ArticlePDF Available

Abstract and Figures

Most studies of student migration focus on interstate migration of college students, largely because the aggregate data typically used are limited in geographic specificity to states. However, interstate migration is only a small part of the total student migration. Public institutions generally get most of their students from within their state; for example, 88 percent of first-time freshmen who enrolled in University System of Georgia institutions in 2002 graduated from Georgia schools. Such intrastate migration is seldom considered. This paper examines intrastate college student migration, using data for Georgia. Aside from such traditional measures of benefits and costs like tuition, financial aid, and school quality, a crucial explanatory variable in our analysis is the distance from a student's home to the different Georgia state institutions. Our empirical results indicate that student intrastate migration is strongly discouraged by greater distance, but with effects that differ across types of higher education institutions.
Content may be subject to copyright.
Economics of Education Review 28 (2009) 728–738
Contents lists available at ScienceDirect
Economics of Education Review
journal homepage:
Distance and intrastate college student migration
James Alma,, John V. Wintersb
aDepartment of Economics, Andrew Young School of Policy Studies, Georgia State University, P. O. Box 3992, Atlanta, GA 30302-3992, USA
bDepartment of Economics, Auburn University at Montgomery, P.O. Box 244023, Montgomery, AL 36124-4023, USA
article info
Article history:
Received 29 November 2007
Accepted 3 June 2009
JEL classification:
College education
Gravity model
Intrastate migration
Most studies of student migration focus on interstate migration of college students, largely
because the aggregate data typically used are limited in geographic specificity to states.
However, interstate migration is only a small part of the total student migration. Public
institutions generally get most of their students from within their state; for example, 88
percent of first-time freshmen who enrolled in University System of Georgia institutions
in 2002 graduated from Georgia schools. Such intrastate migration is seldom considered.
This paper examines intrastate college student migration, using data for Georgia. Aside from
such traditional measures of benefits and costs like tuition, financial aid, and school quality,
a crucial explanatory variable in our analysis is the distance from a student’s home to the
different Georgia state institutions. Our empirical results indicate that student intrastate
migration is strongly discouraged by greater distance, but with effects that differ across
types of higher education institutions.
© 2009 Elsevier Ltd. All rights reserved.
1. Introduction
Pursuing higher education involves a significant migra-
tion decision by students, and most studies of student
migration focus on interstate migration of college stu-
dents (e.g., out-migration, in-migration, net migration),
largely because the aggregate data that are typically used
in these studies are limited in geographic specificity to
states. However, interstate migration is only a small part
of the total student migration that actually occurs. Pub-
lic institutions generally get most of their students from
within their state; for example, 88 percent of first-time
freshmen who enrolled in University System of Georgia
(USG) institutions in 2002 graduated from Georgia schools
(Information Digest, 2002–2003), and many other states
exhibit a similar pattern. Such intrastate migration is sel-
dom considered. This paper examines intrastate student
migration using data for the USG, a statewide system of
Corresponding author. Tel.: +1 404 413 0093; fax: +1 404 413 4985.
E-mail addresses: (J. Alm), (J.V. Winters).
universities and colleges that includes all public institu-
tions of higher education in Georgia except for technical
colleges. Aside from such traditional measures of benefits
and costs like tuition, financial aid, and school quality, a
crucial explanatory variable in our analysis is the distance
from a student’s home to the different Georgia state institu-
tions. Our empirical results indicate that student intrastate
migration is strongly discouraged by greater distance, but
with effects that differ across types of higher education
Indeed, evidence on USG enrollment rates by school dis-
trict indicates that distance may in fact affect enrollments
in USG institutions. Fig. 1 provides a map of Georgia public
school districts and the 33 institutions of the USG. School
districts are shaded according to their USG enrollment rate,
defined as the number of first-time freshmen enrolling in
any institution of the USG divided by the number of high
school graduates; location of a USG institution is indicated
by an open circle. The shading in Fig. 1 suggests that school
districts that contain or are near a USG institution gen-
erally have a higher USG enrollment rate. However, this
evidence is only suggestive, and more systematic analysis
is necessary.
0272-7757/$ – see front matter © 2009 Elsevier Ltd. All rights reserved.
J. Alm, J.V. Winters / Economics of Education Review 28 (2009) 728–738 729
Fig. 1. Map of member institutions of the University System of Georgia
and Georgia public school districts shaded by participation rates. Notes:
School districts are shaded according to their USG enrollment rate, defined
as the number of first-time freshmen enrolling in any institution of the
USG divided by the number of high school graduates. USG institutions
are indicated by open circles. The black random shapes around some
open circles represent the boundaries for independent city school dis-
tricts. (Most school districts are coterminous with county boundaries,
but a few counties have independent city school districts within them.)
Source: constructed by the authors based on data from the USG and the
In-state students face two, sequential migration deci-
sions, both of which seem likely to be affected by distance.
First, a student must decide whether to pursue any type
of higher education. Second, conditional upon deciding to
pursue postsecondary education, the student must decide
which institution to attend. If there is no postsecondary
institution near a student’s home, then pursuing higher
education necessarily involves moving to a new place or
commuting long distances; even when there are higher
education options near the student’s home, the student still
has to decide if the best choice is a nearby institution or one
further away. If the student chooses an institution in his or
her home state, the institution may still be as far away as
two or three hundred miles in an average size state.
In this paper we examine these intrastate migration
decisions for first-time freshmen who graduate from Geor-
gia public high schools and attend a USG institution.
Georgia public high school graduates account for 81 per-
cent of first-time freshmen enrolled in the USG in 2002
(Information Digest, 2002–2003).1We are interested in
two related questions. First, what factors affect a student’s
probability of enrolling in a USG institution? Second, con-
1Graduates of Georgia private high schools account for an additional
seven percent.
ditional upon enrollment in a USG institution, what factors
affect a student’s choice among USG institutions? In both
cases, an important variable is the distance to the nearest
USG institution, and, along the lines of gravity models of
international trade (e.g., Bergstrand, 1985), we hypothesize
that the likelihood of attendance decreases as the distance
to the nearest USG institution increases. It seems likely that
individuals are more likely to enroll at all when they live
closer to an institution, and also that they are more likely to
enroll in institutions that are located closer to their home.2
We estimate the elasticity of higher education atten-
dance with respect to distance from home to college, using
a basic gravity model approach. We find that the overall
probability of attending any USG institution is decreas-
ing with the distance to the nearest USG college, with an
enrollment-distance elasticity of 0.12. We also estimate
the distance elasticity for enrollment in individual insti-
tutions separately for each institution, and we find that
demand for more prestigious institutions is less elastic with
respect to distance than less prestigious institutions.
In the next section we discuss previous research on stu-
dent migration. We then present in Section 3our approach
and our data. In Section 4we present our estimation results,
and we conclude in Section 5.
2. Previous work on student migration3
Much of the literature on student migration focuses on
the role of tuition and financial aid policies in the stu-
dent migration process, and most of this work focuses
upon interstate student migration. For example, using data
on U.S. states Tuckman (1970) finds that higher aver-
age tuition and fees in a state increase out-migration
from the state, while levels of financial aid do not affect
out-migration; Mixon (1992a, 1992b) also estimates that
higher in-state tuition repels students from their home
state. In general, most other studies conclude that stu-
dent enrollment responds in predictable ways to changes
in tuition and financial aid policies.4However, there are
exceptions. Baryla and Dotterweich (2001) find that higher
tuition increases the percentage of non-resident students
in the U.S. South and West.5Other variables that have been
examined include regional amenities such as climate and
participation in NCAA athletics (Mixon, 1992a; Mixon &
Hsing, 1994a, 1994b). Mixon and Hsing (1994a, 1994b) also
2Distance might also affect student retention and degree completion. If
the “costs” of attendance continue to be higher for students from further
away, we might expect distance to have a negative effect on both retention
and completion. A few previous studies support this notion. Card (1995)
finds that proximity to a college while in high school increases years of
completed education, and other research suggests that increases in col-
lege costs in general have a negative effect on college completion (Hoxby,
2004). However, an analysis of the effects of distance on student retention
and degree completion is beyond the scope of this paper.
