Abstract
Community Eligibility Provision (CEP) allows schools that meet certain criteria to provide free meals to all students. I provide new evidence on the impact of CEP with a focus on a particular group of students: those who are Multilingual Learners (MLL). These students may have been less likely to enroll to receive free meals before CEP even when income-eligible and were potentially less likely to take-up free meals when enrolled during the pre-CEP period due to heightened stigma. Using a student-level panel from North Carolina, I estimate the impact of CEP on MLL student math and reading achievement and school suspensions. I estimate student fixed effects models that suggest CEP positively impacts MLL students, whose math and reading test scores improve .032 and .082 of a standard deviation, respectively, on average when they attend schools enrolled in CEP. These students also see a reduction in the chance of being suspended in a given year of ∼2.3%, a 20% reduction of the mean, though this finding is more sensitive to how school-level poverty is measured.
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Notes
1 According to a USDA report on direct certification, ∼10.1 million children were directly certified for free school meals during the 2010–2011 school year. There were ∼20.6 million children reported as free-lunch eligible in the Common Core of Data in 2010–2011. Therefore, ∼49% of participating children were directly certified. This was prior to the introduction of CEP in all states (Moore et al., Citation2012).
2 Categorically eligible students include those served by the migrant education program, students experiencing homelessness, students in Head Start, and students whose families participate in the Food Distribution Program on Indian Reservations (Food Research & Action Center, Citation2018).
3 I do not have suspension data from 2019, so results from these models do not include this year.
4 In accordance with my data sharing agreement with NCERDC, I refer to students who are enrolled to receive free or reduced-price meals at school as economically disadvantaged (ED) from this point forward unless I am directly discussing meal consumption.
5 ISP is missing for about 35% of schools in 2015, likely because schools did not report their ISPs. To fill in these missings, I use ISPs reported in 2016. There are 631 remaining missings between 2015 and 2019 after these replacements are made, and over 90% are charter schools. I replace missing ISPs with 0s and generate a missing flag, which I include in my models.
6 I use two NCERDC variables, frl and eds_code, that indicate if a student receives meals through CEP. If I am unable to generate a clear CEP status for a school with this data, I rely on the status reported by the Food Action Research Center. I exclude students in 16 school/year observations because I am unable to obtain a CEP status for these schools.
7 There are about 45,000 unique MLL students in the panel who have access to CEP one but not all years they are present in the data. About 55% of these students gain access to CEP because their current school enrolls while about 29% gain access due to a school move. About 15% of students have no pre-treatment data, but lose access to CEP at some point either due to a school move or because their school stops participating. Some students also both gain and lose access during the panel.
8 This assumes a 180-day school year with breakfast and lunch served every school day and is based on a free reimbursement rate of $1.66 for breakfast and $3.07 for lunch and a paid reimbursement rate of $0.29 for breakfast and $.029 for lunch. These were the reimbursement rates for 2015–2016 for schools serving fewer than 60% of students at the free rate (Office of Child Nutrition Programs, 2022).