In this paper I mainly used expenditure
records of four types of postsecondary institutions to discover where schools
have been investing in. I used fixed effect (FE) model regression to find out
contributing effects of investment on instruction and research and inferred
quality change of higher education. The results are that private institutions
and public research institutions all experienced some levels of quality
improvement while community colleges are left behind.
There
are several advantages of this approach. Firstly, expenditure data are
relatively complete and accessible to general public[1]. In
addition, using expenditure per FTE student as a measure of quality avoids
particular assumptions about the best, or preferred, way for schools to
allocate their total resources among specific inputs. (Ladd and Loeb, 2012) For
example, an institution can use a certain amount of instruction per FTE student
either for smaller classes with less experienced instructors or larger classes
with more experienced instructors. With no evidence showing that certain categories
of resources are preferred to others in all postsecondary institutions, it would
be inappropriate to conclude that one way of spending is absolutely better than
the other. Finally, this measure allows for straightforward comparisons across
schools, and by using regression tests, researchers can also study how one
category of spending affects total expenditure.
However, there are three main limitations
with this method. Firstly, different schools report data under various accounting
standards, which creates debate upon what data reports to use. Besides, we can
only draw an inference of how quality changes by discovering where money has
gone and whether institutions are putting more money in core missions like
instruction. Lastly, this method takes no account of differences in
effectiveness or efficiency with which dollars are spent. The key assumption of
input measure is that high quality inputs tend to produce better education
provided that market is competitive. We don’t know if students will fully
utilize inputs deemed to be of high quality, such as whether students will have
more contact with professors or do more critical thinking. Even though higher
education market is generally competitive (Hoxby 1997), there would still be concern
about inefficiency given the unique governance of institutions.[2]
Since data on mean SAT scores are
incomplete, I couldn't create a quality index to give numerical assessment of
quality, which is another popular input measure methodology. Two papers can exemplify
and reveal questions about this approach. Black and Smith (2006) used index as
proxy of college quality to discover its impact on graduates’ income. They
first summarized previous related researches and then compared four measures
with multiple proxies. They found that Generalized Method of Moments (GMM)
estimation is the most efficient econometrics model and proxies that are
powerful are mean SAT scores, mean faculty salaries, student-faculty ratio,
application rejection rate and first year retention rate. Cohodes and Goodman
(2013) criticized that Black and Smith’s choice of proxies is partly flawed
because of certain level of perfect correlation. Instead, they introduced
another proxy, namely college completion rate, into their index. One obvious problem
with this approach is that quality index is created rather arbitrarily: there
is no consensus about what proxies should be chosen and what weights should be
assigned. What’s more, most researchers use index to function as a regressor
for graduates’ income. In other words, quality of education is not their major
concern.
Recently there are some new approaches
trying to do a better job. For example, Porter (2012) argued that detailed student
self-report can help discover what they have obtained in college. This attempt
is a value-added measure and is theoretically desirable. However, the validity of this approach depends
on whether reports are well-designed.
Another attempt is more promising. With the
advent of online course platforms like Massive Open Online Course (MOOC) system,
people are able to learn college materials without actually sitting in a
classroom. I think it possible to design a value-added like measure. We can do comparison
tests between two groups of students with similar abilities and motivations and
have them take several courses. One group learns materials in class, while the
other takes online versions. The main control is that the former group is
accessible to all those services related to instruction while the group that
takes online versions is not. At the end of the semester both groups take the
same tests and we compare their test results. If students who learn materials
in class do perform better, then we might conclude that sitting in class and
enjoying services like academic support can help improve students’ performance;
then we can step further and test to what extent does these services help and
find out possible inefficiency in terms of expenditure. This method is not
impeccable, however, since we might also count peer effect as positive effect
of instruction related spending.
As Robert Pirsig (1974) wisely wrote, “Some
things are better than others; that is, they have more quality. But when you
try to say what the quality is, apart from the things that have it, it all goes
poof. ” Quality measure in terms of higher education is hard because many
aspects of college life cannot be quantified, and there are a lot of factors
like student motivation that affects student performance. We might never know
how college undergraduate education really bestows on us, and all we can do is
come up with some ways to get a better approximation.
references:
1. Ladd,
Helen and Susanna Loeb. “The Challenge of Measuring School Quality:
Implications for Educational Equity,” Education,
Justice and Democracy, The University of Chicago Press, pp. 22-55,
forthcoming
2. Black,
Dan and Jeffrey Smith. “Estimating the Returns to College Quality with Multiple
Proxies for Quality.” Journal of Labor
Economics, Vol. 24, No. 3, 2006
3. Cohodes,
Sarah and Joshua Goodman. “Merit Aid, College Quality and College Completion:
Massachusetts’ Adams Scholarship as an In-kind Subsidy.” American Economic Journal: Applied Economics, forthcoming, March
2013
4. Pirsig,
Robert. Zen and the Art of Motorcycle
Maintenance: An Inquiry into Values. Harper Torch; Reprint edition (April
25, 2006) ISBN-10: 0060589469
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