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National Science Foundation

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Doctoral Dissertation Research in DRMS: The Psychology of Political Risk in Repressive Regimes

The US government spends billions per year on democracy and governance assistance in foreign countries. Nevertheless, one in five elections in Africa since 1990 has been afflicted by significant levels of violence, which impedes citizens from freely voting for their preferred candidates. Identifying how and when coercive violence influences voters is critical to effectively reducing its impact on the quality of elections. This study uses the case of Zimbabwe to understand how citizens make decisions about politics when faced with the threat of violence.

Collaborative Research: Multilevel Regression and Poststratification: A Unified Framework for Survey Weighted Inference

This research project will develop a unified framework for survey weighting through novel modifications of multilevel regression and poststratification (MRP) to incorporate design-based information into modeling. Real-life survey data often are unrepresentative due to selection bias and nonresponse. Existing methods for adjusting for known differences between the sample and population from which the sample is drawn have some advantages but also practical limitations.

Collaborative Research: Voter Mobilization and Electoral Subversion in the Workplace

Intellectual Merit: The project is important to US national security interests because it addresses economic coercion that undermines democracy. Scholars have long recognized that rulers in non democracies can extend their tenure by subverting elections, focusing on ballot-box fraud, repression, turnout-buying, vote buying, patronage spending, and the co-optation of opposition elites. However, they have largely overlooked one prominent form of electoral subversion in contemporary hybrid regimes: the coercive mobilization of voters by employers.

Doctoral Dissertation Research: Accountability in Rebel Regimes

The project investigates local governance and the establishment of law-like systems of order in territory controlled by rebel organizations during civil war. In particular, the project explains variation in rebel organizations' provision of public goods and services as well as their use of coercive violence against civilians to control territory. Departing from existing research, the argument examines civilian political mobilization and collective action to constrain rebel organizations, advancing a political accountability theory of rebel governance.

Doctoral Dissertation Research: Homeward Bound: Return Migration and Post-Conflict Governance

This dissertation examines the political impact of return migration after civil war. Violence wrought by civil war forces millions of people to flee their homes. While scholars have demonstrated how these population movements can spread and exacerbate conflict, return-migration is assumed to be a purely logistical issue. Once the war is over, people will simply return home and pick up where they left off. Yet, conflict between returning and non-migrant populations is a nearly ubiquitous issue for post-conflict societies from South Sudan to Iraq and Rwanda.

New Approaches for Dynamic Panel Data Analysis

The project seeks to develop new methods for modeling, estimation, and inference in panel data models with a factor error or approximate factor error structure. This error structure has roots in economics and finance, and the usual additive individual effects and time effects are special cases of this structure. Explanatory variables are correlated with unobserved factors and factor loadings.

Protected Family and Medical Job Leave: Evidence from US Administrative Data

53.3% of employed US mothers were on leave one month after having a child, versus only .8% for fathers [Han et al., 2009]. The 1993 Family and Medical Leave Act is exceptional in being a US policy mandating unpaid job-protected leave for specified family and medical reasons, including childbirth. Surprisingly little is known about the first-order labor market effects of this policy, principally for data reasons. Benefit mandates like the FMLA have costs and benefits which are notoriously difficult to assess.

Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA)

The BIGDATA program seeks novel approaches in computer science, statistics, computational science, and mathematics, along with innovative applications in domain science, including social and behavioral sciences, geosciences, education, biology, the physical sciences, and engineering that lead towards the further development of the interdisciplinary field of data science.

Deadline: 

Monday, May 14, 2018

High-Risk Research in Biological Anthropology and Archaeology

Anthropological research may be conducted under unusual circumstances, often in distant locations. As a result the ability to conduct potentially important research may hinge on factors that are impossible to assess from a distance and some projects with potentially great payoffs may face difficulties in securing funding. This program gives small awards that provide investigators with the opportunity to assess the feasibility of an anthropological research project. The information gathered may then be used as the basis for preparing a more fully developed research program.

Interests: 

Biological Anthropology

The Biological Anthropology Program supports basic research in areas related to human evolution and contemporary human biological variation. Research areas supported by the program include, but are not limited to, human genetic variation, human and nonhuman primate ecology and adaptability, human osteology and bone biology, human and nonhuman primate paleontology, functional anatomy, and primate socioecology. Grants supported in these areas are united by an underlying evolutionary framework, and often by a consideration of adaptation as a central theoretical theme.

Deadline: 

Wednesday, July 20, 2022
Friday, January 20, 2023
Thursday, July 20, 2023

Interests: 

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