3See Greenwood (1975) and Ghatak, Levine, and Price (1996) for gen-
eral surveys of the migration literature.
4For reviews of the literature, see Leslie and Brinkman (1987),Heller
(1997), and Hoxby (2004).
5One explanation offered by McHugh and Morgan (1984) and Baryla
and Dotterweich (2001) is that tuition levels are correlated with educa-
tional quality and that the quality effect may offset the price effect.
730 J. Alm, J.V. Winters / Economics of Education Review 28 (2009) 728–738
find that the percentage of non-resident student enroll-
ment increases for institutions classified as historically
These interstate studies do not incorporate distance into
the framework. However, it seems obvious that distance
might affect student migration, and several other stud-
ies have incorporated distance into the student interstate
migration framework. The earliest work is by Gossman,
Nobbe, Patricelli, Schmid, and Steahr (1968), who find that
distance has a strong deterrent effect on student inter-
state migration, with a distance elasticity of enrollment
between 1.5 and 2.0. Using state-to-state migration
flows, McHugh and Morgan (1984) also find a distance
deterrence effect with a distance elasticity of 1.3. Kyung
(1996) examines student in-migration to New York state,
and estimates a much smaller (but still negative) elasticity
of 0.5.6
These studies are concerned with U.S. interstate student
migration. However, intrastate migration is important as
well. A number of studies examine the importance of dis-
tance for intrastate migration, including McConnell (1965),
Kariel (1968),Ullis and Knowles (1975),Leppel (1993),
Ordovensky (1995),Desjardins, Dundar, and Hendel
(1999), and Ali (2003). All of these works (except Desjardins
et al. (1999)) find a significant deterrent effect. However,
only Ullis and Knowles (1975) and Ali (2003) compute
intrastate distance elasticities. Ullis and Knowles (1975)
estimate separate equations for four state colleges in Wash-
ington state, and estimate distance elasticities ranging from
0.4 to 1.7. Ali (2003) examines interstate and intrastate
student migration to West Virginia University, and esti-
mates an interstate elasticity of 2.2 and an intrastate
elasticity of 0.7, suggesting that out-of-state students are
considerably more deterred by distance than are in-state
Comparing these studies, we see that the distance elas-
ticity of enrollment demand is almost always significantly
negative, but ranges significantly in value. Reasons for the
variance are unclear, but it is certainly likely that enroll-
ment demand in different geographic areas and different
institutions is affected by distance in a non-uniform man-
ner, and these aspects have not been fully examined. Lowe
and Viterito (1989) assess the market penetration of private
colleges and universities in the U.S., and argue that individ-
ual schools have either a local, regional, or national base of
appeal. They also find that market penetration is highly cor-
related with mean SAT scores indicating that more selective
schools tend to have larger bases of appeal. Of course, other
factors could explain the variability as well.
Despite the many insights from this work, there remain
important gaps. Most studies focus on interstate student
migration; of those that examine intrastate migration, the
focus is on flows to only a small number of state institu-
tions. Further, the role of distance is not always considered
in the intrastate studies, and, when it is incorporated,
distance elasticities are not typically generated. Our con-
tributions are to examine the role of distance for all state
6In a more recent study, Sà et al. (2004) examine interregional student
migration in the Netherlands, and report a distance elasticity of 1.3.
institutions in a single state (Georgia), to estimate the
resulting distance elasticities, and to estimate institution-
specific intrastate distance elasticities.
3. Methods
3.1. A gravity model of student migration
We use a gravity model of student migration to estimate
the distance elasticity of enrollment demand for the Uni-
versity System of Georgia. Migration flows Mij from school
district i to institution j can be written as:
Mij =AijP˛1
ij ,(1)
where Piis the student population of school district i (the
origin area), Pjis the student population of institution j (the
destination area), dij is the distance from i to j, Aij =˘k(zk
is a multiplicative shift term that allows for inclusion of
Kvariables measuring school district “push” factors and
institution “pull” factors zkij that influence migration, and
(k,˛1,˛2,ˇ) are parameters. The gravity model specifies
migration flows as a function of the origin area popula-
tion, the destination area population, the distance between
the origin and destination, and a number of push and pull
variables. Larger school districts are expected to send more
students to higher education institutions, and larger insti-
tutions are expected to receive more students, so both ˛1
and ˛2are expected to be positive. The distance between
a school district and a particular institution is expected to
deter the flow of students between the two, so ˇis expected
to be negative. Distance is expected to deter student flows
because greater distance creates both greater monetary
costs and greater “psychic” costs to migration; greater dis-
tance may also deter student flows because it reduces the
amount of information (i.e. familiarity) a student receives
about a school. Examples of push factors include economic
and demographic characteristics of the school district. Pull
factors include characteristics of the institution.
Institutions are not evenly dispersed over space, so the
model also needs to account for the fact that some institu-
tions are geographically closer to other institutions while
some are quite distant from the nearest institution. One
way to do so that has been suggested by Fotheringham,
Nakaya, Yano, Openshaw, and Ishikawa (2001) and by Sà,
Florax, and Rietveld (2004) is to include a centrality index
that measures for each institution j the spatial competi-
tion between institutions. Defining the centrality index to
be the first of the Kvariables in Aij and suppressing the i
subscript (because the index is measured at the institution
level), the index for institution j is computed as:
z1j =˙m(Pm/dmj),(2)
where Pmis the student population of institution m and dmj
is the distance between institutions m and j. The centrality
index is a population-weighted average of the inverse dis-
tance between institution j and the other M1 institutions
in the system. If there are many close institutions with large
student populations, then the centrality index for institu-
tion j would have a large value, indicating that institution j
is subject to greater spatial competition.
J. Alm, J.V. Winters / Economics of Education Review 28 (2009) 728–738 731
We linearize the gravity model by taking the natural
logarithm of both sides of Eq. (1), which yields our basic
estimation equation:
ln Mij =˙kk(ln zkij)+˛1ln(Pi)+˛2ln(Pj)+ˇln(dij ).(3)
The coefficients can now be interpreted as elasticities of
enrollment demand. The transformed gravity model is usu-
ally estimated via Ordinary Least Squares (OLS) methods.
However, it is often the case that the flows are left-censored
at zero, causing OLS to be inconsistent. Therefore in esti-
mation where flows of zero exist, we add one to all flows
before taking the log and then estimate equation (3) using
the standard Tobit model. Amemiya (1973) shows that the
Tobit estimator is consistent and asymptotically normal.
