How many postsecondary students participated in distance learning before and during the coronavirus pandemic? In fall , some 37 percent of postsecondary students 7.
Of the 7. In spring , some 87 percent of undergraduate students experienced any enrollment disruption or change at their postsecondary institution due to COVID, with 84 percent of students experiencing some or all in-person classes moved to online-only source. These findings are based on preliminary data and may differ from estimates that will be available in the full survey sample and dataset released in , which will address missing data in the findings.
Visit our Fast Fact on distance learning among postsecondary students to learn more about distance learning in the United States. During the —19 academic year, how many degrees did colleges and universities award?
NCES publishes a wide range of data on graduation rates, technology in education, college costs, fields of study or majors , number of degrees awarded, and employment outcomes in annual publications, including the Condition of Education and the Digest of Education Statistics.
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Report Highlights. College enrollment totals College enrollment rates have declined by an average of 1. There are currently undergraduate students enrolled in colleges nationwide. College enrollment statistics indicate that more Americans are forgoing higher education; some may be putting off college attendance to build savings. College enrollment statistics indicate that, whether they earn their degree or drop out most undergraduate students make one attempt to complete their college education.
Demographic statistics are imperfect because many people do not easily fit into just one category. Some data, especially when it is historical, uses obsolete definitions and terminology.
The language of this report conforms to the language used in available data sets. Limited data makes no significant representation of genders beyond the standard binary. Updates to data collection policies may make these statistics available in the future. Most college students first enroll in their late teens as full-time students.
Part-time students are more likely to be older. Statistics indicate that while enrollment rates increased at a more-or-less steady pace for decades, the past 10 years have seen a significant decline in college attendance. College enrollment statistics indicate that most students are willing to travel out of their home state for their college education. The percentage of high school graduates referred to sometimes as completers who enroll in either 2-year or 4-year institutions following graduation is referred to as the immediate college enrollment rate.
Some institutions still use these terms. Independent students usually have lower levels of income and are more likely to have children under the age of At peak enrollment, The rate of female college attendance has increased Enrollment peaked in at Among adults over 18, college students make up Among first-time, first-year college students, Enrollment by Degree College enrollment statistics indicate that, whether they earn their degree or drop out most undergraduate students make one attempt to complete their college education.
Graduate students are As a percentage of the entire student population, the White or Caucasian demographic has decreased by Black or African American students have increased among the student population by Since , Black or African American students have decreased among the student population by Asian and Pacific Islander were not considered separate demographics until Since , when it was first listed as a racial category, enrollment as a share of the total student body among students of two or more races has increased Black and Hispanic students enroll in higher numbers in community colleges and less-selective four-year institutions.
Among Black or African American high school graduates with a 3. College Enrollment by Sex or Gender Limited data makes no significant representation of genders beyond the standard binary.
Women are Women are 4. In , College Enrollment by Age Most college students first enroll in their late teens as full-time students. The average age for students enrolled full-time in undergraduate programs is College Enrollment Trends Statistics indicate that while enrollment rates increased at a more-or-less steady pace for decades, the past 10 years have seen a significant decline in college attendance.
The rate of enrollment declines by 2. From to , college enrollment increased at a rate of 2. In the s, college enrollment grew at an annual rate of 3. In the s, however, enrollment rates declined at an annual rate of 0. In , 5. State Enrollment Trends College enrollment statistics indicate that most students are willing to travel out of their home state for their college education.
California has the highest number of enrolled college students at 2. Florida, New York, and Texas are the only other states with more than a million college students enrolled. Wyoming, Montana, and Vermont all have fewer than 50, enrolled college students. New Hampshire and Utah have seen the largest increases in enrollment rates since Utah has seen a Delaware and Idaho have also seen significant increases in enrollment rates. Iowa and Alaska have seen the largest declines in college enrollment since Other states that have seen major losses in postsecondary enrollment include Arizona, Hawaii, and New Mexico.
Enrollment statistics indicate that most District of Columbia residents prefer to attend college elsewhere. Puerto Rico colleges retain the highest percentage of residents, followed by Utah.
