Aktuelle Publikationen

What are the social legacies of civil war and how do they differ for men and women? Despite a growing body of research on civil war consequences, the social legacies of conflict remain among the least understood impacts of war. Furthermore, empirical evidence on the distinct effects wartime violence has on men and women is scarce. Quantitative research mostly overlooks the gendered experiences, consequences, and potential benefits of conflict. The aim of my dissertation is to contribute to research on gendered civil war legacies by combining observational and experimental micro-level evidence in different post-conflict settings. The overarching question of my dissertation is addressed in three self-contained essays which test theories of the gendered impacts of war.

Chapter 2 asks whether civil war (dis)empowers women and explores the causal relationship between civil war and women’s labor force participation by leveraging the arbitrary Côte d'Ivoire--Burkina Faso border as setting for a natural experiment. Using a regression discontinuity design, I find that Ivoirian women are 25 percentage points less likely to work outside the home post-war. I investigate three potential mechanisms and argue that the decline in female labor force participation might be explained by a shift towards more traditional gender norms and a reduction in women's bargaining power.

Chapter 3 investigates social (dis)trust in post-war Sri Lanka and analyzes a list experiment to explore the causal relationship between war-related sexual violence and intra- and inter-ethnic group trust. Combining the list experiment with survey data of the Tamil population, I find evidence that war-related sexual violence affects trust decisions of men and women differently. Although both Tamil men and women lose trust in fellow Tamils, female victims are more trusting towards their ethnic out-group. Possible explanations might be that both context of sexual violence and coping strategies differ by gender.

Chapter 4 focuses on social (dis)integration in the Democratic Republic of Congo and explores how forced recruitment shapes male ex-combatants' violent behavior. Based on survey data, I present evidence that former forced recruits commit significantly more violence against their intimate partners and their children compared to voluntary recruits. Using structural equation modeling, I scrutinize the combatant socialization mechanism to show that more intense exposure to violence as part of armed groups and ensuing mental health problems mediate this relationship.

Taken together, my dissertation highlights the importance of systematic micro-level evidence as well as the significance of applying a gender perspective to conflict research. I argue that it is necessary to account for gendered war experiences and consequences, both in theory-building and methodology, as well as in policy-making. This thesis contributes to different literatures on the consequences of armed conflict, the social implications of violence for individuals, and the risks of recurring violence. In chapter 5, I derive several implications and suggest avenues forward for academic research and policy-making.

This dissertation addresses one of the most serious risks associated with automated decision-making: bias. This is not a new phenomenon, and decisions have always been biased, but automated decision-making multiplies the risks in many ways. The main challenges are: How can we detect biases? Who should be held accountable for biased predictions? And how can biases be mitigated or corrected? The three studies within this dissertation help answer these questions by emphasizing the importance of monitoring our own machine learning (ML) pipelines, auditing third party prediction systems, and exposing the potential abuse of predictive algorithms when given sensitive data.

The first paper (section 2) addresses the question of how to direct ML users to high-performing, robust, and fair models. ML systems have been shown to harm human lives via discrimination, distortion, exploitation, or misjudgment. Although bias is often associated with malicious behavior, this is not always the case. Inductive biases, for example, such as knowledge about parameter ranges or priors can help to stabilize a model optimization process. Furthermore, decomposing into statistical bias and variance, allows for model selection with minimum future risk. Since "all models are wrong, but some are useful", we should analyze as many biases in ML as feasible before putting faith in our predictions.

The second paper (section 3) addresses the question about how to audit recommender bias on social media. The goal of this experiment is to quantify the causes of algorithmic filter bubbles by analyzing amplification bias in the recommender system of Twitter. Using simulation of human behavior with bots we can show that 'filter bubbles' exist and that they add an additional layer of bias to 'echo chambers'. More precisely, the algorithm responded far more strongly to bots that actually engage with content than to bots that just follow human accounts. This demonstrates that the Twitter algorithm significantly depends on human interactions to adapt to preferences of its users. This has serious consequences since users may be unaware of the large personalization bias that happens when they like or share content.

The third paper (section 4) addresses the question whether online communication is predictive of offline political behavior. We can predict the party affiliation and turnout likelihood of a person with fair accuracy using a unique dataset consisting of thousands of ordinary citizens, including their Twitter statuses, integrated with public US voter registration files. Our results show social media communication is sufficiently biased to provide information about attitudes and political behavior of an average person in the real world. We demonstrate how, in addition to us, political, commercial, or bad faith actors may acquire this sensitive data to build prediction models, for example, to influence a customers retail journey or perhaps worse discourage them from voting on scale.