3.2. Data
Our data come from several sources. We obtain data on
first-time freshman student flows from 175 public school
districts in Georgia to the 33 member institutions of the
University System of Georgia for 2002 using the USG’s High
School Feedback Reports.7We merge these data with infor-
mation on school district characteristics from the National
Center for Educational Statistics (NCES) Common Core of
Data (CCD), School District Demographics System (SDDS),
and USG institutional characteristics from the NCES Inte-
grated Postsecondary Education Data System (IPEDS). The
year 2002 was chosen for the analysis largely because of
data limitations. The High School Feedback Reports are only
available beginning in 2000, and the additional data used in
the analysis are not available for more recent years. Because
of our focus on the year 2002, we are unable to determine
if the effects of distance are rising or falling over time. Our
dataset excludes Georgia private high school graduates,
non-resident students attending USG institutions, and stu-
dents not attending a USG institution. While the migration
patterns of these student groups are certainly interesting,
the focus of our study is on the intrastate college migration
of Georgia public high school graduates to schools within
the USG.
One of our key variables is distance. Our variable Dis-
tance (as well as its variants) is calculated using Geographic
Information Systems (GIS) via ArcMap, and is measured in
miles (before taking logs). We use Census boundary maps to
calculate geographic centroids for each school district. USG
institutions are then geocoded, and straight-line distances
from each institution to each of the 175 school district cen-
troids and between institutions are computed using the
great circle distance formula.8,9
7According to the National Center for Education Statistics (NCES), there
are 183 unified school districts in Georgia. However, only 175 of them go
through the 12th grade and produce high school graduates.
8One could also compute the “over-the-road” distance between school
districts and colleges, but, because the network of highways in Georgia is
fairly dense, these two measures are very highly correlated.
9In computing distance for institutions with multiple campuses, we
consider only the geographic location of the main campus. Unfortunately,
enrollment flows from school districts to institutions are not broken down
by campus, so incorporating distance to satellite campuses would not be
helpful unless enrollment flows to an institution can be allocated among
its multiple campuses. Given that most USG institutions have a single
As for other variables, the school district population Pi
is calculated as the total number of persons receiving high
school diplomas from a given school district in 2002 (HS
Diplomas). Similarly, the destination area population Pjis
defined as the total number of freshmen who graduated
from a Georgia public high school in 2002 and chose to
attend a given institution minus the flow from school dis-
trict i (GA Public HS Freshmen (not i)). The flow from i is
subtracted to avoid having an explanatory variable that is
a linear function of the dependent variable. Distance and
population are also used to compute the centrality index
Other variables include institution-specific pull factors
and origin-specific push factors, as well as some basic con-
trol variables. Pull variables include mean SAT scores (SAT),
the percentage of freshmen receiving the Georgia HOPE
scholarship (Percent HOPE), per pupil USG expenditures
(Expenditures USG), the estimated total cost of attendance
for resident students living off-campus (Cost), and the per-
centage of students who are Black in a given USG institution
(Percent Black USG). The first three variables measure the
quality of the institution, and are expected to attract stu-
dents. The cost of attending a particular school is expected
to have a deterrent effect on student flows. We use the
total cost of attendance instead of tuition for several rea-
sons. Tuition is set by the USG for each sector of the system
with virtually the only variation occurring across different
sectors and not within them. Also, 78 percent of first-time
freshmen in the USG from Georgia high schools received
the Georgia HOPE scholarship in 2002, rendering tuition
even less important.10 The cost of attendance is also more
appropriate than tuition because Cost encompasses dif-
ferences in the cost of living at the different institutions.
Students on average may be either drawn to or repelled
by historically Black institutions, so the expected sign on
Percent Black USG is unclear. When appropriate, we also
include a dummy variable equal to one if an institution
is located in the Atlanta metropolitan area (Atlanta), and
dummy variables for the different academic sectors of USG
institutions (Research University,Regional University,State
University,State College).11
The push variables are the percentages of high school
graduates in a school district that are Black, Hispanic, and
Asian (Percent Diplomas Black,Percent Diplomas Hispanic,
Percent Diplomas Asian), as well as the median household
income in a school district (Median Income), the percent-
age of persons 25 and older in a school district with a
campus and that those with multiple campuses tend to have most of their
enrollment at their main campus, we do not believe that the existence of
satellite campuses is a major problem.
10 Note that some of these students lost the HOPE Scholarship in later
semesters due to failure to maintain the required grade point average.
11 The USG consists of 35 higher education institutions, with 33 of them
enrolling undergraduates in 2002. These institutions are classified into
five sectors, each with different missions. In 2002 the five sectors included
three research universities, two regional universities, thirteen state uni-
versities, two state colleges, and thirteen two-year colleges. Eight of these
are located in the Atlanta metropolitan area: Atlanta Metropolitan College,
Clayton State University, Georgia Institute of Technology, Georgia Perime-
ter College, Georgia State University, Kennesaw State University, Southern
Polytechnic State University, and the University of West Georgia.
732 J. Alm, J.V. Winters / Economics of Education Review 28 (2009) 728–738
Table 1
Summary statistics.
Variable Mean Standard deviation Minimum Maximum
ln flow 0.497 1.027 0 6.913
ln (flow + 1) 0.636 1.048 0 6.914
ln distance 4.667 0.708 0.906 5.841
ln distance to closest USG institution 2.658 0.960 0.9066 3.715
ln distance to closest college 3.137 0.779 0.664 4.418
ln distance to closest university 3.096 0.992 0.906 4.367
ln HS diplomas 5.198 1.063 2.197 8.719
ln GA public HS freshmen 6.444 0.763 4.977 7.995
ln GA public HS freshmen (not i) 6.438 0.764 4.249 7.995
Percent diplomas Black 0.320 0.254 0 1
Percent diplomas Hispanic 0.016 0.030 0 0.272
Percent diplomas Asian 0.011 0.016 0 0.105
ln expenditures school district 9.003 0.171 8.674 9.994
Percent BA degree 0.147 0.0820 0.055 0.559
ln median income 10.425 0.240 9.902 11.175
ln expenditures USG 9.498 0.391 8.943 10.703
Percent HOPE 69.164 16.877 40.100 98.900
ln SAT 6.869 0.105 6.697 7.189
ln cost 9.328 0.213 8.721 9.733
ln centrality 5.886 0.619 5.110 7.505
Percent Black USG 30.539 25.252 2.100 94.400
bachelor’s degree or higher (Percent BA Degree), and expen-
ditures per pupil for each school district (Expenditures
School District).12 Demographic controls for school districts
are included because some ethnic minorities might have
lower rates of college enrollment than white students. Sim-
ilarly, students in wealthier districts and more educated
districts might be more likely to enroll in the USG. In some
estimations, we include four school district metropolitan
status indicator variables. Metro1 is a dummy variable for
the city of Atlanta, Metro2 represents central city school
districts in all other metropolitan areas in Georgia, Metro3
represents school districts in the Atlanta metropolitan
area excluding the city itself, and Metro4 represents non-
central city school districts in metropolitan areas other
than Atlanta. Non-metropolitan districts are the excluded
group when metropolitan dummies are employed.13 Sum-
mary statistics for all continuous variables are in Table 1.14
4. Empirical findings
4.1. Attending any USG institution?
Table 2 presents estimation results on how the like-
lihood of attending any USG institution varies with the
distance to the closest college or university, the number
of high school graduates in a district, and the various push
12 While we include the percent Black, Hispanic, and Asian of high school
graduates in a school district, we do not include percent Hispanic and
percent Asian for institutions in the USG. The rationale is that Hispanics
and Asians are disproportionately concentrated in certain school districts
but are not particularly concentrated within USG institutions. On the other
hand, Blacks tend to be highly concentrated in both school districts and
USG institutions.
13 Of the 175 school districts, one is classified as Metro1,14areMetro2,
25 are Metro3, and 11 are Metro4. The remaining 124 school districts are
not in metropolitan areas.
