The SPEC Lab conducts interdisciplinary, policy-relevant research on issues at the intersection of social issues, security, and economic development. In a collaborative team setting, students develop data science and other research skills that are then applied directly to the policy challenges facing national governments and international institutions. We focus in particular on recruiting and serving female, minority, and first-generation students early in their undergraduate career. In a close-knit, supportive research community we are building the next generation of social science researchers. If you are interested in the substance of the lab and the training we provide, please take a look at the SPEC Lab website to find out more about us.
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Powersharing institutions are often prescribed to enhance civil peace, democratic survival, and the equitable provision of public services, and these institutions have become more prevalent over time. Nonetheless, the past decade has seen a rise in democratic backsliding and competitive authoritarianism, raising questions about how the relationship between democracy and powersharing may be evolving.
This paper introduces an update to the Inclusion, Dispersion, and Constraints (IDC) powersharing data that adds nine years of data, up through 2019. These new data also include enhanced intercoder reliability checks, a significant reduction in missing values, and the documentation and correction of some coding errors in the original data.
Our new data show that, during the past decade, constraining and dispersive institutions have increasingly been adopted in non-democratic states. These data allow scholars to address urgent questions about whether previously observed relationships between powersharing and democracy and powersharing and civil peace still hold in this new era, and in what contexts powersharing institutions remain advisable.
This research agenda considers the effects of interventions on equitable public service provision. It employs quantitative methods to evaluate the effects of interventions on development outcomes and draws upon regional expertise to integrate field work that complements the causal inference, making for more comprehensive analyses.
This semester students will be working on three projects. One looks at equitable aid allocation to Somali education in Kenya, another at foreign policy and development in Djibouti, and a third will be data collection on development assistance and humanitarian aid.
As a whole, this project demonstrates the extent to which domestic politics can shape the allocation of aid by foreign actors and how this can result in the diversion of aid away from the populations who need it most. Undergraduate research assistants work on data collection, management, and visualization as well as more qualitative tasks related to completed and planned field work that connects students with organizations in the development and humanitarian sectors.
Applied Data Science
In the SPEC Lab we invest a lot of energy training students in applied data science. In particular, we focus on training students in data management and data visualization using R, an open source statistical software package.
These trainings are complementary to coursework in statistics and econometrics. While statistics and econometrics courses generally focus on theory and mathematics, we teach the nuts and bolts of statistical computing. We developed these trainings in collaboration with our Pipeline Partners to prepare students not only for academic social science research, but also for data science careers in government, nonprofits, and the private sector.
These materials are a constant work in progress and we welcome feedback: firstname.lastname@example.org
Funding for the creation of these materials was provided by the National Science Foundation, the Dornsife College of Letters, Arts, and Sciences at the University of Southern California, and individual SPEC Lab Donors. To support our continued work to improve and expand these materials, please donate here.