the case. However, the variables themselves are instructive in outlining the sorts of grievances that may motivate demonstrations. Agricultural land and carbon emissions may indicate a tension between industrial development and traditional agriculture, or potentially land reform issues. Coal rents may point to the role of primary resource rent

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distribution problems. Coal rents in India tend to accrue to elites rather than the first people who live where the mines are. This tension has undoubtedly contributed to the simmering Maoist rebellion in eastern India (Shah, 2019). Trade shocks can exacerbate economic inequality, which in turn may get people angry and out in the streets. This may explain the salience of the trade-related variables in predicting demonstrations. Of course, variables like regime durability and the structure of legislatures may point to simmering discontent with a status quo that is just not working for people. Our point here is that many of the variables noted here may well be the proximate causes for demonstrations. Consequently, the variables listed in Table 7.1 would be important if we were writing a book explaining demonstrations. We are not. Moreover, we find no currently known theoretical reason to elevate one of these as a possible cause for civil conflict instead of demonstrations. Nevertheless, suppose future work can empirically and theoretically justify that one of these variables is the most important cause for demonstrations. In that case, that variable may indeed be a candidate as an alternate for demonstrations. Our algorithm identifies Riots (domestic6) as a feedback variable. CNTS describes riots as “any violent demonstration or clash of more than 100 citizens involving the use of physical force.” As a purely technical matter, we should eliminate this variable to satisfy the backdoor criteria. Yet, riots lead to demonstrations, and demonstrations lead to riots highlights the complex interplay of events as the clouds of war gather around a nation. We also need to eliminate intermediate variables, the ones chosen by the RFE but downstream from the treatment, in this case, peaceful, anti-government demonstrations. Nevertheless, these variables, too, are instructive while thinking about civil conflict as a continuum (Table 7.2). Our EICS process has been quite informative in addition to helping us specify a model that is amenable to meaningful causal analysis. The Table 7.2 Variables intervening between demonstrations and civil conflict

Variables

Variable description

Lag.domestic1.5 Lag.domestic4.5 Lag.domestic6.5

Assassinations Major Government Crises Riots

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search for alternates has identified potential sources for things that can build grievances among peoples. The search for feedback and intermediates suggests a complex relationship between violent and non-violent protests (the feedback loop). At the same time, peaceful demonstrations appear to be followed by violent protests, which mediate the predictive effect of peaceful demonstrations on civil conflict. It now merely remains to be seen whether peaceful demonstrations have a stand-alone causal effect on civil conflict.

7.2

Peaceful Demonstrations Cause Civil Conflict

As we noted at the beginning of this chapter, the civil conflict literature tilts to explicitly rational choice models of conflict that emphasize resources or the opportunity to rebel as a rationale for civil conflict. Yet, from a theoretical perspective, greed, opportunity, and grievance have been the three main approaches to understanding civil war (Cederman & Vogt, 2017). Of these, greed seems to have faded away as an explanation, leaving Fearon and Laitin’s (2003) lack of state capacity as the prime contender for a robust explanation of civil conflict. Weak states are an easier thing to topple than strong states, thus providing an opportunity for rebellion. In a previous chapter, we showed that certain aspects of state capacity might be more salient than others to explain civil conflict. A strand of the civil war literature, since Fearon and Laitin’s (2003) publication has refused to accept that grievances do not matter. Some of these researchers have argued that grievances are hard to measure, and the lack of evidence for grievance as a cause of civil conflict is a measurement problem (Blattman & Miguel, 2010). More specifically, individualist demographic indicators of grievance like ethnolinguistic fractionalization and income inequality may not be relevant indicators of grievance since civil wars are fought among groups of people (Cederman & Girardin, 2007). Thus, horizontal inequity between two groups may be more relevant for capturing grievances. Cederman et al. (2010) show that groups suffering status reversals are more likely to rebel. Cederman et al. (2011) then show that poorer groups are more likely to rebel against more prosperous groups. Both suggest that, though grievances are universal, they may only matter for civil conflict if expressed at the group level. Cederman et al. (2013) indicate that economic and political grievances at the group level increase the risk of civil war because mobilization for the war is easier.