14 Note that ln Distance is negative between three school districts and
institutions because the computed distance is less than one mile. Recoding
the three negative values to zero (e.g., one mile) does not affect our results.
factors. This is not a standard gravity model because there is
only one destination, the USG, so we do not include institu-
tional pull factors.15 Table 2 presents various specifications.
The results in column (1) indicate that distance to the
closest USG institution has a significantly negative effect
on enrollment in the USG. The distance elasticity of 0.075
suggests that a one percent decrease in the distance to the
nearest USG institution increases enrollment in the USG by
0.075 percent. Because this is the first study to estimate the
elasticity of system enrollment with respect to the distance
to the nearest institution of a state university system, we
cannot compare this estimate to other distance elasticity
estimates in the literature. Even so, the conclusion is clear:
distance has an important deterrent effect on enrolling in
the USG. If public policies can decrease the distance Geor-
gia public high school graduates must travel to attend the
nearest USG institution, then more graduates will pursue
higher education; that is, the USG can enroll more students
by creating new institutions and new campuses for exist-
ing institutions that are closer to areas in which high school
graduates live.
The other variables in column (1) are school district
characteristics used as controls to account for heterogene-
ity in origin areas. The most important and significant
variable is the number of persons receiving diplomas from
the school district in 2002. Its estimated coefficient sug-
gests that a one percentage point increase in the number
of diploma recipients from a school district increases the
number of first year USG enrollees from the district by
roughly one percent, or an elasticity of about unity.
The race/ethnicity variables are included because of a
concern that districts with a high percentage of minority
students would send fewer graduates to the USG. However,
the results in column (1) do not suggest that this is the case.
15 However, we did experiment with including characteristics of the
closest institution, closest university, and closest college. These variables
were generally insignificant.
J. Alm, J.V. Winters / Economics of Education Review 28 (2009) 728–738 733
Table 2
Attending any USG institution?
Estimation method Specification
Variable (1) (2) (3) (4) (5) (6)
ln USG enrollment ln USG enrollment ln USG enrollment ln USG enrollment ln college enrollment ln university enrollment
ln distance closest 0.075*** [0.023]
ln distance closest college 0.122*** [0.023] 0.116*** [0.021] 0.647*** (0.061) 0.241*** [0.037]
ln distance closest university 0.017 [0.022] 0.018 [0.022] 0.131** (0.058) 0.088*** [0.030]
ln HS diplomas 1.033*** [0.035] 1.033*** [0.035] 1.034*** [0.036] 1.048*** [0.036] 0.930*** (0.071) 1.032*** [0.043]
Percent diplomas Black 0.101 [0.118] 0.09 [0.116] 0.097 [0.118] 0.101 [0.119] 0.156 (0.228) 0.438*** [0.155]
Percent diplomas Hispanic 1.964*** [0.681] 1.382** [0.666] 1.346** [0.648] 1.999*** [0.751] 0.049 (1.716) 3.232*** [1.210]
Percent diplomas Asian 1.509 [1.259] 1.615 [1.236] 1.564 [1.202] 1.513 [1.175] 0.641 (3.603) 1.87 [1.931]
ln expenditures school district 0.401** [0.195] 0.383** [0.186] 0.384** [0.188] 0.412** [0.202] 0.483 (0.342) 0.175 [0.207]
Percent BA degree 0.463 [0.371] 0.721*[0.380] 0.811** [0.340] 0.851** [0.375] 0.606 (0.869) 1.715*** [0.465]
ln median income 0.184 [0.138] 0.121 [0.132] 0.117 [0.131] 0.117 [0.137] 0.468 (0.330) 0.496** [0.203]
Metro1 0.930*** [0.142] 1.005*** [0.157] 0.949*** [0.140] 0.690*** [0.144] 0.595 (0.652) 0.986*** [0.218]
Metro2 0.072 [0.077] 0.037 [0.067] 0.023 [0.063] 0.021 [0.074] 0.418** (0.206) 0.09 [0.114]
Metro3 0.064 [0.045] 0.048 [0.046] 0.053 [0.047] 0.083*[0.047] 0.071 (0.156) 0.152** [0.071]
Metro4 0.064 [0.072] 0.085 [0.068] 0.084 [0.068] 0.065 [0.076] 0.139 (0.187) 0.005 [0.088]
Observations 175 175 175 175 175 175
Censored observations 00004 0
R-squared 0.95 0.96 0.96 0.95 0.93
Pseudo R-squared 0.50
Notes: Robust standard errors are in brackets, and Tobit standard errors are in parentheses.
*Significant at 10%.
** Significant at 5%.
*** Significant at 1%.
734 J. Alm, J.V. Winters / Economics of Education Review 28 (2009) 728–738
Percent Diplomas Black and Percent Diplomas Hispanic have
positive coefficients of 0.101 and 1.964, respectively, but
only the latter coefficient is statistically significant. Percent
Diplomas Asian is the only one with a negative coefficient
at 1.509, but the coefficient is not statistically significant.
Somewhat surprisingly, the coefficient on school district
expenditures is 0.401 and statistically significant. This
result may suggest that higher spending school districts
have to spend more per pupil because their students come
from more disadvantaged backgrounds and are less pre-
pared for college. It may also be consistent with arguments
by Hanushek (2003) and others that increased spending
on education does not result in improved educational out-
As expected, the percent of adults in the district with a
college degree and the median income in the district have
a positive effect on USG enrollment, but neither coefficient
is significant. Of the four metro dummies, only Metro1, the
dummy for Atlanta City Schools, is statistically significant,
with a coefficient of 0.930. This result suggests that grad-
uates from Atlanta City Schools are disproportionately less
likely to attend a USG institution. Many of the best students
may opt out of the district by enrolling in private schools
or by moving to nearby districts to avoid the problems that
often plague central city school districts such as Atlanta.
Because we treat the entire USG as the destination area,
we lose information on the importance of institutional
characteristics. In order to recover some of these institu-
tional characteristics, we look at different classifications of
USG institutions. We combine the five sectors of the USG
into two groups, colleges and universities, and we test if
enrolling in the USG is differentially affected by distance to
the nearest college and distance to the nearest university.16
Column (2) shows the results. Distance to the closest col-
lege has a significant deterrent effect on enrolling in the
USG, with a distance elasticity of 0.122. Distance to the
closest university has an elasticity of 0.017 but is not
statistically significant. Columns (3) and (4) estimate a sim-
ilar specification including only one distance variable each,
with distance to the closest college in column (3) and dis-
tance to the closest university in column (4), to test if there
is an adverse effect from including the two distance vari-
ables in the same regression. Distance to the closest college
is virtually unchanged in its effect, and distance to the clos-
est university becomes positive but is still insignificant.
These results confirm that the state and two-year col-
leges of the USG play an important role in bringing students
into the system. Bringing colleges closer to students low-
ers the costs of pursuing higher education and encourages
them to enroll in the USG. Results for the other variables
in columns (2), (3), and (4) are virtually unchanged from
column (1), except that the percentage of adults with a
bachelor’s degree is significantly positive in columns (2),
(3), and (4), suggesting that districts with a more educated
populace enroll more students in the USG.
16 The “colleges” category includes the two state colleges and the thir-
teen two-year colleges, while “universities” includes the three research
universities, two regional universities, and thirteen state universities. See
also Note 11.
