Need-based grants provide means-tested financial assistance to low-income students. Need-based grants can come from many sources and in various forms. In this meta-analysis, we focus on need-based federal and state grants with minimal eligibility requirements. Example programs in this review include the Federal Pell Grant Program and state grant programs similar to Washington’s State Need Grant. Grants funded by private entities may also be included if their implementation is similar to that of federal and state need-based grants. We exclude institutional need-based aid, as well as other grant programs that have conditions for aid receipt other than income (such as work study programs or merit-based aid). The studies in this meta-analysis evaluate the effects of need-based grants for students who are already enrolled in college.

 

^*The effect size for this outcome indicates percentage change, not a standardized mean difference effect size.

Meta-analysis is a statistical method to combine the results from separate studies on a program, policy, or topic to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the program impacts measured in the research literature (for example, impacts on crime or educational attainment). Treatment N represents the total number of individuals or units in the treatment group across the included studies.

An effect size (ES) is a standard metric that summarizes the degree to which a program or policy affects a measured outcome. If the effect size is positive, the outcome increases. If the effect size is negative, the outcome decreases. See Estimating Program Effects Using Effect Sizes for additional information on how we estimate effect sizes.

The effect size may be adjusted from the unadjusted effect size estimated in the meta-analysis. Historically, WSIPP adjusted effect sizes to some programs based on the methodological characteristics of the study. For programs reviewed in 2024 or later, we do not make additional adjustments, and we use the unadjusted effect size whenever we run a benefit-cost analysis.

Research shows the magnitude of effects may change over time. For those effect sizes, we estimate outcome-based adjustments, which we apply between the first time ES is estimated and the second time ES is estimated. More details about these adjustments can be found in our Technical Documentation.

Meta-Analysis of Program Effects
Outcomes measured	Treatment age	Primary or secondary participant	No. of effect sizes	Treatment N	Effect sizes (ES) and standard errors (SE)			Unadjusted effect size (random effects model)
					ES	SE	Age	ES	p-value
Earnings* Percent change in labor market earnings, typically weekly or monthly wages.	19	Primary	1	13860	0.053	0.022	25	0.053	0.015
Graduate with 4-year degree Graduate with a degree from a 4-year higher education institution.	19	Primary	2	14460	0.101	0.015	24	0.101	0.001
College grade point average Grade point average in a higher education institution.	19	Primary	7	39463	0.017	0.011	22	0.017	0.117
Persistence within 1st year Persistence from enrollment through the end of the first year of higher education in a 2- or 4-year institution.	19	Primary	4	7797	0.082	0.030	19	0.152	0.001
Graduate with 2-year degree Graduate with a degree from a 2-year higher education institution.	19	Primary	2	772	-0.004	0.105	22	-0.004	0.973
Transfer from 2- to 4-year college Enroll in a 4-year higher education institution.	19	Primary	1	397	0.019	0.071	22	0.019	0.793
Persistence into 2nd year Completion of the first year and re-enrollment in the second year of higher education in a 2- or 4-year institution.	19	Primary	8	37497	0.051	0.017	20	0.080	0.003
Persistence into 3rd year Completion of the second year and enrollment into a third year of higher education in a 2- or 4-year institution.	19	Primary	4	1820	0.023	0.037	21	0.023	0.526

Anderson, D.M. (2015). Essays in public economics. (Doctoral dissertation).

Bert, M.R. (2013). Evaluating the impact of Ohio's College Opportunity Grant on first year student success. (Doctoral dissertation). Cambridge, MA: Harvard Graduate School of Education.

Bettinger, E. (2015). Need-based aid and college persistence: The effects of the Ohio College Opportunity Grant. Educational Evaluation and Policy Analysis, 37 (1), 102S-119S.

Chen, R., & DesJardins, S. (2008). Exploring the effects of financial aid on the gap in student dropout risks by income level. Research in Higher Education, 49(1), 1-18.

Davidson, J.C. (2015). The effects of a state need-based access grant on traditional and nontraditional student persistence. Higher Education Policy, 28 (2), 235-257.

Denning, J.T., Marx, B.M., & Turner, L.J. (2017). ProPelled: The effects of grants on graduation, earnings, and welfare. Cambridge, Mass: National Bureau of Economic Research.

Friedmann, E.Z. (2016). Addressing disparities in postsecondary success: The role of need-based financial aid. (Doctoral dissertation). University of California, Davis.

Goldrick-Rab, S., Kelchen, R., Harris, D., & Benson, J. (2016). Reducing income inequality in educational attainment: experimental evidence on the impact of financial aid on college completion. American Journal of Sociology, 121(6), 1762-1817.

Mendoza, P., Mendez, J.P., & Malcom, Z. (2009). Financial aid and persistence in community colleges: Assessing the effectiveness of federal and state financial aid programs in Oklahoma. Community College Review, 37 (2), 112-135.

St. John, E.P., Hu, S., & Weber, J. (2001). State policy and the affordability of public higher education: The influence of state grants on persistence in Indiana. Research in Higher Education, 42(4), 401-428.

110 FIFTH AVENUE SE, SUITE 214
P O BOX 40999
OLYMPIA, WA 98501