In , , students were enrolled in college in Alabama. In , , were enrolled. College Enrollment in Alaska In , 28, students enrolled in college in Alaska. In , 9, were enrolled. College Enrollment in Arizona In , , students enrolled in college in Arizona. College Enrollment in Arkansas In , , students enrolled in college in Arkansas. In , 52, were enrolled. College Enrollment in California In , 2. In , 1. The top chart in this figure, corresponding to high income countries, shows a very clear pattern: households contribute the largest share of expenses in tertiary education, and the smallest share in primary education.
Roughly speaking, this pattern tends to be progressive, since students from wealthier households are more likely to attend tertiary education, and those individuals who attend tertiary education are likely to perceive large private benefits. Such distribution of private household contributions to education is regressive.
The visualization presents three scatter plots using data to show the cross-country correlation between i education expenditure as a share of GDP , ii mean years of schooling, and iii mean PISA test scores. At a cross-sectional level, expenditure on education correlates positively with both quantity and quality measures; and not surprisingly, the quality and quantity measures also correlate positively with each-other.
But correlation does not imply causation: there are many factors that simultaneously affect education spending and outcomes. Indeed, these scatterplots show that despite the broad positive correlation, there is substantial dispersion away from the trend line — in other words, there is substantial variation in outcomes that does not seem to be captured by differences in expenditure. The visualization presents the relationship between PISA reading outcomes and average education spending per student, splitting the sample of countries by income levels.
It shows that income is an important factor that affects both expenditure on education and education outcomes: we can see that above a certain national income level, the relationship between PISA scores and education expenditure per pupil becomes virtually inexistent.
Several studies with more sophisticated econometric models corroborate the fact that expenditure on education does not explain well cross-country differences in learning outcomes. The fact that expenditure on education does not explain well cross-country differences in learning outcomes is indicative of the intricate nature of the process through which such outcomes are produced.
This conceptualization highlights that, for any given level of expenditure, the output achieved will depend on the input mix. And consequently, this implies that in order to explain education outcomes, we must rely on information about specific inputs. Available evidence specifically on the importance of school inputs , suggests that learning outcomes may be more sensitive to improvements in the quality of teachers, than to improvements in class sizes.
And regarding household inputs , the recent experimental evidence suggests that interventions that increase the benefits of attending school e. Policy experiments have also shown that pre-school investment in demand-side inputs leads to large positive impacts on education — and other important outcomes later in life. The environment that children are exposed to early in life, plays a crucial role in shaping their abilities, behavior and talents.
Education is a valuable investment, both individually and collectively. Here we analyse available evidence of the private i. The most common way to measure the private returns to education, is to study how attainment improves individual labor market outcomes — usually by attempting to measure the effect of education on wages. Regarding social returns, the most common approach is to measure the effect of education on pro-social behaviour e.
In each case, we provide a discussion of the robustness of the evidence. The chart shows the earnings of tertiary-educated workers, by level of tertiary education, relative to the earnings of workers with upper secondary education. As we can see, in all OECD countries for which information is available, the higher the level of education, the greater the relative earnings.
The countries in this chart are ordered in ascending order of relative earnings. As we can see, the countries with the greatest returns to tertiary education Brazil, Chile and Colombia are also those where tertiary education is less prevalent among the adult population.
This is indicative of the demand-and-supply dynamics that contribute to determine wage differentials across different countries. These figures are simply correlations, and cannot be interpreted causally: individuas with more education are different in many ways to individuals with less education, so we cannot attribute wage differences solely to education choices.
The previous graph gave a cross-country comparison of earning by education level. As pointed out, those figures were difficult to interpret causally, because they failed to account for important underlying differences in things like hours worked, experience profiles, etc.
The visualization, from Card 20 , attempts to pin down the relationship between education and earnings, by comparing wages across education levels, genders and age groups. Education levels correspond to individuals with 10, 12 and 16 years of education. The marks show averages for each corresponding group, and the smooth lines show the predictions made by a simple econometric model explaining wages by education and experience.