We are also interested in how the choice between col-
leges and universities is affected by the distance to the
nearest college and distance to the nearest university. Col-
umn (5) looks at enrollment in colleges (and is estimated
with Tobit methods), and column (6) looks separately at
enrollment in universities. Column (5) shows that college
enrollment is significantly deterred by distance to the near-
est college with an elasticity of 0.647 but that the distance
to the nearest university increases college enrollment with
an elasticity of 0.131. Column (6) shows parallel results
for university enrollment. Distance to the nearest univer-
sity decreases university enrollment with an elasticity of
0.088, but distance to the nearest college increases uni-
versity enrollment with an elasticity of 0.241. Together,
these results suggest that, when students choose between
attending a college or a university, they again take distance
into consideration in assessing the relative costs and ben-
efits. In particular, students who live nearer to colleges are
more likely to attend colleges, and students who live nearer
to universities are more likely to attend universities. This
result is quite similar to the findings of Ordovensky (1995)
for the NCES High School and Beyond longitudinal survey
respondents. He finds that the probability of attending a
two-year college is decreasing in the distance to the near-
est two-year college and increasing in the distance to the
nearest four-year school, and that the probability of attend-
ing a four-year school is increasing in the distance to the
nearest two-year college and decreasing in the distance to
the nearest four-year school.
These results have important implications for the USG
and other state systems of higher education. Decreas-
ing distance to the nearest university has little effect on
the overall likelihood of enrolling in any USG institution,
but increases the likelihood of enrolling in a university.
Decreasing distance to the nearest college increases the
likelihood students will enroll in the USG by increasing
the likelihood that they will enroll in colleges. However,
decreasing distance to the nearest college also has the
negative effect of decreasing the number of first-time fresh-
men enrolling in universities. These results suggest that we
can think of students as choosing between three ordered
choices: attending a university, attending a college, and
not attending either. The distance elasticities suggest that
there are two types of students making marginal choices.
First, there are students choosing between attending a col-
lege and attending neither. Decreasing the distance to the
nearest college increases the likelihood the students will
choose to attend college. Second, there are students who
have decided to pursue higher education but must choose
between attending a college or a university. A decrease in
the distance to the nearest university increases the likeli-
hood the students will choose a university. However, the
closer is the nearest college the more likely it is that a stu-
dent will choose a college.
These findings are important because colleges, espe-
cially two-year colleges, are not perfect substitutes for
universities. The apparent hope is that students in aca-
demic tracks at two-year colleges will eventually transfer
to four-year schools and obtain a bachelor’s degree. How-
ever, there is some evidence that this is not necessarily
the case. Alfonso (2006) finds that enrolling in a two-
J. Alm, J.V. Winters / Economics of Education Review 28 (2009) 728–738 735
Table 3
Attending a specific USG institution? USG gravity model estimates.
Variable ln flow
ln distance 1.345*** (0.027)
ln GA public HS freshman (not i) 0.625*** (0.042)
ln expenditures USG 0.963*** (0.152)
Percent HOPE 0.011*** (0.003)
ln SAT 4.170*** (0.915)
ln cost 0.514*** (0.137)
ln centrality 0.279*** (0.085)
Percent Black USG 0.008*** (0.003)
Atlanta 0.272*** (0.098)
Research university 0.679*** (0.241)
Regional university 1.296*** (0.182)
State university 0.360** (0.140)
State college 0.771*** (0.105)
Observations 5775
Censored observations 3547
Pseudo R-squared 0.31
Notes: Tobit standard errors are in parentheses.
** Significant at 5%.
*** Significant at 1%.
year college decreases a student’s probability of ultimately
obtaining a bachelor’s degree relative to enrolling in a four-
year institution. The costs of migrating from a two-year to
a four-year school might deter some students from trans-
ferring after their two years are complete. It may also be
the case that two-year colleges provide a lower quality
education, so that students who transfer from a two-year
college are less well-prepared for upper level classes than
students who enrolled in four-your schools as freshmen. If
this is true, then the USG faces a major dilemma concerning
the expansion of two-year colleges. Increasing accessibil-
ity to two-year colleges will bring more students into the
system, but will divert some students from universities to
colleges where they may be less likely to eventually obtain
a bachelor‘s degree.
4.2. Conditional upon attending any USG institution,
attending a specific USG institution?
Thus far we have been concerned with how enrollment
in any USG institution is affected by distance to the near-
est college or university. In this subsection we examine
how various factors (including distance) affect one’s choice
among the different USG institutions. We estimate a stan-
dard gravity model that includes the flows from each of
the 175 school districts to the 33 institutions, for a total
of 5775 flows. As suggested by Sà et al. (2004), we esti-
mate the model using school district fixed effects so that
all of the push variables disappear. This is done to con-
trol for unobserved heterogeneity in origin areas that is
imperfectly captured by the push factors. Because we are
examining more than one destination, we now include the
centrality index, the destination population, and the vari-
ous pull factors. We also include the Atlanta metropolitan
area institutional dummy variable and dummy variables
for the sectors of the institutions. Given the number of zero
flows, we use Tobit estimation.
Table 3 reports the results. All of the variables are sig-
nificant at the 5 percent level or greater. Distance has
a very strong negative effect, with an estimated elastic-
ity of 1.345. The destination population has a positive
coefficient of 0.625 that is significantly less than one. Insti-
tutional expenditures per pupil has a positive pull on
enrollment flows with a coefficient of 0.963, as does the
percentage of first-time freshmen who are Georgia res-
idents receiving the HOPE scholarship with a coefficient
of 0.011. However, SAT scores has a negative coefficient
of 4.17, so the evidence is mixed as to whether stu-
dents are drawn to higher quality institutions. Higher cost
of attendance and a higher percentage of Black students
deter enrollment flows with coefficients of 0.514 and
0.008, respectively. The centrality index has a coefficient
of 0.279, suggesting that greater spatial competition via
proximity to other institutions decreases enrollment in a
given institution.
The gravity model in Table 3 includes all 33 USG institu-
tions, and so implicitly assumes that the distance elasticity
and other coefficients are the same for every institution.
However, as pointed out by Ullis and Knowles (1975),it
is likely that the distance elasticity of enrollment demand
varies by institution. In particular, we might expect that
more prestigious and higher quality institutions have a less
elastic demand, and students may be more willing to travel
greater distances to attend a research university than a
two-year college. There also might be variables other than
educational quality that affect the distance elasticity.
To test this hypothesis we estimate for each USG insti-
tution a modified gravity model via the Tobit estimator,
including only the distance from the school district to
the institution and the push variables included in the
estimations in Table 2. Estimated distance elasticities of
enrollment demand for each of the 33 USG institutions are
reported in Table 4, along with standard errors and the
institution’s sector classification in 2002. Schools are listed
in descending order by the magnitude of their distance
Looking first at the bottom of Table 4, we see that
three schools have distance elasticities that are not signifi-
cantly different than zero. Georgia Institute of Technology’s
distance elasticity is positive but insignificant, Georgia
State University’s is negative and insignificant, and Georgia
Perimeter College’s is negative and close to significant.18
All other distance elasticities are negative and significantly
different from zero at the 1 percent level, and institutions
of the USG have distance elasticities that range from 0.016
(Georgia Tech) to 3.248 (Waycross College).
Echoing the sentiments of Lowe and Viterito (1989),
some institutions have statewide appeal while others have
a much more local appeal. While the institution-specific
distance elasticities are not perfectly ordered by sector,
there is a general pattern. The three research universities
of the USG have the lowest (in magnitude) distance elas-
ticities, and two-year and state colleges generally have the
highest elasticities; regional and state universities are gen-
17 The USG includes four institutions that are historically Black. These
are Albany State University, Savannah State University, Fort Valley State
University, and Atlanta Metropolitan College.
