97 research outputs found

    MEASURING PRODUCTION EFFICIENCY USING AGGREGATE DATA

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    This paper develops a measure of efficiency when data have been aggregated. Unlike the most commonly used efficiency measures, our estimator handles the heteroskedasticity created by aggregation appropriately. Our estimator is compared to estimators currently used to measure school efficiency. Theoretical results are supported by a Monte Carlo experiment. Results show that for samples containing small schools (sample average may be about 100 students per school but sample includes several schools with about 30 students), the proposed aggregate data estimator performs better than the commonly used OLS and only slightly worse than the multilevel estimator. Thus, when school officials are unable to gather multilevel or disaggregate data, the aggregate data estimator proposed here should be used. When disaggregate data is available, standardizing the value-added estimator should be considered.Productivity Analysis,

    Containing a firestorm: adaptive policies needed to address changing foreclosure landscape

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    Like a wildfire leaving devastation in its path, the foreclosure crisis continues to wreak havoc on many families and communities throughout the Fourth District, especially in the largest urban areas. Only a year ago the primary reason for foreclosures centered on subprime mortgages. Today, the primary driver is unemployment, further widening the consumption arc of this blaze.Foreclosure

    Spatial Differences of Land Use Change within Oklahoma's Wheat Belt

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    Farm Service Agency acreage data for the nine Oklahoma Agricultural Statistics Service districts is analyzed to determine the degree of price response in wheat acreage allocation decisions. Some critics have stated that land use after Freedom to Farm would change little, however these findings show acreage shifted greatly after the policy throughout the state.Land Economics/Use,

    SROI in the Pay for Success Context: Are They at Odds?

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    The Pay For Success (PFS) and Social Impact Bond (SIB) movements to date have focused heavily on shorter-term outcomes that can be monetized and show clear savings to government entities. In part, this focus derives from the need to specify contract payments based on a narrow set of well measured outcomes (e.g., avoided days in jail and foster care, decreased use of behavioral health services). Meanwhile efforts to measure the social return on investment (SROI) of interventions have sought to expand the view of relevant outcomes to include domains that lend themselves less clearly to monetization. This paper explores the intersection between these two movements with illustrations from a SIB initiative underway focused on homeless families with children in foster care. Challenges and potential for SROI in a third-party payor environment will be discussed as well as opportunities to better leverage the strengths of both types of initiative

    USING BOTH SOCIOLOGICAL AND ECONOMIC INCENTIVES TO REDUCE MORAL HAZARD

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    Economists tend to focus on monetary incentives. In the model developed here, both sociological and economic incentives are used to diminish the apparent moral hazard problem existing in commodity grading. Training that promotes graders' response to sociological incentives is shown to increase expected benefits. The model suggests that this training be increased up to the point where the marginal benefit due to training equals its marginal cost. It may be more economical to influence the grader's behavior by creating cognitive dissonance through training and rules rather than by using economic incentives alone.Marketing,

    Using Integrated Data Systems (IDS) to Design and Support Pay For Success Interventions: Cuyahoga County, Ohio

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    Pay for Success (PFS) interventions are increasingly being implemented in the U.S. and worldwide to assess social programs under a risk-sharing financial agreement between the public and private sectors. They seek to mitigate risk for the public sector and promote wider experimentation of programs to improve social outcomes. PFS contracts encourage coordination and alignment of goals, outcomes, and metrics across all agents involved - government, service providers, service recipients, funders and investors. Accordingly, these interventions rely heavily on access to high quality data and analysis, making integrated data systems (IDS) valuable assets to support the design, implementation, and evaluation phases of these projects. The ChildHood Integrated Longitudinal Data (CHILD) System, one of the most comprehensive county-level IDS in the nation, has been used to support and inform two Pay for Success projects in Cuyahoga County (Cleveland). Partnering for Family Success is a county-level intervention in the areas of child welfare and housing instability, now into its fourth year of operation. While the intervention was implemented under a randomized controlled trial, analysis with CHILD proved instrumental to inform the project design and address challenges in program implementation. CHILD has also been used to study the feasibility of PFS as a model to expand high quality preschool, under a grant awarded to eight communities nationally. A case study of both initiatives will be presented, highlighting the role of integrated data in supporting and facilitating PFS design and analysis of outcomes, challenges encountered and lessons learned
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