The first conclusion from this charts is that for both genders, at any given age, individuals with more education receive higher wages. Moreover, these estimates suggest that the incremental benefit from additional education grows with experience: the differences in wages between people with varying degrees of education become larger as they advance in their careers.
In other words, education pay-offs are not constant over the life cycle. Other studies using different data have found similar results see, for instance, Blundell et al. These estimates can still not be interpreted causally, because there are yet other potential sources of bias that are unaccounted for, such as innate ability.
To address this issue, the economics literature has developed different strategies. For example, by contrasting the wages of genetically identical twins with different schooling level, researchers have found a way of controlling for unobservable characteristics such as family background and innate ability.
In other words, there is robust evidence supporting the causal effect of education on wages for more details see Card The chart uses OECD results from the Survey of Adult Skills to show how self-reported trust in others correlates with educational attainment. More precisely, this chart plots the percentage-point difference in the likelihood of reporting to trust others, by education level of respondents. Those individuals with upper secondary or post-secondary non-tertiary education are taken as the reference group, so the percentage point difference is expressed in relation to this group.
As we can see, in all countries those individuals with tertiary education were by far the group most likely to report trusting others. And in almost every country, those with post-secondary non-tertiary education were more likely to trust others than those with primary or lower secondary education. The report concludes that adults with higher qualifications are more likely to report desirable social outcomes, including good or excellent health, participation in volunteer activities, interpersonal trust, and political efficacy.
And these results hold after controlling for literacy, gender, age and monthly earnings. As usual, correlation does not imply causation — but it does show an important pattern that supports the idea that education is indeed necessary to produce social capital. Under this hypothesis, therefore, we should expect that education levels in a country correlate positively with measures of democratisation in subsequent years.
The visualization shows that this positive correlation is indeed supported by the data. As we can see, countries where adults had a higher average education level in , are also more likely to have democratic political regimes today you can read more about measures of democracy in our entry on Democracy. As usual, these results should be interpreted carefully, because they do not imply a causal link: it does not prove that increasing education necessarily produces democratic outcomes everywhere in the world.
The visualization shows the strong cross-country correlation between child mortality and educational attainment. The economics literature has long studied whether the level of education in a country is a determinant of economic growth. While early studies found that schooling levels were poor predictors of economic growth, more recent studies — that crucially made use of better data — confirm the expected positive link.
The plot, from Hanushek and Woessmann , 28 provides a basic representation of the association between test scores and economic growth using data over the period to As we can see, there is a strong positive relationship. This coincides with other studies showing that historical increases in the number of universities across countries are positively associated with subsequent growth of GDP per capita Valero and Van Reenen A number of studies have found that it is actually education in the form of cognitive skills, rather than mere school attainment, what really matters for predicting individual earnings and economic growth.
You can read more about this in Delgado, Henderson and Parmeter 31 , Hanushek 32 and Pritchett There are two important sources of long-run cross-country data on education attainment. The most recent one is Lee and Lee The authors relied on information about the years of establishment of the oldest schools at different education levels in individual countries, in order to adjust their estimates; and they also used data on repetition ratios to adjust for school repeaters. These institutions reconstructed educational attainment distributions by age and sex for countries for the years — You can read more about this source of data and the underlying methodology in our entry on Education Projections.
You find more research, data visualisations, and detailed lists of data sources in the more specific entries:. The Evolution of Education Outcomes. How has global literacy evolved in the last two centuries? Click to open interactive version. How is literacy distributed across the globe? How fast are we closing cross-country inequalities in literacy? Primary school enrollment around the world increased drastically in the last century.
Primary school attendance remains a challenge in many developing countries. Out-of-school children. The world is more educated than ever before. How has the stock of human capital evolved in the long run? World distribution of years of schooling for selected years — Figure 7B in Lee and Lee 9.
School life expectancy captures the years of schooling children can expect. What has been the evolution of gender inequalities in education?
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