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Rajashri Chakrabarti, Michael Lovenheim, and Kevin Morris
Editor’s note: The labels for “Elite private” and “Non-elite private, not-for-profit” institutions in the charts have been corrected; they were initially transposed. We regret the error. (September 12, 12:45 p.m.)
This is the final post in a four-part series examining the evolution of enrollment, student loans, graduation and default in the higher education market over the course of the past fifteen years. In the first post, we found a marked increase in enrollment of 35 percent between 2000 and 2015, led mostly by the for-profit sector—which increased enrollment by 177 percent. The second post showed that these new enrollees were quite different from the traditional enrollees. Yesterday’s post demonstrated an unprecedented increase in loan origination amounts during this period—nearly tripling between 2000 and 2015. This surge was driven most prominently by a massive increase in the number of borrowers in the public community college sector and the private for-profit college sector. Given the large increase in the borrower pool and loan originations, it is paramount to understand the consequences of these changes for the student loan default rate. This post aims to do just that. We focus on three-year cohort default rates reported by the United States Department of Education. The three-year cohort default rate is defined as the percentage of a school's borrowers who enter repayment during a particular federal fiscal year—running from October 1 to September 30—and default prior to the end of the second following fiscal year. Most federal loans enter default when payments are more than 270 days past due.
Rajashri Chakrabarti, Michael Lovenheim, and Kevin Morris
Editor’s note: The chart sources cited in this post have been corrected. (September 9, 12:55 p.m.)
In the first post in this series, we characterized the rapid transformation of the higher education market over the 2000-2015 period, a transformation that was led by explosive growth of the for-profit sector of higher education. In the second post, we found that most of this growth was driven by nontraditional students entering these institutions. Given this growth and the marked change in student composition, it is important to understand what impact these patterns might have on student loan originations, student loan volume, and the borrower pool in the various sectors of higher education. While a causal analysis is beyond the scope of this post, we instead examine descriptive patterns in these critical postsecondary outcomes. Was the growth in for-profit enrollment associated with a higher incidence of student loans? Were for-profit students, the main contributors of this growth, more or less likely to take student loans, and were they more or less likely to originate larger student loans? How about community-college borrowers, especially since community college enrollment increased noticeably over the period? This post focuses on these questions.
Rajashri Chakrabarti, Michael Lovenheim, and Kevin Morris
The higher education landscape changed drastically over the last decade and a half. This evolution was largely characterized by the unprecedented growth of the private for-profit sector. In this post, we examine whether the evolution of the higher education market was associated with changes in the types of students who attended the institutions in various sectors of the market. Was the growth in enrollment spurred by an increased entry of traditional students? Or was it driven by an inflow of nontraditional students? Has student composition in higher education changed differentially between sectors? It is important for us to understand not only the growth in the higher education market but also which types of students contributed to this growth, because any changes in the composition of students may have implications for the composition of skilled workers in the labor market, for student loans, for loan repayment, and for the labor market returns to education investments.
Rajashri Chakrabarti, Giacomo De Giorgi, and Rachel Schuh
Educational attainment is an important element of human capital; however a series of recent papers highlights the crucial role of the quality of education—which determines the skills actually learned, rather than the number of years spent in a classroom—as a main driver of growth. In fact, Hanushek and Woessmann argue that the importance of more appropriately measuring skills is seen in the very tight relationship between quality of skills, or knowledge capital, and growth. Moreover, the researchers state, “The knowledge capital–growth relationship suggests little mystery for East Asia, Latin America, or other regions: Growth rates are accounted for by cognitive skills.” Similarly, “Considering knowledge capital dramatically increases our ability to account for differences in growth.”
This morning, the Federal Reserve Bank of New York released a set of interactive visuals that present data on school spending and its various components—such as instructional spending, instructional support, leadership support, and building services spending—across all thirty-two community school districts (CSD) in New York City and map their progression over time. A key feature of these interactive visuals is that they present the data in two forms: as adjusted data, which control for student categories that receive differential funding from the City based on their needs, and as raw data that do not include this adjustment. The interactive features allow the user to easily view (and compare) the adjusted and raw data, to observe trends for different spending categories, and to compare spending profiles across community school districts for each form of data. Demographic and socioeconomic characteristics of each CSD can be viewed by clicking on the district of interest. Our purpose is to make data on education finance and education indicators more accessible to a broader audience, including education researchers.
Many newly minted college graduates entering the labor market in the wake of the Great Recession have had a tough time finding good jobs. But just how difficult has it been, and are things getting better? And for which graduates? These questions can be difficult to answer because timely information on the employment prospects of college graduates has been hard to come by. To address this gap, today we are launching a new interactive web feature to provide data on a wide range of job market metrics for recent college graduates, including trends in unemployment rates, underemployment rates, and wages. We also provide data on the demand for college-educated workers, as well as differences in labor market outcomes across college majors. These data will be updated regularly and are available for download.
With the college graduation season well under way, a new crop of freshly minted graduates is entering the job market and many bright young minds are hoping to land a good first job. It’s no wonder if they are approaching the job hunt with some trepidation. For a number of years now, recent college graduates have been struggling to find good jobs. However, the labor market for college graduates is improving. After declining for nearly two years, openings for jobs requiring a college degree have picked up since last summer. Not only has this increase in the demand for educated workers continued to push down the unemployment rate for recent graduates, but it has also finally started to help reduce underemployment, though the underemployment rate remains high. While successfully navigating the job market will likely remain a challenge, it appears that finding a good job has become just a little bit easier for the class of 2015.
This morning, the Federal Reserve Bank of New York released a set of interactive visuals that present school spending and its various components—such as instructional spending, instructional support, leadership support, and building services spending—across all thirty-two Community School Districts (CSD) in New York City and map their progression over time. The interactive features allow the user to easily view the data and observe spending trends. Our purpose is to make data on education finance more accessible to a broader audience.
Uncertainty is of considerable interest for understanding the behavior of individuals as well as the movements in key macroeconomic and financial variables. Despite its importance, direct measures of uncertainty aren’t widely available. Because of this data limitation, a common practice is to use survey-based measures of forecast dispersion—reflecting disagreement among respondents—to proxy for uncertainty. Is this a reliable practice? Here, we review the distinction between disagreement and uncertainty as concepts, and show that this conceptual distinction carries over to their empirical counterparts, suggesting that disagreement is not generally a good proxy for uncertainty.
In the first of this two post series, we investigated the relationship between state aid and local funding before and after the Great Recession. We presented robust evidence that sharp changes in state aid brought about by the prolonged downturn influenced local budget decision-making. More specifically, we found that relative to the pre-recession relationship, a dollar decline in state aid resulted in a $0.19 increase in local revenue and a $0.14 increase in property tax revenue in New York school districts. In this post, we dive deeper to consider whether there were variations in this compensatory response across school districts, using an approach described in our recent study. For example, one might expect that there would be differences in willingness and ability to offset cuts in state aid across districts with varying levels of property wealth, which in turn might lead to differences in responses. Was this really the case?
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