attention of policy makers on the formula for calculating cohort default rates.
Using four years rather than the more common 12 to 24 months as the time
frame, Choy and Li (2006) showed that default rates increased by as much as 6
percent among some groups of students and by as much as 60 percent among
some types of institutions (Lederman, 2008). It is not surprising that federal
policy makers looking at these numbers were asking again how much default is
acceptable and what factors contribute to it. Their efforts to define default and
to decide if default rates should be used as indicators of institutional quality or
loan program efficacy raise complicating questions. Is default a function of the
characteristics of students or of the institutions they attend? Do the types of
loans influence the probabilities of default? Do life circumstances—like the
types of jobs and income levels of students after they graduate—have an impact
on default rates? To help policy makers and practitioners answer these and
other questions surrounding the reauthorization process, we offer this review
of the research literature on the predictors of student loan default.
Method Our literature search for studies of student loan default targeted peer-reviewed
journals in the fields of higher education as well as economics, sociology, and
finance. We also used a variety of databases—such as EBSCO, Lexis-Nexis
Academic, and JSTOR—to identify relevant reports or articles that may not
have been published in journals. Using a template to systematically note key
themes and important features of the reviewed studies—such as the study’s
quality and scope and the database the researchers used—we identified,
reviewed, and summarized 41 studies of student loan default conducted
between 1978 and 2007, most of which were done after 1991.
While writing each summary, we used qualitative data analysis software
(ATLAS.ti 5.2) to flag key findings and significant points with predetermined
codes such as race/ethnicity or institutional type as well as emergent codes.
These 45 codes were then grouped into thematic areas, forming the basis for
the synthesis below. Although some research in this area has treated race,
gender, and loan default separately, they are manifestly entangled. Using
qualitative data analysis software enabled us to see the overlapping and
intersecting themes across the literature on student loan default and to develop
a systematic, comprehensive map of this complex terrain.
Empirical research employing multivariate statistical techniques that
controlled for multiple complicating factors received the most attention in our
review. While descriptive studies often make for simple and interesting trend
analyses, they do not reveal underlying interactions between student
characteristics and other factors—such as choice of major, type of institution,
type of student loan, graduation status, postcollege employment and income,
and student loan repayment status. Only the studies that simultaneously
controlled for a range of variables could identify the predictors of student loan
default. In addition, we focused more on studies that used national databases
and that had larger samples.
Among the studies we reviewed, the chief limitation was that the research
that was most robust in scope and methodology was conducted during the late
1980s and, especially, in the mid to late 1990s. Because few multivariate studies
using national databases have been undertaken in the last seven years, much of
the best research on this topic was conducted a decade or more ago—during a
different historical context. It is possible that some patterns or trends have
changed since the late 1990s. For example, Baum and O’Malley (2003a, 2003b)
reported a fall in the debt levels of African American students between 1997 and
20 Journal of Student Financial Aid Volume 39 • Number 1 • 2009
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