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In short, grievances increase group cohesion and provide the opportunity for the collective action of rebellion. Demonstrations and riots are also artifacts of collective action driven by grievance (Hendrix et al., 2009). People may enter into contentious politics expressed in protests, demonstrations, and ultimately rebellion when people feel relatively deprived (Gurr, 1970). But mere feelings of deprivation alone may not culminate in collective action. The opportunity for collective action must exist as well. There need to be elements of the political environment that encourage people to form groups because they feel such collective action may be successful (Tarrow, 1994). Hendrix et al. (2009) note that the political opportunity landscape has two features, the ability to organize and the state’s capacity to manage discontent. They find some evidence for the latter in explaining how grievances stemming from food price shocks translated into widespread protests. Of note here is that food price shock, the independent variable, was a proxy for grievance. Kurer et al. (2019) suggest a correlation between unemployment risk, yet another proxy for grievance, and protesting. In the previous paragraph, we noted that studies show that grievances afflicting entire groups are more likely to lead to conflict. Grievances unite people and ease the collective action problem. Opp (1988), too, suggests that grievances, in this case stemming from the Chernobyl incident, increased social movement participation. We cannot measure grievance directly. Therefore, students of the subject often use observable proxies to measure the extent of grievance. The literature on the role of grievances in fomenting conflict so far suggests that the sort of grievances stemming from relative deprivation common to an entire group may be more likely to lead to civil conflict. Such grievances may also lead to demonstrations since they reduce the opportunity cost for collective action. Thus, demonstrations are an expression of grievance and can be used as a measurable proxy for grievances. Indeed, Bodea and Elbadawi (2007) have done just that with violent demonstrations. Peaceful demonstrations, too, are driven by grievances. If unobserved grievances motivate conflict, then there should be a causal link between observable expressions of grievances like peaceful demonstrations and civil conflict. We observe just such a causal link. The BART with propensity score is our technology of choice because, as we noted in Chapter 3, it is more accurate than other contenders for developing partial dependence plots (PDPs). Recall too that we can interpret all the PDPs in Fig. 7.1 causally since they, according

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Fig. 7.1 Peaceful demonstrations increase the risk of conflict

to our machine-assisted judgment, satisfy the backdoor criteria through the EICS process. Peaceful demonstrations do increase the risk of civil conflict. However, a comparison with Logit PDP is also instructive. Logit and other such parametric models are relatively more common in the civil war literature than our approach. The causal effect delineated by Logit is muted relative to BART. This relative muting may be why grievance has not been the focus of academic attention as, say, the rational choice “opportunity” argument. A more comprehensively specified model combined with more accurate technology can change how we think about the causes of conflict. Thus, we add to a small but growing literature that uses more modern technologies to suggest a pathway from grievance to peaceful demonstrations to violence (see, for example, Gustafson, 2020).

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On a technical level, this chapter also highlights why our approach of formally acknowledging the causal model building process through EICS and considering the relative accuracy of different predictive technologies can give us better insights into civil war. Most recent papers do take care to theoretically justify the idea of causality or, at least, do not claim to be causal. For the research that considers causality, the empirical models are ad hoc. So are the results. EICS brings discipline to this process and may help to get consistent results. The question of model accuracy should also become the norm. Studies do report robustness checks in many of the papers we cite here. Yet, these checks are geared toward describing a particular correlational methodology that may or may not be interpreted causally. The robustness checks consist of using different methods or different data sets to see if the results stay similar. We suggest that we achieve the same goal of judging robustness more consistently by comparing out-of-sample machine learning predictive accuracy. The machine learning approach automates the data sampling process and does it repeatedly. Thus, it checks whether results hold over different subsets of the data. Out-of-sample predictive accuracy (or the lack thereof) matters too.1 Our finding here is that the less accurate Logit models the relationship between demonstrations and civil war very differently than BART is instructive. Logit suggests there is virtually no relationship. BART suggests there is a steadily increasing relationship. Both can be interpreted causally because both have been developed through the same EICS process. Which relationship should we believe? The more accurate one, BART with the propensity scores. The current literature on civil conflict gives precedence to explanations that think about the opportunity of conflict over grievance. This makes a certain sense because human grievances are universal, civil war is less common. So, if grievances cause conflict, we should observe conflict universally. We simply do not. However, our results add to a growing literature that suggests a contextual reconciliation between grievances and opportunity as causes for civil war. Grievances do matter. But only if there are opportunities that increase the likelihood of collective action. This likelihood is expressed in peaceful demonstrations. And as we see here, peaceful demonstrations causally increase the risk of civil war.2 1 See Chapter 3. 2 We have also tested whether riots have a causal impact on civil conflict or not. It

does, in a manner very similar to peaceful protests. This is consistent with Bodea and

