Socio-economic predictors
of student mobility in Russia
Ilya Prakhov, Maria Bocharova
First International Conference “Trajectories and Educational Choice”
HSE, Moscow, September 16, 2016
Motivation
Recent institutional transformation of admission to higher
education: the introduction of the Unified State Exam (USE)
instead of high school exit exams and university-specific entry
exams
‘Local’ applicants may not have the benefit of being closer to
the desired university they had before, while for applicants from
other regions the admission procedure has been simplified,
which can contribute to educational migration
However, even under the simplified access to higher
education, university applicants can face barriers which can
influence their decision to move to another region (for example,
in the absence of additional student support such as grants and
student loans, individual and regional socio-economic
characteristics may play a significant role in such a decision)
2
Research question
 This study analyses the factors which drive student
choice between moving to another region to study at
university or to stay in their home region, under USE.
 Why Russia?
 More than 80 regions with highly differentiated economic
conditions and varying regional higher education
markets.
 Institutional characteristics of ‘school’ and ‘university’
regions can also be the important predictors of
educational migration, and in some cases can still
restrict the level of mobility despite the unification of
admission procedures
Theoretical grounds
 Human capital theory (Becker, 1962) applied to the
higher education market, when the applicant compares
potential benefits and costs of migration to another
region
 Student as an investor in his human capital
 Moving to another region to study is beneficial if the net
benefits from studying and migrating to another location
exceed the added costs of migration (Alecke, Burgard, &
Mitze, 2013; Mak & Moncur, 2003; Mchugh & Morgan,
1984).
Empirical results of previous studies: factors of
educational mobility
 Individual factors
 Gender (Becker et al., 2010; Alecke et al., 2013)
 Academic achievement (Kyung, 1996; Fenske et al., 1974)
 Family income (Fenske et al., 1974; Fenske, 1972; Hübner, 2012;
Kyung, 1996; Mak & Moncur, 2003)
 External factors
 Economic climate in the regions (Tuckman, 1967; Long, 1977;
Fenske, 1972; Mchugh & Morgan, 1984; Mihi-Ramirez & Kumpikaite, 2014;
Rodríguez et al., 2011; Thorn & Holm-Nielsen, 2006)
 Institutional characteristics of regional educational
systems (Alecke et al., 2013; Kyung, 1996; Mak & Moncur, 2003;
Mchugh & Morgan, 1984; Fenske et al., 1974; Fenske, 1972; Mchugh &
Morgan, 1984)
Hypotheses
 Higher academic achievement positively contributes to
educational mobility
 A higher socio-economic background positively affects the
probability of educational migration
 Interregional difference in salaries, and the average salary in
the ‘university region’ positively influence the decision to
move.
 Interregional differences in the costs of living in the university
region would deter students from educational migration, as
they will face a strong financial barrier.
 The concentration of universities in the ‘university’ region and
the difference in concentration between ‘university’ and
‘school’ regions will positively affect educational mobility:
students will tend to move from the regions with poorly
developed systems of higher education to those with better
educational opportunities.
Data and methodology of the study
 Data: Trajectories in Education and Career project – a
national representative longitudinal study devoted to the
investigation of educational trajectories
 Sample: Students who in 2014 either started or
continued their education at university; 1169 obs.
 For every observation we have manually added the
variables which reflect educational mobility, the socioeconomic conditions of ‘school’ and ‘university’ regions,
and the indices of the development of the regional higher
2
education markets:
 x jk
HHI j   
k  Xj





where xjk is a number of students in university k located
in the region j; and Xj is the total number of university
students in the region j
Mobility variables
 Mobility based on a distance is a binary variable that equals ‘1’, if
the distance between the settlement, in which the respondent
graduated from high school and the location of the university where
he was admitted is equal to or greater than 100 km. In this case, the
individual is considered a mobile student based on distance, and ‘0’
otherwise and the student is considered in the empirical analysis as
immobile (by distance). 43% of students are mobile by distance in
the sample.
 Interregional mobility is a binary variable that takes a value of ‘1’ if
the respondent moved to another region in order to study in the
university (‘university’ region). 23% of students in the sample
changed region after high school.
 Distance is a variable which reflects the geographical distance (in
km) between the geographical centres of ‘school’ and ‘university’
towns. This variable is obtained with the help of Google Maps and is
calculated not only for interregional mobility, but for all mobile
students (even if the distance travelled does not exceed 100 km).
The mean distance between ‘school’ and ‘university’ towns is 336
km with the longest distance travelled 8838 km.