18 Georgia Perimeter College is somewhat of an outlier perhaps because
it is one of the few USG institutions with relatively large satellite cam-
736 J. Alm, J.V. Winters / Economics of Education Review 28 (2009) 728–738
Table 4
Institution-specific distance elasticities.
Institution Sector Elasticity Standard error
Waycross College Two-year college 3.248*** 0.584
Bainbridge College Two-year college 2.584*** 0.350
Dalton State College State college 2.476*** 0.332
Darton College Two-year college 2.161*** 0.187
Coastal Georgia Community College Two-year college 2.104*** 0.281
Gainesville College Two-year college 2.088*** 0.283
Georgia Highlands College Two-year college 2.085*** 0.213
Macon State College State college 2.060*** 0.272
Augusta State University State university 2.027*** 0.222
South Georgia College Two-year college 1.863*** 0.197
Gordon College Two-year college 1.614*** 0.207
Middle Georgia College Two-year college 1.563*** 0.171
East Georgia College Two-year college 1.384*** 0.159
Kennesaw State University State university 1.374*** 0.216
Georgia Southwestern State University State university 1.349*** 0.168
Columbus State University State university 1.320*** 0.156
Armstrong Atlantic State University State university 1.274*** 0.154
Clayton State University State university 1.270*** 0.381
Georgia College & State University State university 1.269*** 0.154
Atlanta Metropolitan College Two-year college 1.227*** 0.405
North Georgia College & State University State university 1.074*** 0.132
Abraham Baldwin Agricultural College Two-year college 1.033*** 0.106
Valdosta State University Regional university 0.910*** 0.091
Georgia Southern University Regional university 0.878*** 0.077
University of West Georgia State university 0.847*** 0.102
Southern Polytechnic State University State university 0.819*** 0.142
Fort Valley State University State university 0.719*** 0.129
Savannah State University State university 0.624*** 0.136
Georgia Perimeter College Two-year college 0.400 0.268
Albany State University State university 0.399*** 0.144
University of Georgia Research university 0.303*** 0.072
Georgia State University Research university 0.086 0.139
Georgia Institute of Technology Research university 0.016 0.134
Notes: All parameters are estimated using Tobit methods.
*** Significant at 1%.
erally in between. This ordering supports the notion that
higher quality institutions in the USG have a less elastic
Lowe and Viterito (1989) show that the market penetra-
tion index of a private college is correlated with its mean
SAT score, which is consistent with the notion that more
selective schools have wider market areas. To investigate
this notion, we use OLS to regress the estimated elastic-
ities in Table 4 on numerous institutional characteristics
to explain the variance in the distance elasticity across
institutions, using the various pull variables and the sector
dummy variables. These results are reported in Table 5.
Table 5
Determinants of the variation in distance elasticity.
Variable Distance elasticity
Percent Black USG 0.011*** (0.003)
ln GA public HS freshmen 0.450*** (0.150)
ln centrality 0.276** (0.123)
Research university 0.929*** (0.293)
Regional university 0.546** (0.262)
State university 0.467** (0.180)
State college 0.391** (0.141)
Observations 33
R-squared 0.78
Notes: Robust standard errors are in parentheses.
** Significant at 5%.
*** Significant at 1%.
These results indicate that 78 percent of the variance
in the distance elasticities can be explained by seven vari-
ables: the percentage Black, Georgia public high school
freshmen enrollment, the centrality index, and the dummy
variables for research universities, regional universities,
state universities and state colleges. All coefficients are sig-
nificant, and all but the state college dummy variable are
positive. A positive sign means that an increase in a variable
decreases the absolute magnitude of the distance elasticity
and so makes demand less elastic. The sector dummies are
ordered as we might expect, except that state colleges have
a more elastic demand than the omitted group, two-year
colleges. Research universities have the lowest demand
elasticity, followed by regional universities and then state
universities. The centrality index has a positive coefficient
suggesting that institutions facing greater spatial compe-
tition have lower distance elasticities and are therefore
less likely to get all of their students from their immediate
vicinity and must bring in students from outside areas. The
positive coefficient on percent Black indicates that Black
students value attending a historically African–American
institution, and are willing to migrate greater distances to
do so.19 Schools with higher enrollments of Georgia pub-
19 We also experimented with replacing the percent Black variable in
Table 5 with an indicator variable for a historically Black college or univer-
J. Alm, J.V. Winters / Economics of Education Review 28 (2009) 728–738 737
lic high school graduate entering freshmen also have less
elastic demands with respect to distance. Not surprisingly,
this suggests that within sectors large schools are better
able to bring in students from further away.
5. Summary and conclusions
This paper examines how accessibility affects enroll-
ment in and within the University System of Georgia. We
find several important results. First, greater distance to
the nearest college decreases the likelihood of enrollment
in any USG institution with an elasticity of 0.12, while
greater distance to the nearest university has little or no
effect on the likelihood of enrollment in the USG. However,
once students decide to enroll in the USG, they must choose
between colleges and universities. Greater distance to the
nearest college decreases the likelihood that a student will
attend a college and therefore increases the likelihood
that he or she will attend a university. Greater distance
to the nearest university has a similar effect: it decreases
the likelihood of attending a university and increases the
likelihood of attending a college. If colleges offer a lower
quality education than universities, then this presents a
dilemma for policymakers. Making colleges more acces-
sible will both bring more students into the system and
draw students away from universities. Making universities
more accessible does not bring in more students to the sys-
tem, but does encourage more to go to universities rather
than colleges, which may mean that those in the system
receive a better education. Clearly, administrators for the
USG as well as other states’ higher education systems need
to understand the implications of improving accessibility
for both colleges and universities.
We also estimate traditional distance elasticities for the
USG as a whole and for each institution of the USG sepa-
rately. We find that the intrastate distance elasticity for the
USG is 1.345, which is very similar to previous findings by
McHugh and Morgan (1984) and Sà et al. (2004). However,
the intrastate distance elasticity varies across institutions,
ranging from roughly 0 for Georgia Institute of Technology
to 3.248 for Waycross College. This variance can in part be
explained by the academic classification of the institution,
the percentage of the institution’s students that are Black,
freshmen enrollment in the institution, and the degree
of spatial competition the institution faces. Knowledge of
these distance elasticities can help institution adminis-
trators predict how their institution-specific demand will
increase as the population around them grows, and can
also help USG administrators promote greater enrollment
in the system through the creation of new institutions and
Though the analysis in this paper is for Georgia, the
results are also likely to have important implications for
other states. There are some peculiarities to Georgia such
sity. The coefficient on this indicator variable was positive and significant
with a coefficient of 0.810. Given that the mean percent Black for HBCUs
and non-HBCUs is 92.4 percent and 22.0 percent, respectively, the mag-
nitude of the HBCU indicator variable is consistent with what would be
expected based on the coefficient of the percent Black variable.
as the presence of the HOPE Scholarship, but for the most
part we expect that the results would be similar for most
other states. Distance is likely to play an important role in
whether students enroll in higher education and distance
is likely to be very important for students in determining
the specific institution they attend.
We are grateful to the editor and to two anonymous
referees for many helpful comments.
Alfonso, M. (2006). The impact of community college attendance on bac-
calaureate attainment. Research in Higher Education,47(8), 873–903.
Ali, M. K. (2003). Analysis of enrollment: A spatial-interaction model. The
Journal of Economics,29(2), 67–86.
Amemiya, T. (1973). Regression analysis when the dependent variable is
truncated normal. Econometrica,41(6), 997–1016.