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Our finding has interesting policy concerns. If peaceful demonstrations cause civil war then the solution must be simple. Stop demonstrations. The current Myanmarese regime (at the time this book was written in 2021) is actually following this policy. Students of political contention and civil conflict have been acutely aware that repressive regimes are repressive because they squelch anti-government demonstrations. This may stave off civil war according to our finding here. However, perhaps context matters as well. At one level, the ability to squelch demonstrations is an expression of state power. Thus, Fearon and Laitin (2003) are not wrong in thinking that civil wars would be less likely in stronger countries. However, surely why people are rebelling should matter. Here, an understanding that demonstrations are an expression of grievance may be helpful. The solution to civil war then is not repression but being responsive to the grievances of different groups of people in a country. Canceling grievance as a possible cause for civil war may eliminate a real cause for conflict. Our result here suggests that we, as an academic community, cannot claim to eliminate grievance as a cause for conflict as long as variables like peaceful demonstrations can be shown to cause conflict. These sorts of links should be studied more consistently and in the wider context. Our methodological approach makes studying many variables at the same time easier.

7.3

Takeaways

1. EICS can inform future directions of research. This chapter has identified how the search for alternate treatments informs us about the possible causes of demonstrations. 2. Civil conflict may be part of a continuum of political contention. 3. Peaceful demonstrations cause civil conflict. This suggests that grievance remains a causal contender as a cause for conflict and should be considered in any study modeling the causes of conflict. 4. The relative accuracy of different algorithms matters. The less accurate Logit, for example, posits a very different causal relationship between demonstrations and civil war than BART. This difference

Elbadawi (2007). We do not report this here because that result is not new. We do have that graph in an appendix along with many of the other results we do not formally report for space limitations.

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may be why the literature on civil war has not pursued grievance as a cause of conflict. 5. Our methodology, by handling many variables at the same time makes it easier to study the many facets of conflict simultaneously and consistently. This approach provides much needed context, and therefore nuance, to the study of civil conflict.

References Blattman, C., & Miguel, E. (2010). Civil war. Journal of Economic Literature, 48(1), 3–57. Bodea, C., & Elbadawi, I. A. (2007). Riots, coups and civil war: Revisiting the greed and grievance debate. Cederman, L. E., & Girardin, L. (2007). Beyond fractionalization: Mapping ethnicity onto nationalist insurgencies. American Political Science Review, 173–185. Cederman, L. E., Gleditsch, K. S., & Buhaug, H. (2013). Inequality, grievances, and civil war. Cambridge University Press. Cederman, L. E., & Vogt, M. (2017). Dynamics and logics of civil war. Journal of Conflict Resolution, 61(9), 1992–2016. Cederman, L. E., Weidmann, N. B., & Gleditsch, K. S. (2011). Horizontal inequalities and ethnonationalist civil war: A global comparison. American Political Science Review, 105(3), 478–495. Cederman, L. E., Wimmer, A., & Min, B. (2010). Why do ethnic groups rebel? New Data and Analysis. World Politics, 62(1), 87–119. Chiba, D., & Gleditsch, K. S. (2017). The shape of things to come? Expanding the inequality and grievance model for civil war forecasts with event data. Journal of Peace Research, 54(2), 275–297. Collier, P., & Hoeffler, A. (2004). Greed and grievance in civil war. Oxford Economic Papers, 56(4), 563–595. Fearon, J. D., & Laitin, D. D. (2003). Ethnicity, insurgency, and civil war. American Political Science Review, 97 (1), 75–90. Gurr, T. R. (1970). Sources of rebellion in Western societies: Some quantitative evidence. The Annals of the American Academy of Political and Social Science, 391(1), 128–144. Gustafson, D. (2020). Hunger to violence: Explaining the violent escalation of non-violent demonstrations. Journal of Conflict Resolution, 64(6), 1121–1145.

