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Evaluating a state child care assistance program using administrative data.

Shelley Horak1, Caitlin Ward2

  • 1The Harkin Institute, 2800 University Avenue, Des Moines, IA 50311, USA.

Evaluation and Program Planning
|April 26, 2022
PubMed
Summary
This summary is machine-generated.

Government-supported child care assistance (CCA) programs can be better evaluated using administrative data. This study presents a novel framework to assess CCA program success, aiding decision-makers and improving outcomes for low-income families.

Keywords:
Administrative dataChild care assistanceData-driven decision makingPolicy evaluation

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Area of Science:

  • Public Policy
  • Social Work
  • Data Science

Background:

  • Government-supported child care assistance (CCA) programs aim to aid low-income families by subsidizing child care costs for working parents.
  • Federal policy provides goals for state CCA programs, but quantifying intended outcomes remains challenging due to data limitations.
  • Effective program evaluation is hindered by a lack of data analysis on participating families and providers, leaving administrators and advocates without crucial decision-making information.

Purpose of the Study:

  • To present a novel evaluation framework for assessing the success of child care assistance (CCA) programs.
  • To guide the utilization of administrative data for program evaluation, grounded in policy and literature.
  • To demonstrate the framework's utility using Iowa's CCA data system and provide actionable recommendations.

Main Methods:

  • Developed a novel evaluation framework integrating policy goals and existing literature.
  • Utilized administrative data from state-level child care assistance (CCA) programs.
  • Applied the framework to Iowa's CCA data system to illustrate its practical application.

Main Results:

  • The proposed framework offers a structured approach to analyze complex administrative data from CCA programs.
  • Demonstrated the feasibility and benefits of using administrative data for program assessment.
  • Identified opportunities for data-driven decision-making to enhance CCA program effectiveness.

Conclusions:

  • Administrative data presents a low-cost, effective method for evaluating CCA program performance.
  • The novel framework provides guidance for distilling insights from complex administrative records.
  • Data-driven evaluation is essential for optimizing CCA programs and supporting low-income families.