In this article, we construct and test a micro-level event-centered approach to the study of armed conflict and behavioral responses in the general population. Event-centered approaches have been successfully used in the macro-political study of armed conflict but have not yet been adopted in micro-behavioral studies. The micro-level event-centered approach that we advocate here includes decomposition of a conflict into discrete political and violent events, examination of the mechanisms through which they affect behavior, and consideration of differential risks within the population. We focus on two mechanisms: instability and threat of harm. We test this approach empirically in the context of the recent decade-long armed conflict in Nepal, using detailed measurements of conflict-related events and a longitudinal study of first migration, first marriage, and first contraceptive use. Results demonstrate that different conflict-related events independently shaped migration, marriage, and childbearing and that they can simultaneously influence behaviors in opposing directions. We find that violent events increased migration, but political events slowed migration. Both violent and political events increased marriage and contraceptive use net of migration. Overall, this micro-level event-centered approach yields a significant advance for the study of how armed conflict affects civilian behavioral responses.

In the past decade, large scale mobile phone data have become available for the study of human movement patterns. These data hold an immense promise for understanding human behavior on a vast scale, and with a precision and accuracy never before possible with censuses, surveys or other existing data collection techniques. There is already a significant body of literature that has made key inroads into understanding human mobility using this exciting new data source, and there have been several different measures of mobility used. However, existing mobile phone based mobility measures are inconsistent, inaccurate, and confounded with social characteristics of local context. New measures would best be developed immediately as they will influence future studies of mobility using mobile phone data. In this article, we do exactly this. We discuss problems with existing mobile phone based measures of mobility and describe new methods for measuring mobility that address these concerns. Our measures of mobility, which incorporate both mobile phone records and detailed GIS data, are designed to address the spatial nature of human mobility, to remain independent of social characteristics of context, and to be comparable across geographic regions and time. We also contribute a discussion of the variety of uses for these new measures in developing a better understanding of how human mobility influences micro-level human behaviors and well-being, and macro-level social organization and change.

Although sociologists have identified education as likely determinant of migration, the ways in which education affects migration are unclear and empirical results are disparate. This paper addresses the relationship between educational attainment, enrolment, and migration, focusing on the role of gender and how it changes with evolving social contexts. Using empirical analyses based in Nepal, results indicate that educational attainment has positive effects and enrolment has negative effects on out-migration and including enrolment in the model increases the effect of attainment. In the case of women, with the changing role of gender, increased education and labor force participation, the affect of educational attainment changes drastically over time, from almost no effect, to a strong positive effect. Consideration of enrolment, and the role of gender in education, employment, and marriage may help to explain the disparate results in past research on education and migration.

Research shows a strong association between traumatic life experience and mental health and important gender differences in that relationship in the Western European Diaspora, but much less is known about these relationships in other settings. We investigate these relationships in a poor rural Asian setting that recently experienced a decade-long armed conflict. We use data from 400 adult interviews in rural Nepal. The measures come from World Mental Health survey instruments clinically validated for this study population to measure Depression, Post Traumatic Stress Disorder (PTSD), and Intermittent Explosive Disorder (IED). Our results demonstrate that traumatic life experience significantly increases the likelihood of mental health disorders in this setting and that these traumatic experiences have a larger effect on the mental health of women than men. These findings offer important clues regarding the potential mechanisms producing gender differences in mental health in many settings.

With the aim to contribute to humanitarian response to disasters and violent events, scientists have proposed the development of analytical tools that could identify emergency events in real-time, using mobile phone data. The assumption is that dramatic and discrete changes in behavior, measured with mobile phone data, will indicate extreme events. In this study, we propose an efficient system for spatiotemporal detection of behavioral anomalies from mobile phone data and compare sites with behavioral anomalies to an extensive database of emergency and non-emergency events in Rwanda. Our methodology successfully captures anomalous behavioral patterns associated with a broad range of events, from religious and official holidays to earthquakes, floods, violence against civilians and protests. Our results suggest that human behavioral responses to extreme events are complex and multi-dimensional, including extreme increases and decreases in both calling and movement behaviors. We also find significant temporal and spatial variance in responses to extreme events. Our behavioral anomaly detection system and extensive discussion of results are a significant contribution to the long-term project of creating an effective real-time event detection system with mobile phone data and we discuss the implications of our findings for future research to this end.