Descriptive statistics
Variable
Mobility based on a distance (=1 if mobile)
Interregional mobility (=1 if mobile)
Distance (km)
Male
The USE score in Russian
Mother’s education (=1 if higher education)
Incomplete family (=1 if yes)
Family income (rub per month)
School specialization (=1 if any)
Average monthly salary in a ‘school’ region (rub)
Average monthly salary in a ‘university’ region
(rub)
The cost of living in a ‘school’ region (rub per
month)
The cost of living in a ‘university’ region (rub per
month)
Herfindahl-Hirshman index (HHI) in a ‘school
region’
HHI in a ‘university’ region
Interregional difference in salary
Interregional difference in the cost of living
Interregional difference in HHI
N obs.
1166
1169
1166
1169
1147
1104
1169
1072
1152
1169
Min.
0
0
0
0
12
0
0
10000
0
15264
Max.
1
1
8838
1
100
1
1
95000
1
58040.4
Mean
0.43
0.23
335.68
0.43
69.24
0.54
0.23
31856.00
0.55
25854.00
Std. Dev.
0.49
0.42
790.22
0.49
12.98
0.50
0.42
22535.50
0.50
10167.58
1166
14432.8
58040.4
28488.00
12689.84
1169
5979
12077
7345.60
1469.37
1166
5979
12077
7598.70
1636.49
1166
0.02
0.60
0.15
0.12
1166
1166
1166
1166
0.02
-40500
-5709
-0.58
0.69
42061.3
5512
0.54
0.13
2644.00
253.47
-0.02
0.11
9350.91
1182.35
0.09
Empirical models
Pr Mobility based on a distancei  1  f  X i ; Ei ; Ri ,
Pr Interregio nal mobility i  1  g  X i ; Ei ; Ri , where
Pr(·) is the probability of the corresponding type of educational mobility;
Xi is a vector of individual characteristics;
Ei is a vector of socio-economic regional characteristics;
Ri is a vector of the characteristics of the regional higher education
markets.
Distance i    X i  Ei  Ri   i , where
Distancei is the distance (in km) travelled by the student i;
Xi, Ei, Ri are the sets of explanatory variables, as in the previous case;
α, β, γ, δ are the regression coefficients;
εi is the error term.
Results of regression analysis-1
Model
Dependent variable
Independent variables
Male
The USE result in Russian
Mother’s education
Incomplete family
Family income / 1000
School specialization (=1 if
any)
Average monthly salary in a
‘school’ region
Average monthly salary in a
‘university’ region
The cost of living in a ‘school’
region
The cost of living in a
‘university’ region
HHI in a ‘school’ region
HHI in a ‘university’ region
Interregional difference in
salary
Interregional difference in the
cost of living
Interregional difference in HHI
Pseudo R2
Number of observations
Sample
1
2
3
4
Mobility based on a
Interregional mobility
distance
0.081**
0.081**
0.055*
0.055*
(0.035)
(0.035)
(0.029)
(0.029)
0.006***
0.006***
0.004***
0.004***
(0.001)
(0.001)
(0.001)
(0.001)
0.032
0.031
0.034
0.035
(0.034)
(0.034)
(0.027)
(0.028)
-0.057
-0.065*
-0.042
-0.046
(0.039)
(0.03875)
(0.030)
(0.031)
-0.001
-0.002***
-0.000001
-0.001
(0.000)
(0.000)
(0.000)
(0.000)
-0.037
-0.432
0.014
0.008
(0.034)
(0.033)
(0.027)
(0.028)
-0.00002***
-0.00003***
(0.000)
(0.000)
0.00001**
0.00002***
(0.000)
(0.000)
0.0001***
0.0001***
(0.000)
(0.000)
-0.00006
-0.00008***
(0.000)
(0.000)
0.412
0.228
(0.303)
(0.214)
-0.714**
-0.174
(0.334)
(0.251)
0.00002***
0.00002***
(0.000)
(0.000)
-0.0001**
-0.0001***
(0.000)
(0.000)
-0.565*
-0.357*
(0.296)
(0.222)
0.062
0.052
0.161
0.136
999
999
1002
1002
All observations
5
6
Mobility based on a
distance
0.168**
0.149*
(0.083)
(0.080)
0.146*
(0.082)
-0.138
(0.096)
-0.003
(0.000)
-0.032
(0.078)
-0.00005***
(0.000)
0.00003**
(0.000)
0.0002**
(0.000)
-0.0001
(0.000)
0.626
(0.762)
-0.163
(0.784)
0.146
215
0.113
(0.079)
-0.147
(0.093)
-0.005***
(0.000)
-0.059
(0.075)
7
8
Interregional mobility
0.044
(0.084)
0.045
(0.082)
0.182***
(0.073)
-0.110
(0.084)
-0.0003
(0.000)
0.070
(0.073)
-0.00005***
(0.000)
0.00003***
(0.000)
0.0002**
(0.000)
-0.0001
(0.000)
0.119
(0.660)
0.335
(0.703)
0.154**
(0.073)
-0.106
(0.086)
-0.002
(0.000)
0.045
(0.074)
0.00003**
(0.000)
-0.0002*
(0.000)
-0.583
(0.758)