Baryla, E. A., Jr., & Dotterweich, D. (2001). Student migration: Do significant
factors vary by region? Education Economics,9(3), 269–280.
Bergstrand, J. H. (1985). The gravity equation in international trade: Some
microeconomic foundations and empirical evidence. The Review of
Economics and Statistics,67(3), 474–481.
Card, D. (1995). Using geographic variation in college proximity to esti-
mate the return to schooling. In L. Christofides, R. Swideinsky, & E. K.
Grant (Eds.), Aspects of labour market behaviour: Essays in honor of John
Vanderkamp (pp. 201–222). Toronto: University of Toronto Press.
Desjardins, S. L., Dundar, H., & Hendel, D. D. (1999). Modeling the college
application process in a land-grant university. Economics of Education
Review,18(1), 117–132.
Fotheringham, A. S., Nakaya, T., Yano, K., Openshaw, S., & Ishikawa, Y.
(2001). Hierarchical destination choice and spatial interaction mod-
eling: A simulation experiment. Environment and Planning A,33(5),
Ghatak, S., Levine, P., & Price, S. W. (1996). Migration theories and evi-
dence: An assessment. Journal of Economic Surveys,10(2), 159–198.
Gossman, C. S., Nobbe, C. E., Patricelli, T. J., Schmid, C. F., & Steahr, T. E.
(1968). Migration of college and university students in the United States.
Seattle: The University of Washington Press.
Greenwood, M. J. (1975). Research on internal migration in the United
States: A survey. Journal of Economic Literature,13(2), 397–433.
Hanushek, E. A. (2003). The failure of input-based schooling policies. Eco-
nomic Journal,113(485), F64–F98.
Heller, D. E. (1997). Student price response in higher education: An update
to Leslie and Brinkman. Journal of Higher Education,68(6), 624–659.
Hoxby, C. M. (Ed.). (2004). College choices: The economics of where to go,
when to go, and how to pay for it. Chicago: University of Chicago Press
for NBER.
Information Digest, 2002–2003. University System of Georgia,
Office of Strategic Research and Analysis. Available online at stats/info digest/2002/.
Kariel, H. G. (1968). Student enrollment and spatial interaction. Annals of
Regional Science,2(1), 114–127.
Kyung, W. (1996). In-migration of college students to the state of New
York. Journal of Higher Education,67(3), 349–358.
Leppel, K. (1993). Logit estimation of a gravity model of the college enroll-
ment decision. Research in Higher Education,34(3), 387–398.
Leslie, L. L., & Brinkman, P. T. (1987). Student price response in higher
education. Journal of Higher Education,58(2), 181–204.
Lowe, J. C., & Viterito, A. (1989). Differential spatial attraction of private
colleges and universities in the United States. Economic Geography,
65(3), 208–215.
McConnell, H. (1965). Spatial variability of college enrollment as a function
of migration potential. The Professional Geographer,17(6), 29–37.
McHugh, R., & Morgan, J. N. (1984). The determinants of interstate student
migration: A place-to-place analysis. Economics of Education Review,
3(4), 269–278.
Mixon, F. G., Jr. (1992a). Factors affecting college student migration across
states. International Journal of Manpower,13(1), 25–32.
Mixon, F. G., Jr. (1992b). A public choice note on college student migration.
International Journal of Manpower,13(3), 63–68.
Mixon, F. G., Jr., & Hsing, Y. (1994a). College student migration and human
capital theory: A research note. Education Economics,2(1), 65–73.
738 J. Alm, J.V. Winters / Economics of Education Review 28 (2009) 728–738
Mixon, F. G., Jr., & Hsing, Y. (1994b). The determinants of out-of-state
enrollment in higher education: A tobit analysis. Economics of Edu-
cation Review,13(4), 329–335.
Ordovensky, J. F. (1995). Effects of institutional attributes on enrollment
choice: Implications for postsecondary vocational education. Eco-
nomics of Education Review,14(4), 335–350.
Sà, C., Florax, R. J. G. M., & Rietveld, P. (2004). Determinants of the regional
demand for higher education in the Netherlands: A gravity model
approach. Regional Studies,38(4), 375–392.
Tuckman, H. P. (1970). Determinants of college student migration. South-
ern Economic Journal,37(2), 184–189.
Ullis, J. J., & Knowles, P. L. (1975). A study of the intrastate migration of
Washington college freshmen: A further test of the gravity model.
Annals of Regional Science,9(1), 112–121.
... The application of universal gravitation occurs in many areas, such as transportation (Jung et al., 2008;Kaluza et al., 2010;Odlyzko, 2015;Hong & Jung, 2016;Bartzokas-Tsiompras & Photis, 2019;Azad et al., 2021), population flows and commuting (Griffith, 2009;Murat, 2010;Lenormand et al., 2012;Liang et al., 2013;Masucci et al., 2013;Thomas & Tutert, 2013;Liu et al., 2014;Lenormand et al., 2016;Kluge & Schewe, 2021), freight transport (Kaluza et al., 2010), international trade (Bergstrand, 1985;Fagiolo, 2010), telecommunications (Krings et al., 2009), scientific collaboration (Pan et al., 2012) and the consideration of human mobility in the context of infectious disease spreads (Viboud et al., 2006;Balcan et al., 2009;Tizzoni et al., 2014;Sallah, 2017Marshall et al., 2018Tuite et al., 2018). Estimates of student migration flows also use gravity models (Sá et al., 2004;Alm & Winters, 2009;Cooke & Boyle, 2011;Faggian & Franklin, 2014). ...
Full-text available
Classically, gravity models have been used to estimate mobility flows. However, in recent years, a number of new models, such as radiation models, have been introduced to estimate human mobility. The focus has generally been on mod- els dealing with commuting movements. There is no systematic application of different versions of the laws of gravity to student mobility. The application of these models to student mobility provides the opportunity to calculate reliable forecasts of student mobility flows at the micro level, make medium- to long-term decisions at the university level, and implement sustainable strategic orientation. Therefore, this article uses different models to estimate interactions to improve the fore- cast of the regional distribution of students in Germany under data limitations. Using publicly available data on high school graduates and historical data on student flows between German counties, we show that radiative models with parameters are best suited to predict student flows at the level of German counties. Among parameter-free models, the population- weighted odds model yields the best results.
... Given that regional public universities were established to improve local access to higher education and opportunity, ours is an especially relevant sample for understanding the impact of universities on mobility. Second, given the relatively few number of observations inherent to either empirical strategies, bringing more observations Montgomery (2009); Alm and Winters (2009). Bedard (2001) finds that areas with universities have higher high school drop out rates in the 1960s and early 1970s, consistent with a signaling model, as higher rates of college attendance decrease the value of pooling with high school graduates. ...
Full-text available
Regional universities educate approximately 70 percent of students at four-year public universities, and an even larger share from disadvantaged backgrounds. They aim to increase education and social mobility, in part by locating near potential students. We use the historical assignment of normal schools and insane asylums (normal schools became regional universities while asylums remain small) and Opportunity Insights data to identify regional universities’ effects on the social mobility of nearby children. Children in normal school counties attain more education and better economic and social outcomes, especially lower-income children. For key outcomes we show this is a causal effect on children.
... negative effect of distance among those moving for education purposes is in line with the literature on college student migration that also reports a discouraging effect of distance (e.g., Alm and Winters, 2009). Among the network variables the first and third network measures have statistically significant effects on location choices across all migration motives while the second network measure is significant only for those relocating due to the earthquake. ...
