Loyola University Chicago

The Graduate School


Political Science

In discussions of the implications of climate change in both politics and academia, conjecture and some supporting evidence is suggestive of a link between climate and conflict. The resource scarcity model is widely used in this literature, and new data and innovative methods allow for the relationship to be tested more comprehensibly. These new data and methods offer an opportunity to clarify causal paths. Building on the extant literature, I parse integral components of the causal pathway linking climate events to conflict. The factor of grievances towards the government proves integral in this linkage. I seek to show sudden water-related climate events are the most conducive pathway to conflict. An emerging consensus of the causal theories in this literature shows migration to be a key mediating variable. Since urban migration creates problems with the distribution of economic resources, we can expect conflict in the form of acts against the government to ensue. I employ a survey in Guatemala to assess the reality of the proposed pathway. I will supplement this survey with a structural equation mediation model using cross national gridded data spanning from 2000-2015. My research will allow policymakers to form conflict preventative policies and to mitigate the negative implications of climate-induced scarcity and urban migration.

My research assistant will learn how to search for academic articles and data sources. In the first two weeks of this mentorship, the student’s goal will be to identify countries from Table 1 with publicly available sub-national data for urban migration, climate events, and conflict variables. Depending on the nature of this data gathering, the student will spend 2-3 weeks gathering the indicators for my three main variables into the spreadsheet. The main objective of this phase is for the undergraduate student to gain first-hand experience in data collection for a major research project. Once this data is complete, I, with the student, will run an OLS multiple regression analysis (and/or a structural equation mediation model depending on the country) in STATA. If the student does not have any prior experience with data analysis software such as STATA, I will spend a few hours teaching the student how to work with STATA and run some basic statistical commands. I will then provide guidance on how to interpret the results and we will write a short summary of the results. We will then discuss future implications of the research and how it fits into the broader literature. The main objective of this phase is for the student to gain experience with data analysis. Over the course of this program, the mentee will gain experience on different stages of a research project from data collection to data analysis and interpretation.