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Hendrix, C., Haggard, S., & Magaloni, B. (2009, February). Grievance and opportunity: Food prices, political regime, and protest. In Presentation at the International Studies Association Convention. New York (August, 2009). Kirby, J. (2021, April 24). “Dying by blood or by hunger”: The war in Ethiopia’s Tigray region, explained. Vox. https://www.vox.com/22370629/ethiopia-tig ray-eritrea-amhara-war-ethnic-cleansing (Last Accessed 30 Apr 2021). Kurer, T., Häusermann, S., Wüest, B., & Enggist, M. (2019). Economic grievances and political protest. European Journal of Political Research, 58(3), 866–892. Shah, A. (2019). Nightmarch: Among India’s revolutionary guerrillas. University of Chicago Press. Tarrow, S. (1994). Power in movement: Social movements, collective action and politics. Cambridge University Press.

CHAPTER 8

Epilogue

Abstract This chapter summarizes the main findings of the book. Keywords Methodological insights · Civil conflict · New results for civil conflict

In the end we want to make a few overarching points while listing, in one place, the key takeaways from this book. First of all, the highest risk of conflict in all of our cases is still pretty low. Nevertheless, each of our chosen variables do increase the risk of conflict. Moreover, changes in the risk of conflict caused by these variables are nonlinear. That means some countries may be more at risk from certain variables than others. This nuance has been missing in the literature in conflict. Second, a variable’s predictive salience does not mean it is causal. But if it is causal it should be predictive. In this book we highlight how to operationalize this concept. We have developed a consistent framework to identify variables that cause and predict civil war. The literature on civil conflict is a mess of competing explanations. Our framework also allows us to sift between these competing ideas in a formally validated way. What we find is curious. There are two major strands of explanations; one focuses on whether there is an opportunity for rebellion and the other on the motivation for rebellion, greed © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Basuchoudhary et al., Identifying the Complex Causes of Civil War, https://doi.org/10.1007/978-3-030-81993-4_8

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or grievance. We find evidence for all of these. Our findings suggest that finding an explanation for civil conflict should be less a matter of competition among different explanations and more on how these explanations work together. This conclusion is consistent with what the partial dependence plots tell in our causal analysis. Different countries may fall into civil conflict for different reasons. This has been part of our intuitive and predictive and intuitive understanding of civil war in spite of attempts to find one single cause for conflict.1 In this book we now have a formal causal analysis that suggests this holistic, yet nuanced view is true. In what follows we have tried to summarize our key insights from this book. We organize these insights as (A) Methodological and (B) Contributions to the literature on civil conflict.

8.1 8.1.1

Insights

Methodological Insights:

1. Empirically Informed Covariate Selection (EICS) is a systematic way to justify whether a model specification satisfies the backdoor criteria. 2. Bayesian Additive Regression Trees (BART) and other ways to derive partial dependence plots can only be interpreted causally if the backdoor criteria are satisfied. 3. BART is the most predictively accurate way to identify the marginal effect of a variable on some target like civil conflict. 4. Our approach develops theory from data, thus reducing bias. 5. Choosing BART matters since algorithmically derived relationships can vary quite widely. 6. Our algorithmic approach allows interpretation and moves machine learning away from its “black-box” reputation. 7. Parametric approaches can be misleading. This has dangerous implications for policy. For example, increased government consumption expenditures can increase conflict in some ranges of the variable even if the average effect is to reduce conflict. 8. Our approach allows us to be more precise about the kinds of variables we should or should not include in a causal model. This may 1 Basuchoudhary, A., Bang, J. T., & Shughart II, W. F. Predicting State Failure: Different Pathways into the Abyss.

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help resolve whether aid increases or decreases the risk of conflict. We find that net secondary income increases conflict risk only for a specific range for that variable. 9. Our finding here suggests that there should be greater theoretical scrutiny for why only a small range of values for net secondary income increases the risk of conflict. We believe a game-theoretic approach models the interaction between donors and recipients to identify such equilibrium switches. 10. EICS can inform future directions of research. This chapter has identified how the search for alternate treatments informs us about the possible causes of demonstrations. 11. The relative accuracy of different algorithms matters. The less accurate Logit, for example, posits a very different causal relationship between demonstrations and civil war than BART. This difference may be why the literature on civil war has not pursued grievance as a cause of conflict. 12. Our methodology, by handling many variables at the same time makes it easier to study the many facets of conflict simultaneously and consistently. This approach provides much needed context, and therefore nuance, to the study of civil conflict. 13. The relative accuracy of different algorithms matters. The less accurate logit, for example, posits a very different causal relationship between demonstrations and civil war than BART. This difference may be why the literature on civil war has not pursued grievance as a cause of conflict. 8.1.2

Implications for the Civil Conflict Literature

1. The process of constitution-making can cause civil conflict. 2. The process of constitution can derail the peaceful intent of a constitution. Thus, the process of constitution-making influences whether the development of a peaceful and just constitution is even possible. 3. The causal relationship between constitutional changes and civil conflict is nonlinear. 4. Inflection points characterize the nonlinear relationship between civil conflict and constitutional changes.