Biases can limit the potential of ML for business and society by cultivating distrust and delivering distorting or discriminating results. However, if our societies can (1) implement effective data privacy regulations (2) require internal debaising steps and encourage external independent auditing (3) educate the broader public of biases and ways to report them (4) and invest in training interdisciplinary computational scientists, we may be better prepared for negative consequences of the next industrial revolution.

In the present work I answer pressing questions on the nature of economic statecraft and economic leverage. I provide an integrated model of economic coercion and military statecraft within an interstate bargaining model. Furthermore, this thesis identifies and discusses two failure points of economic statecraft: lack of trade leverage and sanction busting by third parties. This thesis consists of five chapters, starting with an extended theoretical overview and literature review. The main body of work is structured into three substantive chapters, each representing a unique and independent contribution. The final chapter summarizes the results and spells out scientific as well as policy conclusions.

The first chapter motivates and frames the thesis by reference to current developments in international relations. In addition, it gives an overview of the previous literature on trade, its connection to war, economic sanctions, and the multipolarity of the international system. Drawing from the collective understanding of economic statecraft, I draw out the overall research question of this thesis: What is the nature of economic leverage and economic statecraft? Furthermore, which factors determine the success and failure of economic statecraft? The three substantive chapters in this work then proceed to shine light on all of these questions.

According to conventional wisdom, domestic political concerns govern trade policies. In chapter 2, I develop a theoretical model that reconsiders the role of trade policy as a foreign policy instrument, arguing that the choice between protectionism and other foreign policy instruments depends on the nature and level of economic interdependence. Economic interdependence empowers states to use trade and investment as a bargaining chip during interstate disputes, but trade wars and sanctions can also "use up'' this leverage and thus make a violent conflict more likely. This theoretical argument is supported by a case study of pre-World War I Balkan politics as well as a game theoretical model of bargaining that includes both economic policy and military deterrence. The corresponding chapter extends both the currently existing literature on trade policy by taking into consideration national security considerations, and the commercial liberalism literature by incorporating determination of trade levels via an endogenous foreign policy choice.

The third chapter further extends the empirical literature on the trade-conflict nexus so as to include economic statecraft. Traditionally, this literature has been divided into the realist and the liberal schools of thought. While the realist position is that trade either has negative or no effect at all on peace, realists argue that trade indeed ensures peace. In order to cross this divide, I argue that trade interdependence leads to peace in general, even though states still can and do exploit asymmetries. To this end, I construct empirical measures of trade leverage and economic coercion. I further argue that the presence or absence of trade leverage determines whether states choose economic coercion or military statecraft when faced with an international crisis. This is supported by empirical testing of the theoretically derived hypotheses on a data set of post-World War II trade flows and conflict indicators. I conclude that trade leverage leads states to prefer economic coercion, but a lack of trade leverage makes it more likely that they choose war to resolve their differences.

Chapter 4 focuses on the issue of sanction busting. Sanctions are one common instrument of economic statecraft, and sanction busting is a major obstacle to the their success. Sanctions research has largely neglected the impact of sanctions on trade with third countries, focusing much more on the relationship between the sender and the target. Up to now, no systematic overview of the factors that determine the diversion of trade between the sender and its target towards third countries during a sanction case exists. To fill the gap, in chapter 4 I first formulate a theoretical account of sanction busting that focuses on the attributes of potentially sanction busting third countries, rather than the relationship between the sender and the target. In particular, I point out that the state capacity of sanction busting countries has been previously overlooked. This leads to two complementary explanations of sanction busting: politically motivated sanction busting driven by political calculations and market based sanction busting driven by economic incentives. I empirically test the hypotheses derived from this theoretical argument, employing different measures of state capacity and comparing their effect on sanction busting with that of political alignment. Measures of state capacity as well as political alignment are both associated with the prevalence of sanction busting, with state capacity consistently being the more important factor. The weaker a third country’s state capacity, the more prone it is to become a jurisdiction through which sanction busting trade is conducted. Finally, I show that including measures of state capacity in supervised machine learning algorithms meaningfully improves out of sample predictions of sanction busting trade flows. I conclude by suggesting that taking into account state capacity when designing sanctions will improve sanction success compared to focusing solely on the political alignment of potential sanction busters. In the final chapter, I summarize my contribution to our collective understanding of economic statecraft. In addition, I draw out concrete policy prescriptions as well as potential avenues for future research.

The main contribution of this thesis lies in its theoretical definition of economic leverage as a function of economic costs states can impose upon each other. This definition is complemented empirically by a formulation of trade leverage as a function of relative market exposures of two interacting states. Beyond this vital definitional work, my thesis shows that economic leverage broadly, and trade leverage narrowly, can lead states to choose economic coercion over war. On the flipside, economic coercion in the form of sanctions may fail if low state capacity sanction busters succeed in sabotaging the sanction sender's efforts.