The Learning Curve is a regular essay series that aims to bring new data to timely policy debates, blending academic rigor with accessibility and a focus on solutions. This effort is designed to address policymakers’ needs and make education data actionable and accessible, including by publishing data and code whenever possible. We also seek to bring more data-driven voices into the policy conversation and to broaden and diversify the pool of scholars who do this kind of timely, rigorous, and relevant work.
We are seeking proposals to analyze the effects of education policies that have been enacted in the past 10 years or the likely effects of proposed policies. Authors invited to turn their proposal into an essay for the Learning Curve will receive technical and editorial support from Urban Institute staff, have their product published on urban.org, and receive a $2,500 honorarium.
Proposals will be reviewed on a rolling basis, with a final deadline of Wednesday, August 31, 2022. Review criteria will include the proposal’s originality, feasibility, and relevance to policy. Authors are welcome to submit multiple proposals.
Analyses We’re Seeking
We are looking for analyses of existing or proposed policies in PK–12 and higher education.
Analyses of existing policies can show how it affected students (and education systems), unintended consequences that resulted, and broader lessons learned. For example, a May 2022 Learning Curve essay on New York’s free college policy (enacted in 2017) showed how program participation, scholarship funds, and retention rates varied by student income and race or ethnicity. Existing policies should have been in place long enough for data to be available to assess their impact and should be relevant to current policy debates. The goal of these analyses is to inform policy discussions in both the place that adopted the original policy and others that are considering similar policies.
Analyses of proposed policies (including both legislation and informal proposals by legislators, candidates for public office, advocacy groups, and others) can estimate how the proposal is likely to affect different groups of students, institutions, or government budgets. For example, the November 2021 Learning Curve essay on the proposed expansion of free school meals in Build Back Better shows that the proposed policy design might discourage the states with the highest poverty rates from participating. The goal of these analyses is to inform an active debate, so it should be feasible to complete the analysis while the policy is still under consideration.
More general descriptive analyses of education policy topics should not be submitted through this call for proposals but through the normal process for pitching a Learning Curve essay by email to firstname.lastname@example.org.
Audience: This opportunity is open to all policy researchers and analysts with an interest in education, including early-career researchers. Joint proposals are welcome.
Datasets: The proposed analysis may draw on any dataset the researcher has access to, including both publicly available datasets and restricted-use datasets from state longitudinal data systems, the federal government, and other sources.
We encourage researchers using national institution-level datasets to access these data through the Education Data Portal, which includes most major national datasets on schools, districts, and colleges. PK–12 datasets include the Common Core of Data, the Civil Rights Data Collection, and EDFacts. Higher education datasets include the Integrated Postsecondary Education Data System, College Scorecard, and the Office of Federal Student Aid. Schools and colleges are linked to various geographic identifiers maintained by the National Historical Geographic Information System. A full list of datasets and elements is available at the Education Data Portal’s documentation site.
We also encourage potential authors to consider using Urban’s new nationally comparable measure of student poverty in schools if such a measure is relevant.
Authors should expect that the data and code underlying their analysis will be published on the Learning Curve’s GitHub repository. Exceptions to this policy can be granted (e.g., for restricted-access datasets) but should be requested in the proposal.
Process for Selected Proposals
The authors of selected proposals will develop their idea into a short research product of approximately 2,000 words and a small number of supporting figures or tables.
Authors invited to develop their proposals into policy research products will receive the support of Urban staff as they refine their idea, conduct the analysis, and write up their results, including feedback on both substance and style. All products will go through Urban’s quality assurance and editorial processes. Authors will own their work, while providing a license to Urban to publish and disseminate the work, and will be free to further develop their analyses for publication in other outlets (e.g., academic journals).
Authors will receive a $2,500 honorarium upon publication (divided equally among coauthors).
The proposal form asks applicants to respond to the following questions:
- Do you propose to analyze a recent or proposed policy?
- For existing policies: What did the policy do, when and where was it enacted, and how is an analysis of it relevant to current policy debates?
- For proposed policies: What would the policy do, where has it been proposed, and who proposed it? When do you expect policymakers to make a decision (i.e., by when will the analysis need to be published to inform the debate)?
- Which dataset(s) will you use to analyze the policy? Will you be able to share the data and code publicly?
- What questions will you answer about the policy?
- Why are you the right person to write this analysis?
Feel free to contact us with any questions at email@example.com