Estimation of Educational Borrowing Constraints Using Returns to ...

educational borrowing constraints 145
because they are not drawn according to exogenously determined characteristics.
15 Second, we use only males because their schooling and labor
supply decisions are less complicated by fertility and labor market participation
considerations. Third, because information about events occurring
before January 1978 is retrospective and limited, we confine
our sample to males between ages 13 and 16 as of that date in order
to have reliable information on schooling attendance, parental income,
and county of residence. County of residence is used to construct measures
of labor market conditions and measures of college proximity.
Finally, 13 percent of the sample was eliminated because respondents
did not complete the Armed Services Vocational Aptitude Battery
(ASVAB) (see below) or because of missing data in county of residence
during high school (measured at age 17 when available), family income,
or one of the family background variables. Final sample sizes are reported
in table 1.
We construct panel data from NLSY annual observations. Annual observations
on each respondent begin no later than age 16 and extend
through ages 29–33. For the analysis of wages, we exclude annual wage
observations taken before age 22 because most college graduates were
still in school before that age. 16
Summary statistics of the main variables used in the analysis are presented
in table 1. Panels A–C show summary statistics of static variables
for years of schooling completed, racial-ethnic identity, family background,
and geographic location measured at age 17. Details of the
variables are provided in the notes to the table.
Panel D shows summary statistics for test score variables. The scores
are taken from the 10-part ASVAB. The test was administered to NLSY
respondents in the summer of 1980, when respondents in our sample
were between ages 15 and 18. The set of four variables word score, math
score, science score, and automotive score are raw scores on four of the
ASVAB sections. The variable AFQT score is the score on the Armed
Forces Qualification Test, a weighted sum of ASVAB components that
measure literacy and basic mathematics skills. This variable has been
widely used in recent empirical work. The other four test scores are
used together in the analysis below as an alternative to AFQT score.
Taber (2001) contends that these four scores represent a parsimonious
15
Thus our data include all observations from the random sample and the random
black and Hispanic oversamples. The military and the nonblack, non-Hispanic disadvantaged
samples are small relative to the data we include. In addition, the military oversample
was discontinued after the 1984 wave of the NLSY.
16
Starting the panel at an earlier age creates a data set unrepresentative of the population
since it would contain more annual observations for high school dropouts and
graduates than for college graduates. The estimates presented below are not sensitive to
higher age cutoffs.