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5. The lack of state capacity is a well-known theoretical explanation for civil conflict. Our approach suggests that certain aspects of state capacity matter more for causing civil conflict than others. This nuance adds to the literature on the relationship between state capacity and civil conflict. 6. The sort of aid reflected in the current account may matter more than a broader definition of aid. EICS helps us find variables that are more precisely linked to civil conflict. 7. Military aid may not drive conflict. 8. Foreign direct investment may not matter in helping explain conflict. 9. Civil conflict may be part of a continuum of political contention. 10. Peaceful demonstrations cause civil conflict. This suggests that grievance remains a causal contender as a cause for conflict and should be considered in any study modeling the causes of conflict. 11. Our methodology, by handling many variables at the same time makes it easier to study the many facets of conflict simultaneously and consistently. This approach provides much needed context, and therefore nuance, to the study of civil conflict.

Appendices

Appendix 1 The 261 Variables Considered for Feature Extraction Names Agricultural land (sq. km) Agricultural land (% of land area) Arable land (hectares per person) Arable land (% of land area) Permanent cropland (% of land area) Land area (sq. km) Crop production index (2004–2006 = 100) Food production index (2004–2006 = 100) Livestock production index (2004–2006 = 100) Cereal yield (kg per hectare) Autonomous regions Goods imports (BoP, current US$) Current account balance (BoP, current US$) Net primary income (BoP, current US$) Net errors and omissions (BoP, current US$) Net secondary income (BoP, current US$) Grants, excluding technical cooperation (BoP, current US$) Technical cooperation grants (BoP, current US$) Foreign direct investment, net inflows (BoP, current US$) Foreign direct investment, net inflows (% of GDP) Measure of checks and balance in legislatures (continued)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 A. Basuchoudhary et al., Identifying the Complex Causes of Civil War, https://doi.org/10.1007/978-3-030-81993-4

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APPENDICES

(continued) Names Share of household consumption at current PPPs Share of government consumption at current PPPs Share of gross capital formation at current PPPs Share of merchandise imports at current PPPs Share of residual trade and GDP statistical discrepancy at current PPPs Share of merchandise exports at current PPPs Average depreciation rate of the capital stock % Annual Increase: Population (Scaling: 0.01) % Annual Increase: Population Density (Scaling: 0.01) % Annual Increase: Imports Per Capita (Scaling: 0.01) % Annual Increase: Exports Per Capita (Scaling: 0.01) % Annual Increase: Telephones Per Capita (Scaling: 0.01) % Annual Increase: Primary School Enrollment Per Capita (Scaling: 0.01) % Annual Increase: Secondary School Enrollment Per Capita (Scaling: 0.01) % Annual Increase: Primary + Secondary School Enrollment Per Capita (Scaling: 0.01) % Annual Increase: Gross Domestic Product Per Capita (Scaling: 0.01) % Annual Increase: Gross National Product Per Capita (Scaling: 0.01) Assassinations General Strikes Government Crises Purges Riots Anti-Government Demonstrations Net official development assistance and official aid received (constant 2015 US$) Regime Durability Official/Principal Exchange Rate, Local Currency/ $US (Scaling: 0.01) Executive index of electoral competition CO2 emissions from gaseous fuel consumption (kt) CO2 emissions from gaseous fuel consumption (% of total) CO2 emissions (kg per 2010 US$ of GDP) CO2 emissions from liquid fuel consumption (% of total) CO2 emissions (metric tons per capita) CO2 emissions from solid fuel consumption (% of total) Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent) Total greenhouse gas emissions (kt of CO2 equivalent) Methane emissions (kt of CO2 equivalent) in agriculture Methane emissions (% change from 1990) Methane emissions in energy sector (thousand metric tons of CO2 equivalent) Energy related methane emissions (% of total) Nitrous oxide emissions (thousand metric tons of CO2 equivalent) in agriculture Nitrous oxide emissions (% change from 1990) in agriculture Nitrous oxide emissions (thousand metric tons of CO2 equivalent), energy sector (continued)

