Washington DC, July 28, 2020
This ongoing study, How America Pays for College, provides insights on how families paid for college in the most recent academic year (AY 2019–20). The report also examines the decision dynamics of American families when selecting and paying for higher education.
The data collection period for this study took place at an unprecedented time in this country. In April 2020, our nation was greatly affected by COVID-19: stay-at-home orders were in place, COVID-19 deaths were on the rise, more than 20M Americans lost their jobs, and stimulus checks through the $2 trillion Cares Act were starting to reach households around the country.
Given this environment, the 2020 study was augmented with forward-looking questions to help us begin to understand how, if at all, the pandemic influences families’ higher education perceptions and plans.
Despite the overall uncertainty caused by the global pandemic, How America Pays for College 2020 reveals that families continue to value and invest in a college education. For the thirteenth year in a row, 9 in 10 American college families agree that college is an investment in the student’s future. In fact, most families are not changing education plans for the upcoming academic year despite the pandemic—78% said the student will continue studying at the school where he or she is currently enrolled. The majority of families believe the current situation will not have an impact on the student’s education in the long run.
On average, American families spent $30,017 on college, which represents an increase from the previous two years. Parent income and savings covered the largest portion of education costs, 44% or $13,072, and free money— scholarships and grants—covered 25% or $7,626. Money borrowed by students and parents paid for 21% or $6,581 of total cost. For the third year in a row, fewer families report submitting the Free Application for Federal Student Aid (FAFSA). Seven in 10 families (71%) completed the FAFSA for the academic year 2019–20 compared to 77% in 2018–19 and 83% in 2017–18. This decrease coincides with fewer families reporting using scholarships or grants to cover the cost of education: 73% of families used “free money” this academic year compared with 82% in 2018–19. The most frequently cited reason for not submitting the FAFSA continues to be that families don’t believe they will qualify.
While it is the student who most often makes the decision about which school to attend, parents typically play a more active role in deciding how to pay for that education. Forty-five percent of families reported that the parent and the student made paying-for-college decisions together, in 36% of families it was the parent’s responsibility, and, in 19%, the student was the primary decision-maker. Parental involvement in paying-for-college decisions has implications for which sources of funds the family uses, how confident the family is in their approach to paying for college, and how much responsibility for paying for college the students take on themselves.
About this Study
Sallie Mae has again partnered with Ipsos, a global to conduct this study. How America Pays for College 2020 reflects the results of online interviews Ipsos conducted, in English, with:
- 996 parents of children ages 18-24 enrolled as undergraduate students
- 1,000 undergraduate students ages 18-24
The research was fielded between March 30 and April 27, 2020.
Dollar and proportional amounts in this report are averages that reflect composite representations intended to illustrate how the “typical” family pays for college. The
composite is a computed formula that spreads individual responses across all survey respondents.
Low-income households are defined as those with annual income of less than $35,000; middle-income as those with annual income from $35,000 to less than $100,000; and high-income as those with annual income of $100,000 or more. Geographic regions discussed mirror those used by the U.S. Census Bureau.
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The sample for this study was randomly drawn from Ipsos’ online panel, partner online panel sources, and “river” sampling and does not rely on a population frame in the traditional sense. Ipsos uses fixed sample targets, unique to the study, in drawing sample. The sample design was a disproportionate stratified sample of parents of college students and college students. After a sample has been obtained, Ipsos calibrates respondent characteristics to be representative of the U.S. Population using standard procedures such as raking-ratio adjustments. The source of these population targets is 2016 American Community Survey data. The sample was stratified by additional variables, such as region and student enrollment status.
To correct for the disproportionate stratified sample, both samples were weighted using a statistical technique called raking, in which all of the population marginal profiles of interest are replicated in the sample. The sample of parents was weighted by gender, age, race/ethnicity, region, education and by college information (region, size and type). The sample of students was weighted by gender, age, race/ethnicity, region, and by college information (region, size and type). All of the demographic profiles used for both parents and students in the weights were sourced from the Current Population Survey (CPS). The National Center for Educational Statistics provided additional data for the college information weights.
Bayesian Credibility Intervals
The calculation of credibility intervals assumes that Y has a binomial distribution conditioned on the parameter θ\, i.e., Y|θ~Bin(n,θ), where n is the size of our sample. In this setting, Y counts the number of “yes”, or “1”, observed in the sample, so that the sample mean (y ̅) is a natural estimate of the true population proportion θ. This model is often called the likelihood function, and it is a standard concept in both the Bayesian and the Classical framework. The Bayesian 1 statistics combines both the prior distribution and the likelihood function to create a posterior distribution. The posterior distribution represents our opinion about which are the plausible values for θ adjusted after observing the sample data. In reality, the posterior distribution is one’s knowledge base updated using the latest survey information. For the prior and likelihood functions specified here, the posterior distribution is also a beta distribution (π(θ/y)~β(y+a,n-y+b)), but with updated hyper-parameters.
Our credibility interval for θ is based on this posterior distribution. As mentioned above, these intervals represent our belief about which are the most plausible values for θ given our updated knowledge base. There are different ways to calculate these intervals based on π(θ/y). Since we want only one measure of precision for all variables in the survey, analogous to what is done within the Classical framework, we will compute the largest possible credibility interval for any observed sample. The worst case occurs when we assume that a=1 and b=1 and y=n/2. Using a simple approximation of the posterior by the normal distribution, the 95% credibility interval is given by, approximately 2.5.
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