0.102
0.243
215
215
High achievers
0.00003**
(0.000)
-0.0001**
(0.000)
-0.269
(0.702)
0.198
215
Results of regression analysis-2
Model
Dependent variable
Independent variables
Male
The USE result in Russian
Mother’s education
Incomplete family
Family income / 1000
School specialization (=1 if any)
Average monthly salary in a
‘school’ region
Average monthly salary in a
‘university’ region
The cost of living in a ‘school’
region
The cost of living in a
‘university’ region
HHI in a ‘school’ region
HHI in a ‘university’ region
9
Mobility based on a distance
0.071*
(0.039)
0.006***
(0.002)
0.036
(0.037)
-0.018
(0.044)
-0.002**
(0.000)
-0.081**
(0.037)
-0.00002**
(0.000)
0.00003***
(0.000)
0.00009**
(0.000)
-0.00005
(0.000)
-0.526
(0.343)
-0.082
(0.360)
Interregional difference in
salary
Interregional difference in the
cost of living
Interregional difference in HHI
Pseudo R2
Number of observations
Sample
10
0.121
905
0.084**
(0.038)
0.007***
(0.002)
0.028
(0.037)
-0.030
(0.043)
-0.0002
(0.000)
-0.057
(0.036)
11
12
13
Interregional mobility
0.048
(0.035)
0.005***
(0.001)
0.025
(0.033)
-0.031
(0.038)
0.0004
(0.000)
-0.008
(0.033)
-0.00004***
(0.000)
0.00004***
(0.000)
0.0005***
(0.000)
-0.0001***
(0.000)
-0.454
(0.287)
0.389
(0.308)
0.00003***
(0.000)
-0.0001***
(0.000)
0.280
(0.317)
0.086
0.198
905
907
Moscow school graduates excluded
0.053
(0.035)
0.005***
(0.001)
0.026
(0.033)
-0.031
(0.038)
0.001
(0.000)
-0.006
(0.032)
14
Distance
119.929***
(39.846)
5.500***
(1.552)
125.600***
(40.532)
5.521***
(1.564)
-0.137***
(0.007)
0.112***
(0.007)
1.049***
(0.046)
-0.842***
(0.049)
-1668.979***
(265.790)
1121.642***
(319.111)
0.00004***
(0.000)
-0.0001***
(0.000)
0.445
(0.276)
0.192
907
0.128***
(0.006)
-0.972***
(0.045)
1653.359***
(264.802)
0.334
0.309
1141
1141
All observations
Results-1
 A positive relationship between the results of USE and
the applicant's probability of moving. In this case, the
simplification of the admission process contributes to
educational mobility, but even high achievers face some
cultural, and more importantly, financial barriers, which
can prevent educational migration.
 Mother’s education level has a strong influence on the
decision to move only for high-achievers. The level of
cultural capital can contribute to student mobility for
applicants who have the widest choice of where to apply.
 For family income there are ambiguous results, but
because of the interregional differences the income, the
effect may be expressed through macroeconomic
indicators of regional economic development.
Results-2
The characteristics of regional economic development have a strong
influence on the mobility of applicants, i.e., individuals tend to evaluate not
only the quality of education, but also the economy of the ‘university’ region.
The significance of financial factors highlights the importance of student
support. However, the mechanisms of student aid in Russia are ill-developed.
We argue that the simplification of the admission system is not enough in the
absence of such mechanisms.
The regional differences in characteristics of higher education markets
raise questions about the equality of educational opportunity. Together with
the importance of the financial aspects, the regional differentiation of the
higher education market creates unequal conditions for applicants from
different regions. Thus, USE only partially solves the problem of equality of
accessibility of higher education.
If the implementation of the new mechanisms of student support is costly to
the state, it can invest in the development of the regional educational
markets. As a result, the development of regional educational markets and
the quality of education in local universities, can contribute to increased
human capital in the region leading to economic growth in the future.
Thank you for your attention!
Ilya Prakhov
[email protected]