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(continued) Names Nitrous oxide emissions (% of total), energy sector Population in largest city Population in the largest city (% of urban population) Energy Production, Metric Tons Oil Equivalent (Scaling: 1000) Aquaculture fisheries production (metric tons) Capture fisheries production (metric tons) Total fisheries production (metric tons) Executive party nationalist Executive party regional Executive party religious Executive party right/left/center Executive party rural Executive election Total reserves (includes gold, current US$) Total reserves minus gold (current US$) Executive finite term? Net foreign assets (current LCU) Consumer price index (2010 = 100) Inflation, consumer prices (annual %) Government party 1 seats Government party 2 seats Government party 3 seats Government party 3 vote share Government other seats Government party other vote share 0/1/2: relative price data for consumption, investment, and government is extrapolated (0), benchmark (1), or interpolated (2) 0/1: the observation on pl_gdpe or pl_gdpo is not an outlier (0) or an outlier (1) 0/1/2: relative price data for exports and imports is extrapolated (0), benchmark (1), or interpolated (2) 0/1: the exchange rate is market-based (0) or estimated (1) Electric Power Production (kwh) Per Capita (Scaling 0.1) Mobile cellular subscriptions (per 100 people) Fixed telephone subscriptions (per 100 people) Legislative election Number of Seats, Largest Party in Legislature Size of Legislature (Lower House) Party Coalitions Party Legitimacy Size of Legislature/Number of Seats, Largest Party (Scaling: 0.01) Composite Index Legislative index of electoral competition Military Executive (continued)

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APPENDICES

(continued) Names General government final consumption expenditure (% of GDP) Households and NPISHs final consumption expenditure (% of GDP) Final consumption expenditure (% of GDP) Exports of goods and services (% of GDP) Gross capital formation (% of GDP) Imports of goods and services (% of GDP) External balance on goods and services (current LCU) External balance on goods and services (% of GDP) Trade (% of GDP) total number of seats held by all government parties Number of opposition seats Unaligned parties seats Total vote share of all government parties Agriculture, forestry, and fishing, value added (% of GDP) Industry (including construction), value added (% of GDP) Adjusted savings: education expenditure (% of GNI) Adjusted savings: carbon dioxide damage (% of GNI) Adjusted savings: net forest depletion (current US$) Adjusted savings: net forest depletion (% of GNI) Adjusted savings: consumption of fixed capital (% of GNI) Adjusted savings: mineral depletion (current US$) Adjusted savings: mineral depletion (% of GNI) Adjusted savings: energy depletion (current US$) Adjusted savings: energy depletion (% of GNI) Adjusted savings: natural resources depletion (% of GNI) Coal rents (% of GDP) Inflation, GDP deflator (annual %) GDP deflator (base year varies by country) Forest rents (% of GDP) Mineral rents (% of GDP) GDP growth (annual %) Natural gas rents (% of GDP) GDP per capita (constant 2010 US$) GDP per capita growth (annual %) GDP per capita (constant LCU) Oil rents (% of GDP) Total natural resources rents (% of GDP) Gross domestic savings (% of GDP) Net primary income (Net income from abroad) (current US$) Net primary income (Net income from abroad) (current LCU) Opposition party 1 seats Opposition party 2 seats Opposition party 3 seats (continued)

APPENDICES

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(continued) Names Opposition party other seats Vote Share of Opposition Parties Official exchange rate (LCU per US$, period average) Telephones, excluding Cellular Per Capita (Scaling: 0.00001) All Telephones, including Cellular, Per Capita (Scaling: 0.00001) Price level of government consumption, price level of USA GDPo in 2011 = 1 Price level of CGDPo (PPP/XR), price level of USA GDPo in 2011 = 1 Price level of capital formation, price level of USA GDPo in 2011 = 1 Price level of imports, price level of USA GDPo in 2011 = 1 Price level of the capital stock, price level of USA in 2011 = 1 Price level of exports, price level of USA GDPo in 2011 = 1 Type of Regime Number of Coups d’état Number of Major Constitutional Changes Head of State Premier Effective Executive (Type) Effective Executive (Selection) Degree of Parliamentary Responsibility Size of Cabinet Number of Major Cabinet Changes Changes in Effective Executive Legislative Effectiveness Legislative Selection Number of Legislative Elections Population Density (Scaling: 0.1) PITF: Revolutionary Civil War Primary School Enrollment Per Capita (Scaling: 0.0001) Secondary School Enrollment Per Capita (Scaling: 0.0001) Primary + Secondary School Enrollment Per Capita (Scaling: 0.0001) Primary/Primary + Secondary School Enrollment (Scaling: 0.01) University Enrollment Per Capita (Scaling: 0.0001) All School Enrollment Per Capita (Scaling: 0.0001) Preprimary education, duration (years) Primary school starting age (years) Primary education, duration (years) Primary education, pupils (% female) School enrollment, primary (% gross) Lower secondary school starting age (years) Secondary education, duration (years) Number of infant deaths Number of under-five deaths Number of neonatal deaths (continued)

