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The Gap within the Gap: Using Longitudinal Data to Understand Income Differences in Student Achievement

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  • Katherine Michelmore
  • Susan Dynarski

Abstract

Gaps in educational achievement between high- and low-income children are growing. Administrative datasets maintained by states and districts lack information about income but do indicate whether a student is eligible for subsidized school meals. We leverage the longitudinal structure of these datasets to develop a new measure of persistent economic disadvantage. Half of 8th graders in Michigan are eligible for a subsidized meal, but just 14 percent have been eligible for subsidized meals in every grade since kindergarten. These children score 0.94 standard deviations below those never eligible for subsidies and 0.23 below those occasionally eligible. There is a negative, linear relationship between grades spent in economic disadvantage and 8th grade test scores. This is not an exposure effect: the relationship is almost identical in 3rd grade, before children have been differentially exposed to five more years of economic disadvantage. Survey data show that the number of years that a child will spend eligible for subsidized lunch is negatively correlated with her current household income. Years eligible for subsidized meals can therefore be used as a reasonable proxy for income. Our proposed measure can be used in evaluations to estimate heterogeneous effects, to improve value-added calculations, and to better target resources.

Suggested Citation

  • Katherine Michelmore & Susan Dynarski, 2016. "The Gap within the Gap: Using Longitudinal Data to Understand Income Differences in Student Achievement," NBER Working Papers 22474, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:22474
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    Cited by:

    1. Thomas Odongo Ololo & Dr. Mary Anyango Onditi & Dr. Benard Mwebi, 2024. "Influence of Principals’ Allocation of Teaching and Learning Resources on Learners’ Academic Performance in Kenya Certificate of Secondary Education in Kisii County, Kenya," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(5), pages 975-1004, May.
    2. Burgess, Simon & Metcalfe, Robert & Sadoff, Sally, 2021. "Understanding the response to financial and non-financial incentives in education: Field experimental evidence using high-stakes assessments," Economics of Education Review, Elsevier, vol. 85(C).
    3. Lois Miller & Humberto Barreto, 2017. "The Role Of Distance In College Undermatching," Working Papers 2017-01, DePauw University, School of Business and Leadership and Department of Economics and Management.
    4. Thomas Odongo Ololo & Dr. Mary Anyango Onditi & Dr. Benard Mwebi, 2024. "Influence of Principals’ Instructional Supervisory Practices on Learners’ Academic Performance in Kenya Certificate of Secondary Education in Kisii County, Kenya," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(5), pages 557-579, May.
    5. Thurston Domina & Quentin Brummet & Nikolas Pharris-Ciurej & Sonya R. Porter & Andrew Penner & Emily Penner & Tanya Sanabria, 2017. "Capturing More Than Poverty: School Free and Reduced-Price Lunch Data and Household Income," CARRA Working Papers 2017-09, Center for Economic Studies, U.S. Census Bureau.

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    More about this item

    JEL classification:

    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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