Full-text available
This paper estimates effect of birthplace migrant networks on destination choices of internal migrants. In addition, we study the effects by skill group, reason for migration, marital status, and age at migration. The results show a robust effect of birthplace networks on destination choices with significant heterogeneity across migrant types. Networks matter less for individuals who are more educated, single, and moving for employment related reasons. More educated and single individuals also move longer distances and labor market conditions play a significant role only on employment related moves. JEL Classification: J61, O15, R23, Z13
... As noted by John V. Winters (2011), one of the most important determinants of the local level of human capital is the presence of colleges and universities in the area. Universities increase the local stock of human capital in at least two ways: 1) they increase accessibility to higher education for local residents (Alm & Winters, 2009;Card, 1995) and 2) they bring in students from outside the area seeking education, some of whom stay in the city after completing their education (Blackwell et al., 2002;Groen, 2004;Groen & White, 2004;Hickman, 2009). Winters (2011) suggests that the migration of students to cities with high human capital results from the fact that they transfer to higher education where there are good universities, hoping to graduate from them and -consequently -find a good job that will allow them to assess their quality of life and standard of living highly. ...
Full-text available
The main objective of the article is to depict access to education, both in terms of objective factors (quality of life) and subjective factors (standard of living), in the assessment of the Polish community living in the greater Toronto metropolitan area (GTA). The results of the research are presented on the basis of a questionnaire conducted among 583 Polish people living in the GTA. The results confirm that the Canadian Polish community evaluates highly the schools and other educational institutions operating within the study area. Another goal of the research was to investigate the possible influence of gender, income, or household size on the evaluation of access to education, which translates into a higher assessment of quality of life. In the study, the women and the respondents with higher income rated access to education more highly, while people in households with two to four people evaluated it the highest.
... Cilt / Volume 25 Sayı / Issue 3 students and it is preferred to get an education in closer places to reduce financial needs. Alm and Winters (2009), studied intrastate college student migration in the US state of Georgia. Using gravity analysis, they found that greater distances to the closest University System of Georgia (USG) institution have a significantly negative effect on student enrollment in USG schools. ...
University education is one of the primary incentives for internal migration and most educational migration in Turkey is directed toward the city of İstanbul. In addition to vocational and academic achievement, university education also provides autonomy for young people by allowing them to live in different and perhaps distant areas from their families. In this article, we analyze students who have moved to İstanbul with regard to characteristics of gender and migratory distance in order to determine whether there is a gender difference in the realization of distant resettlement for education. To accomplish this, we use a database of students in the 2017-2018 academic year who applied for accommodation to the General Directorate of Credit and Hostels (KYK), which is the largest public institution that provides housing opportunities for university students in Turkey. This dataset includes 27,643 students, 49% of whom are female while 51% are male. Controlling for the demographic, social, and economic characteristics of the students and their scores on the university entrance exam, we reveal that male students move greater distances to study in universities and they have more opportunities to migrate to İstanbul from settlements farther away than female students.
... Those who don't quite belong are more apt to move in part to look for a place where they do belong. Furthermore, when people do move, they are especially likely to move short distances and to places that are similar to their previous locations (Alm and Winters, 2009;Kremer, 2022;Krupka, 2009;Molloy et al., 2011;Wilson, 2021). Thus, belonging is not just to a particular place or set of people. ...
Full-text available
Place-based attachments are important but often overlooked. Place-based attachments can be beneficial but often harm individuals tied to struggling areas. In this address, I discuss my own education and migration experiences and then more generally discuss sense of belonging as a friction to migration. I also present descriptive statistics related to place-based attachments. Most persons born in the U.S. live in their birth state as adults. Birth-state residence has increased over time, especially among the highly educated. I also present evidence that college graduates who reside in their birth state experience a wage penalty that is increasing over time.
Full-text available
This paper makes an attempt towards exploring the trends and patterns of educational migration among 15-29 years’ age using various rounds of the unit level data from NSSO in India during 1992-93 to 2007-08. The study shows that access to higher education through migration has undoubtedly improved for socially backward communities, low income and residing in rural areas. However, if we look at the relative change in the odds of educational migration for different layers of the socio-economic strata, it is found that improvement for those with higher socio-income brackets, but not for those belonging to the lower socio-income groups.
The education sector in India was among the most affected sectors during the COVID-19 pandemic. While considerable attention has been paid to informal workers' return or reverse migration to their home communities, not much has been reported about the challenges faced by migrant students. Using a mixed-method approach, the current study presents an overview of internal student migration in India prior to the COVID-19 pandemic using data from the 2001 and 2011 Census of India and the 2007–2008 National Sample Survey Organization, and discusses challenges faced by selected migrant learners during the COVID-19 pandemic based on primary research. Based on the census data, nearly 3.3 million migrants in India move for study reasons with 2.9 million migrating within the state (with the duration of residence less than five years) from their last residence within India. The pattern of female student migration suggests an increasingly localized interdistrict migration. Findings from the qualitative data indicate that during the pandemic, students had compromised learning and placement experience, inadequate digital resources and pressure to repay loans. Student migrants experienced varying degrees of the impact of the COVID-19 pandemic based on their destination and migration stream.
In this paper, we use a gravity approach to study the impact of distance on desired migratory flows of candidates to undergraduate courses in Portugal. We employ a large administrative database that includes the entire population of candidates to public HE institutions in Portugal from 2008 to 2019. We document, in line with the literature, that distance is a significant deterrent to mobility, with flows showing an average elasticity to distance between −2 and −1, a value that tends to decay as distance increases. We find that flows are particularly sensitive when distance is measured by travel time. We show, however, that the deterrence effect is not homogeneous. Among other results, we identify and measure important differences across fields of education. Finally, we illustrate how using different probability density functions can be of service in finding the most appropriate gravity model specification.
This study assesses the market penetration of private colleges and universities in the conterminous United States. A hierarchy of geographic impact is evident. Examination of enrollments for 90 schools reveals that individual schools have either a local, regional, or national base of appeal. Correlation of market penetration and SAT scores clearly indicates that a school's academic competitiveness is a key determinant of its spatial impact. "Reputation" determines the size of a school's impact area, with significant implications for alternative recruitment strategies.
This study investigates the determinants affecting the number of college student immigrants from other states to the state of New York. The present analysis, based on 1986 New York migration data, provides considerable evidence that student migrants' home state characteristics influence student migration. It also provides evidence on educational policy implications.
Previous researchers have studied the interstate migration of college students employing a gravity conception of movement. Such a conceptualization has been found to fit the data with a high degree of correlation. The present research applies the gravity concept to the intrastate migration of Washington college freshmen from their counties of residence to their college of attendance for Fall quarter, 1972. The predictive utility of the model is tested by comparing actual migration against migration volume expected visa-vis the population and distance terms in the model. A high degree of correlation is obtained between the amount of actual and expected migration, indicating the general utility of the model to an analysis of intrastate student migration.
The present note offers improvements on previous work by Tuckman (1970) and Mixon (1992a, b) that describes college student migration as a from of human capital investment. WHile previous work employed macro-data and single equation estimation techniques, the present study utilizes a large institution-level data set of four-year colleges in the US and two-stage least-squares estimation to account properly for the simultaneity within the modeling process. This study suggests that small class sizes, college selectivity, successful athletic programmes, the availability of diverse cultural alternatives, and highly qualified and productive faculty are important in attracting non-resident students to colleges and universities across states. In summary, this research note lends support to human capital theory and offers an alternative to the screening hypothesis.