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(continued) Names Immunization, DPT (% of children ages 12–23 months) Immunization, measles (% of children ages 12–23 months) Adolescent fertility rate (births per 1000 women ages 15–19) Birth rate, crude (per 1000 people) Life expectancy at birth, total (years) Population ages 0–14 (% of total population) Population ages 15–19, male (% of male population) Population ages 20–24, female (% of female population) Population ages 20–24, male (% of male population) Population ages 25–29, female (% of female population) Population ages 25–29, male (% of male population) Population ages 30–34, female (% of female population) Population ages 30–34, male (% of male population) Population ages 35–39, female (% of female population) Population ages 35–39, male (% of male population) Age dependency ratio (% of working-age population) Age dependency ratio, old (% of working-age population) Population growth (annual %) Population, total Population, female Population, female (% of total population) Population, male (% of total population) Rural population Rural population growth (annual %) Rural population (% of total population) Urban population growth (annual %) Urban population Urban population (% of total population) 4 measures of the percent of veto players who drop from the government in any given year, depending on the kind of legislature Presidential/parliamentary system 4 measures of the longest tenure of a veto player, depending on the kind of legislature How long has the country been autocratic or democratic, respectively Merchandise trade (% of GDP) Merchandise imports from economies in the Arab World (% of total merchandise imports) Merchandise imports from high-income economies (% of total merchandise imports) Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports) Merchandise imports from low- and middle-income economies in East Asia and Pacific (% of total merchandise imports) Merchandise imports from low- and middle-income economies in Europe and Central Asia (% of total merchandise imports) (continued)

APPENDICES

111

(continued) Names Merchandise imports from low- and middle-income economies in Latin America and the Caribbean (% of total merchandise imports) Merchandise imports from low- and middle-income economies in Middle East and North Africa (% of total merchandise imports) Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports) Merchandise imports from low- and middle-income economies in Sub-Saharan Africa (% of total merchandise imports) Merchandise imports by the reporting economy, residual (% of total merchandise imports) Import value index (2000 = 100) Total legislative seats Imports Per Capita (Scaling: 0.01) Exports Per Capita (Scaling: 0.01) Proportion of World Trade (Scaling: 0.00001) Merchandise exports to economies in the Arab World (% of total merchandise exports) Merchandise exports to high-income economies (% of total merchandise exports) Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports) Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports) Merchandise exports to low- and middle-income economies in Europe & Central Asia (% of total merchandise exports) Merchandise exports to low- and middle-income economies in Latin America & the Caribbean (% of total merchandise exports) Merchandise exports to low- and middle-income economies in Middle East & North Africa (% of total merchandise exports) Merchandise exports to low- and middle-income economies in South Asia (% of total merchandise exports) Merchandise exports to low- and middle-income economies in Sub-Saharan Africa (% of total merchandise exports) Merchandise exports by the reporting economy, residual (% of total merchandise exports) Merchandise exports by the reporting economy (current US$) unaligned parties vote share Exchange rate, national currency/USD (market estimated) Executive years in office

112

APPENDICES

Appendix 2: The Code A2.1 Pre-processing the Data Random Forest Missing Value Imputation Once we have merged the data from the various sources, we impute the missing values in the 353 variables in our dataset that do not include any of the conflict variables, country-id variables, or the variables that had very high levels of correlation with one or more of the other variables using the missForest function in the missForest package in R (R Core Team, 2021; Stekhoven & Bühlmann, 2012). set.seed(8976) ConflictCausalBook.missForest