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National Science Foundation

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Doctoral Dissertation Research in Economics: Residential Segregation and Neighbor-Based Informal Hiring

Place-based job policies, such as informal neighborhood job search, are common tools to promote local job growth and reduce regional economic inequality. It is however not clear what makes a neighborhood good for job search. This research project will study one such mechanism---the use of neighbor networks in job search. It explores which type of neighborhood---segregated or integrated, by race and by education---is more conducive to a successful job search. Disentangling the neighborhood effects on job search is difficult partly because people self-select into neighborhoods.

Doctoral Dissertation Research in Economics: Information Asymmetry in Job Search

Wage gaps across race and gender persist among equally educated individuals, and have been attributed in significant part to differences in behavior during job search. Economic theory suggests that access to information about the labor market influences behavior. If information differs across groups, either in quantity or quality, this can lead to differences in job choice. Across race and gender, unequal access to networks and mentoring has been shown to give rise to these information gaps.

Doctoral Dissertation Research: The political economy of migration, labor, and documentation

Regularized bureaucratic strategies are demonstrated to prevent migrants with legal status from lapsing into illegal status. This can also adversely impact communities whose livelihoods depend on migrant labor. This doctoral dissertation research asks how individuals negotiate securing documentation to support their claims in changing political contexts. It focuses directly on the continuum of documents that support claims of national identity in an attempt to move beyond binary characterizations of legal status.

Trans-Atlantic Platform: Recovery, Renewal, and Resilience in a Post-Pandemic World

The Trans-Atlantic Platform Recovery, Renewal, and Resilience in a Post-Pandemic World (T-AP RRR) opportunity supports international, collaborative research projects that address key gaps in our understanding of the complex societal effects of COVID-19.

Deadline: 

Monday, July 12, 2021

Designing Accountable Software Systems

he Designing Accountable Software Systems (DASS) program solicits foundational research aimed towards a deeper understanding and formalization of the bi-directional relationship between software systems and the complex social and legal contexts within which software systems must be designed and operate.

Deadline: 

Monday, April 19, 2021

CAREER: Accountable Democracy: Mathematical Reasoning and Democratic Processes in America

n recent years, it has become increasingly apparent that computational techniques are deeply embedded in every stage of the American democratic process. Prominent examples include computational redistricting and the increased use of statistical analysis in polling and forecasts. This project will historicize the role of computers, as well as algorithmic thinking and mathematical rationales, in the constitution of American representative democracy in the twentieth century.

Mid-Career Advancement

An academic career often does not provide the uninterrupted stretches of time necessary for acquiring and building new skills to enhance and advance one’s research program. Mid-career scientists in particular are at a critical career stage where they need to advance their research programs to ensure long-term productivity and creativity but are often constrained by service, teaching, or other activities that limit the amount of time devoted to research.

Deadline: 

Monday, February 7, 2022
Monday, February 6, 2023
Monday, February 5, 2024
Monday, February 3, 2025

RAPID: Flexible, Efficient, and Available Bayesian Computation for Epidemic Models

Decisions about coronavirus response are necessarily based on statistical models of prevalence, transmission risks, case fatality rate, projection of future spread of infection, and estimated effects of medical and social interventions. Much of this modeling and inference is being done using the Bayesian framework, an approach to statistics that is well suited to integration of information from different sources and accounting for uncertainty in predictions that can be input into decision analysis.

Collaborative Research: PPoSS: Planning: Scalable Systems for Probabilistic Programming

Statistical methods have had great successes for exploring data, making predictions, and solving problems in a wide range of problems. But in the world of big data, methods need to be scalable, so as to handle larger problems while modeling the real-world problems of messy and nonrepresentative data. The project?s novelties are developments in software and hardware facilitating full-stack integration of Bayesian inference to allow complex and realistic models to be fit to large datasets.

Doctoral Dissertation Research in Economics: The Social Dimension of Quality

Why do consumers willingly pay more for brand name products compared to non-branded products even though the two have the same attributes. This doctoral dissertation research in economics (DDRIE) research project will use economic theory and experimental methods to investigate the social network source of value for a product. The researchers argue that people pay more for a product to signal prestige or because people they look up to consume that product. The researchers will collect data on a number imported and domestically produced consumer goods to test this theory.

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